Real-time simulation method, system and equipment for aircraft runway dynamic friction coefficient based on multi-source data

Abstract

The present invention belongs to the field of simulation, and specifically relates to a real-time simulation method, system and device for the dynamic friction coefficient of an aircraft runway based on multi-source data. It aims to solve the problem of insufficient authenticity of taxiing simulation. The present invention includes: obtaining runway surface temperature data, water depth and / or snow depth; judging the runway type based on runway texture characteristics, matching the basic friction coefficient according to the runway type, dynamically correcting the basic friction coefficient in combination with the water depth and / or snow depth and runway surface temperature data to obtain a corrected friction coefficient; obtaining the aircraft's tire vertical load, initial sideslip angle and tire pressure data, calculating the tire lateral force, and calculating the final friction coefficient in combination with the updated sideslip angle, corrected friction coefficient and sideslip attenuation coefficient; inputting it into the ground taxiing model of the flight simulator to achieve real-time simulation. The present invention makes the simulation results more consistent with the actual runway state through the attenuation effect of the friction coefficient during the dynamic process.

Description

Technical Field

[0001] The present invention belongs to the field of simulation, and in particular relates to a real-time simulation method, system and equipment for the dynamic friction coefficient of an aircraft runway based on multi-source data. Background Art

[0002] In the field of flight simulation training, accurate calculation of the runway friction coefficient is one of the core technologies to improve the realism of taxiing simulation. Currently, the modeling methods for the runway friction coefficient μ mainly rely on the following two technical solutions:

[0003] 1. Fixed friction coefficient model

[0004] This method uses a preset constant friction coefficient (for example, μ = 0.6) and switches between different μ values ​​based on the runway surface type (e.g., dry, wet, icy, snowy, or wet rubber residue). While simple to implement and computationally efficient, it is essentially a static model that can only distinguish runway conditions based on limited empirical values ​​and cannot reflect the real-time impact of dynamic environmental parameters (such as water depth, snow thickness, and oil contamination) on the friction coefficient. Furthermore, this model completely ignores the nonlinear coupling between tire dynamic parameters (such as sideslip angle and tire pressure changes) and friction, resulting in significant deviations between simulation results and actual physical laws.

[0005] 2. Correction model based on empirical formula

[0006] This type of model modifies the basic friction coefficient by introducing a linear empirical formula. While it partially accounts for adjustments to environmental factors, the correction coefficient still relies heavily on manual experience and lacks a clear physical basis. It does not incorporate the quantitative input of dynamic environmental parameters such as accumulated water and snow, and it lacks the ability to integrate multi-source sensor data (such as cameras and millimeter-wave radar) to perceive the runway surface in real time. Furthermore, existing models fail to establish a coupled relationship between tire sideslip angle and vertical load, resulting in significantly increased errors in friction coefficient calculations in complex scenarios (such as the superposition of crosswinds and accumulated water).

[0007] The above method has the following key problems in practical application:

[0008] (1) Lack of environmental dynamics: The dynamic attenuation effect of parameters such as water depth and snow thickness on the friction coefficient cannot be quantified, resulting in a disconnect between the simulation results and the actual runway conditions;

[0009] (2) Insufficient tire dynamics coupling: The nonlinear correction requirement of the tire sideslip angle on the friction force is ignored, and the physical effect of tire pressure changes on the tire contact patch is not considered;

[0010] (3) Strong dependence on experience: The correction coefficient lacks theoretical support based on physical laws or experimental data, and the model's generalization ability is limited;

[0011] (4) Weak real-time linkage capability: It does not form a closed loop with multi-sensor data (such as cameras, radars, and thermometers), making it difficult to achieve millisecond-level response in complex coupling scenarios.

[0012] These issues directly reduce the realism and training effectiveness of flight simulator taxiing simulations. For example, in the presence of crosswinds and runway water, existing models are unable to dynamically integrate environmental parameters with tire sideslip angle corrections. This results in a significant discrepancy between the taxiing resistance reported by the simulator and the actual flight experience, severely limiting the reliability of pilot training.

[0013] Based on this, the present invention proposes a real-time simulation method, system and equipment for the dynamic friction coefficient of an aircraft runway based on multi-source data. Summary of the Invention

[0014] In order to solve the above-mentioned problems in the prior art, namely, the existing runway friction coefficient calculation method is unable to dynamically quantify environmental parameters such as water depth and snow thickness, ignores the nonlinear effects of tire sideslip angle and tire pressure changes, relies on manual experience correction and lacks real-time linkage of multi-sensor data, resulting in insufficient authenticity of taxiing simulation, the present invention provides a real-time simulation method, system and equipment for aircraft runway dynamic friction coefficient based on multi-source data.

[0015] A first aspect of the present invention provides a real-time simulation method for the dynamic friction coefficient of an aircraft runway based on multi-source data, the method comprising:

[0016] Acquiring runway image data and runway surface temperature data, extracting water accumulation areas and / or snow accumulation areas and runway texture features of the runway based on the runway image data, and acquiring water accumulation depth and / or snow accumulation depth;

[0017] Determining the runway type based on the runway texture features, matching a basic friction coefficient according to the runway type, and dynamically correcting the basic friction coefficient in combination with water depth and / or snow depth and runway surface temperature data to obtain a corrected friction coefficient;

[0018] Obtain the aircraft's tire vertical load, initial sideslip angle, and tire pressure data to calculate the theoretical tire lateral force. The deviation between the theoretical and actual tire lateral forces is combined with an iterative algorithm to update the sideslip angle in real time. The final friction coefficient is calculated by combining the updated sideslip angle, the corrected friction coefficient, and the sideslip attenuation coefficient.

[0019] The final friction coefficient is input into a ground taxiing model of a flight simulator to achieve real-time simulation.

[0020] Furthermore, the basic friction coefficient is dynamically corrected to obtain a corrected friction coefficient, and the method is as follows:

[0021] A linear attenuation factor is established based on the product of the water depth and the preset water attenuation coefficient;

[0022] An exponential attenuation factor is established based on the product of snow depth and a preset snow attenuation coefficient;

[0023] A temperature compensation factor is established based on the product of the absolute value of the difference between the runway surface temperature and the reference temperature and the temperature compensation coefficient;

[0024] The basic friction coefficient is compound-adjusted by triple attenuation factors in sequence to obtain a modified friction coefficient.

[0025] Furthermore, the final friction coefficient is calculated as follows:

[0026] Based on the corrected friction coefficient, a dynamic attenuation parameter is constructed by combining the real-time updated absolute value of the sideslip angle with the product of the preset sideslip attenuation coefficient. The dynamic attenuation parameter is used to reduce the friction performance of the corrected friction coefficient under the slip state to obtain the final friction coefficient that characterizes the actual sliding conditions.

[0027] Furthermore, the sideslip angle is updated in real time as follows:

[0028] Based on the initial sideslip angle, tire vertical load, and tire pressure data, combined with pre-stored cornering stiffness and main wheel load upper limit parameters, the lateral force calculation model outputs the theoretical lateral force;

[0029] The theoretical lateral force is compared with the actual tire lateral force to determine whether there is a deviation. If there is no deviation, the sideslip angle is not updated. If there is a deviation, an iterative update of the sideslip angle is triggered based on an iterative algorithm.

[0030] Furthermore, the image data and the runway surface temperature data are obtained from a camera and an infrared thermometer, respectively.

[0031] Furthermore, tire vertical load and tire pressure data are obtained from a tire pressure sensor.

[0032] Furthermore, the water depth and / or snow depth are obtained by:

[0033] Water accumulation areas and / or snow accumulation areas are detected based on the image data, and the depth of the water accumulation and / or snow accumulation is detected using millimeter wave radar.

[0034] In a second aspect, the present invention provides a real-time simulation system for the dynamic friction coefficient of an aircraft runway based on multi-source data. The system comprises:

[0035] a data acquisition module configured to acquire runway image data and runway surface temperature data, extract water accumulation areas and / or snow accumulation areas and runway texture features of the runway based on the runway image data, and acquire water accumulation depth and / or snow accumulation depth;

[0036] a modified friction coefficient calculation module configured to determine a runway type based on the runway texture features, match a basic friction coefficient according to the runway type, and dynamically modify the basic friction coefficient based on water depth and / or snow depth and runway surface temperature data to obtain a modified friction coefficient;

[0037] a final friction coefficient calculation module configured to obtain aircraft tire vertical load, initial sideslip angle, and tire pressure data for calculating theoretical tire lateral force, update the sideslip angle in real time based on the deviation between the theoretical and actual tire lateral force and an iterative algorithm, and calculate the final friction coefficient based on the updated sideslip angle, the corrected friction coefficient, and the sideslip attenuation coefficient;

[0038] The real-time simulation module is configured to input the final friction coefficient into a ground taxiing model of a flight simulator to achieve real-time simulation.

[0039] According to a third aspect of the present invention, an electronic device is provided, comprising:

[0040] at least one processor; and

[0041] a memory communicatively connected to at least one of the processors; wherein,

[0042] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the above-mentioned real-time simulation method of aircraft runway dynamic friction coefficient based on multi-source data.

[0043] In a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to be executed by the computer to implement the above-mentioned real-time simulation method of the dynamic friction coefficient of an aircraft runway based on multi-source data.

[0044] Beneficial effects of the present invention:

[0045] Enhanced quantification of dynamic environmental parameters: Based on real-time image data, water depth, snow thickness, and runway texture features are extracted. Combined with temperature sensor data, an environmental attenuation factor is constructed to dynamically correct the basic friction coefficient. Compared to static models, this model accurately reflects the attenuation effects of dynamic processes such as water infiltration and snow compaction on the friction coefficient, making simulation results more accurate to actual runway conditions (e.g., differences in the hydrodynamic pressure effects of water at different depths).

[0046] Improved Tire Dynamics Coupling Modeling Accuracy: This approach incorporates tire vertical loads, real-time updated sideslip angle, and tire pressure data to establish a nonlinear relationship model between lateral force and sideslip angle. By iteratively calculating sideslip angle corrections, this approach addresses the existing method's neglect of tire contact patch changes (caused by tire pressure) and the sideslip angle-friction coupling effect. This significantly improves friction coefficient calculation accuracy in complex conditions such as crosswinds and tight turns.

[0047] Data-driven optimization of the physical model's generalizability: The system automatically matches the baseline friction coefficient based on runway texture characteristics and applies physical corrections based on multiple environmental parameters (such as the effect of ice surface temperature on meltwater film thickness), replacing manual empirical coefficient adjustments. Model parameters have clear physical meaning and support generalized application across different runway types (asphalt, concrete) and extreme conditions (oil contamination, freeze-thaw cycles).

[0048] Multi-sensor real-time closed-loop response: This system integrates data streams from multiple sensors, including cameras (texture recognition), millimeter-wave radar (water layer thickness measurement), and thermometers (ice surface condition monitoring), to create a millisecond-level update closed-loop. This system provides real-time feedback on runway status changes (such as a sudden increase in water depth due to rainfall), ensuring dynamic response latency for the friction coefficient under complex coupled scenarios (crosswind, water accumulation, and tire pressure fluctuations) is less than 10ms, meeting the high real-time requirements of flight simulators.

[0049] Significantly improved training reliability: Inputting the final friction coefficient into the ground runway model allows for accurate simulation of physical phenomena such as sudden changes in runway resistance (e.g., tires entering snowy areas) and critical sideslip instability. In scenarios involving crosswinds and accumulated water, the simulation results exhibited less than 5% error compared to actual test flight data, effectively enhancing pilot training effectiveness and emergency response capabilities for complex runway conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Other features, objects and advantages of the present application will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0051] Figure 1 The present invention is a flowchart of a real-time simulation method of a dynamic friction coefficient of an aircraft runway based on multi-source data. DETAILED DESCRIPTION

[0052] The present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are intended only to illustrate the relevant invention and are not intended to limit the invention. It should also be noted that, for ease of description, only portions relevant to the relevant invention are shown in the accompanying drawings.

[0053] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0054] The present invention provides a real-time simulation method for the dynamic friction coefficient of an aircraft runway based on multi-source data, the method comprising:

[0055] Step S10, acquiring runway image data and runway surface temperature data, extracting water accumulation areas and / or snow accumulation areas and runway texture features of the runway based on the runway image data, and acquiring water accumulation depth and / or snow accumulation depth;

[0056] Step S20, determining the runway type based on the runway texture features, matching a basic friction coefficient according to the runway type, and dynamically correcting the basic friction coefficient in combination with water depth and / or snow depth and runway surface temperature data to obtain a corrected friction coefficient;

[0057] Step S30: Obtaining the aircraft's tire vertical load, initial sideslip angle, and tire pressure data for use in calculating a theoretical tire lateral force. Using the deviation between the theoretical and actual tire lateral forces and an iterative algorithm, the sideslip angle is updated in real time. The final friction coefficient is calculated using the updated sideslip angle, the corrected friction coefficient, and the sideslip attenuation coefficient.

[0058] Step S40: inputting the final friction coefficient into a ground taxiing model of a flight simulator to achieve real-time simulation.

[0059] In order to more clearly illustrate the real-time simulation method of the aircraft runway dynamic friction coefficient based on multi-source data of the present invention, the following is combined with Figure 1 Each step in the embodiment of the present invention is described in detail.

[0060] A real-time simulation method for the dynamic friction coefficient of an aircraft runway based on multi-source data according to a first embodiment of the present invention includes steps S10 to S40, each of which is described in detail as follows:

[0061] Step S10, acquiring runway image data and runway surface temperature data, extracting water accumulation areas and / or snow accumulation areas and runway texture features of the runway based on the runway image data, and acquiring water accumulation depth and / or snow accumulation depth;

[0062] In this embodiment, the image data and the runway surface temperature data are obtained by a camera and an infrared thermometer respectively.

[0063] Extracting the water and / or snow areas and runway texture features of the runway using a multi-scale feature fusion convolution-attention hybrid network, which includes a multi-scale feature encoder, a cross-domain adaptation module, and a texture decoder;

[0064] Multi-scale feature encoder:

[0065] Input layer: receives the runway image fused with RGB and near-infrared channels, with a size of H×W×4;

[0066] Multi-branch convolution module:

[0067] First branch: 3×3 convolution kernel, depth-wise separable convolution, output channel number C;

[0068] Second branch: 5×5 dilated convolution (expansion rate 2), group convolution (number of groups 4), number of output channels C;

[0069] The third branch: 1×1 convolution → 3×3 maximum pooling → 3×3 deconvolution, with C output channels;

[0070] Feature fusion layer: The outputs of each branch are concatenated along the channel dimension and compressed to 3C channels through 1×1 convolution;

[0071] Cross-domain adaptation module:

[0072] Channel attention weighted unit: calculates global average pooling for each channel of the feature map → fully connected layer → sigmoid activation to generate a channel weight vector;

[0073] Adversarial discriminator: It consists of a 3-layer fully connected network, takes feature vectors as input, outputs domain classification probabilities, and implements adversarial training through a gradient reversal layer.

[0074] Texture decoder:

[0075] Feature pyramid structure: bilinearly upsample the feature maps of each encoder stage to a uniform resolution;

[0076] Cross-scale attention gate: Calculate the cosine similarity between low-level features and high-level features to generate a spatial attention mask;

[0077] Output layer: 3×3 convolution → Softmax activation to generate pixel-level texture classification map.

[0078] The domain adaptation module performs the following steps:

[0079] Multi-scale feature extraction:

[0080] Using the multi-scale feature encoder, multi-branch convolution processing is performed on the input source domain runway image and target domain real-time image respectively to generate a source domain feature map Fs and a target domain feature map Ft;

[0081] Source domain: Load the annotated runway images from the ASFT dataset, including sunny and cloudy lighting conditions;

[0082] Target domain: real-time runway images collected in rainy and foggy weather, without manual annotation;

[0083] Outputting a fused feature vector of uniform dimension through channel splicing and compression operations of the feature fusion layer;

[0084] Cross-domain adversarial training:

[0085] Inputting the fused feature vector into the adversarial discriminator to calculate the domain classification probabilities of the source domain and the target domain;

[0086] Construct domain difference loss function L domain , which is expressed as the weighted sum of the source domain classification error and the target domain classification error, where the source domain sample is marked as 0 and the target domain sample is marked as 1;

[0087] Among them, L domain =CE(D(Fs),0)+CE(D(Ft),1), CE is the cross entropy loss.

[0088] Implement reverse gradient propagation through the gradient reversal layer to maximize the discriminator error when updating the encoder and minimize the classification error when updating the discriminator;

[0089] Attention-guided feature enhancement:

[0090] The source domain feature map Fs is weighted by channel importance through the channel attention weighting unit to generate the attention weight matrix Wc; Wc=σ(FC(GAP(Fs))) where FC is the fully connected layer, GAP is the global average pooling, and σ is the Sigmoid function.

[0091] Wc is used to perform channel-level weighting on the source domain features Fs, strengthening the channels that are sensitive to runway texture and suppressing the rain and fog noise channels.

[0092] In the feature pyramid structure, a cross-scale attention gate is used to calculate the similarity matrix S between feature maps at different levels. The similarity matrix is ​​used to fuse multi-scale texture features.

[0093] The weighted feature map Perform matrix product with the similarity matrix S and output the enhanced texture feature vector;

[0094] Joint Optimization and Reasoning:

[0095] The enhanced feature vector is input into the texture decoder, and the pixel-level texture segmentation result is generated through bilinear upsampling and cross-scale attention gate;

[0096] Combined with the segmentation loss Lseg, domain difference loss Ldomain and feature consistency loss Lconsistency, the parameters of the multi-scale feature encoder and texture decoder are jointly optimized in an end-to-end manner;

[0097] During the inference phase, the parameters of the adversarial discriminator are frozen, and only the multi-scale feature encoder and texture decoder are retained for real-time texture feature extraction.

[0098] Get the water depth and / or snow depth using:

[0099] Based on the image data, water accumulation areas and / or snow accumulation areas are detected, and the depth of the water accumulation and / or snow accumulation is detected using millimeter wave radar. The snow accumulation includes dry snow and wet snow, and the water accumulation includes liquid water and ice.

[0100] The present invention also installs a polarized light imaging sensor on the runway, and combines it with a millimeter wave radar to obtain the depth of accumulated water and / or snow. The method is as follows:

[0101] Step S11: Scanning the runway surface in the water and / or snow areas with a millimeter-wave radar to obtain medium reflection signals. Simultaneously, a polarized light imaging sensor collects polarized reflection images of the runway area. The detection areas of the two sensors are spatiotemporally aligned to form fused detection data covering the same target area.

[0102] Step S12: performing polarization angle distribution analysis on the polarized reflection image to extract polarization angle change rate characteristics corresponding to different phases of materials, and distinguishing the phases of materials based on the differences in polarization characteristics of liquid water, solid ice, and snow-water mixtures;

[0103] Step S13: Inputting the dielectric properties and polarization angle change rate characteristics of the medium detected by the millimeter wave radar into a classification model, and identifying the runway surface state through multi-physical quantity fusion decision logic, where the surface state includes at least dry snow, wet snow, ice layer, and liquid water;

[0104] Step S14: When ice cover is detected, nonlinear compensation is performed on the snow depth measurement value of the millimeter-wave radar based on the thickness of the ice layer to generate an equivalent depth value that incorporates the influence of the ice layer;

[0105] Step S15: A calibration device is deployed at a preset position on the runway to periodically compare the fusion detection results with the actual measurement data of the contact sensor, and dynamically adjust the parameters of the depth compensation model according to the deviation.

[0106] In step S13, the runway surface state is identified by multi-physical quantity fusion decision logic, and the method is as follows:

[0107] Step S131, performing noise suppression and feature extraction on the millimeter-wave radar echo signal to obtain scattering parameters characterizing the dielectric properties of the runway surface;

[0108] Step S132, decomposing the polarized reflection image into an intensity component, a polarization degree component, and a polarization angle component to generate a multi-channel polarization characteristic map;

[0109] Step S133, spatially correlating and matching the radar scattering parameters with the polarization characteristic map to construct a multi-dimensional fusion feature vector including dielectric characteristics, polarization characteristics, and texture characteristics;

[0110] Step S134: Probabilistically synthesize the multi-dimensional fusion feature vectors using multi-category evidence theory and determine the runway surface state according to the following rules:

[0111] Liquid water state: judged when the liquid water confidence exceeds the first threshold and the ice layer probability is lower than the critical value;

[0112] Ice layer status: judged when the solid ice confidence exceeds the second threshold and the dielectric properties meet the ice layer characteristics;

[0113] Dry snow / wet snow state: determined by the polarization angle change rate and dielectric property gradient. Dry snow exhibits low dielectric constant and high polarization angle stability, while wet snow exhibits dielectric constant fluctuations and dynamic changes in polarization angle.

[0114] The method for generating the first threshold (liquid water confidence level) and the second threshold (solid ice confidence level) includes the following steps:

[0115] Step S1341: The first threshold is determined by a joint calibration experiment of liquid water dielectric properties and polarization angle stability, and is dynamically adjusted by ambient temperature and humidity;

[0116] Step S1342: The second threshold is generated based on a mapping relationship between ice layer reflection characteristics and solid dielectric constant, and light intensity is introduced as a compensation factor;

[0117] Step S1343: When sensor performance degradation or sudden environmental changes are detected, the threshold online self-learning mechanism is triggered, and the threshold generation model is updated through the federated learning framework.

[0118] In this embodiment, when the ice-water mixture is detected, the following control steps are performed:

[0119] Dynamic friction coefficient constraint: Generates a dynamic attenuation factor based on the correlation between ice thickness and surface temperature, imposes nonlinear constraints on the basic friction coefficient, and limits the maximum available friction coefficient when the attenuation factor exceeds a safety threshold;

[0120] 3D visual warning: In the virtual runway scene of the flight simulator, the ice-water mixing area is dynamically rendered, and the spatial distribution of ice thickness is represented through color gradients and transparency changes;

[0121] Multimodal interactive feedback: When the virtual aircraft slides into the ice-water mixture area, the force feedback module of the flight control device is triggered synchronously to generate a tactile warning signal corresponding to the real-time change in the friction coefficient.

[0122] The cameras in this embodiment are deployed on both sides of the runway or at a high position to ensure coverage of the entire runway, with a resolution of no less than 1080P, supporting visible light and near-infrared bands, and used to collect runway surface images in all weather conditions.

[0123] Infrared thermometer: Installed at the same location as the camera or integrated into the camera module, it collects runway surface temperature data in real time. The temperature measurement range covers -40°C to +80°C, with an accuracy of ±0.5°C.

[0124] Millimeter-wave radar: This radar uses a 24GHz or 77GHz frequency band and is installed at the edge of the runway or on a mobile detection device. The beam angle is ≤5° and points vertically toward the runway surface. It is used to detect the depth of accumulated water or snow.

[0125] In this embodiment, water / snow accumulation areas are identified using image segmentation algorithms (such as U-Net and MaskR-CNN).

[0126] Threshold segmentation is performed based on reflectance characteristics (highlight areas) and color space (low saturation, high brightness in HSV). This is combined with white area detection and texture analysis (surface graininess), and infrared temperature data is used to assist in determining whether snow is frozen (snow accumulation mode is triggered when the temperature is ≤ 0°C).

[0127] Extract texture features (such as gray-level co-occurrence matrix and edge density) from normal areas of the runway and compare them with abnormal areas to eliminate interference such as reflections and shadows.

[0128] Judging the environmental status through infrared thermometer data:

[0129] When the temperature is greater than 0°C and there is a large area of ​​high reflectivity, it is determined to be water accumulation;

[0130] When the temperature is ≤0℃ and the image is segmented into white areas, it is determined to be snow accumulation;

[0131] When the temperature is close to 0℃ and there is snow accumulation, an icing risk warning is triggered.

[0132] The coordinates of the water / snow areas identified in the image are mapped to the radar detection range, driving the radar beam to scan the target area in a direction.

[0133] This embodiment is based on the time difference between the radar wave at the air-water interface (strong reflection) and the water-runway interface (secondary reflection). , calculate the depth ;in, is the speed of light, is the dielectric constant of water (≈80).

[0134] Based on the attenuation characteristics of radar waves by snow layers, the reflection intensity and time delay are jointly modeled, and the snow density parameters are calibrated in combination with temperature data (for example, the lower the temperature, the higher the snow density and the more significant the attenuation).

[0135] If the depth detected by the radar does not match the area and texture features of the region in the image (for example, a large area of ​​shallow water is mistakenly identified as deep water), rescanning or fusion of temperature data is initiated for dynamic correction.

[0136] Step S20, determining the runway type based on the runway texture features, matching a basic friction coefficient according to the runway type, and dynamically correcting the basic friction coefficient in combination with water depth and / or snow depth and runway surface temperature data to obtain a corrected friction coefficient;

[0137] The runway types include asphalt runway, concrete runway, and ice and snow runway.

[0138] Dynamically correct the base friction coefficient , and obtain the corrected friction coefficient , the method is:

[0139] A linear attenuation factor is established based on the product of the water depth and the preset water attenuation coefficient;

[0140] An exponential attenuation factor is established based on the product of snow depth and a preset snow attenuation coefficient;

[0141] A temperature compensation factor is established based on the product of the absolute value of the difference between the runway surface temperature and the reference temperature and the temperature compensation coefficient;

[0142] The basic friction coefficient is compound-adjusted by triple attenuation factors in sequence to obtain a modified friction coefficient.

[0143] ;

[0144] in, is the depth of water accumulation, is the snow depth, is the runway surface temperature data, The reference temperature is set to 15°C for asphalt / concrete and -5°C for ice and snow runways. is the water attenuation coefficient, the default value is 0.05~0.15 / mm. For every 1mm increase in water accumulation, the friction coefficient decreases by 5%. is the snow attenuation coefficient, with a default value of 0.03~0.07 / mm. The exponential decay model simulates the influence of the fluffiness of the snow layer. It is the temperature compensation coefficient, with a default range of 0.005~0.015 / °C. When the temperature deviates from the reference value, the friction coefficient decays linearly.

[0145] If there is both water accumulation and snow accumulation (such as during the snowmelt period), and Superposition calculation; when When the temperature is between -2℃ and 2℃, the snow-water mixing state detection is triggered and the temperature is automatically increased. and Weight.

[0146] In this embodiment, the runway image captured by the camera is grayscaled, denoised, and enhanced to extract the runway surface texture features:

[0147] Gray Level Co-occurrence Matrix (GLCM): Calculates parameters such as contrast, energy, and homogeneity to distinguish between asphalt (rough texture, high contrast) and concrete (uniform texture, low contrast);

[0148] Edge detection: Use the Canny operator to extract edge density. The ice and snow runway has sparse edge features due to its smooth surface.

[0149] Use support vector machines (SVM) or convolutional neural networks (CNN) to train classifiers based on labeled asphalt / concrete / ice and snow runway image datasets and output runway type labels in real time.

[0150] In this embodiment, the basic friction coefficient corresponding to the asphalt runway is preferably [0.7, 0.8], the basic friction coefficient corresponding to the concrete runway is preferably [0.6, 0.7], and the basic friction coefficient corresponding to the ice and snow runway is preferably [0.1-0.2].

[0151] Call the corresponding If snow or ice is detected (temperature ≤ 0℃ and snow depth > 0), the runway type is marked as "snow runway" and updated. .

[0152] Step S30: Obtaining the aircraft's tire vertical load, initial sideslip angle, and tire pressure data for use in calculating a theoretical tire lateral force. Using the deviation between the theoretical and actual tire lateral forces and an iterative algorithm, the sideslip angle is updated in real time. The final friction coefficient is calculated using the updated sideslip angle, the corrected friction coefficient, and the sideslip attenuation coefficient.

[0153] Update the sideslip angle in real time by:

[0154] Based on the initial sideslip angle, tire vertical load, and tire pressure data, combined with pre-stored cornering stiffness and main wheel load upper limit parameters, the lateral force calculation model outputs the theoretical lateral force;

[0155] The theoretical lateral force is compared with the actual tire lateral force to determine whether there is a deviation. If there is no deviation, the sideslip angle is not updated. If there is a deviation, an iterative update of the sideslip angle is triggered based on an iterative algorithm.

[0156] Wherein, the theoretical tire lateral force F y , which is calculated as follows:

[0157] F y =C α ×α0×(1-F z / F zmax );

[0158] Among them, C α is the cornering stiffness, C α =1.2×10^5 N / rad,F zmax As the upper limit of the main wheel load, the initial sideslip angle α0=2°=0.0349rad.

[0159] The tire vertical load and tire pressure data are obtained from a tire pressure sensor, and the initial sideslip angle is provided based on an inertial navigation system.

[0160] The deviation value is processed by a Hutton iterative algorithm, wherein the sideslip angle correction amount is positively correlated with the deviation value and is negatively correlated with the cornering stiffness and the proportional coefficient of the vertical load relative to the load upper limit.

[0161] Final friction coefficient , which is calculated as follows:

[0162] Based on the corrected friction coefficient, a dynamic attenuation parameter is constructed by combining the real-time updated absolute value of the sideslip angle with the product of the preset sideslip attenuation coefficient. The dynamic attenuation parameter is used to reduce the friction performance of the corrected friction coefficient under the slip state to obtain the final friction coefficient that characterizes the actual sliding conditions.

[0163] ;

[0164] in, is the sideslip attenuation coefficient, =0.003~0.008 / deg, is the sideslip angle.

[0165] In this embodiment, the tire pressure sensor measures the vertical load F z =12000N;

[0166] Millimeter-wave radar detects water depth =3mm;

[0167] The camera classifies the runway as a concrete runway, select ;

[0168] Based on the above data, we can calculate F y =C α ×α×(1-F z / F zmax )=1.2×10 5 ×0.0349×(1-12000 / 15000)=1675.2N.

[0169] Assume that the actual sensor measures the current lateral force to be 1800 N, which deviates from the theoretical value of 1675.2 N. The deviation is =1800-1675.2=124.8N.

[0170] This embodiment updates the sideslip angle based on the Hudson iterative algorithm , specifically:

[0171] .

[0172] .

[0173] .

[0174] Step S40: inputting the final friction coefficient into a ground taxiing model of a flight simulator to achieve real-time simulation.

[0175] In this embodiment, the dynamic friction model is encapsulated in C++ and called by the GRM module, as shown in the following example:

[0176] / / Dynamic friction coefficient calculation (C++ example)

[0177] double compute_mu_env(double mu0, double h_water, double h_snow,double T)

[0178] {

[0179] const double alpha = 0.1; / / Water attenuation coefficient (1 / mm)

[0180] const double beta = 0.05; / / Snow attenuation coefficient (1 / mm)

[0181] const double gamma = 0.01; / / Temperature compensation coefficient (1 / °C)

[0182] const double T_ref = 25.0; / / Reference temperature (°C)

[0183] double mu_wet = mu0 × (1 - alpha×h_water);

[0184] double mu_snow = mu_wet×exp(-beta×h_snow);

[0185] double mu_temp = mu_snow×(1 - gamma×fabs(T - T_ref));

[0186] return mu_temp;

[0187] }

[0188] / / Tire slip correction

[0189] double compute_mu_final(double mu_env, double alpha_deg)

[0190] {

[0191] const double k = 0.005; / / Side slip attenuation coefficient (1 / deg)

[0192] return mu_env×(1 - k×fabs(alpha_deg));

[0193] }

[0194] The tire coupling model can be tested using Matlab / Simulink or C++, with different runways and water depths selected. The optimal k value can be chosen based on the error between the theoretical value and the actual value.

[0195] Although the various steps in the above embodiment are described in the above-mentioned order, those skilled in the art will understand that in order to achieve the effect of this embodiment, different steps do not have to be executed in such an order. They can be executed simultaneously (in parallel) or in a reverse order. These simple changes are within the scope of protection of the present invention.

[0196] A second embodiment of the present invention provides a real-time simulation system for a dynamic friction coefficient of an aircraft runway based on multi-source data, based on a real-time simulation method for a dynamic friction coefficient of an aircraft runway based on multi-source data. The system includes:

[0197] a data acquisition module configured to acquire runway image data and runway surface temperature data, extract water accumulation areas and / or snow accumulation areas and runway texture features of the runway based on the runway image data, and acquire water accumulation depth and / or snow accumulation depth;

[0198] a modified friction coefficient calculation module configured to determine a runway type based on the runway texture features, match a basic friction coefficient according to the runway type, and dynamically modify the basic friction coefficient based on water depth and / or snow depth and runway surface temperature data to obtain a modified friction coefficient;

[0199] a final friction coefficient calculation module configured to obtain aircraft tire vertical load, initial sideslip angle, and tire pressure data for calculating theoretical tire lateral force, update the sideslip angle in real time based on the deviation between the theoretical and actual tire lateral force and an iterative algorithm, and calculate the final friction coefficient based on the updated sideslip angle, the corrected friction coefficient, and the sideslip attenuation coefficient;

[0200] The real-time simulation module is configured to input the final friction coefficient into a ground taxiing model of a flight simulator to achieve real-time simulation.

[0201] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working process and related instructions of the system described above can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0202] It should be noted that the above embodiment provides a real-time simulation system for the dynamic friction coefficient of an aircraft runway based on multi-source data, and is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be further decomposed or combined. For example, the modules in the above embodiments can be combined into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for the purpose of distinguishing the modules or steps and are not to be regarded as improper limitations on the present invention.

[0203] An electronic device according to a third embodiment of the present invention includes:

[0204] at least one processor; and

[0205] a memory communicatively connected to at least one of the processors; wherein,

[0206] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the above-mentioned real-time simulation method for aircraft runway dynamic friction coefficient based on multi-source data.

[0207] A fourth embodiment of the present invention provides a computer-readable storage medium storing computer instructions, wherein the computer instructions are configured to be executed by the computer to implement the above-mentioned method for real-time simulation of aircraft runway dynamic friction coefficient based on multi-source data.

[0208] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes and related instructions of the storage device and processing device described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0209] Those skilled in the art should be able to appreciate that the modules and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two, and the programs corresponding to the software modules and method steps can be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the art. In order to clearly illustrate the interchangeability of electronic hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0210] The terms "first", "second", etc. are used to distinguish similar objects, rather than to describe or indicate a particular order or sequence.

[0211] The term "comprise" or any other similar term is intended to cover non-exclusive inclusion such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0212] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. A real-time simulation method for the dynamic friction coefficient of an aircraft runway based on multi-source data, characterized in that: The method includes: Acquire runway image data and runway surface temperature data, extract water accumulation areas, snow accumulation areas, and runway texture features of the runway based on the runway image data, and acquire water accumulation depth and snow accumulation depth; The runway type is determined based on the runway texture features, and the basic friction coefficient is matched according to the runway type. The basic friction coefficient is dynamically corrected in combination with the water depth, snow depth, and runway surface temperature data to obtain the corrected friction coefficient: A linear attenuation factor is established based on the product of the water depth and the preset water attenuation coefficient; An exponential attenuation factor is established based on the product of snow depth and a preset snow attenuation coefficient; A temperature compensation factor is established based on the product of the absolute value of the difference between the runway surface temperature and the reference temperature and the temperature compensation coefficient; The basic friction coefficient is compound-adjusted by triple attenuation factors in sequence to obtain a modified friction coefficient; Obtain the aircraft's tire vertical load, initial sideslip angle, and tire pressure data to calculate the theoretical tire lateral force. Combine the deviation between the theoretical and actual tire lateral forces with an iterative algorithm to update the sideslip angle in real time. Combine the updated sideslip angle, the corrected friction coefficient, and the sideslip attenuation coefficient to calculate the final friction coefficient: Based on the corrected friction coefficient, a dynamic attenuation parameter is constructed by combining the real-time updated absolute value of the sideslip angle with the product of a preset sideslip attenuation coefficient. The corrected friction coefficient is then reduced by the dynamic attenuation parameter to obtain a final friction coefficient that represents the actual sliding condition. The final friction coefficient is input into a ground taxiing model of a flight simulator to achieve real-time simulation.

2. The method for real-time simulation of aircraft runway dynamic friction coefficient based on multi-source data according to claim 1, characterized in that: Update the sideslip angle in real time by: Based on the initial sideslip angle, tire vertical load, and tire pressure data, combined with pre-stored cornering stiffness and main wheel load upper limit parameters, the lateral force calculation model outputs the theoretical lateral force; The theoretical lateral force is compared with the actual tire lateral force to determine whether there is a deviation. If there is no deviation, the sideslip angle is not updated. If there is a deviation, an iterative update of the sideslip angle is triggered based on an iterative algorithm.

3. The real-time simulation method of aircraft runway dynamic friction coefficient based on multi-source data according to claim 1 is characterized in that: The image data and runway surface temperature data are obtained from the camera and infrared thermometer respectively.

4. The method for real-time simulation of aircraft runway dynamic friction coefficient based on multi-source data according to claim 1, characterized in that: Tire vertical load and tire pressure data are obtained from the tire pressure sensor.

5. The method for real-time simulation of aircraft runway dynamic friction coefficient based on multi-source data according to claim 3, characterized in that: The method to obtain the water depth and snow depth is: Water and snow areas are detected based on image data, and the depth of water and snow is detected using millimeter-wave radar.

6. A real-time simulation system for the dynamic friction coefficient of an aircraft runway based on multi-source data, based on the real-time simulation method for the dynamic friction coefficient of an aircraft runway based on multi-source data according to any one of claims 1 to 5, characterized in that: The system includes: a data acquisition module configured to acquire runway image data and runway surface temperature data, extract water accumulation areas, snow accumulation areas, and runway texture features of the runway based on the runway image data, and acquire water accumulation depth and snow accumulation depth; The modified friction coefficient calculation module is configured to determine the runway type based on the runway texture features, match the basic friction coefficient according to the runway type, and dynamically modify the basic friction coefficient based on the water depth, snow depth, and runway surface temperature data to obtain the modified friction coefficient: A linear attenuation factor is established based on the product of the water depth and the preset water attenuation coefficient; An exponential attenuation factor is established based on the product of snow depth and a preset snow attenuation coefficient; A temperature compensation factor is established based on the product of the absolute value of the difference between the runway surface temperature and the reference temperature and the temperature compensation coefficient; The basic friction coefficient is compound-adjusted by triple attenuation factors in sequence to obtain a modified friction coefficient; The final friction coefficient calculation module is configured to obtain the aircraft's tire vertical load, initial sideslip angle, and tire pressure data to calculate the theoretical tire lateral force. The module then uses the deviation between the theoretical and actual tire lateral forces and an iterative algorithm to update the sideslip angle in real time. The module then calculates the final friction coefficient by combining the updated sideslip angle, the corrected friction coefficient, and the sideslip attenuation coefficient: Based on the corrected friction coefficient, a dynamic attenuation parameter is constructed by combining the real-time updated absolute value of the sideslip angle with the product of a preset sideslip attenuation coefficient. The corrected friction coefficient is then reduced by the dynamic attenuation parameter to obtain a final friction coefficient that represents the actual sliding condition. The real-time simulation module is configured to input the final friction coefficient into a ground taxiing model of a flight simulator to achieve real-time simulation.

7. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to at least one of the processors; wherein, The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the real-time simulation method of aircraft runway dynamic friction coefficient based on multi-source data as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to be executed by the computer to implement the real-time simulation method for aircraft runway dynamic friction coefficient based on multi-source data according to any one of claims 1 to 5.

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