Road slope prestress guyed displacement intelligent monitoring and early warning system

Abstract

The invention discloses a highway slope prestress guyed displacement intelligent monitoring and early warning system, and relates to the technical field of highway slope displacement monitoring and early warning, the highway slope prestress guyed displacement intelligent monitoring and early warning system comprises a monitoring module, an early warning module and a database, the monitoring module comprises a vehicle sensing assembly and a soil sensing assembly, the vehicle sensing assembly is connected with the database through signal transmission, and the soil sensing assembly is connected with the database through signal transmission. The vehicle sensing assembly comprises a weighing unit, a vibration and stress coupling monitoring unit and a model building unit, the weighing unit collects vehicle information and transmits the vehicle information to the database, and the vibration and stress coupling monitoring unit is used for collecting vibration frequency, energy distribution and soil stress dynamic changes caused by vehicle passing; the information is transmitted to a database, and the model building unit is used for building a vehicle load-slope stress transfer model. According to the method, the vehicle load-slope stress transfer model is constructed by collecting the vehicle driving information, and the function of quantifying the influence of the vehicle load on the slope is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of highway slope displacement monitoring and early warning, and specifically to an intelligent monitoring and early warning system for highway slope prestressed cable - type displacement. Background Technique

[0002] Traditional geological exploration methods rely on on - site inspection by professionals and drilling for sampling. There are problems such as long monitoring periods, limited coverage, and difficulties in night operations, making it difficult to meet the all - weather monitoring requirements of high - speed roads. Early monitoring technologies mostly focused on single parameters and lacked the ability to synergistically perceive multiple factors affecting slope stability, resulting in delayed early warnings. The vibration load generated by vehicle traffic can cause instantaneous displacement fluctuations on the slope surface, which may lead to misjudgments. At the same time, the fatigue effect on the slope caused by repeated rolling of heavy vehicles cannot be quantified solely through displacement data, masking the true deformation trend and resulting in delayed early warnings. Natural factors such as rainfall and geological activities exhibit similar characteristics to vehicle vibrations in displacement data. Without information on vehicle traffic periods, it is impossible to effectively separate interference sources and reduce the early warning accuracy.

[0003] Patent CN118675073B discloses a method for predicting highway slope displacement. The above - mentioned patent realizes the improvement of prediction accuracy through the comprehensive utilization of multi - source data and advanced deep - learning models.

[0004] The above - mentioned patent constructs a comprehensive data acquisition system by integrating various sensor data, satellite remote sensing, and UAV images. It uses a convolutional network combined with a long - short - term memory neural network to extract spatio - temporal features from images, and a graph neural network combined with a long - short - term memory neural network to extract spatio - temporal features from sensor data. It fuses features through an attention mechanism to form comprehensive spatio - temporal feature data, applies ensemble empirical mode decomposition to decompose and reconstruct the fused features, removes noise, retains core features, and inputs the main spatio - temporal features into a Transformer model for prediction. Utilizing the powerful spatio - temporal dependence capture ability of the Transformer model, it realizes the prediction of highway slope displacement, but there is room for optimization in vehicle travel information acquisition.

[0005] Therefore, this application proposes an intelligent monitoring and early warning system for highway slope prestressed cable - type displacement that collects vehicle travel information to construct a vehicle load - slope stress transfer model. Summary of the Invention

[0006] The purpose of the present invention is to provide an intelligent monitoring and early warning system for highway slope prestressed cable - type displacement to solve the technical problem in the above - mentioned background technique that the monitoring and early warning system does not refer to vehicle travel information.

[0007] To achieve the above object, the present invention provides the following technical solutions: a prestressed cable - type displacement intelligent monitoring and early warning system for highway slopes, including a monitoring module, an early warning module, and a database. The monitoring module includes a vehicle sensing component and a soil sensing component. The vehicle sensing component is connected to the database through signal transmission; The vehicle sensing component includes: a weighing unit, a vibration - stress coupling monitoring unit, and a model construction unit. The weighing unit collects vehicle axle weight, vehicle speed, vehicle type information, vehicle real - time trajectory, and vehicle aggregation status, and transmits the information to the database; The vibration - stress coupling monitoring unit is used to collect the vibration frequency, energy distribution, and dynamic changes in soil body stress caused by vehicle passage, and transmits the information to the database; The model construction unit, by comprehensively considering vehicle load, vibration field, and stress field, establishes a three - dimensional transfer function, builds a slope entity model based on Abaqus, calculates stress seam filling under different working conditions using the strength reduction method, follows the principle of densification at the slope toe for mesh division, and constructs a vehicle load - slope stress transfer model.

[0008] Preferably, the vehicle sensing component further includes a visual sensor and an infrared sensor. The visual sensor is used to collect vehicle type, license plate, and body color information and transmit it to the database. The infrared sensor is used to collect vehicle passing frequency and speed, and transmit the information to the database.

[0009] Preferably, the model construction unit constructs a displacement - humidity - water level coupling equation, calculates the displacement field distribution using the elastic layer theory to establish a vehicle load - displacement transfer matrix, obtains the three - dimensional real - scene model of the slope terrain in the database, establishes a parametric model of the slope soil layer through CAE software, maps the information collected by the soil sensing component to the three - dimensional real - scene model, calculates the stress propagation path of vehicle load in three - dimensional space through the finite element method, dynamically updates the soil layer parameters based on the Bayesian network, and introduces the LSTM network to predict the lag effect of water level change on displacement to construct a vehicle - slope - environment model.

[0010] Preferably, the early warning module is connected to the database through signal transmission. An early warning model is set inside the early warning module. Based on the vehicle load - slope stress transfer model and the vehicle - slope - environment model, the early warning model dynamically corrects the early warning threshold according to the displacement trend predicted by the LSTM network, introduces the Bayesian network to evaluate the joint triggering probability of multiple indicators, and issues an early warning to the management personnel according to the prediction result.

[0011] Preferably, the monitoring and early warning system further includes a recovery module. The recovery module is connected to the monitoring module and the early warning module through signal lines; The recovery module includes: a piezoelectric energy harvesting unit, a wind power generation unit, a heat energy recovery unit, an electromagnetic power generation unit, and an energy storage unit. The energy storage unit distributes electrical energy to the monitoring module and the warning module through a database; The piezoelectric energy harvesting unit is disposed in the road surface layer and the roadbed. The piezoelectric effect is triggered by the road surface deformation caused by vehicle decompression, and mechanical energy is converted into electrical energy and stored in the energy storage unit; The wind power generation unit is disposed on the slope guardrail and the isolation belt. By capturing the turbulent wind energy generated by the vehicle driving at a high speed and adjusting the angle according to the wind direction, the generator is driven to generate electricity; The heat energy recovery unit recovers heat energy through the heat gradient in the area where the tire contacts the ground; The electromagnetic power generation unit generates an induced current by embedding magnets and coils in the flexible road surface and the slope structural layer, and releasing the magnetic field periodically under vehicle load compression.

[0012] Preferably, the weighing unit includes a piezoelectric sensor and an on-vehicle positioning terminal. The piezoelectric sensor is used to collect vehicle axle weight, vehicle speed, and vehicle type information in real time and transmit the information to the database. The on-vehicle positioning terminal is used to track the real-time trajectory of the vehicle, monitor the aggregation state of the vehicle, and transmit the information to the database.

[0013] Preferably, the vibration and stress coupling monitoring unit includes a fiber Bragg grating vibration sensor and a digital earth pressure gauge. The fiber Bragg grating vibration sensor is arranged along the slope toe to detect the vibration frequency caused by vehicle passage and transmit the information to the database. The digital earth pressure gauge is arranged on the surface layer and deep layer of the slope to record the dynamic change of soil stress when the vehicle passes.

[0014] Preferably, the soil sensing component is connected to the database through signal transmission, and the soil sensing component is connected to the database through signal transmission; The soil sensing component includes a GNSS displacement monitoring station, a wire-pulling displacement meter, an inclinometer, a soil moisture sensor, a groundwater level gauge, and a rain gauge. The GNSS displacement monitoring station measures the surface displacement of the slope through satellite positioning technology. The wire-pulling displacement meter is used to measure the local displacement of the slope. The inclinometer is used to monitor the deep horizontal displacement inside the slope. The soil moisture sensor is used to measure the water content of the surface soil layer. The groundwater level gauge is used to monitor the groundwater level fluctuation. The rain gauge is used to collect rain data in real time.

[0015] Preferably, the warning is divided into three levels. A first-level warning is issued when a single index exceeds the limit and the LSTM prediction trend is stable. A second-level warning is issued when two indicators exceed the standard synergistically for 30 minutes. A third-level warning is issued when the slip surface shown by the three-dimensional model is penetrated.

[0016] Preferably, the piezoelectric energy harvesting unit provides energy for the monitoring module, the wind power generation unit provides energy for the vision sensor and the infrared sensor, the heat energy recovery unit is used to maintain the operating temperature of the monitoring module and the warning module, and the electromagnetic power generation unit provides energy for the warning module.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. By designing to collect vehicle driving information to construct a vehicle load - slope stress transfer model, the present invention realizes the function of quantifying the influence of vehicle load on the slope, solves the problems of being unable to capture the instantaneous influence of vehicle dynamic load on the slope and the difficulty in quantifying the coupling relationship between vibration frequency and soil stress change, can finely map the dynamic vehicle load, broadens the application scenario of the system, and improves the accuracy of early warning. 2. By designing to collect vehicle, slope and environment information to construct a vehicle - slope - environment model, the present invention realizes the function of multi - source perception data fusion, solves the problems of isolated data analysis, static data collection and single warning threshold, can comprehensively monitor vehicle, slope and environment data, improves the warning accuracy rate, extends the service life of the three - dimensional model, shortens the warning response time, and reduces the system operation and maintenance cost. 3. By designing a warning module, the present invention realizes the function of dynamically adjusting the warning threshold, solves the problems of single information collection, large judgment error and low prediction accuracy, can match the warning threshold with environmental load and structural response, shortens the warning time, and improves the warning sensitivity and accuracy rate. 4. By designing an energy recovery module, the present invention realizes the function of recovering dissipated energy, solves the problems of limitations in single energy recovery mode, dependence on external power supply and high cost, can avoid monitoring interruption caused by power supply interruption, improves the energy recovery efficiency and coverage scenario, enhances the system autonomy and stability, reduces the cost, and improves the environmental protection performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a schematic diagram of the composition of the intelligent monitoring and warning system of the present invention; Figure 2 is a schematic diagram of the composition of the vehicle perception component of the present invention; Figure 3 is a schematic diagram of the composition of the soil perception component of the present invention; Figure 4 is a schematic diagram of the warning module of the present invention; Figure 5 is a schematic diagram of the composition of the recovery module of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0020] Embodiment 1: Please refer to Figure 1 and Figure 2 , a prestressed cable-pull type intelligent monitoring and early warning system for highway slopes, including a monitoring module, an early warning module, and a database. The monitoring module includes a vehicle sensing component and a soil sensing component. The vehicle sensing component is connected to the database through signal transmission; The vehicle sensing component includes: a weighing unit, a vibration and stress coupling monitoring unit, and a model building unit. The weighing unit collects vehicle axle weight, vehicle speed, vehicle type information, vehicle real-time trajectory, and vehicle aggregation state, and transmits the information to the database; The vibration and stress coupling monitoring unit is used to collect the vibration frequency, energy distribution, and dynamic changes in soil stress caused by vehicle passage, and transmits the information to the database; The model building unit establishes a three-dimensional transfer function by comprehensively considering vehicle load, vibration field, and stress field, builds a slope entity model based on Abaqus, calculates stress seam filling under different working conditions using the strength reduction method, and follows the principle of densification at the slope toe for mesh division to construct a vehicle load - slope stress transfer model; The vehicle sensing component further includes a visual sensor and an infrared sensor. The visual sensor is used to collect vehicle type, license plate, and body color information and transmit it to the database. The infrared sensor is used to collect vehicle passage frequency and speed, and transmit the information to the database; The weighing unit includes a piezoelectric sensor and an on-vehicle positioning terminal. The piezoelectric sensor is used to collect vehicle axle weight, vehicle speed, and vehicle type information in real time and transmit the information to the database. The on-vehicle positioning terminal is used to track the vehicle real-time trajectory and monitor the vehicle aggregation state, and transmit the information to the database; The vibration and stress coupling monitoring unit includes a fiber Bragg grating vibration sensor and a digital earth pressure gauge. The fiber Bragg grating vibration sensor is arranged along the slope toe of the slope to detect the vibration frequency caused by vehicle passage and transmit the information to the database. The digital earth pressure gauge is arranged on the surface layer and deep layer of the slope to record the dynamic changes in soil stress when the vehicle passes; Furthermore, through the dynamic weighing technology of piezoelectric sensors, the total weight of passing vehicles is obtained in real time, the weight of each axle and the total number of axles of the vehicle are accurately measured, whether the axle load distribution is compliant is identified, the vehicle type is inferred based on the wheelbase and the number of axles of each vehicle, the real-time speed of vehicle passing is collected, combined with the vehicle information collected by the vision sensor, the vehicle information of the vehicle in the database is improved. At night or in low visibility conditions, the passing frequency and density of vehicles are collected by infrared sensors. According to the collected vehicle information, combined with the vibration frequency collected by the fiber grating vibration sensor and the dynamic change of soil stress collected by the digital earth pressure gauge when the vehicle passes, a three-dimensional transfer function is established. Based on Abaqus, a slope solid model is established. The strength reduction method is used to calculate the stress seam filling under different working conditions. The mesh division follows the principle of densification at the slope toe. A vehicle load-slope stress transfer model is constructed. After adding the vehicle information as a parameter to the intelligent monitoring and prediction system, the critical traffic flow density threshold can be used to dynamically adjust the values of vehicle speed limit and traffic flow limit, which is helpful for traffic control and management. The function of quantifying the influence of vehicle load on the slope is realized, and the problems of being unable to capture the instantaneous influence of vehicle dynamic load on the slope and being difficult to quantify the coupling relationship between vibration frequency and soil stress change are solved. The dynamic vehicle load can be finely mapped, the application scenario of the system is broadened, and the accuracy of early warning is improved.

[0021] Embodiment 2: Please refer to Figure 1 and Figure 3 , a prestressed cable displacement intelligent monitoring and warning system for highway slopes. The model construction unit establishes a vehicle load-displacement transfer matrix by constructing a displacement-humidity-water level coupling equation and calculating the displacement field distribution using the elastic layer theory, obtains a three-dimensional real-scene model of the slope terrain in the database, establishes a parametric model of the slope soil layer through CAE software, maps the information collected by the soil sensing component to the three-dimensional real-scene model, calculates the stress propagation path of vehicle load in three-dimensional space by the finite element method, dynamically updates the soil layer parameters based on the Bayesian network, and introduces the LSTM network to predict the lag effect of water level change on displacement to construct a vehicle-slope-environment model; The soil sensing component is connected to the database through signal transmission, and the soil sensing component is connected to the database through signal transmission; The soil sensing component includes a GNSS displacement monitoring station, a cable displacement meter, an inclinometer, a soil humidity sensor, a groundwater level gauge and a rain gauge. The GNSS displacement monitoring station measures the surface displacement of the slope through satellite positioning technology. The cable displacement meter is used to measure the local displacement of the slope. The inclinometer is used to monitor the deep horizontal displacement inside the slope. The soil humidity sensor is used to measure the water content of the surface soil layer. The groundwater level gauge is used to monitor the fluctuation of the groundwater level. The rain gauge is used to collect rain data in real time; Furthermore, GNSS displacement monitoring stations are arranged along the slope surface with a sampling frequency greater than or equal to 10 Hz, and millimeter-level surface displacement monitoring is achieved through the Beidou / GPS dual-mode differential positioning technology. The wire-pulling displacement meter is arranged at the potential slip surface, the inclinometer is installed vertically in a borehole, and the soil moisture sensors are arranged in a double layer with buried depths of 0.5 m and 1.5 m. Based on the vehicle load-slope stress transfer model, by collecting slope displacement data and environmental parameters and integrating the two into the vehicle load-slope stress transfer model, using the coordinates of the GNSS displacement monitoring station as a reference, the local coordinates of devices such as the wire-pulling displacement meter and the inclinometer are transformed. Combining the three-dimensional real-scene model in the database as the spatial reference for the device arrangement position, by constructing the coupling equation of displacement-humidity-water level, and combining the inclinometer data to invert the position of the deep slip surface, the elastic layer theory is used to calculate the displacement field distribution, establish the vehicle load-displacement transfer matrix, obtain the three-dimensional real-scene model of the slope terrain in the database, establish a parametric model of the slope soil layer through CAE software, map the information collected by the soil perception component to the three-dimensional real-scene model, calculate the stress propagation path of the vehicle load in the three-dimensional space through the finite element method, dynamically update the soil layer parameters based on the Bayesian network, introduce the LSTM network to predict the lag effect of water level change on displacement, and construct a vehicle-slope-environment model, realizing the function of multi-source perception data fusion, solving the problems of isolated data analysis, static data collection, and single warning threshold, being able to comprehensively monitor vehicle, slope, and environmental data, improving the warning accuracy rate, extending the service life of the three-dimensional model, shortening the warning response time, and reducing the system operation and maintenance cost.

[0022] Embodiment 3: Please refer to Figure 1 and Figure 4 , a prestressed wire-pulling displacement intelligent monitoring and warning system for highway slopes. The warning module is connected to the database through signal transmission. An early warning model is set inside the warning module. The warning model is based on the vehicle load-slope stress transfer model and the vehicle-slope-environment model. The displacement trend predicted by the LSTM network is used to dynamically correct the warning threshold. The Bayesian network is introduced to evaluate the joint triggering probability of multiple indicators, and a warning is sent to the management personnel according to the prediction result. The warning is divided into three levels. A first-level warning is issued when a single indicator exceeds the limit and the LSTM prediction trend is stable. A second-level warning is issued when two indicators cooperate to exceed the standard for 30 minutes. A third-level warning is issued when the three-dimensional model shows that the slip surface is penetrated. Furthermore, based on the information collected by the monitoring module, combined with the vehicle load - slope stress transfer model and the vehicle - slope - environment model, the displacement trend is predicted through the LSTM network, and the Bayesian network is introduced to evaluate the joint triggering probability of multiple indicators. The first - level warning trigger is adjusted when one of the following conditions is met: the proportion of vehicle - load - induced displacement exceeds 15% for the first time, the predicted displacement trend increase of the LSTM model in the next 24 hours is less than 5%, the horizontal or vertical surface displacement rate is greater than 5 mm / day, the cumulative rainfall in 24 hours is greater than 50 mm, and the increase in soil moisture content is greater than 20%. The second - level warning trigger conditions are as follows: the vehicle - load proportion is greater than 25% for more than 35 minutes continuously, the landslide probability evaluated by the Bayesian network is 0.5 - 0.7, the deep - horizontal displacement mutation rate is greater than 0.1 mm / h, the surface displacement rate is greater than 8 mm / day, the groundwater level rises by more than or equal to 1 m / 24 h, and the pore water pressure is greater than 0.3 MPa. The third - level warning trigger conditions are as follows: the proportion of vehicle - load - induced displacement is greater than 30% and the slope stress and strain are greater than 150% of the design value, the joint probability of the Bayesian network is greater than 0.7, the sliding - surface displacement rate is greater than 0.5 mm / h, the three - dimensional model shows that the sliding surface is penetrated, the rainfall is greater than 100 mm / 24 h, and the soil moisture content is greater than 95%. By integrating slope information, soil information, environmental information, and vehicle information, it predicts and warns of possible safety risks. When giving a warning, it uses the LSTM neural network to dynamically model the displacement time - series data monitored by GNSS, predicts the displacement change trend in the next 6 hours through a sliding window, and dynamically adjusts the threshold interval in combination with displacement acceleration. The Bayesian network calculates the joint triggering probability through multiple indicators. When the joint triggering probability is greater than 0.7, it automatically reduces the single - indicator threshold by 15% - 20%, realizing the function of dynamically adjusting the warning threshold, solving the problems of single information collection, large judgment error, and low prediction accuracy, being able to match the warning threshold with environmental loads and structural responses, shortening the warning time, and improving the warning sensitivity and accuracy.

[0023] Example 4: Please refer to Figure 1 and Figure 5 , the intelligent monitoring and warning system for prestressed cable - type displacement of highway slopes. The monitoring and warning system further includes a recovery module, and the recovery module is connected to the monitoring module and the warning module through signal lines; The recovery module includes: a piezoelectric energy - harvesting unit, a wind - power generation unit, a heat - energy recovery unit, an electromagnetic - power generation unit, and an energy - storage unit. The energy - storage unit distributes electric energy to the monitoring module and the warning module through a database; The piezoelectric energy - harvesting unit is arranged on the road surface layer and the roadbed. The piezoelectric effect is triggered by the road - surface deformation caused by vehicle decompression, converting mechanical energy into electrical energy and storing it in the energy - storage unit; The wind power generation unit is installed on the slope guardrail and the isolation belt. By capturing the turbulent wind energy generated by the vehicle's high-speed driving and adjusting the angle according to the wind direction, it drives the generator to generate electricity. The heat energy recovery unit recovers heat energy through the heat gradient in the area where the tire contacts the ground. The electromagnetic power generation unit embeds magnets and coils into the flexible road surface and the slope structural layer. When the vehicle load periodically compresses and releases the magnetic field, an induced current is generated. The piezoelectric energy harvesting unit provides energy for the monitoring module, the wind power generation unit provides energy for the visual sensor and the infrared sensor, the heat energy recovery unit is used to maintain the working temperature of the monitoring module and the warning module, and the electromagnetic power generation unit provides energy for the warning module. Furthermore, the piezoelectric energy harvesting unit arranged in the roadbed and subgrade triggers the piezoelectric effect through dynamic pressure changes when the vehicle passes by, converting mechanical energy into electrical energy. The instantaneous power generated by a single vehicle rolling can reach 0.5W - 1.2W, which can meet the real-time power supply requirements of the micro sensor. The energy collected when the vehicle passes is used to supply the monitoring module. The wind power generation unit arranged on the slope guardrail and the isolation belt can adjust the angle according to the wind direction, improving the efficiency of wind power generation. A single wind power generation unit can stably output 20W - 50W power under the traffic flow density of 40km / h, and can provide electrical energy for monitoring devices such as visual sensors and infrared sensors. When the vehicle passes on the highway, due to different vehicle models, speeds, weights, and driver operations, the load on the ground is also different. In areas where the vehicle frequently starts and stops, such as long downhill braking sections, curves, variable slope sections, and service areas, by setting up a heat energy recovery unit, the heat generated by the friction between the wheels and the ground is recovered, and electricity is generated using the temperature difference, forming a heat - electricity closed loop. Magnets and coils are embedded in the flexible road surface and the slope structural layer, and an induced current is generated by using the vehicle load to periodically compress and release the magnetic field. A single vehicle passing can generate a pulsed voltage of 3V - 5V, which can provide energy for the warning module after rectification. It realizes the function of recovering the dissipated energy during vehicle driving, solves the problems of limitations in a single energy recovery mode, dependence on external power supply, and high cost, can avoid monitoring interruption caused by power supply interruption, improves the energy recovery efficiency and coverage scenarios, enhances the system's autonomy and stability, reduces costs, and improves the environmental performance of the system.

[0024] Example 5: Please refer to Figure 1 、 Figure 2 and Figure 5 ., a prestressed cable - type displacement intelligent monitoring and warning system for highway slopes, includes a monitoring module, a warning module, and a database. The monitoring module includes a vehicle perception component and a soil perception component. The vehicle perception component is connected to the database through signal transmission. The vehicle perception component includes: a weighing unit, a vibration and stress coupling monitoring unit, and a model construction unit. The weighing unit collects vehicle axle weight, vehicle speed, vehicle type information, real-time vehicle trajectory, and vehicle aggregation status, and transmits the information to the database; The vibration and stress coupling monitoring unit is used to collect the vibration frequency, energy distribution, and dynamic changes in soil stress caused by vehicle passage, and transmit the information to the database; The model construction unit establishes a three-dimensional transfer function by comprehensively considering vehicle load, vibration field, and stress field, builds a slope entity model based on Abaqus, calculates stress seam repair under different working conditions using the strength reduction method, and follows the principle of densification at the slope toe for mesh division to construct a vehicle load-slope stress transfer model; The monitoring and warning system further includes a recovery module, and the recovery module is connected to the monitoring module and the warning module through signal lines; The recovery module includes: a piezoelectric energy harvesting unit, a wind power generation unit, a heat energy recovery unit, an electromagnetic power generation unit, and an energy storage unit. The energy storage unit distributes electric energy to the monitoring module and the warning module through the database; The piezoelectric energy harvesting unit is arranged on the road surface and subgrade, and triggers the piezoelectric effect through the deformation of the road surface caused by vehicle decompression, converting mechanical energy into electric energy and storing it in the energy storage unit; The wind power generation unit is arranged on the slope guardrail and isolation belt, captures the turbulent wind energy generated by the vehicle driving at high speed, adjusts the angle according to the wind direction, and drives the generator to generate electricity; The heat energy recovery unit recovers heat energy through the heat gradient in the contact area between the tire and the ground; The electromagnetic power generation unit generates induced current by embedding magnets and coils in the flexible road surface and slope structural layer, and periodically compressing and releasing the magnetic field under vehicle load; Furthermore, during the vehicle passage, the energy is recovered by the recovery module to provide energy support for the monitoring module and the warning module. When the vehicle sensing component detects that the axle load or vehicle type of the vehicle exceeds the design threshold, or when the vehicle aggregation causes a sharp increase in local load, to avoid overloading and causing excessive deformation of the road surface, resulting in the fracture of the piezoelectric material or the damage of the coil, the piezoelectric energy collection unit and the electromagnetic power generation unit in this area are turned off in advance. When the deep soil pressure of the slope is abnormal, the buried electromagnetic power generation unit is turned off. When the vehicle load-slope stress transfer module calculates and shows that the slope deformation may cause the instability of the guardrail structure when the slope stress compensation approaches the yield strength of the material, the wind power generation unit needs to be turned off. When the fiber Bragg grating sensor detects that the vibration frequency is close to the natural frequency of the slope and there is a risk of resonance, the piezoelectric energy collection unit, the electromagnetic power generation unit and the heat energy recovery unit need to be turned off to avoid resonance from exacerbating mechanical fatigue and damaging the heat conduction material. The function of dynamically protecting the recovery module is realized, the problem of relying on physical protection and static threshold protection is solved, the damage to the recovery module can be reduced, the service life of the recovery module is extended, and the energy recovery efficiency is improved.

[0025] Working principle: Through the dynamic weighing technology of piezoelectric sensors, the total weight of passing vehicles is obtained in real time, the weight of each axle and the total number of axles of the vehicle are accurately measured, whether the axle load distribution is compliant is identified, the vehicle type is inferred based on the wheelbase and the number of axles of each vehicle, the real-time speed of vehicle passage is collected, and combined with the vehicle information collected by the vision sensor, the vehicle information of this vehicle in the database is improved. When it is night or the visibility is low, the passing frequency and density of vehicles are collected by infrared sensors. According to the collected vehicle information, combined with the dynamic changes of the vibration frequency collected by the fiber Bragg grating vibration sensor and the soil stress collected by the digital soil pressure gauge when the vehicle passes, a three-dimensional transfer function is established. Based on Abaqus, a slope solid model is established, and the strength reduction method is used to calculate the stress compensation under different working conditions. The mesh division follows the principle of densification at the slope toe, and a vehicle load-slope stress transfer model is constructed. After adding the vehicle information as a parameter to the intelligent monitoring and prediction system, the numerical values of vehicle speed limit and current limit can be dynamically adjusted through the critical traffic flow density threshold, which is helpful for traffic control and management; GNSS displacement monitoring stations are arranged along the slope surface, with a sampling frequency greater than or equal to 10 Hz. Millimeter-level surface displacement monitoring is achieved through Beidou / GPS dual-mode differential positioning technology. Wire-pulling displacement gauges are arranged at potential slip surfaces, inclinometers are installed vertically in boreholes, and soil moisture sensors are arranged in a double layer with buried depths of 0.5 m and 1.5 m. Based on the vehicle load-slope stress transfer model, by collecting slope displacement data and environmental parameters and integrating the two into the vehicle load-slope stress transfer model, using the coordinates of the GNSS displacement monitoring station as a reference, the local coordinates of equipment such as wire-pulling displacement gauges and inclinometers are transformed. Combining the three-dimensional real-scene model in the database as the spatial reference for the equipment layout position, by constructing a coupling equation of displacement-humidity-water level, combining inclinometer data to invert the position of the deep slip surface, using the elastic layer theory to calculate the displacement field distribution, establishing a vehicle load-displacement transfer matrix, obtaining the three-dimensional real-scene model of the slope terrain in the database, establishing a parametric model of the slope soil layer through CAE software, mapping the information collected by the soil sensing component to the three-dimensional real-scene model, calculating the stress propagation path of vehicle load in three-dimensional space through the finite element method, dynamically updating the soil layer parameters based on the Bayesian network, and introducing the LSTM network to predict the lag effect of water level change on displacement to construct a vehicle-slope-environment model; The information collected by the monitoring module, combined with the vehicle load - slope stress transfer model and the vehicle - slope - environment model, predicts the displacement trend through the LSTM network, and introduces the Bayesian network to evaluate the joint trigger probability of multiple indicators. The first - level warning trigger is adjusted to one of the following conditions: the proportion of displacement induced by vehicle load exceeds 15% for the first time, the predicted displacement trend increase of the LSTM model in the next 24 hours is less than 5%, the horizontal or vertical surface displacement rate is greater than 5 mm / day, the cumulative rainfall in 24 hours is greater than 50 mm, and the increase in soil water content is greater than 20%. The second - level warning trigger conditions are one of the following: the vehicle load proportion is greater than 25% for more than 35 minutes, the landslide probability evaluated by the Bayesian network is 0.5 - 0.7, the deep - level horizontal displacement mutation rate is greater than 0.1 mm / h, the surface displacement rate is greater than 8 mm / day, the groundwater level rises by more than or equal to 1 m / 24 h, and the pore water pressure is greater than 0.3 MPa. The third - level warning trigger conditions are one of the following: the proportion of displacement induced by vehicle load is greater than 30% and the slope stress and strain are greater than 150% of the design value, the joint probability of the Bayesian network is greater than 0.7, the sliding surface displacement rate is greater than 0.5 mm / h and the three - dimensional model shows that the sliding surface is connected, and the rainfall is greater than 100 mm / 24 h and the soil water content is greater than 95%. By integrating slope information, soil information, environmental information, and vehicle information, it predicts and warns of possible safety risks. When giving a warning, it uses the LSTM neural network to dynamically model the displacement time - series data monitored by GNSS, predicts the displacement change trend in the next 6 hours through a sliding window, and dynamically adjusts the threshold interval in combination with displacement acceleration. The Bayesian network calculates the joint trigger probability through multiple indicators. When the joint trigger probability is greater than 0.7, it automatically reduces the single - indicator threshold by 15% - 20%; The piezoelectric energy harvesting units installed in the roadbase course and subgrade trigger the piezoelectric effect through dynamic pressure changes when vehicles pass by, converting mechanical energy into electrical energy. Each time a vehicle runs over, it can generate an instantaneous power of 0.5W - 1.2W, which can meet the real-time power supply requirements of micro sensors. The energy collected when vehicles pass by is used to supply the monitoring module. The wind power generation units installed on the slope guardrails and isolation belts can adjust the angle according to the wind direction to improve the wind power generation efficiency. Each single wind power generation unit can stably output a power of 20W - 50W under the traffic flow density of 40km / h, which can provide electrical energy for monitoring devices such as vision sensors and infrared sensors. When vehicles pass on the highway, due to different vehicle models, speeds, weights and driver operations, the loads on the ground are also different. In areas where vehicles frequently start and stop, such as long downhill braking sections, curves, grade change sections, service areas, etc., by setting up heat energy recovery units, the heat generated by the friction between the wheels and the ground is recovered, and electricity is generated by using the temperature difference to form a heat energy - electrical energy closed loop. Magnets and coils are embedded in the flexible road surface and slope structural layer, and the vehicle load periodically compresses and releases the magnetic field to generate an induced current. Each time a vehicle passes, a pulse voltage of 3V - 5V can be generated, which can provide energy for the warning module after rectification. During the vehicle passing process, the energy is recovered through the recovery module to provide energy support for the monitoring module and the warning module. When the vehicle sensing component detects that the axle weight or vehicle model of the vehicle exceeds the design threshold, or when the vehicle aggregation causes a sudden increase in local load, in order to avoid overloading resulting in excessive deformation of the road surface and causing the fracture of the piezoelectric material or the damage of the coil, the piezoelectric energy harvesting unit and the electromagnetic power generation unit in this area are turned off in advance. When the deep soil pressure of the slope is abnormal, the buried electromagnetic power generation unit is turned off. The vehicle load - slope stress transfer module calculation shows that when the slope stress seam repair approaches the yield strength of the material, the slope deformation may cause the instability of the guardrail structure, and the wind power generation unit needs to be turned off. When the fiber Bragg grating sensor detects that the vibration frequency is close to the natural frequency of the slope and there is a risk of resonance, the piezoelectric energy harvesting unit, the electromagnetic power generation unit and the heat energy recovery unit need to be turned off to avoid resonance aggravating mechanical fatigue and damaging the heat conduction material.

[0026] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced by the present invention. Any reference signs in the claims should not be regarded as limiting the claims involved.

Claims

1. The intelligent monitoring and early warning system for prestressed cable - type displacement of highway slopes, comprising a monitoring module, an early warning module and a database, is characterized in that: The monitoring module includes a vehicle sensing component and a soil sensing component. The vehicle sensing component is connected to the database through signal transmission; The vehicle sensing component includes: a weighing unit, a vibration and stress coupling monitoring unit, and a model building unit. The weighing unit collects vehicle axle weight, vehicle speed, vehicle type information, real-time vehicle trajectory, and vehicle aggregation status, and transmits the information to the database; The vibration and stress coupling monitoring unit is used to collect the vibration frequency, energy distribution, and dynamic changes in soil stress caused by vehicle passage, and transmit the information to the database; The model building unit establishes a three-dimensional transfer function by integrating vehicle load, vibration field, and stress field, builds a slope entity model based on Abaqus, calculates stress seam filling under different working conditions using the strength reduction method, and follows the principle of densification at the slope toe for mesh division to build a vehicle load-slope stress transfer model.

2. The intelligent monitoring and early warning system for prestressed cable - type displacement of highway slopes according to claim 1, wherein: The vehicle sensing component further includes a visual sensor and an infrared sensor. The visual sensor is used to collect vehicle type, license plate, and body color information and transmit it to the database. The infrared sensor is used to collect vehicle passage frequency and speed, and transmit the information to the database.

3. The intelligent monitoring and early warning system for prestressed cable - type displacement of highway slopes according to claim 1, characterized in that: The model building unit constructs a displacement-humidity-water level coupling equation, calculates the displacement field distribution using the elastic layer theory to establish a vehicle load-displacement transfer matrix, obtains the three-dimensional real scene model of the slope terrain in the database, builds a parametric model of the slope soil layer through CAE software, maps the information collected by the soil sensing component to the three-dimensional real scene model, calculates the stress propagation path of vehicle load in three-dimensional space through the finite element method, dynamically updates the soil layer parameters based on the Bayesian network, and introduces the LSTM network to predict the lag effect of water level change on displacement to build a vehicle-slope-environment model.

4. The intelligent monitoring and early warning system for prestressed cable-pulling displacement of highway slopes according to claim 1, wherein: The warning module is connected to the database through signal transmission. An early warning model is set inside the warning module. The early warning model is based on the vehicle load-slope stress transfer model and the vehicle-slope-environment model, dynamically corrects the warning threshold according to the displacement trend predicted by the LSTM network, introduces the Bayesian network to evaluate the joint trigger probability of multiple indicators, and issues a warning to the management personnel according to the prediction result.

5. The intelligent monitoring and early warning system for prestressed cable-pulled displacement of highway slopes according to claim 1, wherein: The monitoring and warning system further includes a recovery module. The recovery module is connected to the monitoring module and the warning module through signal lines; The recovery module includes: a piezoelectric energy harvesting unit, a wind power generation unit, a heat energy recovery unit, an electromagnetic power generation unit, and an energy storage unit. The energy storage unit distributes electrical energy to the monitoring module and the warning module through the database; The piezoelectric energy harvesting unit is arranged on the road surface and the roadbed. The piezoelectric effect is triggered by the road surface deformation caused by vehicle decompression, and mechanical energy is converted into electrical energy and stored in the energy storage unit; The wind power generation unit is arranged on the slope guardrail and the isolation belt. It captures the turbulent wind energy generated by the vehicle driving at a high speed and adjusts the angle according to the wind direction to drive the generator to generate electricity; The heat energy recovery unit recovers heat energy through the heat gradient in the contact area between the tire and the ground; The electromagnetic power generation unit generates an induced current by embedding magnets and coils in the flexible road surface and the slope structural layer, and periodically compressing and releasing the magnetic field under vehicle load.

6. The intelligent monitoring and early warning system for prestressed cable - type displacement of highway slopes according to claim 1, characterized in that: The weighing unit includes a piezoelectric sensor and a vehicle-mounted positioning terminal. The piezoelectric sensor is used to collect vehicle axle weight, vehicle speed, and vehicle type information in real time and transmit the information to the database. The vehicle-mounted positioning terminal is used to track the real-time trajectory of the vehicle, monitor the aggregation state of the vehicle, and transmit the information to the database.

7. The intelligent monitoring and early warning system for prestressed cable-pull type displacement of highway slopes according to claim 1, wherein: The vibration and stress coupling monitoring unit includes a fiber Bragg grating vibration sensor and a digital earth pressure gauge. The fiber Bragg grating vibration sensor is arranged along the toe of the slope and is used to detect the vibration frequency caused by vehicle passing and transmit the information to the database. The digital earth pressure gauge is arranged on the surface layer and deep layer of the slope and is used to record the dynamic change of soil stress when the vehicle passes.

8. The intelligent monitoring and early warning system for prestressed cable - type displacement of highway slopes according to claim 1, characterized in that: The soil sensing component is connected to the database through signal transmission; The soil sensing component includes a GNSS displacement monitoring station, a wire-pulling displacement meter, an inclinometer, a soil moisture sensor, a groundwater level gauge, and a rain gauge. The GNSS displacement monitoring station measures the surface displacement of the slope through satellite positioning technology. The wire-pulling displacement meter is used to measure the local displacement of the slope. The inclinometer is used to monitor the deep horizontal displacement inside the slope. The soil moisture sensor is used to measure the water content of the surface soil layer. The groundwater level gauge is used to monitor the fluctuation of the groundwater level. The rain gauge is used to collect rainfall data in real time.

9. The intelligent monitoring and early warning system for prestressed cable-pull type displacement of highway slopes according to claim 4, wherein: The early warning is divided into three levels. A first-level early warning is issued when a single index exceeds the limit and the LSTM prediction trend is stable. A second-level early warning is issued when two indicators exceed the standard synergistically for 30 minutes. A third-level early warning is issued when the slip surface shown in the three-dimensional model penetrates.

10. The intelligent monitoring and early warning system for prestressed cable - type displacement of highway slopes according to claim 5, characterized in that: The piezoelectric energy harvesting unit provides energy for the monitoring module. The wind power generation unit provides energy for the vision sensor and the infrared sensor. The heat energy recovery unit is used to maintain the working temperature of the monitoring module and the early warning module. The electromagnetic power generation unit provides energy for the early warning module.

Images