A highway bridge slope maintenance drone inspection method and system

Abstract

The present invention relates to the field of drone inspection, and specifically to a method and system for drone inspection of highway bridge slope maintenance. The method utilizes a multi-sensor system carried by a drone to collect high-definition images and multi-dimensional data of the slope in real time, and constructs a three-dimensional model through GIS technology. The system connects to authoritative meteorological warnings, uses machine learning models to predict slope disease risks, and automatically adjusts inspection plans to ensure that key areas are inspected before severe weather. When the battery is low or the weather is severe, the drone automatically navigates to a preset base station for wireless charging and maintenance to ensure the continuity and safety of the inspection mission. The drone inspection system of the present invention improves the efficiency and safety of slope maintenance, collects data in real time, connects with meteorological warnings, intelligently adjusts inspection plans, accurately constructs three-dimensional models, identifies disease risks early, and ensures the safety of drones in severe weather.

Description

Technical Field

[0001] The present invention relates to the field of drone inspection, and in particular to a drone inspection method and system for highway bridge slope maintenance. Background Art

[0002] With the rapid development of highway and bridge infrastructure, slope maintenance has become a key link in ensuring road safety. Traditional slope maintenance methods mainly rely on manual inspections, which have problems such as low efficiency, limited coverage area, and high safety risks. Manual inspections are greatly restricted by weather conditions. Bad weather often makes inspections impossible or delayed, affecting the timeliness of maintenance work. In addition, manual inspections are highly subjective and may have errors and omissions, making it difficult to achieve objective and accurate risk assessments. For example, patent CN114281107A describes a highway slope maintenance inspection method, which obtains slope location information by manually driving a vehicle along the highway for inspection, and uses drones for fixed-point photography and sampling, but is still limited by the efficiency and accuracy of manual operations.

[0003] With the development of drone technology, its application in highway bridge slope maintenance has improved the efficiency and safety of inspections. However, existing drone inspection methods still face challenges, such as flight power limitations, difficulty ensuring flight safety and data acquisition quality in adverse weather conditions, and reliance on simple image acquisition. The lack of in-depth analysis and processing capabilities for acquired data makes it impossible to accurately predict and warn of slope disease risks. Patent CN114353876A proposes a loess highway slope health monitoring method that uses drone aerial surveys to obtain multi-period image data and utilizes a neural network model to preliminarily identify slope diseases. However, this method primarily focuses on image recognition, and its real-time monitoring and predictive maintenance capabilities still need to be improved.

[0004] Meteorological factors significantly influence the stability of highway bridge slopes. Severe weather conditions such as rain, snow, and high winds can cause slope erosion, slippage, and even collapse, posing a serious threat to road safety. Therefore, real-time monitoring of these meteorological factors and risk assessment based on specific slope conditions are crucial for preventing and mitigating highway bridge slope disasters. While previous studies have attempted to integrate meteorological data with slope monitoring, these methods often lack real-time and predictive capabilities, failing to meet practical maintenance needs.

[0005] In summary, the existing technology has poor accuracy, lacks predictability and is restricted by weather conditions. There is an urgent need for a drone inspection method and system for highway bridge slope maintenance. Summary of the Invention

[0006] The purpose of the present invention is to provide a highway bridge slope maintenance drone inspection method and system to solve the problems of poor accuracy, lack of predictability and weather conditions in the existing technology.

[0007] To achieve the above objectives, the following technical solutions are adopted.

[0008] A method for inspecting highway bridge slopes using drones, comprising:

[0009] Use drones equipped with multi-sensor systems to fly over highway bridge slopes and collect high-definition images and multi-dimensional data of bridge slopes in real time;

[0010] By connecting to the early warning system interface of authoritative meteorological agencies, it receives early warning information about future weather conditions, including rain, snow and strong winds;

[0011] Utilizing collected high-definition images and multi-dimensional data, combined with geographic information system technology, a three-dimensional model of highway bridge slopes was constructed;

[0012] The received weather warning information is input into a pre-trained machine learning prediction model and combined with the 3D slope model to perform predictive simulation and assess potential slope damage risks;

[0013] Based on the results of predictive simulation, if the risk level assessed by the simulation results reaches or exceeds the predetermined threshold, the system automatically issues a maintenance warning;

[0014] When receiving a severe weather warning, the system automatically analyzes the current inspection plan and intelligently adjusts the drone's inspection route and time based on weather conditions, focusing on key areas before the weather deteriorates.

[0015] During the inspection mission, if the drone encounters low battery or bad weather conditions, the system will automatically direct the drone to enter the preset base station for wireless charging and maintenance, ensuring the safety of the drone and the continuity of the inspection mission.

[0016] Optionally, the steps of using a drone equipped with a multi-sensor system to fly over the highway bridge slope and collect high-definition images and multi-dimensional data of the bridge slope in real time include:

[0017] Set the drone's flight path to cover all designated highway bridge slope areas;

[0018] Activate the multi-sensor system onboard the drone, including high-definition cameras, lidar, infrared cameras, and hyperspectral sensors;

[0019] During the drone flight, a multi-sensor system collects high-definition images and multi-dimensional data of the bridge slope in real time, including topography, vegetation cover, geological structure, and temperature changes;

[0020] The collected raw data is preprocessed, including image denoising, contrast adjustment, color correction, and multidimensional data filtering and normalization, to obtain preprocessed high-definition images and multidimensional data.

[0021] Optionally, the steps of constructing a three-dimensional model of the highway bridge slope using the collected high-definition images and multi-dimensional data in combination with geographic information system technology include:

[0022] Receive high-definition images and multi-dimensional data collected by drones, where the multi-dimensional data includes but is not limited to lidar data, infrared data, and elevation data;

[0023] Using image registration technology, serialized high-definition images are aligned into a common reference frame to form a set of images with spatial consistency;

[0024] By using computer vision technology, feature points and feature descriptors are extracted from the aligned image set to establish the correspondence between images;

[0025] Using the structure-from-motion algorithm, the relative posture between images and the 3D structure of the scene are calculated based on the correspondence between feature points, generating a sparse 3D point cloud with visual features.

[0026] Using multi-view stereo matching technology, sparse 3D point clouds are densified to construct a detailed 3D model;

[0027] Fusing multidimensional data with dense 3D point clouds, optimizing the accuracy and integrity of the 3D model through data fusion algorithms, and generating accurate 3D models of highway bridge slopes;

[0028] In a geographic information system, aligning a precise three-dimensional model with a geographic coordinate system, integrating geographic coordinates and attribute data to form a georeferenced three-dimensional model;

[0029] Verify the accuracy of the generated 3D model by comparing it with known geographic control points or historical data to obtain verification results;

[0030] According to the verification results, the 3D model is adjusted and optimized, the deviation is corrected, and the final 3D model of the highway bridge slope is obtained.

[0031] Optional, pre-training step for machine learning prediction models, including,

[0032] Collect historical inspection data, including high-definition images, multi-dimensional data, and corresponding slope disease annotation information;

[0033] Extract features from the collected data, including but not limited to image texture features, shape features, structural features, and spatial and topographic features in multidimensional data;

[0034] Use the extracted features and corresponding slope disease annotation information to conduct supervised learning training on the machine learning model;

[0035] Validate and test the trained machine learning model to ensure its prediction accuracy meets the predetermined performance indicators;

[0036] Adjust and optimize the model based on the verification and test results, including adjusting model parameters, increasing or decreasing training data, changing feature selection, etc.

[0037] The final trained and optimized machine learning model is deployed to the inspection system to receive new high-definition images and multi-dimensional data, as well as weather warning information, in real time.

[0038] The model is regularly updated with new inspection data to adapt to changes in slope disease development and new environmental conditions.

[0039] Optionally, the received weather warning information is input into a pre-trained machine learning prediction model and combined with the three-dimensional slope model to perform predictive simulation to assess the potential slope disease risk, specifically including:

[0040] Receive and interpret real-time weather warning information provided by authoritative meteorological agencies, including but not limited to warning levels, warning times, and impact areas for rain, snow, and strong winds;

[0041] Extract key meteorological parameters from acquired weather warning information;

[0042] Match the extracted meteorological parameters with the three-dimensional model of the highway bridge slope to determine the correlation between the warning information and the specific locations and features in the slope model;

[0043] Using the matching results, the meteorological parameters are input into a pre-trained machine learning prediction model, which then performs internal calculations based on the input parameters and historical data.

[0044] The machine learning prediction model uses mapping relationships to simulate the development trends and possible impacts of slope diseases under different meteorological conditions;

[0045] The prediction model outputs simulation results, including the risk level of slope damage, the location and time of possible occurrence.

[0046] Optionally, upon receiving a severe weather warning, the system automatically analyzes the current inspection plan and intelligently adjusts the drone's inspection route and time based on the weather conditions. The specific steps for conducting focused inspections of key areas before the weather deteriorates include:

[0047] Upon receiving severe weather warning information, the system immediately triggers the preset emergency analysis program to quickly evaluate the current inspection plan;

[0048] Determine the inspection areas that may be affected based on the weather type, severity, and expected impact period in the warning information;

[0049] Using the 3D slope model and combining it with historical inspection data, we can identify key slope areas susceptible to severe weather.

[0050] Utilizes path planning algorithms to automatically re-plan drone inspection routes, prioritizing coverage of identified critical slope areas; assesses the drone's current battery life and projected power consumption to determine the feasibility of completing inspections of key areas before inclement weather arrives.

[0051] If the assessment results indicate that the inspection cannot be completed before severe weather, the system automatically adjusts the inspection plan, postponing or canceling inspection tasks in non-critical areas to ensure inspection priority in critical areas;

[0052] Automatically adjust the drone's takeoff and landing times to avoid bad weather periods;

[0053] Monitor weather changes in real time and dynamically update inspection plans;

[0054] The adjusted inspection plan is displayed to the operator through the user interface, and instructions are sent to the drone to perform the new inspection task;

[0055] During the execution of the adjusted inspection mission, the status of the drone is continuously monitored to ensure that the drone safely returns to or enters the preset base station before the arrival of severe weather.

[0056] Optionally, if the drone encounters low battery or bad weather during the inspection mission, the system will automatically command the drone to enter the preset base station for wireless charging and maintenance to ensure the safety of the drone and the continuity of the inspection mission. Specifically,

[0057] The UAV's onboard meteorological sensors detect the flight environment in real time, including temperature, humidity, air pressure, and wind speed parameters;

[0058] When the parameters detected by the meteorological sensor exceed the safety threshold for normal flight or the battery level drops to the low battery threshold, the emergency procedure is automatically triggered;

[0059] Evaluate the drone's current battery level and remaining flight time to determine if it has enough battery to safely return or travel to the nearest base station;

[0060] If the battery is insufficient to support the return operation, the drone will immediately fly to the nearest preset base station;

[0061] During the flight to the base station, the drone's status and environmental changes are continuously monitored to ensure flight safety;

[0062] When the drone reaches the base station, it automatically enters wireless charging mode. At the same time, the system performs necessary maintenance on the drone, including cleaning sensors and calibrating measurement equipment.

[0063] During maintenance, the system records details of maintenance activities, including maintenance time, maintenance operations performed, and parts replaced;

[0064] After maintenance is completed, assess weather conditions and drone status to decide whether to resume inspection missions or keep the drone on standby;

[0065] If weather conditions improve and the drone is in good condition, the drone will continue or reschedule the inspection mission.

[0066] A highway bridge slope maintenance drone inspection system, comprising:

[0067] An unmanned aerial vehicle platform, comprising at least one unmanned aerial vehicle equipped with a multi-sensor system for collecting high-definition images and multi-dimensional data of highway bridge slopes;

[0068] Meteorological data interface module, used to connect to the warning system interface of authoritative meteorological agencies to receive warning information on future weather conditions including rainfall, snowfall and strong winds;

[0069] A geographic information system unit, used to combine collected high-definition images and multi-dimensional data to construct a three-dimensional model of the highway bridge slope;

[0070] Machine learning prediction models are used to combine received weather warning information with three-dimensional slope models to perform predictive simulations and assess potential slope damage risks;

[0071] A risk assessment unit, configured to assess the risk level based on the results of the predictive simulation and automatically issue a maintenance warning when the risk level reaches or exceeds a predetermined threshold;

[0072] The inspection plan adjustment unit is used to automatically analyze the current inspection plan when receiving a severe weather warning and intelligently adjust the drone's inspection route and time according to weather conditions;

[0073] The drone command and control unit is used to automatically direct the drone to a preset base station for wireless charging and maintenance when the drone encounters low battery or bad weather conditions;

[0074] Base station network, including a series of pre-set base stations equipped with wireless charging facilities and maintenance equipment for emergency shelter and maintenance of drones;

[0075] Data processing and storage center, used to store and process data collected by drones, as well as weather warning information and inspection results;

[0076] User interface system, which provides operators with an interface for monitoring inspection activities, adjusting inspection plans and receiving maintenance warning information;

[0077] Communication systems for data transmission and communication between UAVs, base stations, data processing and storage centers, and interfaces with meteorological warning systems;

[0078] Power management system for monitoring and managing drone battery status.

[0079] Optionally, the inspection plan adjustment unit includes:

[0080] The weather impact assessment subunit is used to analyze real-time weather data and predict the potential impact of weather changes on inspection tasks, and to assess flight risks under different weather conditions;

[0081] Inspection route optimization algorithm, used to dynamically adjust the drone inspection route based on weather impact assessment results and 3D slope models, avoiding high-risk areas and prioritizing key inspection points;

[0082] A task scheduling manager is used to automatically adjust the execution order and time of inspection tasks so that inspections of vulnerable areas can be completed before severe weather arrives;

[0083] The drone status monitoring subunit is used to monitor the drone's power, performance, and maintenance needs in real time, allowing the drone to perform inspection tasks in a safe state;

[0084] An emergency plan generator, which automatically generates emergency plans when severe weather or other emergencies are predicted, including adjusting inspection plans, specifying alternative flight routes, and safe return strategies;

[0085] A user interface for operators to manually adjust the inspection plan, enter specific instructions or override the automatically generated inspection route, and directly control the drone when necessary;

[0086] Historical data analyzer, used to analyze historical inspection data and weather events, learn historical trends, and improve inspection plans and response strategies.

[0087] Optionally, the base station network includes:

[0088] Multiple strategically deployed base stations: The base stations are distributed in key areas of the highway bridge slopes, so that the UAV can reach any base station within the maximum flight radius when performing a mission;

[0089] Each base station is equipped with an automatic identification system for confirming the identity of the drone entering the base station and recording the time and status of its entry and exit from the base station;

[0090] Each base station is equipped with a high-efficiency wireless charging device to provide fast charging services for returning drones;

[0091] Each of the base stations is equipped with an automatic maintenance and repair workstation for performing routine maintenance and emergency repair tasks on the drone;

[0092] Each base station is designed with an environmental control cabin to provide stable temperature and humidity conditions for the drone;

[0093] Each of the base stations is equipped with a data synchronization and transmission module for synchronizing the data collected by the drone to the central data processing center in real time;

[0094] Each base station emergency evacuation instruction system is configured to receive instructions via the network and send emergency evacuation instructions to the drone in the event of severe weather or other emergencies;

[0095] Each of the base stations has a built-in energy management system for monitoring and managing the energy consumption of the base station, including solar energy and backup battery systems;

[0096] Each of the base stations is provided with a security monitoring system, including video monitoring and intrusion detection systems;

[0097] Each base station is equipped with a status indicator light and communication equipment for displaying the operating status and communication signal strength of the base station to the drone and the operator.

[0098] Compared with the prior art, the present invention has the following beneficial effects:

[0099] Improving inspection efficiency and safety: The present invention's drone inspection method for highway bridge slope maintenance utilizes a multi-sensor drone system to achieve real-time collection of high-definition images and multi-dimensional data on highway bridge slopes. Compared to traditional manual inspections, drone inspections are not restricted by terrain and can quickly cover a wide area, while also avoiding the safety risks of personnel operating in high-risk areas. Furthermore, when the drone encounters low battery or inclement weather, it automatically returns to a pre-set base station for wireless charging and maintenance, ensuring the continuity of inspection tasks and the safety of the drone.

[0100] Real-time weather monitoring and early warning: By interfacing with the early warning systems of authoritative meteorological agencies, this system can receive real-time warning information on future weather conditions, including rain, snow, and strong winds. This capability enables the inspection system to adjust inspection plans based on weather changes, optimize resource allocation, and conduct focused inspections in key areas before severe weather arrives, thereby improving the timeliness and relevance of inspections.

[0101] Accurate 3D model construction and risk assessment: Utilizing collected high-definition imagery and multidimensional data, combined with geographic information system technology, a 3D model of highway bridge slopes is constructed, providing precise spatial information for slope maintenance. Combined with pre-trained machine learning prediction models, the system performs predictive simulations and assesses potential slope damage risks, enabling early identification and early warning of slope damage, enabling preventive measures to mitigate the likelihood of disasters.

[0102] Intelligent Inspection Plan Adjustment: When receiving a severe weather warning, the system automatically analyzes the current inspection plan and intelligently adjusts the drone's inspection route and schedule based on weather conditions. This intelligent adjustment mechanism allows inspections to more flexibly adapt to environmental changes, ensuring that key areas are inspected before the weather deteriorates, improving inspection efficiency and effectiveness.

[0103] Improved response speed and maintenance warning accuracy: Based on predictive simulation results, the system automatically issues maintenance warnings if the risk level reaches or exceeds a predetermined threshold. This automated warning mechanism significantly improves response speed, enabling timely maintenance work and reducing traffic accidents and property losses caused by slope damage.

[0104] In summary, the present invention provides an intelligent and automated drone inspection method for highway bridge slope maintenance by integrating technologies such as multi-sensor systems, meteorological warning interfaces, geographic information systems, and machine learning prediction models. It significantly improves the efficiency, safety, and accuracy of inspections, and provides strong technical support for the maintenance and management of highway bridge slopes. BRIEF DESCRIPTION OF THE DRAWINGS

[0105] Figure 1 The figure is a schematic diagram of the steps of an embodiment of a method for drone inspection of highway bridge slope maintenance according to the present invention. DETAILED DESCRIPTION

[0106] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, unless there is a conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

[0107] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise indicated, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present invention.

[0108] Example 1

[0109] like Figure 1 As shown, a highway bridge slope maintenance drone inspection method uses a drone equipped with a multi-sensor system

[0110] In this embodiment, the multi-sensor system carried by the drone includes a high-definition camera, a laser radar (LiDAR), an infrared camera, and a hyperspectral sensor. These sensors can collect high-definition images and multi-dimensional data of the highway bridge slope in real time, including information such as topography, vegetation cover, geological structure, and temperature changes.

[0111] The system connects to the warning systems of authoritative meteorological agencies and receives real-time warnings of future weather conditions, including rain, snow, and strong winds. This ensures that the system can adjust inspection plans in a timely manner based on weather changes.

[0112] Utilizing collected high-definition imagery and multidimensional data, combined with geographic information system (GIS) technology, a 3D model of the highway bridge slope was constructed. The collected high-definition imagery and multidimensional data were input into a geographic information system (GIS). Image registration techniques were used to align the images into a common reference frame, forming a spatially consistent image set. Computer vision techniques were used to extract feature points and descriptors, establishing correspondences between the images. Structure from Motion algorithms and multi-view stereo matching techniques were used to generate a detailed 3D model, which was then aligned to a geographic coordinate system. Geographic coordinates and attribute data were integrated to form a georeferenced 3D model.

[0113] Received weather warning information is fed into a pre-trained machine learning prediction model. This model combines the received weather warning information with a three-dimensional slope model to perform predictive simulations and assess potential slope damage risks. The model uses the learned mapping relationships to simulate the development trends and potential impacts of slope damage under different meteorological conditions.

[0114] Based on the results of predictive simulation, maintenance warnings are automatically issued. If the risk level assessed by the predictive simulation results reaches or exceeds the predetermined threshold, the system automatically issues a maintenance warning, prompting maintenance personnel to take appropriate preventive or repair measures.

[0115] When receiving a severe weather warning, the drone's inspection route and schedule are intelligently adjusted. The system automatically analyzes the current inspection plan and intelligently adjusts the drone's inspection route and schedule based on weather conditions, focusing on key areas before the weather deteriorates. This step ensures that key areas are fully inspected and necessary maintenance is carried out before severe weather arrives.

[0116] Emergency response during drone inspections: If a drone encounters low battery or inclement weather, the system will automatically direct the drone to a pre-set base station for wireless charging and maintenance. Each base station is equipped with an automatic identification system, wireless charging equipment, an automated maintenance and repair station, and an environmental control cabin to ensure drone safety and mission continuity.

[0117] Example 2

[0118] The drone's flight path is pre-programmed by the ground control station to cover all designated highway bridge slope areas. The flight path takes into account the bridge slope geometry, terrain characteristics, and flight safety requirements. The path planning algorithm ensures the drone efficiently and safely covers all critical areas while avoiding obstacles and hazardous areas.

[0119] Activate the drone's multi-sensor system, which includes a high-definition camera, laser radar (LiDAR), infrared camera, and hyperspectral sensor. These sensors are activated before flight and undergo a self-test to ensure all sensors are functioning properly. The HD camera captures high-resolution images, the LiDAR obtains precise distance and shape information, the infrared camera detects temperature changes, and the hyperspectral sensor analyzes the chemical composition of materials.

[0120] During the drone's flight, the multi-sensor system collects high-definition images and multi-dimensional data of the bridge slope in real time. This data includes, but is not limited to, topography, vegetation cover, geological structure, and temperature changes. The high-definition images are transmitted in real time to the ground control station via the drone's camera, while the multi-dimensional data is transmitted simultaneously via a wireless communication system.

[0121] The collected raw data is preprocessed at the ground control station. Image data preprocessing includes noise removal, contrast adjustment, and color correction to improve image quality and highlight disease features. Multidimensional data preprocessing includes filtering and normalization to reduce noise and errors, improving data accuracy and reliability. Preprocessed data is used for subsequent data analysis and 3D model construction.

[0122] Example 3

[0123] The multi-sensor system carried by the drone includes a high-definition camera, laser radar (LiDAR), an infrared camera, and a hyperspectral sensor. These sensors collect high-definition images and multidimensional data of highway bridge slopes in real time, including topography, vegetation cover, geological structure, and temperature changes. Multidimensional data such as LiDAR, infrared, and elevation data provide precise spatial information for the construction of 3D models.

[0124] Image registration technology is used to align serialized high-definition images into a common reference frame. This step involves preliminary matching, creating sample subsets, establishing a reliable set, and calculating the model. After completing a certain number of iterations, the final model is obtained, achieving the goal of efficiently and reliably eliminating matching errors during the image registration process.

[0125] Using computer vision technology, feature points and feature descriptors are extracted from the aligned image set to establish correspondences between the images. This involves extracting and matching feature points, providing key information for subsequent 3D structure recovery.

[0126] Using the Structure from Motion (SfM) algorithm, the relative pose between images and the 3D structure of the scene are calculated based on the correspondence between feature points, generating a sparse 3D point cloud with visual features. The SfM algorithm can be implemented by the following steps:

[0127] Select a set of scenes (two pictures), estimate the camera pose based on these two pictures, and reconstruct the 3D coordinate points;

[0128] Optimize with Bundle Adjustment;

[0129] For each remaining scene (image), repeat the above steps to perform 3D reconstruction.

[0130] Multi-view stereo matching technology is used to densify the sparse 3D point cloud and construct a detailed 3D model. This step involves matching images from multiple perspectives to increase the density of the point cloud and improve the refinement of the model.

[0131] By fusing multidimensional data with dense 3D point clouds, data fusion algorithms are used to optimize the accuracy and integrity of the 3D model, generating a precise 3D model of the highway bridge slope. Data fusion algorithms can include feature-level fusion, decision-level fusion, and other methods to ensure model accuracy and reliability.

[0132] In a geographic information system, a precise 3D model is aligned with a geographic coordinate system, integrating geographic coordinates and attribute data to form a georeferenced 3D model. This involves matching the 3D model with the actual geographic location to provide accurate geographic information for subsequent analysis and applications.

[0133] The generated 3D model is verified for accuracy and compared with known geographic control points or historical data to obtain verification results, ensuring the accuracy and precision of the model.

[0134] Based on the verification results, the 3D model is adjusted and optimized, deviations are corrected, and the final 3D model of the highway bridge slope is obtained. This involves fine-tuning and optimization of the model to ensure its optimal performance and applicability.

[0135] Example 4

[0136] Pre-training steps for machine learning prediction models include,

[0137] Collect historical inspection data, including high-definition images, multidimensional data, and corresponding slope disease annotations. This data will serve as the basis for training machine learning models. Multidimensional data may include lidar data, infrared data, and elevation data, providing comprehensive slope information.

[0138] Extract features from the collected data, including but not limited to image texture features, shape features, structural features, and spatial and topographic features in multidimensional data. This step is critical for machine learning model training because the selection of features directly affects model performance.

[0139] Using the extracted features and the corresponding slope disease annotation information, the machine learning model is trained through supervised learning. This process involves selecting an appropriate machine learning algorithm, such as support vector machine (SVM), random forest, or neural network, and adjusting the model parameters for optimal performance.

[0140] Validation and testing of trained machine learning models to ensure their predictive accuracy meets predetermined performance metrics. This is typically done by splitting the dataset into a training set and a test set, with the model trained on the training set and evaluated on the test set.

[0141] Based on the validation and testing results, the model is adjusted and optimized, including adjusting model parameters, increasing or decreasing training data, changing feature selection, etc. This step may require multiple iterations to ensure that the model maintains stable performance under different conditions.

[0142] The trained and optimized machine learning model is deployed to the inspection system, which receives new high-definition images, multi-dimensional data, and weather warning information in real time. After the model is deployed, it is necessary to ensure that it can be seamlessly integrated with the existing inspection system and can process real-time data.

[0143] Regularly update the model with new inspection data to adapt to changes in slope disease development and new environmental conditions. This may involve retraining the model or using online learning methods to ensure that the model can adapt to the new data distribution.

[0144] Example 5

[0145] The steps involved in inputting the received weather warning information into a pre-trained machine learning prediction model and combining it with the 3D slope model to perform predictive simulation and assess potential slope damage risks include:

[0146] The system connects to the warning system of authoritative meteorological agencies and receives real-time warning information on future weather conditions, including rain, snow, and strong winds. This information includes key meteorological parameters such as warning level, warning time, and impact range.

[0147] From the weather warning information obtained, the system automatically extracts key meteorological parameters such as rainfall, snowfall, wind speed and direction, which are crucial for assessing slope damage risks.

[0148] The system matches the extracted meteorological parameters with a three-dimensional model of the highway bridge slope to determine the correlation between the warning information and the specific locations and features in the slope model. This step utilizes Geographic Information System (GIS) technology to combine meteorological data with spatial data to provide precise location information for the prediction model.

[0149] Using the matching results, the system feeds the meteorological parameters into a pre-trained machine learning prediction model. The model performs internal calculations based on the input parameters and historical data, applying mapping relationships to simulate the development trends and potential impacts of slope damage under different meteorological conditions.

[0150] The machine learning prediction model uses the learned mapping relationships to simulate the development trends and potential impacts of slope damage under different meteorological conditions. This process involves complex algorithms and a large amount of historical data to ensure the accuracy of the simulation results.

[0151] The prediction model outputs simulation results, including the risk level of slope damage, the location and time of possible occurrence. These results provide a scientific basis for the maintenance of highway bridge slopes, making maintenance work more targeted and timely.

[0152] Example 6

[0153] When receiving a severe weather warning, the system automatically analyzes the current inspection plan, intelligently adjusts the drone's inspection route and time according to weather conditions, and conducts key inspections in key areas before the weather deteriorates.

[0154] Specifically, the system connects to the warning system of authoritative meteorological agencies and receives real-time severe weather warning information, including rain, snow, and strong winds. This information includes key parameters such as warning level, warning time, and impact range.

[0155] Once a severe weather warning is received, the system immediately triggers the preset emergency analysis program to quickly evaluate the current inspection plan.

[0156] Based on the weather type, severity and expected impact period in the warning information, the system determines the inspection areas that may be affected.

[0157] The system uses the three-dimensional slope model and combines it with historical inspection data to identify key slope areas that are susceptible to severe weather.

[0158] Using a path planning algorithm, the system automatically replans the drone’s inspection route, prioritizing coverage of identified critical slope areas.

[0159] The system evaluates the drone's current power and projected consumption to determine the feasibility of completing inspections of key areas before severe weather arrives.

[0160] If the assessment results indicate that the inspection cannot be completed before severe weather, the system will automatically adjust the inspection plan, postpone or cancel inspection tasks in non-critical areas, and ensure inspection priority in critical areas.

[0161] The system automatically adjusts the drone's take-off and landing times to avoid periods of bad weather.

[0162] The system monitors weather changes in real time and dynamically updates inspection plans to respond to real-time weather changes.

[0163] The adjusted inspection plan is displayed to the operator through the user interface, and instructions are sent to the drone to perform the new inspection task.

[0164] During the execution of the adjusted inspection mission, the system continuously monitors the status of the drone to ensure that the drone safely returns to or enters the preset base station before the arrival of bad weather.

[0165] Example 7

[0166] During the inspection mission, if the drone encounters low battery or bad weather conditions, the system will automatically direct the drone to enter the preset base station for wireless charging and maintenance, ensuring the safety of the drone and the continuity of the inspection mission.

[0167] Specifically, the drone's onboard meteorological sensors include temperature, humidity, air pressure, and wind speed sensors, which can detect flight environment parameters in real time. The sensor data is sent to the ground control station via a wireless transmission module for immediate analysis and decision-making.

[0168] When parameters detected by meteorological sensors exceed preset safety thresholds, such as wind speed exceeding the safe flight speed or battery power falling below a preset low-battery threshold, the drone's flight control system automatically triggers an emergency procedure. This procedure ensures that the drone can respond quickly to dangerous situations.

[0169] The drone's flight control system evaluates the current battery level and estimated consumption to determine if there is enough power to safely return or proceed to the nearest base station. This assessment is based on a real-time battery performance monitoring system that monitors the drone's battery status in real time to ensure flight safety.

[0170] If the battery level is insufficient to support a return, the drone will immediately fly to the nearest pre-set base station. The base station location information is stored in the drone's flight control system and works in conjunction with the GPS positioning system to ensure the drone accurately navigates to the correct base station.

[0171] During the flight to the base station, the drone's flight control system continuously monitors the drone's status and environmental changes to ensure flight safety. During this process, the drone may use a path planning algorithm to automatically plan the safest flight path, avoiding obstacles and dangerous areas.

[0172] Upon arrival at the base station, the drone automatically enters wireless charging mode. The base station is equipped with a wireless charging system that provides rapid charging for the drone. The system also performs necessary maintenance on the drone, including cleaning sensors and calibrating measurement equipment.

[0173] During the maintenance process, the system records the details of the maintenance activities, including the maintenance time, maintenance operations performed, and parts replaced. These records can be tracked and managed through the drone maintenance technology detailed explanation.

[0174] After maintenance is completed, the system evaluates weather conditions and drone status to decide whether to resume the inspection mission or keep the drone in standby mode. If weather conditions improve and the drone is in good condition, the drone will resume the inspection mission or reschedule it.

[0175] Example 8

[0176] A highway bridge slope maintenance drone inspection system includes a drone platform, a meteorological data interface module, a geographic information system unit, a risk assessment unit, an inspection plan adjustment unit, a drone command and control unit, a base station network, a data processing and storage center, a user interface system, a communication system, and a power management system.

[0177] The UAV platform includes at least one drone equipped with a multi-sensor system for collecting high-definition images and multi-dimensional data of highway bridge slopes. These sensors include high-definition cameras, laser radar (LiDAR), infrared cameras, and hyperspectral sensors, capable of collecting high-definition images and multi-dimensional data of highway bridge slopes in real time.

[0178] The Meteorological Data Interface Module interfaces with the warning systems of authoritative meteorological agencies to receive warning information about future weather conditions, including rain, snow, and strong winds. This module can receive and interpret real-time weather warning information from authoritative meteorological agencies, including warning level, warning time, and impact range.

[0179] The Geographic Information System (GIS) unit combines collected high-definition imagery with multidimensional data to construct a 3D model of the highway bridge slope. Using image registration technology, the unit aligns the sequenced high-definition images into a common reference frame, creating a spatially consistent image set. Furthermore, the unit applies structure-from-motion algorithms and multi-view stereo matching techniques to generate a detailed 3D model.

[0180] The machine learning prediction model combines received weather warning information with a three-dimensional slope model to perform predictive simulations and assess potential slope damage risks. The model uses learned mapping relationships to simulate the development trends and potential impacts of slope damage under different meteorological conditions.

[0181] The risk assessment unit evaluates risk levels based on the results of predictive simulations and automatically issues maintenance alerts when risk levels reach or exceed predetermined thresholds. Based on the output of machine learning predictive models, the unit can determine the risk level of slope damage and the location and time of its likely occurrence.

[0182] The inspection plan adjustment unit automatically analyzes the current inspection plan upon receiving a severe weather warning and intelligently adjusts the drone's inspection route and schedule based on the weather conditions. The unit identifies critical slope areas susceptible to severe weather and, using a path planning algorithm, automatically reroutes the drone.

[0183] The drone's command and control unit automatically directs the drone to a pre-set base station for wireless charging and maintenance when the drone encounters low battery or inclement weather. The unit assesses the drone's current battery level and remaining flight time, determines if there is sufficient power to safely return to or proceed to the nearest base station, and directs the drone to the nearest pre-set base station if necessary.

[0184] The base station network comprises a series of pre-configured base stations equipped with wireless charging facilities and maintenance equipment for emergency drone evacuation and maintenance. Each base station is equipped with an automatic identification system, wireless charging equipment, an automated maintenance and repair workstation, and an environmentally controlled chamber to ensure drone safety and the continuity of inspection missions.

[0185] The data processing and storage center is used to store and process data collected by drones, as well as weather warning information and inspection results. The center is capable of processing and analyzing large amounts of inspection data to provide scientific decision support.

[0186] The user interface system provides operators with an interface for monitoring inspection activities, adjusting inspection plans, and receiving maintenance warnings. Operators can use the user interface system to monitor the drone's flight status and inspection progress in real time and manually adjust the inspection plan when necessary.

[0187] The communication system is used for data transmission and communication between drones, base stations, data processing and storage centers, and interfaces with the meteorological warning system. This system ensures real-time data transmission and reliable communication, supporting the efficient operation of the drone inspection system.

[0188] The power management system monitors and manages the drone's battery status, ensuring it maintains energy during inspection missions. The system monitors the drone's battery level in real time and automatically triggers an emergency procedure when the battery is low, directing the drone to return to the base station for recharging.

[0189] Specifically, the UAV composition structure system:

[0190] The UAV platform includes the basic components of key mechanical systems such as the airborne platform, aircraft platform, remote control operation perception and operation system.

[0191] The UAV carrier is designed as a helicopter without a cockpit, which can automatically sense the flight azimuth altitude, speed and spatial position parameters.

[0192] Drone operation process steps:

[0193] Design the route and determine the take-off point, and scientifically determine the overall flight plan.

[0194] Load an operational flight plan to ensure the drone reaches a specific altitude and implement automated hovering procedures.

[0195] After the drone enters the preset flight route, it begins spraying pesticides.

[0196] Accurately return to the preset take-off point to realize automatic flight spraying control operation.

[0197] UAV operating parameter settings:

[0198] Technicians reasonably set operating parameter indicators to ensure the achievement of automated and intelligent flight control adjustment effects.

[0199] Detailed explanation of drone maintenance technology:

[0200] Drone maintenance includes core technologies such as basic structural understanding, cleaning and dust prevention, motor and propeller maintenance, battery maintenance and storage.

[0201] The core technologies of drone maintenance also include seven aspects: sensor calibration inspection, flight control system diagnosis, and fastener and connection inspection.

[0202] Drone communication protocol:

[0203] UAV communication protocols include MAVLink, OcuSync series communication protocols, UDP and TCP / IP, etc., to ensure communication between the UAV and the ground station.

[0204] Drone Energy Management Strategies:

[0205] A solar / hydrogen hybrid energy power system integration solution is proposed for small, low-altitude, long-flight electric UAVs.

[0206] An overall design method considering the weight-energy coupling relationship of the entire aircraft and an energy management strategy driven by mission profile are proposed.

[0207] Drone security monitoring system:

[0208] The drone management and control platform includes functions such as real-time monitoring, air traffic control, drone identification, flight restrictions, data recording and analysis.

[0209] UAV status indication and communication equipment:

[0210] The drone platform is equipped with status indicators and communication equipment to display the operating status of the base station and the communication signal strength to the drone and operator.

[0211] Drone online management platform:

[0212] The management platform supports functions such as route, record analysis, task zoom, focus, etc., supports L1 lens preheating, point cloud recording start and end, and data return.

[0213] Drone Emergency Response:

[0214] The applications of drones in emergency rescue include early warning reconnaissance, close-range reconnaissance, mobile deployment, fire scene reconnaissance, fire source location, and fire scene investigation.

[0215] Specifically, the composition of geographic information system units.

[0216] Data Input and Verification Subunit: This subunit is responsible for converting survey data, map data, remote sensing data, statistical data, and text reports into computer-compatible digital formats. Tools used include digitizers, scanners, total stations, GPS, etc.

[0217] Data storage and management system: Design a spatial database architecture that suits project requirements, determine the required spatial and attribute data types, and collect, process, and organize data, including data format conversion, coordinate system selection, and correction.

[0218] Data transformation subunit: performs data transformation operations, including scale transformation, data and projection matching (projection transformation), logical retrieval of data, area and side length calculation, etc., to eliminate errors, update data, and match with other databases.

[0219] Data display and output subunit: The original data or analysis and processing result data are presented in various forms such as maps, tables, images, etc., and displayed on the screen or output through a printer or plotter.

[0220] User interface module: provides tools for interaction between users and the system, including user interface, program interface and data interface, and receives user instructions and program or data input.

[0221] Topological modeling capabilities: GIS can identify and analyze spatial relationships in digital spatial data, such as connection, containment, and proximity, allowing for complex spatial modeling and analysis.

[0222] Network modeling capabilities: GIS can simulate the diffusion paths of pollutants along linear networks (such as rivers) and is often used in transportation planning, hydrological modeling, and underground pipeline network modeling.

[0223] Geospatial Data Analysis: Use GIS hardware and software to perform geospatial analysis and generate visualizations that show spatial relationships—that is, how different geospatial elements relate to each other.

[0224] Mapping and Visualization: Use GIS software to create maps to visualize data and analysis results, and design appropriate visual elements such as legends, labels, and color schemes.

[0225] Application Development: Develop custom GIS applications or tools as needed, using programming languages ​​and GIS development frameworks to ensure the technical requirements of the project are met.

[0226] Specifically, the specific implementation of the machine learning prediction model:

[0227] Data collection: Based on the problem definition, data related to highway bridge slope maintenance is collected, including historical inspection data, slope disease cases, environmental parameters, etc.

[0228] Data cleaning: handle missing values ​​and outliers, unify data formats, and ensure data quality.

[0229] Feature engineering: Extract useful features from raw data, such as slope geometry, material properties, historical disease records, etc.

[0230] Candidate models: Based on the problem type (such as classification, regression) and data characteristics, select a suitable machine learning model, such as random forest, support vector machine (SVM), neural network, etc.

[0231] Model training involves data splitting: dividing the dataset into training and test sets, usually in an 80 / 20 or 70 / 30 ratio. Cross-validation: using K-fold cross-validation to assess the stability and generalization ability of the model.

[0232] Grid Search: Systematically tries predefined parameter combinations to find the optimal hyperparameter settings.

[0233] Random search: Randomly sample the parameter space to find the optimal parameters.

[0234] Evaluation metrics: Determine the metrics for evaluating the model based on the problem type, such as accuracy, precision, recall, F1 score, etc.

[0235] Performance comparison: Use the test set to evaluate model performance and compare it with business objectives to ensure that the model meets business requirements.

[0236] Overfitting and underfitting processing: Overfitting can be handled by increasing training data, using regularization, simplifying the model, etc.; underfitting can be handled by increasing model complexity, adding new features, and reducing regularization.

[0237] Deployment strategy: Choose the appropriate technology and platform deployment model, such as cloud services or on-premises servers.

[0238] Performance monitoring: Establish a monitoring system to track model performance and ensure the stability and accuracy of the model in practical applications.

[0239] Real-time prediction: Apply the trained model to real-time data to make real-time predictions of slope damage risks.

[0240] Specifically, the inspection plan adjustment unit consists of a weather impact assessment subunit, an inspection route optimization algorithm, a task scheduling manager, a drone status monitoring subunit, an emergency plan generator, a user interaction interface, and a historical data analyzer.

[0241] Receive and analyze warning information from authoritative meteorological agencies in real time, including data on rainfall, snowfall, and strong winds.

[0242] Utilize statistical and probabilistic models to assess flight risks under different weather conditions and determine the potential impact of weather on inspection missions.

[0243] The inspection route of the drone is dynamically adjusted based on the weather impact assessment results and the three-dimensional slope model.

[0244] Use path planning algorithms, such as the A* algorithm or the TEB algorithm, to avoid high-risk areas and prioritize key inspection points.

[0245] Automatically adjust the execution order and time of inspection tasks to ensure that inspections of vulnerable areas are completed before severe weather arrives.

[0246] Considering the drone's battery life and flight speed, optimize task scheduling to maximize inspection efficiency.

[0247] Monitor the drone's power, performance, and maintenance needs in real time to ensure it performs inspection tasks in a safe state.

[0248] Leverage sensor data and flight status information to assess the drone's health and remaining flight time.

[0249] Automatically generate emergency plans when severe weather or other emergencies are predicted.

[0250] The plan includes adjusting inspection plans, specifying alternative flight routes and safe return strategies to ensure the safety of drones and data.

[0251] Provides an interface for operators to manually adjust the inspection plan, allowing them to enter specific instructions or overwrite the automatically generated inspection route.

[0252] When necessary, operators can directly control the drone to respond to emergencies or perform special missions.

[0253] Analyze historical inspection data and weather events to learn historical trends and improve inspection plans and response strategies.

[0254] Leverage machine learning algorithms to extract patterns and trends from historical data to optimize future inspection plans.

[0255] Specific, strategically deployed base stations

[0256] Base station deployment follows an optimization algorithm to ensure that drones can reach any base station within their maximum flight radius. Assuming the drone’s flight radius is R, the maximum distance d between base stations should satisfy d ≤ R.

[0257] Use GIS technology to determine the optimal location for base stations, taking into account factors such as terrain, vegetation, and buildings.

[0258] Each base station is equipped with an RFID or QR code scanner for drone authentication. The system records the time when the drone enters and leaves the base station. in and t out and state S.

[0259] The wireless charging device provides the corresponding charging power P according to the battery status of the drone. The charging time T can be calculated by the formula T=E needed / P calculation, where E needed is the energy required.

[0260] Automatic maintenance and repair workstation, the drone maintenance cycle M is determined by the flight hours F and the preset maintenance interval I, M = F mod I.

[0261] The environmental control chamber maintains the temperature T and humidity H at the set point, and adjusts them through the air conditioning system to ensure that T and H are within the preset range.

[0262] Data synchronization and transmission module data, transmission rate: The data synchronization module calculates the transmission time T based on the data volume D and available bandwidth B sync , T sync =D / B.

[0263] Emergency evacuation instruction system, evacuation response time: after receiving the emergency instruction, the system response Respond within a short time and send evacuation instructions to the drone.

[0264] Energy management system, the energy management system monitors the energy consumption of the base station E 消耗 , and optimize energy distribution to ensure that solar panels A and backup batteries B 备用 effective use of.

[0265] Security monitoring system, the security monitoring system uses video analysis algorithms to detect unauthorized intrusions, and the system response time is R intrusion Should be smaller than the preset threshold.

[0266] Status indicator and communication device, communication device displays signal strength S 强度 , use indicator lights or digital displays to ensure that operators can understand the communication status in real time.

[0267] It is understood from common technical knowledge that the present invention may be implemented by other embodiments that do not depart from its spirit or essential features. Therefore, the embodiments disclosed above are, in all respects, merely illustrative and not exclusive. All modifications within the scope of the present invention or equivalent to the scope of the present invention are intended to be encompassed by the present invention.

Claims

1. A method for inspecting highway bridge slopes using drones, characterized in that: include, Use drones equipped with multi-sensor systems to fly over highway bridge slopes and collect high-definition images and multi-dimensional data of bridge slopes in real time; By connecting to the early warning system interface of authoritative meteorological agencies, it receives early warning information about future weather conditions, including rain, snow and strong winds; Utilizing collected high-definition images and multi-dimensional data, combined with geographic information system technology, a three-dimensional model of highway bridge slopes was constructed; The received weather warning information is input into a pre-trained machine learning prediction model and combined with the 3D slope model to perform predictive simulation and assess potential slope damage risks; Among them, the machine learning prediction model uses mapping relationships to simulate the development trend and possible impact of slope diseases under different meteorological conditions; Based on the results of predictive simulation, if the risk level assessed by the simulation results reaches or exceeds the predetermined threshold, the system automatically issues a maintenance warning; When receiving a severe weather warning, the system automatically analyzes the current inspection plan and intelligently adjusts the drone's inspection route and time based on weather conditions, focusing on key areas before the weather deteriorates. Specifically, upon receiving severe weather warning information, the system immediately triggers the preset emergency analysis program to quickly evaluate the current inspection plan; Determine the inspection areas that may be affected based on the weather type, severity, and expected impact period in the warning information; Using the 3D slope model and combining it with historical inspection data, we can identify key slope areas susceptible to severe weather. Using path planning algorithms, the drone's inspection route is automatically replanned to prioritize coverage of identified critical slope areas; Evaluate the drone's current battery life and projected consumption to determine the feasibility of completing inspections of key areas before severe weather arrives; If the assessment results indicate that the inspection cannot be completed before severe weather, the system automatically adjusts the inspection plan, postponing or canceling inspection tasks in non-critical areas to ensure inspection priority in critical areas; Automatically adjust the drone's takeoff and landing times to avoid bad weather periods; Monitor weather changes in real time and dynamically update inspection plans; The adjusted inspection plan is displayed to the operator through the user interface, and instructions are sent to the drone to perform the new inspection task; During the adjusted inspection mission, the drone's status will be continuously monitored to ensure it safely returns to or enters the preset base station before severe weather arrives. If a drone encounters low battery or bad weather during an inspection mission, the system will automatically direct the drone to a preset base station for wireless charging and maintenance, ensuring the safety of the drone and the continuity of the inspection mission. The base stations are distributed in key areas of the highway bridge slopes so that the UAV can reach any base station within the maximum flight radius when performing a mission; Each base station emergency evacuation indication system is used to receive instructions through the network and send emergency evacuation instructions to the drone in severe weather or other emergency situations.

2. A highway bridge slope maintenance drone inspection method according to claim 1, characterized in that: The specific steps of using a drone equipped with a multi-sensor system to fly over the highway bridge slope and collect high-definition images and multi-dimensional data of the bridge slope in real time include: Set the drone's flight path to cover all designated highway bridge slope areas; Activate the multi-sensor system onboard the drone, including high-definition cameras, lidar, infrared cameras, and hyperspectral sensors; During the drone flight, a multi-sensor system collects high-definition images and multi-dimensional data of the bridge slope in real time, including topography, vegetation cover, geological structure, and temperature changes; The collected raw data is preprocessed, including image denoising, contrast adjustment, color correction, and multidimensional data filtering and normalization, to obtain preprocessed high-definition images and multidimensional data.

3. A highway bridge slope maintenance drone inspection method according to claim 1, characterized in that: The steps of constructing a three-dimensional model of the highway bridge slope using the collected high-definition images and multi-dimensional data combined with geographic information system technology include: Receive high-definition images and multi-dimensional data collected by drones, where the multi-dimensional data includes but is not limited to lidar data, infrared data, and elevation data; Using image registration technology, serialized high-definition images are aligned into a common reference frame to form a set of images with spatial consistency; By using computer vision technology, feature points and feature descriptors are extracted from the aligned image set to establish the correspondence between images; Using the structure-from-motion algorithm, the relative posture between images and the 3D structure of the scene are calculated based on the correspondence between feature points, generating a sparse 3D point cloud with visual features. Using multi-view stereo matching technology, sparse 3D point clouds are densified to construct a detailed 3D model; Fusing multidimensional data with dense 3D point clouds, optimizing the accuracy and integrity of the 3D model through data fusion algorithms, and generating accurate 3D models of highway bridge slopes; In a geographic information system, aligning a precise three-dimensional model with a geographic coordinate system, integrating geographic coordinates and attribute data to form a georeferenced three-dimensional model; Verify the accuracy of the generated 3D model by comparing it with known geographic control points or historical data to obtain verification results; According to the verification results, the 3D model is adjusted and optimized, the deviation is corrected, and the final 3D model of the highway bridge slope is obtained.

4. A highway bridge slope maintenance drone inspection method according to claim 1, characterized in that: Pre-training steps for machine learning prediction models include, Collect historical inspection data, including high-definition images, multi-dimensional data, and corresponding slope disease annotation information; Extract features from the collected data, including but not limited to image texture features, shape features, structural features, and spatial and topographic features in multidimensional data; Use the extracted features and corresponding slope disease annotation information to conduct supervised learning training on the machine learning model; Validate and test the trained machine learning model to ensure its prediction accuracy meets the predetermined performance indicators; Adjust and optimize the model based on the verification and testing results, including adjusting model parameters, increasing or decreasing training data, and changing feature selection; The final trained and optimized machine learning model is deployed to the inspection system to receive new high-definition images and multi-dimensional data, as well as weather warning information, in real time. The model is regularly updated with new inspection data to adapt to changes in slope disease development and new environmental conditions.

5. The method for inspecting highway bridge slopes using a drone according to claim 1, characterized in that: The steps of inputting the received weather warning information into the pre-trained machine learning prediction model and combining it with the three-dimensional slope model to conduct predictive simulation and assess the potential slope disease risk include: Receive and interpret real-time weather warning information provided by authoritative meteorological agencies, including but not limited to warning levels, warning times, and impact areas for rain, snow, and strong winds; Extract key meteorological parameters from acquired weather warning information; Match the extracted meteorological parameters with the three-dimensional model of the highway bridge slope to determine the correlation between the warning information and the specific locations and features in the slope model; Using the matching results, the meteorological parameters are input into a pre-trained machine learning prediction model, which then performs internal calculations based on the input parameters and historical data. The machine learning prediction model uses mapping relationships to simulate the development trends and possible impacts of slope diseases under different meteorological conditions; The prediction model outputs simulation results, including the risk level of slope damage, the location and time of possible occurrence.

6. A highway bridge slope maintenance drone inspection method according to claim 1, characterized in that: During the inspection mission, if the drone encounters low battery or bad weather, the system will automatically command the drone to enter the preset base station for wireless charging and maintenance to ensure the safety of the drone and the continuity of the inspection mission. The specific steps include: The UAV's onboard meteorological sensors detect the flight environment in real time, including temperature, humidity, air pressure, and wind speed parameters; When the parameters detected by the meteorological sensor exceed the safety threshold for normal flight or the battery level drops to the low battery threshold, the emergency procedure is automatically triggered; Evaluate the drone's current battery level and remaining flight time to determine if it has enough battery to safely return or travel to the nearest base station; If the battery is insufficient to support the return operation, the drone will immediately fly to the nearest preset base station; During the flight to the base station, the drone's status and environmental changes are continuously monitored to ensure flight safety; When the drone reaches the base station, it automatically enters wireless charging mode. At the same time, the system performs necessary maintenance on the drone, including cleaning sensors and calibrating measurement equipment. During maintenance, the system records details of maintenance activities, including maintenance time, maintenance operations performed, and parts replaced; After maintenance is completed, assess weather conditions and drone status to decide whether to resume inspection missions or keep the drone on standby; If weather conditions improve and the drone is in good condition, the drone will continue or reschedule the inspection mission.

7. A highway bridge slope maintenance drone inspection system, based on a highway bridge slope maintenance drone inspection method according to any one of claims 1 to 6, characterized in that: include, An unmanned aerial vehicle platform, comprising at least one unmanned aerial vehicle equipped with a multi-sensor system for collecting high-definition images and multi-dimensional data of highway bridge slopes; Meteorological data interface module, used to connect to the warning system interface of authoritative meteorological agencies to receive warning information on future weather conditions including rainfall, snowfall and strong winds; A geographic information system unit, used to combine collected high-definition images and multi-dimensional data to construct a three-dimensional model of the highway bridge slope; Machine learning prediction models are used to combine received weather warning information with three-dimensional slope models to perform predictive simulations and assess potential slope damage risks; A risk assessment unit, configured to assess the risk level based on the results of the predictive simulation and automatically issue a maintenance warning when the risk level reaches or exceeds a predetermined threshold; The inspection plan adjustment unit is used to automatically analyze the current inspection plan when receiving a severe weather warning and intelligently adjust the drone's inspection route and time according to weather conditions; The drone command and control unit is used to automatically direct the drone to a preset base station for wireless charging and maintenance when the drone encounters low battery or bad weather conditions; Base station network, including a series of pre-set base stations equipped with wireless charging facilities and maintenance equipment for emergency shelter and maintenance of drones; Data processing and storage center, used to store and process data collected by drones, as well as weather warning information and inspection results; User interface system, which provides operators with an interface for monitoring inspection activities, adjusting inspection plans and receiving maintenance warning information; Communication systems for data transmission and communication between UAVs, base stations, data processing and storage centers, and interfaces with meteorological warning systems; Power management system for monitoring and managing drone battery status.

8. The highway bridge slope maintenance drone inspection system according to claim 7 is characterized in that: The inspection plan adjustment unit includes: The weather impact assessment subunit is used to analyze real-time weather data and predict the potential impact of weather changes on inspection tasks, and to assess flight risks under different weather conditions; Inspection route optimization algorithm, used to dynamically adjust the drone inspection route based on weather impact assessment results and 3D slope models, avoiding high-risk areas and prioritizing key inspection points; A task scheduling manager is used to automatically adjust the execution order and time of inspection tasks so that inspections of vulnerable areas can be completed before severe weather arrives; The drone status monitoring subunit is used to monitor the drone's power, performance, and maintenance needs in real time, allowing the drone to perform inspection tasks in a safe state; An emergency plan generator, which automatically generates emergency plans when severe weather or other emergencies are predicted, including adjusting inspection plans, specifying alternative flight routes, and safe return strategies; A user interface for operators to manually adjust the inspection plan, enter specific instructions or override the automatically generated inspection route, and directly control the drone when necessary; Historical data analyzer, used to analyze historical inspection data and weather events, learn historical trends, and improve inspection plans and response strategies.

9. The highway bridge slope maintenance drone inspection system according to claim 7 is characterized in that: The base station network includes: Multiple strategically deployed base stations: The base stations are distributed in key areas of the highway bridge slopes, so that the UAV can reach any base station within the maximum flight radius when performing a mission; Each base station is equipped with an automatic identification system for confirming the identity of the drone entering the base station and recording the time and status of its entry and exit from the base station; Each base station is internally equipped with a wireless charging device for providing fast charging services for returning drones; Each of the base stations is equipped with an automatic maintenance and repair workstation for performing routine maintenance and emergency repair tasks on the drone; Each base station is designed with an environmental control cabin to provide stable temperature and humidity conditions for the drone; Each of the base stations is equipped with a data synchronization and transmission module for synchronizing the data collected by the drone to the central data processing center in real time; Each base station emergency evacuation instruction system is configured to receive instructions via the network and send emergency evacuation instructions to the drone in the event of severe weather or other emergencies; Each of the base stations has a built-in energy management system for monitoring and managing the energy consumption of the base station, including solar energy and backup battery systems; Each of the base stations is provided with a security monitoring system, including video monitoring and intrusion detection systems; Each base station is equipped with a status indicator light and communication equipment for displaying the operating status and communication signal strength of the base station to the drone and the operator.

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