A method and device for measuring wind by optical wave space-time synchronization

Abstract

The application relates to a light wave space-time synchronization wind measurement method and equipment, which comprises the following steps: obtaining laser radar observation data and millimeter wave radar observation data; calculating real-time characteristic parameters based on the laser radar observation data and the millimeter wave radar observation data; calculating laser main body weight and millimeter wave main body weight based on the real-time characteristic parameters; calculating a dew point temperature difference based on temperature and relative humidity, and calculating a meteorological adjustment factor based on the dew point temperature difference and the relative humidity; correcting the laser main body weight and the millimeter wave main body weight by using the meteorological adjustment factor to obtain corrected weights; performing wind vector estimation based on the laser radar observation data and the millimeter wave radar observation data, and performing weighted fusion on the wind vector estimation results by using the corrected weights to obtain an actual wind speed; and the equipment is used for realizing the method. Compared with the prior art, the application provides a high-precision wind measurement method which can adapt to complex weather changes.

Description

Technical Field

[0001] This invention relates to the field of wind field detection and multi-radar fusion algorithm technology, and in particular to a method and device for synchronous optical wave spatiotemporal wind measurement. Background Technology

[0002] Wind speed and direction information play a crucial role in wind farm power prediction, aviation flight safety, meteorological monitoring, and national defense applications. With the continuous development of large-scale wind farms and the increasing demand for refined weather forecasts, wind measurement technology not only needs to achieve long-distance, all-weather, and high-resolution detection, but also needs to maintain continuity and stability under complex weather conditions such as rain and fog. Currently, lidar and millimeter-wave radar are commonly used for wind measurement. Lidar employs a coherent Doppler system, with echoes primarily generated by atmospheric aerosol scattering. Within the clear sky boundary layer, aerosol content is relatively abundant, atmospheric extinction in the visible and near-infrared bands is minimal, and the short wavelength results in a larger Doppler frequency shift for the same velocity, while narrow beamwidth allows for a smaller detection volume. Therefore, higher spatial resolution and velocity measurement accuracy can be obtained under clear weather conditions. However, when entering low-visibility environments such as fog, rain, and dust storms, Mie scattering and absorption cause a sharp attenuation of the optical echo, shortening the effective distance, steepening the echo slope, and reducing the reliability of wind measurement. Millimeter-wave radar retrieves wind fields through Doppler analysis of the echoes scattered by water droplets, cloud particles, and other water condensates. In rain, fog, snow, and other weather conditions, the particle size of water condensates is comparable to the wavelength of millimeter waves, and the volume scattering coefficient is significant. At the same time, millimeter waves have better penetration ability for fog droplets than optical bands, thus maintaining high echo intensity and operational stability in adverse weather conditions. However, due to the limitations of longer wavelength and larger beam scattering volume, its spatial resolution and velocity measurement accuracy are usually lower than those of lidar.

[0003] To improve the adaptability of wind measurement systems under complex weather conditions and achieve all-weather operation, a combined approach using lidar and millimeter-wave radar offers significant advantages: lidar, under clear skies, can obtain high-precision, high-resolution wind field information thanks to its short wavelength and narrow beamwidth; millimeter-wave radar exhibits strong response to water condensation scattering in low-visibility environments such as fog and rain, resulting in more stable detection. For example, Chinese patent application CN116520312A provides a wind measurement method based on the fusion of millimeter-wave radar and lidar data. This method collects two types of radial wind speeds at the same time and altitude, selects the dominant mode based on valid bits or flag bits, and then uses fixed or scenario-specific weights for weighted output of the results. While this overcomes the limitations of single-mode measurement, it still suffers from the following problems: its weighting decisions essentially rely on posterior usability or unavailability judgments and empirical rules, ignoring the observational characteristics of different laser channels' applicability. This leads to limited wind measurement accuracy when facing cross-weather scenarios, and the switching from clear to foggy weather depends on flag bits and fixed thresholds, resulting in limited interpretability and continuity. For example, Chinese patent application CN116859384A provides a radar wind measurement method based on multi-radar data fusion. Although it avoids the problem of relying on empirical rules for weight decision-making in Chinese patent application CN116520312A by introducing adaptive weights, its calculation only relies on data variance and does not consider the impact of real-time meteorological parameters such as visibility and precipitation intensity on the reliability of sensor measurements. As a result, when the weather changes rapidly, the weight adjustment cannot respond to environmental changes in a timely manner, which can easily lead to lag or jumps in the fusion results. In addition, because it ignores the fact that the sensor quality will also change with complex weather changes when calculating the weights, the fusion algorithm will not be able to effectively identify and compensate for the decline in sensor data quality under some complex conditions, which will reduce the continuity and consistency of the measurement results and affect the overall stability and accuracy reliability of the system in variable environments.

[0004] Therefore, providing a high-precision wind measurement method that can adapt to complex weather changes is a technical problem that needs to be solved. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a method and device for optical wave spatiotemporal synchronous wind measurement. By utilizing the combined information of channel availability and meteorological priors in the weighting construction, it is possible to identify visibility changes in advance, suppress switching jumps, and significantly improve the accuracy and engineering applicability of fusion wind measurement under complex weather conditions.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] According to a first aspect of the present invention, a method for optical wave spatiotemporal synchronous wind measurement is provided, the method comprising:

[0008] Simultaneously acquire lidar observation data and millimeter-wave radar observation data within the same spatiotemporal unit with the same dwell time, the same field of view, and the same distance gate;

[0009] Real-time characterization parameters are calculated based on the lidar observation data and the millimeter-wave radar observation data, respectively; the real-time characterization parameters include lidar real-time characterization parameters and millimeter-wave real-time characterization parameters.

[0010] The weights of the laser host and the millimeter-wave host are calculated based on the real-time characterization parameters described above.

[0011] Obtain the temperature and relative humidity of the wind measurement scenario, calculate the dew point temperature difference based on the temperature and relative humidity, and calculate meteorological data based on the dew point temperature difference and relative humidity. Regulatory factors;

[0012] Using the aforementioned meteorological The adjustment factor corrects the weights of the laser main body and the millimeter wave main body to obtain the corrected weights;

[0013] Wind vector estimation is performed based on the aforementioned lidar observation data and millimeter-wave radar observation data, and the wind vector estimation results are weighted and fused using the aforementioned corrected weights to obtain the actual wind speed.

[0014] As a preferred technical solution, the laser real-time characterization parameters include signal-to-noise ratio, effective distance, and echo slope;

[0015] The method for obtaining the effective distance is as follows: obtain the maximum distance corresponding to the laser echo intensity decrease value when a set intensity threshold is set, and the maximum distance is the effective distance;

[0016] The method for obtaining the echo slope is as follows: within a preset distance range, the laser echo energy curve is linearly fitted based on the laser radar observation data, and the slope is calculated based on the fitting result. This slope is the echo slope.

[0017] As a preferred technical solution, the real-time characterization parameters of the millimeter-wave light include signal-to-noise ratio, reflectivity, and substrate noise;

[0018] The reflectivity mentioned is calculated based on the millimeter-wave transmission power using radar equations;

[0019] The method for obtaining the aforementioned floor noise is as follows: calculate the average received power of millimeter waves in a signal-free region, and the average received power is the floor noise; the signal-free region includes long-range gates or range gates not occupied by laser echoes.

[0020] As a preferred technical solution, the method for calculating the weight of the laser body is as follows:

[0021] The laser relative quantity is calculated based on the laser real-time characterization parameters. The laser relative quantity includes the effective distance relative quantity and the echo slope relative quantity. The effective distance relative quantity is the ratio of the real-time acquired effective distance to the preset reference value of the effective distance. The echo slope relative quantity is the ratio of the preset reference value of the echo slope to the real-time acquired echo slope.

[0022] The laser relative quantity is mapped to a laser characteristic quantity, wherein the laser characteristic quantity includes an effective distance characteristic quantity and an echo slope characteristic quantity, the value of the effective distance relative quantity is the value of the effective distance characteristic quantity; the value of the echo slope relative quantity is the value of the echo slope characteristic quantity;

[0023] The laser harmonic mean is calculated based on the reciprocal of the laser characteristic quantity, and the laser scaling factor is calculated based on the laser signal-to-noise ratio and the millimeter-wave signal-to-noise ratio.

[0024] The laser subject weight is calculated based on the laser scaling factor and the laser harmonic mean.

[0025] As a preferred technical solution, the method for calculating the weight of the millimeter wave main body is as follows:

[0026] The millimeter wave relative quantities are calculated based on the millimeter wave real-time characterization parameters. The millimeter wave relative quantities include the relative reflectivity and the relative floor noise. The relative reflectivity is the ratio of the real-time acquired reflectivity to the preset reference value of reflectivity. The relative floor noise is the ratio of the preset reference value of floor noise to the real-time acquired floor noise.

[0027] The millimeter-wave relative quantities are mapped to millimeter-wave characteristic quantities, wherein the millimeter-wave characteristic quantities include reflectivity characteristic quantities and floor noise characteristic quantities. The reflectivity characteristic quantity is the ratio between the reflectivity relative quantity and the sum of itself plus one; the floor noise characteristic quantity is the ratio between the floor noise relative quantity and the sum of itself plus one.

[0028] The millimeter-wave harmonic mean is calculated based on the reciprocal of the millimeter-wave characteristic quantities, and the millimeter-wave scaling factor is calculated based on the millimeter-wave signal-to-noise ratio and the millimeter-wave signal-to-noise ratio.

[0029] The millimeter-wave main weight is calculated based on the millimeter-wave scaling factor and the millimeter-wave harmonic mean.

[0030] As a preferred technical solution, the meteorological data is calculated. The method for adjusting factors is as follows:

[0031] Based on the aforementioned dew point temperature difference and relative humidity, a continuous and bounded soft threshold mapping is constructed. The soft thresholds include a dew point temperature soft threshold and a relative humidity soft threshold, as follows:

[0032] ,

[0033] ,

[0034] in, This indicates the preset dew point temperature difference reference value; Indicates the dew point temperature difference; Indicates relative humidity; This indicates the preset relative humidity reference value; and These represent the slopes for controlling the soft threshold of dew point temperature and the soft threshold of relative humidity, respectively. Represents a computation function, and has ; Indicates the soft threshold temperature of the dew point; This indicates the soft threshold for relative humidity.

[0035] Meteorological calculations based on the aforementioned soft threshold The adjustment factor is: , and This represents adjustable weights, and satisfies... , .

[0036] As a preferred technical solution, the method for calculating the correction weight is as follows:

[0037] ,

[0038] ,

[0039] in, Indicates the weight of the laser body; Indicates weather Regulatory factors; Indicates the main weight of millimeter waves; Indicates the laser correction weight; This indicates the millimeter-wave correction weight.

[0040] As a preferred technical solution, the weighted fusion method is as follows:

[0041] ,

[0042] in, This represents the laser correction weight of the laser beam measured at azimuth angle i; This represents the estimated wind vector value of the laser beam measured at azimuth angle i; This represents the time it takes for the laser to measure wind speed at azimuth angle i; This indicates the wind direction measured by the laser at azimuth angle i; This represents the millimeter-wave correction weight of the millimeter-wave measurement beam at azimuth angle i; This represents the estimated wind vector of the millimeter wave beam measured at azimuth angle i; This represents the time it takes for the millimeter wave to measure the wind speed at azimuth angle i; This indicates the wind direction measured by millimeter waves at azimuth angle i; This indicates the total number of beams.

[0043] According to a second aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described thereon.

[0044] Compared with existing technologies, this invention provides a wind speed fusion weighting calculation method that considers the impact of complex environmental factors on the accuracy of multiple radar devices. It introduces core observation features of radar devices, including signal-to-noise ratio, effective detection range, echo slope, reflectivity, and floor noise, to construct the main weights. Furthermore, considering the impact of environmental factors on measurement accuracy, this invention also incorporates meteorological data. The adjustment factor corrects the main weights, and through the above methods, a continuous and bounded weight trajectory is calculated. This avoids the lag or jump problems in the fusion results caused by hard switching and abrupt changes under boundary weather conditions, making the fusion results more continuous, consistent, and traceable in terms of time and altitude profiles. Specifically:

[0045] 1) In the process of calculating the main weight, this invention first calculates the relative value based on the characterization parameter to weaken the difference in parameter variation; then, the relative value is monotonically bounded to generate feature quantity to ensure that different physical quantities are comparable; then, the harmonic mean is calculated based on the feature quantity to suppress the influence of single index anomalies and accidental peaks on the weight; next, a scaling factor based on the signal-to-noise ratio is introduced to characterize the reliability of laser and millimeter wave in the same spatial unit; finally, the main weight with engineering properties such as physical interpretability and anti-anomaly is calculated based on the harmonic mean and scaling factor. This main weight ensures the complementary relationship between high precision of laser and stability of millimeter wave under different weather conditions.

[0046] 2) Based on meteorology When adjusting the weights of the adjustment factors, the dew point difference is used. Dominant, relative humidity To assist in this process, a bounded and continuous meteorological ΔT–RH adjustment factor is constructed to apply a forward-looking and smooth meteorological correction to the main weights. This ensures that when a decrease in ΔT or an increase in RH indicates a decline in visibility, the meteorological... By increasing the adjustment factor, the millimeter wave weight is increased in advance, while the laser weight decreases synchronously. Combined with a soft threshold, the weight remains smooth and controllable as the environment evolves. This significantly reduces variance spikes and short-term oscillations in boundary weather, ensuring continuous weight transition and consistency of test results even when the weather changes, thereby achieving more accurate wind measurement. Attached Figure Description

[0047] Figure 1 This is a flowchart of the method of the present invention;

[0048] Figure 2 This is a schematic diagram of the operation of the optical wave spatiotemporal synchronous wind measurement system of the present invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0050] To address the challenge of balancing accuracy and continuity in wind measurement under varying weather conditions in existing technologies, this invention proposes a spatiotemporally synchronized optical wind measurement method. This method constructs a main weighting mechanism and incorporates meteorological data. The adjustment factor corrects the main weights, and the corrected weights are used to fuse the laser wind measurement and millimeter-wave wind measurement results to obtain the final wind measurement result. The method flow is as follows: Figure 1 As shown, it includes the following steps:

[0051] S1. Simultaneously acquire lidar observation data and millimeter-wave radar observation data within the same spatiotemporal unit with the same dwell time, same field of view, and same distance gate.

[0052] In this embodiment, the following is adopted: Figure 2 The system shown measures wind speed. Figure 2 The isomorphic radar emits lidar beams and millimeter-wave radar beams in spatiotemporal units t1, t2, ..., tn, respectively, and collects lidar observation data and millimeter-wave radar observation data based on the emitted lidar beams and millimeter-wave radar beams.

[0053] S2. Calculate real-time characterization parameters based on lidar observation data and millimeter-wave radar observation data respectively.

[0054] In this embodiment, the real-time characterization parameters include laser real-time characterization parameters and millimeter-wave real-time characterization parameters.

[0055] For real-time laser characterization parameters, these include signal-to-noise ratio (SNR), effective range, and echo slope. To obtain a more accurate SNR, bandpass filtering, pulse accumulation, or coherent integration are performed on the laser echo to improve the SNR. The maximum range corresponding to the laser echo intensity drop value set at a threshold (e.g., 3–5 times the noise baseline) is obtained, and the maximum range is the effective range. Within a preset range, the laser echo energy curve is linearly fitted based on lidar observation data, and the slope is calculated based on the fitting result; this slope is the echo slope.

[0056] For real-time characterization parameters of millimeter-wave light, these include signal-to-noise ratio (SNR), reflectivity, and floor noise. Similarly, to obtain a more accurate SNR, baseline correction of the millimeter-wave echo is required to remove static clutter. Reflectivity is based on the millimeter-wave transmit power and... Figure 2 The system constants of the provided system are obtained by converting radar equations. It should be noted that the radar equations used in this invention are common techniques used by those skilled in the art, and therefore will not be described in detail here. The average received power of millimeter waves is calculated in the signal-free region. The average received power is the basis noise. The signal-free region includes the long-range gate or the range gate not occupied by the laser echo.

[0057] In addition, this step requires filtering clear-sky segments from historical data of the test environment. These clear-sky segments are defined as segments with no precipitation, no fog, high laser signal-to-noise ratio, and low millimeter-wave reflectivity. Within a stable window, the effective distance... echo slope Reflectivity and floor noise Obtain the corresponding reference value from the statistical values. , , as well as .

[0058] S3. Calculate the weights of the laser main body and the millimeter-wave main body based on the real-time characterization parameters.

[0059] S31. Calculate the weight of the laser body.

[0060] S311. Calculate the relative laser quantities based on the real-time laser characterization parameters. The relative laser quantities include the relative quantities of effective distance and echo slope.

[0061] The effective distance relative quantity is the ratio of the real-time acquired effective distance to the preset reference value of the effective distance; the echo slope relative quantity is the ratio of the preset reference value of the echo slope to the real-time acquired echo slope, and the corresponding expression is:

[0062] ,

[0063] ,

[0064] in, Indicates the relative quantity of effective distance; This indicates the effective distance acquired in real time. This indicates the preset reference value for the effective distance; This represents the relative amount of echo slope; This indicates the preset reference value for the echo slope; This indicates the echo slope acquired in real time.

[0065] S312. Map the relative laser quantities to laser characteristic quantities, where the laser characteristic quantities include effective distance characteristic quantities and echo slope characteristic quantities.

[0066] The value of the relative quantity of effective distance is the value of the effective distance characteristic quantity, and the value of the relative quantity of echo slope is the value of the echo slope characteristic quantity, that is:

[0067] ,

[0068] ,

[0069] in, Represents the effective distance feature quantity; This represents the characteristic quantity of echo slope.

[0070] S313. Calculate the harmonic mean of the laser based on the reciprocal of the laser characteristic quantity, and calculate the laser scaling factor based on the laser signal-to-noise ratio and the millimeter-wave signal-to-noise ratio:

[0071] .

[0072] S314. The laser subject weight is calculated based on the laser scaling factor and the laser harmonic mean as follows:

[0073] ,

[0074] in, Indicates the laser signal-to-noise ratio; This indicates the millimeter-wave signal-to-noise ratio.

[0075] S32. Calculate the weight of the millimeter wave main body.

[0076] S321. Calculate millimeter wave relative quantities based on millimeter wave real-time characterization parameters. Millimeter wave relative quantities include relative reflectivity and relative floor noise.

[0077] The relative reflectance is the ratio of the real-time acquired reflectance to the preset reference value of reflectance; the relative floor noise is the ratio of the preset reference value of floor noise to the real-time acquired floor noise, and the corresponding calculation expression is:

[0078] ,

[0079] ,

[0080] in, Represents a relative measure of reflectivity; This represents the reflectance acquired in real time. This indicates the preset reference value for reflectivity; Indicates the relative amount of base noise; This indicates the preset reference value for the base noise. This represents the real-time acquired floor noise.

[0081] S322. Map millimeter-wave relative quantities to millimeter-wave characteristic quantities, where millimeter-wave characteristic quantities include reflectivity characteristic quantities and floor noise characteristic quantities.

[0082] The reflectivity characteristic is the ratio between the relative reflectivity and the sum of its value plus one; the floor noise characteristic is the ratio between the relative floor noise and the sum of its value plus one. The corresponding expressions are:

[0083] ,

[0084] ,

[0085] in, Represents the reflectivity characteristic quantity; This represents the characteristic quantity of the base noise.

[0086] S323. Calculate the millimeter-wave harmonic mean based on the reciprocal of millimeter-wave characteristic quantities, and calculate the millimeter-wave scaling factor based on the millimeter-wave signal-to-noise ratio (SNR) and millimeter-wave SNR, as follows:

[0087] ,

[0088] in, Represents the reflectivity characteristic quantity; This represents the characteristic quantity of the base noise.

[0089] S324. The millimeter-wave matrix weight is calculated based on the millimeter-wave scaling factor and the millimeter-wave harmonic mean as follows:

[0090] ,

[0091] in, Indicates the millimeter-wave signal-to-noise ratio; This indicates the millimeter-wave signal-to-noise ratio.

[0092] S4. Obtain the temperature and relative humidity of the wind measurement scene, calculate the dew point temperature difference based on the temperature and relative humidity, and calculate meteorological data based on the dew point temperature difference and relative humidity. Regulatory factors.

[0093] S41. Calculate the dew point temperature difference.

[0094] Temperature T and relative humidity RH are obtained from conventional weather sensors, and dew point temperature is calculated using the Magnus formula or other empirical formulas. Its expression can be:

[0095] ,

[0096] in, and Represents empirical constants, and is commonly used , ; Represents the Gamma function; Indicates temperature; This indicates relative humidity.

[0097] S42, Calculating Meteorology Regulatory factors.

[0098] S421. Construct a continuous and bounded soft threshold mapping based on the dew point temperature difference and relative humidity. The soft thresholds include the dew point temperature soft threshold and the relative humidity soft threshold.

[0099] Construct a continuous bounded soft threshold mapping and , Introduction and The slope used to control the soft threshold enables adaptive adjustment of the slope to ensure... and The adjustment range varies within a certain range, thereby achieving a moderate and smooth regulation effect under different meteorological conditions. The corresponding expression is:

[0100] ,

[0101] ,

[0102] in, This indicates the preset dew point temperature difference reference value; Indicates the dew point temperature difference; Indicates relative humidity; This indicates the preset relative humidity reference value; and These represent the slopes for controlling the soft threshold of dew point temperature and the soft threshold of relative humidity, respectively. Represents a computation function, and has ; Indicates the soft threshold temperature of the dew point; This indicates the soft threshold for relative humidity.

[0103] Furthermore, dynamic recording is required during online operation. The correlation between RH and the signal-to-noise ratio of laser / millimeter wave is used to determine the boundary between clear sky and low visibility based on classification or clustering methods, and the results are updated in real time. and relative humidity reference value .

[0104] S422, Meteorological calculation based on soft threshold Regulatory factors.

[0105] reflect Aiming at establishing a dominant-RH-assisted decision-making relationship, a moderating factor is synthesized using a two-factor model, with the corresponding expression as follows: , and This represents adjustable weights, and satisfies... , .

[0106] meteorological The adjustment factor takes a lower value under clear sky conditions, limiting the weight correction effect and ensuring the laser channel's dominance; however, under foggy or precipitation conditions... Approaching zero, RH rising, weather As the adjustment factor increases, the weight of the millimeter-wave channel gradually increases, while the weight of the laser channel decreases accordingly. This adjustment factor ensures that the entire process is continuous and controllable, avoiding hard weight switching caused by sudden weather changes.

[0107] S5, Utilizing Meteorology The adjustment factor corrects the weights of the laser and millimeter-wave components, yielding the corrected weights, expressed as follows:

[0108] ,

[0109] ,

[0110] in, Indicates the weight of the laser body; Indicates weather Regulatory factors; Indicates the main weight of millimeter waves; Indicates the laser correction weight; This indicates the millimeter-wave correction weight.

[0111] S6. Based on lidar and millimeter-wave radar observation data, wind vector estimation is performed separately. The wind vector estimation results are then weighted and fused using corrected weights to obtain the actual wind speed, expressed as follows:

[0112] ,

[0113] in, This represents the laser correction weight of the laser beam measured at azimuth angle i; This represents the estimated wind vector value of the laser beam measured at azimuth angle i; This represents the time it takes for the laser to measure wind speed at azimuth angle i; This indicates the wind direction measured by the laser at azimuth angle i; This represents the millimeter-wave correction weight of the millimeter-wave measurement beam at azimuth angle i; This represents the estimated wind vector of the millimeter wave beam measured at azimuth angle i; This represents the time it takes for the millimeter wave to measure the wind speed at azimuth angle i; This indicates the wind direction measured by millimeter waves at azimuth angle i; This indicates the total number of beams.

[0114] To verify that the correction weights calculated by the method provided in this invention can change with weather variations, a test was conducted in this embodiment for a specific scenario. Clear-sky segments were selected from the historical data of this test environment, and the following reference values ​​were calculated: , , as well as .

[0115] And it can be calculated that, under clear sky conditions, the following holds true:

[0116] ,

[0117] As can be seen from the above parameters, in clear skies, wind measurement is mainly performed by lidar, and millimeter waves are hardly involved in the calculation.

[0118] Under light fog conditions:

[0119] ,

[0120] It is evident that millimeter-wave radar initially dominates under these conditions, but both radars participate together.

[0121] Under light rain conditions:

[0122] ,

[0123] It is evident that during light rain, the weighting is dominated by millimeter-wave radar, while lidar hardly participates in the calculation.

[0124] Under moderate to heavy rain conditions:

[0125]

[0126] It is evident that the weighting of moderate to heavy rain is dominated by millimeter-wave radar, while lidar hardly participates in the calculation.

[0127] Therefore, under the same measurement area and altitude, the weight will continuously shift from clear sky to fog and rain as the weather changes. In clear sky, the laser channel dominates, and the millimeter wave channel gradually takes over as visibility decreases. The meteorological factor only serves as a mild adjustment, smoothly pushing and pulling the main weight to make the transition continuous and without abrupt changes. Moreover, neither channel is cleared to zero under any non-extreme conditions. The main weight reflects the channel quality and relative signal-to-noise ratio, and works with the meteorological factor to achieve all-weather adaptiveness.

[0128] The present invention also provides an electronic device including a central processing unit (CPU) that can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0129] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0130] The processing unit executes the various methods and processes described above, such as methods S1 to S6. For example, in some embodiments, methods S1 to S6 may be implemented as computer software programs tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of methods S1 to S6 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute methods S1 to S6 by any other suitable means (e.g., by means of firmware).

[0131] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0132] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for spatiotemporal synchronous optical wind measurement, characterized in that the method... The application relates to a wind speed estimation method and device. The method comprises the following steps: Synchronously acquiring laser radar observation data and millimeter wave radar observation data in a same time-space unit of a same residence time, a same field of view and a same distance gate; Calculating real-time characteristic parameters based on the laser radar observation data and the millimeter wave radar observation data; obtaining temperature and relative humidity of a wind measurement scene, calculating dew point temperature difference based on the temperature and relative humidity, calculating meteorological modulating factor; The weather is utilized The adjustment factor corrects the laser main body weight and the millimeter wave main body weight to obtain a corrected weight. Calculating laser main body weight and millimeter wave main body weight based on the real-time characteristic parameters; 2. The method according to claim 1, wherein, Estimating wind vectors based on the laser radar observation data and the millimeter wave radar observation data, and performing weighted fusion on the wind vector estimation results by using the correction weight to obtain an actual wind speed. The laser real-time characteristic parameters comprise a signal-to-noise ratio, an effective distance and a return slope. The effective distance is obtained by acquiring a maximum distance corresponding to a set intensity threshold of laser return intensity drop value, and the maximum distance is the effective distance.

3. The method of claim 1, wherein the method is characterized by, The return slope is obtained by performing linear fitting on a laser return energy curve in a preset distance interval based on the laser radar observation data, and calculating a slope based on a fitting result, and the slope is the return slope. The millimeter wave real-time characteristic parameters comprise a signal-to-noise ratio, a reflectivity and a background noise. The reflectivity is obtained by converting a millimeter wave transmission power by using a radar equation; 4. The method of claim 1, wherein the method is characterized by, The background noise is obtained by calculating a mean value of millimeter wave receiving power in a signal-free area, and the mean value of the receiving power is the background noise; the signal-free area comprises a long-distance gate or a distance gate not occupied by laser return. The laser main body weight is calculated by the following method: Calculating laser relative quantities based on the laser real-time characteristic parameters, wherein the laser relative quantities comprise an effective distance relative quantity and a return slope relative quantity; the effective distance relative quantity is a ratio of the real-time acquired effective distance to an effective distance preset reference value; the return slope relative quantity is a ratio of a return slope preset reference value to the real-time acquired return slope; Mapping the laser relative quantities into laser characteristic quantities, wherein the laser characteristic quantities comprise an effective distance characteristic quantity and a return slope characteristic quantity; the value of the effective distance relative quantity is the value of the effective distance characteristic quantity; the value of the return slope relative quantity is the value of the return slope characteristic quantity; Calculating a laser harmonic mean based on the reciprocal of the laser characteristic quantities, and calculating a laser proportion factor based on a laser signal-to-noise ratio and a millimeter wave signal-to-noise ratio; 5. The method of claim 1, wherein the method is characterized by, Calculating the laser main body weight based on the laser proportion factor and the laser harmonic mean. The millimeter wave main body weight is calculated by the following method: Calculating millimeter wave relative quantities based on the millimeter wave real-time characteristic parameters, wherein the millimeter wave relative quantities comprise a reflectivity relative quantity and a background noise relative quantity; the reflectivity relative quantity is a ratio of the real-time acquired reflectivity to a reflectivity preset reference value; The background noise relative quantity is a ratio of a background noise preset reference value to the real-time acquired background noise. mapping the millimeter wave relative quantity into a millimeter wave characteristic quantity, wherein the millimeter wave characteristic quantity comprises a reflectivity characteristic quantity and a background noise characteristic quantity, the reflectivity characteristic quantity being a ratio between the reflectivity relative quantity and a sum of the reflectivity relative quantity and one, and the background noise characteristic quantity being a ratio between the background noise relative quantity and a sum of the background noise relative quantity and one; calculating a millimeter wave harmonic mean based on an inverse of the millimeter wave characteristic quantity, and calculating a millimeter wave scaling factor based on the millimeter wave signal-to-noise ratio and the millimeter wave signal-to-noise ratio; calculating a millimeter wave body weight based on the millimeter wave scaling factor and the millimeter wave harmonic mean.

6. The method of claim 1, wherein the method is characterized by, computing said weather the method of adjusting the factor is calculating a dew point temperature difference and a relative humidity based on the dew point temperature difference and the relative humidity, and constructing a continuous bounded soft threshold mapping, the soft threshold comprising a dew point temperature soft threshold and a relative humidity soft threshold; Computing weather based on the soft threshold Adjustment factor, is: , and denotes an adjustable weight and satisfies , ; denotes a dew point temperature soft threshold; denotes a relative humidity soft threshold.

7. The optical wave spatiotemporal synchronous wind measurement method according to claim 6, characterized in that, the method for calculating the dew point temperature soft threshold and the relative humidity soft threshold comprises: ， ， wherein, represents a preset dew point temperature difference reference value; represents a dew point temperature difference; represents a relative humidity; represents a preset relative humidity reference value; and respectively represent a slope of a control dew point temperature soft threshold and a relative humidity soft threshold; represents a calculation function, and has .

8. The method of claim 1, wherein the method is characterized by, the method for calculating the correction weight comprises: ， ， wherein, represents a laser main weight; represents a weather adjustment factor; represents a millimeter wave main weight; represents a laser correction weight; represents a millimeter wave correction weight.

9. The method of claim 1, wherein the method is characterized by, the method for weighted fusion comprises: ， wherein, represents a laser correction weight for the laser measuring the beam at azimuth i; represents a wind vector estimate for the laser measuring the beam at azimuth i; represents a time at which the laser measures the wind speed at azimuth i; represents a wind direction measured by the laser at azimuth i; represents a millimeter wave correction weight for the millimeter wave measuring the beam at azimuth i; represents a wind vector estimate for the millimeter wave measuring the beam at azimuth i; represents a time at which the millimeter wave measures the wind speed at azimuth i; represents a wind direction measured by the millimeter wave at azimuth i; represents the total number of beams.

10. An electronic device comprising a memory and a processor, said memory having stored thereon a computer program, characterized in that, the processor executes the program to implement the method in any one of claims 1-9.

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