Cloud data acquisition device, cloud data acquisition instrument, processing method, equipment and medium

Abstract

The invention discloses a cloud and mist data acquisition device, a cloud and mist data acquisition instrument, a processing method, equipment and a medium, and relates to the technical field of meteorological radar. The packaging container is internally provided with a miniature millimeter wave radar used for collecting original cloud and mist data frames, a miniature laser radar used for collecting echo signals representing the cloud and mist intensity, and a network sharing device. A first serial data bus used for transmitting cloud original data frames is arranged between the network sharing device and the miniature millimeter wave radar, and a second serial data bus used for transmitting echo signals is arranged between the network sharing device and the miniature laser radar, so that the network sharing device wirelessly transmits the cloud original data frames and the echo signals to a data processing end. The miniature millimeter wave radar and the miniature laser radar are small in size and light in weight, the network sharing device is used for wirelessly transmitting cloud original data frames and echo signals to the data processing end, and the system is more suitable for being matched with an unmanned aerial vehicle to collect cloud data.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological radar, and particularly to a cloud and fog data acquisition device, a cloud and fog data acquisition instrument, a processing method, a device, and a medium. Background Art

[0002] Clouds and fog are aerosol systems composed of a large number of tiny water droplets or ice crystals suspended in the air near the ground layer, and the tiny water droplets or ice crystals are the products of the condensation (or sublimation) of water vapor in the air near the ground layer. Clouds and precipitation have important impacts on the energy budget between the earth and the atmosphere, climate change, ecological environment, disaster prevention and relief, etc. The heavy fog causing low visibility has become one of the important disastrous weather affecting economic and social development and people's health. Conventional cloud and fog data acquisition devices usually have difficulty supporting large-scale intensive observations, and are large in both volume and weight. When installed on an unmanned aerial vehicle (UAV), they cannot cooperate with the UAV for flexible observations.

[0003] In summary, how to design a cloud and fog data acquisition device to cooperate with a UAV for cloud and fog data acquisition is a problem to be solved in this field. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a cloud and fog data acquisition device, a cloud and fog data acquisition instrument, a processing method, a device, and a medium, and the designed cloud and fog data acquisition device can cooperate with a UAV for cloud and fog data acquisition. The specific solutions are as follows:

[0005] In a first aspect, the present application discloses a cloud and fog data acquisition device, including:

[0006] A packaging container, including an installation base and a container cover;

[0007] A micro millimeter-wave radar, disposed in the packaging container, for collecting cloud and fog raw data frames;

[0008] A micro lidar, disposed in the packaging container, for collecting echo signals representing the intensity of cloud and fog;

[0009] A network sharer, disposed in the packaging container, and a first serial data bus for transmitting the cloud and fog raw data frames is provided between the network sharer and the micro millimeter-wave radar, and a second serial data bus for transmitting the echo signals is provided between the network sharer and the micro lidar, so that the network sharer wirelessly transmits the cloud and fog raw data frames and the echo signals to a data processing end.

[0010] Optionally, the installation base is a foam plastic box body.

[0011] Optionally, the container cover includes a foam plastic layer and / or a waterproof layer.

[0012] Optionally, the waterproof layer is located in the observation direction of the micro lidar.

[0013] Optionally, the waterproof layer is an infrared highly transparent glass.

[0014] Optionally, the cloud and fog data acquisition device further includes:

[0015] A support rod passing through the packaging container, used to fix the micro millimeter wave radar in the packaging container so that the micro millimeter wave radar does not directly contact the inner wall of the packaging container.

[0016] Optionally, the first serial data bus and the second serial data bus are universal asynchronous receivers and transmitters.

[0017] In a second aspect, the present application discloses a cloud and fog data collector, including the cloud and fog data acquisition device disclosed above.

[0018] In a third aspect, the present application discloses a cloud and fog data processing method, which is applied to a data processing end; the cloud and fog data processing method includes:

[0019] Receiving the cloud and fog raw data frame and the echo signal returned by the network sharer in the target acquisition device; the target acquisition device is the cloud and fog data acquisition device disclosed above;

[0020] Based on the echo signal, performing noise removal on the cloud and fog raw data frame to obtain a pure cloud and fog power spectral density matrix, and using the pure cloud and fog power spectral density matrix for parameter inversion and parameter correction to obtain target cloud and fog microphysical parameters.

[0021] Optionally, the performing noise removal on the cloud and fog raw data frame based on the echo signal to obtain a pure cloud and fog power spectral density matrix includes:

[0022] Performing noise removal on the cloud and fog raw data frame in the distance dimension and the velocity dimension in sequence to obtain a target power spectral density matrix;

[0023] Determining an effective cloud and fog time window and an ineffective cloud and fog time window based on the intensity of the echo signal, and respectively screening out an initial cloud and fog power spectral density matrix corresponding to the effective cloud and fog time window and a non-cloud and fog power spectral density matrix corresponding to the ineffective cloud and fog time window from the target power spectral density matrix;

[0024] Using the non-cloud and fog power spectral density matrix to perform noise removal on the initial cloud and fog power spectral density matrix to obtain a pure cloud and fog power spectral density matrix.

[0025] Optionally, performing denoising in the distance dimension and denoising in the velocity dimension on the original cloud data frame in sequence to obtain a target power spectral density matrix, including:

[0026] Performing sidelobe suppression in the distance dimension on the original cloud data frame by using a Hanning window to obtain a distance-sidelobe-suppressed signal, and performing a fast Fourier transform in the distance dimension on the distance-sidelobe-suppressed signal to obtain a distance-frequency-domain signal;

[0027] Performing sidelobe suppression in the velocity dimension on the distance-frequency-domain signal by using a Hanning window to obtain a velocity-sidelobe-suppressed signal, and performing a fast Fourier transform in the velocity dimension on the velocity-sidelobe-suppressed signal to obtain a velocity-frequency-domain signal;

[0028] Performing spectral averaging on the velocity-frequency-domain signal to obtain a target power spectral density matrix.

[0029] Optionally, performing noise removal on the initial cloud power spectral density matrix by using the non-cloud power spectral density matrix to obtain a pure cloud power spectral density matrix, including:

[0030] Determining the average value of the non-cloud power spectral density matrix as a noise estimation value, and determining the difference between the initial cloud power spectral density matrix and the noise estimation value as the pure cloud power spectral density matrix.

[0031] Optionally, performing parameter inversion and parameter correction by using the pure cloud power spectral density matrix to obtain target cloud microphysical parameters, including:

[0032] Obtaining a radial velocity spectrum width and a radar reflectivity factor by using the pure cloud power spectral density matrix;

[0033] Based on the radial velocity spectrum width, the radar reflectivity factor, and a preset shape parameter of the Gamma distribution function, and using the substitution integral method for preliminary inversion to obtain initial cloud microphysical parameters;

[0034] Determining a visibility estimation value according to the initial cloud microphysical parameters, and correcting the preset shape parameter based on the visibility estimation value to obtain a corrected shape parameter;

[0035] Based on the radial velocity spectrum width, the radar reflectivity factor, and the corrected shape parameter, and using the substitution integral method for re-inversion to obtain target cloud microphysical parameters.

[0036] Optionally, obtaining a radial velocity spectrum width and a radar reflectivity factor by using the pure cloud power spectral density matrix, including:

[0037] Determine the zero - order matrix of the pure cloud and fog power spectral density matrix as the echo signal power, and obtain the radial velocity spectral width according to the second - order matrix of the pure cloud and fog power spectral density matrix;

[0038] According to the meteorological radar equation, and use the echo signal power to obtain the radar reflectivity factor

[0039] In a fourth aspect, the present application discloses an electronic device, including:

[0040] A memory for storing a computer program;

[0041] A processor for executing the computer program to implement the steps of the cloud and fog data processing method disclosed above.

[0042] In a fifth aspect, the present application discloses a computer - readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the steps of the cloud and fog data processing method disclosed above are implemented.

[0043] The beneficial effects of the present application are as follows: The cloud and fog data acquisition device of the present application includes a packaging container, including a mounting base and a container cover; a micro - millimeter - wave radar disposed in the packaging container for collecting cloud and fog original data frames; a micro - lidar disposed in the packaging container for collecting echo signals representing the cloud and fog intensity; a network sharer disposed in the packaging container, and a first serial data bus for transmitting the cloud and fog original data frames is provided between the network sharer and the micro - millimeter - wave radar, and a second serial data bus for transmitting the echo signals is provided between the network sharer and the micro - lidar, so that the network sharer wirelessly transmits the cloud and fog original data frames and the echo signals to a data processing end. Thus, it can be seen that the micro - millimeter - wave radar and micro - lidar in the cloud and fog data acquisition device of the present application are small in volume and light in weight, and support large - range multi - point observation. Therefore, they can be set on an unmanned aerial vehicle (UAV) to cooperate with the UAV to collect cloud and fog original data frames and echo signals representing the cloud and fog intensity. Further, the cloud and fog data acquisition device further includes a network sharer, and a first serial data bus and a second serial data bus are respectively provided between the network sharer and the micro - millimeter - wave radar and the micro - lidar, which can be respectively used to transmit the cloud and fog original data frames and echo signals. In this way, the network sharer wirelessly transmits the cloud and fog original data frames and echo signals to the data processing end. That is to say, because the cloud and fog data acquisition device is small in volume and light in weight, and can wirelessly transmit the collected cloud and fog data to the data processing end, it can be set in the UAV and cooperate with the UAV to collect cloud and fog data. Description of the Drawings

[0044] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on the provided drawings.

[0045] Figure 1 Schematic diagram of the structure of a cloud and fog data acquisition device disclosed in the present application;

[0046] Figure 2 Schematic diagram of data communication of a specific cloud and fog data acquisition device disclosed in the present application;

[0047] Figure 3 Flowchart of a cloud and fog data processing method disclosed in the present application;

[0048] Figure 4 Structure diagram of an electronic device disclosed in the present application. Detailed implementation manners

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0050] Cloud and fog are aerosol systems composed of a large number of tiny water droplets or ice crystals suspended in the air near the ground layer, and the tiny water droplets or ice crystals are the products of the condensation (or sublimation) of water vapor in the air near the ground layer. Clouds and precipitation have important impacts on the energy budget between the earth and the atmosphere, climate change, ecological environment, disaster prevention and mitigation, etc. The thick fog that causes low visibility has become one of the important disastrous weather affecting economic and social development and people's health. Conventional cloud and fog data acquisition devices usually have difficulty supporting large-scale intensive observations, and are large in volume and weight. When installed on an unmanned aerial vehicle, they cannot cooperate with the unmanned aerial vehicle for flexible observations.

[0051] Therefore, the present application correspondingly provides a cloud and fog data acquisition device, a cloud and fog data acquisition instrument, a processing method, a device and a medium. The designed cloud and fog data acquisition device can cooperate with an unmanned aerial vehicle for cloud and fog data acquisition.

[0052] See Figure 1 As shown, the embodiments of the present application disclose a cloud and fog data acquisition device, including:

[0053] Packaging container 1, including an installation base and a container cover;

[0054] A micro-millimeter wave radar 12 is arranged in the packaging container 1 and is used for collecting the original cloud data frames;

[0055] A micro-lidar 13 is arranged in the packaging container 1 and is used for collecting the echo signals representing the cloud intensity;

[0056] A network sharer 14 is arranged in the packaging container 1, and a first serial data bus for transmitting the original cloud data frames is provided between the network sharer 14 and the micro-millimeter wave radar 12, and a second serial data bus for transmitting the echo signals is provided between the network sharer 14 and the micro-lidar 13, so that the network sharer 14 wirelessly transmits the original cloud data frames and the echo signals to the data processing end.

[0057] The packaging container 1 in the cloud data acquisition device is used for installing and fixing the micro-millimeter wave radar 12, the micro-lidar 13 and the network sharer 14, and installing the micro-millimeter wave radar 12, the micro-lidar 13 and the network sharer 14 in the packaging container 1, and can also play a waterproof role. Because there is a lot of moisture in the cloud, the damage to the device caused by years of accumulation cannot be ignored. Therefore, setting the micro-millimeter wave radar 12, the micro-lidar 13 and the network sharer 14 in the packaging container 1 can not only fix each radar, but also protect the radar and the network sharer from being damaged by the fog water.

[0058] The micro-millimeter wave radar 12 is arranged in the packaging container 1 so that the micro-millimeter wave radar 12 can detect vertically upward to collect the original cloud data frames. The micro-lidar 13 is arranged in the packaging container 1 so that the micro-lidar 13 can detect vertically upward to collect the echo signals representing the cloud intensity. Compared with the large millimeter wave cloud radar, the micro-millimeter wave radar 12 and the micro-lidar 13 have extremely low costs, very small field of view blind areas, and relatively high resolutions, which are more conducive to analyzing the fine spatial structure of the cloud. Compared with the fog droplet spectrometer, they have extremely low costs, extremely small volume and weight, can support large-scale multi-point observations, and can cooperate with unmanned aerial vehicle observations.

[0059] For example Figure 2 As shown in a specific schematic diagram of data communication of the cloud data acquisition device, the network sharer 14 is arranged in the packaging container 1. Among them, the network sharer 14 is respectively connected to the micro-millimeter wave radar 12 and the micro-lidar 13 through the first serial data bus and the second serial data bus. In this way, the network sharer 14 can receive the original cloud data frames collected by the micro-millimeter wave radar 12 through the first serial data bus, and the network sharer 14 can also receive the echo signals collected by the micro-lidar 13 through the second serial data bus, so that the network sharer 14 wirelessly transmits the original cloud data frames and the echo signals to the data processing end.

[0060] In this embodiment, the mounting base is a foam plastic box. Specifically, the mounting base can be a foam plastic box, and the shape of the foam plastic box can be a cuboid. Specifically, the mounting base can be composed of five faces, namely the bottom face, the front face, the back face, the left face and the right face, and these faces can all be specifically composed of foam plastic boards. In this way, the mounting base is a foam plastic box.

[0061] In this embodiment, the container cover includes a foam plastic layer and / or a waterproof layer. In the first specific case, the container cover can specifically include a foam plastic layer. In the second specific case, the container cover can specifically include a waterproof layer. In the third specific case, the container cover includes a foam plastic layer and a waterproof layer. The cost of the foam plastic is relatively low, and its impact on millimeter waves and lasers is not very high. Therefore, the container cover can specifically be a foam plastic layer. The cost of the waterproof layer is generally relatively high, and its impact on millimeter waves and lasers is generally less than that of the foam plastic. Therefore, the container cover can specifically be a waterproof layer. It can be understood that the container cover can also be a combination of a foam plastic layer and a waterproof layer.

[0062] In this embodiment, the waterproof layer is located in the observation direction of the micro lidar. The lidar senses the surrounding environment by emitting laser beams and receiving the signals reflected back. This process has extremely high requirements for the cleanliness and integrity of the optical components. Moisture (such as rain, fog, etc.) may adhere to the lens or housing of the lidar, resulting in abnormal laser scattering, absorption or reflection, thus affecting the accuracy and stability of the signals. Due to the sensitivity of the micro lidar to moisture and the requirements of the application scenarios, a waterproof layer is usually added to ensure normal operation.

[0063] In this embodiment, the waterproof layer is an infrared highly transparent glass. The waterproof layer is an infrared highly transparent glass, which can increase the transmittance of infrared light, thereby reducing the attenuation of the lidar signal. Using infrared highly transparent glass as the radar radome can significantly improve the detection range and imaging resolution of the lidar, while reducing the interference of environmental factors on the performance of the lidar.

[0064] In this embodiment, the cloud and fog data acquisition device further includes a support rod passing through the packaging container, which is used to fix the micro-millimeter wave radar in the packaging container so that the micro-millimeter wave radar does not directly contact the inner wall of the packaging container. Millimeter waves refer to electromagnetic waves with wavelengths ranging from 0.1 to 1 cm, corresponding to a frequency range of 30 to 300 GHz. If the packaging container directly contacts the millimeter wave radar, the inner wall of the packaging container may interfere with the propagation of radar waves, resulting in signal attenuation or misjudgment. Moreover, if the surface of the packaging container is relatively rough, that is, there are many small unevennesses on the surface of the packaging container, these uneven surfaces may scatter millimeter waves, reducing the detection accuracy and stability of the radar. For example Figure 2 As shown, the support rod passes through the packaging container, and then the micro-millimeter wave radar is arranged on the support rod. Therefore, the support rod can be used to fixedly arrange the micro-millimeter wave radar in the packaging container and make the micro-millimeter wave radar not directly contact the inner wall of the packaging container. The support rod can specifically be a wooden rod.

[0065] In this embodiment, the first serial data bus and the second serial data bus are universal asynchronous transceivers. Specifically, the first serial data bus and the second serial data bus are universal asynchronous transceivers (Universal Asynchronous Receiver / Transmitter, i.e., UART). The universal asynchronous transceiver does not need to share the same clock. It only requires that the clock frequencies of both communication parties be the same during the transmission process. That is, the micro-millimeter wave radar and the micro-lidar detect the cloud and fog echo and perform signal transmission with the USB network sharer through the UART serial port.

[0066] The beneficial effects of this application are as follows: The cloud and fog data acquisition device of this application includes a packaging container, which includes a mounting base and a container lid; a micro millimeter-wave radar, which is arranged in the packaging container and is used to acquire the original cloud and fog data frames; a micro lidar, which is arranged in the packaging container and is used to acquire the echo signal representing the cloud and fog intensity; a network sharer, which is arranged in the packaging container, and a first serial data bus for transmitting the original cloud and fog data frames is provided between the network sharer and the micro millimeter-wave radar, and a second serial data bus for transmitting the echo signal is provided between the network sharer and the micro lidar, so that the network sharer wirelessly transmits the original cloud and fog data frames and the echo signal to the data processing end. It can be seen that the micro millimeter-wave radar and the micro lidar in the cloud and fog data acquisition device of this application are small in size and light in weight, and support large-range multi-point observation. Therefore, they can be arranged on an unmanned aerial vehicle (UAV) and cooperate with the UAV to acquire the original cloud and fog data frames and the echo signal representing the cloud and fog intensity. Further, the cloud and fog data acquisition device further includes a network sharer, and a first serial data bus and a second serial data bus are respectively provided between the network sharer and the micro millimeter-wave radar and the micro lidar, which can be respectively used to transmit the original cloud and fog data frames and the echo signal. In this way, the network sharer wirelessly transmits the original cloud and fog data frames and the echo signal to the data processing end. That is to say, because the cloud and fog data acquisition device is small in size and light in weight, and can wirelessly transmit the acquired cloud and fog data to the data processing end, it can be arranged in the UAV and cooperate with the UAV to acquire the cloud and fog data.

[0067] Further, this application also provides a cloud and fog data acquisition instrument, which includes the cloud and fog data acquisition device disclosed above. The cloud and fog data acquisition instrument includes a cloud and fog data acquisition device, which includes a packaging container, a micro millimeter-wave radar, a micro lidar, and a network sharer. The micro millimeter-wave radar, the micro lidar, and the network sharer are all arranged in the packaging container. A first serial data bus for transmitting the original cloud and fog data frames is provided between the network sharer and the micro millimeter-wave radar, and a second serial data bus for transmitting the echo signal is provided between the network sharer and the micro lidar. The network sharer is used to wirelessly transmit the original cloud and fog data frames and the echo signal to the data processing end.

[0068] See Figure 3 As shown, the embodiment of this application discloses a cloud and fog data processing method, which is applied to the data processing end; the cloud and fog data processing method includes:

[0069] Step S11: Receive the original cloud and fog data frames and the echo signal returned by the network sharer in the target acquisition device; the target acquisition device is the cloud and fog data acquisition device disclosed above.

[0070] The target acquisition device is the cloud data acquisition device disclosed above, that is, the target acquisition device includes a packaging container, a micro millimeter-wave radar, a micro lidar, and a network sharer. Among them, the packaging container includes a mounting base and a container lid. The micro millimeter-wave radar disposed in the packaging container is used to collect the original cloud data frames; the micro lidar disposed in the packaging container is used to collect the echo signals characterizing the cloud intensity; a first serial data bus for transmitting the original cloud data frames is provided between the network sharer disposed in the packaging container and the micro millimeter-wave radar, and a second serial data bus for transmitting the echo signals is provided between the network sharer and the micro lidar, so that the network sharer wirelessly transmits the original cloud data frames and the echo signals to the data processing end.

[0071] Further, the mounting base is a foam plastic box body, and the container lid includes a foam plastic layer and / or a waterproof layer. When the container lid includes a waterproof layer, the waterproof layer is located in the observation direction of the micro lidar. The waterproof layer is specifically an infrared highly transparent glass. The cloud data acquisition device further includes a support rod penetrating the packaging container. The support rod is used to fix the micro millimeter-wave radar in the packaging container so that the micro millimeter-wave radar does not directly contact the inner wall of the packaging container. The first serial data bus and the second serial data bus are universal asynchronous receivers and transmitters.

[0072] Step S12: Remove noise from the original cloud data frames based on the echo signals to obtain a pure cloud power spectral density matrix, and use the pure cloud power spectral density matrix for parameter inversion and parameter correction to obtain the target cloud microphysical parameters.

[0073] In this embodiment, the removing noise from the original cloud data frames based on the echo signals to obtain a pure cloud power spectral density matrix includes: performing denoising in the distance dimension and the velocity dimension on the original cloud data frames in sequence to obtain a target power spectral density matrix; determining an effective cloud time window and an ineffective cloud time window based on the intensity of the echo signals, and respectively screening out an initial cloud power spectral density matrix corresponding to the effective cloud time window and a non-cloud power spectral density matrix corresponding to the ineffective cloud time window from the target power spectral density matrix; using the non-cloud power spectral density matrix to remove noise from the initial cloud power spectral density matrix to obtain a pure cloud power spectral density matrix.

[0074] Before performing distance - dimension denoising and velocity - dimension denoising on the original cloud data frame in sequence, the original cloud data frame can be cleaned to remove outliers in the original cloud data frame. Specifically, 3x3 median filtering can be used to process the original data to eliminate abnormal data points caused by factors such as equipment failures and environmental interferences. Then, distance - dimension denoising and velocity - dimension denoising are performed on the original cloud data frame with outliers removed in sequence.

[0075] Perform distance - dimension denoising and velocity - dimension denoising on the original cloud data frame in sequence. And to improve the stability and accuracy of the data, spectral averaging is performed on the denoised data. The specific operation is to perform spectral averaging on every 256 data matrices to obtain the target power spectral density matrix. Determine the effective cloud time window and the ineffective cloud time window based on the intensity of the echo signal. Specifically, select the time period with the echo signal intensity greater than - 4.0 dbz as the effective cloud time window, and select the ineffective cloud time window in the preset time period before and after the effective cloud time window from the time period with the echo signal intensity not greater than - 4.0 dbz. For example, the preset time period is 5 minutes. That is to say, the time period of 5 minutes before and after the effective cloud time window is used as the ineffective cloud time window, and the initial cloud power spectral density matrix corresponding to the effective cloud time window and the non - cloud power spectral density matrix corresponding to the ineffective cloud time window are respectively selected from the target power spectral density matrix; use the non - cloud power spectral density matrix to remove noise from the initial cloud power spectral density matrix to obtain the pure cloud power spectral density matrix.

[0076] In this embodiment, the denoising in the distance dimension and the denoising in the velocity dimension are sequentially performed on the original cloud data frame to obtain a target power spectral density matrix, which includes: using a Hanning window to perform sidelobe suppression on the original cloud data frame in the distance dimension to obtain a distance-sidelobe-suppressed signal, and performing a fast Fourier transform (FFT) on the distance-sidelobe-suppressed signal in the distance dimension to obtain a distance-frequency domain signal; using a Hanning window to perform sidelobe suppression on the distance-frequency domain signal in the velocity dimension to obtain a velocity-sidelobe-suppressed signal, and performing a fast Fourier transform (FFT) on the velocity-sidelobe-suppressed signal in the velocity dimension to obtain a velocity-frequency domain signal; performing spectral averaging on the velocity-frequency domain signal to obtain a target power spectral density matrix. In the distance dimension, a Hanning window is used to perform sidelobe suppression on the signal to reduce the influence of radar signal sidelobes on subsequent data processing. For the signal after sidelobe suppression in the distance dimension, a fast Fourier transform (FFT) is performed to convert the signal from the time domain to the frequency domain, facilitating the analysis of the frequency components of the signal. In the velocity dimension, the signal after FFT transformation is again subjected to sidelobe suppression using a Hanning window to further reduce the influence of sidelobes on the signal and improve the accuracy of the signal. For the signal after sidelobe suppression in the velocity dimension, a fast Fourier transform (FFT) is performed again to convert the signal from the time domain (or the frequency domain after FFT in the distance dimension) to the velocity domain (or a finer frequency domain), facilitating the analysis of the velocity distribution and frequency components of the signal.

[0077] In this embodiment, the noise removal of the initial cloud power spectral density matrix by using the non-cloud power spectral density matrix to obtain a pure cloud power spectral density matrix includes: determining the average value of the non-cloud power spectral density matrix as the noise estimation value, and determining the difference between the initial cloud power spectral density matrix and the noise estimation value as the pure cloud power spectral density matrix. Select the power spectral density matrices 5 minutes before and after the cloud-containing time period as noise samples, calculate their average value as the noise estimation value, and subtract the noise estimation value from each power spectral density matrix in the cloud-containing time period to obtain a pure cloud power spectral density matrix, that is, determine the difference between the initial cloud power spectral density matrix and the noise estimation value as the pure cloud power spectral density matrix.

[0078] In this embodiment, performing parameter inversion and parameter correction using the pure cloud and fog power spectral density matrix to obtain target cloud and fog microphysical parameters includes: obtaining the radial velocity spectral width and the radar reflectivity factor using the pure cloud and fog power spectral density matrix; based on the radial velocity spectral width, the radar reflectivity factor, and a preset shape parameter of the Gamma distribution function, and using the substitution integral method to perform a preliminary inversion to obtain initial cloud and fog microphysical parameters; determining an estimated visibility value according to the initial cloud and fog microphysical parameters, and correcting the preset shape parameter based on the estimated visibility value to obtain a corrected shape parameter; based on the radial velocity spectral width, the radar reflectivity factor, and the corrected shape parameter, and using the substitution integral method to perform a re-inversion to obtain the target cloud and fog microphysical parameters.

[0079] Obtain the radial velocity spectral width and the radar reflectivity factor Z using the pure cloud and fog power spectral density matrix. Assume that the pure cloud and fog power spectral density matrix follows a Gamma distribution, and assume that the preset shape parameter μ of the Gamma distribution is a fixed value. For example, the preset shape parameter is 17. Then, according to the radar reflectivity factor, the radial velocity spectral width, and the preset shape parameter, use the substitution integral method to perform a preliminary inversion to obtain the initial cloud and fog microphysical parameters. The initial cloud and fog microphysical parameters specifically include the number concentration, the liquid water content, and the effective radius. Next, according to the initial cloud and fog microphysical parameters, determine the estimated visibility value. The specific formula is as follows:

[0080] ;

[0081] In the formula, Vis represents the estimated visibility value, LWC represents the liquid water content, and N represents the number concentration;

[0082] Further, according to the visibility value and shape parameter mapping table, find the shape parameter corresponding to the estimated visibility value, that is, correct the preset shape parameter to obtain the corrected shape parameter. The visibility value and shape parameter mapping table is specifically shown in Table 1:

[0083] Table 1

[0084]

[0085] Substitute the corrected shape parameter into the previous inversion process, and repeat the parameter inversion step until the final cloud and fog microphysical parameter value is obtained, that is, based on the radial velocity spectral width, the radar reflectivity factor, and the corrected shape parameter, and use the substitution integral method to perform a re-inversion to obtain the target cloud and fog microphysical parameters.

[0086] In this embodiment, obtaining the radial velocity spectrum width and the radar reflectivity factor by using the pure cloud and fog power spectral density matrix includes: determining the zero-order matrix of the pure cloud and fog power spectral density matrix as the echo signal power, and obtaining the radial velocity spectrum width according to the second-order matrix of the pure cloud and fog power spectral density matrix; obtaining the radar reflectivity factor according to the meteorological radar equation and by using the echo signal power. The specific process of obtaining the radial velocity spectrum width and the radar reflectivity factor is as follows:

[0087] 1) First, obtain the echo signal power, that is, determine the zero-order matrix of the pure cloud and fog power spectral density matrix as the echo signal power;

[0088] 2) Secondly, obtain the radial velocity spectrum width and the radar reflectivity factor respectively:

[0089] 2.1) Obtain the radial velocity spectrum width according to the second-order matrix of the pure cloud and fog power spectral density matrix;

[0090] 2.2) Obtain the radar reflectivity factor according to the meteorological radar equation and by using the echo signal power.

[0091] The beneficial effects of this application are as follows: The cloud and fog data acquisition device of this application includes a packaging container, which includes a mounting base and a container lid; a micro-millimeter wave radar, which is arranged in the packaging container and is used to collect the original cloud and fog data frames; a micro-lidar, which is arranged in the packaging container and is used to collect the echo signal representing the cloud and fog intensity; a network sharer, which is arranged in the packaging container, and a first serial data bus for transmitting the original cloud and fog data frames is provided between the network sharer and the micro-millimeter wave radar, and a second serial data bus for transmitting the echo signal is provided between the network sharer and the micro-lidar, so that the network sharer wirelessly transmits the original cloud and fog data frames and the echo signal to the data processing end. It can be seen that the micro-millimeter wave radar and the micro-lidar in the cloud and fog data acquisition device of this application are small in volume and light in weight, and support large-range multi-point observations. Therefore, they can be arranged on an unmanned aerial vehicle (UAV) to cooperate with the UAV to collect the original cloud and fog data frames and the echo signal representing the cloud and fog intensity. Further, the cloud and fog data acquisition device further includes a network sharer, and a first serial data bus and a second serial data bus are respectively provided between the network sharer and the micro-millimeter wave radar and the micro-lidar, which can be respectively used to transmit the original cloud and fog data frames and the echo signal. In this way, the network sharer wirelessly transmits the original cloud and fog data frames and the echo signal to the data processing end. That is to say, because the cloud and fog data acquisition device is small in volume and light in weight, and can wirelessly transmit the collected cloud and fog data to the data processing end, it can be arranged in the UAV and cooperate with the UAV to collect the cloud and fog data.

[0092] Furthermore, an embodiment of this application also provides an electronic device. Figure 4It is a structural diagram of an electronic device 20 shown according to an exemplary embodiment. The content in the figure should not be regarded as any limitation on the scope of use of this application.

[0093] Figure 4 This is a schematic structural diagram of an electronic device provided by an embodiment of this application. Specifically, it may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. Among them, the memory 22 is used to store a computer program, and the computer program is loaded and executed by the processor 21 to implement the relevant steps in the cloud data processing method executed by the electronic device disclosed in any of the foregoing embodiments.

[0094] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device; the communication interface 24 can create a data transmission channel between the electronic device and external devices, and the communication protocol it follows is any communication protocol applicable to the technical solution of this application, and no specific limitation is imposed on it here; the input / output interface 25 is used to obtain external input data or output data to the outside, and its specific interface type can be selected according to specific application needs, and no specific limitation is made here.

[0095] Among them, the processor 21 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 21 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), or PLA (Programmable Logic Array). The processor 21 may also include a main processor and a coprocessor. The main processor is a processor used to process data in the wake state, also known as the CPU (Central Processing Unit); the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 21 may be integrated with a GPU (Graphics Processing Unit), and the GPU is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 21 may also include an AI (Artificial Intelligence) processor, and the AI processor is used to process computing operations related to machine learning.

[0096] In addition, as a carrier for storing resources, the memory 22 can be a read-only memory, a random access memory, a magnetic disk, an optical disc, etc. The resources stored thereon include an operating system 221, a computer program 222, data 223, etc. The storage method can be transient storage or permanent storage.

[0097] Among them, the operating system 221 is used to manage and control each hardware device and the computer program 222 on the electronic device, so as to realize the operation and processing of the massive data 223 in the memory 22 by the processor 21. It can be Windows, Unix, Linux, etc. In addition to the computer program that can be used to complete the cloud data processing method executed by the electronic device disclosed in any of the foregoing embodiments, the computer program 222 can further include computer programs that can be used to complete other specific tasks. The data 223 can include not only the data transmitted by the external device received by the electronic device, but also the data collected by its own input / output interface 25, etc.

[0098] Furthermore, the present application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the cloud data processing method disclosed above is implemented. For the specific steps of this method, reference can be made to the corresponding content disclosed in the foregoing embodiments, and details will not be repeated here.

[0099] In this specification, the various embodiments are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts between the various embodiments, reference can be made to each other. For the device disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and reference can be made to the description of the method part for the relevant parts.

[0100] Those skilled in the art may further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described according to their functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application. The steps of the methods or algorithms described in combination with the embodiments disclosed herein can be directly implemented by hardware, software modules executed by a processor, or a combination of the two. The software modules can be placed in a random access memory (RAM), memory, read-only memory (ROM), electrically programmable EPROM (Erasable Programmable Read Only Memory), electrically erasable programmable EEPROM (Electrically Erasable Programmable read only memory), registers, hard disks, removable disks, CD-ROM (Compact Disc Read-Only Memory), or any other form of storage medium known in the art.

[0101] Finally, it should also be noted that in this document, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising a..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0102] The above has introduced in detail a cloud and fog data acquisition device, a cloud and fog data acquisition instrument, a processing method, a device and a medium provided by the present invention. Specific examples are used in this article to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A cloud and fog data collection device, characterized in that: include: A packaging container, including a mounting base and a container cover; A miniature millimeter-wave radar is disposed in the packaging container and is used to collect cloud and fog raw data frames; A micro laser radar is arranged in the packaging container and is used to collect echo signals representing the intensity of clouds and fog; A network sharer is arranged in the packaging container, and a first serial data bus for transmitting the cloud and fog original data frame is provided between the network sharer and the micro millimeter wave radar, and a second serial data bus for transmitting the echo signal is provided between the network sharer and the micro laser radar, so that the network sharer can wirelessly transmit the cloud and fog original data frame and the echo signal to the data processing end.

2. The cloud and fog data collection device according to claim 1, characterized in that: The installation base is a foam plastic box.

3. The cloud and fog data collection device according to claim 2, characterized in that: The container cover comprises a foam plastic layer and / or a waterproof layer.

4. The cloud and fog data collection device according to claim 3, characterized in that: The waterproof layer is located in the observation direction of the micro laser radar.

5. The cloud and fog data collection device according to claim 3, characterized in that: The waterproof layer is infrared high-transmittance glass.

6. The cloud and fog data collection device according to any one of claims 1 to 5, characterized in that: Also includes: The support rod passing through the packaging container is used to fix the micro millimeter wave radar in the packaging container so that the micro millimeter wave radar does not directly contact the inner wall of the packaging container.

7. The cloud and fog data collection device according to claim 1, characterized in that: The first serial data bus and the second serial data bus are universal asynchronous receivers and transmitters.

8. A cloud and fog data collector, characterized in that: It comprises a cloud and fog data collection device as described in any one of claims 1 to 7.

9. A cloud and fog data processing method, characterized in that: Applied to the data processing end; the cloud fog data processing method includes: Receive the cloud and fog original data frame and the echo signal returned by the network sharer in the target acquisition device; the target acquisition device is the cloud and fog data acquisition device according to any one of claims 1 to 7; The cloud and fog original data frame is de-noised based on the echo signal to obtain a pure cloud and fog power spectrum density matrix, and the pure cloud and fog power spectrum density matrix is ​​used to perform parameter inversion and parameter correction to obtain target cloud and fog microphysical parameters.

10. The cloud and fog data processing method according to claim 9, characterized in that: The step of removing noise from the cloud original data frame based on the echo signal to obtain a pure cloud power spectrum density matrix includes: The cloud and fog original data frame is subjected to distance dimension denoising and speed dimension denoising in sequence to obtain a target power spectrum density matrix; Determine a valid fog time window and an invalid fog time window based on the intensity of the echo signal, and select an initial fog power spectrum density matrix corresponding to the valid fog time window and a non-fog power spectrum density matrix corresponding to the invalid fog time window from a target power spectrum density matrix; The non-cloud power spectrum density matrix is ​​used to remove noise from the initial cloud power spectrum density matrix to obtain a pure cloud power spectrum density matrix.

11. The cloud and fog data processing method according to claim 10, characterized in that: The distance dimension denoising and the speed dimension denoising are sequentially performed on the cloud and fog original data frame to obtain a target power spectrum density matrix, including: Using a Hanning window to perform sidelobe removal processing on the cloud and fog original data frame in the distance dimension to obtain a range sidelobe removal signal, and performing a fast Fourier transform on the range sidelobe removal signal in the distance dimension to obtain a range frequency domain signal; Using a Hanning window, the distance frequency domain signal is subjected to a speed-removed sidelobe process to obtain a speed-removed sidelobe signal, and the speed-removed sidelobe signal is subjected to a fast Fourier transform in the speed dimension to obtain a speed frequency domain signal; The speed frequency domain signal is subjected to spectrum averaging processing to obtain a target power spectrum density matrix.

12. The cloud and fog data processing method according to claim 10, characterized in that: The method of removing noise from the initial cloud power spectrum density matrix by using the non-cloud power spectrum density matrix to obtain a pure cloud power spectrum density matrix includes: The average value of the non-cloud power spectrum density matrix is ​​determined as the noise estimation value, and the difference between the initial cloud power spectrum density matrix and the noise estimation value is determined as the pure cloud power spectrum density matrix.

13. The cloud and fog data processing method according to any one of claims 9 to 12, characterized in that: The method of performing parameter inversion and parameter correction using the pure cloud power spectrum density matrix to obtain target cloud microphysical parameters includes: The radial velocity spectrum width and radar reflection factor are obtained by using the pure cloud power spectrum density matrix; Based on the radial velocity spectrum width, the radar reflection factor and the preset shape parameters of the Gamma distribution function, a preliminary inversion is performed using a substitution integration method to obtain initial cloud and fog microphysical parameters; Determining a visibility estimation value according to the initial cloud and fog microphysical parameters, and correcting the preset shape parameters based on the visibility estimation value to obtain corrected shape parameters; Based on the radial velocity spectrum width, the radar reflection factor and the corrected shape parameter, a re-inversion is performed using the substitution integration method to obtain the target cloud microphysical parameters.

14. The cloud and fog data processing method according to claim 13, characterized in that: The method of obtaining the radial velocity spectrum width and the radar reflection factor by using the pure cloud power spectrum density matrix includes: Determine the zero-order matrix of the pure cloud power spectrum density matrix as the echo signal power, and obtain the radial velocity spectrum width according to the second-order matrix of the pure cloud power spectrum density matrix; According to the weather radar equation, the radar reflection factor is obtained by using the echo signal power.

15. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, used to execute the computer program to implement the steps of the cloud and fog data processing method as described in any one of claims 9 to 14.

16. A computer-readable storage medium, characterized in that: Used to store computer programs; wherein, when the computer program is executed by a processor, the steps of the cloud and fog data processing method as described in any one of claims 9 to 14 are implemented.

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