Raman laser radar ozone three-dimensional real-time imaging method based on edge calculation

Abstract

The invention relates to the technical field of radar detection, in particular to a Raman laser radar ozone three-dimensional real-time imaging method based on edge calculation, and the method can capture the influence of instantaneous temperature fluctuation on a signal through the independent deduction of the spectrum fidelity and the topological transformation potential energy, and can also capture the influence of the temperature fluctuation on the signal. The change of the continuous wind field to the air mass form can be accurately evaluated, and the contradiction of different physical processes on the space-time scale is solved; according to the method, the compression and turbulence intensity of the fluid is directly quantified by utilizing topological transformation potential energy, and sensing data is mapped into a standard three-dimensional GIS grid by utilizing a Lagrange trajectory backtracking algorithm and a Gaussian kernel function, so that blind areas among sensors are filled, each voxel data of the standard three-dimensional GIS grid is ensured to have a clear observation source, and the accuracy of measurement is improved. And the essential three-dimensional characteristics of the atmospheric environment are restored.

Description

Technical Field

[0001] This invention relates to the field of radar detection technology, and in particular to a Raman lidar three-dimensional real-time imaging method for ozone based on edge computing. Background Technology

[0002] Raman lidar is currently the mainstream technology for detecting the vertical distribution of atmospheric ozone. Its basic principle is to utilize the Raman scattering echo signal generated by the interaction of emitted laser light with atmospheric molecules, and then use radar equations to invert ozone concentration. To achieve large-scale monitoring, the current technological trend is to deploy single-point lidar on mobile platforms or build distributed monitoring networks, and to integrate them with geographic information systems to construct a three-dimensional imaging system.

[0003] Despite the progress made in existing lidar 3D imaging technology, fundamental technical shortcomings remain when dealing with complex urban terrain and variable weather conditions, leading to physical distortions in the imaging results: Existing technologies typically employ static atmospheric assumptions or simple linear interpolation to process meteorological data. However, microscopic physical processes (temperature fluctuations altering the Raman scattering cross section) are transient and high-frequency; while macroscopic dynamic processes (wind-driven air mass diffusion) are continuous and spatially lagging. Existing technologies cannot decouple these two drastically different scales within a unified framework, resulting in either concentration inversion errors due to ignoring instantaneous temperature fluctuations or misalignment between the imaging grid and the actual air mass location due to ignoring wind turbulence in rapidly changing environments. Traditional GIS grid division is usually static or adjusted solely based on simple concentration gradients. However, the atmosphere is a fluid, and ozone masses undergo stretching, compression, and distortion (topological deformation) under wind influence. Existing technologies lack real-time perception of fluid dynamic characteristics, causing fixed grids to fail to effectively cover deformed air masses in highly turbulent regions, resulting in discontinuous imaging results. Summary of the Invention

[0004] The main objective of this invention is to provide a Raman lidar method for real-time three-dimensional ozone imaging based on edge computing. By independently extrapolating spectral fidelity and topological variation energy, it can capture the impact of instantaneous temperature fluctuations on the signal and accurately assess the changes in air mass morphology caused by continuous wind fields, thus resolving the contradictions between different physical processes on spatiotemporal scales. It directly quantifies fluid compression and turbulence intensity using topological variation energy, and maps sensor data to a standard three-dimensional GIS grid using a Lagrange trajectory backtracking algorithm and a Gaussian kernel function, filling the blind spots between sensors and ensuring that each voxel data point in the standard three-dimensional GIS grid has a clear observation source, thus restoring the essential three-dimensional characteristics of the atmospheric environment.

[0005] The technical solution of the present invention is as follows: Firstly, a Raman lidar three-dimensional real-time ozone imaging method based on edge computing is proposed, which includes the following steps: S1. Construct a standard three-dimensional GIS grid based on the urban geographic information system and label the ozone concentration detection value. Collect real-time photon count, real-time temperature, real-time air pressure, real-time three-dimensional wind speed vector, and laser beam divergence angle of the laser emission system from the lidar. S2. Based on the real-time photon count and real-time temperature of the lidar, calculate the Raman scattering cross section correction coefficient using the second-order polynomial fitting formula, and calculate the spectral fidelity along the distance distribution. S3. Based on the real-time three-dimensional wind speed vector, calculate the rate of change of the three-dimensional wind speed vector magnitude over time to characterize the air mass expansion intensity. At the same time, calculate the rate of change of the vertical component of the three-dimensional wind speed vector over time to characterize the vertical shear intensity of the air mass, and further obtain the real-time topological deformation energy of the wind field on the standard three-dimensional GIS grid. S4. Obtain historical three-dimensional wind speed vector and historical topological change potential energy. Starting from lidar, calculate the coordinates of air mass movement trajectory points based on the Lagrange trajectory backtracking algorithm. For any voxel in the standard three-dimensional GIS grid, calculate the dynamic potential energy and physical fidelity, and further obtain the three-dimensional light-kinetic energy resolvable potential energy field. S5. For any voxel in the standard 3D GIS grid, output the preliminary ozone concentration field through the differential absorption algorithm and calculate the ozone vertical column concentration. Compare the ozone vertical column concentration with the labeled ozone concentration detection value, calculate the relative deviation, and output the final corrected 3D ozone concentration grid matrix.

[0006] A further improvement of the present invention is that step S2 includes the following specific steps: S21. Based on real-time temperature, calculate the Raman scattering cross section correction coefficient using a second-order polynomial fitting formula. The formula for calculating the Raman scattering cross section correction coefficient is as follows: ; in, This represents the real-time temperature of the detection node at time k, in Kelvin (K). Standard temperature, in Kelvin (K). All are the rotational Raman scattering temperature coefficients of ozone molecules, with values ​​of respectively. Let be the Raman scattering cross section correction factor at time k; S22. Calculate the spectral fidelity along the distance distribution. The formula for calculating the spectral fidelity is as follows: ; in, This represents the spectral fidelity of the r-th range cell in the line-of-sight direction of the lidar at time k. This is the sensitivity coefficient, with a value of 1. This represents the number of photons in the r-th range cell along the line-of-sight direction of the lidar at time k. It is a constant, and its value is... This is the normalization constant.

[0007] A further improvement of the present invention is that step S3 includes the following specific steps: S31. Based on the real-time three-dimensional wind speed vector, calculate the rate of change of the three-dimensional wind speed vector magnitude over time to characterize the intensity of air mass expansion. The calculation formula is as follows: ; in, This represents the intensity of air mass expansion at time k. Let k be the three-dimensional wind speed vector magnitude at time k. Let k be the three-dimensional wind speed vector magnitude at time k-1. The time interval between time k and time k-1; S32. Based on the real-time three-dimensional wind speed vector, calculate the rate of change of the vertical component of the three-dimensional wind speed vector over time to characterize the vertical shear intensity of the air mass. The calculation formula is as follows: ; in, This represents the vertical shear intensity of the air mass at time k. Let K be the vertical component of the three-dimensional wind speed vector at time k. Let be the vertical component of the three-dimensional wind speed vector at time k-1; S33. Based on the air mass expansion intensity and the air mass vertical shear intensity, calculate the real-time topological deformation energy of the wind field on the three-dimensional spatial grid. The calculation formula is as follows: ; in, Let k be the topological variation energy of the wind field on a standard 3D GIS grid at time k. This is a weighting factor for the intensity of air mass expansion. This is the weighting factor for the vertical shear intensity of the air mass.

[0008] A further improvement of the present invention is that step S4 includes the following specific steps: S41. Obtain historical 3D wind speed vectors and historical topological variation energy. Starting from lidar, calculate the coordinates of the air mass movement trajectory points based on the Lagrange trajectory backtracking algorithm. The calculation formula is as follows: ; in, Let k be the coordinates of the trajectory point of an air mass at a historical time k. To fix the three-dimensional coordinates of the lidar, Let be the three-dimensional wind speed vector at historical moment kn, where n takes values ​​from 0 to m; S42. For any voxel in a standard 3D GIS mesh Obtain center coordinates The kinetic potential energy is calculated using the inverse distance weighted method, and the formula is as follows: ; in, For standard 3D GIS grid voxel index, voxel center coordinates kinetic potential energy, Denotes the Euclidean distance norm; S43, For any voxel in a standard 3D GIS grid Obtain center coordinates Projected length on the laser axis At the same time, obtain the center coordinates Vertical distance to the laser axis Voxels are calculated using Gaussian kernel functions. The physical fidelity is calculated using the following formula: ; in, voxels Physical fidelity For time k voxels Corresponding distance unit Spectral fidelity, The beam divergence angle of the laser emission system; S44. Based on kinetic potential energy and physical fidelity, calculate the three-dimensional light-kinetic energy resolvable potential energy field. The formula is: .

[0009] A further improvement of the present invention is that step S5 includes the following specific steps: S51, For arbitrary voxels in standard 3D GIS mesh The distance cells along the line-of-sight of the lidar are obtained and mapped to their corresponding heights in a standard 3D GIS grid. Based on the real-time photon count, temperature, and air pressure of the lidar at the corresponding height, a preliminary ozone concentration field is output using a differential absorption algorithm. ;calculate The ozone concentration at the vertical column is calculated using the following formula: ; in, for Ozone concentration at the vertical column. For grid layers in a standard 3D GIS grid Vertical thickness, This represents the maximum number of layers in the vertical direction of a standard 3D GIS grid. S52, Ozone vertical column concentration Compared with the labeled ozone concentration detection value A comparison is made, and the relative deviation is calculated. The formula for calculating the relative deviation is: ; S53, Based on the preliminary ozone concentration field Three-dimensional light-kinetic energy resolvable potential energy field and relative deviation The final corrected 3D ozone concentration grid matrix is ​​output, calculated using the following formula: ; in, voxels in a standard 3D GIS grid The final corrected ozone concentration, For grid layers in a standard 3D GIS grid The weight.

[0010] Secondly, a computer-readable storage medium is proposed, on which a computer program is stored. When the computer program is executed by a processor, it implements the above-mentioned Raman lidar ozone three-dimensional real-time imaging method based on edge computing.

[0011] Thirdly, an electronic device is proposed, including a memory for storing instructions and a processor for executing the instructions, causing the device to perform the above-described edge computing-based Raman lidar ozone three-dimensional real-time imaging method.

[0012] The technical effects of this invention are as follows: A real-time 3D ozone imaging method based on edge computing Raman lidar was constructed. By independently extrapolating spectral fidelity and topological variation energy, it can capture the impact of instantaneous temperature fluctuations on the signal and accurately assess the changes in air mass morphology caused by continuous wind fields, thus resolving the contradictions between different physical processes at different spatiotemporal scales. The compression and turbulence intensity of the fluid are directly quantified using topological variation energy. The sensor data is mapped to a standard 3D GIS grid using the Lagrange trajectory backtracking algorithm and Gaussian kernel function, filling the blind spots between sensors and ensuring that each voxel data in the standard 3D GIS grid has a clear observation source, thus restoring the essential 3D characteristics of the atmospheric environment. Attached Figure Description

[0013] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1This is a schematic flowchart of the aerosol radar optical path calibration method based on digital twin empowerment according to Embodiment 1 of the present invention. Detailed Implementation

[0014] Example 1: This example proposes a Raman lidar method for real-time 3D ozone imaging based on edge computing. By independently extrapolating spectral fidelity and topological variation energy, it can capture the impact of instantaneous temperature fluctuations on the signal and accurately assess the changes in air mass morphology caused by continuous wind fields, resolving the contradictions between different physical processes at different spatiotemporal scales. It directly quantifies fluid compression and turbulence intensity using topological variation energy, and maps sensor data to a standard 3D GIS grid using the Lagrange trajectory backtracking algorithm and Gaussian kernel function, filling the blind spots between sensors and ensuring that each voxel data point in the standard 3D GIS grid has a clear observation source, thus restoring the essential 3D characteristics of the atmospheric environment. Specifically, such as... Figure 1 As shown, the Raman lidar ozone three-dimensional real-time imaging method based on edge computing proposed in this embodiment includes the following specific steps: S1. Construct a standard three-dimensional GIS grid based on the urban geographic information system and label the ozone concentration detection value. Collect real-time photon count, real-time temperature, real-time air pressure, real-time three-dimensional wind speed vector, and laser beam divergence angle of the laser emission system from the lidar. In this embodiment, this step aims to construct a unified spatial data carrying framework and acquire multi-dimensional raw observation data. Operationally, a standard three-dimensional GIS grid is established based on the urban geographic information system as the coordinate benchmark for subsequent calculations. Ozone concentration detection values ​​provided by satellites or ground stations are marked in the grid as references. At the same time, real-time photon counts from lidar, real-time temperature and air pressure from meteorological sensors, and real-time three-dimensional wind speed vectors from ultrasonic anemometers are collected in real time through edge computing nodes. The inherent beam divergence angle parameters of the laser emission system are also read to provide basic input data for subsequent spectral calibration and hydrodynamic deduction.

[0015] S2. Based on the real-time photon count and real-time temperature of the lidar, calculate the Raman scattering cross section correction coefficient using the second-order polynomial fitting formula, and calculate the spectral fidelity along the distance distribution. In this embodiment, step S2 includes the following specific steps: S21. Based on real-time temperature, calculate the Raman scattering cross section correction coefficient using a second-order polynomial fitting formula. The formula for calculating the Raman scattering cross section correction coefficient is as follows: ; in, This represents the real-time temperature of the detection node at time k, in Kelvin (K). Standard temperature, in Kelvin (K). All are the rotational Raman scattering temperature coefficients of ozone molecules, with values ​​of respectively. Let be the Raman scattering cross section correction factor at time k; S22. Calculate the spectral fidelity along the distance distribution. The formula for calculating the spectral fidelity is as follows: ; in, This represents the spectral fidelity of the r-th range cell in the line-of-sight direction of the lidar at time k. This is the sensitivity coefficient, with a value of 1. This represents the number of photons in the r-th range cell along the line-of-sight direction of the lidar at time k. It is a constant, and its value is... This is the normalization constant.

[0016] In this embodiment, the core design idea of ​​this step is to eliminate the nonlinear effect of temperature fluctuations on Raman scattering efficiency at the microscopic spectroscopy level. This is because changes in ambient temperature directly lead to changes in the population of the ozone molecule rotational energy level, which in turn causes a drift in the Raman scattering cross section. If this is not corrected, it will lead to a systematic error in concentration inversion. Therefore, step S21 uses a second-order polynomial fitting formula to calculate the Raman scattering cross section correction coefficient. ,in, This represents the real-time temperature of the detection node at time k, in Kelvin (K). This is the standard temperature, measured in Kelvin (K), typically 296 K or 273.15 K. These are the rotational Raman scattering temperature coefficients of ozone molecules. Based on spectral databases and experimental calibration, their preferred values ​​are as follows: These two coefficients accurately quantify the first and second order response relationships of the scattering cross section as a function of temperature; based on this, step S22 further calculates the spectral fidelity along the distance distribution. The aim is to comprehensively evaluate signal quality by combining signal strength and cross-section correction factor. This represents the spectral fidelity of the r-th range cell in the line-of-sight direction of the lidar at time k. This is a sensitivity coefficient, preferably set to 1 in this embodiment, used to adjust the weight of the influence of cross-sectional deviation on fidelity. This represents the number of raw photons received by the r-th range cell in the line-of-sight direction of the lidar at time k. Let be a constant, and the preferred value is . Its function is to prevent the denominator from being zero and to smooth numerical fluctuations under low photon numbers. This is a normalization constant used to map the calculation results to a standard range. This step provides a microscopic confidence index for subsequent weighted imaging by quantifying the signal reliability under the influence of temperature.

[0017] S3. Based on the real-time three-dimensional wind speed vector, calculate the rate of change of the three-dimensional wind speed vector magnitude over time to characterize the air mass expansion intensity. At the same time, calculate the rate of change of the vertical component of the three-dimensional wind speed vector over time to characterize the vertical shear intensity of the air mass, and further obtain the real-time topological deformation energy of the wind field on the standard three-dimensional GIS grid. In this embodiment, step S3 includes the following specific steps: S31. Based on the real-time three-dimensional wind speed vector, calculate the rate of change of the three-dimensional wind speed vector magnitude over time to characterize the intensity of air mass expansion. The calculation formula is as follows: ; in, This represents the intensity of air mass expansion at time k. Let k be the three-dimensional wind speed vector magnitude at time k. Let k be the three-dimensional wind speed vector magnitude at time k-1. The time interval between time k and time k-1; S32. Based on the real-time three-dimensional wind speed vector, calculate the rate of change of the vertical component of the three-dimensional wind speed vector over time to characterize the vertical shear intensity of the air mass. The calculation formula is as follows: ; in, This represents the vertical shear intensity of the air mass at time k. Let K be the vertical component of the three-dimensional wind speed vector at time k. Let be the vertical component of the three-dimensional wind speed vector at time k-1; S33. Based on the air mass expansion intensity and the air mass vertical shear intensity, calculate the real-time topological deformation energy of the wind field on the three-dimensional spatial grid. The calculation formula is as follows: ; in, Let k be the topological variation energy of the wind field on a standard 3D GIS grid at time k. This is a weighting factor for the intensity of air mass expansion. This is the weighting factor for the vertical shear intensity of the air mass.

[0018] In this embodiment, the design principle of this step is to evaluate the changes in the morphology of ozone clouds caused by atmospheric wind fields from a macroscopic fluid dynamics perspective. Because the expansion or shearing of air clouds can cause deformation of their spatial topology, thereby affecting the continuity and accuracy of imaging, step S31 first calculates the air cloud expansion intensity. Using the three-dimensional wind speed vector magnitude Length of the model at the previous moment The difference is compared with the time interval to quantify the stretching or compression rate of the air mass in the isotropic direction. Step S32 also calculates the vertical shear intensity of the air mass. Utilizing vertical components Vertical component from the previous time step The difference between the two components and the time interval quantifies the degree of tearing of the air mass in the vertical direction. Step S33 then combines the two components into real-time topological variation energy. This is a weighting factor for the intensity of air mass expansion. The two weights are weighting factors for the vertical shear intensity of the air mass. The selection of the two weights is based on the topographic complexity and atmospheric stability of the monitoring area. They are used to balance the influence of different deformation modes on the overall topology. This step realizes the quantitative characterization of atmospheric fluid instability.

[0019] S4. Obtain historical three-dimensional wind speed vector and historical topological change potential energy. Starting from lidar, calculate the coordinates of air mass movement trajectory points based on the Lagrange trajectory backtracking algorithm. For any voxel in the standard three-dimensional GIS grid, calculate the dynamic potential energy and physical fidelity, and further obtain the three-dimensional light-kinetic energy resolvable potential energy field. In this embodiment, step S4 includes the following specific steps: S41. Obtain historical 3D wind speed vectors and historical topological variation energy. Starting from lidar, calculate the coordinates of the air mass movement trajectory points based on the Lagrange trajectory backtracking algorithm. The calculation formula is as follows: ; in, Let k be the coordinates of the trajectory point of an air mass at a historical time k. To fix the three-dimensional coordinates of the lidar, Let be the three-dimensional wind speed vector at historical moment kn, where n takes values ​​from 0 to m; S42. For any voxel in a standard 3D GIS mesh Obtain center coordinates The kinetic potential energy is calculated using the inverse distance weighted method, and the formula is as follows: ; in, For standard 3D GIS grid voxel index, voxel center coordinates kinetic potential energy, Denotes the Euclidean distance norm; S43, For any voxel in a standard 3D GIS grid Obtain center coordinates Projected length on the laser axis At the same time, obtain the center coordinates Vertical distance to the laser axis Voxels are calculated using Gaussian kernel functions. The physical fidelity is calculated using the following formula: ; in, voxels Physical fidelity For time k voxels Corresponding distance unit Spectral fidelity, The beam divergence angle of the laser emission system; S44. Based on kinetic potential energy and physical fidelity, calculate the three-dimensional light-kinetic energy resolvable potential energy field. The formula is: .

[0020] In this embodiment, step S4 further performs spatiotemporal mapping and field reconstruction to address the spatial blind zone problem in single-point radar observations. The Lagrange trajectory backtracking algorithm is used to map historical observation data in the time dimension to the three-dimensional spatial dimension. Step S41 calculates the coordinates of the air mass movement trajectory points. This coordinate represents the position of an air mass at a historical time k at the current time k. For the fixed three-dimensional coordinates of the lidar, The three-dimensional wind speed vector at historical time kn, where n ranges from 0 to m, is used to extrapolate the trajectory by accumulating the product of the wind speed vector and the time interval; step S42 targets any voxel in the standard three-dimensional GIS grid. Calculate kinetic potential energy The inverse distance weighting method is used to analyze the historical topological transformation energy. Mapped to mesh voxels, weights Coordinates of the voxel center To the trajectory point The distance is inversely proportional to the square of the Euclidean distance norm, and ϵ is a small quantity to prevent the denominator from being zero, thus ensuring that the voxels closer to the trajectory have a greater weight in terms of the influence of wind field deformation at that moment; step S43 uses the Gaussian kernel function to calculate the physical fidelity of the voxels. The algorithm considers the energy distribution characteristics of the laser beam, and the exponential term in the formula simulates the Gaussian decay of laser energy as the distance from the axis increases; step S44 finally synthesizes a three-dimensional light-kinetic energy resolvable potential field. This demonstrates that the resolvable potential energy of a voxel is proportional to the physical fidelity and decreases exponentially with increasing kinetic potential energy. This design principle profoundly reveals the challenges in regions of intense fluid deformation (high... The inherent logic behind the reduced reliability of imaging.

[0021] S5. For any voxel in the standard 3D GIS grid, output the preliminary ozone concentration field through the differential absorption algorithm and calculate the ozone vertical column concentration. Compare the ozone vertical column concentration with the labeled ozone concentration detection value, calculate the relative deviation, and output the final corrected 3D ozone concentration grid matrix.

[0022] In this embodiment, step S5 includes the following specific steps: S51, For arbitrary voxels in standard 3D GIS mesh The distance cells along the line-of-sight of the lidar are obtained and mapped to their corresponding heights in a standard 3D GIS grid. Based on the real-time photon count, temperature, and air pressure of the lidar at the corresponding height, a preliminary ozone concentration field is output using a differential absorption algorithm. ;calculate The ozone concentration at the vertical column is calculated using the following formula: ; in, for Ozone concentration at the vertical column. For grid layers in a standard 3D GIS grid Vertical thickness, This represents the maximum number of layers in the vertical direction of a standard 3D GIS grid. S52, Ozone vertical column concentration Compared with the labeled ozone concentration detection value A comparison is made, and the relative deviation is calculated. The formula for calculating the relative deviation is: ; S53, Based on the preliminary ozone concentration field Three-dimensional light-kinetic energy resolvable potential energy field and relative deviation The final corrected 3D ozone concentration grid matrix is ​​output, calculated using the following formula: ; in, voxels in a standard 3D GIS grid The final corrected ozone concentration, For grid layers in a standard 3D GIS grid The weight.

[0023] In this embodiment, this step uses a differential absorption algorithm to output a preliminary ozone concentration field and performs a final correction. Step S51 calculates the preliminary ozone concentration field at any voxel and obtains the ozone vertical column concentration at that location by accumulating the product of the concentration of each grid layer and the vertical thickness along the vertical direction. Step S52 compares the calculated vertical column concentration with the labeled satellite or ground detection values ​​and calculates the relative deviation. Step S53 outputs the final corrected three-dimensional ozone concentration grid matrix. The correction formula embodies the idea of ​​weighted fusion. The core logic of this formula is: when the light-kinetic energy resolvable potential field of a voxel... A higher value indicates good laser signal quality and stable airflow at that location, and the correction term... When the potential energy field approaches zero, the final concentration is mainly determined by the preliminary inversion results of the local radar; conversely, when the potential energy field is low, it indicates that the signal is weak or the airflow is turbulent, and the system relies more on external reference values. Corrections were made to achieve adaptive fusion imaging of multi-source data at different confidence levels.

[0024] The threshold and weight settings can be based on the default settings of this invention, or they can be set by the operator.

[0025] Example 2: This example provides an electronic device, including a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the above-described Raman lidar ozone three-dimensional real-time imaging method based on edge computing by calling the computer program stored in the memory.

[0026] The electronic device can vary considerably depending on its configuration or performance. It may include one or more Central Processing Units (CPUs) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the edge computing-based Raman lidar ozone three-dimensional real-time imaging method provided in the above-described embodiment. The electronic device may also include other components for implementing its functions; for example, it may have wired or wireless network interfaces and input / output interfaces for data input and output. Details will not be elaborated upon in this embodiment.

[0027] Those skilled in the art will recognize that this invention can be implemented as a system, method, or computer program product. Therefore, this disclosure can be embodied in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, the invention can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.

[0028] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0029] This invention is described with reference to flowchart illustrations and block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and block diagrams, as well as combinations of blocks in the flowchart illustrations and block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0030] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and boxes Figure 1 The steps of the function specified in one or more boxes.

[0031] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A Raman lidar three-dimensional real-time imaging method for ozone based on edge computing, characterized in that: The specific steps include the following: S1. Construct a standard three-dimensional GIS grid based on the urban geographic information system and label the ozone concentration detection value. Collect real-time photon count, real-time temperature, real-time air pressure, real-time three-dimensional wind speed vector, and laser beam divergence angle of the laser emission system from the lidar. S2. Based on the real-time photon count and real-time temperature of the lidar, calculate the Raman scattering cross section correction coefficient using the second-order polynomial fitting formula, and calculate the spectral fidelity along the distance distribution. S3. Based on the real-time three-dimensional wind speed vector, calculate the rate of change of the three-dimensional wind speed vector magnitude over time to characterize the air mass expansion intensity. At the same time, calculate the rate of change of the vertical component of the three-dimensional wind speed vector over time to characterize the vertical shear intensity of the air mass, and further obtain the real-time topological deformation energy of the wind field on the standard three-dimensional GIS grid. S4. Obtain historical three-dimensional wind speed vector and historical topological change potential energy. Starting from lidar, calculate the coordinates of air mass movement trajectory points based on the Lagrange trajectory backtracking algorithm. For any voxel in the standard three-dimensional GIS grid, calculate the dynamic potential energy and physical fidelity, and further obtain the three-dimensional light-kinetic energy resolvable potential energy field. S5. For any voxel in the standard 3D GIS grid, output the preliminary ozone concentration field through the differential absorption algorithm and calculate the ozone vertical column concentration. Compare the ozone vertical column concentration with the labeled ozone concentration detection value, calculate the relative deviation, and output the final corrected 3D ozone concentration grid matrix.

2. The Raman lidar three-dimensional real-time ozone imaging method based on edge computing according to claim 1, characterized in that: S2 includes the following specific steps: S21. Based on real-time temperature, calculate the Raman scattering cross section correction coefficient using a second-order polynomial fitting formula. The formula for calculating the Raman scattering cross section correction coefficient is as follows: ； in, This represents the real-time temperature of the detection node at time k, in Kelvin (K). Standard temperature, in Kelvin (K). All are the rotational Raman scattering temperature coefficients of ozone molecules, with values ​​of respectively. Let be the Raman scattering cross section correction factor at time k; S22. Calculate the spectral fidelity along the distance distribution. The formula for calculating the spectral fidelity is as follows: ； in, This represents the spectral fidelity of the r-th range cell in the line-of-sight direction of the lidar at time k. This is the sensitivity coefficient, with a value of 1. This represents the number of photons in the r-th range cell along the line-of-sight direction of the lidar at time k. It is a constant, and its value is... This is the normalization constant.

3. The Raman lidar three-dimensional real-time ozone imaging method based on edge computing according to claim 2, characterized in that: S3 includes the following specific steps: S31. Based on the real-time three-dimensional wind speed vector, calculate the rate of change of the three-dimensional wind speed vector magnitude over time to characterize the intensity of air mass expansion. The calculation formula is as follows: ； in, This represents the intensity of air mass expansion at time k. Let k be the three-dimensional wind speed vector magnitude at time k. Let k be the three-dimensional wind speed vector magnitude at time k-1. The time interval between time k and time k-1; S32. Based on the real-time three-dimensional wind speed vector, calculate the rate of change of the vertical component of the three-dimensional wind speed vector over time to characterize the vertical shear intensity of the air mass. The calculation formula is as follows: ； in, This represents the vertical shear intensity of the air mass at time k. Let K be the vertical component of the three-dimensional wind speed vector at time k. Let be the vertical component of the three-dimensional wind speed vector at time k-1; S33. Based on the air mass expansion intensity and the air mass vertical shear intensity, calculate the real-time topological deformation energy of the wind field on the three-dimensional spatial grid. The calculation formula is as follows: ； in, Let k be the topological variation energy of the wind field on a standard 3D GIS grid at time k. This is a weighting factor for the intensity of air mass expansion. This is the weighting factor for the vertical shear intensity of the air mass.

4. The Raman lidar three-dimensional real-time ozone imaging method based on edge computing according to claim 3, characterized in that: S4 includes the following specific steps: S41. Obtain historical 3D wind speed vectors and historical topological variation energy. Starting from lidar, calculate the coordinates of the air mass movement trajectory points based on the Lagrange trajectory backtracking algorithm. The calculation formula is as follows: ； in, Let k be the coordinates of the trajectory point of an air mass at a historical time k. To fix the three-dimensional coordinates of the lidar, Let be the three-dimensional wind speed vector at historical moment kn, where n takes values ​​from 0 to m; S42. For any voxel in a standard 3D GIS mesh Obtain center coordinates The kinetic potential energy is calculated using the inverse distance weighted method, and the formula is as follows: ； in, For standard 3D GIS grid voxel index, voxel center coordinates kinetic potential energy, Denotes the Euclidean distance norm; S43, For any voxel in a standard 3D GIS grid Obtain center coordinates Projected length on the laser axis At the same time, obtain the center coordinates Vertical distance to the laser axis Voxels are calculated using Gaussian kernel functions. The physical fidelity is calculated using the following formula: ； in, voxels Physical fidelity For time k voxels Corresponding distance unit Spectral fidelity, The beam divergence angle of the laser emission system; S44. Based on kinetic potential energy and physical fidelity, calculate the three-dimensional light-kinetic energy resolvable potential energy field. The formula is: .

5. The Raman lidar three-dimensional real-time ozone imaging method based on edge computing according to claim 4, characterized in that: S5 includes the following specific steps: S51, For arbitrary voxels in standard 3D GIS mesh The distance cells along the line-of-sight of the lidar are obtained and mapped to their corresponding heights in a standard 3D GIS grid. Based on the real-time photon count, temperature, and air pressure of the lidar at the corresponding height, a preliminary ozone concentration field is output using a differential absorption algorithm. ;calculate The ozone concentration at the vertical column is calculated using the following formula: ； in, for Ozone concentration at the vertical column. For grid layers in a standard 3D GIS grid Vertical thickness, This represents the maximum number of layers in the vertical direction of a standard 3D GIS grid. S52, Ozone vertical column concentration Compared with the labeled ozone concentration detection value A comparison is made, and the relative deviation is calculated. The formula for calculating the relative deviation is as follows: ； S53, Based on the preliminary ozone concentration field Three-dimensional light-kinetic energy resolvable potential energy field and relative deviation The final corrected 3D ozone concentration grid matrix is ​​output, calculated using the following formula: ； in, voxels in a standard 3D GIS grid The final corrected ozone concentration, For grid layers in a standard 3D GIS grid The weight.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the Raman lidar ozone three-dimensional real-time imaging method based on edge computing as described in any one of claims 1-5.

7. An electronic device, characterized in that, Includes a memory for storing instructions; and a processor for executing the instructions, causing the device to perform the edge computing-based Raman lidar ozone three-dimensional real-time imaging method as described in any one of claims 1 to 5.

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