Airport runway operation and maintenance method based on digital twinborn technology

Abstract

The invention relates to an airport runway operation and maintenance method based on a digital twin technology. The method comprises the following steps: performing high-precision reconstruction on a three-dimensional scene of an airport runway to obtain a runway high-precision three-dimensional scene model; constructing a multi-scale digital twinborn model of the airport runway based on the runway high-precision three-dimensional scene model and the multi-source sensing data; performing high real-time simulation of airplane-runway interaction through the multi-scale digital twin model, and performing dynamic deduction of runway performance by using a simulation result so as to predict states of the runway under different operation conditions; and generating a preventive maintenance strategy according to the state of the runway. According to the method, full-life-cycle management and intelligent operation and maintenance of the runway are achieved, the management and maintenance level of the airport runway can be remarkably improved, runway damage is effectively prevented through real-time monitoring and dynamic simulation, the operation risk is reduced, a scientific decision basis is provided for maintenance personnel, the service life of the runway is prolonged, and flight safety is ensured.

Description

Technical Field

[0001] The present invention relates to the operation and maintenance of airport runways, and specifically relates to an airport runway operation and maintenance method based on digital twin technology, belonging to the technical field of intelligent operation and maintenance of airport runways. Background Art

[0002] With the rapid development of aviation, airport runways, as the most critical infrastructure in the airfield, have a direct impact on the normal operation of air transportation due to their safety and service life. However, the traditional runway management model, which mainly relies on manual inspections and empirical decision-making, has the following major pain points and shortcomings:

[0003] First, traditional manual inspection methods are inefficient and unable to monitor runway condition in real time. Runways are prone to structural damage under frequent aircraft takeoffs and landings and high-load operations. Especially in adverse weather conditions, runway surfaces can rapidly deteriorate, increasing operational risks. However, relying on manual inspections typically means maintenance can only be performed after problems arise. This reactive approach often misses the optimal time for repairs. Second, existing maintenance decisions are often based on experience and historical data, lacking real-time awareness and accurate prediction of runway condition, making them inadequate for complex and changing operating environments. Different aircraft types, takeoff and landing frequencies, and climatic conditions have varying impacts on runways. Traditional empirical decisions struggle to provide scientific maintenance plans, resulting in high runway maintenance costs and suboptimal maintenance results. Furthermore, traditional pavement management lacks effective preventative maintenance mechanisms, making it impossible to detect potential structural problems early. This leads to rapid runway deterioration, increasing safety risks for airport operations. According to statistics, over 30% of aviation safety incidents worldwide are related to runway performance degradation. Especially given the rapid growth of air traffic in my country, scientific and efficient runway management and maintenance have become a pressing challenge.

[0004] Therefore, the existing runway management method cannot meet the needs of modern airports for efficient and precise maintenance. There is an urgent need for an airport runway operation and maintenance solution that meets the above needs to improve runway operation and maintenance efficiency and ensure the safe operation of the airport. Summary of the Invention

[0005] The present invention aims to address the deficiencies in the above-mentioned prior art and provide an airport runway operation and maintenance method based on digital twin technology. This method is oriented to the digital twin technology for intelligent operation and maintenance of airport runways, and establishes a digital twin system that can map the physical state of the runway in real time and perform dynamic simulation and deduction, so as to improve the maintenance efficiency of the airport runway, extend the service life of the runway, and significantly improve flight safety.

[0006] The technical solution adopted to achieve the purpose of the present invention is an airport runway operation and maintenance method based on digital twin technology, which includes:

[0007] Reconstruct the three-dimensional scene of the airport runway with high precision to obtain a high-precision three-dimensional scene model of the runway;

[0008] Constructing a multi-scale digital twin model of the airport runway based on the high-precision three-dimensional scene model of the runway and multi-source sensor data;

[0009] Performing high-real-time simulation of aircraft-runway interaction through the multi-scale digital twin model, and using the simulation results to dynamically simulate runway performance to predict the state of the runway under different operating conditions;

[0010] Generate preventive maintenance strategies based on the condition of the runway.

[0011] In the above technical solution, high-precision reconstruction of the three-dimensional scene of the airport runway includes:

[0012] Collect point cloud data through lidar;

[0013] Preprocessing the collected point cloud data;

[0014] Generate a 3D scene model of the airport runway based on the pre-processed point cloud data;

[0015] Verify whether the three-dimensional model is of high accuracy.

[0016] Furthermore, verifying whether the three-dimensional model is of high precision includes: comparing key point data of the airport runway field measurement with the corresponding point positions in the three-dimensional model. If all position deviations are within ±3 mm, the three-dimensional model is of high precision.

[0017] In the above technical solution, the construction of a multi-scale digital twin model of an airport runway includes:

[0018] Collect data from sensors on the airport runway to obtain multi-source sensor data;

[0019] Preprocessing and fusing the multi-source sensor data, and constructing a multi-scale digital twin model of the airport runway based on the fused data;

[0020] The sensor data collected in real time is continuously input into the multi-scale digital twin model to achieve real-time mapping of the runway status, and the multi-scale digital twin model is corrected through a feedback mechanism.

[0021] In the above technical solution, constructing a multi-scale digital twin model of the airport runway based on the fused data includes:

[0022] Micro-model construction: Based on the physical properties of the runway material, the finite element method is used to construct the material property model of each finite element at the micro-scale;

[0023] Macro model construction: At the macro scale, the overall structural response model of the runway is constructed by combining the constructed high-precision three-dimensional scene model of the runway and the actual stress data.

[0024] In the above technical solution, the correction of the multi-scale digital twin model through the feedback mechanism includes: when there is a deviation between the measured data and the model prediction results, the least squares method is used to adjust the model parameters to ensure that the model always reflects the actual working status of the runway, thereby achieving correction; the correction results of the model will be automatically recorded and fed back to the data acquisition system, so that the sensor data acquisition strategy can be optimized according to the changes in the model to obtain more detailed data.

[0025] In the above technical solution, performing high-real-time simulation of aircraft-runway interaction using the multi-scale digital twin model includes:

[0026] Establish aircraft-runway dynamics model;

[0027] Using the aircraft-runway dynamics model to perform high real-time simulation to simulate the dynamic response of the aircraft when taxiing on the runway;

[0028] Based on the high-real-time simulation results of the aircraft-runway dynamics model, the long-term performance of the runway is dynamically simulated through the multi-scale digital twin model to predict the status of the airport runway;

[0029] Generate preventive maintenance strategies based on the status of airport runways.

[0030] In the above technical solution, the above-mentioned airport runway operation and maintenance method based on digital twin technology is characterized by including the use of a physical-digital closed-loop feedback mechanism to dynamically maintain and optimize the management of the airport runway, specifically including:

[0031] Collect data through sensors on the airport runway and conduct preliminary analysis of the collected data;

[0032] Data that is found to be normal after preliminary analysis is fed into the multi-scale digital twin model. The model uses adaptive updating to adjust the virtual state of the airport runway in real time, ensuring that the model remains consistent with the actual runway state. Furthermore, the multi-scale digital twin model uses real-time data input to perform more accurate simulations and predict future runway performance.

[0033] A maintenance strategy is generated based on the simulation results of the multi-scale digital twin model and the actual data of the airport runway, and relevant maintenance operations are sent to the operation and maintenance personnel.

[0034] After each maintenance work is completed, the system will compare the actual data and simulation results to determine whether the maintenance effect has met expectations. If any discrepancies are found, the system will record the problems and adjust the model parameters to provide a more accurate basis for the next simulation and decision-making.

[0035] Furthermore, 3D visualization tools are used to visualize the real-time status, stress distribution, deformation and future performance prediction of the runway.

[0036] The present invention achieves full lifecycle management and intelligent operation and maintenance of runways through multi-source data fusion, digital twin modeling, high-real-time simulation, and physical-digital closed-loop feedback. It has the following advantages: it can significantly improve the management and maintenance level of airport runways, effectively prevent runway damage and reduce operational risks through real-time monitoring and dynamic simulation, and provide maintenance personnel with a scientific decision-making basis, thereby extending the service life of the runway and ensuring flight safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a flow chart of an airport runway operation and maintenance method based on digital twin technology in the present invention.

[0038] Figure 2 Schematic diagram of the process for high-precision reconstruction of the runway 3D scene.

[0039] Figure 3 Schematic diagram of the process of modeling multi-scale digital twins based on multi-source sensor data.

[0040] Figure 4 Schematic diagram of the process of high-real-time simulation and dynamic deduction of aircraft-runway interaction.

[0041] Figure 5 Schematic diagram of the process for dynamic maintenance and optimization management of airport runways using a physical-digital closed-loop feedback mechanism. DETAILED DESCRIPTION

[0042] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0043] like Figure 1 As shown, the present invention provides an airport runway operation and maintenance method based on digital twin technology, including:

[0044] S1, high-precision reconstruction of the runway 3D scene, such as Figure 2 As shown, the following steps are included:

[0045] S1.1. Data collection through LiDAR

[0046] In this example, a lidar system was mounted on a drone platform, flying at an altitude of 20 meters to cover the entire runway area. The scanning angle was set to 360 degrees, ensuring full coverage of the runway surface and surrounding facilities. Each scan produced a point cloud data density of 1,000 points per square meter, with a scanning error controlled within ±1 mm. To ensure data integrity, three scans were performed in the morning, midday, and evening, collecting a total of 1 billion points of high-density point cloud data.

[0047] S1.2. Preprocessing of point cloud data

[0048] The multiple scan point cloud data collected by S1.1 are preprocessed to ensure the accuracy and integrity of the model. The specific steps are as follows: Use the ICP (Iterative Closest Point) algorithm to align the multiple scan data and accurately align the scan data. The registration accuracy is controlled within 0.5 mm. The median filter algorithm is used to remove noise in the point cloud and filter out abnormal points caused by environmental interference (such as raindrops and dust) to ensure that the noise ratio of the filtered data is less than 0.1%. For blank areas caused by obstacles, linear interpolation technology is used to supplement them so that the interpolated area is seamlessly connected with the surrounding point cloud data, and the error of the supplemented area is controlled within ±2 mm.

[0049] S1.3. Generation of 3D Model

[0050] After data preprocessing, a high-precision 3D model of the runway was generated based on the preprocessed point cloud data. The specific operation was as follows: The surface mesh model of the runway was generated using the Delaunay triangulation algorithm, with the side length of the triangle mesh set to 5 cm to ensure the model's precision.

[0051] Constrained Delaunay triangulation was applied to ensure that the generated mesh boundaries were precisely aligned with the runway boundaries in the GIS data, thus guaranteeing the geometric accuracy of the overall model.

[0052] To obtain visual details on the model surface, texture mapping is performed using images with a resolution of 2 cm / pixel. The captured runway surface image is mapped onto the 3D model, realistically reproducing details such as cracks and markings on the runway surface.

[0053] S1.4. Model Accuracy Verification

[0054] In order to verify the accuracy of the 3D model built by S1.3, the specific verification operations are as follows:

[0055] Twenty key points were selected from the runway surface and accurately measured using a total station.

[0056] Compare the key point data measured on site with the corresponding point position data in the 3D model. If all position deviations are within ±3 mm, the high accuracy of the model is verified. The model after accuracy verification is saved in OBJ format. The generated file size is about 3GB and is imported into the virtual reality system for further analysis and display.

[0057] S2, Multi-scale digital twin modeling based on multi-source sensor data, such as Figure 3 As shown, the following steps are included:

[0058] S2.1. Collection of runway multi-source sensor data

[0059] A variety of sensors are deployed on the runway to obtain accurate physical quantity data, including but not limited to acceleration, stress, temperature and humidity. Acceleration sensors are deployed in key areas of the runway (such as the take-off and landing area, the middle section and the end), with 10 sensors deployed in each area, for a total of 30 acceleration sensors. These sensors can monitor the vibration and impact response of the runway in real time at a frequency of 1000Hz. Stress sensors are deployed in the runway structure layer, with each sensor spaced 10 meters apart. A total of 100 stress sensors are deployed to monitor the stress distribution of the runway structure. Temperature and humidity sensors are deployed on the runway surface at intervals of 20 meters. Each sensor monitors the environmental conditions of a specific area, for a total of 50 sensors.

[0060] Data Collection: Each sensor transmits data to the central control system in real time via a wireless communication network at a data rate of 1 Gbps, ensuring zero-delay transmission. In this embodiment, the data collection cycle is set to 1 second, meaning that data from all sensors is collected and recorded in the central database every second, ensuring that dynamic changes in the runway under various operating conditions are captured.

[0061] S2.2 Preprocessing and Fusion of Multi-Source Data

[0062] The multi-source sensor data collected in real time is preprocessed and fused to build a complete multi-scale digital twin model. The specific operations are as follows:

[0063] Data preprocessing: De-noising algorithms (such as Kalman filtering) are used to perform real-time denoising on acceleration and stress data, removing data fluctuations caused by environmental noise or accidental interference. The denoised data error is controlled within ±0.1%. Temperature and humidity data are standardized, converting temperature and humidity data from different regions to a unified scale for subsequent fusion processing.

[0064] Data Fusion: A multidimensional data fusion algorithm is used to fuse preprocessed acceleration, stress, temperature, and humidity data. A BP neural network combined with fuzzy set theory is used to construct a multidimensional data fusion model. The trained model is capable of modeling the relationships between various physical quantities. The data fusion results in a multidimensional state vector representing the runway's overall state at each moment. The key parameters of the state vector include: overall stress level, average vibration acceleration, ambient temperature, and humidity.

[0065] S2.3 Multiscale Coupling Modeling

[0066] Based on the fused data, a multi-scale digital twin model of the runway is constructed to reflect all-round information from microscopic material properties to macroscopic structural responses.

[0067] Micro-model construction: At the microscopic scale, the finite element method (FEM) was used to construct a material property model for each finite element based on the physical properties of the runway material (such as elastic modulus and Poisson's ratio). The size of each finite element was set to 10 cm to ensure model accuracy.

[0068] Macro-model construction: At the macroscale, a comprehensive structural response model of the runway is constructed by combining a high-precision 3D runway scenario model with actual stress data. This model encompasses all layers of the runway, including the surface layer, base layer, and foundation layer. Parameters of the multi-scale model are identified using a coupled least squares method. The model is calibrated and optimized using historical runway operational data (such as aircraft takeoffs and landings, load types, etc.), ensuring that it more accurately reflects the actual operating conditions of the runway.

[0069] S2.4. Real-time state mapping and model correction

[0070] Once the multi-scale digital twin model of the runway is built, real-time sensor data is continuously fed into the model to map the runway status in real time. The model is then corrected through a feedback mechanism, specifically:

[0071] Real-time Mapping: The collected comprehensive state vector is input into a multi-scale digital twin model of the runway, which calculates the stress, deformation, and vibration of the runway in real time under current conditions. The model's dynamic calculations provide a real-time display of the runway's health status and generate a real-time performance map.

[0072] Model calibration: When there is a deviation between the measured data and the model prediction results, the least squares method is used to adjust the model parameters. The model calibration process is performed every 10 seconds to ensure that the model always reflects the actual working status of the runway and the deviation is controlled within ±2%.

[0073] Feedback mechanism: Model correction results are automatically recorded and fed back to the data acquisition system, allowing the sensor's data acquisition strategy to be optimized based on model changes. For example, if stress in a certain area is found to be excessive, the system will increase the sampling frequency in that area to obtain more detailed data.

[0074] S3, High-Real-Time Simulation and Dynamic Deduction of Aircraft-Runway Interaction

[0075] The present invention uses a multi-scale digital twin model of the runway to perform high-real-time simulation of aircraft-runway interaction, and uses the simulation results to dynamically deduce runway performance to predict the performance and life of the runway under different operating conditions, such as Figure 4 As shown, the following steps are included:

[0076] S3.1. Establishing an aircraft-runway dynamics model

[0077] Before simulation, an accurate aircraft-runway dynamics model must be established.

[0078] Aircraft parameter selection: Select three typical aircraft models: Boeing 737, Airbus A320, and Boeing 777, representing mid-size, narrow-body, and wide-body passenger aircraft, respectively. Obtain key parameters for each model, such as gross weight, landing gear layout, tire pressure, and contact patch.

[0079] For example, the Boeing 737 has a gross weight of 70 tons, a nose wheel pressure of 10 MPa, and a rear wheel pressure of 15 MPa. The contact area is 0.35 square meters. These parameters are used as input conditions for building a simulation model.

[0080] The runway's structural parameters were determined based on the output of the digital twin model, including a 30-centimeter thickness for the surface layer, a 200-MPa elastic modulus for the base layer, and a 300-MPa elastic modulus for the foundation layer. These parameters ensure that the mechanical response of the runway structure in the simulation is consistent with actual conditions.

[0081] Based on these parameters, an aircraft-runway contact (dynamic) model was established using the finite element method (FEM). This model takes into account the nonlinear characteristics of the aircraft's landing gear (such as the spring-damper system) and the elastic-plastic properties of the tires, accurately simulating the stress distribution, deformation, and vibration of the aircraft as it taxis on the runway.

[0082] S3.2, Finite Element Simulation and Real-time Correction

[0083] The constructed aircraft-runway dynamics model is used to perform high-real-time simulation to simulate the dynamic response of the aircraft when taxiing on the runway, including:

[0084] Simulation Settings: ABAQUS finite element analysis software was used for simulation, with a time step of 0.01 seconds and a total simulation time of 10 seconds, covering the entire aircraft process from takeoff to taxiing. The computational grid size was set to 5 cm to ensure sufficient accuracy and speed at a small time step.

[0085] Simulation Process: During the simulation, the stress, strain, and vibration acceleration of the runway surface are calculated in real time as the aircraft taxis, and the changes in these mechanical quantities over time are recorded. The simulation results show that during takeoff, the maximum stress in the center of the runway reaches 2.5 MPa, the maximum deformation is 5 mm, and the maximum vibration acceleration is 0.2 g.

[0086] Real-time correction: Dynamic model simulation results are compared with actual sensor data in real time, and simulation accuracy is assessed using the root mean square error (RMSE) method. If a deviation of more than 5% is detected between the simulated and measured data, the model parameters are immediately adjusted using the least squares method and recalculated. For example, in the simulation of the Boeing 737, the actual measured maximum stress was 2.6 MPa, which deviated by 4% from the simulation result, so no correction was required. However, for the deformation simulation result, the actual measured value was 5.5 mm, with a deviation of 10%. The model parameters needed to be adjusted to an elastic modulus of 210 MPa. After recalculation, the deviation was reduced to 3%.

[0087] S3.3 Dynamic Deduction and Performance Prediction

[0088] Based on the high-real-time simulation results of the aircraft-runway dynamics model, the long-term performance of the runway is dynamically simulated through a multi-scale digital twin model of the runway. The runway's lifespan and damage accumulation under different conditions are predicted, including:

[0089] Simulation of future operating conditions: Based on the airport's three-year operational plan, the daily number of takeoffs and landings, aircraft types, and load conditions are simulated. For example, 100 Boeing 737 takeoffs and landings, 30 Airbus A320s, and 20 Boeing 777s are expected to occur daily.

[0090] Combined with weather forecast data, operations under different climate conditions (such as high and low temperatures, and rain) were simulated to analyze the impact of climate on runway stress and deformation. Under high temperatures (such as 40°C), the runway's elastic modulus is expected to decrease by 10%, resulting in a 0.3 MPa increase in maximum stress.

[0091] Runway life prediction: Damage accumulation models (such as Miner's linear damage theory) are used to calculate the cumulative damage to the runway under different operating conditions. Daily stress and deformation data are used to estimate the runway's fatigue life. Predictions indicate that under current operating conditions, the runway's fatigue life is approximately 15 years, with high temperatures and frequent use resulting in a 30% loss of life. If the airport plans to increase the frequency of heavy aircraft takeoffs and landings, the runway life is expected to be further shortened to 12 years.

[0092] Maintenance Strategy Generation: Based on the runway life prediction results, a preventive maintenance strategy is generated. For example, a comprehensive runway overhaul, including resurfacing and base reinforcement, is performed every five years to extend the runway's service life. Furthermore, based on dynamic simulation results, it is recommended to reduce the frequency of heavy aircraft takeoffs and landings during hot weather to slow the accumulation of runway damage and extend its service life.

[0093] S3.4 Visualization and Application of Simulation Results

[0094] Visualize the simulation and deduction results to facilitate understanding and application by decision makers and operation and maintenance personnel. The specific operations are as follows:

[0095] Using 3D visualization tools, the stress distribution, deformation, and vibration response of the runway are presented in the form of color maps. For example, during a Boeing 737 takeoff, the maximum stress area in the center of the runway is shown in red, the deformation area is shown in yellow, and the unaffected area is shown in green.

[0096] Dynamically display runway performance changes and damage accumulation under different future operating conditions. For example, users can drag the timeline to view runway status at different points in time over the next three years, as well as stress distribution under different climate conditions.

[0097] Based on the visual damage accumulation map, operation and maintenance personnel can prioritize repair work in areas with more severe damage to avoid further deterioration.

[0098] S4. Use the physical-digital closed-loop feedback mechanism to dynamically maintain and optimize the management of airport runways to ensure the long-term safety and efficient operation of the runways. The closed-loop feedback mechanism combines the simulation results of the runway's digital twin model with actual operation data. Through continuous real-time monitoring, analysis and feedback, it can achieve intelligent adjustment of the runway status and optimization of maintenance strategies, such as Figure 5 As shown, the following steps are included:

[0099] S4.1. Real-time data collection and feedback

[0100] During the daily operation of the runway, the deployed sensor network is used to continuously collect real-time data, and this data is input into the digital twin model of the runway for dynamic updating.

[0101] Real-time data collection: Various sensors (including stress, acceleration, temperature, and humidity sensors) collect data every second and transmit it to the central control system via the 5G communication network. Sensors are deployed across the entire runway surface and its key structural layers to ensure comprehensive and accurate data. Approximately 50GB of data is generated daily, including multi-dimensional data on the runway surface, including stress, vibration, temperature, and humidity. This data is transmitted to the central control system in real time and used to dynamically update the digital twin model.

[0102] Data Feedback and Preliminary Analysis: The central control system performs preliminary analysis of collected real-time data, automatically identifying abnormal values (such as sudden stress increases and abnormal vibration). If an anomaly is detected, the system immediately generates an alarm and notifies maintenance personnel. For example, if the stress value in a certain area exceeds the design stress by 20%, the system automatically triggers an alert and recommends that maintenance personnel conduct further inspection of the area.

[0103] S4.2 Dynamic Update of the Digital Twin Model of the Runway

[0104] The data collected in real time is fed into the digital twin model of the runway to continuously update the virtual state of the runway, and this data is used for simulation corrections and performance predictions.

[0105] Dynamic model updates: Real-time data is fed into the runway's digital twin model, and the model's adaptive update function adjusts the runway's virtual state in real time. The runway's digital twin model automatically corrects various parameters (such as elastic modulus and stress distribution) based on the latest data to ensure that the model remains consistent with the actual runway's state. For example, if the temperature sensor indicates that the current runway surface temperature is 10°C higher than the standard temperature, the model automatically adjusts the elastic modulus of the runway material to reflect the changes in material performance at high temperatures.

[0106] Simulation Calibration and Performance Prediction: Using real-time data input, the runway's digital twin model performs more accurate simulations and predicts future runway performance. The model calculates potential stress concentrations, deformation areas, and potential damage, generating a performance prediction report. For example, simulations predict that if rainfall exceeds 50 mm within the next 48 hours, the center section of the runway will experience an additional 5 mm of deformation. Based on this prediction, the system recommends drainage measures to mitigate the negative impact of rain on the runway.

[0107] S4.3. Generation and implementation of maintenance strategies

[0108] Based on the simulation results and actual operation conditions, the system automatically generates maintenance strategies and guides operation and maintenance personnel to perform relevant maintenance operations.

[0109] Maintenance Strategy Generation: Based on simulation predictions and real-time data analysis, the system automatically generates a maintenance strategy, including the optimal maintenance timing, specific maintenance areas, and maintenance measures. For example, if a certain area is predicted to reach critical stress within seven days, the system will recommend local reinforcement in advance to prevent structural damage. The maintenance strategy also includes specific material selection and construction methods to ensure efficient and accurate maintenance.

[0110] Maintenance Implementation and Monitoring: Operations and maintenance personnel carry out actual runway maintenance work based on the system-generated maintenance strategy. During maintenance, the system continuously monitors the runway's status and compares and analyzes pre- and post-maintenance data to verify maintenance effectiveness. For example, during maintenance, the system monitors stress changes in reinforced areas in real time to ensure that reinforcement measures are effectively reducing stress concentrations. After maintenance is completed, the system continues to monitor the long-term performance of the area to ensure that problems do not recur.

[0111] S4.4. Closed-loop feedback and continuous optimization

[0112] Continuous optimization of maintenance strategies is achieved through continuous correction and feedback of the physical runway status and digital twin model.

[0113] Real-time Feedback and Adjustment: After each maintenance task is completed, the system compares actual data with simulation results to determine whether the maintenance achieved the expected results. If discrepancies are found, the system records the issues and adjusts model parameters to provide a more accurate basis for the next simulation and decision-making. For example, if stress levels in a maintained area are still higher than expected, the system may recommend using higher-strength materials or increasing the thickness of the reinforcement layer for the next maintenance task.

[0114] Continuous Optimization: Over time, through repeated maintenance and feedback, the system continuously refines the digital twin model, making its predictions of runway conditions more accurate. Simultaneously, the system optimizes maintenance strategies, reduces unnecessary maintenance work, and improves the lifespan and safety of the runway. For example, through long-term data analysis, the system may discover that a certain type of aircraft causes significant runway damage and recommend that the airport adjust its takeoff and landing schedule to reduce the frequency of use of that type of aircraft, thereby extending the runway's lifespan.

[0115] S4.5. System Integration and Visualization

[0116] To facilitate the understanding and decision-making of operation and maintenance personnel, the system also provides a visual interface to display runway status, simulation results and maintenance recommendations.

[0117] Visual Interface Design: The system provides a 3D visualization interface that displays the runway's real-time status, stress distribution, deformation, and future performance forecasts. Operations and maintenance personnel can view detailed information on key areas and easily identify potential issues using color-coded and graphical indicators. The interface also integrates historical data and trend analysis, allowing operators to view runway status changes over the past few months and future performance forecasts using a sliding timeline.

[0118] Decision Support and Report Generation: Based on simulation and feedback results, the system generates detailed maintenance reports, including the current health status of the runway, predicted damage accumulation, and recommended maintenance measures. These reports are automatically generated and sent to relevant management for timely decision-making. For example, the system automatically generates a monthly runway maintenance report that covers the previous month's operating data, simulation analysis results, and the maintenance plan for the following month. The report also includes an assessment of the expected effectiveness of each maintenance measure, helping management optimize resource allocation.

Claims

1. An airport runway operation and maintenance method based on digital twin technology, characterized in that: include: Reconstruct the three-dimensional scene of the airport runway with high precision to obtain a high-precision three-dimensional scene model of the runway; Constructing a multi-scale digital twin model of the airport runway based on the high-precision three-dimensional scene model of the runway and multi-source sensor data; Performing high-real-time simulation of aircraft-runway interaction through the multi-scale digital twin model, and using the simulation results to dynamically simulate runway performance to predict the state of the runway under different operating conditions; Generate preventive maintenance strategies based on the condition of the runway.

2. The airport runway operation and maintenance method based on digital twin technology according to claim 1 is characterized in that: High-precision reconstruction of the 3D scene of the airport runway includes: Collect point cloud data through lidar; Preprocessing the collected point cloud data; Generate a 3D scene model of the airport runway based on the pre-processed point cloud data; Verify whether the three-dimensional model is of high accuracy.

3. The airport runway operation and maintenance method based on digital twin technology according to claim 2 is characterized in that: Verifying whether the three-dimensional model is of high precision includes: comparing key point data of the airport runway field measurement with the corresponding point positions in the three-dimensional model. If all position deviations are within ±3 mm, the three-dimensional model is of high precision.

4. The airport runway operation and maintenance method based on digital twin technology according to claim 1 is characterized in that: The construction of a multi-scale digital twin model of an airport runway includes: Collect data from sensors on the airport runway to obtain multi-source sensor data; Preprocessing and fusing the multi-source sensor data, and constructing a multi-scale digital twin model of the airport runway based on the fused data; The sensor data collected in real time is continuously input into the multi-scale digital twin model of the runway to achieve real-time mapping of the runway status, and the multi-scale digital twin model of the runway is corrected through a feedback mechanism.

5. The airport runway operation and maintenance method based on digital twin technology according to claim 4 is characterized in that: The multi-scale digital twin model of the airport runway constructed based on the fused data includes: Micro-model construction: Based on the physical properties of the runway material, the finite element method is used to construct the material property model of each finite element at the micro-scale; Macro model construction: At the macro scale, the overall structural response model of the runway is constructed by combining the constructed high-precision three-dimensional scene model of the runway and the actual stress data.

6. The airport runway operation and maintenance method based on digital twin technology according to claim 5 is characterized in that: The correction of the multi-scale digital twin model through the feedback mechanism includes: when there is a deviation between the measured data and the model prediction results, the least squares method is used to adjust the model parameters to ensure that the model always reflects the actual working status of the runway, thereby achieving correction; the correction results of the model are automatically recorded and fed back to the data acquisition system, so that the sensor data acquisition strategy can be optimized according to the changes in the model to obtain more detailed data.

7. The airport runway operation and maintenance method based on digital twin technology according to claim 1 is characterized in that: High-real-time simulation of aircraft-runway interactions using a multi-scale digital twin of the runway includes: Establish aircraft-runway dynamics model; Using the aircraft-runway dynamics model to perform high real-time simulation to simulate the dynamic response of the aircraft when taxiing on the runway; Based on the high-real-time simulation results of the aircraft-runway dynamics model, the long-term performance of the runway is dynamically simulated through the multi-scale digital twin model of the runway to predict the status of the airport runway; Generate preventive maintenance strategies based on the status of airport runways.

8. The airport runway operation and maintenance method based on digital twin technology according to any one of claims 1 to 7, characterized in that: It also includes the use of physical-digital closed-loop feedback mechanisms to dynamically maintain and optimize airport runways, including: Collect data through sensors on the airport runway and conduct preliminary analysis of the collected data; Data that is found to be normal after preliminary analysis is fed into the multi-scale digital twin model. The model uses adaptive updating to adjust the virtual state of the airport runway in real time, ensuring that the model remains consistent with the actual runway state. Furthermore, the multi-scale digital twin model uses real-time data input to perform more accurate simulations and predict future runway performance. A maintenance strategy is generated based on the simulation results of the multi-scale digital twin model and the actual data of the airport runway, and relevant maintenance operations are sent to the operation and maintenance personnel.

9. The airport runway operation and maintenance method based on digital twin technology according to claim 8 is characterized by: After each maintenance work is completed, the system will compare the actual data and simulation results to determine whether the maintenance effect has met expectations. If any discrepancies are found, the system will record the problems and adjust the model parameters to provide a more accurate basis for the next simulation and decision-making.

10. The airport runway operation and maintenance method based on digital twin technology according to claim 9 is characterized by: Using 3D visualization tools, the real-time status, stress distribution, deformation and future performance prediction of the runway are visualized.

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