A method and system for inverting the low-altitude temperature field of unmanned aerial vehicles

Abstract

The present invention relates to a method and system for inverting the low-altitude temperature field of an unmanned aerial vehicle (UAV). The method comprises: obtaining raw temperature image data collected by an infrared imager of a UAV and the flight log data of the UAV; processing the relative temperature of each pixel in all raw temperature images in the raw temperature image data to obtain calibrated and inverted absolute temperature image data; parsing the flight log data to obtain the UAV's camera attitude data; reconstructing the flight attitude based on the camera attitude data to obtain the lens attitude trajectory corresponding to the raw temperature image data; reconstructing an inverted three-dimensional temperature field point cloud based on the absolute temperature image data and the lens attitude trajectory using a three-dimensional temperature field reconstruction algorithm; and visualizing the inverted three-dimensional temperature field point cloud based on a point cloud model to output an inverted three-dimensional temperature field. The method utilizes UAV technology, temperature inversion technology, and three-dimensional reconstruction technology to achieve three-dimensional temperature field reconstruction of small and medium-scale layers in urban built-up areas.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicles (UAVs), and in particular relates to a method and system for inverting the low-altitude temperature field of an UAV. Background Art

[0002] In nature, all objects with temperatures above absolute zero (-273.15°C) continuously emit radiation energy into the surrounding space due to the thermal motion of their molecules. In principle, if this radiation energy can be detected and collected, the signals from the detector can be rearranged to form an infrared image corresponding to the object's radiation distribution. Furthermore, the energy level and wavelength distribution of an object's infrared radiation are closely related to its surface temperature. Therefore, by measuring the infrared energy radiated by an object, its surface temperature can be accurately determined. Infrared thermal imaging technology converts the temperature distribution and reflectivity differences between the target and various parts of the scene into corresponding signals, which are then converted into a visible light image. A device that converts invisible infrared radiation energy into a visible light image is an infrared thermal imager. Infrared thermal imaging cannot achieve the effect of "seeing through" because the infrared radiation emitted by the target is blocked by obstacles. The color and temperature values ​​captured by the infrared thermal imaging camera only reflect the obstacles and do not represent the target itself.

[0003] A temperature field is defined as the set of temperature values ​​at each point in an object at a given moment, also known as the temperature distribution. For a dynamic temperature field, where the temperature changes over time, the value is: T = f(x, y, z, t); for a steady-state temperature field, where the temperature remains constant, the value is: T = f(x, y, z), where T is the temperature, x, y, and z are the three-dimensional coordinates of the point, and t is time.

[0004] The steady-state temperature field of an urban built environment over a specific time period. Because temperature-collecting drones spend a considerable amount of time in flight, temperature acquisition at a single moment cannot achieve multi-perspective 3D temperature field reconstruction. However, temperature changes in urban environments are continuous throughout the day, with no temperature jumps. For a limited time period when the temperature field changes smoothly, it can be conditionally assumed that the temperature field follows a steady-state distribution.

[0005] Traditional temperature field perception methods are divided into three scale levels: large and medium scale, small and micro scale, and medium and small scale. For large and medium scale, multi-band remote sensing data inversion is mostly performed through satellite call. The data volume is large, and temperature measurement focuses more on the overall temperature changes in a large scale range. The data scale is about 30-90m / pixel, the data granularity is relatively coarse, and there are only two-dimensional temperature changes. It is only suitable for large-scale research and applications such as urban heat islands and regional climate research. For small and micro scale, point temperature measurement or local temperature measurement is mostly carried out by setting or holding small temperature sensors. The temperature measurement is accurate, but the amount of data collected in a single time is small, the data collection workload is large, and the temperature field is not coherent. Alternatively, the temperature field is directly derived by physical temperature analysis after modeling. It is only suitable for small and micro-scale research and applications such as local precise temperature measurement, local temperature extreme value detection, and microclimate environment temperature measurement. For small and medium scale, there is currently no dedicated temperature field inversion method or temperature measurement method. Most of them are based on the cropping of large-scale method results or interpolation based on small-scale method results.

[0006] Therefore, for the reconstruction of the temperature field in urban built-up areas at the small and medium scale levels, if satellite remote sensing data at the large and medium scale levels are used to reconstruct the temperature field, the data will be relatively rough and the temperature measurement accuracy will be insufficient; if handheld small temperature sensors at the small and micro scale levels are used to carry out point temperature measurement or local temperature measurement to reconstruct the temperature field, it will be highly dependent on point temperature measurement interpolation and the temperature measurement confidence will be insufficient; as a result, the three-dimensional temperature field inversion of urban built-up areas cannot be completed. Summary of the Invention

[0007] In order to make up for the defects of the prior art, the present invention provides a method and system for inverting the low-altitude temperature field of an unmanned aerial vehicle.

[0008] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0009] In the first aspect, a method for inverting the low-altitude temperature field of a UAV is provided, comprising:

[0010] Obtain the original temperature image data collected by the drone's infrared imager and the drone's flight log data;

[0011] Process the relative temperature of each pixel of all original temperature images in the original temperature image data to obtain the absolute temperature image data after calibration and inversion;

[0012] Parse flight log data to obtain the drone's camera attitude data;

[0013] Reconstruct the flight attitude based on the camera attitude data to obtain the lens attitude trajectory corresponding to the original temperature image data;

[0014] According to the absolute temperature image data and the lens posture trajectory, the inverted three-dimensional temperature field point cloud is reconstructed through the three-dimensional temperature field reconstruction algorithm;

[0015] The inverted three-dimensional temperature field point cloud is visualized according to the point cloud model and the inverted three-dimensional temperature field is output.

[0016] Furthermore, the relative temperature of each pixel of all the original temperature images in the original temperature image data is processed to obtain the absolute temperature image data after calibration and inversion, including:

[0017] For each original temperature image Img_O in the original temperature image data, extract the relative temperature of each pixel Tuv_O located in the two-dimensional image coordinate system of the horizontal axis U and the vertical axis V of the image;

[0018] Based on the user-preset relative temperature to absolute temperature mapping parameters, the absolute temperature radiation intensity of the corresponding position of each pixel Tuv_O is extracted;

[0019] Based on the user-preset comprehensive ambient temperature inversion parameters, the inversion temperature measurement is performed, and the absolute temperature radiation intensity of each pixel Tuv_O in the shooting environment is measured, which corresponds to the inverted absolute temperature;

[0020] The absolute temperature is then mapped into an absolute temperature two-dimensional pixel Tuv (Ruv, Guv, Buv) composed of eight-bit pixels in three channels of R, G, and B. The expression of the absolute temperature two-dimensional pixel Tuv is:

[0021] Tuv = Wr×Ruv + Wg×Guv + Wb×Buv;

[0022] Among them, Wr is the weight of the R channel, Wg is the weight of the G channel, and Wb is the weight of the B channel;

[0023] The absolute temperature two-dimensional pixels Tuv are combined to form the calibrated and inverted absolute temperature image Img_T, thereby obtaining the calibrated and inverted absolute temperature image data.

[0024] Furthermore, the camera attitude data includes image frame name parameters, camera gimbal position parameters, gimbal attitude direction parameters, lens parameters and image frame sequence number parameters. The camera gimbal position parameters include longitude value, latitude value and altitude value. The gimbal attitude direction parameters include yaw angle value, pitch angle value and roll angle value. The lens parameters include focal length value.

[0025] Furthermore, the flight attitude is reconstructed based on the camera attitude data to obtain the lens attitude trajectory corresponding to the original temperature image data, including:

[0026] The camera gimbal position parameters corresponding to each original temperature image in the original temperature image data are read from the camera attitude data, and the camera position attitude is reconstructed according to the camera gimbal position parameters;

[0027] The gimbal attitude direction parameters corresponding to each original temperature image in the original temperature image data are read from the camera attitude data, and the camera gimbal attitude is reconstructed according to the gimbal attitude direction parameters;

[0028] The lens parameters corresponding to each original temperature image in the original temperature image data are read from the camera posture data, and the lens focal length posture is reconstructed according to the lens parameters;

[0029] According to the camera position posture, camera gimbal posture and lens focal length posture, the three-dimensional posture of the aerial lens corresponding to each raw temperature image in the raw temperature image data is obtained;

[0030] Each aerial lens three-dimensional posture is used as a lens posture trajectory point sequence, and all lens posture trajectory points in the lens posture trajectory point sequence constitute a lens posture trajectory.

[0031] Furthermore, the camera gimbal position parameters corresponding to each original temperature image in the original temperature image data are read from the camera attitude data, and the camera position attitude is reconstructed according to the camera gimbal position parameters, including:

[0032] Reading the camera gimbal position parameters corresponding to each raw temperature image in the raw temperature image data from the camera attitude data;

[0033] Get the longitude, latitude and altitude values ​​based on the camera gimbal position parameters;

[0034] Use map projection to calculate the longitude, latitude, and altitude values ​​to obtain the camera position and posture.

[0035] Furthermore, the gimbal attitude direction parameters corresponding to each original temperature image in the original temperature image data are read from the camera attitude data, and the camera gimbal attitude is reconstructed according to the gimbal attitude direction parameters, including:

[0036] Reading the gimbal attitude direction parameters corresponding to each original temperature image in the original temperature image data from the camera attitude data;

[0037] Obtain the yaw angle value, pitch angle value and roll angle value according to the gimbal attitude direction parameters;

[0038] Use the North East Earth (NED) coordinate system to perform three-dimensional vector orientation processing on the yaw angle, pitch angle, and roll angle values ​​to obtain the camera gimbal posture.

[0039] Furthermore, the lens parameters corresponding to each original temperature image in the original temperature image data are read from the camera attitude data, and the lens focal length attitude is reconstructed according to the lens parameters, including:

[0040] Reading the lens parameters corresponding to each raw temperature image in the raw temperature image data from the camera posture data;

[0041] Get the focal length value according to the lens parameters;

[0042] Get the lens focal length attitude according to the focal length value.

[0043] Furthermore, based on the absolute temperature image data and the lens posture trajectory, the 3D temperature field reconstruction algorithm is used to reconstruct the inverted 3D temperature field point cloud, including:

[0044] Based on the absolute temperature image data and lens posture trajectory, the inverted three-dimensional temperature field point cloud is reconstructed through the bundle method regional block adjustment reconstruction algorithm, the neural radiation field reconstruction algorithm or the three-dimensional Gaussian splash reconstruction algorithm. Each temperature measuring point in the inverted three-dimensional temperature field point cloud includes spatial parameters (X, Y, Z) and three-channel parameters (Rxyz, Gxyz, Bxyz) that indirectly reflect the temperature.

[0045] Secondly, a UAV low-altitude temperature field inversion system is provided, including:

[0046] The acquisition module is used to obtain the original temperature image data collected by the drone's infrared imager and the drone's flight log data;

[0047] The temperature processing module is used to process the relative temperature of each pixel of all the original temperature images in the original temperature image data to obtain the absolute temperature image data after calibration and inversion;

[0048] The attitude reconstruction module is used to parse the flight log data to obtain the camera attitude data of the UAV; the flight attitude is reconstructed based on the camera attitude data to obtain the lens attitude trajectory corresponding to the original temperature image data;

[0049] The inverted 3D temperature field point cloud reconstruction module is used to reconstruct the inverted 3D temperature field point cloud based on the absolute temperature image data and the lens posture trajectory through the 3D temperature field reconstruction algorithm;

[0050] The inverted three-dimensional temperature field output module is used to visualize the inverted three-dimensional temperature field point cloud according to the point cloud model and output the inverted three-dimensional temperature field.

[0051] The beneficial effects achieved by the present invention are:

[0052] The original temperature image data collected by the drone's infrared imager and the drone's flight log data are obtained; the relative temperature of each pixel in the original temperature image data is processed to obtain the absolute temperature image data after calibration and inversion; the flight log data is parsed to obtain the drone's camera attitude data; the flight attitude is reconstructed based on the camera attitude data to obtain the lens attitude trajectory corresponding to the original temperature image data; based on the absolute temperature image data and the lens attitude trajectory, a 3D temperature field reconstruction algorithm is used to reconstruct an inverted 3D temperature field point cloud; the inverted 3D temperature field point cloud is visualized based on the point cloud model to output the inverted 3D temperature field. The drone equipped with an infrared imager can realize the thermal environment collection of local microclimates at small and medium scales. The drone's aerial perspective can more comprehensively reflect the overall site conditions and is more suitable for small and medium scale data collection. The 3D temperature field reconstruction of small and medium scale layers in urban built-up areas is achieved by utilizing drone technology, temperature inversion technology, and 3D reconstruction technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flow chart of the method for inverting the low-altitude temperature field of a UAV according to the present invention;

[0054] Figure 2 is a pixel relative temperature map of the two-dimensional image coordinate system of the original temperature image Img_O of the present invention;

[0055] Figure 3 This is a structural diagram of the UAV low-altitude temperature field inversion system of the present invention. DETAILED DESCRIPTION

[0056] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0057] like Figure 1 As shown, an embodiment of the present invention provides a method for inverting the low-altitude temperature field of a UAV, comprising:

[0058] 101, obtaining the original temperature image data collected by the infrared imager of the drone and the flight log data of the drone;

[0059] In this embodiment, the drone is equipped with an infrared imager. A typical infrared imager mainly consists of an optical system, a detector, an electronic signal processing system, a display system, and other components. Taking the DJI Mavic 3T as an example, it uses an uncooled vanadium oxide VOx microbolometer for temperature sensing. The temperature measurement range is -20°C to 150°C (high gain) and 0°C to 500°C (low gain), and the temperature measurement accuracy is ±2°C or ±2% (whichever is greater).

[0060] The drone collects raw temperature image data through infrared imagers in urban built-up areas. The raw temperature image data contains multiple raw temperature image images. The data is saved in real time when the drone is collecting. The saved data formats include flight log .log files and .rjpg image files containing temperature radiation information.

[0061] 102, processing the relative temperature of each pixel of all original temperature images in the original temperature image data to obtain calibrated and inverted absolute temperature image data;

[0062] For each original temperature image Img_O in the original temperature image data, the relative temperature of each pixel Tuv_O located in the two-dimensional image coordinate system of the horizontal axis U and the vertical axis V of the image is extracted; Figure 2 As shown, 0≤u≤U, 0≤v≤V, O represents the original uninverted state of the temperature image;

[0063] Based on the user-preset relative temperature to absolute temperature mapping parameters, the mapping parameters are determined by the upper and lower limits of the shooting environment temperature. For example, the mapping parameters of a single image during shooting include the lowest temperature measured in the single image Tmin_O and the highest temperature measured in the single image Tmax_O. The absolute temperature radiation intensity at the corresponding position of each pixel Tuv_O is extracted;

[0064] Since the absolute temperature radiation intensity at the corresponding position of each pixel Tuv_O directly reflects the thermal radiation intensity received by the sensor, it is affected by the ambient temperature and humidity, distance, and physical emissivity, resulting in different temperature measurement results. Only by setting the correct environmental parameters can the correct temperature measurement results be obtained. Based on the preset comprehensive ambient temperature inversion parameters (including temperature measurement distance, ambient humidity, target emissivity, and ambient reflection temperature), the temperature is inverted and measured. The absolute temperature radiation intensity of each pixel Tuv_O in the shooting environment corresponds to the inverted absolute temperature.

[0065] Due to the measurement range and counting requirements of absolute temperature, the direct export value of absolute temperature needs to be counted in long bytes (16-bit or 32-bit count, and its storage format is .RAW, which is similar to the temperature storage pixel mechanism in GIS heat island inversion, and the format is TIF or TIFF). In order to facilitate the three-dimensional temperature field modeling calculation, the absolute temperature is remapped into an absolute temperature two-dimensional pixel Tuv (Ruv, Guv, Buv) composed of eight-bit pixels in the three channels of R, G and B. The expression of the absolute temperature two-dimensional pixel Tuv is:

[0066] Tuv = Wr×Ruv + Wg×Guv + Wb×Buv;

[0067] Among them, Wr is the weight of the R channel, Wg is the weight of the G channel, and Wb is the weight of the B channel;

[0068] The absolute temperature two-dimensional pixels Tuv are combined to form the calibrated inverted absolute temperature image Img_T, where T represents the state of the temperature image after inversion, thereby obtaining the calibrated inverted absolute temperature image data.

[0069] 103, parsing the flight log data to obtain the camera attitude data of the drone;

[0070] The camera attitude data can be read from the flight log .log file, which can usually be displayed in a tabular .csv file.

[0071]

[0072] As shown in Table 1 above, the camera attitude data includes image frame name parameters, camera gimbal position parameters, gimbal attitude direction parameters, lens parameters, and image frame sequence number parameters. The camera gimbal position parameters include longitude, latitude, and altitude values. The gimbal attitude direction parameters include yaw angle, pitch angle, and roll angle values. The lens parameters include focal length values.

[0073] 104, reconstructing the flight attitude according to the camera attitude data to obtain a lens attitude trajectory corresponding to the original temperature image data;

[0074] Among them, flight attitude reconstruction includes three aspects: camera position attitude reconstruction, camera gimbal attitude reconstruction, and lens focal length attitude reconstruction. The specific reconstructed lens attitude trajectory is as follows:

[0075] The camera gimbal position parameters corresponding to each raw temperature image in the raw temperature image data are read from the camera attitude data, and the camera position attitude is reconstructed based on the camera gimbal position parameters; the camera gimbal position parameters corresponding to each raw temperature image in the raw temperature image data are read from the camera attitude data; the longitude value, latitude value and altitude value are obtained based on the camera gimbal position parameters; the longitude value, latitude value and altitude value are solved using a map projection method to obtain the camera position attitude; for example, the image frame name of the raw temperature image in the raw temperature image data is obtained and the spatial position of the camera at the time of shooting, i represents the image frame number: longitude (°), latitude (°), altitude (m), longitude (°), latitude (°), altitude (m) are projected using map projection and resolved into the camera position and attitude, which is expressed as longitude (m), latitude (m), altitude (m);

[0076] The gimbal attitude direction parameters corresponding to each raw temperature image in the raw temperature image data are read from the camera attitude data, and the camera gimbal attitude is reconstructed according to the gimbal attitude direction parameters; the gimbal attitude direction parameters corresponding to each raw temperature image in the raw temperature image data are read from the camera attitude data; the yaw angle value, pitch angle value and roll angle value are obtained according to the gimbal attitude direction parameters; the yaw angle value, pitch angle value and roll angle value are processed by three-dimensional vector orientation using the North East Down (NED) coordinate system to obtain the camera gimbal attitude, for example, obtain At this moment, the camera's spatial orientation angle, also known as the Euler angle in the industry, actually includes the pitch angle, yaw angle, and roll angle. The specific process of three-dimensional vector orientation using the NED coordinate system is as follows:

[0077] (1) Reference to the world coordinate system: The orientation coordinate method is the NED coordinate system. With the current drone camera position point as the center, the NED coordinate system of the world coordinate system is defined as: Axis North, Axis east, Axis downward;

[0078] (2) Define the world reference rotation plane, The plane perpendicular to the axis is the plane of rotation , The plane perpendicular to the axis is the plane of rotation , The plane perpendicular to the axis is the plane of rotation ;

[0079] (3) Camera body coordinate system: The direction of the lens posture is defined with the current drone camera lens position point as the center, and the coordinate system is fixedly connected to the camera. The axis points to the center of the axis directly in front of the lens shooting direction. The axis is perpendicular to the plane of left-right symmetry of the lens body and points to the right side of the camera body. The axis is in the plane of left-right symmetry of the fuselage, and The axis is vertical and points directly below the lens body;

[0080] (4) Define the fuselage self-rotation plane: The plane perpendicular to the axis is the plane of rotation , The plane perpendicular to the axis is the plane of rotation , The plane perpendicular to the axis is the plane of rotation Under the conditions of defining the reference world coordinate system, the world reference rotation plane, the camera body coordinate system, and the camera body coordinate system, the camera's aerial attitude direction can be described as the pitch angle, yaw angle, and roll angle, which are defined as follows:

[0081] Yaw angle yaw: with the current drone camera position as the center, is the rotation axis, along The plane rotates clockwise, with the axis in front of the camera lens Deviation The angle of the axis is The projection of the plane is defined as the yaw angle yaw. For drones, the yaw angle yaw is generally set to [0°, 360°], where 0° means the camera lens is facing north, 90° means the camera lens is facing east, 180° means the camera lens is facing south, and 270° means the camera lens is facing west.

[0082] Pitch angle: With the current drone camera position as the center, is the rotation axis, along The plane rotates clockwise, with the axis above the camera lens Deviation The angle of the axis is The projection of the plane is defined as the pitch angle. For drones, the pitch angle is generally set to [-90°, 90°], where -90° means the camera lens is looking down and pointing vertically to the ground, -45° means the camera lens is looking down at the ground at 45 degrees, 0° means the camera lens is looking forward horizontally, 45° means the camera lens is looking up at the sky at 45 degrees, and 90° means the camera lens is looking up and pointing vertically to the sky.

[0083] Roll angle: With the current drone camera position as the center, is the rotation axis, along The plane rotates clockwise, with the axis to the right of the camera lens Deviation The angle of the axis is The projection of the plane is defined as the roll angle. For drones, the roll angle is generally set to [-90°, 90°], where -90° means the camera rolls 90 degrees to the left and the left side of the camera body is perpendicular to the ground; -45° means the camera rolls 45 degrees to the left; 0° means the camera lens is horizontal and has no roll; 45° means the camera rolls 45 degrees to the right; and 90° means the camera rolls 90 degrees to the right and the right side of the camera body is perpendicular to the ground.

[0084] Reading the lens parameters corresponding to each original temperature image in the original temperature image data from the camera attitude data, obtaining the focal length value according to the lens parameters, and obtaining the lens focal length attitude according to the focal length value;

[0085] The three-dimensional posture of the aerial lens is obtained according to the camera position posture, the camera gimbal posture and the lens focal length posture; the lens parameters corresponding to each raw temperature image in the raw temperature image data are obtained from the camera posture data;

[0086] Each aerial lens 3D posture is taken as a lens posture trajectory point sequence, and all lens posture trajectory points in the lens posture trajectory point sequence constitute a lens posture trajectory.

[0087] 105, based on the absolute temperature image data and the lens posture trajectory, a 3D temperature field reconstruction algorithm is used to reconstruct an inverted 3D temperature field point cloud;

[0088] Since the absolute temperature image Img_T after calibration inversion in step 102 only retains temperature information and does not contain any spatial positioning and attitude parameters, it is impossible to directly implement spatial three-dimensional solution. The lens posture trajectory reconstructed in step 104 needs to be converted into the image frame name. Re-match the calibrated inverted absolute temperature image Img_T one by one to obtain a calibrated inverted absolute temperature image group containing the drone lens attitude trajectory, which can be used for spatial three-dimensional solution;

[0089] Based on the absolute temperature image data and the lens posture trajectory, the 3D temperature field reconstruction algorithm includes but is not limited to the bundle method regional block adjustment reconstruction algorithm, the neural radiation field reconstruction algorithm (NeRF), or the 3D Gaussian Splatting reconstruction algorithm (3DGS). Accordingly, the 3D temperature field reconstruction module software that can be used includes but is not limited to Bentley iTwin (formerly context capture), PIX4D Mapper, DJI Zhitu and other 3D reconstruction software. The inverted 3D temperature field point cloud is reconstructed. Each temperature measurement point in the inverted 3D temperature field point cloud includes spatial parameters (X, Y, Z) and three-channel parameters (Rxyz, Gxyz, Bxyz) that indirectly reflect the temperature;

[0090] The spatial position of the spatial temperature measurement point Pxyz is X, Y, and Z;

[0091] The inverted three-dimensional spatial temperature corresponding to the spatial temperature measurement point Pxyz is Txyz, and its value calculation formula is:

[0092] Txyz = Wr×Rxyz + Wg×Gxyz + Wb×Bxyz.

[0093] 106. Visualize the inverted three-dimensional temperature field point cloud according to the point cloud model and output the inverted three-dimensional temperature field.

[0094] The generated point cloud and its corresponding point cloud visualization method can be freely applied or replaced with a variety of modeling software and low-code platform combinations, including but not limited to Rhino + Grasshopper, Blender + Geometry Node, Revit + Dynamo, C4D, etc. Its wide applicability, strong display capabilities, and good scalability facilitate the subsequent series of calculations for surface envelope thermal engineering, green building renovation, microclimate, microenvironment, and micro-renewal in urban building environments. Because it involves the 3D perception of fundamental physical quantities, it has a wide range of application scenarios.

[0095] The implementation principle of the embodiment of the present invention is:

[0096] Obtain the original temperature image data collected by the drone's infrared imager and the drone's flight log data; process the relative temperature of each pixel in all the original temperature images in the original temperature image data to obtain the absolute temperature image data after calibration and inversion; parse the flight log data to obtain the drone's camera attitude data; reconstruct the flight attitude based on the camera attitude data to obtain the lens attitude trajectory corresponding to the original temperature image data; reconstruct the inverted three-dimensional temperature field point cloud based on the absolute temperature image data and the lens attitude trajectory through the three-dimensional temperature field reconstruction algorithm; visualize the inverted three-dimensional temperature field point cloud based on the point cloud model and output the inverted three-dimensional temperature field. The drone equipped with an infrared imager can realize the thermal environment collection of small and medium-scale local microclimates. The drone's aerial perspective can more comprehensively reflect the overall site conditions and is more suitable for small and medium-scale data collection.

[0097] The three-dimensional temperature field reconstruction of small and medium-scale layers in urban built-up areas is achieved by using drone technology, temperature inversion technology, and three-dimensional reconstruction technology.

[0098] In combination with the UAV low-altitude temperature field inversion method described in the above embodiments, the UAV low-altitude temperature field inversion system is described below through an embodiment.

[0099] like Figure 3As shown, an embodiment of the present invention provides a UAV low-altitude temperature field inversion system, comprising:

[0100] The acquisition module 301 is used to acquire the original temperature image data collected by the infrared imager of the drone and the flight log data of the drone;

[0101] The temperature processing module 302 is used to process the relative temperature of each pixel of all the original temperature images in the original temperature image data to obtain the absolute temperature image data after calibration and inversion;

[0102] The attitude reconstruction module 303 is used to parse the flight log data to obtain the camera attitude data of the UAV; reconstruct the flight attitude based on the camera attitude data to obtain the lens attitude trajectory corresponding to the original temperature image data;

[0103] The inverted 3D temperature field point cloud reconstruction module 304 is used to reconstruct the inverted 3D temperature field point cloud based on the absolute temperature image data and the lens posture trajectory using a 3D temperature field reconstruction algorithm;

[0104] The inverted three-dimensional temperature field output module 305 is used to perform visualization processing on the inverted three-dimensional temperature field point cloud according to the point cloud model and output the inverted three-dimensional temperature field.

[0105] The implementation principles of the embodiments of the present invention are as follows:

[0106] The acquisition module 301 acquires the raw temperature image data captured by the drone's infrared imager and the drone's flight log data. The temperature processing module 302 processes the relative temperature of each pixel in the raw temperature image data to obtain calibrated and inverted absolute temperature image data. The attitude reconstruction module 303 parses the flight log data to obtain the drone's camera attitude data. The inverted 3D temperature field point cloud reconstruction module 304 reconstructs the flight attitude based on the camera attitude data to obtain the lens attitude trajectory corresponding to the raw temperature image data. The inverted 3D temperature field output module 305 reconstructs the inverted 3D temperature field point cloud based on the absolute temperature image data and the lens attitude trajectory using a 3D temperature field reconstruction algorithm. The inverted 3D temperature field point cloud is visualized based on the point cloud model to output the inverted 3D temperature field. Using a drone equipped with an infrared imager, the thermal environment of a local microclimate can be acquired at small and medium scales. The drone's aerial perspective can more comprehensively reflect the overall site conditions and is more suitable for small and medium scale data acquisition. Using drone technology, temperature inversion technology, and 3D reconstruction technology, 3D temperature field reconstruction of small and medium scale layers in urban built-up areas can be achieved.

[0107] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0108] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0109] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0110] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0111] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A method for inverting the low-altitude temperature field of an unmanned aerial vehicle, characterized in that: include: Obtaining raw temperature image data collected by the infrared imager of the drone and flight log data of the drone; Processing the relative temperature of each pixel of all original temperature images in the original temperature image data to obtain calibrated and inverted absolute temperature image data; Parsing the flight log data to obtain the camera attitude data of the UAV; Reconstructing the flight attitude according to the camera attitude data to obtain a lens attitude trajectory corresponding to the original temperature image data; Reconstructing an inverted three-dimensional temperature field point cloud using a three-dimensional temperature field reconstruction algorithm based on the absolute temperature image data and the lens posture trajectory; The inverted three-dimensional temperature field point cloud is visualized according to the point cloud model, and the inverted three-dimensional temperature field is output.

2. The method for inverting the low-altitude temperature field of a UAV according to claim 1, characterized in that: The processing of the relative temperature of each pixel of all the original temperature images in the original temperature image data to obtain the calibrated and inverted absolute temperature image data includes: For each original temperature image Img_O in the original temperature image data, extract the relative temperature of each pixel Tuv_O located in the two-dimensional image coordinate system of the horizontal axis U and the vertical axis V of the image; Based on the user-preset relative temperature to absolute temperature mapping parameters, the absolute temperature radiation intensity at the corresponding position of each pixel Tuv_O is extracted; the mapping parameters are determined by the upper and lower extremes of the shooting environment temperature; Based on the user-preset comprehensive ambient temperature inversion parameters, the inversion temperature measurement is performed, and the absolute temperature radiation intensity of each pixel Tuv_O in the shooting environment is measured, which corresponds to the inverted absolute temperature; The absolute temperature is remapped into an absolute temperature two-dimensional pixel Tuv (Ruv, Guv, Buv) composed of eight-bit pixels in three channels of R, G, and B. The expression of the absolute temperature two-dimensional pixel Tuv is: Tuv = Wr×Ruv + Wg×Guv + Wb×Buv; Wherein, Wr is the weight of the R channel, Wg is the weight of the G channel, and Wb is the weight of the B channel; The absolute temperature two-dimensional pixels Tuv are combined to form a calibrated and inverted absolute temperature image Img_T, thereby obtaining calibrated and inverted absolute temperature image data.

3. The method for inverting the low-altitude temperature field of a UAV according to claim 1, characterized in that: The camera attitude data includes image frame name parameters, camera gimbal position parameters, gimbal attitude direction parameters, lens parameters and image frame sequence number parameters. The camera gimbal position parameters include longitude values, latitude values ​​and altitude values. The gimbal attitude direction parameters include yaw angle values, pitch angle values ​​and roll angle values. The lens parameters include focal length values.

4. The method for inverting the low-altitude temperature field of a UAV according to claim 3 is characterized in that: The reconstructing the flight attitude according to the camera attitude data to obtain the lens attitude trajectory corresponding to the original temperature image data includes: Reading from the camera attitude data the camera gimbal position parameters corresponding to each original temperature image in the original temperature image data, and reconstructing the camera position attitude according to the camera gimbal position parameters; Reading from the camera attitude data a gimbal attitude direction parameter corresponding to each original temperature image in the original temperature image data, and reconstructing the camera gimbal attitude according to the gimbal attitude direction parameter; Reading the lens parameters corresponding to each original temperature image in the original temperature image data from the camera posture data, and reconstructing the lens focal length posture according to the lens parameters; Obtaining a three-dimensional aerial lens posture corresponding to each original temperature image in the original temperature image data according to the camera position posture, the camera gimbal posture, and the lens focal length posture; Each aerial lens three-dimensional posture is used as a lens posture trajectory point sequence, and all lens posture trajectory points in the lens posture trajectory point sequence constitute a lens posture trajectory.

5. The method for inverting the low-altitude temperature field of a UAV according to claim 4 is characterized in that: The step of reading from the camera attitude data a camera gimbal position parameter corresponding to each original temperature image in the original temperature image data, and reconstructing the camera position attitude according to the camera gimbal position parameter comprises: Reading from the camera posture data to obtain the camera gimbal position parameters corresponding to each original temperature image in the original temperature image data; Obtaining a longitude value, a latitude value, and an altitude value according to the camera gimbal position parameters; The longitude value, the latitude value, and the altitude value are calculated using a map projection method to obtain a camera position and posture.

6. The method for inverting the low-altitude temperature field of a UAV according to claim 4, characterized in that: The step of reading from the camera attitude data a pan / tilt attitude direction parameter corresponding to each original temperature image in the original temperature image data, and reconstructing the camera pan / tilt attitude according to the pan / tilt attitude direction parameter comprises: Reading from the camera attitude data to obtain the gimbal attitude direction parameter corresponding to each original temperature image in the original temperature image data; Obtaining a yaw angle value, a pitch angle value, and a roll angle value according to the gimbal attitude direction parameters; A North East Earth (NED) coordinate system is used to perform three-dimensional vector orientation processing on the yaw angle value, the pitch angle value, and the roll angle value to obtain a camera gimbal posture.

7. The method for inverting the low-altitude temperature field of a UAV according to claim 4, characterized in that: The step of reading from the camera attitude data the lens parameters corresponding to each original temperature image in the original temperature image data, and reconstructing the lens focal length attitude according to the lens parameters comprises: Reading from the camera posture data to obtain lens parameters corresponding to each original temperature image in the original temperature image data; Obtaining a focal length value according to the lens parameters; The lens focal length posture is obtained according to the focal length value.

8. The method for inverting the low-altitude temperature field of a UAV according to claim 7, characterized in that: The method of reconstructing an inverted three-dimensional temperature field point cloud based on the absolute temperature image data and the lens posture trajectory using a three-dimensional temperature field reconstruction algorithm includes: According to the absolute temperature image data and the lens posture trajectory, an inverted three-dimensional temperature field point cloud is reconstructed through a three-dimensional temperature field reconstruction algorithm. Each temperature measurement point in the inverted three-dimensional temperature field point cloud includes spatial parameters (X, Y, Z) and three-channel parameters (Rxyz, Gxyz, Bxyz) that indirectly reflect the temperature.

9. A UAV low-altitude temperature field inversion system, characterized in that: include: An acquisition module, configured to acquire raw temperature image data collected by the infrared imager of the drone and flight log data of the drone; a temperature processing module, configured to process the relative temperature of each pixel of all the original temperature images in the original temperature image data to obtain absolute temperature image data after calibration and inversion; A posture reconstruction module is used to parse the flight log data to obtain the camera posture data of the UAV; reconstruct the flight posture according to the camera posture data to obtain the lens posture trajectory corresponding to the original temperature image data; An inverted three-dimensional temperature field point cloud reconstruction module is used to reconstruct an inverted three-dimensional temperature field point cloud based on the absolute temperature image data and the lens posture trajectory using a three-dimensional temperature field reconstruction algorithm; The inverted three-dimensional temperature field output module is used to perform visualization processing on the inverted three-dimensional temperature field point cloud according to the point cloud model and output the inverted three-dimensional temperature field.

Images