A data processing method and apparatus

Abstract

This application discloses a data processing method. Specifically, the control system controls multiple radars to perform line-of-sight wind speed measurements simultaneously and acquires the echo signals received by each radar. The echo signals received by each radar are uniformly preprocessed to obtain preprocessing results. Wind speed inversion is performed based on the preprocessing results for each radar to obtain the first wind speed measurement result for each radar. Simultaneously, the measurement quality of each radar is evaluated based on its preprocessing results to obtain a quality weight for that radar. A consistency evaluation is performed based on the first wind speed measurement results and quality weights for each radar to obtain a consistency evaluation index. Based on this consistency evaluation index, the consistency of the wind speed measurement results for each radar among the multiple radars is determined. Therefore, the above scheme can automatically detect the consistency of measurement results from multiple radars, reducing manual intervention and improving detection efficiency.

Description

Technical Field

[0001] This application relates to the field of radar wind measurement, specifically to a data processing method and apparatus. Background Technology

[0002] With the development of atmospheric sounding and wind speed observation technologies, wind-measuring lidar has been widely used in meteorological monitoring, wind energy assessment, and environmental monitoring due to its advantages such as high spatiotemporal resolution, all-weather operation capability, and long-distance non-contact measurement. In practical applications, to improve the observation range and data reliability, multiple wind-measuring lidars are often used for joint observation to achieve coordinated detection of the wind field in the target area.

[0003] Wind-measuring lidar typically acquires the radial wind speed component in the target direction by measuring along the line-of-sight (LOS) and inverts the wind field using different scanning modes (such as directional scanning, PPI scanning, RHI scanning, etc.). In multi-device joint observation scenarios, to ensure the comparability of measurement results from different devices, it is necessary to verify the consistency of measurement performance of different wind-measuring lidars.

[0004] Currently, calibration methods for single wind-measuring lidars are relatively mature, such as calibration using reference equipment or standard wind fields. However, for scenarios involving multiple devices working collaboratively, there is still a lack of a comprehensive solution that can achieve unified control, synchronous data acquisition, and automated analysis. Therefore, how to achieve automated consistency assessment of measurement results during joint observations by multiple wind-measuring lidars is a technical problem that urgently needs to be solved. Summary of the Invention

[0005] In view of this, this application provides a data processing method and apparatus that can automatically verify the consistency of measurement results from multiple wind-measuring lidars.

[0006] To solve the above problems, the technical solution provided in this application is as follows: In a first aspect of this application, a data processing method is provided. This method can be executed by a control system. Specifically, the control system controls multiple radars to perform line-of-sight wind speed measurements simultaneously and acquires the echo signals received by each radar. The echo signals received by each radar are uniformly preprocessed to obtain preprocessing results. Wind speed inversion is performed based on the preprocessing results for each radar to obtain the first wind speed measurement result for each radar. Simultaneously, the measurement quality of each radar is evaluated based on its preprocessing results to obtain a quality weight for that radar. A consistency evaluation is performed based on the first wind speed measurement results and quality weights for each radar to obtain a consistency evaluation index. Based on this consistency evaluation index, the consistency of the wind speed measurement results for each radar among the multiple radars is determined. Therefore, the above scheme can automatically detect the consistency of measurement results from multiple radars, reducing manual intervention and improving detection efficiency.

[0007] In a second aspect of this application, a data processing apparatus is provided, the apparatus comprising: The acquisition unit is used to control multiple radars to perform line-of-sight wind speed measurement at the same time and acquire the echo signals received by the multiple radars. The acquisition unit is also used to perform unified preprocessing on the echo signals received by the multiple radars to obtain preprocessing results. The acquisition unit is also used to perform wind speed inversion based on the preprocessing results corresponding to each of the plurality of radars to obtain the first wind speed measurement result corresponding to each radar. The acquisition unit is further configured to evaluate the measurement quality of the radar based on the preprocessing results corresponding to each radar among the plurality of radars, and obtain the quality weight corresponding to the radar. The evaluation unit is used to conduct a consistency evaluation based on the first wind speed measurement results and mass weights of each radar to obtain a consistency evaluation index. The determination unit is used to determine the consistency of wind speed measurement results for each of the multiple radars based on the consistency evaluation index.

[0008] In a third aspect of this application, a data processing apparatus is provided, including a processor and a memory, wherein the memory is used to store programs, instructions or code, and the processor is used to execute the programs, instructions or code in the memory to perform the method as described in the first aspect.

[0009] In a fourth aspect of this application, a computer-readable storage medium is provided storing a computer program that is loaded by a processor to execute the method described in the first aspect.

[0010] In a fifth aspect of this application, a computer program product is provided, the computer program product comprising: a computer program (also referred to as code or instructions) that, when the computer program is run, causes a computer to perform the method described in the first aspect above. Attached Figure Description

[0011] Figure 1 A flowchart of a data processing method provided in an embodiment of this application; Figure 2 A data processing framework diagram provided for an embodiment of this application; Figure 3 A structural diagram of a data processing device provided in an embodiment of this application; Figure 4 This is a structural diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0012] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the embodiments of this application will be further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0013] To facilitate understanding of the technical solutions provided in the embodiments of this application, the technical knowledge of this application will be explained below.

[0014] The working principle of a wind-measuring lidar (hereinafter referred to as radar) is as follows: when the emitted laser propagates through the atmosphere, it interacts with targets in the atmosphere (such as aerosol particles and / or atmospheric molecules). A portion of the light is scattered back to the radar receiving system; this received scattered light is the echo signal. Specifically, the radar's transmitting system first generates a highly stable, single-frequency, narrow-linewidth laser pulse, which is then pointed towards the target space via a scanning unit. As the emitted laser propagates through the atmosphere along the line of sight, it interacts with aerosol particles (and air molecules) at each spatial location (range gate) along its path, resulting in scattering. The portion of the light scattered in a direction exactly pointing towards the radar (approximately 180°) is called backscattered light, which is collected by the radar receiver, forming the echo signal at that location.

[0015] If the aerosol particles at that location have a certain velocity along the line of sight with the wind field, the echo signal will be superimposed with a Doppler shift. The relationship between the shift Δf and the radial wind speed V is Δf = 2V / λ (λ is the laser wavelength), which is the basis for subsequent wind speed measurements.

[0016] In practical applications, multiple wind-measuring lidars commonly encounter the following problems: First, the lack of a unified time synchronization mechanism among the devices leads to time discrepancies in data acquisition, resulting in inconsistent measured target wind field states and affecting the comparability of measurement results. Second, due to installation errors and control precision limitations, the azimuth and elevation angles of each wind-measuring lidar deviate, causing inconsistencies in the actual spatial positions observed by each device. Even with identical settings, it is difficult to guarantee consistent observation directions. Furthermore, differences in hardware performance, signal processing algorithms, and calibration status among different wind-measuring lidars lead to systematic biases in their measurement results. Simultaneously, under low signal-to-noise ratio or complex meteorological conditions, inconsistent measurement errors among different devices further complicate the consistency assessment of multi-device measurement results.

[0017] Currently, the method of comparing and analyzing measurement results from different devices relies heavily on human experience, and there is a lack of a unified, automated, and quantitative consistency assessment method.

[0018] To address the aforementioned issues, this application provides a data processing method that automates the process from data acquisition to consistency assessment, reducing manual intervention and improving detection efficiency.

[0019] To facilitate understanding of the technical solution of this application, specific embodiments will be described below.

[0020] See Figure 1 The figure is a flowchart of a data processing method provided in an embodiment of this application, as shown below. Figure 1 As shown, the method includes: S101: Controls multiple radars to perform line-of-sight wind speed measurements at the same time and acquires the echo signals received by each radar.

[0021] In this implementation, the control system can send measurement commands to multiple radars, instructing them to perform LOS wind speed measurements simultaneously. Upon receiving the measurement command, the radar can transmit a detection signal at a specified time. This detection signal is scattered by the target, and the scattered signal is received by the radar as an echo signal. The echo signal is a time-domain signal.

[0022] Since the echo signal originates from the backscattering of aerosol particles in space, and these particles drift with the wind, only radars that are time-synchronized and spatially aligned can "see" the same particle swarm, thus ensuring the comparability of measurement results. To ensure that multiple radars establish a unified observation record in both time and space dimensions, the control system can synchronize the time of multiple radars through a unified timing mechanism, enabling them to observe under the same time record. Furthermore, a cooperative scanning control mechanism can be used to uniformly constrain the observation direction of each radar. Specifically, a first command is sent to each of the multiple radars, instructing them to establish a unified time reference; and / or, a second command is sent to each of the multiple radars, instructing them to point in the same spatial direction.

[0023] In practical implementation, unified time synchronization can be achieved in the following ways: BeiDou time synchronization: Each radar is equipped with a BeiDou receiving module to directly receive high-precision time signals broadcast by BeiDou satellites, achieving nanosecond-level time synchronization; Network time synchronization: Based on the Network Time Protocol (NTP) or the Precision Time Protocol (PTP), the time of each radar is calibrated through a network time server. NTP is suitable for millisecond-level accuracy requirements, while PTP is suitable for sub-microsecond-level accuracy requirements. External trigger signal synchronization: The control system sends trigger pulses to each radar simultaneously via wired or wireless means. After receiving the trigger signal, each radar starts data acquisition synchronously, achieving nanosecond-level hardware synchronization.

[0024] It should be noted that the above three methods can be used individually or in combination (such as BeiDou providing an absolute time reference and external triggering providing instantaneous synchronization accuracy), thus adapting to different deployment environments and accuracy requirements.

[0025] In practical implementation, the second command may include azimuth and elevation angles. These two angles can uniquely determine the line-of-sight direction of a laser beam, thereby enabling the radar to accurately point to a certain space.

[0026] Due to individual differences in mechanical installation, encoder zero point, and structural deformation among various wind-measuring lidars, there is often a deviation between their actual pointing and the commanded pointing. Without correction, even if the control system sends the same azimuth and elevation angle commands to each radar, the actual observation directions of each radar may still differ, resulting in measurement results from multiple radars not corresponding to the same wind field area in a physical sense, thus affecting the effectiveness of consistency assessment. To solve this problem, the second command can also include pointing error correction. This pointing error correction involves obtaining error compensation parameters for each radar through pre-calibration, and then correcting the command values ​​during actual scanning to ensure that the actual pointing of each radar reaches a unified target direction, thereby achieving spatial pointing consistency.

[0027] Considering that a single consistency check typically only verifies the measurement results in a specific spatial direction (such as a fixed line-of-sight direction), such verification may be incomplete. For example, the errors of different radars may be direction-dependent (e.g., large deviations in some directions and small deviations in others); the uniformity of atmospheric wind fields varies in different directions, etc. To more comprehensively and rigorously evaluate the measurement consistency of multiple radars, the second command can also include a multi-directional scanning sequence, which can sequentially control each radar to perform a "synchronous measurement, then consistency evaluation" process in each direction, thereby comprehensively verifying the measurement consistency of multiple radars in multiple directions and improving the reliability of the evaluation.

[0028] It is evident that the control system can make the observation data acquired by each radar corresponding to the same observation target by sending first and second commands, based on a unified spatiotemporal reference, thus achieving comparability.

[0029] S102: Perform unified preprocessing on the echo signals received by multiple radars to obtain the preprocessing results.

[0030] In this embodiment, in order to ensure that the measurement results of different radars are consistent in terms of data structure, processing flow and expression, the echo signals received by each radar will be preprocessed in a unified manner to obtain standardized preprocessing results. This avoids systematic deviations in the measurement results of the same wind field caused by different radars using different peak detection algorithms (such as maximum peak method, centroid method, Gaussian fitting method).

[0031] Specifically, unified preprocessing may include the following procedures: The frequency domain power spectrum is determined based on the echo signal received by the first radar; noise estimation is performed based on the frequency domain power spectrum to obtain the noise estimation result; signal features are extracted from the frequency domain power spectrum based on the noise estimation result, and these signal features include the frequency positions corresponding to the spectral peaks. Here, the first radar refers to any one of multiple radars.

[0032] In this implementation, the time-domain echo signal received by the radar undergoes a Fast Fourier Transform (FFT) to obtain the frequency-domain power spectrum, from which Doppler shift information is extracted. A noise floor is determined from the frequency-domain power spectrum to distinguish the effective signal from background noise. Based on the noise floor, a detection threshold is determined. Based on this threshold, the maximum peak value exceeding the detection threshold is located in the frequency-domain power spectrum, and the frequency position and power of this maximum peak value are recorded. After determining the frequency position of the maximum peak value, the spectral width can also be determined.

[0033] S103: Based on the preprocessing results of each radar in multiple radars, wind speed inversion is performed to obtain the first wind speed measurement result corresponding to each radar.

[0034] After obtaining the preprocessing results for each radar, wind speed inversion is performed based on the preprocessing results to obtain the first wind speed measurement result for that radar.

[0035] Specifically, the preprocessing results include the frequency positions corresponding to the spectral peaks, and the wind speed measurement results of the first radar are determined based on these frequency positions. For example, if the spectral peak position is fpeak, the corresponding wind speed inversion formula is V=λ / 2. fpeak.

[0036] S104: Evaluate the measurement quality of each radar based on the preprocessing results of each radar among multiple radars, and obtain the quality weight corresponding to that radar.

[0037] In this embodiment, after acquiring the preprocessing results for each radar, the control system evaluates the measurement quality of the radar based on the preprocessing results to obtain the quality weight corresponding to the radar.

[0038] Specifically, the preprocessing results include the power of the spectral peak. Based on the power of the spectral peak and the noise estimation results, the mass weight corresponding to the first radar is determined. For example, the spectral peak is detected from the power spectrum to obtain the peak power Ppeak; using the estimated noise floor Pnoise, the signal-to-noise ratio SNR = (Ppeak - Pnoise) / Pnoise is calculated; then the mass weight wi = SNRi / SNRmax. Where SNRi represents the signal-to-noise ratio corresponding to the i-th radar.

[0039] S105: Based on the first wind speed measurement results and mass weights of each radar, a consistency assessment is conducted to obtain a consistency evaluation index.

[0040] In this embodiment, considering the differences in signal quality, noise level, and measurement stability among different radars, a measurement quality-based evaluation mechanism is introduced. The measurement results from different radars are weighted differently, giving high-quality data a higher impact on the evaluation results, thereby improving the accuracy and robustness of the overall evaluation. Specifically, the first wind speed measurement result corresponding to the first radar is weighted according to its quality weight to obtain the second wind speed measurement result. Then, based on the second wind speed measurement results corresponding to each radar, a deviation statistic is determined.

[0041] For example, Vi=wi vi. Where vi represents the first wind speed measurement result of the i-th radar, wi represents the mass weight of the i-th radar, and Vi represents the second wind speed measurement result of the i-th radar.

[0042] The deviation statistics can be the mean deviation, standard deviation, or mean square error. That is, based on the second wind speed measurement results corresponding to each radar, the mean deviation, standard deviation, or mean square error corresponding to these multiple radars is determined. The specific implementations of the mean deviation, standard deviation, and mean square error are relatively mature technologies and will not be elaborated upon in this embodiment.

[0043] S106: Based on the consistency evaluation index, the consistency of wind speed measurement results of each radar among multiple radars is determined.

[0044] After obtaining the consistency evaluation index, it can be compared with a preset threshold, and a consistency judgment can be made based on the comparison result. For example, if the consistency evaluation index is less than the preset threshold, it indicates that the measurement results of multiple radars are relatively consistent; if the consistency evaluation index is greater than the preset threshold, it indicates that the measurement results of multiple radars are relatively inconsistent.

[0045] In some implementations, if the consistency evaluation index exceeds a preset threshold, the control system adjusts the scanning parameters of at least one of the multiple radars. For example, it adjusts the azimuth and elevation angles to change the observation direction.

[0046] In some implementations, if the consistency evaluation index is greater than a first preset threshold, indicating that a single measurement is unreliable, a remeasurement can be triggered.

[0047] In some implementations, if the consistency evaluation index corresponding to multiple measurements is greater than a preset threshold, it indicates the existence of a systematic deviation, and system calibration can be prompted. System calibration refers to adjusting or correcting the radar's internal parameters to ensure that the measurement results achieve a preset accuracy.

[0048] In some implementations, the absolute value of the deviation between the first wind speed measurement value of the second radar and a reference value (such as the average value of all radars) can also be calculated. When the absolute value of the deviation of the second radar exceeds a second preset threshold, the second radar is determined to be abnormal.

[0049] When the second radar is determined to be an abnormal device, its identifier can be output to achieve automatic location of the abnormal device. Simultaneously, before executing S105, the first wind speed measurement result of the second radar can be discarded to avoid the impact of abnormal data on the consistency evaluation results.

[0050] It is evident that this method automates the process from data collection to consistency assessment, reducing manual intervention and improving detection efficiency.

[0051] For a better understanding of the overall implementation of this application, please refer to [link / reference]. Figure 2 The data processing framework diagram shown is as follows: Figure 2 As shown, the following processing steps are included: S1: Unified time synchronization among multiple radars.

[0052] S2: Scanning control between multiple radars.

[0053] S3: Acquire the echo signals received by each radar.

[0054] S4: Preprocess the echo signals received by each radar and perform wind speed inversion.

[0055] S5: Based on the wind speed measurement results of each radar, obtain the consistency evaluation index.

[0056] S6: Make consistency determination based on consistency assessment indicators.

[0057] S7: Take action based on the consistency determination results.

[0058] The processing strategies can include adjusting scanning parameters, triggering remeasurement, or performing system calibration.

[0059] It should be noted that the specific implementation details of each of the above steps can be found in [link to documentation]. Figure 1 The relevant descriptions in the embodiments will not be repeated here.

[0060] Based on the above method embodiments, this application provides a data processing apparatus and device, which will be described below in conjunction with the embodiments.

[0061] See Figure 3 This figure is a structural diagram of a data processing device provided in an embodiment of this application, as shown below. Figure 3As shown, the device 300 includes an acquisition unit 301, an evaluation unit 302, and a determination unit 303. The data processing device 300 can implement the functions of the above-described method embodiments.

[0062] The acquisition unit 301 is used to control multiple radars to perform line-of-sight wind speed measurement at the same time and acquire the echo signals received by the multiple radars. The acquisition unit 301 is also used to perform unified preprocessing on the echo signals received by the multiple radars to obtain preprocessing results. The acquisition unit 301 is also used to perform wind speed inversion based on the preprocessing results corresponding to each of the plurality of radars to obtain the first wind speed measurement result corresponding to each radar. The acquisition unit 301 is further configured to evaluate the measurement quality of the radar based on the preprocessing results corresponding to each radar in the plurality of radars, and obtain the quality weight corresponding to the radar. Evaluation unit 302 is used to perform consistency evaluation based on the first wind speed measurement results and mass weights of each radar to obtain consistency evaluation indicators. The determination unit 303 is used to determine the consistency of the wind speed measurement results of each of the plurality of radars according to the consistency evaluation index.

[0063] In some implementations, the acquisition unit 301 is specifically used to determine the frequency domain power spectrum based on the echo signal received by the first radar, wherein the first radar is any one of the plurality of radars; to perform noise estimation based on the frequency domain power spectrum to obtain a noise estimation result; and to extract signal features from the frequency domain power spectrum based on the noise estimation result, wherein the signal features include the frequency positions corresponding to the spectral peaks.

[0064] In some implementations, the acquisition unit 301 is specifically used to determine the wind speed measurement result of the first radar based on the frequency position corresponding to the spectral peak.

[0065] In some implementations, the acquisition unit 301 is specifically used to determine the mass weight corresponding to the first radar based on the power of the spectral peak corresponding to the first radar and the noise estimation result.

[0066] In some implementations, the evaluation unit 302 is specifically used to obtain the second wind speed measurement result corresponding to the first radar based on the first wind speed measurement result corresponding to the first radar and the mass weight; and to determine the deviation statistics based on the second wind speed measurement results corresponding to each of the plurality of radars.

[0067] In some embodiments, the apparatus further includes a transmitting unit for transmitting a first instruction to each of the plurality of radars, the first instruction instructing each radar to establish a unified time reference; The transmitting unit is also used to send a second command to each of the plurality of radars, the second command instructing each radar to point to the same spatial direction.

[0068] In some embodiments, the device further includes an adjustment unit, which is used to adjust the scanning parameters of at least one of the plurality of radars if the consistency evaluation index is greater than a preset threshold.

[0069] It should be noted that the specific implementation of each unit in this embodiment can be found in the relevant descriptions in the above method embodiments.

[0070] The data processing apparatus provided in this application embodiment may include software to implement the code generation method described above. Alternatively, the data processing apparatus provided in this application embodiment may include hardware to implement the data processing method described above. Or, the data processing apparatus provided in this application embodiment may include both software and hardware, using a combination of software and hardware to execute the data processing method described above.

[0071] This application also provides an electronic device. This electronic device is specifically used to implement, as described above. Figure 3 The data processing device 300 in the illustrated embodiment has the following functions.

[0072] Figure 4 A structural schematic diagram of an electronic device 400 is provided, such as... Figure 4 As shown, the electronic device 400 includes a bus 401, a processor 402, a communication interface 403, and a memory 404. The processor 402, the memory 404, and the communication interface 403 communicate with each other via the bus 401.

[0073] Bus 401 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0074] Processor 402 can be any one or more of the following processors: central processing unit (CPU), graphics processing unit (GPU), microprocessor (MP), or digital signal processor (DSP).

[0075] Communication interface 403 is used for communication with external devices. For example, communication interface 403 can be used to communicate with a terminal.

[0076] Memory 404 may include volatile memory, such as random access memory (RAM). Memory 404 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0077] The memory 404 stores executable code, and the processor 402 executes the executable code to perform the aforementioned data processing method.

[0078] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape) or a semiconductor medium (e.g., solid-state disk (SSD)).

[0079] This application also provides a readable storage medium for storing the methods provided in the above embodiments. Examples include random access memory (RAM), flash memory, read-only memory (ROM), EPROM, non-volatile read-only memory (EPROM), registers, hard disks, removable disks, or any other form of storage medium in the art.

[0080] This application also provides a computer program product comprising one or more computer instructions. When the computer instructions are loaded and executed on a computing device, all or part of the processes or functions described in this application are generated.

[0081] The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, or data center to another website, computer, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0082] When the computer program product is executed by a computer, the computer performs any of the aforementioned task flow repair methods. The computer program product can be a software installation package; when any of the aforementioned data processing methods is required, the computer program product can be downloaded and executed on the computer.

[0083] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0084] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A data processing method, characterized in that, The method includes: Control multiple radars to perform line-of-sight wind speed measurement at the same time point, and acquire the echo signals received by each of the multiple radars; The echo signals received by each of the multiple radars are uniformly preprocessed to obtain the preprocessing result; Based on the preprocessing results corresponding to each of the multiple radars, wind speed inversion is performed to obtain the first wind speed measurement result corresponding to each radar. The measurement quality of each radar is evaluated based on the preprocessing results of each radar among the multiple radars, and the quality weight of each radar is obtained. Based on the first wind speed measurement results and mass weights of each radar, a consistency assessment is conducted to obtain a consistency evaluation index. Based on the consistency evaluation index, the consistency of wind speed measurement results for each of the multiple radars is determined.

2. The method according to claim 1, characterized in that, The unified preprocessing of the echo signals received by the multiple radars to obtain the preprocessing result includes: The frequency domain power spectrum is determined based on the echo signal received by the first radar, where the first radar is any one of the plurality of radars. Noise estimation is performed based on the frequency domain power spectrum to obtain noise estimation results; Based on the noise estimation results, signal features are extracted from the frequency domain power spectrum, and the signal features include the frequency positions corresponding to the spectral peaks.

3. The method according to claim 2, characterized in that, The step of performing wind speed inversion based on the preprocessing results corresponding to each of the multiple radars to obtain the wind speed measurement results corresponding to each radar includes: The wind speed measurement result of the first radar is determined based on the frequency position corresponding to the spectral peak.

4. The method according to claim 2 or 3, characterized in that, The step of evaluating the measurement quality of the radar based on the preprocessing results corresponding to each of the plurality of radars, and obtaining the quality weight corresponding to the radar, includes: Based on the power of the spectral peak corresponding to the first radar and the noise estimation result, the mass weight corresponding to the first radar is determined.

5. The method according to claim 1, characterized in that, Based on the first wind speed measurement results and mass weights corresponding to each radar, a consistency assessment is performed to obtain consistency evaluation indicators, including: Based on the first wind speed measurement result corresponding to the first radar and the mass weight, the second wind speed measurement result corresponding to the first radar is obtained. Based on the second wind speed measurement results corresponding to each of the multiple radars, the deviation statistics are determined.

6. The method according to claim 1, characterized in that, The method also includes one or more of the following: Send a first instruction to each of the plurality of radars, the first instruction instructing each radar to establish a unified time reference; A second command is sent to each of the plurality of radars, the second command instructing each radar to point in the same spatial direction.

7. The method according to claim 1, characterized in that, The method further includes: If the consistency evaluation index is greater than a preset threshold, the scanning parameters of at least one of the multiple radars will be adjusted.

8. A data processing apparatus, characterized in that, The device includes: The acquisition unit is used to control multiple radars to perform line-of-sight wind speed measurement at the same time and acquire the echo signals received by the multiple radars. The acquisition unit is also used to perform unified preprocessing on the echo signals received by the multiple radars to obtain preprocessing results. The acquisition unit is also used to perform wind speed inversion based on the preprocessing results corresponding to each of the plurality of radars to obtain the first wind speed measurement result corresponding to each radar. The acquisition unit is further configured to evaluate the measurement quality of the radar based on the preprocessing results corresponding to each radar among the plurality of radars, and obtain the quality weight corresponding to the radar. The evaluation unit is used to conduct a consistency evaluation based on the first wind speed measurement results and mass weights of each radar to obtain a consistency evaluation index. The determination unit is used to determine the consistency of wind speed measurement results for each of the multiple radars based on the consistency evaluation index.

9. A data processing apparatus, characterized in that, It includes a processor and a memory, the memory being used to store programs, instructions, or code, and the processor being used to execute the programs, instructions, or code in the memory to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The device contains a computer program that is loaded by a processor to perform the method as described in any one of claims 1-7.

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