An online calibration method and device for millimeter wave cloud detection radar

Abstract

The present application provides an online calibration method and device for millimeter-wave cloud radar, which relates to the field of data processing. In this method, the environmental parameters of the environment in which the millimeter-wave cloud radar is located are obtained, and the environmental parameters include temperature, humidity and air pressure; according to the temperature, humidity and air pressure, a first calibration strategy is generated, and the first calibration strategy is used to compensate for the signal transmission power corresponding to the millimeter-wave cloud radar; the reflection signal is obtained, and the reflection signal is a signal obtained after preprocessing the signal reflected by the target cloud layer received by the millimeter-wave cloud radar; according to the reflection signal, the real-time wind speed and turbulent kinetic energy are calculated; according to the real-time wind speed and turbulent kinetic energy, a second calibration strategy is generated, and the second calibration strategy is used to adjust the signal receiving sensitivity corresponding to the millimeter-wave cloud radar; based on the first calibration strategy and the second calibration strategy, the millimeter-wave cloud radar is calibrated online. The implementation of this technical solution is convenient for improving the working efficiency of the radar system.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and in particular to an online calibration method and device for a millimeter-wave cloud detection radar. Background Art

[0002] Millimeter-wave cloud radar is a radar system specially designed for detecting clouds, which uses millimeter-wave electromagnetic waves as the detection medium. This radar system transmits millimeter-wave signals and receives echo signals after being reflected by clouds. By further analyzing the intensity and frequency shift information of these echoes, it can accurately obtain key meteorological data such as the vertical structure of clouds, the distribution of particle size, and the liquid water content.

[0003] In the existing millimeter-wave cloud radar system, calibration is a crucial link, which involves the accuracy and reliability of the radar system performance. However, the traditional calibration method usually relies on downtime maintenance, which means that during the calibration process, the radar system must stop working and cannot perform cloud detection tasks normally, which greatly affects the working efficiency of the radar system.

[0004] Therefore, there is an urgent need for an online calibration method and device for millimeter-wave cloud radar. Summary of the invention

[0005] The present application provides an online calibration method and device for a millimeter-wave cloud detection radar, which is convenient for improving the working efficiency of the radar system.

[0006] In a first aspect of the present application, a method for online calibration of a millimeter-wave cloud measuring radar is provided, the method comprising: obtaining environmental parameters of an environment in which the millimeter-wave cloud measuring radar is located, the environmental parameters comprising temperature, humidity and air pressure; generating a first calibration strategy according to the temperature, the humidity and the air pressure, the first calibration strategy being used to compensate for a signal transmission power corresponding to the millimeter-wave cloud measuring radar; obtaining a reflected signal, the reflected signal being a signal obtained after preprocessing a signal reflected by a target cloud layer received by the millimeter-wave cloud measuring radar; calculating a real-time wind speed and turbulent kinetic energy according to the reflected signal; generating a second calibration strategy according to the real-time wind speed and the turbulent kinetic energy, the second calibration strategy being used to adjust the signal receiving sensitivity corresponding to the millimeter-wave cloud measuring radar; and performing online calibration on the millimeter-wave cloud measuring radar based on the first calibration strategy and the second calibration strategy.

[0007] By adopting the above technical solution, the actual condition of the radar working environment can be understood in real time by obtaining environmental parameters (temperature, humidity, air pressure). The first calibration strategy compensates the signal transmission power according to these environmental parameters, ensuring that the radar can maintain stable performance under different environmental conditions. The acquisition and processing of the reflected signal are real-time, so the millimeter wave signal reflected by the cloud layer can be quickly obtained. The calculation of real-time wind speed and turbulent kinetic energy provides the radar system with real-time information about the dynamic changes of the cloud layer. The second calibration strategy adjusts the signal receiving sensitivity based on these real-time data to ensure the rapid response of the radar system to the dynamic changes of the cloud layer. Comprehensive consideration of environmental parameters and real-time meteorological data can provide a more comprehensive understanding of the working status of the radar system. The combination of the first calibration strategy and the second calibration strategy can more accurately compensate for the influence of various factors on the radar measurement accuracy. This enables the radar system to provide more accurate and reliable meteorological data, improving the scientific nature and application value of the data. Online calibration can monitor the performance status of the radar system in real time and adjust and optimize it when necessary. This helps to timely discover and correct potential problems in the radar system and avoid measurement errors and data distortion caused by system performance degradation. Traditional downtime maintenance methods require a lot of manpower, material resources and time, and may damage the components of the radar system. Online calibration technology can be calibrated during the operation of the radar system, without downtime for maintenance, reducing maintenance costs. In addition, since the calibration process is automated, there is no need for professionals to go to the site to operate, further reducing labor costs. Through online calibration technology, the performance parameters of the radar system can be adjusted and optimized in real time to ensure the accuracy and reliability of the measurement data. This is of great significance for data applications in fields such as meteorological observation and environmental monitoring, and can improve data quality and application effects. Accurate data can provide strong support for weather forecasts, disaster warnings, etc., and reduce decision-making errors caused by inaccurate data.

[0008] Optionally, generating a first calibration strategy based on the temperature, the humidity and the air pressure specifically includes: inputting the temperature, the humidity and the air pressure into a preset signal attenuation model to obtain a signal attenuation amount, wherein the preset signal attenuation model pre-stores a correspondence between environmental parameters and signal attenuation amounts; obtaining the first calibration strategy based on the signal attenuation amount, the first calibration strategy including a signal transmission power compensation amount.

[0009] By adopting the above technical solution, different environmental parameters (such as temperature, humidity and air pressure) will have different effects on the propagation of millimeter wave signals in the air, resulting in signal attenuation. By inputting the environmental parameters obtained in real time into the preset signal attenuation model, the signal attenuation in the current environment can be estimated more accurately. This method enhances the adaptability of the radar system to different environmental conditions and ensures that accurate measurement data can be obtained in different environments. The preset signal attenuation model is established based on a large amount of experimental data and experience, and can accurately reflect the relationship between environmental parameters and signal attenuation. Therefore, the signal attenuation calculated according to the model has high accuracy, thereby improving the accuracy of the first calibration strategy. Accurate signal transmission power compensation can ensure that the radar system can maintain the best measurement performance under different environmental conditions. The method can obtain environmental parameters in real time and automatically calculate the signal attenuation and the corresponding signal transmission power compensation. This means that the first calibration strategy can be updated in real time without manual intervention, making the calibration process faster and more efficient. The characteristics of real-time and automation enable the radar system to maintain stable measurement performance in various environments.

[0010] Optionally, obtaining the reflected signal specifically includes: responding to a measurement request input by a user, the measurement request including the target cloud layer; according to the measurement request, controlling the millimeter-wave cloud measurement radar to transmit a measurement signal to the target cloud layer; receiving the original signal sent by the millimeter-wave cloud measurement radar; preprocessing the original signal to obtain the reflected signal, the preprocessing including denoising, filtering and normalization.

[0011] By adopting the above technical solution, the system can respond to the measurement request input by the user and control the radar to transmit signals according to the target cloud layer in the request. This strong user interactivity makes the system more flexible and easy to use, and can perform customized measurements according to user needs. By controlling the millimeter-wave cloud radar to transmit measurement signals to specific target clouds, the pertinence and accuracy of the measurement data can be ensured. This method can accurately obtain the reflection signal of the target cloud layer, providing a reliable basis for subsequent data processing and analysis. The received original signal may contain unfavorable factors such as noise and interference, which affect the data quality. Preprocessing the original signal, such as denoising, filtering and normalization, can significantly improve the quality of the signal. Denoising can eliminate the noise component in the signal, filtering can retain the useful signal and filter out the interference, and normalization can convert the signal into a unified dimension and range for subsequent processing and analysis. The preprocessed reflection signal has a higher signal-to-noise ratio and better data quality, making subsequent data processing and analysis more efficient and accurate. Efficient data processing can speed up data processing, reduce computing costs, and improve the real-time performance of data processing.

[0012] Optionally, the real-time wind speed and turbulent kinetic energy are calculated according to the reflected signal, specifically including: performing FFT transform on the reflected signal to obtain a power spectrum corresponding to the reflected signal; determining a Doppler frequency shift peak according to the power spectrum; and calculating the real-time wind speed according to the Doppler frequency shift peak and a wavelength corresponding to the reflected signal, wherein the wavelength corresponding to the reflected signal is obtained by using a preset calculation method based on the frequency of the millimeter-wave cloud radar.

[0013] By adopting the above technical solution, FFT transformation is an efficient algorithm for converting time domain signals into frequency domain signals. This transformation makes it easier to analyze the frequency components in the signal, especially when the signal contains Doppler shift. The power spectrum obtained by FFT transformation provides information about the intensity of different frequency components in the signal, so that the Doppler shift peak can be accurately determined. The Doppler shift is caused by the relative motion between the radar and the target. For the movement of the wind, this relative motion will cause the frequency of the radar echo signal to shift. By determining the Doppler shift peak, the real-time wind speed can be accurately calculated. This is based on the principle of the Doppler effect, that is, the frequency shift is proportional to the relative speed. The wavelength corresponding to the reflected signal is a key parameter in the wind speed calculation, which ensures the accuracy of the wind speed calculation. This process can be carried out in real time, allowing continuous monitoring and recording of wind speed. This is crucial for applications such as weather forecasting, environmental monitoring, and air traffic control. Real-time wind speed data can provide important information about atmospheric conditions and help decision makers respond in a timely manner.

[0014] Optionally, the real-time wind speed and turbulent kinetic energy are calculated based on the reflected signal, and specifically also includes: obtaining a spectrum broadening corresponding to the reflected signal; inputting the spectrum broadening into a preset turbulence model to obtain turbulence intensity, wherein the preset turbulence model pre-stores a correspondence between spectrum broadening and turbulence intensity; and calculating the turbulent kinetic energy based on the real-time wind speed and the turbulence intensity.

[0015] By adopting the above technical solution, turbulent kinetic energy is a physical quantity that describes the strength of turbulent motion in a fluid, which is of great significance for meteorological and environmental research. By broadening the spectrum of the reflected signal and combining it with the preset turbulence model, the turbulence intensity can be directly calculated, and then the turbulent kinetic energy can be obtained. Spectral broadening is a manifestation of the reflected signal in the frequency domain, which contains important information about the frequency distribution of the signal. By analyzing the spectrum broadening, the turbulent structure inside the cloud layer can be more accurately reflected, thereby improving the accuracy and reliability of the turbulent kinetic energy calculation. This process can be carried out in real time, allowing continuous monitoring and recording of turbulent kinetic energy. Real-time data is essential for applications such as weather forecasting, environmental monitoring, and air traffic control, as they require rapid response and accurate prediction of atmospheric conditions. The preset turbulence model is able to handle different types of reflected signals and spectrum broadening, thereby adapting to different environments and measurement conditions. By simultaneously calculating the real-time wind speed and turbulent kinetic energy, multi-dimensional information about the cloud motion state and turbulent characteristics can be obtained. By accurately calculating the turbulent kinetic energy, the performance parameters and signal processing algorithms of the radar system can be further optimized.

[0016] Optionally, the second calibration strategy is generated based on the real-time wind speed and the turbulent kinetic energy, specifically including: judging the relationship between the real-time wind speed and a preset wind speed threshold, and judging the relationship between the turbulent kinetic energy and a preset kinetic energy; if it is determined that the real-time wind speed is greater than the preset wind speed threshold, and the turbulent kinetic energy is greater than the preset kinetic energy, then the second calibration strategy is obtained, and the second calibration strategy includes a signal reception sensitivity adjustment amount, and the signal reception sensitivity adjustment amount is used to improve the signal reception sensitivity of the millimeter-wave cloud measurement radar.

[0017] By adopting the above technical solution, when the real-time wind speed is high and the turbulent kinetic energy is large, the turbulence and wind movement inside the cloud layer may cause the scattering and attenuation of the radar echo signal to intensify. In this case, by improving the signal reception sensitivity, the system can more effectively capture weak reflected signals, thereby improving the integrity and accuracy of signal reception. The optimized signal reception performance can ensure that the system can obtain higher quality radar data. These data are critical for applications such as meteorological analysis, environmental monitoring, and air traffic control, as they require accurate and reliable data to support decision-making and prediction. The strategy can detect changes in wind speed and turbulent kinetic energy in real time and adjust the radar's signal reception sensitivity according to these changes. This real-time response and adaptability enables the system to maintain optimal working conditions in different environments and conditions, ensuring the continuity and consistency of data. In severe weather conditions, such as strong winds and high turbulence, traditional radar systems may not be able to effectively receive and process reflected signals, resulting in data loss or misjudgment. By adjusting the signal reception sensitivity according to the real-time wind speed and turbulent kinetic energy, the system can reduce this risk and ensure the integrity and accuracy of the data.

[0018] Optionally, the method also includes: responding to a calibration request input by a user, the calibration request including a target signal transmission power and a target signal reception sensitivity; and calibrating the millimeter-wave cloud detection radar using the target signal transmission power and the target signal reception sensitivity.

[0019] By adopting the above technical solution, users can input specific target signal transmission power and receiving sensitivity according to actual needs and environmental conditions. This customization enables the system to adapt to different application scenarios and measurement requirements, improving the flexibility and adaptability of the system. The performance of the radar system can be optimized by precisely controlling the signal transmission power and receiving sensitivity. Appropriate transmission power can ensure that the signal can cover a long enough distance while avoiding interference with the surrounding environment. Appropriate receiving sensitivity can ensure that the system can capture weak reflected signals and improve the integrity and accuracy of the data. A precisely calibrated radar system can provide more accurate and reliable measurement data. Inappropriate signal transmission power and receiving sensitivity may lead to data loss, misjudgment or noise interference. Through the calibration request entered by the user, it can be ensured that the system works with appropriate parameters, thereby reducing these risks. Users can customize the performance of the system to meet their needs by entering calibration requests. This customization enables users to optimize the system according to their actual situation and improve user experience and satisfaction.

[0020] In a second aspect of the present application, an online calibration device for millimeter-wave cloud measuring radar is provided, the online calibration device comprising an acquisition module and a processing module, wherein the acquisition module is used to acquire environmental parameters of the environment in which the millimeter-wave cloud measuring radar is located, the environmental parameters comprising temperature, humidity and air pressure; the processing module is used to generate a first calibration strategy according to the temperature, the humidity and the air pressure, the first calibration strategy being used to compensate for the signal transmission power corresponding to the millimeter-wave cloud measuring radar; the acquisition module is also used to acquire a reflected signal, the reflected signal being a signal obtained after preprocessing the signal reflected by the target cloud layer received by the millimeter-wave cloud measuring radar; the processing module is also used to calculate the real-time wind speed and turbulent kinetic energy according to the reflected signal; the processing module is also used to generate a second calibration strategy according to the real-time wind speed and the turbulent kinetic energy, the second calibration strategy being used to adjust the signal receiving sensitivity corresponding to the millimeter-wave cloud measuring radar; the processing module is also used to perform online calibration on the millimeter-wave cloud measuring radar based on the first calibration strategy and the second calibration strategy.

[0021] In the third aspect of the present application, an electronic device is provided, which includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs the method described above.

[0022] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, the method described above is executed.

[0023] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0024] By acquiring environmental parameters (temperature, humidity, air pressure), the actual conditions of the radar working environment can be understood in real time. The first calibration strategy compensates the signal transmission power according to these environmental parameters, ensuring that the radar can maintain stable performance under different environmental conditions. The acquisition and processing of reflected signals are real-time, so the millimeter wave signals reflected by the cloud layer can be quickly acquired. The calculation of real-time wind speed and turbulent kinetic energy provides the radar system with real-time information about the dynamic changes of the cloud layer. The second calibration strategy adjusts the signal receiving sensitivity based on these real-time data to ensure the rapid response of the radar system to the dynamic changes of the cloud layer. Comprehensive consideration of environmental parameters and real-time meteorological data can provide a more comprehensive understanding of the working status of the radar system. The combination of the first calibration strategy and the second calibration strategy can more accurately compensate for the influence of various factors on the radar measurement accuracy. This enables the radar system to provide more accurate and reliable meteorological data, improving the scientific nature and application value of the data. Online calibration can monitor the performance status of the radar system in real time and adjust and optimize it when necessary. This helps to timely discover and correct potential problems in the radar system and avoid measurement errors and data distortion caused by system performance degradation. Traditional downtime maintenance methods require a lot of manpower, material resources and time, and may damage the components of the radar system. Online calibration technology can be calibrated during the operation of the radar system, without downtime for maintenance, reducing maintenance costs. In addition, since the calibration process is automated, there is no need for professionals to go to the site to operate, further reducing labor costs. Through online calibration technology, the performance parameters of the radar system can be adjusted and optimized in real time to ensure the accuracy and reliability of the measurement data. This is of great significance for data applications in fields such as meteorological observation and environmental monitoring, and can improve data quality and application effects. Accurate data can provide strong support for weather forecasts, disaster warnings, etc., and reduce decision-making errors caused by inaccurate data. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1A flowchart of an online calibration method for millimeter-wave cloud radar provided in an embodiment of the present application.

[0026] Figure 2 Another schematic diagram of the process of an online calibration method for millimeter-wave cloud radar provided in an embodiment of the present application.

[0027] Figure 3 A schematic diagram of a module of an online calibration device for millimeter-wave cloud radar provided in an embodiment of the present application.

[0028] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0029] Explanation of the reference numerals: 31, acquisition module; 32, processing module; 41, processor; 42, communication bus; 43, user interface; 44, network interface; 45, memory. DETAILED DESCRIPTION

[0030] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0031] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0032] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0033] Millimeter-wave cloud radar, as a special radar system for cloud detection, has the core function of accurately detecting clouds with the help of millimeter-wave electromagnetic waves. It transmits millimeter-wave signals and receives echo signals reflected by clouds, and carefully analyzes the intensity and frequency shift information of the echoes, thereby accurately revealing key meteorological information such as the vertical structure of clouds, particle size distribution, and liquid water content.

[0034] In the daily operation of millimeter-wave cloud radar systems, calibration is particularly critical because it is directly related to the stability of radar system performance and the accuracy of data. However, traditional calibration methods often require the radar system to be shut down for calibration, which not only makes the radar unable to perform normal cloud detection tasks during the calibration period, but also greatly reduces the system's working efficiency.

[0035] In order to solve the above technical problems, the present application provides an online calibration method for millimeter wave cloud detection radar. Figure 1 , Figure 1 A flowchart of an online calibration method for millimeter wave cloud radar provided in an embodiment of the present application. The online calibration method is applied to a server and includes steps S110 to S160, which are as follows:

[0036] S110. Obtain environmental parameters of the environment in which the millimeter-wave cloud detection radar is located. The environmental parameters include temperature, humidity, and air pressure.

[0037] Specifically, the server is a data processing and storage center that is responsible for collecting data from millimeter-wave cloud radar or other sensors. The server is connected to the radar equipment through a network or other communication methods to obtain data in real time or periodically. Millimeter-wave cloud radar is a radar system that uses millimeter-wave technology to detect and measure cloud characteristics. Millimeter waves refer to electromagnetic waves with a wavelength between 1 and 10 mm. This type of radar is widely used in meteorological observation, cloud analysis, and weather forecasting. Environmental parameters refer to the environmental conditions at the installation and operation site of the millimeter-wave cloud radar, including temperature, humidity, and air pressure. These parameters not only affect the physical state of the radar equipment (such as thermal expansion and contraction, the performance of electronic components, etc.), but may also affect the propagation and reception of radar signals. Ambient temperature has an important impact on the working performance and life of electronic equipment. High temperature may cause the equipment to overheat, affecting the stability of the circuit board and the accuracy of the radar system; low temperature may cause difficulty in starting the equipment or performance degradation. Ambient humidity may affect the electrical performance and mechanical structure of the radar equipment. Excessive humidity may cause condensation inside the equipment, causing circuit short circuits or component corrosion; too low humidity may increase the risk of electrostatic discharge and damage electronic equipment. Changes in air pressure may affect the propagation speed and direction of radar waves. In high altitude areas, the air pressure is lower, which may affect the refraction and scattering characteristics of radar signals, thereby affecting the accuracy of the measurement results. In an embodiment of the present application, the above-mentioned environmental parameters can be obtained by measuring the millimeter wave cloud radar itself and sending it to the server, or by measuring it with other sensor devices and sending it to the server.

[0038] For example, suppose a millimeter-wave cloud radar is installed at a meteorological observation station on the top of a mountain. This station is connected to a data center (where the server is located) at the bottom of the mountain via a dedicated data transmission line. The data center needs to obtain the environmental parameters of the radar environment in real time to ensure the accuracy and reliability of the radar data. The temperature at the top of the mountain may be much lower than that at the bottom of the mountain, especially in winter. The server needs to obtain this low temperature data to evaluate whether the radar equipment needs to take warming measures in extreme low temperatures, or adjust the operating parameters of the equipment to adapt to the low temperature environment. The humidity at the top of the mountain may fluctuate greatly with weather changes. On rainy or foggy days, the humidity may be very high, and the server needs to obtain this humidity data to evaluate whether the radar equipment has a risk of condensation and whether moisture-proof measures need to be taken. Due to the lower air pressure at the top of the mountain, the server needs to obtain this parameter to adjust the radar signal processing algorithm to ensure the accuracy of measuring cloud height and speed. This adjustment may involve correction of the propagation speed of the radar wave to reflect the impact of air pressure changes on the radar signal.

[0039] S120. Generate a first calibration strategy according to the temperature, humidity, and air pressure, where the first calibration strategy is used to compensate for the signal transmission power corresponding to the millimeter-wave cloud detection radar.

[0040] Specifically, the performance and measurement accuracy of the millimeter-wave cloud radar are significantly affected by the environmental conditions in which it is located. Among them, temperature, humidity and air pressure are three important environmental parameters. Changes in these parameters may cause changes in the working state of the internal components of the radar equipment, thereby affecting the signal transmission power. The server generates a compensation strategy for the signal transmission power of the millimeter-wave cloud radar by analyzing the collected environmental parameter data. The purpose of this strategy is to appropriately adjust the signal transmission power of the radar according to the current environmental conditions to optimize the performance and measurement accuracy of the radar. Changes in temperature, humidity and air pressure may cause changes in the performance of the internal components of the radar, thereby affecting the signal transmission power. Therefore, the server needs to calculate a compensation value based on these changes and apply it to the signal transmission power of the radar. In this way, even when the environmental conditions change, the radar can maintain a stable signal transmission power, thereby ensuring the accuracy of the measurement data.

[0041] In a possible implementation, a first calibration strategy is generated based on temperature, humidity and air pressure, specifically including: inputting temperature, humidity and air pressure into a preset signal attenuation model to obtain a signal attenuation amount, wherein the preset signal attenuation model pre-stores a correspondence between environmental parameters and signal attenuation amounts; based on the signal attenuation amount, a first calibration strategy is obtained, and the first calibration strategy includes a signal transmission power compensation amount.

[0042] Specifically, the preset signal attenuation model is a pre-established tool for predicting or calculating the signal attenuation. In this model, a corresponding relationship is established between environmental parameters (temperature, humidity and air pressure) and signal attenuation. This corresponding relationship is obtained based on experimental data, theoretical calculations or historical data fitting. By inputting the current environmental parameters (temperature, humidity and air pressure), the model can output the corresponding signal attenuation. This signal attenuation represents the degree of attenuation that the radar signal may suffer during the propagation process under the current environmental conditions. The temperature, humidity and air pressure data measured in real time are input into the preset signal attenuation model. The model calculates the signal attenuation under the current environmental conditions based on the input environmental parameters and the stored corresponding relationship. Based on the calculated signal attenuation, the server generates a first calibration strategy. This strategy includes the compensation amount of the signal transmission power. The purpose of the compensation amount is to offset or reduce the signal attenuation caused by environmental factors, thereby ensuring the stability and accuracy of the radar signal.

[0043] For example, assume that the millimeter-wave cloud radar is working in a high temperature and high humidity environment in summer. At this time, the temperature may reach 35°C, the humidity may reach 80%, and the air pressure is normal atmospheric pressure. The server collects the current environmental parameter data: temperature 35°C, humidity 80%, and normal air pressure. These parameters are input into the preset signal attenuation model. According to the corresponding relationship in the model, the server calculates that the signal attenuation that the radar signal may suffer under the current environmental conditions is XdB (decibel). Based on the calculated signal attenuation XdB, the server generates the first calibration strategy. This strategy tells the radar system that the signal transmission power needs to be increased by YdB to compensate for the signal attenuation caused by environmental factors. In this way, even in a high temperature and high humidity environment, the radar can send a signal of sufficient strength to ensure the accuracy of the measurement data.

[0044] S130, obtaining a reflection signal, where the reflection signal is a signal obtained by preprocessing a signal reflected by a target cloud layer received by a millimeter-wave cloud measuring radar.

[0045] Specifically, millimeter-wave cloud radar transmits millimeter-wave signals into the air. When these signals encounter clouds, part of the signals will be reflected back by the clouds. The radar antenna receives these reflected signals, which contain information about the clouds. Since the received reflected signals may contain noise, interference or other unwanted components, preprocessing is required to improve the quality and reliability of the signal. Preprocessing may include steps such as filtering, amplification, and digitization, which are aimed at removing noise, enhancing signal characteristics, or converting signals into digital form for further processing. The preprocessed signal is called a reflected signal. This signal has removed most of the noise and interference and more accurately reflects the characteristics of the target cloud layer. The reflected signal is the basis for subsequent analysis and processing, and is used to extract various physical parameters of the cloud layer.

[0046] In a possible implementation, obtaining a reflected signal specifically includes: responding to a measurement request input by a user, the measurement request including a target cloud layer; according to the measurement request, controlling a millimeter-wave cloud radar to transmit a measurement signal to the target cloud layer; receiving an original signal sent by the millimeter-wave cloud radar; and preprocessing the original signal to obtain a reflected signal, wherein the preprocessing includes denoising, filtering, and normalization.

[0047] Specifically, the user sends a measurement request to the server through an interface (such as a web page, software application, or console). This request contains information about the target cloud layer that the user wants to measure, which may be the specific location of the cloud layer, the height range, or other relevant parameters. After receiving the measurement request, the server will parse the target cloud layer information in the request and control the millimeter-wave cloud radar device accordingly. The server may send control instructions to the radar device, telling it in which direction and with what parameters (such as frequency, power, beam width, etc.) to transmit the measurement signal. After receiving the control instruction, the millimeter-wave cloud radar will transmit the measurement signal to the target cloud layer according to the instruction requirements and receive the signal reflected from the cloud layer. These reflected signals contain information about the cloud layer, but may also contain unnecessary components such as noise and interference. The radar device sends these signals as raw signals to the server. After receiving the raw signal, the server will preprocess it to improve the quality and reliability of the signal. Preprocessing includes the following steps: using various denoising algorithms (such as filters, threshold processing, etc.) to remove noise components in the original signal; using filters to further reduce high-frequency noise or unnecessary frequency components in the signal; adjusting the amplitude or power of the signal to a certain range or standard for subsequent analysis and processing. The preprocessed signal, i.e. the reflected signal, more accurately reflects the characteristics of the target cloud layer and can be used for subsequent analysis and measurement.

[0048] For example, suppose a meteorological research team wants to measure the height and water content of a cumulonimbus cloud. They send a measurement request to the server through the weather station's software application, which specifies the approximate location and height range of the cumulonimbus cloud. After receiving this request, the server parses the information of the target cloud layer and controls the millimeter-wave cloud radar device to transmit the measurement signal in the specified direction. The radar device sends out signals as required by the instructions and receives the original signals reflected from the cumulonimbus cloud. After receiving these original signals, the server first uses a denoising algorithm to remove the noise components in the signal, and then uses a filter to further reduce high-frequency noise. Next, the server normalizes the amplitude of the signal to adapt it to the subsequent analysis algorithm. After these preprocessing steps, the server obtains a reflected signal. This reflected signal more accurately reflects the characteristics of the cumulonimbus cloud, and the meteorological research team can use this signal to analyze parameters such as the height and water content of the cloud layer, providing valuable information for weather forecasting and climate research.

[0049] S140. Calculate the real-time wind speed and turbulent kinetic energy according to the reflected signal.

[0050] Specifically, by analyzing the Doppler shift of the reflected signal, the server can infer the movement speed of particles in the cloud layer, thereby indirectly obtaining the wind speed. Doppler shift refers to the change in the frequency of the signal received by the observer when there is relative motion between the signal source (here, the signal emitted by the radar) and the observer (radar receiver). By measuring this frequency change, the movement speed of the scatterer in the cloud layer can be calculated, and then the wind speed can be estimated. Turbulent kinetic energy is a physical quantity that measures the intensity of turbulence. It represents the kinetic energy of tiny vortices in turbulence. In meteorology, the magnitude of turbulent kinetic energy is closely related to energy exchange, mixing processes, and the formation of weather phenomena in the atmosphere. By analyzing the intensity and change characteristics of the reflected signal, the turbulent kinetic energy inside the cloud layer can be estimated.

[0051] In a possible implementation, the real-time wind speed and turbulent kinetic energy are calculated based on the reflected signal, specifically including: performing FFT transformation on the reflected signal to obtain a power spectrum corresponding to the reflected signal; determining the Doppler frequency shift peak based on the power spectrum; and calculating the real-time wind speed based on the Doppler frequency shift peak and the wavelength corresponding to the reflected signal, where the wavelength corresponding to the reflected signal is obtained by a preset calculation method based on the frequency of the millimeter-wave cloud radar.

[0052] Specifically, FFT is an efficient algorithm for calculating discrete Fourier transform and its inverse transform. In an embodiment of the present application, FFT is used to convert the reflected signal in the time domain into a power spectrum in the frequency domain. This is because the Doppler shift is easier to observe and measure in the frequency domain. After the FFT transformation, the reflected signal is converted from the time domain to the frequency domain to form a power spectrum. The power spectrum shows the intensity or energy distribution of the signal at different frequencies. In the power spectrum, due to the movement of particles in the cloud, the reflected signal will show the Doppler effect, that is, the frequency of the signal will shift. This offset (Doppler shift) is proportional to the movement speed of the particles (i.e., water droplets or ice crystals in the cloud). By analyzing the power spectrum, the peak of the Doppler shift can be found, that is, the frequency point with the strongest signal energy. Once the Doppler shift peak is determined, the peak value and the wavelength of the radar signal can be used to calculate the wind speed. The relationship between the Doppler shift (Δf) and the wind speed (V) and the wavelength (λ) of the radar signal can be expressed by the following formula:

[0053] ;

[0054] Wherein, θ is the angle between the radar beam and the wind speed. Since in practical applications, the radar beam is usually directed toward the ground, θ is usually very small, and cos(θ) can be approximated to 1. Here, the wavelength (λ) of the radar signal is obtained by a preset calculation method based on the frequency of the millimeter-wave cloud radar. In the embodiment of the present application, the wavelength (λ) is calculated by the ratio between the speed of light and the radar frequency.

[0055] In a possible implementation, the real-time wind speed and turbulent kinetic energy are calculated based on the reflected signal, which specifically includes: obtaining the spectrum broadening corresponding to the reflected signal; inputting the spectrum broadening into a preset turbulence model to obtain turbulence intensity, wherein the preset turbulence model pre-stores the corresponding relationship between the spectrum broadening and the turbulence intensity; and calculating the turbulent kinetic energy based on the real-time wind speed and turbulence intensity.

[0056] Specifically, after the power spectrum of the reflected signal is obtained through FFT transformation, the spectrum broadening is a parameter that describes the width of the power spectrum distribution. It reflects the range of variation of the frequency components in the signal and is related to the velocity distribution of particles in the cloud layer (i.e., turbulence). Among them, the larger the spectrum broadening, the stronger the turbulence. The preset turbulence model is a pre-established mathematical model or algorithm that describes the correspondence between spectrum broadening and turbulence intensity. This model is pre-established based on experimental data, theoretical derivation, or a combination of the two. The server takes the spectrum broadening as an input parameter, and the model outputs the corresponding turbulence intensity value. Through the preset turbulence model, the server can calculate the turbulence intensity based on the input spectrum broadening. Turbulence intensity is a physical quantity that describes the degree of turbulence activity, which is usually related to the velocity fluctuation or energy dissipation caused by turbulence. After obtaining the real-time wind speed and turbulence intensity, the turbulent kinetic energy can be calculated through a certain formula or model. Turbulent kinetic energy is the sum of the kinetic energy of all vortices in turbulence, which is related to the turbulence intensity and the distribution of the velocity field. For example, the turbulent kinetic energy calculated by the model is 0.05m 2 / s 2 .

[0057] S150. Generate a second calibration strategy according to the real-time wind speed and turbulent kinetic energy, where the second calibration strategy is used to adjust the signal receiving sensitivity corresponding to the millimeter-wave cloud detection radar.

[0058] Specifically, signal reception sensitivity refers to the minimum signal strength that the radar system can detect. If the sensitivity is set too high, too much noise may be introduced; if it is set too low, some important echo signals may be missed.

[0059] In a possible implementation, a second calibration strategy is generated based on the real-time wind speed and turbulent kinetic energy, specifically including: determining the relationship between the real-time wind speed and a preset wind speed threshold, and determining the relationship between the turbulent kinetic energy and the preset kinetic energy; if it is determined that the real-time wind speed is greater than the preset wind speed threshold, and the turbulent kinetic energy is greater than the preset kinetic energy, a second calibration strategy is obtained, and the second calibration strategy includes a signal reception sensitivity adjustment amount, and the signal reception sensitivity adjustment amount is used to improve the signal reception sensitivity of the millimeter-wave cloud detection radar.

[0060] Specifically, the preset wind speed threshold is a preset wind speed value used to determine whether the current wind speed reaches a level where the signal receiving sensitivity needs to be adjusted. The server detects the wind speed in real time and compares it with the preset wind speed threshold. The preset kinetic energy is a preset turbulent kinetic energy threshold used to determine whether the current turbulence is strong enough to adjust the signal receiving sensitivity. The server also calculates the turbulent kinetic energy in real time and compares it with the preset kinetic energy. If the real-time wind speed is greater than the preset wind speed threshold, and the turbulent kinetic energy is greater than the preset kinetic energy, then the server will determine that the current environment is relatively harsh and the signal receiving sensitivity of the millimeter wave cloud measurement radar needs to be adjusted to improve the data receiving quality. In this case, the server will generate a second calibration strategy, which includes a signal receiving sensitivity adjustment amount. This adjustment amount is used to guide the radar system on how to adjust its signal receiving sensitivity, usually to increase the sensitivity to capture more signals. Conversely, when the real-time wind speed is less than or equal to the preset wind speed threshold, and the turbulent kinetic energy is less than or equal to the preset kinetic energy, the generated second calibration strategy will point to reducing the signal receiving sensitivity. Among them, the increase in wind speed usually causes the turbulent kinetic energy to increase, so the embodiment of the present application is only analyzed for the above situation.

[0061] For example, suppose a millimeter-wave cloud radar is observing a cloud layer, and the server is analyzing the wind speed and turbulent kinetic energy in real time. The real-time wind speed is 10m / s, and the preset wind speed threshold is 15m / s. The real-time turbulent kinetic energy is 50J / kg, and the preset kinetic energy is 100J / kg. Because neither the real-time wind speed nor the turbulent kinetic energy has reached the preset threshold, the server believes that the current environment has little impact on radar signal reception, so it does not adjust the signal reception sensitivity. Subsequently, the wind speed in the cloud layer suddenly increases, and the real-time wind speed reaches 20m / s. At the same time, due to the increase in cloud turbulence caused by the increase in wind speed, the real-time turbulent kinetic energy also increases to 120J / kg. The server detects that the real-time wind speed is greater than the preset wind speed threshold (20m / s>15m / s). At the same time, the real-time turbulent kinetic energy is also greater than the preset kinetic energy (120J / kg>100J / kg). Based on these two conditions, the server determines that the current environment has a greater impact on radar signal reception and needs to adjust the signal reception sensitivity. The server calculates an appropriate signal reception sensitivity adjustment amount based on the specific values ​​of the current wind speed and turbulent kinetic energy, for example, a 10% increase. This adjustment amount is included in the second calibration strategy and sent to the radar system for execution. After receiving the second calibration strategy, the radar system increases the signal reception sensitivity accordingly based on the adjustment amount. In this way, under severe weather conditions, the radar system can better capture signals in the clouds and improve the accuracy and reliability of data reception.

[0062] S160: Perform online calibration on the millimeter-wave cloud detection radar based on the first calibration strategy and the second calibration strategy.

[0063] For example, before the radar starts working, the server performs initial settings for the radar according to the first calibration strategy. For example, the radar's transmit power, antenna pointing and other parameters are set to adapt to the specific observation area and target characteristics. After the radar starts working, the server starts to receive real-time data and calibrates the radar online based on this data. Over time, environmental conditions may change, such as increased wind speed and thicker clouds. These changes may affect the data quality of the radar. The server monitors the changes in environmental conditions in real time and dynamically adjusts the radar according to the second calibration strategy. For example, when the wind speed increases, the server may increase the radar's signal reception sensitivity to capture more cloud information. When the cloud layer becomes thicker, the server may adjust the filter settings to reduce interference inside the cloud layer. Throughout the observation process, the server will comprehensively consider the first calibration strategy and the second calibration strategy to continuously and comprehensively adjust the radar. Such adjustments may include fine-tuning of the transmit power, micro-adjustment of the antenna pointing, adjustment of the filter settings, etc., to ensure that the radar can obtain accurate and reliable data under various conditions.

[0064] Therefore, the combined use of the first calibration strategy and the second calibration strategy can more accurately compensate for the influence of various factors on the radar measurement accuracy. This enables the radar system to provide more accurate and reliable meteorological data, and improves the scientificity and application value of the data. Online calibration can monitor the performance status of the radar system in real time and adjust and optimize it when necessary. This helps to timely discover and correct potential problems in the radar system, avoiding measurement errors and data distortion caused by system performance degradation. The traditional shutdown maintenance method requires a lot of manpower, material resources and time, and may damage the components of the radar system. Online calibration technology can be calibrated during the operation of the radar system without shutdown maintenance, reducing maintenance costs. In addition, since the calibration process is automated, there is no need for professionals to go to the site for operation, which further reduces labor costs. Through online calibration technology, the performance parameters of the radar system can be adjusted and optimized in real time to ensure the accuracy and reliability of the measurement data. This is of great significance for data applications in the fields of meteorological observation and environmental monitoring, and can improve data quality and application effects. Accurate data can provide strong support for weather forecasts, disaster warnings, etc., and reduce decision-making errors caused by inaccurate data.

[0065] In one possible implementation, refer to Figure 2 , Figure 2Another flow chart of an online calibration method for a millimeter-wave cloud detection radar provided in an embodiment of the present application includes steps S210 to S220, and the steps are as follows: S210, responding to a calibration request input by a user, the calibration request including a target signal transmission power and a target signal receiving sensitivity; S220, calibrating the millimeter-wave cloud detection radar using the target signal transmission power and the target signal receiving sensitivity.

[0066] Specifically, the user may send a calibration request to the server through an interface (such as control software, hardware switch, etc.) based on specific observation requirements or experimental purposes. This calibration request usually contains two key parameters: target signal transmission power and target signal receiving sensitivity. The target signal transmission power is the power value of the signal that the user wants the radar system to transmit. Different application scenarios and observation targets may require different transmission powers. Higher transmission power can cover longer distances or penetrate thicker clouds, but it may also increase energy consumption and interfere with other devices. The target signal receiving sensitivity is the minimum signal strength that the user wants the radar system to detect. This parameter determines the radar system's ability to capture weak signals. Higher receiving sensitivity can detect weaker signals, but it may also increase noise interference and false alarm rate. After receiving the calibration request input by the user, the server will parse the target signal transmission power and target signal receiving sensitivity in the request, and apply these parameters to the calibration process of the millimeter wave cloud radar. The calibration process may include adjusting the parameter settings of the radar's transmitter, receiver and other hardware components to ensure that the radar system works according to the target parameters specified by the user.

[0067] For example, suppose a meteorological observation station uses a millimeter-wave cloud radar to observe clouds. Since the height and thickness of clouds vary, users may need to adjust the parameters of the radar according to actual conditions to obtain more accurate observation data. Users find that the current radar's observation range is not far enough to cover some distant clouds. At the same time, users also notice that the radar has some problems when receiving weak signals, which may lead to inaccurate data. The user sends a calibration request to the server through the control software of the meteorological observation station. In the request, the user specifies the target signal transmission power as XX watts (higher than the current transmission power) to cover a longer distance; at the same time, the user also specifies the target signal receiving sensitivity as YY decibels (higher than the current receiving sensitivity) to improve the ability to capture weak signals. After receiving the user's calibration request, the server parses out two parameters: the target signal transmission power and the target signal receiving sensitivity. Then, the server calibrates the millimeter-wave cloud radar based on these parameters. This process may include adjusting the radar's transmitter power, receiver gain and other parameter settings. After calibration, the millimeter-wave cloud radar's transmission power and receiving sensitivity are adjusted to the target values ​​specified by the user. Radar can now cover longer distances and better capture weak signals. Users can view calibrated radar parameters and observation data in real time through the control software or interface of the meteorological observation station to ensure that the radar system works according to user needs.

[0068] The present application also provides an online calibration device for millimeter wave cloud radar, referring to Figure 3 , Figure 3 A schematic diagram of a module for an online calibration device for a millimeter-wave cloud radar provided in an embodiment of the present application. The online calibration device is a server, and the server includes an acquisition module 31 and a processing module 32, wherein the acquisition module 31 acquires environmental parameters of the environment in which the millimeter-wave cloud radar is located, and the environmental parameters include temperature, humidity and air pressure; the processing module 32 generates a first calibration strategy according to the temperature, humidity and air pressure, and the first calibration strategy is used to compensate for the signal transmission power corresponding to the millimeter-wave cloud radar; the acquisition module 31 acquires a reflected signal, and the reflected signal is a signal obtained after preprocessing the signal reflected by the target cloud layer received by the millimeter-wave cloud radar; the processing module 32 calculates the real-time wind speed and turbulent kinetic energy according to the reflected signal; the processing module 32 generates a second calibration strategy according to the real-time wind speed and turbulent kinetic energy, and the second calibration strategy is used to adjust the signal receiving sensitivity corresponding to the millimeter-wave cloud radar; the processing module 32 performs online calibration on the millimeter-wave cloud radar based on the first calibration strategy and the second calibration strategy.

[0069] In a possible implementation, the processing module 32 generates a first calibration strategy based on temperature, humidity and air pressure, specifically including: the processing module 32 inputs the temperature, humidity and air pressure into a preset signal attenuation model to obtain a signal attenuation amount, and the preset signal attenuation model pre-stores a correspondence between environmental parameters and signal attenuation amounts; the processing module 32 obtains a first calibration strategy based on the signal attenuation amount, and the first calibration strategy includes a signal transmission power compensation amount.

[0070] In a possible implementation, the acquisition module 31 acquires the reflected signal, specifically including: the acquisition module 31 responds to a measurement request input by a user, and the measurement request includes a target cloud layer; the processing module 32 controls the millimeter-wave cloud measurement radar to transmit a measurement signal to the target cloud layer according to the measurement request; the acquisition module 31 receives the original signal sent by the millimeter-wave cloud measurement radar; the processing module 32 preprocesses the original signal to obtain a reflected signal, and the preprocessing includes denoising, filtering, and normalization.

[0071] In a possible implementation, the processing module 32 calculates the real-time wind speed and turbulent kinetic energy based on the reflected signal, specifically including: the processing module 32 performs FFT transformation on the reflected signal to obtain a power spectrum corresponding to the reflected signal; the processing module 32 determines the Doppler frequency shift peak based on the power spectrum; the processing module 32 calculates the real-time wind speed based on the Doppler frequency shift peak and the wavelength corresponding to the reflected signal, and the wavelength corresponding to the reflected signal is obtained by a preset calculation method based on the frequency of the millimeter-wave cloud radar.

[0072] In a possible implementation, the processing module 32 calculates the real-time wind speed and turbulent kinetic energy based on the reflected signal, and specifically also includes: the acquisition module 31 obtains the spectrum broadening corresponding to the reflected signal; the processing module 32 inputs the spectrum broadening into a preset turbulence model to obtain the turbulence intensity, and the preset turbulence model pre-stores the corresponding relationship between the spectrum broadening and the turbulence intensity; the processing module 32 calculates the turbulent kinetic energy based on the real-time wind speed and turbulence intensity.

[0073] In a possible implementation, the processing module 32 generates a second calibration strategy based on the real-time wind speed and the turbulent kinetic energy, specifically including: the processing module 32 determines the relationship between the real-time wind speed and the preset wind speed threshold, and determines the relationship between the turbulent kinetic energy and the preset kinetic energy; if the processing module 32 determines that the real-time wind speed is greater than the preset wind speed threshold, and the turbulent kinetic energy is greater than the preset kinetic energy, then a second calibration strategy is obtained, and the second calibration strategy includes a signal reception sensitivity adjustment amount, and the signal reception sensitivity adjustment amount is used to improve the signal reception sensitivity of the millimeter wave cloud measurement radar.

[0074] In a possible implementation, the acquisition module 31 responds to a calibration request input by a user, where the calibration request includes a target signal transmission power and a target signal receiving sensitivity; the processing module 32 uses the target signal transmission power and the target signal receiving sensitivity to calibrate the millimeter-wave cloud detection radar.

[0075] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0076] The present application also provides an electronic device, referring to Figure 4 , Figure 4 The electronic device may include: at least one processor 41 , at least one network interface 44 , a user interface 43 , a memory 45 , and at least one communication bus 42 .

[0077] The communication bus 42 is used to realize the connection and communication between these components.

[0078] The user interface 43 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 43 may also include a standard wired interface and a wireless interface.

[0079] The network interface 44 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0080] Among them, the processor 41 may include one or more processing cores. The processor 41 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 45, and calling data stored in the memory 45. Optionally, the processor 41 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 41 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 41, and it can be implemented separately through a chip.

[0081] Among them, the memory 45 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 45 includes a non-transitory computer-readable storage medium. The memory 45 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 45 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 45 may also be optionally at least one storage device located away from the aforementioned processor 41. As Figure 4 As shown, the memory 45 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program for an online calibration method for millimeter-wave cloud radar.

[0082] exist Figure 4In the electronic device shown, the user interface 43 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 41 can be used to call an application stored in the memory 45 for an online calibration method applied to a millimeter-wave cloud radar. When executed by one or more processors, the electronic device executes one or more methods in the above-mentioned embodiments.

[0083] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0084] The present application also provides a computer-readable storage medium, which stores instructions. When executed by one or more processors, the electronic device executes one or more of the methods described in the above embodiments.

[0085] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0086] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0087] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0088] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0089] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0090] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, it will be easy for those skilled in the art to think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. An online calibration method for millimeter wave cloud radar, characterized in that: The method comprises: Acquire environmental parameters of the environment in which the millimeter-wave cloud detection radar is located, wherein the environmental parameters include temperature, humidity and air pressure; Generate a first calibration strategy according to the temperature, the humidity, and the air pressure, wherein the first calibration strategy is used to compensate for the signal transmission power corresponding to the millimeter-wave cloud detection radar; Acquire a reflection signal, where the reflection signal is a signal obtained by preprocessing a signal reflected by a target cloud layer received by the millimeter-wave cloud radar; According to the reflected signal, the real-time wind speed and turbulent kinetic energy are calculated; generating a second calibration strategy according to the real-time wind speed and the turbulent kinetic energy, wherein the second calibration strategy is used to adjust the signal receiving sensitivity corresponding to the millimeter-wave cloud detection radar; Based on the first calibration strategy and the second calibration strategy, online calibration is performed on the millimeter-wave cloud detection radar; The generating a second calibration strategy according to the real-time wind speed and the turbulent kinetic energy specifically includes: Determining the magnitude relationship between the real-time wind speed and a preset wind speed threshold, and determining the magnitude relationship between the turbulent kinetic energy and a preset kinetic energy; If it is determined that the real-time wind speed is greater than the preset wind speed threshold and the turbulent kinetic energy is greater than the preset kinetic energy, the second calibration strategy is obtained, and the second calibration strategy includes a signal receiving sensitivity adjustment amount, and the signal receiving sensitivity adjustment amount is used to improve the signal receiving sensitivity of the millimeter-wave cloud detection radar.

2. The online calibration method for millimeter wave cloud radar according to claim 1, characterized in that: The generating a first calibration strategy according to the temperature, the humidity and the air pressure specifically includes: Inputting the temperature, the humidity and the air pressure into a preset signal attenuation model to obtain a signal attenuation amount, wherein the preset signal attenuation model pre-stores a corresponding relationship between environmental parameters and signal attenuation amounts; The first calibration strategy is obtained according to the signal attenuation amount, and the first calibration strategy includes a signal transmission power compensation amount.

3. The online calibration method for millimeter wave cloud radar according to claim 1, characterized in that: The obtaining of the reflection signal specifically includes: In response to a measurement request input by a user, the measurement request includes the target cloud layer; According to the measurement request, controlling the millimeter-wave cloud measurement radar to transmit a measurement signal to the target cloud layer; Receiving the original signal sent by the millimeter-wave cloud detection radar; The original signal is preprocessed to obtain the reflected signal, wherein the preprocessing includes denoising, filtering and normalization.

4. The online calibration method for millimeter wave cloud radar according to claim 1, characterized in that: The calculating of the real-time wind speed and turbulent kinetic energy according to the reflected signal specifically includes: Performing FFT transformation on the reflected signal to obtain a power spectrum corresponding to the reflected signal; Determining a Doppler shift peak value according to the power spectrum; The real-time wind speed is calculated according to the Doppler frequency shift peak and the wavelength corresponding to the reflected signal, and the wavelength corresponding to the reflected signal is obtained by a preset calculation method according to the frequency of the millimeter-wave cloud radar.

5. The online calibration method for millimeter wave cloud radar according to claim 4, characterized in that: The step of calculating the real-time wind speed and turbulent kinetic energy according to the reflected signal specifically includes: Obtaining a spectrum broadening corresponding to the reflected signal; Inputting the spectrum broadening into a preset turbulence model to obtain turbulence intensity, wherein the preset turbulence model pre-stores a corresponding relationship between spectrum broadening and turbulence intensity; The turbulence kinetic energy is calculated according to the real-time wind speed and the turbulence intensity.

6. The online calibration method for millimeter wave cloud radar according to claim 1, characterized in that: The method further comprises: In response to a calibration request input by a user, the calibration request includes a target signal transmission power and a target signal reception sensitivity; The target signal transmission power and the target signal receiving sensitivity are used to calibrate the millimeter-wave cloud detection radar.

7. An online calibration device for millimeter wave cloud radar, characterized in that: The online calibration device comprises an acquisition module (31) and a processing module (32), wherein: The acquisition module (31) is used to acquire environmental parameters of the environment in which the millimeter wave cloud detection radar is located, wherein the environmental parameters include temperature, humidity and air pressure; The processing module (32) is used to generate a first calibration strategy according to the temperature, the humidity and the air pressure, wherein the first calibration strategy is used to compensate for the signal transmission power corresponding to the millimeter wave cloud detection radar; The acquisition module (31) is further used to acquire a reflection signal, wherein the reflection signal is a signal obtained by preprocessing a signal reflected by a target cloud layer received by the millimeter wave cloud measurement radar; The processing module (32) is further used to calculate the real-time wind speed and turbulent kinetic energy according to the reflected signal; The processing module (32) is further used to generate a second calibration strategy according to the real-time wind speed and the turbulent kinetic energy, wherein the second calibration strategy is used to adjust the signal receiving sensitivity corresponding to the millimeter wave cloud detection radar; The processing module (32) is further used to perform online calibration on the millimeter wave cloud detection radar based on the first calibration strategy and the second calibration strategy; The processing module (32) is also used to generate a second calibration strategy based on the real-time wind speed and the turbulent kinetic energy, specifically including: judging the magnitude relationship between the real-time wind speed and a preset wind speed threshold, and judging the magnitude relationship between the turbulent kinetic energy and a preset kinetic energy; if it is determined that the real-time wind speed is greater than the preset wind speed threshold, and the turbulent kinetic energy is greater than the preset kinetic energy, then obtaining the second calibration strategy, the second calibration strategy including a signal receiving sensitivity adjustment amount, the signal receiving sensitivity adjustment amount being used to improve the signal receiving sensitivity of the millimeter wave cloud detection radar.

8. An electronic device, characterized in that: The electronic device comprises a processor (41), a memory (45), a user interface (43) and a network interface (44), wherein the memory (45) is used to store instructions, the user interface (43) and the network interface (44) are both used to communicate with other devices, and the processor (41) is used to execute the instructions stored in the memory (45) so that the electronic device executes the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 6 is performed.

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