A road thermal radiation effect evaluation method based on unmanned aerial vehicle-mounted infrared detection

Abstract

The application provides a road thermal radiation effect evaluation method based on unmanned aerial vehicle-mounted infrared detection, which comprises the following steps: acquiring infrared thermal imaging data, flight data and environmental auxiliary data of a road and a predetermined range area on both sides of the road edge through an unmanned aerial vehicle-mounted infrared detection system; pre-processing the acquired data, performing geographic coordinate registration and road area extraction, and generating a road thermal radiation data set; calculating and constructing a multi-dimensional evaluation index set based on spatial statistical analysis and blackbody radiation law; based on multi-index coupling and multi-factor correction, a comprehensive evaluation formula is constructed by using a nonlinear weighted coupling method to calculate a road thermal radiation effect comprehensive index; the K-means clustering algorithm is used to divide the road thermal radiation effect grade, evaluate the road thermal radiation effect, and generate a road thermal radiation effect grade spatial distribution map. The application is helpful to realize multi-dimensional and quantitative comprehensive evaluation of the road thermal radiation effect, and provides direct and scientific decision basis for road engineering maintenance and management.

Description

Technical Field

[0001] This invention belongs to the field of road environment assessment technology, specifically relating to a method for assessing the thermal radiation effect of roads based on UAV-borne infrared detection. Background Technology

[0002] Road thermal radiation is one of the core driving factors of the urban heat island effect, and its intensity is directly related to the aging rate of road materials, the surrounding microclimate, and driving safety. High-temperature road surfaces not only accelerate asphalt softening and road surface cracking, but also affect the comfort of residential areas through heat diffusion, while reducing the friction coefficient between tires and the road surface, significantly increasing braking distance and the risk of traffic accidents. Therefore, comprehensively and accurately quantifying the road thermal radiation effect is a prerequisite for carrying out relevant remediation and optimization work.

[0003] Currently, methods for monitoring and assessing road surface temperature mainly include ground contact measurement and satellite remote sensing inversion. Ground contact measurement (such as deploying thermocouple sensors), while highly accurate, suffers from limitations such as sparse deployment, insufficient spatial representativeness, and traffic disruption, making it difficult to obtain large-scale, continuous road surface temperature field information. Satellite remote sensing inversion methods (such as using MODIS data) can achieve large-scale monitoring, but their spatial resolution is low, making it difficult to effectively identify lane-level temperature differences within a single road, and their long revisit periods cannot meet the real-time, dynamic monitoring needs of road thermal radiation effects. Furthermore, existing assessment methods often rely on a single temperature index (such as average temperature) and fail to establish a correlation model between thermal radiation effects and road material aging, as well as external factors such as atmospheric environment, solar radiation, and wind speed, resulting in limited practical guidance for the assessment results. To address the shortcomings of existing technologies, there is an urgent need for an integrated assessment method with multi-factor coupling assessment capabilities and scenario-based hierarchical classification capabilities to overcome the deficiencies of existing technologies and achieve a comprehensive, accurate, and efficient assessment of road thermal radiation effects. Summary of the Invention

[0004] The purpose of this invention is to propose a method for evaluating the road thermal radiation effect based on UAV-borne infrared detection. This method helps to achieve a multi-dimensional and quantitative comprehensive evaluation of the road thermal radiation effect, providing a direct and scientific basis for decision-making in road engineering maintenance and management.

[0005] To achieve the above objectives, the technical solution of the present invention is: a method for evaluating the road thermal radiation effect based on UAV-borne infrared detection, specifically including the following steps:

[0006] S1. Acquire infrared thermal imaging data, flight data, and environmental auxiliary data of the road and a predetermined area on both sides of the road edge using an infrared detection system mounted on a drone; the infrared thermal imaging data includes thermal images and temperature quantization coefficients; the flight data includes the drone's heading angle, roll angle, and pitch angle; the environmental auxiliary data includes atmospheric temperature, relative humidity, atmospheric pressure, solar altitude angle, solar shortwave radiation, atmospheric transparency, wind speed, and the angle between wind direction and road direction at the time of detection; the environmental auxiliary data is collected synchronously by environmental sensors mounted on the drone.

[0007] S2. Preprocess the acquired data, perform geographic coordinate registration and road area extraction to generate a road thermal radiation dataset;

[0008] S3. Using the road thermal radiation dataset, based on spatial statistical analysis and the blackbody radiation law, calculate and construct a multi-dimensional set of evaluation indicators including the average radiation temperature of the road surface, peak thermal radiation intensity, net road surface radiation, spatial variation coefficient of thermal radiation, thermal radiation diffusion attenuation rate, and regional thermal environment impact.

[0009] S4. Using the road thermal radiation dataset and a multi-dimensional evaluation index set, based on multi-index coupling and multi-factor correction, a comprehensive evaluation formula is constructed using a nonlinear weighted coupling method to calculate the comprehensive index of road thermal radiation effect.

[0010] S5. Use the K-means clustering algorithm to classify the road thermal radiation effect level, evaluate the road thermal radiation effect, and generate a spatial distribution map of the road thermal radiation effect level.

[0011] Preferably, the flight altitude of the UAV is controlled between 50-200m, the flight speed does not exceed 15m / s, the flight path overlap rate is ≥80%, and the spatial resolution of the thermal image is not greater than 0.3m.

[0012] Preferably, the acquired data is preprocessed, geographic coordinates are registered, and road areas are extracted to generate a road thermal radiation dataset, as follows:

[0013] S21. For each frame of thermal imaging, the neighborhood mean filling method is used to repair bad pixels. After removing noise using a combination of median filtering and Gaussian filtering, the gray value of each pixel in the thermal imaging is converted into the actual temperature value through the temperature quantization coefficient to obtain the radiation temperature of each pixel.

[0014] By combining the UAV's GPS positioning coordinates (X,Y,Z) with the UAV's heading angle α, roll angle β, and pitch angle γ, the mapping relationship between each pixel of the thermal image and the ground geographic coordinates is established, and the geographic coordinate registration of each frame of the thermal image is completed.

[0015] A threshold segmentation method combined with morphological closing operation is used to segment the road area from the thermal image (excluding interference from roadside buildings, vegetation and traffic facilities) to obtain a road mask image, which is then used to extract the radiation temperature of each effective pixel corresponding to the road area in the thermal image.

[0016] The main material types of the road surface in the area were identified using image classification algorithms. Road parameters, including the thermal conductivity λ of the road material, were obtained by combining material property databases and road maintenance records. r Road surface emissivity ε r The road pavement lifespan y, the reference value for the road pavement lifespan yr, and the surface reflectivity α;

[0017] S22, based on the radiation temperature T of each effective pixel i and the corresponding atmospheric temperature T e Relative humidity R a Atmospheric pressure P a Solar altitude angle θ s Solar shortwave radiation S ra Atmospheric transparency τ a Wind speed W s Angle between wind direction and road orientation Thermal conductivity λ of road material r Road surface emissivity ε r Road pavement lifespan y, reference value for road pavement lifespan y r And the surface reflectance α, construct the road thermal radiation dataset D={T i T e R a P a θ s S ra , τ a W s , , λ r , ε r ,y,y r , α}.

[0018] Preferably, the construction of the multi-dimensional evaluation index set is as follows:

[0019] S31. Based on the road thermal radiation dataset D, calculate the average radiation temperature T of the road surface using spatial statistical methods. a :

[0020] ;

[0021] Where N is the number of valid pixels;

[0022] S32. Extract the radiation temperature T of each effective pixel. iThe maximum value is used to obtain the peak thermal radiation intensity T. p ;

[0023] S33. Obtain the relative humidity R from the thermal radiation dataset D. a and atmospheric temperature T e Atmospheric back radiation was calculated based on Brutsaert's formula and the blackbody radiation law. And calculate the net radiation R of the road surface. n :

[0024] ;

[0025] Where σ is the Stefan-Boltzmann constant;

[0026] S34. Calculate the spatial variability coefficient of thermal radiation C. v This reflects the uniformity of the spatial distribution of road thermal radiation.

[0027] ;

[0028] S35. Select multiple longitudinal sampling lines along the road direction within the road area, including the road center sampling line and roadside sampling lines located on both sides of the road center sampling line. Extract sampling points for each sampling line at preset intervals and obtain the radiation temperature of the corresponding pixels. Calculate the average temperature of the road center sampling line and the roadside sampling lines respectively, and use it as the average temperature T of the central area of ​​the road. ce With the average temperature T of the edge region ed And calculate the thermal radiation diffusion attenuation rate K. d :

[0029] ;

[0030] Where L is the horizontal distance between the road center sampling line and the roadside sampling line along the direction perpendicular to the road direction;

[0031] S36. Calculate the average radiation temperature T of the unobstructed area within a predetermined range on both sides of the road edge. amb And calculate the regional thermal environment impact degree I. e :

[0032] ;

[0033] Where d is the shortest straight-line distance from the road edge to the unobstructed area within a predetermined range on both sides of the road edge, calculated by loading vector data of the road and the unobstructed area within the predetermined range on both sides of the road edge using GIS software; D is the influence threshold distance;

[0034] S37. Based on the average radiant temperature T of the road surface a Peak thermal radiation intensity Tp , Road surface net radiation R n The spatial variation coefficient of thermal radiation C v Thermal radiation diffusion attenuation rate K d and regional thermal environment impact degree I e Construct a multi-dimensional evaluation index set Ω{T a T p R n C v K d I e}

[0035] Preferably, the calculation of the comprehensive index of road thermal radiation effect is as follows:

[0036] Step S41: Set various reference thresholds, including the typical net radiation value R of a road at normal temperature. n,ref Critical coefficient of variation of spatial distribution of thermal radiation C v,ref Critical value of thermal radiation diffusion attenuation rate K d,ref and the critical value I of regional thermal environment influence e,ref ;

[0037] Step S42: Calculate the multi-dimensional correction coefficients based on the road thermal radiation dataset D; where:

[0038] Atmospheric environmental correction factor The calculation is as follows;

[0039] ;

[0040] Solar radiation correction factor The calculation is as follows;

[0041] ;

[0042] Road attribute correction coefficient The calculation is as follows;

[0043] ;

[0044] Wind speed heat dissipation correction factor The calculation is as follows;

[0045] ;

[0046] Step S43: Based on the multi-dimensional evaluation index set Ω and multi-dimensional correction coefficients, a nonlinear weighted coupling method is used to amplify the contribution of the dominant factor, smooth the fluctuation of the secondary factor, construct a comprehensive evaluation formula, and calculate the comprehensive index I of road thermal radiation effect;

[0047] .

[0048] Preferably, the K-means clustering algorithm is used to classify road thermal radiation effect levels, evaluate road thermal radiation effects, and generate a spatial distribution map of road thermal radiation effect levels, which is specifically as follows;

[0049] S51. Collect the comprehensive index of road thermal radiation effect of sample road sections under different road materials, different traffic loads, different climatic conditions and different time periods (collect historical evaluation data or a large number of representative road sections in the current evaluation area, and calculate the comprehensive index of road thermal radiation effect of each sample road section based on steps S1 to S4 of the present invention) to form a comprehensive index sample set;

[0050] S52. Treat each comprehensive index of road thermal radiation effect in the comprehensive index sample set as a one-dimensional feature, and perform standardized processing to eliminate the influence of dimensional units, so that the data meets the input requirements of the clustering algorithm;

[0051] S53. Input the standardized comprehensive index sample set into the K-means algorithm for clustering, and divide all samples into the low thermal radiation level cluster C low , the medium thermal radiation level cluster C medium , and the high thermal radiation level cluster C high , and output the cluster center of each cluster;

[0052] S54. Calculate the midpoint between the cluster center of the low thermal radiation level cluster C low and the cluster center of the medium thermal radiation level cluster C medium to determine the first road thermal radiation threshold I1; calculate the midpoint between the cluster center of the medium thermal radiation level cluster C medium and the cluster center of the high thermal radiation level cluster C high to determine the second road thermal radiation threshold I2; where I1 < I2;

[0053] S55. According to the comprehensive index I of the road thermal radiation effect of the road to be evaluated, as well as the first road thermal radiation threshold I1 and the second road thermal radiation threshold I2, divide the thermal radiation effect of the road to be evaluated into 3 levels:

[0054] When I ≥ I2, it is classified as the high thermal radiation level, which indicates that the road has high thermal radiation intensity, uneven spatial distribution, fast thermal diffusion speed and significant impact on the regional thermal environment;

[0055] When I1 ≤ I < I2, it is classified as the medium thermal radiation level, which indicates that the road has moderate thermal radiation intensity, relatively uniform spatial distribution, and limited thermal diffusion and regional impact;

[0056] When I < I1, it is classified as the low thermal radiation level, which indicates that the road has low thermal radiation intensity, uniform spatial distribution, and weak thermal diffusion and regional impact;

[0057] S56. Integrate the grading results with the geographic information system to generate a spatial distribution map of the road thermal radiation effect level, and use different colors to display the risk level of different road sections.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] This method utilizes an unmanned aerial vehicle (UAV) platform equipped with infrared detection equipment to acquire high spatiotemporal resolution road surface thermal radiation data. Combined with multi-source geographic and environmental information, it constructs a comprehensive quantitative assessment index system for thermal radiation effects. The method designed in this invention enables refined assessment of the thermal radiation effects of any road segment under real-world conditions; it fills the technical gap caused by insufficient spatial resolution of traditional satellite remote sensing technology and the high cost of on-site measurements; and it avoids the limitations of single-temperature index assessment, enabling a more comprehensive reflection of the spatial heterogeneity of thermal radiation and its interaction with environmental factors. Attached Figure Description

[0060] Figure 1 This is a flowchart of the road thermal radiation effect assessment method based on UAV-borne infrared detection provided in this embodiment of the invention;

[0061] Figure 2 This is a flowchart of the data processing and construction of the road thermal radiation dataset D according to an embodiment of the present invention;

[0062] Figure 3 This is a flowchart illustrating the calculation of the multi-dimensional evaluation index set Ω according to an embodiment of the present invention;

[0063] Figure 4 This is a flowchart illustrating how the present invention constructs a comprehensive evaluation formula, calculates a comprehensive index of road thermal radiation effects, and classifies levels.

[0064] Figure 5 A flowchart illustrating the generation of a spatial distribution map of road thermal radiation effect levels in an embodiment of the invention. Detailed Implementation

[0065] The following is in conjunction with the appendix Figure 1-5 The technical solution of the present invention will be described in detail below.

[0066] To make the features and advantages of this application more apparent and understandable, specific embodiments are provided below for detailed explanation:

[0067] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0068] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0069] like Figure 1 As shown, this invention proposes a method for evaluating the road thermal radiation effect based on UAV-borne infrared detection, which includes the following steps:

[0070] S1. Conduct low-altitude aerial photography using drones equipped with infrared detection systems to acquire infrared thermal imaging data, flight data, and environmental auxiliary data of roads and surrounding environments;

[0071] The infrared thermal imaging data includes at least: a thermal image and a temperature quantization coefficient;

[0072] The flight parameters include at least: heading angle α, roll angle β, and pitch angle γ;

[0073] The environmental auxiliary parameters include the atmospheric temperature T at the time of detection. e Relative humidity R a Atmospheric pressure P a Solar altitude angle θ s Solar shortwave radiation S ra Atmospheric transparency τ a Wind speed W s Angle between wind direction and road orientation The data is collected synchronously by environmental sensors carried on the drone;

[0074] The UAV-borne infrared detection system includes at least: a UAV platform, an infrared thermal imager, a GPS / BeiDou positioning module, an attitude sensor, and an environmental sensor; the selected infrared thermal imager has a detection wavelength range of 8-14μm, a temperature measurement accuracy of ≤±0.5℃, a flight altitude of 50-200m, a flight speed of no more than 15m / s, a flight path overlap rate of ≥80%, and a thermal image spatial resolution of ≥320×240 pixels;

[0075] S2. Using the data obtained in step S1, perform preprocessing, geographic coordinate registration, and road area extraction to generate a road thermal radiation dataset D; the flowchart for this step is as follows: Figure 2 As shown;

[0076] 1) For the acquired infrared thermal imaging data, the neighborhood mean filling method is used to repair bad pixels. The median filter is applied to replace the center pixel value with the median of the pixel gray value in the window to effectively remove noise. Then, Gaussian filtering is applied to suppress Gaussian noise through weighted averaging to make the image smooth. For each bad pixel, its gray value is replaced with the arithmetic mean of the gray values ​​of the surrounding 8 neighboring pixels.

[0077] 2) For the repaired infrared thermal imaging data, calibration is performed using a blackbody radiation source to establish a precise mapping relationship between image grayscale values ​​and target radiation temperature. The grayscale values ​​of each pixel in the thermal imaging image are converted into actual temperature values ​​using a temperature quantization coefficient to obtain the radiation temperature of each pixel.

[0078] 3) Based on the UAV flight parameters obtained in step S1, by combining the UAV GPS positioning coordinates (X,Y,Z) and flight parameters (heading angle α, roll angle β, pitch angle γ), establish the mapping relationship between each pixel of the thermal image and the ground geographic coordinates, and complete the geographic coordinate registration of each frame of the thermal image.

[0079] The mapping relationship between thermal image pixels and ground geographic coordinates can be established using Pix4Dmappe and Agisoft Metashape image processing software, which can directly import POS data and camera / sensor parameters from the UAV for high-precision registration.

[0080] 4) The OTU threshold segmentation method combined with morphological closing operation is used to segment the road area from the thermal imaging data to obtain the road mask image, which is used to extract the radiation temperature of each effective pixel corresponding to the road area in the thermal image, thereby eliminating the interference of roadside buildings, vegetation and traffic facilities; among them, the OTU algorithm automatically calculates an optimal grayscale threshold to divide the thermal image into two categories: "road" and "background"; morphological closing operation fills the possible holes inside the road area, smooths the edges, and connects the areas that are broken due to trees or vehicles.

[0081] 5) For the segmented road area types, the main material type of the road surface is identified using image classification algorithms. Road parameters, including the thermal conductivity λ of the road material, are obtained by combining material property databases (or through road core sampling and indoor thermal tests) with road maintenance archive data. r Road surface emissivity ε r Road pavement lifespan y, reference value for road pavement lifespan y r , surface reflectance α;

[0082] Among them, the image classification algorithm can analyze visible light or infrared texture features based on deep learning convolutional neural networks, thereby automatically identifying the main material type of the road surface;

[0083] 6) Construct a standardized road thermal radiation dataset D={T i T e R a P a θ s S ra , τ a W s , , λ r , ε r ,y,y r ,α}; where the dataset D can be stored using data management applications such as PostgreSQL and MongoDB;

[0084] S3. Using the acquired road thermal radiation dataset D, calculate the multi-dimensional evaluation index set Ω, which includes thermal radiation intensity index, spatial uniformity index, and regional thermal environment impact index; the flowchart for this step is as follows. Figure 3 As shown;

[0085] 1) For the road thermal radiation dataset D, extract the radiation temperature T of all valid pixels corresponding to the masked image. i The mean radiant temperature T of the road surface was calculated using spatial statistical methods. a :

[0086] ;

[0087] Where N is the number of valid pixels;

[0088] 2) Extract the radiation temperature T of all valid pixels corresponding to the mask image. i The maximum value is used to obtain the peak thermal radiation intensity T. p ;

[0089] 3) For the thermal radiation dataset D, obtain the relative humidity R. a Atmospheric temperature T e Solar shortwave radiation S ra Road surface net radiation R n The formula is as follows:

[0090] ;

[0091] Where σ is the Stefan-Boltzmann constant, with a value of 5.67 × 10⁻⁶. −8 Atmospheric back radiation From relative humidity R a Atmospheric temperature T e Calculations based on Brutsaert and the blackbody radiation law;

[0092] 4) Calculate the spatial variability coefficient C of thermal radiation.v This reflects the uniformity of the spatial distribution of road heat radiation and is calculated using the following formula:

[0093] ;

[0094] ;

[0095] Among them, C v is the spatial variation coefficient of thermal radiation; S is the sample standard deviation of the effective pixel radiation temperature;

[0096] 5) Select three evenly distributed sampling lines along the vertical direction of the road (one along the road centerline and one at each of the two sides at 1 / 3 of the road width). Take points at 1m intervals along each sampling line and calculate the average temperature T in the central area of ​​the road. ce With the average temperature T of the edge region ed Thermal radiation diffusion attenuation rate K d Calculate using the following formula:

[0097] ;

[0098] Where L is the horizontal distance between the road center sampling line and the roadside sampling line along the direction perpendicular to the road direction;

[0099] 6) Calculate the average radiation temperature T of the unobstructed area 50-100m on both sides of the road. amb Regional thermal environment impact I e The calculation formula is:

[0100] ;

[0101] Where d is the shortest straight-line distance from the road edge to the unobstructed area within a predetermined range on both sides of the road edge, calculated by loading vector data of the road and the unobstructed area within a predetermined range on both sides of the road edge using GIS software; D is the influence threshold distance.

[0102] 7) Integrate indicators including thermal radiation intensity (average road surface radiation temperature T) a Peak thermal radiation intensity T p Spatial uniformity index (net radiation of road surface R) n The spatial variation coefficient of thermal radiation C v Thermal radiation diffusion attenuation rate K d ) and regional thermal environment impact index (regional thermal environment impact level I) e The set of multi-dimensional evaluation indicators Ω{T) a T p R n C v K d Ie}

[0103] S4. Construct a comprehensive evaluation formula using a nonlinear weighted coupling method and calculate the comprehensive index of road thermal radiation effect; the flowchart for this step is as follows. Figure 4 As shown;

[0104] 1) Set various reference thresholds, including the typical net radiation value R of roads at normal temperature. n,ref Critical coefficient of variation of spatial distribution of thermal radiation C v,ref The critical value for thermal diffusion attenuation rate is set to K. d,ref Critical value I of regional thermal environment impact e,ref The threshold settings are based on relevant specifications and industry-standard judgment criteria.

[0105] 2) Based on the road thermal radiation dataset D; calculate the multi-dimensional correction coefficients; where:

[0106] The formula for the atmospheric environment correction factor is as follows;

[0107] ;

[0108] The formula for the solar radiation correction factor is as follows;

[0109] ;

[0110] The formula for the road attribute correction factor is as follows;

[0111] ;

[0112] The formula for the wind speed heat dissipation correction factor is as follows;

[0113] ;

[0114] 3) For the multi-dimensional evaluation index set Ω and multi-dimensional correction coefficients, a nonlinear weighted coupling method is adopted to amplify the contribution of the dominant factor, smooth the fluctuation of the secondary factor, construct a comprehensive evaluation formula, and calculate the comprehensive index I of road thermal radiation effect;

[0115] ;

[0116] The basic framework of the comprehensive evaluation formula is constructed based on a nonlinear weighted coupling method. The indicators reflecting the core characteristics of road thermal radiation are divided into three functional modules, which are multiplied by environmental and attribute correction coefficients to achieve dynamic correction of multiple factors. Its basic framework is as follows:

[0117] ;

[0118] Among them, M core M is the core module for thermal radiation intensity.uniformity is the spatial uniformity modulation module, M impact is the thermal diffusion and effect amplification module;

[0119] S5. Divide the road thermal radiation effect grades using the K-means clustering algorithm, evaluate the road thermal radiation effect, and generate a spatial distribution map of road thermal radiation effect grades; the flowchart of this step is as shown in Figure 5 below;

[0120] 1) Collect historical evaluation data or a large number of representative road segments in the current evaluation area, and calculate the comprehensive index of road thermal radiation effect of each sample road segment based on steps S1 to S4 of the present invention, so as to form a comprehensive index sample set I set . This sample set should cover various thermal radiation states under different road materials, different traffic loads, different climatic conditions and different time periods as far as possible to ensure its representativeness;

[0121] 2) Treat each comprehensive index of road thermal radiation effect in the comprehensive index sample set as a one-dimensional feature, and perform standardization processing to eliminate the influence of dimensional consistency, so that the data meets the input requirements of the clustering algorithm; wherein, the number of clusters is set as K=3, corresponding to three effect grades: low, medium and high.

[0122] 3) Input the standardized comprehensive index sample set into the K-means algorithm for clustering. The algorithm will automatically divide all samples into three clusters (low thermal radiation grade cluster C low , medium thermal radiation grade cluster C medium , high thermal radiation grade cluster C high ), and output the cluster center of each cluster;

[0123] 4) Based on the calculated clusters, determine the first road thermal radiation threshold I1 and the second road thermal radiation threshold I2 by calculating the midpoint between adjacent cluster centers, where I1 < I2;

[0124] 5) According to the comprehensive index I of the road thermal radiation effect of the road to be evaluated, as well as the first road thermal radiation threshold I1 and the second road thermal radiation threshold I2, divide the thermal radiation effect of the road to be evaluated into 3 grades:

[0125] When I ≥ I2, it is classified as the high thermal radiation grade, which indicates that the road has high thermal radiation intensity, uneven spatial distribution, fast thermal diffusion speed and significant influence on the regional thermal environment, with obvious safety and environmental risks;

[0126] When I1 ≤ I < I2, it is classified as the medium thermal radiation grade, which indicates that the road has moderate thermal radiation intensity, relatively uniform spatial distribution, limited thermal diffusion and regional influence, and basically no significant risks;

[0127] When I<I1, it is classified as a low thermal radiation level, indicating that the road thermal radiation intensity is low, the spatial distribution is uniform, the thermal diffusion and regional influence are weak, and there is no obvious risk;

[0128] 6) Integrate the classification results with the geographic information system to generate a spatial distribution map of road thermal radiation effect levels. In the map, different colors are used to visually display the risk levels of different road sections.

[0129] The road thermal radiation effect assessment can be completed based on the solution provided in this embodiment according to the above key information.

[0130] Those skilled in the art should understand that the embodiments of the present application may be provided as a method, a system, or a computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

[0131] The present application is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowcharts and / or block diagrams, and combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0132] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0133] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process, such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the process Figure 1 one process or multiple processes and / or blocks Figure 1The steps of the function specified in one or more boxes.

[0134] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention to other forms of road thermal radiation effect assessment methods based on UAV-borne infrared detection. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention, without departing from the scope of the present invention, shall still fall within the protection scope of the present invention.

[0135] This application is not limited to the above-described preferred embodiments. Anyone can derive other forms based on the guidance of this application. All equivalent changes and modifications made within the scope of this invention should be included in the scope of this application.

[0136] The instructions to be executed provide the means to implement the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0137] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

[0138] This application is not limited to the above-described preferred embodiments. Anyone can derive other various forms of energy-saving and emission-reduction evaluation methods for pure electric vehicles under complex road alignment conditions based on the teachings of this application. All equivalent changes and modifications made within the scope of this invention should be covered by this application.

Claims

1. A method for evaluating the road thermal radiation effect based on UAV-borne infrared detection, characterized in that, Specifically, the following steps are included: S1. The UAV equipped with an infrared detection system acquires infrared thermal imaging data, flight data, and environmental auxiliary data of the road and a predetermined area on both sides of the road edge. The infrared thermal imaging data includes thermal images and temperature quantization coefficients. The flight data includes the UAV's heading angle, roll angle, and pitch angle. The environmental auxiliary data includes atmospheric temperature, relative humidity, atmospheric pressure, solar altitude angle, solar shortwave radiation, atmospheric transparency, wind speed, and the angle between wind direction and road direction at the time of detection. The environmental auxiliary data is collected synchronously by environmental sensors carried by the UAV. S2. Preprocess the acquired data, perform geographic coordinate registration, and extract road regions to generate a road thermal radiation dataset; details are as follows: S21. For each frame of thermal imaging, the neighborhood mean filling method is used to repair bad pixels. After removing noise using a combination of median filtering and Gaussian filtering, the gray value of each pixel in the thermal imaging is converted into the actual temperature value through the temperature quantization coefficient to obtain the radiation temperature of each pixel. By combining the UAV's GPS positioning coordinates with the UAV's heading angle α, roll angle β, and pitch angle γ, the mapping relationship between each pixel of the thermal image and the ground geographic coordinates is established, and the geographic coordinate registration of each frame of the thermal image is completed. A threshold segmentation method combined with morphological closing operation is used to segment the road region from the thermal image to obtain a road mask image, which is then used to extract the radiation temperature of each effective pixel corresponding to the road region in the thermal image. The main material types of the road surface in the area were identified using image classification algorithms. Road parameters, including the thermal conductivity λ of the road material, were obtained by combining material property databases and road maintenance records. r Road surface emissivity ε r The road pavement lifespan y, the reference value for the road pavement lifespan yr, and the surface reflectivity α; S22, based on the radiation temperature T of each effective pixel i and the corresponding atmospheric temperature T e Relative humidity R a Atmospheric pressure P a Solar altitude angle θ s Solar shortwave radiation S ra Atmospheric transparency τ a Wind speed W s Angle between wind direction and road orientation Thermal conductivity λ of road material r Road surface emissivity ε r Road pavement lifespan y, reference value for road pavement lifespan y r And the surface reflectance α, construct the road thermal radiation dataset D={T i T e R a P a θ s S ra , τ a W s , , λ r , ε r ,y,y r , α}; S3. Using the road thermal radiation dataset, based on spatial statistical analysis and the blackbody radiation law, calculate and construct a multi-dimensional set of evaluation indicators including the average radiation temperature of the road surface, peak thermal radiation intensity, net road surface radiation, spatial variation coefficient of thermal radiation, thermal radiation diffusion attenuation rate, and regional thermal environment impact. S4. Using the road thermal radiation dataset and a multi-dimensional evaluation index set, based on multi-index coupling and multi-factor correction, a comprehensive evaluation formula is constructed using a nonlinear weighted coupling method to calculate the comprehensive index of road thermal radiation effect. S5. Use the K-means clustering algorithm to classify the road thermal radiation effect level, evaluate the road thermal radiation effect, and generate a spatial distribution map of the road thermal radiation effect level.

2. The method for evaluating road thermal radiation effects based on UAV-borne infrared detection according to claim 1, characterized in that, The drone's flight altitude is controlled between 50-200m, the flight speed does not exceed 15m / s, the flight path overlap rate is ≥80%, and the spatial resolution of the thermal image is no greater than 0.3m.

3. The method for evaluating road thermal radiation effects based on UAV-borne infrared detection according to claim 1, characterized in that, The construction of the multi-dimensional evaluation index set is as follows: S31. Based on the road thermal radiation dataset D, calculate the average radiation temperature T of the road surface using spatial statistical methods. a : ； Where N is the number of valid pixels; S32. Extract the radiation temperature T of each effective pixel. i The maximum value is used to obtain the peak thermal radiation intensity T. p ; S33. Obtain the relative humidity R from the thermal radiation dataset D. a and atmospheric temperature T e Atmospheric back radiation was calculated based on Brutsaert's formula and the blackbody radiation law. And calculate the net radiation R of the road surface. n : ； Where σ is the Stefan-Boltzmann constant; S34. Calculate the spatial variability coefficient of thermal radiation C. v This reflects the uniformity of the spatial distribution of road thermal radiation. ； S35. Select multiple longitudinal sampling lines along the road direction within the road area, including the road center sampling line and roadside sampling lines located on both sides of the road center sampling line. Extract sampling points for each sampling line at preset intervals and obtain the radiation temperature of the corresponding pixels. Calculate the average temperature of the road center sampling line and the roadside sampling lines respectively, and use it as the average temperature T of the central area of ​​the road. ce With the average temperature T of the edge region ed And calculate the thermal radiation diffusion attenuation rate K. d : ； Where L is the horizontal distance between the road center sampling line and the roadside sampling line along the direction perpendicular to the road direction; S36. Calculate the average radiation temperature T of the unobstructed area within a predetermined range on both sides of the road edge. amb And calculate the regional thermal environment impact degree I. e : ； Where d is the shortest straight-line distance from the road edge to the unobstructed area within a predetermined range on both sides of the road edge; D is the influence threshold distance; S37. Based on the average radiant temperature T of the road surface a Peak thermal radiation intensity T p , Road surface net radiation R n The spatial variation coefficient of thermal radiation C v Thermal radiation diffusion attenuation rate K d and regional thermal environment impact degree I e Construct a multi-dimensional evaluation index set Ω{T a T p R n C v K d I e } 4. The method for evaluating road thermal radiation effects based on UAV-borne infrared detection according to claim 3, characterized in that, The specific calculation of the comprehensive index of road thermal radiation effect is as follows: Step S41: Set various reference thresholds, including the typical net radiation value R of a road at normal temperature. n,ref Critical coefficient of variation of spatial distribution of thermal radiation C v,ref Critical value of thermal radiation diffusion attenuation rate K d,ref and the critical value I of regional thermal environment influence e,ref ; Step S42: Calculate the multi-dimensional correction coefficients based on the road thermal radiation dataset D; where: Atmospheric environmental correction factor The calculation is as follows; ； Solar radiation correction factor The calculation is as follows; ； Road attribute correction coefficient The calculation is as follows; ； Wind speed heat dissipation correction factor The calculation is as follows; ； Step S43: Based on the multi-dimensional evaluation index set Ω and multi-dimensional correction coefficients, a comprehensive evaluation formula is constructed using a nonlinear weighted coupling method, and the comprehensive index I of road thermal radiation effect is calculated; 。 5. The method for evaluating road thermal radiation effects based on UAV-borne infrared detection according to claim 1, characterized in that, The K-means clustering algorithm is used to classify road thermal radiation effect levels, evaluate road thermal radiation effects, and generate a spatial distribution map of road thermal radiation effect levels, as detailed below; S51. Collect the comprehensive indices of road thermal radiation effect of sample road sections under different road materials, different traffic loads, different climatic conditions and different time periods to form a comprehensive index sample set; S52. Treat each comprehensive index of road thermal radiation effect in the comprehensive index sample set as a one-dimensional feature, and perform standardization processing; S53. Input the standardized composite index sample set into the K-means algorithm for clustering, and divide all samples into the low heat radiation level cluster C. low Cluster C, medium thermal radiation level medium High thermal radiation level cluster C high In the middle, output the cluster center of each cluster; S54, Calculate the low thermal radiation level cluster C low Cluster C of medium thermal radiation level medium The center point of the cluster is used to determine the first road thermal radiation threshold I1; Calculation of thermal radiation level cluster C medium Cluster C with high thermal radiation level high The center point of the cluster is used to determine the second path thermal radiation threshold I2; S55. According to the comprehensive index I of the road thermal radiation effect of the road to be evaluated, as well as the first road thermal radiation threshold I1 and the second road thermal radiation threshold I2, the thermal radiation effect of the road to be evaluated is divided into 3 levels: When I≥I2, it is classified as a high thermal radiation level, indicating that the road has high thermal radiation intensity, uneven spatial distribution, fast thermal diffusion speed and significant impact on the regional thermal environment; When I1≤I<I2, it is classified as a medium thermal radiation level, indicating that the road has moderate thermal radiation intensity, relatively uniform spatial distribution, and limited thermal diffusion and regional impact; When I<I1, it is classified as a low thermal radiation level, indicating that the road has low thermal radiation intensity, uniform spatial distribution, and weak thermal diffusion and regional impact; S56. Integrate the level classification results with the geographic information system, generate a spatial distribution map of road thermal radiation effect levels, and use different colors to display the risk levels of different road sections.

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