Road slope real-time monitoring and active prevention and control method and system

Abstract

The invention provides a road slope real-time monitoring and active prevention and control method and system, and the method comprises the steps: setting a distributed sensing module, and collecting real-time sensing data representing the state of a road slope through the distributed sensing module; the real-time sensing data comprises continuous vibration data; performing full waveform inversion on a preset data driving model by using the continuous vibration data to generate a slope stability model representing the internal structure state of the slope; generating a speed change model representing slope medium change speed based on background noise in the continuous vibration data; in combination with the slope stability model and the speed change model, the risk state of the road slope is evaluated in real time, and a risk index is generated; and performing prevention and control early warning on the safety state of the road slope according to the risk index. Therefore, potential crack extension and medium degradation processes in the slope are sensitively captured, so that the perspectiveness and precision of slope stability evaluation are improved, and measures such as drainage, reinforcement or protection are automatically triggered before risks occur.

Description

Technical Field

[0001] This invention relates to the fields of geotechnical engineering and traffic safety monitoring technology, specifically to a method and system for real-time monitoring and active prevention and control of highway slopes. Background Technology

[0002] Slope instability along highways, such as landslides and rockfalls, is one of the major geological hazards threatening traffic safety. To ensure safe highway operation, real-time monitoring and assessment of slope stability are necessary.

[0003] In existing technologies, a common practice is to deploy multiple sensors, such as displacement gauges and inclinometers, at key locations on highway slopes to collect physical data reflecting the slope's condition in real time. The collected data is then processed through data analysis or machine learning models to assess the slope's health and provide early warnings. For example, some solutions employ multiple sensors and utilize neural network models to perform joint inversion of multiphysics fields, constructing a three-dimensional model of the slope to identify potential internal defects. However, these solutions still have shortcomings. First, assessments relying on single or joint inversion models may not simultaneously possess high sensitivity to both long-term, slow deterioration and short-term, sudden changes caused by different factors (such as stress and water content variations), leading to insufficient accuracy and foresight in the assessment. Second, most existing systems remain at the level of "passive early warning," meaning they only output alarm signals after identifying risks; subsequent drainage, reinforcement, and other remediation measures still require manual decision-making and intervention, resulting in a long and inefficient response chain, making effective automated proactive intervention before disasters occur. Summary of the Invention

[0004] To address these issues, this invention provides a method and system for real-time monitoring and proactive prevention of highway slopes, aiming to solve the technical problems in existing technologies where slope risk assessment models lack sensitivity to multi-scale changes, assessment accuracy needs improvement, and monitoring results are mostly used for passive early warning and lack automated proactive prevention capabilities linked to remediation measures.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: According to a first aspect of the present invention, the present invention provides a method for real-time monitoring and active prevention of highway slopes, the method comprising: Distributed sensing modules are installed at predetermined locations on the highway slope, and real-time sensing data characterizing the state of the highway slope is collected using the distributed sensing modules; the real-time sensing data includes continuous vibration data. The continuous vibration data is used to perform full waveform inversion on the preset data-driven model to generate a slope stability model that characterizes the internal structural state of the slope. Based on the background noise in the continuous vibration data, a velocity variation model characterizing the rate of change of the slope medium is generated. By combining the slope stability model and the velocity change model, the risk status of the highway slope is assessed in real time, and risk indicators are generated. The safety status of the highway slope is monitored and warned based on the aforementioned risk indicators.

[0006] Furthermore, the provision of distributed sensing modules at predetermined locations on the highway slope includes: Multiple sensing devices spaced at a certain distance are installed on the surface and toe of the highway slope to form a sensing device array. The sensing device includes at least one of a seismograph, an accelerometer, and a tiltmeter.

[0007] Furthermore, the step of using the continuous vibration data to perform full waveform inversion on a preset data-driven model to generate a slope stability model characterizing the internal structural state of the slope includes: Construct a neural network model based on an encoder-decoder structure as a data-driven model; The observed waveforms in the continuous vibration data are used as pseudo-labels, and the data-driven model is trained by self-supervised full waveform inversion to obtain the trained slope stability model. The slope stability model is used to predict the synthetic waveform that should be generated under the same source and receiver locations.

[0008] Furthermore, training the data-driven model through self-supervised full waveform inversion includes: The data-driven model is used as the initial model for forward modeling simulation to calculate the waveform mismatch between the synthesized waveform and the observed waveform. Using minimizing the waveform mismatch as the objective function, the weight parameters of the initial model are adjusted using the backpropagation algorithm until the initial model training converges.

[0009] Furthermore, the step of generating a velocity variation model characterizing the rate of change of the slope medium based on the background noise in the continuous vibration data includes: Extract the environmental background noise segment from the continuous vibration data; A velocity variation model is constructed based on the cross-correlation algorithm and imaging technology of the aforementioned environmental background noise segments; The velocity variation model is used to predict the deterioration or stress variation trend of the underground medium structure of the slope.

[0010] Furthermore, the step of constructing a velocity change model based on the cross-correlation algorithm and imaging technology of the environmental background noise segments includes: Long-term continuous cross-correlation calculations are performed on background noise signals from different sensing devices to extract the noise cross-correlation function and calculate the Green's function corresponding to the reference time and the target time. The time series model is inverted based on the changes in the Green's function of multiple sensing devices to obtain a velocity change model that reflects the changes in the underground medium of the slope.

[0011] Furthermore, by combining the slope stability model and the velocity change model, the risk status of the highway slope is assessed in real time, and risk indicators are generated, including: The synthetic waveform of real-time vibration data is analyzed using the slope stability model, and the waveform mismatch value between the synthetic waveform and the observed waveform is calculated. The velocity variation model is used to continuously monitor real-time vibration data over a long period of time. The noise cross-correlation function at different time periods is compared, and the change information of the underground medium of the slope is identified by differential imaging analysis. The change information of the underground medium of the slope includes the crack propagation rate and / or displacement increment. The risk indicators include at least one of waveform mismatch value, crack propagation rate, and displacement increment.

[0012] Furthermore, the step of preventing and issuing early warnings about the safety status of the highway slope based on the risk indicators includes: When any risk indicator exceeds the preset threshold, an early warning message is output, and the governance execution module is linked to implement proactive prevention and control measures. The treatment execution module includes at least one of the following: a slope drainage device, a prestressed adjustment device for anchor bolts or anchor cables, and a flexible protective net control device.

[0013] According to a second aspect of the present invention, the present invention provides a real-time monitoring and active prevention and control system for highway slopes, the system comprising a distributed sensing module and a data processing module; The distributed sensing module is set at a preset location on the highway slope and is used to collect real-time sensing data characterizing the state of the highway slope; the real-time sensing data includes continuous vibration data. The data processing module is used to perform full waveform inversion on a preset data-driven model using the continuous vibration data to generate a slope stability model characterizing the internal structural state of the slope; based on the background noise in the continuous vibration data, a velocity change model characterizing the rate of change of the slope medium is generated; combining the slope stability model and the velocity change model, the risk status of the highway slope is assessed in real time, and risk indicators are generated; and the safety status of the highway slope is prevented and warned based on the risk indicators.

[0014] Furthermore, the system also includes a governance execution module; The governance execution module includes at least one of a slope drainage device, a prestressed adjustment device for anchor bolts or anchor cables, and a flexible protective net control device, for implementing proactive prevention and control measures.

[0015] The present invention, by adopting the above technical solution, has at least the following beneficial effects: The present invention involves setting up distributed sensing modules at predetermined locations on a highway slope to collect real-time sensing data characterizing the slope's condition. This real-time sensing data includes continuous vibration data. The continuous vibration data is used to perform full-waveform inversion on a preset data-driven model, generating a slope stability model characterizing the internal structural state of the slope. Based on background noise in the continuous vibration data, a velocity variation model characterizing the rate of change of the slope's medium is generated. Combining the slope stability model and the velocity variation model, the risk status of the highway slope is assessed in real time, generating risk indicators. Based on these risk indicators, the safety status of the highway slope is monitored and warned against. This allows for the sensitive detection of potential crack propagation and medium degradation processes within the slope, thereby improving the foresight and accuracy of slope stability assessment. It can automatically trigger drainage, reinforcement, or protection measures before risks occur, offering advantages such as high real-time performance, high intelligence, and wide adaptability.

[0016] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0017] To more clearly illustrate the technical solutions 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.

[0018] Figure 1 A flowchart illustrating a method for real-time monitoring and proactive prevention of highway slopes according to an embodiment of the present invention is shown. Figure 2 A schematic diagram of a slope stability model provided in an embodiment of the present invention is shown; Figure 3 A simulation diagram of a velocity variation model provided in an embodiment of the present invention is shown; Figure 4 This diagram illustrates the principle of using a velocity variation model for time-series variation comparison according to an embodiment of the present invention. Figure 5 A schematic diagram of the structure of a real-time monitoring and active prevention system for highway slopes provided in an embodiment of the present invention is shown; Figure 6 A schematic diagram of the structure of a real-time monitoring and active prevention system for highway slopes provided in another embodiment of the present invention is shown. Detailed Implementation

[0019] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0020] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0021] This invention provides a method for real-time monitoring and active prevention of highway slopes, such as... Figure 1 As shown, it may include at least the following steps S101~S105: Step S101: Set up a distributed sensing module at a preset location on the highway slope and use the distributed sensing module to collect real-time sensing data characterizing the state of the highway slope.

[0022] In this embodiment of the invention, the real-time sensing data can be continuous vibration data; the distributed sensing module can be an array of sensing devices formed by multiple sensing devices spaced at certain distances on the surface and toe of the highway slope, used to achieve multi-dimensional and all-round perception of the slope state. The sensing devices include seismic detectors, accelerometers, and inclinometers. Specifically, the seismic detectors can be deployed in an array along the slope surface and toe to collect ground vibration signals caused by environmental factors such as vehicles, micro-seismic events, and wind and rain. These signals are the main data source for subsequently constructing the two core models. The accelerometers and inclinometers can be deployed near key structural points or potential slip surfaces of the slope to directly measure the small displacements, settlements, and tilt angle changes of the slope, providing direct evidence for macroscopic structural stability.

[0023] In an optional embodiment, the sensing device may also include environmental monitoring instruments such as rain gauges, temperature and humidity sensors, and pore water pressure gauges, for collecting data on external environmental factors that may induce slope instability, such as rainfall, soil moisture content, and freeze-thaw cycles, to assist in the judgment of prevention and early warning and the selection of prevention and control measures.

[0024] It should be noted that all sensing devices in the embodiments of the present invention can be connected to the remote data processing module through a high-bandwidth, low-latency wireless communication network (such as 5G or a dedicated wireless bridge) to convert the collected continuous vibration data, tilt angle data, environmental parameters and other real-time sensing data into a form that can be recognized by a computer at a rate of not less than a preset frequency (such as once per minute), and stream them to the data processing module for subsequent data processing.

[0025] Step S102: Use continuous vibration data to perform full waveform inversion on the preset data-driven model to generate a slope stability model that characterizes the internal structural state of the slope.

[0026] Specifically, a neural network model based on an encoder-decoder structure can be constructed as a data-driven model; the observed waveforms in the continuous vibration data are used as pseudo-labels, and the data-driven model is trained through self-supervised full waveform inversion to obtain a trained slope stability model.

[0027] In this embodiment of the invention, the data-driven model is a deep neural network trained using a self-supervised learning method. Its network structure can be a convolutional neural network with an encoder-decoder structure. The training objective of the data-driven model is to generate a slope stability model that characterizes the macroscopic structural stability of the slope's interior. The training principle of the slope stability model is explained below: Specifically, the data-driven model is used as the initial model for forward modeling simulation to calculate the waveform mismatch between the synthesized waveform and the observed waveform. The weight parameters of the initial model are adjusted using the backpropagation algorithm with the objective function of minimizing the waveform mismatch, until the initial model training converges.

[0028] In other words, the data-driven model first uses continuous vibration data received from the distributed sensing module as model input. Unlike traditional supervised learning, which requires a large amount of data with precise geological labels, this embodiment employs a self-supervised learning strategy. That is, the actually observed seismic waveform data itself is used as a "pseudo-label," and the model's task is to predict the synthetic waveform that should be generated at the same source and receiver location based on its internal representation of the slope's physical structure. The model's loss function is defined as the difference between the synthetic waveform and the observed waveform, i.e., waveform mismatch. Through the backpropagation algorithm, the weight parameters of the neural network are continuously adjusted to minimize this difference. This process is called self-supervised full waveform inversion. When the model training converges, the slope P-wave velocity field encoded by the internal parameters of the neural network is considered the final generated slope stability model. Figure 2 As shown, the slope stability model can be a three-dimensional or two-dimensional velocity profile, which can intuitively reflect whether there are low-velocity anomaly zones inside the slope (which may correspond to weak interlayers, water-rich areas, or fracture-developed areas), thereby characterizing the macroscopic structural stability state of the slope.

[0029] Step S103: Based on the background noise in the continuous vibration data, generate a velocity variation model characterizing the rate of change of the slope medium.

[0030] Specifically, environmental background noise segments can be extracted from continuous vibration data; a velocity change model can be constructed using cross-correlation algorithms and imaging techniques based on environmental background noise segments, including: performing long-term continuous cross-correlation calculations on background noise signals from different sensing devices, extracting noise cross-correlation functions, and calculating Green's functions corresponding to reference time and target time; and inverting the time series model based on the changes in Green's functions of multiple sensing devices to obtain a velocity change model reflecting changes in the underground medium of the slope.

[0031] The velocity variation model in this embodiment of the invention is used to predict the deterioration or stress variation trend of the subsurface medium structure of a slope. Unlike the slope stability model, which focuses on waveforms excited by specific seismic events, the velocity variation model focuses on utilizing continuously acquired, ubiquitous environmental background noise. The training principle of the velocity variation model is explained below: First, stable background noise segments (such as traffic noise and microseismic activity) are extracted from continuous vibration data collected by an array of sensors. Then, a long-term cross-correlation calculation is performed on the background noise signals received by any two sensors. Understandably, according to the principles of seismic interferometry, the derivative of the background noise cross-correlation function between two stations approximates the Green's function recorded at another station when one station is a virtual seismic source. By processing and imaging the cross-correlation functions of all station pairs, the shear wave velocity structure of the medium beneath the stations can be deduced; this process is known as background noise imaging technology. Figure 3As shown, the velocity variation model generated is an image characterizing the shear wave velocity distribution in the shallow medium of the slope. Since shear wave velocity is extremely sensitive to the porosity, saturation, and development of microfractures in the medium, the velocity variation model can very sensitively reflect subtle changes in the physical properties of the slope medium caused by rainfall infiltration, changes in groundwater level, or stress adjustment.

[0032] It is understandable that, over time, the speed model changes when compared across different time periods, such as... Figure 4 As shown, this can reflect the deterioration of the underground medium structure or the accumulation of stress on the slope. Furthermore, this result can be combined with a slope stability model derived from real-time full waveform inversion to achieve a more comprehensive risk warning.

[0033] Step S104: Combining the slope stability model and the velocity change model, the risk status of the highway slope is assessed in real time, and risk indicators are generated.

[0034] The risk indicators in this embodiment of the invention include waveform mismatch, crack propagation rate, and displacement increment. Specifically, a slope stability model can be used to analyze the synthetic waveform of real-time vibration data and calculate the waveform mismatch between the synthetic waveform and the observed waveform; a velocity change model can be used to continuously monitor real-time vibration data over a long period, and the noise cross-correlation function at different time periods can be compared. Differential imaging analysis can then be used to identify changes in the underground medium of the slope, including crack propagation rate and displacement increment.

[0035] Understandably, slope stability models provide a snapshot of the macroscopic structural state of a slope, while velocity variation models reveal the dynamic evolution of the microscopic physical properties of the medium. Slope stability models can predict the synthetic waveform that should be generated at the same source and receiver locations. If the mismatch between the synthetic waveform and the observed waveform is too high, it is considered that the slope may have health risks. Velocity variation models, during long-term continuous monitoring, compare the noise cross-correlation functions at different time periods to obtain the velocity changes in multiple time slices. Then, through differential imaging analysis, they can identify subtle changes in the internal structure of the slope over time, thereby discovering potential instability areas in advance. This is suitable for long-term trend monitoring and health diagnosis.

[0036] Step S105: Conduct prevention and early warning of the safety status of highway slopes based on risk indicators.

[0037] In this embodiment of the invention, the risk status is determined to meet the triggering conditions, and the treatment device is controlled to perform prevention and control operations. Specifically, corresponding preset risk thresholds can be set for various risk indicators. Once any risk indicator value exceeds the preset threshold, it is determined that the preset triggering conditions are met, and an early warning information is output. For example, an early warning information is sent to the management personnel through audible and visual alarms, mobile phone text messages, system platform message pushes, etc., and the treatment execution module is linked to implement proactive prevention and control measures. After receiving the prevention and control instruction, the treatment execution module will immediately drive one or more slope treatment devices to perform preset prevention and control operations. In this embodiment of the invention, the proactive treatment unit can integrate multiple treatment devices, such as slope drainage devices, anchor bolt or anchor cable prestress adjustment devices, and flexible protective net control devices. Among them: slope drainage devices, such as intercepting ditches located at the top of the slope or horizontal drainage holes in the slope body, whose water pumps automatically start after receiving instructions to drain excess water in the slope body, reduce pore water pressure, and thus improve slope stability; anchor bolt or anchor cable prestress adjustment devices, such as intelligent tensioning equipment that applies prestress to some key anchor cables or anchor bolts in the slope body, which automatically applies a certain prestress increment to the selected anchor cables after receiving instructions to enhance the slope's anti-sliding force; flexible protective net control devices, such as active or passive flexible protective nets installed at the toe of the slope, whose control system can put them into a ready state after receiving instructions, such as tightening or adjusting their posture, in order to better intercept possible rockfalls.

[0038] In an optional embodiment, the slope stability model and velocity change model provided by the present invention also have the ability to adaptively update. New real-time data accumulates continuously over time. A period (e.g., weekly or monthly) can be set to incrementally train or retrain the parameters of the slope stability model and velocity change model using the newly added data. This enables the model to learn the long-term evolution trends of the slope caused by factors such as seasonal changes, weathering, or human activities, and adaptively adjust its internal parameters, thereby ensuring continuous assessment accuracy.

[0039] Through the above steps, this invention provides a method for real-time monitoring and proactive prevention of highway slopes. Distributed sensing modules are installed at predetermined locations on the highway slope to collect real-time sensing data characterizing the slope's state. This real-time sensing data includes continuous vibration data. The continuous vibration data is used to perform full-waveform inversion on a preset data-driven model, generating a slope stability model characterizing the internal structural state of the slope. Based on background noise in the continuous vibration data, a velocity variation model characterizing the rate of change of the slope medium is generated. Combining the slope stability model and the velocity variation model, the risk status of the highway slope is assessed in real time, generating risk indicators. Based on these risk indicators, the safety status of the highway slope is monitored and warned against. Therefore, this invention has at least the following beneficial effects: 1) Improve the accuracy and foresight of the assessment: By integrating the first stability model, which characterizes the macro structure, and the second velocity model, which is more sensitive to changes in the medium, complementary monitoring of the long-term and short-term trends of slope changes is achieved, which can identify potential instability risks earlier and significantly improve the overall accuracy and foresight of the risk assessment. 2) Achieving proactive closed-loop prevention and control: The intelligent assessment results are directly linked with the slope treatment device, constructing a complete closed loop from data collection and intelligent analysis to automatic control, realizing proactive and pre-emptive intervention in slope risks; compared with the traditional passive early warning mode, it greatly shortens the response time and improves the efficiency and effectiveness of disaster prevention and control. 3) Improve intelligence and adaptability: By adopting a data-driven model, especially through self-supervised learning, the reliance on manually labeled geological model data is reduced, enabling the system to have the ability to learn adaptively and optimize iteratively, and to adapt to the long-term evolution of different geological conditions and slope conditions.

[0040] Furthermore, as Figure 1 In specific implementation, embodiments of the present invention provide a real-time monitoring and active prevention system for highway slopes, such as... Figure 5 As shown, the system may include: a distributed sensing module 510 and a data processing module 520; the distributed sensing module 510 and the data processing module 520 are communicatively connected. The distributed sensing module 510 is set at a preset location on the highway slope and can be used to collect real-time sensing data that characterizes the state of the highway slope; the real-time sensing data includes continuous vibration data. The data processing module 520 may include a model building unit 521 and a risk assessment unit 522; The model building unit 521 can be used to perform full waveform inversion on a preset data-driven model using continuous vibration data to generate a slope stability model that characterizes the internal structural state of the slope; and to generate a velocity variation model that characterizes the rate of change of the slope medium based on the background noise in the continuous vibration data. Risk assessment unit 522 can be used to combine slope stability model and velocity change model to assess the risk status of highway slopes in real time and generate risk indicators; and to prevent and warn of the safety status of highway slopes based on the risk indicators.

[0041] In an alternative embodiment, such as Figure 6 As shown, another embodiment of the present invention provides a real-time monitoring and active prevention and control system for highway slopes, which also includes a treatment execution module 530, and the treatment execution module 530 is communicatively connected to the data processing module 520.

[0042] The governance implementation module 530 includes at least one of a slope drainage device, a prestressed adjustment device for anchor bolts or anchor cables, and a flexible protective net control device, and can be used to implement proactive prevention and control measures.

[0043] It should be noted that other corresponding descriptions of the functional modules involved in the real-time monitoring and active prevention system for highway slopes provided in this embodiment of the invention can be found in [reference needed]. Figure 1 The corresponding description of the method shown will not be repeated here.

[0044] Those skilled in the art will clearly understand that the specific working process of the systems, devices, modules and units described above can be referred to the corresponding process in the foregoing method embodiments. For the sake of brevity, it will not be repeated here.

[0045] Furthermore, the functional units in the various embodiments of the present invention can be physically independent of each other, or two or more functional units can be integrated together, or all functional units can be integrated into one processing unit. The integrated functional units described above can be implemented in hardware, or in software or firmware.

[0046] Those skilled in the art will understand that if the integrated functional unit is implemented in software and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or all or part of it, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computing device (e.g., a personal computer, server, or network device) to execute all or part of the steps of the methods described in the embodiments of the present invention when running the instructions. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0047] Alternatively, all or part of the steps of the foregoing method embodiments can be implemented by hardware (such as a computing device, personal computer, server, or network device) related to program instructions. The program instructions can be stored in a computer-readable storage medium. When the program instructions are executed by the processor of the computing device, the computing device executes all or part of the steps of the methods described in the various embodiments of the present invention.

[0048] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that within the spirit and principles of the present invention, modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the corresponding technical solutions to depart from the protection scope of the present invention.

Claims

1. A method for real-time monitoring and active prevention of highway slopes, characterized in that, The method includes: Distributed sensing modules are installed at predetermined locations on the highway slope, and real-time sensing data characterizing the state of the highway slope is collected using the distributed sensing modules; the real-time sensing data includes continuous vibration data. The continuous vibration data is used to perform full waveform inversion on the preset data-driven model to generate a slope stability model that characterizes the internal structural state of the slope. Based on the background noise in the continuous vibration data, a velocity variation model characterizing the rate of change of the slope medium is generated. By combining the slope stability model and the velocity change model, the risk status of the highway slope is assessed in real time, and risk indicators are generated. The safety status of the highway slope is monitored and warned based on the aforementioned risk indicators.

2. The method according to claim 1, characterized in that, The installation of distributed sensing modules at predetermined locations on the highway slope includes: Multiple sensing devices spaced at a certain distance are installed on the surface and toe of the highway slope to form a sensing device array. The sensing device includes at least one of a seismograph, an accelerometer, and a tiltmeter.

3. The method according to claim 1, characterized in that, The step of using the continuous vibration data to perform full waveform inversion on a preset data-driven model to generate a slope stability model characterizing the internal structural state of the slope includes: Construct a neural network model based on an encoder-decoder structure as a data-driven model; The observed waveforms in the continuous vibration data are used as pseudo-labels, and the data-driven model is trained by self-supervised full waveform inversion to obtain the trained slope stability model. The slope stability model is used to predict the synthetic waveform that should be generated under the same source and receiver locations.

4. The method according to claim 3, characterized in that, The training of the data-driven model through self-supervised full waveform inversion includes: The data-driven model is used as the initial model for forward modeling simulation to calculate the waveform mismatch between the synthesized waveform and the observed waveform. Using minimizing the waveform mismatch as the objective function, the weight parameters of the initial model are adjusted using the backpropagation algorithm until the initial model training converges.

5. The method according to claim 1, characterized in that, The generation of a velocity variation model characterizing the rate of change of the slope medium based on the background noise in the continuous vibration data includes: Extract the environmental background noise segment from the continuous vibration data; A velocity variation model is constructed based on the cross-correlation algorithm and imaging technology of the aforementioned environmental background noise segments; The velocity variation model is used to predict the deterioration or stress variation trend of the underground medium structure of the slope.

6. The method according to claim 5, characterized in that, The process of constructing a velocity change model based on the cross-correlation algorithm and imaging technology of the environmental background noise segments includes: Long-term continuous cross-correlation calculations are performed on background noise signals from different sensing devices to extract the noise cross-correlation function and calculate the Green's function corresponding to the reference time and the target time. The time series model is inverted based on the changes in the Green's function of multiple sensing devices to obtain a velocity change model that reflects the changes in the underground medium of the slope.

7. The method according to claim 1, characterized in that, The method combines the slope stability model and the velocity change model to assess the risk status of the highway slope in real time and generate risk indicators, including: The synthetic waveform of real-time vibration data is analyzed using the slope stability model, and the waveform mismatch value between the synthetic waveform and the observed waveform is calculated. The velocity variation model is used to continuously monitor real-time vibration data over a long period of time. The noise cross-correlation function at different time periods is compared, and the change information of the underground medium of the slope is identified by differential imaging analysis. The change information of the underground medium of the slope includes the crack propagation rate and / or displacement increment. The risk indicators include at least one of waveform mismatch value, crack propagation rate, and displacement increment.

8. The method according to any one of claims 1 to 7, characterized in that, The method of preventing and issuing early warnings about the safety status of the highway slope based on the risk indicators includes: When any risk indicator exceeds the preset threshold, an early warning message is output, and the governance execution module is linked to implement proactive prevention and control measures. The treatment execution module includes at least one of the following: a slope drainage device, a prestressed adjustment device for anchor bolts or anchor cables, and a flexible protective net control device.

9. A real-time monitoring and active control system for highway slopes, characterized in that, The system includes a distributed sensing module and a data processing module; The distributed sensing module is set at a preset location on the highway slope and is used to collect real-time sensing data characterizing the state of the highway slope; the real-time sensing data includes continuous vibration data. The data processing module is used to perform full waveform inversion on a preset data-driven model using the continuous vibration data to generate a slope stability model characterizing the internal structural state of the slope; based on the background noise in the continuous vibration data, a velocity change model characterizing the rate of change of the slope medium is generated; combining the slope stability model and the velocity change model, the risk status of the highway slope is assessed in real time, and risk indicators are generated; and the safety status of the highway slope is prevented and warned based on the risk indicators.

10. The system according to claim 9, characterized in that, The system also includes a governance execution module; The governance execution module includes at least one of a slope drainage device, a prestressed adjustment device for anchor bolts or anchor cables, and a flexible protective net control device, for implementing proactive prevention and control measures.

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