A marine wind profile self-adaptive measurement method, device, medium and equipment

Abstract

This invention discloses an adaptive measurement method, apparatus, medium, and equipment for offshore wind profiles, belonging to the field of wind measurement. This application acquires echo signals and platform attitude change information based on an adaptive wind measurement mode. It dynamically determines the cumulative radial duration in each direction based on attitude changes and uses this to filter and analyze echo signals to obtain radial wind measurement results. These results are then processed and inverted to obtain the wind profile measurement results. This achieves an adaptive response to weather conditions and platform sway during wind measurement on a floating offshore platform, ensuring high-precision measurement of offshore wind profiles. This application effectively solves the problem that existing technologies cannot accurately and efficiently measure the wind profile of offshore wind fields.

Description

Technical Field

[0001] This invention relates to the field of wind measurement, and more particularly to an adaptive measurement method, apparatus, medium, and equipment for marine wind profiles. Background Technology

[0002] In offshore wind power development and marine meteorological observation, accurate measurement of offshore wind profiles is crucial for understanding wind field characteristics, ensuring the safe operation of wind power equipment, and improving the accuracy of weather forecasts. Floating offshore wind measurement platforms, due to their flexible deployment and adaptability to complex open ocean areas, have become an important carrier for offshore wind profile measurement. However, their practical application suffers from significant technical challenges due to the influence of the marine environment. Existing offshore wind measurement technologies largely rely on the fixed operating parameters of land-based wind radars, employing uniform pulse signal parameters and fixed radial data accumulation durations for wind measurement operations. This fails to consider the attitude swaying of floating offshore wind measurement platforms caused by waves and currents. Continuous changes in platform attitude can cause shifts in the radar beam's measurement range. If a fixed accumulation duration is still used for signal acquisition, the superposition of radial signals with different characteristics introduces measurement biases, failing to effectively improve weak signal strength and severely reducing the accuracy of radial wind measurement results, thus affecting the accuracy of wind profile inversion.

[0003] Meanwhile, the marine meteorological environment is complex and changeable, with frequent adverse weather events such as rain, snow, and fog. In rainy or snowy weather, the pulse signals emitted by lidar attenuate rapidly due to obstruction and scattering by raindrops and snowflakes, resulting in a significant decrease in the signal-to-noise ratio of the echo signal, a marked reduction in data acquisition rate, and a severe limitation on the effective detection altitude. Furthermore, existing wind measurement methods lack an adaptive wind measurement mode adjustment mechanism, failing to dynamically optimize core operating parameters such as pulse length and data accumulation time based on real-time weather conditions. Parameter settings in non-rainy / snowy weather conditions cannot adapt to the wind measurement needs of rainy / snowy weather, further exacerbating the loss of wind measurement accuracy under adverse weather conditions and making it difficult to meet the actual requirements of high-precision, high-reliability wind profile measurements in marine wind fields.

[0004] Furthermore, existing wind measurement technologies employ a rather crude approach to analyzing and processing radar echo signals, failing to specifically screen for signal characteristics specific to the complex marine environment. Raindrop and snowflake interference in the echo signals generates invalid radial spectrum data. Directly using this data for radial wind calculations easily leads to erroneous wind speed and direction determinations. Moreover, the lack of scientific spectral data screening standards in current technologies prevents the effective removal of anomalous spectral data, further reducing the reliability of wind profile measurement results. In summary, existing marine wind profile measurement methods suffer from several shortcomings, including a lack of a dynamic adjustment mechanism for cumulative duration linked to the attitude of floating platforms, the absence of adaptive weather-appropriate wind measurement modes, and insufficient signal analysis and processing precision. These deficiencies prevent current technologies from accurately and efficiently measuring the wind profile of marine wind fields. Summary of the Invention

[0005] This invention provides an adaptive measurement method, apparatus, medium, and equipment for offshore wind profiles, to solve the problem that existing technologies cannot accurately and efficiently measure the wind profiles of offshore wind fields.

[0006] In a first aspect, this application provides an adaptive measurement method for marine wind profiles, including: Based on the current adaptive wind measurement mode of the target wind measurement platform, pulse signals are sent to each target location of the target wind field to obtain the echo signals fed back by the target wind field and to obtain the attitude change information of the target wind measurement platform. Based on the attitude change information, the radial cumulative duration corresponding to each target azimuth is determined, and the echo signals of each target azimuth are filtered and analyzed based on the radial cumulative duration to obtain the radial wind measurement results of each target azimuth. The radial wind measurement results are processed, and the target wind field is inverted based on the processed radial wind measurement results to determine the wind profile measurement results of the target wind field.

[0007] This application transmits pulse signals based on an adaptive wind measurement mode, which can dynamically adjust pulse parameters according to weather conditions to adapt to the signal reflection characteristics under different weather conditions and improve the quality of echo signal acquisition. Furthermore, it determines the radial accumulation time based on attitude change information, realizing real-time response to the swaying state of the floating platform at sea. By dynamically adjusting the accumulation time window, the accumulation is extended when the platform attitude change is small to improve the signal-to-noise ratio, and the accumulation is terminated in time when the attitude change is large to avoid the superposition of signals with different characteristics, thus balancing measurement accuracy and data validity. Finally, the radial wind measurement results after screening and analysis are inverted. Since the above steps have ensured the accuracy and consistency of data in all directions, the wind profile measurement results obtained by the inversion can truly reflect the distribution of the sea wind field, effectively solving the problem that the existing technology cannot accurately and efficiently measure the wind profile of the sea wind field, and realizing high-precision wind profile measurement on the sea surface.

[0008] Furthermore, based on the preset adaptive wind measurement mode of the target wind measurement platform, pulse signals are sent to each target location in the target wind field, specifically as follows: Determine the adaptive wind measurement mode of the preset target wind measurement platform; the adaptive wind measurement mode includes a rain and snow weather wind measurement mode and a non-rain and snow weather wind measurement mode, the pulse length corresponding to the rain and snow weather wind measurement mode is greater than the pulse length corresponding to the non-rain and snow weather wind measurement mode, and the maximum cumulative duration corresponding to the rain and snow weather wind measurement mode is greater than the maximum cumulative duration corresponding to the non-rain and snow weather wind measurement mode. Based on the determined adaptive wind measurement mode, the corresponding pulse length is matched, and the wind measurement radar of the preset target wind measurement platform is controlled to send pulse signals to each target location of the target wind field according to the matched pulse length.

[0009] This application, by determining an adaptive wind measurement mode and distinguishing between rain / snow and non-rain / snow modes, can automatically match a longer pulse length to increase pulse energy, thereby enhancing signal penetration and echo intensity, addressing the severe attenuation and complex reflection characteristics of laser signals in rainy / snowy weather. Simultaneously, it configures a longer maximum accumulation time to extend the data accumulation period, thereby improving the signal-to-noise ratio and data acquisition rate. In non-rainy / snowy weather, a shorter pulse length and accumulation time are used to avoid temporal resolution loss due to excessive accumulation. Finally, by controlling the wind measurement radar to send pulse signals according to the matched parameters based on the determined adaptive wind measurement mode, the wind measurement system actively adapts to weather conditions. This effectively solves the technical problems of significantly reduced data acquisition rate and limited measurement height in rainy / snowy weather for traditional fixed-parameter wind measurement radars, ensuring the stability and reliability of wind profile measurements under different weather conditions.

[0010] Furthermore, determining the radial cumulative duration corresponding to each target orientation based on the attitude change information specifically involves: Based on the attitude change information, the real-time attitude difference value of the target wind measurement platform within the preset data accumulation period is determined; Based on the real-time attitude difference values, the preset angle change threshold, and the data accumulation period, the radial accumulation time corresponding to each target orientation is determined. The radial cumulative duration is an integer multiple of the data cumulative period, and the radial cumulative duration does not exceed the preset maximum cumulative duration.

[0011] This application determines real-time attitude difference values ​​based on attitude change information, enabling real-time sensing of the swaying state of a floating platform. It then combines an angle change threshold and a data accumulation period to determine the radial accumulation time. This allows for extended accumulation time to improve signal strength when platform attitude changes are small, while timely termination of accumulation when attitude changes exceed the threshold to avoid measurement accuracy loss due to the superposition of signals with different characteristics. Furthermore, by constraining the radial accumulation time to an integer multiple of the data accumulation period, the high-speed signal acquisition system can operate efficiently with short-cycle judgments, avoiding frequent judgments that consume effective time. Even if the threshold is exceeded, only the most recent accumulation step time needs to be discarded without significantly affecting effective accumulation. Finally, by setting a maximum accumulation time as an upper limit, excessive accumulation leading to a decrease in temporal resolution is prevented. This achieves a dynamic adaptive response to attitude changes in floating platforms, effectively solving the technical problems of unstable beam pointing due to continuous swaying of floating anemometer platforms and signal superposition distortion under traditional fixed accumulation times. It ensures measurement accuracy while also considering data acquisition efficiency and system feasibility.

[0012] Furthermore, the step of determining the radial cumulative duration corresponding to each target orientation based on the real-time attitude difference values, the preset angle change threshold, and the data accumulation period specifically involves: The real-time attitude difference values ​​within the current data accumulation period are summed to obtain the cumulative attitude difference value. Determine whether the cumulative attitude difference value is less than the angle change threshold, and whether the current cumulative duration is less than the maximum cumulative duration; If the cumulative attitude difference value is less than the angle change threshold, and the current cumulative duration is less than the maximum cumulative duration, then continue to accumulate the next data accumulation cycle; If the cumulative attitude difference value is greater than or equal to the angle change threshold, or the current cumulative duration is greater than or equal to the maximum cumulative duration, the current cumulative duration is determined as the radial cumulative duration.

[0013] This application obtains a cumulative attitude difference value by summing the real-time attitude difference values ​​within each data accumulation period, enabling dynamic tracking of the cumulative attitude changes of a floating platform during the measurement process. Furthermore, by determining whether the cumulative attitude difference value is less than an angle change threshold and whether the current accumulation time is less than the maximum accumulation time, it achieves dual constraints on platform attitude stability and measurement time. If the cumulative attitude difference value is less than the angle change threshold and the current accumulation time is less than the maximum accumulation time, the next data accumulation period is continued, thus fully utilizing the accumulation time to improve the signal-to-noise ratio when the platform attitude remains stable. If the cumulative attitude difference value is greater than or equal to the angle change threshold, or the current accumulation time is greater than or equal to the maximum accumulation time, the current accumulation time is determined as the radial accumulation time. This allows for timely termination of accumulation when the platform attitude changes significantly to avoid signal superposition distortion from different directions, or to prevent excessive loss of time resolution when the time limit is reached. Ultimately, through this dynamic determination mechanism, the adaptive determination of the radial accumulation time is achieved, effectively solving the technical problems of unstable measurement beam pointing due to continuous shaking of the floating platform and the impact of signal superposition on accuracy under a fixed accumulation time. This ensures measurement accuracy while also considering data acquisition efficiency and real-time system response.

[0014] Furthermore, the step of filtering and analyzing the echo signals from each target azimuth based on the radial cumulative duration to obtain the radial wind measurement results for each target azimuth is specifically as follows: Based on the radial cumulative duration corresponding to each target azimuth, the first echo signal within the corresponding duration range is extracted from the echo signal, and the original radial spectrum data corresponding to each target azimuth is determined based on the first echo signal. Obtain a preset spectral width threshold, identify and remove data to be removed from the original radial spectral data according to the spectral width threshold, and obtain the removed radial spectral data; The radial spectrum data after rejection is calculated and analyzed to obtain the radial wind measurement results corresponding to each target orientation.

[0015] This application extracts the first echo signal within the corresponding time range from the echo signal based on the radial cumulative duration corresponding to each target azimuth, achieving precise interception of effective signals within the cumulative time window determined by attitude adaptation, thus avoiding the mixing of invalid signals under unstable attitudes. Furthermore, based on the first echo signal, the original radial spectrum data is determined, and a preset spectral width threshold is obtained to identify and remove data to be removed. Since signals with spectral widths exceeding the threshold are usually superimposed with vertical velocity interference introduced by rainfall or snowfall, or spectral broadening caused by individual turbulence, they cannot reflect the true atmospheric horizontal wind field information. Removing such data effectively filters out the influence of weather interference and abnormal turbulence on the measurement results. Finally, the radial wind measurement results are obtained by calculating and analyzing the removed radial spectrum data, ensuring that the wind field data for each target azimuth are generated based on pure, effective signals. This effectively solves the technical problem of data quality degradation and measurement accuracy loss caused by rain and snow weather interference and complex turbulence during marine wind measurement, ensuring the accuracy and reliability of radial wind measurement results.

[0016] Furthermore, the radial wind measurement results are processed, and the target wind field is inverted based on the processed radial wind measurement results to determine the wind profile measurement results of the target wind field, specifically as follows: The radial wind measurement results corresponding to each target orientation are normalized and outlier removal is performed to obtain standardized radial wind measurement results; The standardized radial wind measurement results of each target location are integrated, and the wind field distribution of the target wind field is inverted and calculated according to the preset wind field inversion rules to generate wind speed and wind direction data at different height levels of the target wind field. Based on the wind speed and direction data, the wind profile measurement results of the target wind field are determined.

[0017] This application eliminates dimensional differences and random errors between data from different azimuths by normalizing and removing outliers from radial wind measurement results corresponding to each target azimuth, resulting in standardized radial wind measurement results and improving data consistency and comparability. Furthermore, the standardized radial wind measurement results from each target azimuth are integrated, and the wind field distribution of the target wind field is inverted according to preset wind field inversion rules, generating wind speed and direction data at different height levels of the target wind field. Since the aforementioned steps have ensured the quality of radial data from each azimuth through attitude adaptive accumulation and spectral width threshold filtering, the inversion calculation can reconstruct the wind field structure based on accurate and reliable input data. Finally, the wind profile measurement results of the target wind field are determined based on the wind speed and direction data, achieving accurate reconstruction from multi-azimuth radial measurement to three-dimensional wind field distribution. This effectively solves the technical problem of large dispersion and poor consistency of wind measurement data from offshore floating platforms caused by platform sway and weather interference, ensuring the spatial continuity and vertical resolution of the wind profile measurement results, and providing high-precision wind field data support for offshore wind farm power prediction and unit control.

[0018] Furthermore, this application also includes: The signal-to-noise ratio (SNR) of the echo signal is analyzed, and the current weather observation results are determined based on the SNR analysis results. The current weather observation results are compared with the weather observation results of the previous moment, and the adaptive wind measurement mode of the target wind measurement platform is adjusted according to the comparison results.

[0019] This application determines the current weather observation results by performing signal-to-noise ratio analysis on the echo signal, enabling real-time perception of changes in the weather state of the target wind field. It then compares the current weather observation results with the previous observation results and adjusts the adaptive wind measurement mode of the target wind measurement platform based on the comparison results. This allows the wind measurement system to actively respond to weather evolution and promptly switch to a matching wind measurement mode when weather conditions change significantly. Since key parameters such as pulse length and maximum cumulative duration differ under different weather modes, this dynamic adjustment mechanism ensures that the wind measurement parameters are always adapted to the current weather conditions. This effectively solves the technical problems of traditional fixed-parameter wind measurement radars being unable to adapt to sudden weather changes and experiencing a sharp drop in data acquisition rate under rain and snow. It achieves closed-loop adaptive control of the wind measurement system to weather conditions, ensuring the continuity and stability of wind profile measurements under different weather conditions.

[0020] Secondly, this application provides an adaptive measurement device for marine wind profiles. The adaptive measurement device for marine wind profiles includes: The acquisition module is used to send pulse signals to each target location of the target wind field based on the current adaptive wind measurement mode of the target wind measurement platform, acquire the echo signals fed back by the target wind field, and acquire the attitude change information of the target wind measurement platform. The analysis module is used to determine the radial cumulative duration corresponding to each target azimuth based on the attitude change information, and to filter and analyze the echo signals of each target azimuth based on the radial cumulative duration to obtain the radial wind measurement results of each target azimuth. The measurement module is used to process the radial wind measurement results, invert the target wind field based on the processed radial wind measurement results, and determine the wind profile measurement results of the target wind field.

[0021] This application achieves synchronous data acquisition of weather conditions and platform status monitoring by acquiring pulse signals and obtaining echo signals and attitude change information through an acquisition module based on an adaptive wind measurement mode. This provides multi-dimensional input for subsequent adaptive processing. Furthermore, the analysis module dynamically determines the radial accumulation time based on attitude change information and filters and analyzes the echo signals based on this time. This allows the device to adaptively adjust the data accumulation strategy according to the real-time swaying state of the floating platform. Accumulation is extended when the platform is stable to improve the signal-to-noise ratio, and accumulation is terminated in time when the platform is swaying violently to avoid signal distortion. Simultaneously, filtering and analysis removes interfering data, ensuring the accuracy of the radial wind measurement results. Finally, the measurement module processes the radial wind measurement results and inverts them to determine the wind profile measurement results. Because the aforementioned modules have ensured the quality and consistency of the input data, the inverted wind profile accurately reflects the offshore wind field distribution. Thus, through a modular architecture, the entire process from data acquisition and adaptive processing to wind field inversion is automated, effectively solving the technical problems of decreased measurement accuracy and unstable data acquisition caused by continuous swaying and variable weather of floating wind measurement platforms, and improving the intelligence level and reliability of offshore wind profile measurement.

[0022] Thirdly, this application provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the aforementioned adaptive measurement method for marine wind profiles. Its beneficial effects are the same as those of the adaptive measurement method for marine wind profiles provided in the first aspect of this application.

[0023] Fourthly, this application provides a terminal device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement any of the adaptive measurement methods for marine wind profiles as described in the first aspect. Attached Figure Description

[0024] Figure 1 : A schematic flowchart of an embodiment of the adaptive measurement method for marine wind profiles provided in this application; Figure 2: A schematic diagram of an embodiment of the radial spectrum data signal provided in this application; Figure 3 : A schematic diagram of an embodiment of the radar signal-to-noise ratio characteristics under normal weather and rainy / foggy weather conditions provided in this application; Figure 4 : A schematic diagram of an embodiment of the adaptive measurement device for marine wind profile provided in this application. Detailed Implementation

[0025] 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 embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Example 1 Please refer to Figure 1 To address the problem that existing technologies cannot accurately and efficiently measure the wind profile of offshore wind fields, this invention provides an adaptive measurement method for offshore wind profiles, comprising steps S01-S03.

[0027] S01: Based on the current adaptive wind measurement mode of the target wind measurement platform, send pulse signals to each target location of the target wind field, obtain the echo signals fed back by the target wind field, and obtain the attitude change information of the target wind measurement platform.

[0028] In a preferred embodiment of this invention, the step of sending pulse signals to each target location of the target wind field based on the current adaptive wind measurement mode of the preset target wind measurement platform, obtaining the echo signals fed back by the target wind field, and obtaining the preset attitude change information of the target wind measurement platform, specifically involves: The target wind measurement platform used in this embodiment is a floating offshore wind measurement platform. This platform is equipped with a wind measurement radar and is pre-configured with an adaptive wind measurement mode. This adaptive wind measurement mode specifically includes two types: a rain / snow weather wind measurement mode and a non-rain / snow weather wind measurement mode. The pulse length corresponding to the rain / snow weather wind measurement mode is longer than that corresponding to the non-rain / snow weather wind measurement mode, and the maximum cumulative duration of the rain / snow weather wind measurement mode is also longer than that corresponding to the non-rain / snow weather wind measurement mode. When conducting offshore wind profile measurement operations, the currently activated adaptive wind measurement mode type of the target wind measurement platform is first determined. The corresponding pulse length parameter is matched according to the determined wind measurement mode type. Then, the wind measurement radar on the target wind measurement platform is controlled to send pulse signals to various target azimuths of the target wind field according to the matched pulse length. The wind measurement radar uses a 4-beam DBS detection mode, switching between 90-degree azimuth angles at a fixed elevation angle to complete the pulse signal transmission through four different azimuth angles (north, south, east, and west) to realize the calculation of wind field changes. Based on the requirements for wind measurement accuracy and distance resolution, the pulse width is set to 150 ns and the radial accumulation time is set to 1 s in the non-rainy / snowy weather wind measurement mode (default mode). A sliding window is used to output wind profile results at 1-second time intervals. Since the wind radar uses a fiber laser as its light source, the peak power of the laser pulse is limited, and the emitted pulse power is directly related to the pulse width. Furthermore, considering the limitations of data distance resolution, excessively long laser pulses cannot be used to avoid affecting the vertical resolution of the detection results. Therefore, in the default mode, the system can achieve effective wind profile measurement within the designed range.

[0029] In adverse weather conditions, such as rain or dense fog, the default measurement parameter settings will significantly affect the system's measurement altitude. During rain and snow, the echo signal will contain both conventional atmospheric aerosol echoes and rain / snowflake echoes due to interference from raindrops and snowflakes. The signal strength introduced by raindrops and snowflakes fluctuates significantly, appearing intermittently in second-level signals, which will interfere with wind information detection. Simultaneously, rain and snow cause rapid attenuation of laser pulses and echo signals, significantly impacting the radar's effective detection altitude. Figure 2 This is a schematic diagram of the characteristics of a specific radial spectrum data signal, where Figure 2 (a) is a schematic diagram of the radial spectrum data signal characteristics of radar in normal weather. Figure 2 Figure (b) is a schematic diagram of the radial spectral data signal characteristics of radar in rain and snow weather. As can be seen from the figure, in rain and snow weather, the spectral signal will introduce obvious broadening characteristics to the spectral shape, which will mix the signals of raindrops and snowflakes with the signals of atmospheric molecules. The detection of radial wind speed is based on the wind speed corresponding to the highest position of the spectral signal to determine the radial wind result at the corresponding position. When the interference of raindrop and snowflake signals is significant, the falling raindrop and snowflake signals may be identified as atmospheric wind field signals, resulting in incorrect detection results. Figure 3 This diagram illustrates the comparison of radar signal-to-noise ratio (SNR) characteristics under normal weather and rain / fog conditions. As shown in the diagram, under light rain and light fog, the near-field signal is enhanced due to the significantly increased backscattering intensity, but the far-field signal attenuates rapidly, causing the SNR to drop below the effective detection threshold. This results in a significant reduction in the effective detection altitude under adverse weather conditions. Under conditions of significant rainfall and dense fog, the system SNR will decrease sharply with altitude, and the signal within the designed detection range can only acquire detection data at one or two altitude points.

[0030] Based on the signal characteristics under adverse weather conditions, the weather state can be effectively determined by the real-time acquired echo signals, and the adaptive wind measurement mode can be adjusted according to the determination results. Specifically, by performing signal-to-noise ratio (SNR) analysis on the target echo signal, a judgment threshold B is set at a specific height H (e.g., 150m). If the SNR at this height is lower than the judgment threshold B during operation, it can be determined that the current weather has entered an adverse weather state (including rain, snow, fog, etc.). To ensure the data acquisition rate of the radar system, the effective detection height of the system can be increased while appropriately reducing the temporal resolution and effective spatial resolution. At this time, the default settings of 150 ns pulse length and 1s radial accumulation time are adjusted to enhanced settings (i.e., switching the non-rain / snow weather wind measurement mode to the rain / snow weather wind measurement mode), with the pulse length set to 250 ns and the radial accumulation time set to 4s. The pulse width switching is accompanied by changes in the laser's pump current; a wider pulse corresponds to higher pulse energy, and a longer accumulation time can further improve the SNR, thereby adapting to the wind measurement needs under adverse weather conditions. After transmitting a pulse signal, the wind-measuring radar receives and collects the echo signal formed by the reflection of the pulse signal from the target wind field. At the same time, through the attitude detection component on the target wind-measuring platform, it synchronously acquires the attitude change information of the platform during the pulse signal transmission and echo signal reception process. This attitude change information can fully reflect the attitude swaying of the floating wind-measuring platform caused by the marine environment such as waves and currents, providing data support for the subsequent determination of the radial cumulative duration.

[0031] S02: Based on the attitude change information, determine the radial cumulative duration corresponding to each target azimuth, and filter and analyze the echo signals of each target azimuth based on the radial cumulative duration to obtain the radial wind measurement results of each target azimuth.

[0032] In a preferred embodiment of this invention, the step of determining the radial cumulative duration corresponding to each target azimuth based on the attitude change information, and then filtering and analyzing the echo signals of each target azimuth based on the radial cumulative duration to obtain the radial wind measurement results for each target azimuth, specifically involves: Considering that the target anemometer platform is a floating offshore platform, its observation state will change due to its own attitude variations. Therefore, the radial accumulation time needs to be limited to ensure that each radial result is within a fixed pointing direction, thus improving the accuracy of the radial results. Because the buoy system sways, simply extending the radial accumulation time may introduce deviations due to fluctuations in the pointing direction values. Furthermore, wind speeds vary at different spatial locations, and simple accumulation time under swaying conditions will cause spectral broadening due to differences in radial wind speed results at different times, failing to effectively improve the signal-to-noise ratio. Therefore, it is necessary to determine the radial accumulation time corresponding to each target azimuth based on the attitude change information of the target anemometer platform. Specifically, the radial accumulation time corresponding to each target azimuth needs to be determined by combining the attitude change information of the target anemometer platform, the preset angle change threshold, and the data accumulation period, and this radial accumulation time must be an integer multiple of the data accumulation period.

[0033] When determining the radial cumulative duration, the maximum cumulative duration must first be defined. This maximum cumulative duration must be greater than or equal to the final determined radial cumulative duration and must also be an integer multiple of the data accumulation period. Then, based on the attitude change information of the target wind measurement platform at each target azimuth, the real-time attitude difference value of the platform within each data accumulation period is determined. Finally, combining the real-time attitude difference values, the maximum cumulative duration, and the angle change threshold, the target radial cumulative duration is finally determined. In this embodiment, 0.5s can be used as a radial cumulative unit (i.e., a data accumulation period), constrained by a total change angle threshold C (e.g., 8°), and the radial cumulative duration is dynamically adjusted by the attitude change rate of the floating system. For example, if the sum of the real-time attitude difference values ​​corresponding to 2.5s of continuous data is no higher than 8°, but the azimuth angle change range corresponding to the 3rd second exceeds 8°, then the data from the first 2.5s is used for radial result calculation, the data from the 3rd second is discarded, and the radial data acquisition for the current target azimuth ends. If the attitude change never exceeds the angle change threshold, but the cumulative duration reaches the preset maximum cumulative duration (e.g., 4s), then the radial data acquisition for the current target azimuth also ends. This radial time accumulation adjustment method can effectively improve the data acquisition rate under adverse weather conditions while appropriately reducing the temporal and spatial resolution of the radar system. It is also compatible with the observation accuracy requirements of floating platforms at sea, and the measurement accuracy is ensured by determining the range of angular sway.

[0034] After obtaining the radial cumulative duration corresponding to each target azimuth, the first echo signal within the corresponding duration range is extracted from the echo signals collected from each target azimuth using this radial cumulative duration as the filtering criterion. Based on the extracted first echo signal, the original radial spectrum data corresponding to each target azimuth is determined. Subsequently, a preset spectral width threshold is obtained. This spectral width threshold is determined by statistically analyzing the spectral width value distribution characteristics of the frequency spectrum data under different weather conditions after continuous testing of wind field frequency spectrum data under different weather conditions, based on the difference in spectral width value distribution between rainy / snowy weather and non-rainy / snowy weather. For example, the proportion of cases with a spectral width of 1 under stable weather is 99%, the proportion of cases with a spectral width of 1 under rainy weather is 60%, and the proportion of cases with a spectral width of 1.5 or higher increases to 20%. Therefore, the spectral width threshold can be set to 1.5. The current state of rain or snow is identified by the proportion of cases exceeding the threshold within a certain period of time. This can accurately identify rainy / snowy weather without affecting the judgment due to individual deviations. Based on this spectral width threshold, data that does not meet the requirements is identified from the original radial spectral data and needs to be removed. Simultaneously, the spectral width of the signal at each distance gate is identified. If the spectral width is A times the spectral width of a conventional atmospheric signal (A can be set to 2 or 3), then the second-level data at the corresponding altitude point is removed. The removed data is not included in subsequent inversion. This spectral data identification and removal process effectively improves the accuracy of wind profile measurements under adverse weather conditions. After the removal of the data to be removed, the radial spectral data after removal is obtained. Finally, professional calculations and analyses are performed on the removed radial spectral data to obtain the radial wind measurement results corresponding to each target azimuth.

[0035] S03: Process the radial wind measurement results, and invert the target wind field based on the processed radial wind measurement results to determine the wind profile measurement results of the target wind field.

[0036] In a preferred embodiment of this invention, the step of processing the radial wind measurement results and inverting the target wind field based on the processed radial wind measurement results to determine the wind profile measurement results of the target wind field specifically involves: After obtaining the radial wind measurement results for each target azimuth, all radial wind measurement results are first standardized. A normalization algorithm is used to eliminate dimensional differences in data from different target azimuths and measurement periods. Simultaneously, an outlier removal algorithm is employed to identify and remove abnormal radial wind data caused by equipment interference, sudden changes in extreme environments, and other factors, resulting in standardized radial wind measurement results with a unified format and reliable data. Subsequently, the standardized radial wind measurement results for all target azimuths are integrated, and inversion calculations are performed according to preset wind field inversion rules (these rules are based on the principle of vector synthesis and are formulated in conjunction with the mapping relationship between radar beam angles and radial wind data). During the inversion process, the beam configuration parameters of the wind-measuring radar and the angle information of each target azimuth are combined, and the radial wind data from different azimuths are converted into horizontal and vertical wind speed components of the target wind field using a vector synthesis algorithm, thereby obtaining the wind speed and direction data corresponding to each altitude level.

[0037] The division of each altitude level needs to be combined with the detection resolution requirements of the wind-measuring radar to ensure complete coverage of the target wind field. Based on the wind speed and direction data at each altitude level, a vertical wind profile model of the target wind field is constructed. This model clearly shows the variation of wind speed with altitude, and also marks the wind direction information corresponding to each altitude, ultimately forming a complete wind profile measurement result for the target wind field. Through the above process, after the target wind-measuring platform acquires the echo signals of the target wind field at each target azimuth using the wind-measuring radar based on the current adaptive wind-measuring mode, it determines the corresponding radial accumulation time based on the platform's own attitude change information. By dynamically adjusting the radial accumulation time, the data acquisition rate under adverse weather conditions is improved, and the observation accuracy requirements of the floating wind-measuring platform at sea are met. Then, combined with the radial accumulation time, each echo signal is analyzed in a targeted manner to obtain accurate radial wind measurement results. Finally, the wind profile measurement result of the target wind field is obtained through inversion, realizing high-precision wind profile measurement on the sea surface.

[0038] After the wind profile measurement results are generated, visualized charts or structured data can be output according to actual application needs, providing accurate wind field data support for turbine selection, turbine location optimization, and marine meteorological forecasting in offshore wind power projects. Furthermore, after determining the current wind profile measurement results for the target wind field, new target azimuths can be determined, and the process jumps to step S01. The wind-measuring radar of the target wind measurement platform sends pulse signals to each new target azimuth based on the current adaptive wind measurement mode. This process repeats sequentially, acquiring echo signals and attitude change information, determining the radial cumulative duration, filtering and analyzing echo signals, and retrieving wind profile measurement results. This enables continuous inversion of the target wind field and dynamic monitoring of its state.

[0039] In summary, this application transmits pulse signals based on an adaptive wind measurement mode, which can dynamically adjust pulse parameters according to weather conditions, thereby adapting to the signal reflection characteristics under different weather conditions and improving the quality of echo signal acquisition. Furthermore, it determines the radial accumulation time based on attitude change information, realizing real-time response to the swaying state of the floating platform at sea. By dynamically adjusting the accumulation time window, the accumulation is extended when the platform attitude change is small to improve the signal-to-noise ratio, and the accumulation is terminated in time when the attitude change is large to avoid the superposition of signals with different characteristics, thus balancing measurement accuracy and data validity. Finally, the radial wind measurement results after screening and analysis are inverted. Since the aforementioned steps have ensured the accuracy and consistency of data in all directions, the wind profile measurement results obtained by the inversion can truly reflect the distribution of the sea wind field, effectively solving the problem that the existing technology cannot accurately and efficiently measure the wind profile of the sea wind field, and realizing high-precision wind profile measurement on the sea surface.

[0040] Example 2 Please refer to Figure 4 This is an adaptive measurement device for marine wind profile provided in the embodiments of this application.

[0041] In this embodiment, the marine wind profile adaptive measurement device includes an acquisition module 10, an analysis module 20, and a measurement module 30.

[0042] The acquisition module 10 is used to send pulse signals to each target location of the target wind field based on the current adaptive wind measurement mode of the target wind measurement platform, acquire the echo signal fed back by the target wind field, and acquire the attitude change information of the target wind measurement platform. Analysis module 20 is used to determine the radial cumulative duration corresponding to each target orientation based on the attitude change information, and to filter and analyze the echo signals of each target orientation based on the radial cumulative duration to obtain the radial wind measurement results of each target orientation. The measurement module 30 is used to process the radial wind measurement results, invert the target wind field based on the processed radial wind measurement results, and determine the wind profile measurement results of the target wind field.

[0043] For ease of description and brevity, the embodiments of the device of the present invention include all the implementation methods described in the above embodiments of the adaptive measurement method for marine wind profiles, and will not be repeated here.

[0044] Example 3 This application provides a computer-readable storage medium, which includes a stored computer program, wherein the computer program controls the device where the computer-readable storage medium is located to execute the aforementioned adaptive measurement method for marine wind profiles when it is executed. The aforementioned adaptive measurement method for marine wind profiles, when implemented as a software functional unit and used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0045] Example 4 This embodiment provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements any one of the adaptive measurement methods for marine wind profiles as described in Embodiment 1.

[0046] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. An adaptive measurement method for marine wind profiles, characterized in that, include: Based on the current adaptive wind measurement mode of the target wind measurement platform, pulse signals are sent to each target location of the target wind field to obtain the echo signals fed back by the target wind field and to obtain the attitude change information of the target wind measurement platform. Based on the attitude change information, the radial cumulative duration corresponding to each target azimuth is determined, and the echo signals of each target azimuth are filtered and analyzed based on the radial cumulative duration to obtain the radial wind measurement results of each target azimuth. The radial wind measurement results are processed, and the target wind field is inverted based on the processed radial wind measurement results to determine the wind profile measurement results of the target wind field.

2. The adaptive measurement method for marine wind profiles according to claim 1, characterized in that, The method of sending pulse signals to each target location in the target wind field based on the current adaptive wind measurement mode of the preset target wind measurement platform is as follows: Determine the adaptive wind measurement mode of the preset target wind measurement platform; the adaptive wind measurement mode includes a rain and snow weather wind measurement mode and a non-rain and snow weather wind measurement mode, the pulse length corresponding to the rain and snow weather wind measurement mode is greater than the pulse length corresponding to the non-rain and snow weather wind measurement mode, and the maximum cumulative duration corresponding to the rain and snow weather wind measurement mode is greater than the maximum cumulative duration corresponding to the non-rain and snow weather wind measurement mode. Based on the determined adaptive wind measurement mode, the corresponding pulse length is matched, and the wind measurement radar of the preset target wind measurement platform is controlled to send pulse signals to each target location of the target wind field according to the matched pulse length.

3. The adaptive measurement method for marine wind profiles according to claim 1, characterized in that, The step of determining the radial cumulative duration corresponding to each target orientation based on the attitude change information specifically involves: Based on the attitude change information, the real-time attitude difference value of the target wind measurement platform within the preset data accumulation period is determined; Based on the real-time attitude difference values, the preset angle change threshold, and the data accumulation period, the radial accumulation time corresponding to each target orientation is determined. The radial cumulative duration is an integer multiple of the data cumulative period, and the radial cumulative duration does not exceed the preset maximum cumulative duration.

4. The adaptive measurement method for marine wind profiles according to claim 3, characterized in that, The radial cumulative duration corresponding to each target orientation is determined based on the real-time attitude difference values, the preset angle change threshold, and the data accumulation period, specifically as follows: The real-time attitude difference values ​​within the current data accumulation period are summed to obtain the cumulative attitude difference value; Determine whether the cumulative attitude difference value is less than the angle change threshold, and whether the current cumulative duration is less than the maximum cumulative duration; If the cumulative attitude difference value is less than the angle change threshold, and the current cumulative duration is less than the maximum cumulative duration, then continue to accumulate the next data accumulation cycle; If the cumulative attitude difference value is greater than or equal to the angle change threshold, or the current cumulative duration is greater than or equal to the maximum cumulative duration, the current cumulative duration is determined as the radial cumulative duration.

5. The adaptive measurement method for marine wind profiles according to claim 1, characterized in that, The step of filtering and analyzing the echo signals from each target azimuth based on the radial cumulative duration to obtain the radial wind measurement results for each target azimuth is as follows: Based on the radial cumulative duration corresponding to each target azimuth, the first echo signal within the corresponding duration range is extracted from the echo signal, and the original radial spectrum data corresponding to each target azimuth is determined based on the first echo signal. Obtain a preset spectral width threshold, identify and remove data to be removed from the original radial spectral data according to the spectral width threshold, and obtain the removed radial spectral data; The radial spectrum data after rejection is calculated and analyzed to obtain the radial wind measurement results corresponding to each target orientation.

6. The adaptive measurement method for marine wind profiles according to claim 1, characterized in that, The process of processing the radial wind measurement results and inverting the target wind field based on the processed radial wind measurement results to determine the wind profile measurement results of the target wind field specifically involves: The radial wind measurement results corresponding to each target orientation are normalized and outlier removal is performed to obtain standardized radial wind measurement results; The standardized radial wind measurement results of each target location are integrated, and the wind field distribution of the target wind field is inverted and calculated according to the preset wind field inversion rules to generate wind speed and wind direction data at different height levels of the target wind field. Based on the wind speed and direction data, the wind profile measurement results of the target wind field are determined.

7. The adaptive measurement method for marine wind profiles according to any one of claims 1-6, characterized in that, Also includes: The signal-to-noise ratio (SNR) of the echo signal is analyzed, and the current weather observation results are determined based on the SNR analysis results. The current weather observation results are compared with the weather observation results of the previous moment, and the adaptive wind measurement mode of the target wind measurement platform is adjusted according to the comparison results.

8. An adaptive measurement device for marine wind profiles, characterized in that, include: The acquisition module is used to send pulse signals to each target location of the target wind field based on the current adaptive wind measurement mode of the target wind measurement platform, acquire the echo signals fed back by the target wind field, and acquire the attitude change information of the target wind measurement platform. The analysis module is used to determine the radial cumulative duration corresponding to each target azimuth based on the attitude change information, and to filter and analyze the echo signals of each target azimuth based on the radial cumulative duration to obtain the radial wind measurement results of each target azimuth. The measurement module is used to process the radial wind measurement results, invert the target wind field based on the processed radial wind measurement results, and determine the wind profile measurement results of the target wind field.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the adaptive measurement method for marine wind profiles as described in any one of claims 1 to 7.

10. A terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the adaptive measurement method for marine wind profiles as described in any one of claims 1 to 7.

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