Highway slope monitoring system and method

Abstract

The invention belongs to the technical field of expressway monitoring, and discloses an expressway slope monitoring system and method, and the method comprises the steps: carrying out the fixed-point detection of an expressway slope through a fixed-point monitoring mechanism; the route planning module plans the driving route of the mobile monitoring mechanism according to the monitoring task; the mobile monitoring mechanism comprehensively monitors the highway slope according to the planned driving route; the data integration analysis module is used for integrating, processing and analyzing data monitored by the fixed-point monitoring mechanism and the mobile monitoring mechanism; intelligent analysis is carried out on monitoring data, abnormal conditions are identified, slope stability is evaluated, and potential risks are predicted; the alarm module gives an alarm to a control center in time. According to the invention, the highway slope is comprehensively and accurately monitored through the combination of a movable trolley patrol monitoring method and a fixed-point monitoring method. The monitoring data is intelligently analyzed through the data integration and analysis module, abnormal conditions are identified, the slope stability is evaluated, and potential risks are predicted.

Description

Technical Field

[0001] This application relates to the technical field of highway monitoring, and more specifically, to a highway slope monitoring system and method. Background Art

[0002] In China, the mountainous area is vast, and the mileage of mountainous highways is long, and it will continue to increase in the future. In this case, it is particularly important to ensure the safe and stable performance of mountainous highways. The mountainous terrain is complex and the topography is changeable. The highway index requirements are high, and a large number of high slope projects have emerged. Moreover, the natural geological conditions in mountainous areas are complex. Due to the influence of natural factors (mainly rainfall) and highway construction, the original geological balance conditions are damaged, and road area geological disasters such as instability and landslides of highway slopes may occur, seriously affecting the smooth passage of the highway and people's travel safety. It is particularly important to monitor the stability status of slopes and obtain monitoring data in a timely manner, which is the basis for controlling and preventing high slope landslides and collapses and reducing the losses of disaster accidents. Due to the complex mountainous terrain and sparse population, conventional monitoring means cannot obtain slope displacement and other data in a timely manner under harsh conditions (such as heavy rain, etc.).

[0003] The prior art document with the publication number of CN101788551A provides an automatic monitoring device applied to a highway disease slope. The device is unattended, powered by solar energy, wirelessly transmits detection data, and provides a monitoring center database system for receiving and storing monitoring data and an Internet data query system. The invention system consists of two parts: a monitoring field station part and a monitoring data center part. The various data collected in real time by the monitoring field station part are sent to the monitoring data center through a wireless transmission method. The processed data quickly, automatically stores, transmits, updates, statistically analyzes and manages the above data, automatically monitors and transmits the real-time data of the slope, manages and retrieves the slope files, and provides a true and reliable data basis for the research on slope disease prediction and stability analysis - automatic monitoring of highway slope diseases. For the automatic real-time monitoring of various sensor data of highway slopes and the management of slope geology and design files, it provides data support for the highway management department to make effective decisions.

[0004] Although the above prior art solutions can achieve relevant beneficial effects through the structure of the prior art, there are still the following defects: The highway has a wide coverage area and a very long length, and it is impossible to conduct comprehensive and accurate monitoring of the highway; if fixed-point monitoring is carried out at all highway slopes, the cost will be very high.

[0005] In view of this, we propose a highway slope monitoring system and method. Summary of the Invention

[0006] 1. Technical Problems to be Solved

[0007] The purpose of this application is to provide a highway slope monitoring system and method, which solves the technical problems proposed in the above background technology, and realizes comprehensive and accurate monitoring of highway slopes by combining two methods: mobile trolley inspection monitoring and fixed-point monitoring. By collecting and analyzing data in real time, potential safety hazards can be discovered and processed in a timely manner to ensure the safe and stable operation of the highway; through the data integration and analysis module, intelligent analysis of the monitoring data is carried out to identify abnormal situations, evaluate the slope stability, and predict potential risks.

[0008] 2. Technical solution

[0009] The technical solution of this application provides a highway slope monitoring system, including:

[0010] Fixed-point monitoring mechanism: including equipment such as theodolites, levels, vibrating wire displacement gauges, differential resistance strain gauges, rainfall detectors, temperature detectors, seismic monitors, high-definition cameras, etc.; several fixed-point monitoring mechanisms are set on both sides of the highway to monitor the highway slopes at fixed points.

[0011] Mobile monitoring mechanism: including an automatically controlled mobile trolley, and monitoring equipment such as high-definition cameras, LED lights, displacement monitors, radar detectors, and water level gauges are carried on the automatically controlled mobile trolley; various types of data of the slope protection are collected in real time and sent to the monitoring center in real time through a wireless transmission system for analysis and processing.

[0012] Route planning module: According to the needs of the monitoring task, plan the driving route of the mobile monitoring mechanism to ensure the comprehensiveness and efficiency of the monitoring;

[0013] Data integration and analysis module: Integrate, process and analyze the data monitored by the fixed-point monitoring mechanism and the mobile monitoring mechanism, timely identify abnormal situations on the highway slopes, evaluate the slope stability, and predict potential risks.

[0014] Alarm module: including an alarm, when abnormal situations are detected on the highway slopes, an alarm is sent in a timely manner to remind the control center to take corresponding measures.

[0015] Control center: Network-connected to the fixed-point monitoring mechanism, the mobile monitoring mechanism and the data integration and analysis module, receives and analyzes the monitoring data, responds to abnormal situations in a timely manner, and takes corresponding countermeasures.

[0016] Through the above technical solution, through the combination of fixed-point monitoring institutions and mobile monitoring institutions, comprehensive monitoring of highway slopes is achieved to ensure no omission. Through the data integration and analysis module, intelligent analysis of monitoring data can be carried out to identify abnormal situations, evaluate the slope stability, and predict potential risks. The alarm module and the control center can respond to abnormal situations in a timely manner to ensure the safety of highway slopes.

[0017] As an alternative solution of the present invention, the automatic control mobile trolley can monitor and avoid vehicles and personnel, specifically including the following:

[0018] Obstacle avoidance algorithm: Install ultrasonic sensors, laser rangefinders, lidar (LiDAR), or infrared sensors on the trolley to detect vehicles and personnel in the surrounding environment. Combining the data collected by the sensors, the trolley is equipped with an intelligent obstacle avoidance algorithm to determine whether avoidance measures need to be taken. The algorithm can calculate the optimal avoidance path and speed based on factors such as the distance, speed, and direction of the obstacle.

[0019] Control system: The control system of the trolley should be able to receive the output of the obstacle avoidance algorithm and control the movement of the trolley to achieve avoidance operations. The control system should have a high degree of flexibility and accuracy to ensure that the trolley can maintain stability during avoidance and avoid collisions with other obstacles.

[0020] Intersection avoidance measures: During the driving process of the trolley, identify the upcoming intersection through GPS, map data, or pre-set route information. When approaching the intersection, increase the scanning frequency and range of the sensors to detect possible vehicles or personnel in advance.

[0021] Priority judgment: According to traffic rules or pre-set priority rules, judge the right of way of the trolley at the intersection. If the trolley has the right of way, it maintains normal driving; otherwise, start the avoidance measures.

[0022] Avoidance operation: When avoidance is required, calculate the optimal avoidance path and speed according to the obstacle avoidance algorithm. The control system executes the avoidance operation to ensure the trolley passes through the intersection safely.

[0023] Install warning lights, buzzers and other devices on the trolley to give warnings to surrounding personnel when avoidance is needed.

[0024] Through the above technical solution, the safety and reliability of the movable trolley during the inspection and monitoring of highway slopes are ensured. Greatly reduce the risk of the trolley colliding with other vehicles or personnel, and ensure the smooth progress of the inspection and monitoring work.

[0025] As an alternative solution of the present invention, when calculating the obstacle avoidance decision, it is necessary to consider the dynamic parameters of the vehicle, such as the starting and braking accelerations, as well as the distance and speed between the vehicle and the obstacles (vehicles or pedestrians), and guide the obstacle avoidance decision and speed adjustment by calculating the Time to Collision (TTC).

[0026] As an alternative solution of the present invention, the data integration and analysis module integrates, processes and analyzes the data monitored by the fixed-point monitoring mechanism and the mobile monitoring mechanism to identify abnormal conditions on the highway slope, evaluate the slope stability and predict potential risks. The specific method steps are as follows:

[0027] Data collection and collation: Collect the original data such as displacement monitoring, deformation monitoring, stress monitoring, radar monitoring, etc. from the fixed-point monitoring mechanism and the mobile monitoring mechanism. Conduct preliminary collation on the collected data to ensure the integrity and accuracy of the data.

[0028] Data integration: Convert the data from different monitoring mechanisms into a unified format for subsequent processing and analysis. Integrate multi-source monitoring data such as displacement, deformation, stress and radar to form a complete slope monitoring data set.

[0029] Data processing and analysis: Clean the integrated data to remove outliers and noise data. Apply appropriate mathematical and statistical methods to process and analyze the data, such as time series analysis, trend prediction, correlation analysis, etc.

[0030] Abnormality identification: Set reasonable thresholds or use machine learning algorithms to identify outliers or abnormal patterns based on the processed data. Identify abnormal conditions by comparing and analyzing the collected images.

[0031] Slope stability evaluation: Calculate the slope stability coefficient using the processed data to evaluate the stability degree of the slope. Combine common methods such as qualitative analysis method, limit equilibrium analysis method, numerical analysis method, etc. to conduct a comprehensive evaluation of the slope stability.

[0032] Potential risk prediction: Establish a slope deformation prediction model based on historical data and analysis results to predict the future deformation trend of the slope. Combine external factors such as geological conditions and climate conditions to predict and evaluate the potential risks of the slope.

[0033] Result report and decision support: Organize the results of abnormality identification, stability evaluation and potential risk prediction into a report and provide it to relevant departments and decision-makers. According to the report content, formulate targeted slope maintenance and treatment measures to ensure the safe operation of the highway.

[0034] As an alternative solution of the present invention, the support vector regression (SVR) method is used to identify abnormal situations in displacement monitoring results, deformation monitoring results, stress monitoring results, and radar monitoring results, which specifically includes the following steps:

[0035] 1. Data collection and preprocessing: Collect historical data of displacement, deformation, stress, and radar monitoring. Clean the data to remove noise, errors, or missing values. Standardize or normalize the data for comparison between monitoring data with different dimensions.

[0036] 2. Feature selection and extraction: Select or extract appropriate features according to the characteristics of each monitoring type. For example, displacement monitoring includes displacement amounts in the X, Y, and Z directions; stress monitoring includes stress values in each direction; radar monitoring includes reflection signal intensity, frequency, etc.

[0037] 3. Train the SVR model: Use historical data to train multiple SVR models, with each model corresponding to a monitoring type (displacement, deformation, stress, radar). Select appropriate kernel functions (such as RBF, linear, etc.), regularization parameter C, and insensitive loss function ε according to the characteristics of the data and business requirements.

[0038] 4. Prediction and calculation of residuals: For new monitoring data points, use the trained SVR model for prediction. Calculate the residuals, that is, the errors, between the actual monitoring values and the predicted values.

[0039] 5. Set thresholds: Set a reasonable threshold for each monitoring type according to the distribution of residuals of each monitoring type or business requirements. Use statistical methods (such as standard deviation method, percentile method) or methods based on business requirements to determine the thresholds.

[0040] 6. Identify outliers: For each monitoring type, if the residual of a data point exceeds the corresponding threshold, it is identified as an outlier. Further confirm the authenticity of the anomaly through expert knowledge, on-site inspection, or other auxiliary sensor data.

[0041] As an alternative solution of the present invention, the collected images are compared and analyzed to identify abnormal situations, including the following steps:

[0042] Sample data collection: Collect a large number of images of highway slopes, including normal images and abnormal situation images (including cracks, collapses, water body loss, etc.), and annotate the images as reference samples;

[0043] Image preprocessing: Preprocess the collected image data, including operations such as denoising, grayscale conversion, and contrast enhancement, to improve the image quality and the accuracy of subsequent processing. The denoising operation can eliminate noise interference in the image, such as rain, snow, sand, and dust; grayscale conversion and contrast enhancement can highlight the details and features in the image.

[0044] Feature extraction and recognition: Extract the edge information in the image through edge detection algorithms (such as Canny operator, Sobel operator, etc.), and then identify the crack area. Use morphological processing and filtering algorithms to further analyze and identify the cracks to distinguish different types of cracks (such as tensile cracks, shear cracks, etc.). Divide the slope image into different regions or objects through image segmentation technology, and then identify the collapse area.

[0045] Combined with terrain and geological information, perform three-dimensional reconstruction and analysis on the collapse area to evaluate its scale and influence range.

[0046] Water loss identification: Extract the water body area in the image through color analysis and threshold segmentation technology. Combine terrain and vegetation information to analyze the causes and trends of water loss, such as erosion ditches, surface runoff, etc.

[0047] Defect evaluation and classification: Compare and analyze the image after feature extraction with the reference sample to identify abnormal situations, including cracks, collapses, water loss, etc.; according to the identified defect types and characteristics, evaluate and classify the defects, such as minor cracks, severe collapses, water loss risks, etc. Combine historical data and on-site investigations to verify and correct the evaluation results to improve the accuracy and reliability of identification.

[0048] Result output and application: Output and store the identification results in the form of images, reports, databases, etc., for convenient subsequent analysis and application. Relevant departments formulate corresponding maintenance and management measures according to the identification results, such as strengthening slopes, repairing cracks, improving drainage systems, etc., to improve the safety and stability of expressways.

[0049] As an alternative solution of the present invention, the slope stability assessment comprehensively and deeply evaluates the stability of the expressway slope by combining qualitative analysis methods, limit equilibrium analysis methods, and numerical analysis methods. Through steps such as on-site investigation, data collection, analysis and calculation, comprehensive evaluation and decision-making of results, and monitoring and feedback, provide a scientific basis for the safe operation of the slope. The specific contents include the following:

[0050] Step 1, Preliminary assessment and data collection:

[0051] On-site investigation: Conduct a detailed on-site investigation on factors such as the size, slope shape, geological structure, and geological environment of the slope, and record and take pictures and video materials of key parts.

[0052] Data collection: Collect relevant data such as geological exploration reports, historical monitoring data, and meteorological data, and organize them into a database for subsequent analysis and use.

[0053] Step 2: Qualitative analysis:

[0054] Geological structure analysis: Analyze the geological structure of the slope, including lithology, bedding attitude, faults, joints, etc., to understand the basic geological conditions of the slope.

[0055] Slope shape and size assessment: Based on the slope shape and size, combined with the geological structure analysis, preliminarily judge its stability.

[0056] Geological environment analysis: Consider the geological environment where the slope is located, such as groundwater conditions, seismic activities, etc., and conduct a qualitative assessment of the stability.

[0057] Step 3: Limit equilibrium analysis:

[0058] Sliding surface identification: Based on the geological structure and on-site investigation, identify the possible sliding surface and determine its location and shape.

[0059] Rigid body assumption: Assume the potentially sliding rock and soil masses as rigid bodies and simplify their geometric shapes for subsequent calculations.

[0060] Stability coefficient calculation: Combine various monitoring data (displacement, deformation, stress, and radar monitoring) and corresponding weights to conduct a stability assessment, including the following steps:

[0061] Determine the characteristic values and weights: According to the actual monitoring data, determine the characteristic values of displacement (D), deformation (Df), stress (S), and radar monitoring (R). According to expert experience, historical data, or machine learning models, determine the weights of each monitoring index (WD, WDf, WS, WR).

[0062] Calculate the stability coefficient (SI): Calculate the stability coefficient SI through the following formula:

[0063] SI = D × W , S , D , S , f , D , , , R , Df , R , Df , D ,

[0065] ,

[0064] + D f × W Df + S × W S + R × W R ;

[0064] W D + W Df + W S + W R = 1;

[0065] In the formula, D is the characteristic value of the displacement monitoring result; W D The weight of the displacement monitoring result;

[0066] D f is the eigenvalue of the deformation monitoring result, and W Df is the weight of the deformation monitoring result;

[0067] S is the eigenvalue of the stress monitoring result; W S is the weight of the stress monitoring result;

[0068] R is the eigenvalue of the radar monitoring result, and W R is the weight of the radar monitoring result;

[0069] Weight normalization: Ensure that the sum of all weights is 1, that is, WD + WDf + WS + WR = 1. If the calculated sum of weights is not equal to 1, the weight values need to be adjusted proportionally.

[0070] Result analysis: According to the magnitude of the stability coefficient (SI), judge the stability state of the slope. This usually requires one or more thresholds to determine whether the slope is stable, relatively stable or unstable.

[0071] Based on the calculation results of the stability coefficient, judge the stability state of the slope and formulate corresponding reinforcement or monitoring measures.

[0072] Step 4: Numerical analysis method:

[0073] Establish a numerical model: According to the on-site investigation data and geological exploration report, use professional software to establish a numerical model of the slope (such as a finite element model, a discrete element model, etc.).

[0074] Boundary condition and parameter setting: According to the actual situation of the slope, set the boundary conditions of the model (such as displacement constraints, stress boundaries, etc.) and the physical and mechanical parameters of the rock and soil masses.

[0075] Calculation and analysis: Use numerical analysis methods to calculate the displacement field and stress field of the slope, and combine with the strength criteria of the rock and soil masses to evaluate the stability of the slope.

[0076] Sensitivity analysis: By changing the parameters or boundary conditions of the model, analyze its influence on the slope stability, and provide a scientific basis for reinforcement or monitoring measures.

[0077] Step 5: Result comprehensive evaluation and decision-making:

[0078] Result comparison: Compare the results of qualitative analysis, limit equilibrium analysis and numerical analysis, analyze the similarities and differences between them, and find out possible errors or deficiencies.

[0079] Stability judgment: Considering the results of the three methods comprehensively, judge the stability state of the slope and determine its safety level.

[0080] Decision-making suggestions: Based on the stability assessment results, propose corresponding reinforcement measures or monitoring suggestions, and formulate a detailed implementation plan.

[0081] Step 6: Monitoring and feedback: According to the stability assessment results and reinforcement or monitoring suggestions, formulate a monitoring plan for the slope, including the layout of monitoring points, monitoring periods, monitoring methods, etc. Feed the monitoring data back into the stability assessment for real-time update and correction to ensure the accuracy and reliability of the assessment results.

[0082] As an alternative solution of the present invention, the XGBoost model is used to predict and evaluate the potential risks of the slope by using historical data such as displacement, deformation, stress, and radar monitoring results, combined with external factors such as geological conditions and climate conditions. The specific steps are as follows:

[0083] 1. Data preparation:

[0084] 1.1 Collect historical data: Collect historical data such as displacement, deformation, stress, and radar monitoring results of the slope. Collect geological condition data and climate condition data related to the slope stability.

[0085] 1.2 Data cleaning and preprocessing: Clean outliers, missing values, etc. in the data. Perform normalization or standardization on numerical features to eliminate the dimensional differences between different features. Encode categorical features.

[0086] 1.3 Feature selection and construction: Select features related to slope deformation and stability, including direct monitoring data such as displacement, deformation, stress, and radar monitoring results. According to external factors such as geological conditions and climate conditions, construct corresponding features, such as cumulative rainfall value, temperature fluctuation, etc.

[0087] 2. Establish an XGBoost model:

[0088] 2.1 Data partitioning: Partition the cleaned and preprocessed data set into a training set, a validation set, and a test set.

[0089] 2.2 Model training: Use the training set data to train the XGBoost model, and set appropriate model parameters (such as learning rate, tree depth, subsample ratio, etc.). Adjust the model parameters through methods such as cross-validation to optimize the model performance.

[0090] 2.3 Model validation: Use the validation set data to validate the model performance, and evaluate indicators such as the prediction accuracy, recall rate, and F1 score of the model.

[0091] 3. Predict the future slope deformation trend: Prepare the predicted or estimated values of external factors such as geological conditions and climate conditions for a period of time in the future. Input these external factor data as features into the trained XGBoost model. Use the model to predict the future slope deformation trend.

[0092] 4. Potential risk prediction and assessment:

[0093] 4.1 Combine the predicted deformation trend with external factors (geological conditions and climate conditions) to analyze their impact on slope stability.

[0094] 4.2 Risk assessment: Based on the predicted deformation trend and external factors, develop a set of risk assessment criteria or indicators. According to these criteria or indicators, quantitatively or qualitatively evaluate the potential risks of the slope. Divide the slope into different risk levels (such as low risk, medium risk, high risk).

[0095] 4.3 Result output:

[0096] Output the prediction results and risk assessment results in the form of reports or charts for relevant personnel to understand and reference.

[0097] 5. Countermeasures and suggestions;

[0098] According to the risk assessment results, formulate corresponding countermeasures and measures, such as strengthening monitoring, taking reinforcement measures, controlling the groundwater level, etc. Regularly monitor and evaluate the slope, and adjust the countermeasures in a timely manner.

[0099] As an alternative embodiment of the present invention, the mobile monitoring mechanism includes an automatically controlled mobile trolley, a lifting mechanism, a telescopic mechanism, a U-shaped frame, a motor, a radar detector, and a high-definition camera;

[0100] An electric turntable is fixedly arranged inside the automatically controlled mobile trolley;

[0101] The lifting mechanism is fixedly arranged on the electric turntable; the lifting mechanism is an electric push rod;

[0102] The telescopic mechanism is fixedly arranged at the upper end of the lifting mechanism; the telescopic mechanism is an electric telescopic rod, preferably a multi-stage electric telescopic rod. The end of the lifting mechanism is fixedly provided with a U-shaped frame;

[0103] A motor is fixedly arranged on the U-shaped frame, and a support plate is fixedly arranged at the output end of the motor; s

[0104] A radar detector, a high-definition camera, and a laser rangefinder are fixedly arranged on the support plate.

[0105] Through the above technical solutions, the automatic control mobile trolley closely adheres to the side of the highway during the mobile monitoring process, without affecting the normal passage of vehicles on the highway. The direction of the telescopic mechanism is adjusted by an electric turntable; the height of the telescopic mechanism and the U-shaped frame is adjusted by a lifting mechanism, and then the angles of the radar detector, the high-definition camera, and the laser rangefinder are adjusted by the telescopic mechanism. Image acquisition is performed by the high-definition camera; distance detection is performed by the laser rangefinder. The radar detector is used to detect abnormal conditions such as cavities and cracks on the highway slope.

[0106] The present invention provides a highway slope monitoring system and method, including the following steps:

[0107] S1. Perform fixed-point detection on the highway slope through a fixed-point monitoring mechanism; send the monitoring data to the control center in real time to ensure the timeliness and accuracy of the data;

[0108] S2. The route planning module plans the driving route of the mobile monitoring mechanism according to the needs of the monitoring task to ensure the comprehensiveness and efficiency of the monitoring;

[0109] S3. The mobile monitoring mechanism conducts comprehensive monitoring of the highway slope according to the planned driving route; sends the monitoring data to the control center in real time to ensure the timeliness and accuracy of the data;

[0110] S31. Automatically control the mobile trolley to closely adhere to the side of the highway during the mobile monitoring process, without affecting the normal passage of vehicles on the highway;

[0111] S32. Adjust the direction of the telescopic mechanism through an electric turntable; adjust the height of the telescopic mechanism and the U-shaped frame through a lifting mechanism, and then adjust the angles of the radar detector, the high-definition camera, and the laser rangefinder through the telescopic mechanism;

[0112] S33. Perform image acquisition through the high-definition camera; perform distance detection through the laser rangefinder; detect abnormal conditions such as cavities and cracks on the highway slope through the radar detector;

[0113] S4. The data integration and analysis module performs intelligent analysis on the monitoring data, identifies abnormal conditions, evaluates the slope stability, and predicts potential risks.

[0114] S5. When an abnormal condition is detected on the slope, the alarm module issues an alarm in a timely manner, and the control center responds in a timely manner to ensure the safety of the highway slope. ……

[0115] 3. Beneficial effects

[0116] One or more of the technical solutions provided in the technical solutions of this application have at least the following technical effects or advantages:

[0117] 1. The design scheme of the present invention combines two methods of mobile trolley inspection and monitoring and fixed-point monitoring to comprehensively and accurately monitor the highway slope.

[0118] 2. By collecting and analyzing data in real time, potential safety hazards can be discovered and handled in a timely manner to ensure the safe and stable operation of the highway.

[0119] 3. The monitoring data is intelligently analyzed by the data integration and analysis module to identify abnormal situations, evaluate the slope stability, and predict potential risks.

[0120] 4. When an abnormal situation is detected on the slope, the alarm module issues an alarm in a timely manner, and the control center responds in a timely manner to ensure the safety of the highway slope.

[0121] 5. The width of the automatically controlled mobile trolley is small, and it travels on the side of the highway without affecting the normal driving of vehicles on the highway; it can monitor the slope in all directions.

[0122] 6. It is applicable to the monitoring tasks of various highway slopes, especially in areas with complex geological conditions and poor slope stability, and areas where drone cruise monitoring is not suitable. Through the real-time monitoring and intelligent analysis of the automatically controlled mobile trolley system, abnormal situations on the slope can be discovered and handled in a timely manner, reducing the occurrence probability of geological disasters, not affecting the normal driving of vehicles on the highway, and ensuring the safe operation of the highway. BRIEF DESCRIPTION OF THE DRAWINGS

[0123] Figure 1 It is a schematic diagram of the overall highway slope monitoring system disclosed in a preferred embodiment of the present application;

[0124] Figure 2 It is a schematic diagram of the mobile monitoring mechanism of the highway slope monitoring system disclosed in a preferred embodiment of the present application.

[0125] Reference Signs:

[0126] 1. Automatically controlled mobile trolley; 2. Lifting mechanism; 3. Telescopic mechanism; 4. U-shaped frame; 5. Motor; 6. Support plate; 7. Radar detector; 8. High-definition camera; 9. Laser rangefinder. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0127] The present application will be further described in detail below with reference to the accompanying drawings of the specification.

[0128] Refer to Figure 1 , the embodiment of the present application provides a highway slope monitoring system, including:

[0129] Fixed-point monitoring institutions: Include equipment such as theodolites, levels, vibrating wire displacement gauges, differential resistance strain gauges, rainfall detectors, temperature detectors, seismic monitors, and high-definition cameras; several fixed-point monitoring institutions are set on both sides of the highway to monitor the highway slopes at fixed points and collect high-definition images; to obtain accurate data on the sliding and movement conditions of the slope rock mass. The differential resistance strain gauge is a high-precision measuring instrument mainly used to measure the strain changes of an object under the action of external forces. Stress monitoring is to monitor the stress changes of the slope main body and the retaining and protection structures.

[0130] Mobile monitoring institutions: Include an automatically controlled mobile trolley, and the automatically controlled mobile trolley is equipped with monitoring equipment such as high-definition cameras, LED lights, displacement monitors, radar detectors, and water level gauges; the width dimension of the automatically controlled mobile trolley is small, and it travels on the side of the highway without affecting the normal driving of vehicles on the highway; it monitors the slope; to collect various types of data of the slope protection in real time and send them to the monitoring center for analysis and processing in real time through a wireless transmission system.

[0131] Route planning module: According to the needs of the monitoring task, plan the driving route of the mobile monitoring institution to ensure the comprehensiveness and efficiency of the monitoring;

[0132] Data integration and analysis module: Integrate, process, and analyze the data monitored by the fixed-point monitoring institution and the mobile monitoring institution, timely identify abnormal situations on the highway slope, evaluate the stability of the slope, and predict potential risks.

[0133] Alarm module: Include an alarm. When an abnormal situation is detected on the highway slope, an alarm is issued in a timely manner. Remind the control center to take corresponding measures.

[0134] Control center: Network-connected to the fixed-point monitoring institution, the mobile monitoring institution, and the data integration and analysis module, receive and analyze the monitoring data, respond to abnormal situations in a timely manner, and take corresponding countermeasures.

[0135] In this technical solution, through the combination of the fixed-point monitoring institution and the mobile monitoring institution, the comprehensive monitoring of the highway slope is realized to ensure no omission. Using the wireless transmission system, the monitoring data is sent to the monitoring center in real time to ensure the timeliness and accuracy of the data. Through the data integration and analysis module, the monitoring data can be intelligently analyzed, abnormal situations can be identified, the slope stability can be evaluated, and potential risks can be predicted. The alarm module and the control center can respond to abnormal situations in a timely manner to ensure the safety of the highway slope. It is applicable to the monitoring tasks of various highway slopes, especially applicable to areas with complex geological conditions and poor slope stability. Through the real-time monitoring and intelligent analysis of the system, abnormal situations on the slope can be discovered and processed in a timely manner, reducing the occurrence probability of geological disasters and ensuring the safe operation of the highway.

[0136] Furthermore, the automatically controlled mobile trolley can monitor and avoid vehicles and personnel, specifically including the following:

[0137] Obstacle avoidance algorithm: Install ultrasonic sensors, laser rangefinders, lidar (LiDAR), or infrared sensors on the trolley to detect vehicles and personnel in the surrounding environment. These sensors should be able to scan the environment around the trolley in real time and identify potential obstacles. Combining the data collected by the sensors, the trolley is equipped with an intelligent obstacle avoidance algorithm to determine whether avoidance measures need to be taken. The algorithm can calculate the optimal avoidance path and speed based on factors such as the distance, speed, and direction of the obstacle.

[0138] Control system: The control system of the trolley should be able to receive the output of the obstacle avoidance algorithm and control the movement of the trolley to achieve avoidance operations. The control system should have a high degree of flexibility and accuracy to ensure that the trolley can remain stable during avoidance and avoid collisions with other obstacles.

[0139] Avoidance measures at intersections: During the driving process of the trolley, identify the upcoming intersections through GPS, map data, or pre-set route information. When approaching an intersection, increase the scanning frequency and range of the sensors to detect possible vehicles or personnel in advance.

[0140] Priority judgment: According to traffic rules or pre-set priority rules, judge the right of way of the trolley at intersections. If the trolley has the right of way, it maintains normal driving; otherwise, it activates avoidance measures.

[0141] Avoidance operation: When avoidance is required, calculate the optimal avoidance path and speed according to the obstacle avoidance algorithm. The control system performs the avoidance operation to ensure that the trolley safely passes through the intersection.

[0142] Install warning lights, buzzers, and other devices on the trolley to give warnings to surrounding personnel when avoidance is needed. In case of emergencies, such as when the sensors detect obstacles or personnel that cannot be avoided, the trolley should have an emergency stop function.

[0143] In summary, to ensure the safety and reliability of the mobile trolley during the inspection and monitoring of highway slopes, it is very necessary to install a vehicle and personnel monitoring system and take corresponding avoidance measures. These measures can greatly reduce the risk of the trolley colliding with other vehicles or personnel and ensure the smooth progress of the inspection and monitoring work.

[0144] Furthermore, the obstacle avoidance algorithm includes the following:

[0145] When calculating the obstacle avoidance decision, it is necessary to consider the dynamic parameters of the vehicle, such as the starting and braking accelerations, as well as the distance and speed between the vehicle and the obstacle (vehicle or person). The time to collision (TTC) is calculated to guide the obstacle avoidance decision and speed adjustment.

[0146] The time to collision is a commonly used metric to estimate when two objects will collide while maintaining their current speed and direction. For vehicles and people, it is calculated according to the following formulas.

[0147] For vehicles:

[0148] TTC_vehicle = {[L1 / (V 车辆 - V 小车 ), when V 车辆 > V 小车 ; [L1 / (V 车辆 + V 小车 ), when V 车辆 ≤

[0149] V 小车 or moving towards each other]};

[0150] For people:

[0151] TTC_person = {[L2 / (V 人 - V 小车 ), when V 人 > V 小车 ; [L2 / (V 人 + V 小车 ), when V 人 ≤ V 小车

[0152] or moving towards each other]};

[0153] Calculation of the braking distance d brake :

[0154] The braking distance d brake is calculated by the following formula:

[0155] d brake = V 小车 2 / (2a);

[0156] Where V 小车 is the current speed of the vehicle, and a is the braking acceleration of the vehicle (positive value, indicating deceleration). V 人 is the current speed of the person; V 车辆 is the current speed of the vehicle, L1 is the distance from the vehicle to the vehicle, and L2 is the distance from the person to the vehicle.

[0157] Obstacle avoidance decision logic:

[0158] A. Calculate TTC: Calculate the TTC based on the relative speed and distance between the vehicle and the obstacle (vehicle or person).

[0159] B. Judge the collision risk: If the TTC is less than a certain threshold (e.g., 2 seconds), it is considered that there is a collision risk.

[0160] C. Calculate the braking distance: Calculate the braking distance using the current speed and braking acceleration of the vehicle.

[0161] Collision avoidance decision-making: If there is a collision risk and the braking distance is less than the distance between the vehicle and the obstacle, the vehicle should brake immediately.

[0162] If braking is not sufficient to avoid a collision, or more urgent avoidance is required, the vehicle may need to steer or accelerate (if road conditions permit and it is safe).

[0163] Furthermore, the data integration and analysis module integrates, processes, and analyzes the data monitored by the fixed-point monitoring agency and the mobile monitoring agency to identify abnormal conditions on the highway slope, evaluate the slope stability, and predict potential risks. The specific method steps are as follows:

[0164] Data collection and collation: Collect original data such as displacement monitoring, deformation monitoring, stress monitoring, and radar monitoring from the fixed-point monitoring agency and the mobile monitoring agency. Conduct preliminary collation on the collected data to ensure the integrity and accuracy of the data.

[0165] Data integration: Convert the data from different monitoring agencies into a unified format for subsequent processing and analysis. Integrate multi-source monitoring data such as displacement, deformation, stress, and radar to form a complete slope monitoring data set.

[0166] Data processing and analysis: Clean the integrated data to remove outliers and noise data. Apply appropriate mathematical and statistical methods to process and analyze the data, such as time series analysis, trend prediction, correlation analysis, etc. Combine the requirements of technical specifications and regulations to perform physical quantity calculations, form tables, draw graphs, etc.

[0167] Abnormality identification: Set reasonable thresholds or use machine learning algorithms to identify outliers or abnormal patterns based on the processed data. Identify abnormal conditions through comparative analysis of the collected images.

[0168] Slope stability evaluation: Use the processed data to calculate the slope stability coefficient (such as the anti-sliding stability coefficient η) to evaluate the stability degree of the slope. Combine common methods such as qualitative analysis method, limit equilibrium analysis method, and numerical analysis method to comprehensively evaluate the slope stability.

[0169] Potential risk prediction: Based on historical data and analysis results, a slope deformation prediction model is established to predict future slope deformation trends. Combined with external factors such as geological conditions and climate conditions, potential slope risks are predicted and assessed.

[0170] Results reporting and decision support: The results of anomaly identification, stability assessment, and potential risk prediction are compiled into reports and provided to relevant departments and decision makers. Based on the report content, targeted slope maintenance and control measures are formulated to ensure safe operation of the expressway.

[0171] Furthermore, when using the support vector regression (SVR) method to identify abnormal conditions of displacement monitoring results, deformation monitoring results, stress monitoring results, and radar monitoring results, the following steps are specifically included:

[0172] 1. Data Collection and Preprocessing: Collect historical data on displacement, deformation, stress, and radar monitoring. Clean the data to remove noise, errors, or missing values. Standardize or normalize the data to facilitate comparisons between monitoring data of different dimensions.

[0173] 2. Feature selection and extraction: Select or extract appropriate features based on the characteristics of each monitoring type. For example, displacement monitoring includes displacement in the X, Y, and Z directions; stress monitoring includes stress values in all directions; and radar monitoring includes reflected signal strength and frequency. Consider using sliding windows or time series features to capture the temporal dependencies of the data.

[0174] 3. Training SVR models: Use historical data to train multiple SVR models, each corresponding to a monitoring type (displacement, deformation, stress, radar). Based on the characteristics of the data and business needs, select the appropriate kernel function (such as RBF, linear, etc.), regularization parameter C, and insensitive loss function ε.

[0175] 4. Prediction and calculation of residuals: For new monitoring data points, use the trained SVR model to make predictions. Calculate the residual between the actual monitoring value and the predicted value, i.e., the error.

[0176] 5. Set thresholds: Set a reasonable threshold for each monitoring type based on the distribution of residuals for each monitoring type or business needs. Use statistical methods (such as standard deviation method, percentile method) or methods based on business needs to determine the threshold.

[0177] 6. Identify outliers: For each monitoring type, if the residual of a data point exceeds the corresponding threshold, it is identified as an outlier. The authenticity of the anomaly can be further confirmed through expert knowledge, on-site inspections, or other auxiliary sensor data.

[0178] Furthermore, the collected images are compared and analyzed to identify abnormal situations, including the following steps:

[0179] Sample data collection: A large number of images of highway slopes are collected, including normal images and abnormal situation images (including cracks, collapses, water body loss, etc.), and the images are labeled as reference samples.

[0180] Image preprocessing: The collected image data is preprocessed, including operations such as denoising, grayscale conversion, and contrast enhancement, to improve the image quality and the accuracy of subsequent processing. The denoising operation can eliminate noise interference in the image, such as rain, snow, and sand; grayscale conversion and contrast enhancement can highlight the details and features in the image.

[0181] Feature extraction and recognition: Edge information in the image is extracted through edge detection algorithms (such as Canny operator, Sobel operator, etc.), and then the crack area is recognized. Morphological processing and filtering algorithms are used to further analyze and identify the cracks to distinguish different types of cracks (such as tensile cracks, shear cracks, etc.). The slope image is divided into different regions or objects through image segmentation technology, and then the collapse area is recognized.

[0182] Combined with terrain and geological information, three-dimensional reconstruction and analysis are carried out on the collapse area to evaluate its scale and influence range.

[0183] Water body loss recognition: The water body area in the image is extracted through color analysis and threshold segmentation technology. Combined with terrain and vegetation information, the causes and trends of water body loss are analyzed, such as erosion ditches, surface runoff, etc.

[0184] Defect evaluation and classification: The images after feature extraction are compared and analyzed with the reference samples to identify abnormal situations, including cracks, collapses, water body loss, etc.; according to the identified defect types and features, the defects are evaluated and classified, such as minor cracks, severe collapses, water body loss risks, etc. Combined with historical data and on-site investigations, the evaluation results are verified and corrected to improve the accuracy and reliability of recognition.

[0185] Result output and application: The recognition results are output and stored in the form of images, reports, or databases for convenient subsequent analysis and application. Corresponding maintenance and management measures are formulated according to the recognition results, such as strengthening slopes, repairing cracks, improving drainage systems, etc., to improve the safety and stability of highways.

[0186] Furthermore, the slope stability assessment comprehensively and deeply evaluates the stability of highway slopes by combining qualitative analysis methods, limit equilibrium analysis methods, and numerical analysis methods. Through steps such as on-site investigation, data collection, analysis and calculation, comprehensive evaluation and decision-making of results, and monitoring and feedback, it provides a scientific basis for the safe operation of slopes. The specific contents include the following:

[0187] Step 1: Preliminary assessment and data collection:

[0188] On-site investigation: Conduct a detailed on-site investigation of factors such as the size, slope shape, geological structure, and geological environment of the slope, and record and take pictures and video materials of key parts.

[0189] Data collection: Collect relevant materials such as geological exploration reports, historical monitoring data, and meteorological data, and organize them into a database for subsequent analysis and use.

[0190] Step 2: Qualitative analysis:

[0191] Geological structure analysis: Analyze the geological structure of the slope, including lithology, attitude of rock strata, faults, joints, etc., to understand the basic geological conditions of the slope.

[0192] Slope shape and size assessment: Based on the slope shape and size of the slope, combined with geological structure analysis, preliminarily judge its stability.

[0193] Geological environment analysis: Consider the geological environment where the slope is located, such as groundwater conditions, seismic activities, etc., and conduct a qualitative assessment of stability.

[0194] Step 3: Limit equilibrium analysis:

[0195] Sliding surface identification: Based on the geological structure and on-site investigation, identify possible sliding surfaces and determine their positions and shapes.

[0196] Rigid body assumption: Assume the potentially sliding rock and soil masses as rigid bodies and simplify their geometric shapes for subsequent calculations.

[0197] Calculation of stability coefficient: Combining multiple monitoring data (displacement, deformation, stress, and radar monitoring) and corresponding weights, conduct stability assessment, including the following steps:

[0198] Determine characteristic values and weights: According to actual monitoring data, determine the characteristic values of displacement (D), deformation (Df), stress (S), and radar monitoring (R). According to expert experience, historical data, or machine learning models, determine the weights (WD, WDf, WS, WR) of each monitoring index.

[0199] Calculate the stability coefficient (SI): Calculate the stability coefficient SI through the following formula:

[0200] SI = D × W D + D f × W Df + S × W S + R × W R ;

[0201] W D + W Df + W S + W R = 1;

[0202] In the formula, D is the eigenvalue of displacement monitoring result; W D is the weight of displacement monitoring result;

[0203] D f is the eigenvalue of deformation monitoring result, and W Df is the weight of deformation monitoring result;

[0204] S is the eigenvalue of stress monitoring result; W S is the weight of stress monitoring result;

[0205] R is the eigenvalue of radar monitoring result, and W R is the weight of radar monitoring result;

[0206] Weight normalization: Ensure that the sum of all weights is 1, that is, WD + WDf + WS + WR = 1. If the calculated sum of weights is not equal to 1, the weight values need to be adjusted proportionally.

[0207] Result analysis: Judge the stability state of the slope according to the magnitude of the stability coefficient (SI). This usually requires one or more thresholds to determine whether the slope is stable, relatively stable or unstable.

[0208] Judge the stability state of the slope according to the calculation result of the stability coefficient, and formulate corresponding reinforcement or monitoring measures.

[0209] Step 4: Numerical analysis method:

[0210] Establish a numerical model: According to the on-site investigation data and geological exploration report, use professional software to establish a numerical model of the slope (such as a finite element model, a discrete element model, etc.).

[0211] Boundary condition and parameter setting: According to the actual situation of the slope, set the boundary conditions of the model (such as displacement constraints, stress boundaries, etc.) and the physical and mechanical parameters of the rock and soil masses.

[0212] Calculation and analysis: Use numerical analysis methods to calculate the displacement field and stress field of the slope, and evaluate the stability of the slope in combination with the strength criteria of the rock and soil masses.

[0213] Sensitivity analysis: By changing the parameters or boundary conditions of the model, analyze its impact on slope stability, and provide a scientific basis for reinforcement or monitoring measures.

[0214] Step 5: Comprehensive evaluation and decision-making of results:

[0215] Result comparison: Compare the results of qualitative analysis, limit equilibrium analysis, and numerical analysis, analyze the similarities and differences among them, and identify possible errors or deficiencies.

[0216] Stability judgment: Considering the results of the three methods comprehensively, judge the stability state of the slope and determine its safety level.

[0217] Decision-making suggestions: Based on the stability assessment results, propose corresponding reinforcement measures or monitoring suggestions, and formulate a detailed implementation plan.

[0218] Step 6: Monitoring and feedback: According to the stability assessment results and reinforcement or monitoring suggestions, formulate a monitoring plan for the slope, including the layout of monitoring points, monitoring cycle, monitoring methods, etc. Feed the monitoring data back into the stability assessment for real-time update and correction to ensure the accuracy and reliability of the assessment results.

[0219] Furthermore, data processing and analysis include the following:

[0220] 1. Data cleaning: Data cleaning is the primary step in data processing. It involves checking the consistency, integrity, and accuracy of data, and correcting or deleting incorrect, duplicate, or invalid data. The following are some recommended cleaning steps:

[0221] Handling missing values: Determine the reasons for missing values (random missing, completely random missing, missing due to mechanical reasons, etc.), and select an appropriate filling method according to the characteristics of the data and the analysis purpose (such as filling with mean, median, mode, or using a machine learning model for prediction filling).

[0222] Handling duplicate values: Identify and delete or merge duplicate records.

[0223] Format unification: Ensure the uniformity of data formats, such as date format, currency format, etc.

[0224] Handling invalid values: Delete or correct data that is obviously unreasonable or outside the reasonable range.

[0225] 2. Removing outliers and noise data:

[0226] Outliers and noise data may have an adverse impact on the results of data analysis. Therefore, it is necessary to identify and properly handle these values.

[0227] Outlier identification: Use statistical methods (such as Z-score, IQR method, etc.) or visualization methods (such as box plot, scatter plot, etc.) to identify outliers.

[0228] Outlier handling: According to the cause of outliers and the purpose of data analysis, one can choose to delete outliers, replace outliers with the median, mean or model predicted values, or keep outliers and consider their impact during analysis.

[0229] Noise data handling: Noise data is usually generated randomly, and its impact can be reduced through smoothing techniques (such as moving average method, exponential smoothing method, etc.) or filtering techniques (such as low-pass filtering, high-pass filtering, etc.).

[0230] 3. Apply mathematical and statistical methods for data processing and analysis:

[0231] The cleaned and preprocessed data can be used for the analysis of various mathematical and statistical methods. The following are some common analysis methods:

[0232] Time series analysis: Use time series data to predict future trends or behaviors. Common methods include moving average method, exponential smoothing method, ARIMA model, etc.

[0233] Trend prediction: Predict future trends based on historical data. Machine learning models such as linear regression, polynomial regression, decision tree, random forest, neural network, etc. can be used for prediction.

[0234] Correlation analysis: Study the relationship between two or more variables. Methods such as Pearson correlation coefficient, Spearman correlation coefficient, etc. can be used to measure the correlation between variables.

[0235] Cluster analysis: Divide data into different groups or clusters so that the data within the same group is as similar as possible, while the data between different groups is as different as possible. Common clustering methods include K-means, hierarchical clustering, DBSCAN, etc.

[0236] Classification analysis: Divide data into predefined categories. Classification algorithms such as logistic regression, support vector machine, naive Bayes, random forest, etc. can be used.

[0237] Principal component analysis (PCA) and factor analysis: Used to reduce the dimension of data while retaining most of the information in the data. These techniques are particularly useful for dealing with high-dimensional data.

[0238] Furthermore, the XGBoost model uses historical data such as displacement, deformation, stress and radar monitoring results, combined with external factors such as geological conditions and climate conditions, to predict and evaluate the potential risks of slopes, specifically the following steps:

[0239] 1. Data Preparation:

[0240] 1.1 Collect historical data

[0241] Collect historical data such as displacement, deformation, stress, and radar monitoring results of the slope.

[0242] Collect geological condition data (such as rock layer type, rock strength, groundwater level, etc.) and climate condition data (such as rainfall, temperature, humidity, etc.) related to slope stability.

[0243] 1.2 Data cleaning and preprocessing:

[0244] Clean outliers, missing values, etc. in the data. Perform normalization or standardization on numerical features to eliminate the dimensional differences between different features. Perform encoding on categorical features (such as one-hot encoding or label encoding).

[0245] 1.3 Feature selection and construction

[0246] Select features related to slope deformation and stability, including direct monitoring data such as displacement, deformation, stress, and radar monitoring results.

[0247] According to external factors such as geological conditions and climate conditions, construct corresponding features, such as cumulative rainfall value, temperature fluctuation, etc.

[0248] 2. Establish an XGBoost model:

[0249] 2.1 Data partitioning: Partition the cleaned and preprocessed dataset into a training set, a validation set, and a test set.

[0250] 2.2 Model training: Use the training set data to train the XGBoost model, and set appropriate model parameters (such as learning rate, tree depth, subsample ratio, etc.). Adjust the model parameters through methods such as cross-validation to optimize the model performance.

[0251] 2.3 Model validation: Use the validation set data to validate the model performance, and evaluate indicators such as prediction accuracy, recall rate, and F1 score of the model.

[0252] 3. Predict the future slope deformation trend: Prepare predicted or estimated values of external factors such as geological conditions and climate conditions for a future period of time. Use these external factor data as features and input them into the trained XGBoost model. Use the model to predict the future slope deformation trend.

[0253] 4. Potential risk prediction and assessment:

[0254] 4.1 Combine external factors, combine the predicted deformation trend with external factors (geological conditions and climate conditions), and analyze their impact on slope stability.

[0255] 4.2 Risk assessment: Based on the predicted deformation trend and external factors, develop a set of risk assessment criteria or indicators. According to these criteria or indicators, quantitatively or qualitatively evaluate the potential risks of the slope. Divide the slope into different risk levels (such as low risk, medium risk, high risk).

[0256] 4.3 Result output: Output the prediction results and risk assessment results in the form of reports or charts for relevant personnel to understand and refer to.

[0257] 5. Countermeasures and suggestions

[0258] According to the risk assessment results, formulate corresponding countermeasures and measures, such as strengthening monitoring, taking reinforcement measures, controlling the groundwater level, etc. Regularly monitor and evaluate the slope, and adjust the countermeasures in a timely manner.

[0259] Refer to Figure 2 , the mobile monitoring mechanism includes an automatically controlled mobile trolley 1, a lifting mechanism 2, a telescopic mechanism 3, a U-shaped frame 4, a motor 5, a radar detector 7, and a high-definition camera 8;

[0260] The automatically controlled mobile trolley 1 adopts a six-wheel mobile scheme, with two driven wheels installed at the front and rear respectively, and two independent driving wheels installed in the middle. The DC servo motor transmits power to the driving wheels through a speed reducer to achieve forward or backward movement. By controlling two independent servo motors to drive the driving wheels to form an angular difference, the trolley can turn, including in-place turning with a zero turning radius. The mechanical structure of the trolley is divided into four parts: the vehicle body, the driving device, the driven wheels, and the safety protection device. The width of the automatically controlled mobile trolley 1 is preferably 30 cm. Through a very small width design, the automatically controlled mobile trolley 1 closely adheres to the side of the highway during the mobile monitoring process and will not affect the normal passage of vehicles on the highway.

[0261] An electric turntable is fixedly installed inside the automatically controlled mobile trolley 1;

[0262] The electric turntable is fixedly installed with a lifting mechanism 2; the lifting mechanism 2 is an electric push rod;

[0263] The upper end of the lifting mechanism 2 is fixedly installed with a telescopic mechanism 3; the telescopic mechanism 3 is an electric telescopic rod, preferably a multi-stage electric telescopic rod.

[0264] The end of the lifting mechanism 2 is fixedly installed with a U-shaped frame 4;

[0265] A motor 5 is fixedly installed on the U-shaped frame 4, and a support plate 6 is fixedly installed at the output end of the motor 5;

[0266] A radar detector 7, a high-definition camera 8, and a laser rangefinder 9 are fixedly installed on the support plate 6.

[0267] In this technical solution, the automatically controlled mobile trolley 1 closely adheres to the side of the highway during the mobile monitoring process, without affecting the normal passage of vehicles on the highway. The direction of the telescopic mechanism 3 is adjusted through the electric turntable; the height of the telescopic mechanism 3 and the U-shaped frame 4 is adjusted through the lifting mechanism 2, and then the angles of the radar detector 7, the high-definition camera 8, and the laser rangefinder 9 are adjusted through the telescopic mechanism 3. Image acquisition is performed through the high-definition camera 8; distance detection is performed through the laser rangefinder 9. The radar detector 7 is used to detect abnormal conditions such as cavities and cracks on the highway slope. The radar detector 7 is installed on the automatically controlled mobile trolley to conduct all-round detection of the holes, cracks, and internal soil erosion on the highway slope, featuring high efficiency, accuracy, and real-time performance, providing a strong guarantee for the safe operation of the highway.

[0268] The present invention provides a highway slope monitoring system and method, including the following steps:

[0269] S1. Fixed-point detection is carried out on the highway slope through the fixed-point monitoring mechanism; the monitoring data is sent to the control center in real time to ensure the timeliness and accuracy of the data;

[0270] S2. The route planning module plans the driving route of the mobile monitoring mechanism according to the needs of the monitoring task to ensure the comprehensiveness and efficiency of the monitoring;

[0271] S3. The mobile monitoring mechanism conducts comprehensive monitoring of the highway slope according to the planned driving route; the monitoring data is sent to the control center in real time to ensure the timeliness and accuracy of the data;

[0272] S31. The automatically controlled mobile trolley 1 closely adheres to the side of the highway during the mobile monitoring process, without affecting the normal passage of vehicles on the highway;

[0273] S32. The direction of the telescopic mechanism 3 is adjusted through the electric turntable; the height of the telescopic mechanism 3 and the U-shaped frame 4 is adjusted through the lifting mechanism 2, and then the angles of the radar detector 7, the high-definition camera 8, and the laser rangefinder 9 are adjusted through the telescopic mechanism 3;

[0274] S33. Image acquisition is performed through the high-definition camera 8; distance detection is performed through the laser rangefinder 9; the radar detector 7 is used to detect abnormal conditions such as cavities and cracks on the highway slope;

[0275] S4. The data integration and analysis module conducts intelligent analysis on the monitoring data, identifies abnormal conditions, evaluates the slope stability, and predicts potential risks.

[0276] S5. When an abnormal situation is detected on the slope, the alarm module issues an alarm in a timely manner, and the control center responds in a timely manner to ensure the safety of the highway slope.

[0277] The working principle of a highway slope monitoring system of the present invention is as follows: fixed-point monitoring institutions conduct fixed-point detection on the highway slope; the monitoring data is sent to the control center in real time to ensure the timeliness and accuracy of the data; the route planning module plans the driving route of the mobile monitoring institution according to the needs of the monitoring task to ensure the comprehensiveness and efficiency of the monitoring; the mobile monitoring institution conducts comprehensive monitoring of the highway slope according to the planned driving route; the monitoring data is sent to the control center in real time to ensure the timeliness and accuracy of the data; the automatic control mobile trolley 1 closely adheres to the side of the highway during the mobile monitoring process without affecting the normal passage of vehicles on the highway; the direction of the telescopic mechanism 3 is adjusted through the electric turntable; the height of the telescopic mechanism 3 and the U-shaped frame 4 is adjusted through the lifting mechanism 2, and then the angles of the radar detector 7, the high-definition camera 8 and the laser rangefinder 9 are adjusted through the telescopic mechanism 3; image acquisition is carried out through the high-definition camera 8; distance detection is carried out through the laser rangefinder 9; the radar detector 7 detects abnormal situations such as cavities and cracks on the highway slope; the data integration and analysis module conducts intelligent analysis on the monitoring data, identifies abnormal situations, evaluates the slope stability, and predicts potential risks. When an abnormal situation is detected on the slope, the alarm module issues an alarm in a timely manner, and the control center responds in a timely manner to ensure the safety of the highway slope.

[0278] The design scheme of the present invention combines two methods of mobile trolley inspection monitoring and fixed-point monitoring to comprehensively and accurately monitor the highway slope. By collecting and analyzing data in real time, potential safety hazards can be discovered and processed in a timely manner to ensure the safe and stable operation of the highway. At the same time, the design scheme also considers factors such as manufacturing cost, assembly efficiency and operation stability, and has high feasibility and practicability. The data integration and analysis module conducts intelligent analysis on the monitoring data, identifies abnormal situations, evaluates the slope stability, and predicts potential risks. When an abnormal situation is detected on the slope, the alarm module issues an alarm in a timely manner, and the control center responds in a timely manner to ensure the safety of the highway slope.

[0279] As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for monitoring highway slopes, characterized in that, It includes the following steps: S1. The fixed-point monitoring agency conducts fixed-point detection on the highway slope and sends the monitoring data to the control center in real time; S2. The route planning module plans the driving route of the mobile monitoring agency according to the monitoring task; S3. The mobile monitoring agency comprehensively monitors the highway slope according to the planned driving route; Send the monitoring data to the control center in real time; S4. The data integration and analysis module integrates, processes and analyzes the data monitored by the fixed-point monitoring agency and the mobile monitoring agency; conducts intelligent analysis on the monitoring data, identifies abnormal situations, evaluates the slope stability and predicts potential risks; S5. When an abnormal situation is detected on the slope, the alarm module promptly sends an alarm to the control center.

2. The highway slope monitoring method according to claim 1, wherein: Step S3 includes the following steps: S31. Automatically control the mobile trolley to keep close to the side of the highway during mobile monitoring without affecting the normal passage of vehicles on the highway; S32. Adjust the direction of the telescopic mechanism through the electric turntable; adjust the height of the telescopic mechanism and the U-shaped frame through the lifting mechanism, and adjust the angles of the radar detector, high-definition camera and laser rangefinder through the telescopic mechanism; S33. Conduct image acquisition through the high-definition camera; detect abnormal situations such as cavities and cracks on the highway slope through the radar detector.

3. The highway slope monitoring method according to claim 2, wherein: The data integration and analysis module identifies abnormal situations on the highway slope, evaluates the slope stability and predicts potential risks, including the following steps: S41. Data collection and collation: Collect the original data of displacement monitoring, deformation monitoring, stress monitoring and radar monitoring from the fixed-point monitoring agency and the mobile monitoring agency; perform unified format conversion on the data from different monitoring agencies; integrate the displacement, deformation, stress and radar monitoring data to form a complete slope monitoring data set; clean the integrated data to remove outliers and noise data; S42. Abnormality identification: Identify outliers or abnormal patterns according to the processed data; conduct comparative analysis on the collected images to identify abnormal situations; S43. Slope stability evaluation: Calculate the slope stability coefficient using the processed data; comprehensively evaluate the slope stability by combining qualitative analysis method, limit equilibrium analysis method and numerical analysis method; S44. Potential risk prediction: Establish a slope deformation prediction model based on historical data and analysis results to predict the future deformation trend of the slope; combine geological conditions and climate conditions to predict and evaluate the potential risks of the slope; S45. Result reporting and decision support: Organize the results of abnormality identification, stability evaluation and potential risk prediction into a report and provide it to relevant departments and decision-makers.

4. The highway slope monitoring method according to claim 3, characterized in that: The support vector regression (SVR) method is used to identify abnormal situations in the displacement monitoring results, deformation monitoring results, stress monitoring results and radar monitoring results, specifically including the following steps: S421. Data collection and preprocessing: Collect historical data of displacement, deformation, stress and radar monitoring; clean the data to remove noise, errors or missing values; standardize or normalize the data; S422. Feature selection and extraction: Select or extract appropriate features according to the characteristics of each monitoring type; S423. Train the SVR model: Use historical data to train multiple SVR models, with each model corresponding to a monitoring type; Train and test the models with historical data; S424. Predict and calculate the residuals: For new monitoring data points, use the trained SVR models for prediction; Calculate the residuals between the actual monitoring values and the predicted values; S425. Set the thresholds: Based on the distribution of residuals for each monitoring type or business requirements, set a threshold for each monitoring type; S426. Identify outliers: For each monitoring type, if the residual of a data point exceeds the corresponding threshold, identify it as an outlier.

5. The highway slope monitoring method according to claim 3, characterized in that: Perform comparative analysis on the collected images to identify abnormal situations, including the following steps: S427. Sample data collection: Collect a large number of images of highway slopes, including normal images and images of abnormal situations, and annotate the images as reference samples; S428. Image preprocessing: Preprocess the collected image data, including denoising, grayscale conversion, and contrast enhancement operations; S429. Feature extraction and identification: Extract the edge information in the images through edge detection algorithms, and then identify the crack areas; Use morphological processing and filtering algorithms to further analyze and identify the cracks to distinguish different types of cracks; Divide the slope images into different regions or objects through image segmentation techniques, and then identify the collapse areas; Combine topographic and geological information to perform 3D reconstruction and analysis on the collapse areas to evaluate their scale and influence range; S4210. Water loss identification: Extract the water areas in the images through color analysis and threshold segmentation techniques; Combine topographic and vegetation information to analyze the causes and trends of water loss; S4211. Defect evaluation and classification: Compare and analyze the images after feature extraction with the reference samples to identify abnormal situations; Evaluate and classify the defects according to the identified defect types and features; S4212. Result output and application: Output and store the identification results in the form of images, reports, or databases; Relevant departments formulate corresponding maintenance and management measures based on the identification results.

6. The highway slope monitoring method according to claim 2, wherein: Comprehensively and deeply evaluate the stability of highway slopes by combining qualitative analysis methods, limit equilibrium analysis methods, and numerical analysis methods, specifically including the following: S431. Preliminary evaluation and data collection: Conduct a detailed on-site investigation of the slope's dimensions, slope shape, geological structure, and the geological environment factors it is in; Collect geological exploration reports, historical monitoring data, and meteorological data, and organize them into a database; S432. Qualitative analysis: Include slope shape and dimension evaluation, geological structure analysis, and geological environment analysis; S433. Limit equilibrium analysis: Include slip surface identification, rigid body assumption, and stability coefficient calculation; S434. Numerical analysis: Include boundary condition and parameter setting, calculation and analysis, establishment of numerical models, and sensitivity analysis; S435. Comprehensive evaluation and decision-making of results: Compare the results of qualitative analysis, limit equilibrium analysis, and numerical analysis, analyze the similarities and differences among them, and identify possible errors or deficiencies; comprehensively consider the results of the three methods, judge the stability state of the slope, and determine its safety level. S436. Monitoring and feedback: According to the stability evaluation results and reinforcement or monitoring suggestions, formulate a monitoring plan for the slope.

7. The highway slope monitoring method according to claim 6, characterized in that: The calculation of the stability coefficient combines a variety of monitoring data and corresponding weights for stability evaluation, including the following: Determine the characteristic values and weights: According to the actual monitoring data, determine the characteristic values of displacement D, deformation Df, stress S, and radar monitoring R, and determine the weights of each monitoring index. Calculate the stability coefficient SI through the following formula: SI = D × W D + D f × W Df + S × W S + R × W R ; W D +W Df +W S +W R = 1; Where D is the eigenvalue of the displacement monitoring result; W D The weight of the displacement monitoring result D f is the eigenvalue of the deformation monitoring result, and W Df is the weight of the deformation monitoring result; S is the characteristic value of the stress monitoring result. W S is the weight of the stress monitoring result; R is the eigenvalue of the radar monitoring result, and W R is the weight of the radar monitoring result; If the sum of the calculated weights is not equal to 1, the weight values need to be adjusted proportionally; judge the stability state of the slope according to the magnitude of the stability coefficient.

8. The highway slope monitoring method according to claim 3, characterized in that: In step S44, the prediction and evaluation of the potential risks of the slope include the following steps: S441. Collect historical data, clean and preprocess the data; select features related to slope deformation and stability. S442. Establish an XGBoost model: Divide the cleaned and preprocessed data set into a training set, a validation set, and a test set; use the training set data to train the XGBoost model; use the validation set data to verify the performance of the model. S443. Predict the future slope deformation trend: Prepare predicted or estimated values of geological conditions, climate conditions, and external factors for a future period of time; input the external factor data as features into the trained XGBoost model; use the model to predict the future slope deformation trend. S444. Potential risk prediction and evaluation: Combine the predicted deformation trend with external factors to analyze the impact on slope stability; conduct quantitative or qualitative evaluation of the potential risks of the slope. S445. Countermeasures and suggestions: According to the risk assessment results, formulate corresponding countermeasures and measures, regularly monitor and evaluate the slope, and adjust the countermeasures in a timely manner.

9. The highway slope monitoring method according to claim 1, characterized in that: The mobile monitoring mechanism includes an automatically controlled mobile trolley, a lifting mechanism, a telescopic mechanism, a U-shaped frame, a motor, a radar detector, and a high-definition camera. An electric turntable is fixedly arranged inside the automatically controlled mobile trolley; the electric turntable is fixedly provided with a lifting mechanism; the upper end of the lifting mechanism is fixedly provided with a telescopic mechanism; the end of the lifting mechanism is fixedly provided with a U-shaped frame; a motor is fixedly arranged on the U-shaped frame, and a support plate is fixedly arranged at the output end of the motor; a radar detector, a high-definition camera, and a laser rangefinder are fixedly arranged on the support plate.

10. A highway slope monitoring system, comprising: A fixed-point monitoring mechanism, a mobile monitoring mechanism, a route planning module, a data integration and analysis module, a warning module, and a control center; characterized in that: Fixed-point monitoring mechanism: including a theodolite, a level, a vibrating wire displacement meter, a differential resistance strain gauge, a rainfall detector, a temperature detector, a seismic monitor, and a high-definition camera; several fixed-point monitoring mechanisms are arranged on both sides of the highway to monitor the highway slope at fixed points. Mobile monitoring mechanism: It includes an automatically controlled mobile trolley, on which a high-definition camera, an LED lamp, a displacement monitor, a radar detector and a water level gauge are mounted; it collects various data of the slope in real time and sends them to the monitoring center in real time through a wireless transmission system; Route planning module: Plans the driving route of the mobile monitoring mechanism; Data integration and analysis module: Integrates, processes and analyzes the data monitored by the fixed-point monitoring mechanism and the mobile monitoring mechanism, promptly identifies abnormal situations on the highway slope, evaluates the stability of the slope, and predicts potential risks; Alarm module: It includes an alarm. When an abnormal situation is detected on the highway slope, it issues an alarm in a timely manner; Control center: It is network-connected to the fixed-point monitoring mechanism, the mobile monitoring mechanism and the data integration and analysis module, receives and analyzes the monitoring data, responds promptly to abnormal situations, and takes corresponding countermeasures.

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