Unmanned aerial vehicle low-altitude temperature field inversion method and system

Abstract

The invention relates to an unmanned aerial vehicle low-altitude temperature field inversion method and system. The method comprises the steps that original temperature image data collected by an infrared imager of an unmanned aerial vehicle and flight log data of the unmanned aerial vehicle are acquired; processing the relative temperature of each pixel of all original temperature images in the original temperature image data to obtain absolute temperature image data after calibration inversion; analyzing the flight log data to obtain camera attitude data of the unmanned aerial vehicle; performing flight attitude reconstruction according to the camera attitude data to obtain a lens attitude track corresponding to the original temperature image data; according to the absolute temperature image data and the lens attitude track, through a three-dimensional temperature field reconstruction algorithm, reconstruction is carried out to obtain an inversion three-dimensional temperature field point cloud; and performing visualization processing on the point cloud of the inversion three-dimensional temperature field according to the point cloud model, and outputting the inversion three-dimensional temperature field. An unmanned aerial vehicle technology, a temperature inversion technology and a three-dimensional reconstruction technology are utilized to realize three-dimensional temperature field reconstruction of a meso-scale level in an urban building built-up area.

Description

Technical Field

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

[0002] In nature, all objects with temperatures above absolute zero (i.e. -273.15℃) are constantly emitting radiation energy into the surrounding space due to the thermal motion of their own molecules. In principle, as long as these radiation energies can be detected and collected, an infrared image corresponding to the radiation distribution of the object can be formed by rearranging the signals from the detector. At the same time, the energy size and wavelength distribution of the infrared radiation of an object are closely related to its surface temperature. Therefore, by measuring the infrared energy radiated by the object itself, the surface temperature of the object can be accurately measured. Infrared thermal imaging technology can convert the temperature distribution and reflectivity differences of the target and various parts of the scene into corresponding signals, and then convert them into visible light images. This device that converts invisible infrared radiation energy into visible light images is an infrared thermal imager. Infrared thermal imaging cannot achieve the effect of "perspective" because the infrared rays radiated by the target are blocked by obstacles. The color and temperature values ​​in the infrared thermal imaging camera only represent the situation of the obstacles, not the information of the target itself.

[0003] The definition of temperature field is: the set of temperature values ​​of each point in an object at a certain moment, also known as temperature distribution. For a dynamic temperature field with temporal changes in temperature, it is: T = f (x, y, z, t); for a steady-state temperature field with stable temperature, it is: T = f (x, y, z); where T is temperature, x, y, z are the three-dimensional coordinates of the point, and t is time.

[0004] Steady-state temperature field in a certain period of time in the urban built environment. Since the temperature collection drone flies in the air for a certain period of time, the temperature collection at a single moment cannot achieve multi-view 3D temperature field reconstruction. However, the temperature change in the urban environment changes continuously over time during the day, and there is no temperature jump. In a limited period of time when the temperature field changes slowly, it is conditionally approximated that the temperature field in the area obeys the steady-state temperature field distribution.

[0005] Traditional temperature field perception methods are divided into three scale levels, namely 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 by satellite call, and the data volume is large. Temperature measurement pays more attention to the overall temperature change 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, which 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 at a time is small, the workload of collecting data is large, and the temperature field cannot be coherent. Alternatively, the temperature field is directly obtained by physical temperature analysis after modeling, which 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, which is mostly based on the cutting 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 for temperature field reconstruction, 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 the original temperature images in the original temperature image data to obtain the absolute temperature image data after calibration and inversion;

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

[0013] Reconstruct the flight attitude according to 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 relative temperature to absolute temperature mapping parameters preset by the user, 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 to measure the absolute temperature radiation intensity of each pixel Tuv_O in the shooting environment, which corresponds to the inverted absolute temperature;

[0020] The absolute temperature is remapped into an absolute temperature two-dimensional pixel Tuv (Ruv, Guv, Buv) composed of eight-bit pixels of 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 a 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 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.

[0025] Furthermore, the flight attitude is reconstructed according to 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 attitude data, and the lens focal attitude 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 original temperature image in the original 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] Further, 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 from the camera attitude data, the camera gimbal position parameters corresponding to each raw temperature image in the raw temperature image data are obtained;

[0033] Get the longitude, latitude and altitude values ​​according to 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] Further, 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 from the camera attitude data, the gimbal attitude direction parameter corresponding to each original temperature image in the original temperature image data is obtained;

[0037] According to the gimbal attitude direction parameters, the yaw angle value, pitch angle value and roll angle value are obtained;

[0038] The North East Earth (NED) coordinate system is used to perform three-dimensional vector orientation processing on the yaw angle value, pitch angle value and roll angle value to obtain the camera gimbal attitude.

[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 attitude is reconstructed according to the lens parameters, including:

[0040] Reading from the camera posture data, obtaining lens parameters corresponding to each original temperature image in the original temperature image 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 inverted three-dimensional temperature field point cloud is reconstructed through the three-dimensional temperature field reconstruction algorithm, including:

[0044] According to the absolute temperature image data and the 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] In the second aspect, a UAV low-altitude temperature field inversion system is provided, comprising:

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

[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 drone; the flight attitude is reconstructed according to 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 infrared imager of the drone and the flight log data of the drone are obtained; 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 inversion; the flight log data is parsed to obtain the camera attitude data of the drone; the flight attitude is reconstructed according to the camera attitude data to obtain the lens attitude trajectory corresponding to the original temperature image data; according to the absolute temperature image data and the lens attitude trajectory, the inverted three-dimensional temperature field point cloud is reconstructed through the three-dimensional temperature field reconstruction algorithm; 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. The thermal environment of the local microclimate at a small and medium scale can be collected by the drone equipped with an infrared imager. The aerial perspective of the drone can more comprehensively reflect the overall site conditions and is more suitable for small and medium scale data collection; the three-dimensional temperature field reconstruction of the small and medium scale level of the urban building built-up area is realized by using drone technology, temperature inversion technology, and three-dimensional 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 of the present invention;

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

[0055] Figure 3 It 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 solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0057] like Figure 1 As shown, an embodiment of the present invention provides a method for inverting a 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 is mainly composed of optical systems, detectors, electronic signal processing systems, display systems and other components. Taking DJI's typical model Mavic 3T as an example, it uses an uncooled vanadium oxide VOx micro-thermal bolometer to sense temperature, with a temperature measurement range of -20°C to 150°C (high gain), 0°C to 500°C (low gain), and a temperature measurement accuracy of ±2°C or ±2% (the larger value is taken);

[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 collects the data. 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 the original temperature images in the original temperature image data to obtain the absolute temperature image data after calibration and inversion;

[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 mapping parameters of relative temperature to absolute temperature, 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 single image minimum temperature Tmin_O and the single image maximum temperature Tmax_O, and the absolute temperature radiation intensity of the corresponding position of each pixel Tuv_O is extracted;

[0064] Since the absolute temperature radiation intensity of the corresponding position of each pixel Tuv_O is data that directly reflects the thermal radiation intensity received by the sensor, it is affected by the ambient temperature and humidity, distance, and physical emissivity, and the temperature measurement results will be different. 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 Distance, ambient humidity Humidity, target emissivity Emissivity, and ambient reflection temperature Reflection), 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 of 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 and inverted absolute temperature image Img_T, where T represents the state of the temperature image after inversion, thereby obtaining the calibrated and inverted absolute temperature image data.

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

[0070] From the flight log .log file, the camera attitude data can be read, which can usually be presented 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 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.

[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 contents: 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 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; the camera gimbal position parameters corresponding to each original temperature image in the original temperature image data are read from the camera attitude data; the longitude value, latitude value and altitude value are obtained according to the camera gimbal position parameters; the longitude value, latitude value and altitude value are solved and processed using a map projection method to obtain the camera position attitude; for example, the image frame name of the original temperature image in the original 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 calculated into the camera position and attitude, which is represented by longitude (m), latitude (m), altitude (m);

[0076] 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; the gimbal attitude direction parameters corresponding to each original temperature image in the original temperature image data are read from the camera attitude data; the yaw angle value, the pitch angle value and the roll angle value are obtained according to the gimbal attitude direction parameters; the yaw angle value, the pitch angle value and the 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 spatial orientation angle of the camera, 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. 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:

[0081] Yaw angle: 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; for drones, the yaw angle yaw is generally taken as [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 Pitch; for drones, the pitch angle pitch is generally taken as [-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 roll. For drones, the roll angle roll is generally taken as [-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] The lens parameters corresponding to each original temperature image in the original temperature image data are obtained by reading from the camera attitude data, the focal length value is obtained according to the lens parameters, and the lens focal length attitude is obtained 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 original temperature image in the original temperature image data are obtained by reading from the camera posture data;

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

[0087] 105, reconstructing an inverted three-dimensional temperature field point cloud through a three-dimensional temperature field reconstruction algorithm according to the absolute temperature image data and the lens posture trajectory;

[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. It is necessary to reconstruct the lens attitude trajectory in step 104 according to 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] According to the absolute temperature image data and the lens posture trajectory, the 3D temperature field reconstruction algorithm includes but is not limited to the beam method regional block adjustment reconstruction algorithm, the neural radiation field reconstruction algorithm (NeRF) or the 3D Gaussian Splatting reconstruction algorithm (3DGS). Correspondingly, 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, and 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 space 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 in a combination of several modeling software + low-code platforms, including but not limited to: rhino + grasshopper, blender + geometry node, Revit + dynamo, C4D, etc.; it has wide applicability, strong display and good expansibility, and is convenient for carrying out a number of subsequent series of urban building built environment surface enclosure structure thermal engineering, green building transformation, microclimate, microenvironment, micro-renewal and other calculations. Because it is a three-dimensional perception study of basic 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 infrared imager of the drone and the flight log data of the drone; 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; parse the flight log data to obtain the camera attitude data of the drone; reconstruct the flight attitude according to 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 through the three-dimensional temperature field reconstruction algorithm based on the absolute temperature image data and the lens attitude trajectory; visualize the inverted three-dimensional temperature field point cloud according to 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 aerial perspective of the drone 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 at small and medium scale levels in urban built-up areas is reconstructed 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; perform flight attitude reconstruction 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 according to the absolute temperature image data and the lens posture trajectory through the 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 principle of the embodiment of the present invention is:

[0106] The acquisition module 301 acquires the original temperature image data collected by the infrared imager of the drone and the flight log data of the drone; the temperature processing module 302 processes 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 inversion; the attitude reconstruction module 303 parses the flight log data to obtain the camera attitude data of the drone; the inversion three-dimensional temperature field point cloud reconstruction module 304 reconstructs the flight attitude according to the camera attitude data to obtain the lens attitude trajectory corresponding to the original temperature image data; the inversion three-dimensional temperature field output module 305 reconstructs the inversion three-dimensional temperature field point cloud according to the absolute temperature image data and the lens attitude trajectory through the three-dimensional temperature field reconstruction algorithm; the inversion three-dimensional temperature field point cloud is visualized according to the point cloud model, and the inversion three-dimensional temperature field is output. The thermal environment acquisition of the local microclimate at a small and medium scale can be realized by carrying an infrared imager on the drone. The aerial perspective of the drone can more comprehensively reflect the overall site conditions and is more suitable for small and medium scale data acquisition; the three-dimensional temperature field reconstruction of the small and medium scale level of the urban building built-up area is realized by using drone technology, temperature inversion technology, and three-dimensional reconstruction technology.

[0107] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. 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 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 flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, 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 flowchart and / or block diagram. 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 capable of directing a computer or other programmable data processing device to operate 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 A 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 operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified 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 the original temperature images in the original temperature image data to obtain the absolute temperature image data after calibration and inversion; Parsing the flight log data to obtain the camera attitude data of the drone; Reconstruct the flight attitude according to the camera attitude data to obtain the 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 according to 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 an unmanned aerial vehicle according to claim 1 is characterized in that: The step of processing 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 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 relative temperature to absolute temperature mapping parameters preset by the user, the absolute temperature radiation intensity of the corresponding position of each pixel Tuv_O is extracted; the mapping parameters are determined by the upper and lower limits of the shooting environment temperature; Based on the user-preset comprehensive ambient temperature inversion parameters, the inversion temperature measurement is performed to measure the absolute temperature radiation intensity of each pixel Tuv_O in the shooting environment, 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 of 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 an unmanned aerial vehicle according to claim 1 is 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 to obtain 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 to obtain the gimbal attitude direction parameters corresponding to each original temperature image in the original temperature image data, and reconstructing the camera gimbal attitude according to the gimbal attitude direction parameters; Reading from the camera attitude data to obtain lens parameters corresponding to each original temperature image in the original temperature image data, and reconstructing the lens focal attitude according to the lens parameters; Obtaining the aerial lens three-dimensional 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 an unmanned aerial vehicle according to claim 4 is characterized in that: The step of reading from the camera attitude data to obtain 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 includes: 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 the camera position and posture.

6. The method for inverting the low-altitude temperature field of an unmanned aerial vehicle according to claim 4 is characterized in that: The step of reading from the camera attitude data to obtain 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 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; The yaw angle value, the pitch angle value, and the roll angle value are subjected to three-dimensional vector orientation processing using a North East Earth (NED) coordinate system to obtain a camera gimbal posture.

7. 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 posture data to obtain lens parameters corresponding to each original temperature image in the original temperature image data, and reconstructing the lens focal length posture according to the lens parameters includes: 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 UAV low-altitude temperature field inversion system according to claim 7 is characterized in that: The inverted three-dimensional temperature field point cloud is reconstructed based on the absolute temperature image data and the lens posture trajectory through a three-dimensional temperature field reconstruction algorithm, including: 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 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.

9. A UAV low-altitude temperature field inversion system, characterized in that: include: An acquisition module, used to acquire the original temperature image data collected by the infrared imager of the drone and the flight log data of the drone; A temperature processing module, used for processing 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; A posture reconstruction module is used to parse the flight log data to obtain the camera posture data of the UAV; perform flight posture reconstruction 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 according to the absolute temperature image data and the lens posture trajectory through 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.

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