A weather radar wind turbine clutter recognition method and system based on fuzzy reasoning

Abstract

The application discloses a weather radar wind turbine clutter recognition method and system based on fuzzy reasoning, and belongs to the technical field of fuzzy reasoning. The monitoring area of a target weather radar is divided into multiple echo observation subunits according to distance gates and scanning azimuth angles, and the reflectivity factor, radial velocity, spectral width and differential reflectivity of each echo observation subunit are continuously acquired within a preset data acquisition time sequence. By analyzing the discrete characteristics of the reflectivity factor and the radial velocity in the time sequence, echo stability characteristic quantities are constructed, echo shape consistency characteristic quantities are constructed based on the consistency deviation of the spectral width and the differential reflectivity in the neighborhood, the echo stability characteristic quantities and the echo shape consistency characteristic quantities are subjected to fuzzy mapping, and the wind turbine clutter and meteorological echoes are distinguished and recognized through a fuzzy comprehensive judgment quantity, so that the accuracy and robustness of the wind turbine clutter recognition are improved, and the weather radar data quality control under complex background conditions is suitable.

Description

Technical Field

[0001] This invention relates to the field of fuzzy reasoning technology, specifically to a weather radar wind turbine clutter identification method and system based on fuzzy reasoning. Background Technology

[0002] With the continuous development of next-generation Doppler weather radar and dual-polarization weather radar technologies, the application capabilities of radar in precipitation monitoring, severe convective weather warning, and hazardous weather identification have been continuously improved. By comprehensively utilizing multi-dimensional radar echo parameters such as reflectivity factor, radial velocity, spectral width, and differential reflectivity, weather radar has evolved from traditional qualitative observation to refined and quantitative detection. Simultaneously, the improvement in radar spatial and temporal resolution allows radar observations to more finely characterize the spatiotemporal evolution of atmospheric echoes, providing a data foundation for target identification and classification in complex echo environments. Against this backdrop, suppression and identification techniques for non-meteorological echoes have gradually become an important research direction in the field of weather radar signal processing, with related research continuously deepening its understanding of echo statistical characteristics, spatial structure features, and temporal stability characteristics.

[0003] However, in complex terrain environments, especially in areas with dense wind farms, non-meteorological echoes generated by large rotating structures such as wind turbines significantly interfere with weather radar observations. Wind turbine clutter typically exhibits strong reflectivity response, anomalous radial velocity distribution, and unstable spectral width characteristics. Its spatial morphology and temporal evolution share certain similarities with some meteorological echoes, making traditional clutter identification methods based on fixed thresholds or single parameters ineffective. Existing technologies rely on empirical thresholds or simple rules to eliminate clutter, resulting in insufficient adaptability and difficulty in handling echo differences across different radar systems, observation distances, and wind turbine operating conditions. Other methods attempt to introduce statistical discrimination or machine learning models, but these often have strong sample dependence, insufficient model interpretability, and high computational complexity in real-time applications, hindering engineering deployment. Furthermore, most existing technologies fail to simultaneously consider the temporal stability of echoes and the morphological consistency within their spatial neighborhood, leading to limited robustness in identifying wind turbine clutter and a tendency for misjudgments or omissions. Summary of the Invention

[0004] The purpose of this invention is to provide a weather radar wind turbine clutter identification method and system based on fuzzy reasoning, so as to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] A method for identifying wind turbine clutter in weather radar based on fuzzy reasoning, comprising the following steps: Step S1: Dividing the monitoring area of ​​the target weather radar into several echo observation sub-units; constructing a radar echo data acquisition device and a data acquisition time series, and acquiring the reflectivity factor, radial velocity, spectral width, and differential reflectivity of the echo observation sub-unit at each data acquisition time point; Step S2: Calculating the dispersion of the reflectivity factor and radial velocity of the echo observation sub-unit on the data acquisition time series; constructing the echo stability feature quantity of the echo observation sub-unit on the data acquisition time series; Step S3: Constructing an echo neighborhood set, and calculating the correlation between the echo observation sub-unit and the echo observation area. Step S4: Calculate the echo morphology consistency characteristics of the echo observation sub-units in the echo neighborhood set, based on the echo stability characteristics of the echo observation sub-units in the data acquisition time series and the echo morphology consistency characteristics of the echo observation sub-units in the data acquisition time series; Step S5: Calculate the fuzzy comprehensive judgment quantity of the echo observation sub-units in the data acquisition time series based on the echo stability characteristics and the echo morphology consistency characteristics of the echo observation sub-units in the data acquisition time series, preset the threshold, and analyze and identify wind turbine clutter.

[0007] As a preferred embodiment of the fuzzy reasoning-based weather radar wind turbine clutter identification method described in this invention, the monitoring area of ​​the target weather radar is obtained from the weather radar data storage system. Based on the scanning azimuth and range gate of the target weather radar, the monitoring area of ​​the target weather radar is divided into several echo observation sub-units of the target weather radar according to spatial units, and a set of echo observation sub-units is constructed, denoted as […]. ,in, This represents the echo observation sub-unit corresponding to the m-th range gate and the n-th scanning azimuth angle, where M represents the total number of range gates and N represents the total number of scanning azimuth angles.

[0008] A radar echo data acquisition device is constructed, comprising a reflectivity acquisition unit, a Doppler velocity acquisition unit, a spectral width acquisition unit, and a polarization parameter acquisition unit. The reflectivity acquisition unit is used to acquire the reflectivity factor of the echo observation sub-unit; the Doppler velocity acquisition unit is used to acquire the radial velocity of the echo observation sub-unit; the spectral width acquisition unit is used to acquire the spectral width of the echo observation sub-unit; and the polarization parameter acquisition unit is used to acquire the differential reflectivity of the echo observation sub-unit.

[0009] As a preferred embodiment of the weather radar wind turbine clutter identification method based on fuzzy reasoning described in this invention, a preset data acquisition time series is denoted as... ,in, This represents the t-th data acquisition time point, where T represents the total number of data acquisition time points. At each data acquisition time point, the reflectivity factor, radial velocity, spectral width, and differential reflectivity of the echo observation sub-unit are collected. The data acquisition time points are then... Echo observation subunit acquired from the bottom The reflectivity factor, radial velocity, spectral width, and differential reflectivity are denoted as... and .

[0010] As a preferred embodiment of the weather radar wind turbine clutter identification method based on fuzzy reasoning described in this invention, based on data acquisition time points... Echo observation subunit acquired from the bottom reflectivity factor and radial velocity Calculate the reflectivity factor separately. and radial velocity The dispersion of the data acquisition time series is calculated using the following formula:

[0011] ;

[0012] in, and Reflectivity factors and radial velocity The dispersion over the data acquisition time series, where T represents the total number of data acquisition time points. This represents the mean reflectance factor across all data acquisition time points. This represents the average radial velocity across all data acquisition time points.

[0013] Based on reflectivity factor and radial velocity Based on the dispersion of the data acquisition time series, an echo observation sub-unit is constructed on the data acquisition time series. The echo stability characteristic is calculated using the following formula:

[0014] ;

[0015] in, Represents the echo observation sub-unit on the data acquisition time series. The echo stability characteristic quantity, This represents the maximum reflectivity factor in all echo observation sub-units at all data acquisition time points. This represents the maximum radial velocity value in all echo observation sub-units at all data acquisition time points.

[0016] As a preferred embodiment of the weather radar wind turbine clutter identification method based on fuzzy reasoning described in this invention, the echo observation subunit is used. Centered on the echo observation sub-unit set Selecting and echoing observation sub-units Adjacent echo observation sub-units are identified, and an echo neighborhood set is constructed, denoted as... ;

[0017] Based on data collection time points Echo observation subunit acquired from the bottom spectral width and differential reflectivity Calculate the echo observation sub-unit With echo neighborhood set The formulas for calculating the spectral width consistency deviation and differential reflectivity consistency deviation of the echo observation sub-unit are as follows:

[0018] ;

[0019] in, and These represent echo observation sub-units. With echo neighborhood set The spectral width consistency deviation and differential reflectivity consistency deviation of the echo observation sub-unit in the data. Represents the echo neighborhood set The number of echo observation sub-units in the middle, Let represent the echo observation sub-cell corresponding to the p-th range gate and the q-th scan azimuth angle, and . and These represent the data collection time points. Echo observation subunit acquired from the bottom Spectral width and differential reflectance;

[0020] Based on echo observation subunit With echo neighborhood set The spectral width consistency deviation and differential reflectivity consistency deviation of the echo observation sub-unit are calculated at the data acquisition time point. The echo observation subunit below The echo shape consistency characteristic is calculated using the following formula:

[0021] ;

[0022] in, Indicates the data collection time point Lower echo observation subunit The echo morphology is consistent with the characteristic quantity. This represents the maximum spectral width across all echo observation sub-units at all data acquisition time points. This represents the maximum differential reflectivity of all echo observation sub-units at all data acquisition time points.

[0023] As a preferred embodiment of the weather radar wind turbine clutter identification method based on fuzzy reasoning described in this invention, it is based on the echo observation subunit of the data acquisition time series. echo stability characteristic and data collection time point Lower echo observation subunit echo morphology consistent characteristic Calculate the echo stability characteristic respectively Characteristic quantities consistent with echo morphology The membership degree is as follows:

[0024] The formula for calculating the membership degree of the echo stability characteristic is as follows:

[0025] ;

[0026] in, Indicates the membership degree of the echo stability characteristic. This represents the preset threshold for echo stability characteristics. This represents the preset echo stabilization characteristic quantity, the asymmetric attenuation coefficient.

[0027] The formula for calculating the membership degree of the echo morphology consistency feature is as follows:

[0028] ;

[0029] in, This indicates the membership degree of the echo morphology consistency characteristic. This represents the preset threshold for echo pattern consistency characteristics. This represents the asymmetric adjustment coefficient of the preset echo pattern consistency characteristic quantity.

[0030] As a preferred embodiment of the weather radar wind turbine clutter identification method based on fuzzy reasoning described in this invention, the method is based on the membership degree of the echo stability feature. Membership degree of features consistent with echo morphology Calculate the data collection time point Lower echo observation subunit The fuzzy comprehensive decision quantity is calculated using the following formula:

[0031] ;

[0032] in, Indicates the data collection time point Lower echo observation subunit Fuzzy comprehensive decision quantity, and These represent the preset membership degrees of the echo stabilization characteristic. Membership degree of features consistent with echo morphology Influence factors;

[0033] Based on data collection time points Lower echo observation subunit Fuzzy comprehensive decision quantity The preset wind turbine noise judgment threshold range is used, and the fuzzy comprehensive judgment quantity is... If the noise exists within the wind turbine clutter determination threshold range, then the echo observation subunit is determined. The corresponding echo characteristics are wind turbine clutter, if the fuzzy comprehensive decision quantity If the echo observation subunit does not exist within the wind turbine clutter determination threshold range, then the echo observation subunit is determined. The corresponding echo characteristics are meteorological echoes;

[0034] The fuzzy comprehensive judgment value is acquired in real time at each data acquisition time point to perform dynamic identification of wind turbine clutter.

[0035] A weather radar wind turbine clutter identification system based on fuzzy reasoning. The system includes: a unit partitioning and data acquisition module, a discreteness calculation and stable feature quantity calculation module, a deviation quantity calculation and consistency feature quantity calculation module, a membership degree calculation module, and a fuzzy comprehensive judgment quantity calculation, analysis, and identification module.

[0036] The unit division and data acquisition module divides the monitoring area of ​​the target weather radar into several echo observation sub-units; constructs a radar echo data acquisition device and a data acquisition time series, and acquires the reflectivity factor, radial velocity, spectral width and differential reflectivity of the echo observation sub-unit at each data acquisition time point;

[0037] The discreteness calculation and stable characteristic quantity calculation module calculates the discreteness of the reflectivity factor and radial velocity of the echo observation sub-unit on the data acquisition time series, respectively; and constructs the echo stable characteristic quantity of the echo observation sub-unit on the data acquisition time series.

[0038] The deviation calculation and consistency feature calculation module: constructs an echo neighborhood set, calculates the spectral width consistency deviation and differential reflectivity consistency deviation between the echo observation sub-unit and the echo observation sub-unit in the echo neighborhood set; and calculates the echo morphology consistency feature of the echo observation sub-unit at the data acquisition time point.

[0039] The membership calculation module calculates the membership of the echo stability feature and the echo morphology consistency feature of the echo observation sub-unit at the data acquisition time series, respectively.

[0040] The fuzzy comprehensive judgment quantity calculation and analysis identification module calculates the fuzzy comprehensive judgment quantity of the echo observation subunit at the data acquisition time point based on the membership degree of the echo stability feature quantity and the membership degree of the echo shape consistency feature quantity, presets a threshold, and analyzes and identifies wind turbine clutter.

[0041] Furthermore, the discreteness calculation and stable feature quantity calculation module includes a discreteness calculation unit and a stable feature quantity calculation unit;

[0042] The discreteness calculation unit calculates the discreteness of the reflectivity factor and radial velocity of the echo observation subunit acquired at the data acquisition time point, respectively, on the data acquisition time series.

[0043] The stable characteristic calculation unit: Based on the reflectivity factor and the dispersion of radial velocity on the data acquisition time series, it constructs the echo stable characteristic of the echo observation subunit on the data acquisition time series.

[0044] Furthermore, the deviation calculation and consistency feature calculation module includes a deviation calculation unit and a consistency feature calculation unit;

[0045] The deviation calculation unit: taking the echo observation sub-unit as the center, selects the echo observation sub-unit adjacent to the echo observation sub-unit from the echo observation sub-unit set, and constructs the echo neighborhood set; based on the spectral width and differential reflectance of the echo observation sub-unit acquired at the data acquisition time point, it calculates the spectral width consistency deviation and differential reflectance consistency deviation between the echo observation sub-unit and the echo observation sub-units in the echo neighborhood set.

[0046] The consistency feature calculation unit calculates the echo morphology consistency features of the echo observation subunit at the data acquisition time point based on the spectral width consistency deviation and differential reflectivity consistency deviation between the echo observation subunit and the echo observation subunit in the echo neighborhood set.

[0047] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: This invention provides a weather radar wind turbine clutter identification method and system based on fuzzy reasoning. By dividing the weather radar monitoring area into echo observation sub-units based on range gates and scanning azimuth angles, and continuously acquiring multi-dimensional radar parameters such as reflectivity factor, radial velocity, spectral width, and differential reflectivity under a unified data acquisition time series, standardized modeling of the radar observation object is achieved, providing a stable and consistent data foundation for subsequent analysis. Based on this, by analyzing the reflectivity factor and radial velocity dispersion of the echo observation sub-units in the time series, stable echo characteristic quantities are constructed to characterize the changing patterns of echo characteristics from a time dimension, enabling effective differentiation between wind turbine clutter with relatively stable time characteristics and meteorological echoes that change with weather evolution. Furthermore, by introducing an echo neighborhood set, the spectral width is calculated... By analyzing the consistency deviation of differential reflectivity within its spatial neighborhood, an echo morphology consistency feature is constructed to reflect the continuity and consistency of the echo morphology from a spatial structure perspective, suppressing the interference of isolated outliers on the identification results. Based on this, an asymmetric membership function is used to perform fuzzy mapping on the echo stability feature and the echo morphology consistency feature, respectively, to achieve flexible characterization of sensitive intervals for different feature discriminations, avoiding instability issues caused by hard threshold judgments. Finally, a fuzzy comprehensive judgment quantity is obtained by fusing multiple membership degrees, and combined with a preset threshold interval, the echo observation sub-unit is dynamically discriminated, thereby achieving continuous identification and differentiation of wind turbine clutter. This method, while balancing temporal stability and spatial consistency, improves the reliability and engineering applicability of wind turbine clutter identification in complex environments, providing effective support for weather radar data quality control and subsequent meteorological applications. Attached Figure Description

[0048] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0049] Figure 1 This is a schematic diagram illustrating the steps of a weather radar wind turbine clutter identification method based on fuzzy reasoning according to the present invention.

[0050] Figure 2 This is a schematic diagram of the structure of a weather radar wind turbine clutter identification system based on fuzzy reasoning according to the present invention. Detailed Implementation

[0051] 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.

[0052] Please see Figure 1 In this first embodiment: a weather radar wind turbine clutter identification method based on fuzzy reasoning is provided, which includes the following steps:

[0053] Step S1: Divide the monitoring area of ​​the target weather radar into several echo observation sub-units; construct a radar echo data acquisition device and a data acquisition time series, and collect the reflectivity factor, radial velocity, spectral width and differential reflectivity of the echo observation sub-unit at each data acquisition time point.

[0054] Specifically, the monitoring area of ​​the target weather radar is obtained from the weather radar data storage system. Based on the scanning azimuth and range gate of the target weather radar, the monitoring area is divided into several echo observation sub-units of the target weather radar according to spatial units, and a set of echo observation sub-units is constructed, denoted as . ,in, This represents the echo observation sub-unit corresponding to the m-th range gate and the n-th scanning azimuth angle, where M represents the total number of range gates and N represents the total number of scanning azimuth angles.

[0055] A radar echo data acquisition device is constructed, comprising a reflectivity acquisition unit, a Doppler velocity acquisition unit, a spectral width acquisition unit, and a polarization parameter acquisition unit. The reflectivity acquisition unit is used to acquire the reflectivity factor of the echo observation sub-unit; the Doppler velocity acquisition unit is used to acquire the radial velocity of the echo observation sub-unit; the spectral width acquisition unit is used to acquire the spectral width of the echo observation sub-unit; and the polarization parameter acquisition unit is used to acquire the differential reflectivity of the echo observation sub-unit.

[0056] Furthermore, a preset data collection time series is defined as follows: ,in, This represents the t-th data acquisition time point, where T represents the total number of data acquisition time points. At each data acquisition time point, the reflectivity factor, radial velocity, spectral width, and differential reflectivity of the echo observation sub-unit are collected. The data acquisition time points are then... Echo observation subunit acquired from the bottom The reflectivity factor, radial velocity, spectral width, and differential reflectivity are denoted as... and .

[0057] In this invention, the scanning azimuth angle is the angular direction of the radar antenna rotating and scanning on the horizontal plane. Typically, true north is defined as 0°, and the azimuth angle covers a full circle clockwise from 0° to 360°. The 360° circle is divided into N uniform angular units, each unit being a scanning azimuth angle. For example, when N=360°, each azimuth angle is spaced 1° apart; when N=720°, the spaced 0.5° apart. The more azimuth angles there are, the higher the horizontal resolution. It can be used to determine the position of the echo in the horizontal direction; for example, the nth scanning azimuth angle corresponds to a specific direction (such as 30° or 120°) pointed by the radar antenna.

[0058] A range gate represents the range interval unit along the propagation path of the electromagnetic wave emitted by the radar. The radar calculates the target distance by measuring the round-trip time of the signal and divides this range into multiple equally spaced small units. Assuming the radar can detect a maximum distance of 200 kilometers, if it is divided into M=2000 range gates, then the interval between each range gate is 100 meters (200km / 2000=100m). The more range gates there are, the higher the radial range resolution. The position of the echo in the radial distance is determined; for example, the m-th range gate corresponds to a position m × the range gate interval in front of the radar antenna (e.g., the 500th gate corresponds to 50 kilometers).

[0059] Each range gate (m) + scanning azimuth angle (n) corresponds to a unique echo observation sub-unit, which is equivalent to drawing a two-dimensional grid map of the radar monitoring area. Each grid is an independent analysis unit, ensuring that the subsequently collected data such as reflectivity factor and radial velocity can be accurately located to the specific spatial position.

[0060] Step S2: Calculate the reflectivity factor and radial velocity of the echo observation sub-unit on the data acquisition time series respectively; construct the echo stability characteristic of the echo observation sub-unit on the data acquisition time series.

[0061] Specifically, based on the data collection time point Echo observation subunit acquired from the bottom reflectivity factor and radial velocity Calculate the reflectivity factor separately. and radial velocity The dispersion of the data acquisition time series is calculated using the following formula:

[0062] ;

[0063] in, and Reflectivity factors and radial velocity The dispersion over the data acquisition time series, where T represents the total number of data acquisition time points. This represents the mean reflectance factor across all data acquisition time points. This represents the average radial velocity across all data acquisition time points.

[0064] It should be noted that dispersion is an indicator that measures the degree of data fluctuation. Here, the magnitude of the fluctuation of the parameters over time is quantified by calculating the average absolute deviation of the reflectivity factor and radial velocity from the mean in the entire time series (T time points).

[0065] Wind turbine clutter is generated by rotating components and exhibits small temporal fluctuations (small dispersion); meteorological echoes (such as precipitation and convection) fluctuate significantly with weather changes (large dispersion). This formula quantifies the difference in fluctuations, providing a temporal dimension for distinguishing between the two types of echoes, such as those from the echo observation subunit where the wind turbine is located. and It will be significantly lower than the surrounding meteorological echo units;

[0066] Based on reflectivity factor and radial velocity Based on the dispersion of the data acquisition time series, an echo observation sub-unit is constructed on the data acquisition time series. The echo stability characteristic is calculated using the following formula:

[0067] ;

[0068] in, Represents the echo observation sub-unit on the data acquisition time series. The echo stability characteristic quantity, This represents the maximum reflectivity factor in all echo observation sub-units at all data acquisition time points. This represents the maximum radial velocity value in all echo observation sub-units at all data acquisition time points.

[0069] It should be noted that the dispersion and Divide by the maximum reflectivity factor of the entire scene respectively and maximum radial velocity Normalize and then sum to obtain the comprehensive stable characteristic. This ensures that the feature values ​​are in the [0,2] interval, which facilitates subsequent unified analysis.

[0070] Normalization eliminates differences in parameter magnitudes (such as the different units of reflectivity factor and radial velocity). The smaller the value, the stronger the echo time stability, and the more likely it is wind turbine noise; A larger value indicates weaker stability and a higher likelihood of a meteorological echo. For example, if a certain unit... A value of 0.1 (close to 0) is most likely wind turbine noise; if If the value is 1.5 (close to 2), it is most likely a meteorological echo.

[0071] By condensing the two dimensions of reflectivity fluctuation and radial velocity fluctuation into a single stable feature, the core information is preserved while reducing the dimensionality of subsequent calculations, thus improving the overall algorithm efficiency. At the same time, the logic that greater stability equals greater likelihood of clutter is made more intuitive.

[0072] The normalization process is independent of the parameter range of a specific radar. Whether it is a Doppler radar or a dual-polarization radar, as long as the output reflectivity factor and radial velocity are obtained, it can be directly applied. This solves the problem of high dependence on specific radar systems in traditional methods and improves the versatility of the method.

[0073] Step S3: Construct an echo neighborhood set, calculate the spectral width consistency deviation and differential reflectivity consistency deviation between the echo observation sub-unit and the echo observation sub-unit in the echo neighborhood set; calculate the echo morphology consistency characteristic of the echo observation sub-unit at the data acquisition time point.

[0074] Specifically, taking the echo observation subunit Centered on the echo observation sub-unit set Selecting and echoing observation sub-units Adjacent echo observation sub-units are identified, and an echo neighborhood set is constructed, denoted as... ;

[0075] Based on data collection time points Echo observation subunit acquired from the bottom spectral width and differential reflectivity Calculate the echo observation sub-unit With echo neighborhood set The formulas for calculating the spectral width consistency deviation and differential reflectivity consistency deviation of the echo observation sub-unit are as follows:

[0076] ;

[0077] in, and These represent echo observation sub-units. With echo neighborhood set The spectral width consistency deviation and differential reflectivity consistency deviation of the echo observation sub-unit in the data. Represents the echo neighborhood set The number of echo observation sub-units in the middle, Let represent the echo observation sub-cell corresponding to the p-th range gate and the q-th scan azimuth angle, and . and These represent the data collection time points. Echo observation subunit acquired from the bottom Spectral width and differential reflectance;

[0078] It should be noted that, based on the echo observation subunit Centered on a central element, neighboring units are selected to form a neighborhood set. The average absolute deviation between the central cell and its neighboring cells in spectral width and differential reflectance is calculated to quantize the parameter consistency in the quantization space.

[0079] Wind turbine clutter is a localized, isolated echo with significant differences in spectral width and differential reflectivity compared to surrounding units; meteorological echoes, on the other hand, are spatially continuous and exhibit minimal parameter differences compared to surrounding units. This formula quantifies spatial differences, providing a spatial dimensional basis for distinguishing between the two types of echoes, such as the unit where the wind turbine is located. It will be significantly higher than the surrounding meteorological echo units.

[0080] By using the mean parameter deviation between the central unit and neighboring units, the spatial continuity of the echo is accurately characterized—wind turbine clutter is a localized, isolated echo with a large deviation from surrounding units, while meteorological echoes are spatially continuous with a small deviation, thus compensating for the lack of temporal dimension features. Spectral width and differential reflectivity are chosen as spatial comparison parameters because these two parameters are more sensitive to isolated clutter—the echoes generated by wind turbine rotation have an abnormal spectral width, and the differential reflectivity differs significantly from the surrounding atmospheric echoes, making spatial comparison more accurate than traditional methods using reflectivity factors and reducing misjudgments.

[0081] Based on echo observation subunit With echo neighborhood set The spectral width consistency deviation and differential reflectivity consistency deviation of the echo observation sub-unit are calculated at the data acquisition time point. The echo observation subunit below The echo shape consistency characteristic is calculated using the following formula:

[0082] ;

[0083] in, Indicates the data collection time point Lower echo observation subunit The echo morphology is consistent with the characteristic quantity. This represents the maximum spectral width across all echo observation sub-units at all data acquisition time points. This represents the maximum differential reflectivity of all echo observation sub-units at all data acquisition time points.

[0084] It should be noted that the consistency deviation amount, and Divide by the maximum spectral width of the entire scene respectively and maximum differential reflectivity Normalization, summation, and subtraction by 1 yield the morphologically consistent feature quantity. ,make The value is in the range [0,1]. A larger value indicates stronger spatial consistency.

[0085] To supplement the deficiencies in time characteristics and solve the problem of distinguishing between stationary targets (such as buildings) and wind turbine clutter—although building clutter is time-stable ( The value is small, but the spatial difference from the surrounding units may not be significant. (Value is large), while wind turbine noise is time-stable ( Small value), and spatially isolated ( (Small value). For example, the wind turbine unit's The value may be 0.2, for building units. The value may be 0.8, which is the value of the meteorological echo unit. The value could be 0.9.

[0086] Step S4: Based on the echo stability feature of the echo observation sub-unit on the data acquisition time series and the echo morphology consistency feature of the echo observation sub-unit at the data acquisition time point, calculate the membership degree of the echo stability feature and the echo morphology consistency feature respectively.

[0087] Specifically, based on the echo observation subunit of the data acquisition time series. echo stability characteristic and data collection time point Lower echo observation subunit echo morphology consistent characteristic Calculate the echo stability characteristic respectively Characteristic quantities consistent with echo morphology The membership degree is as follows:

[0088] The formula for calculating the membership degree of the echo stability characteristic is as follows:

[0089] ;

[0090] in, Indicates the membership degree of the echo stability characteristic. This represents the preset threshold for echo stability characteristics. This represents the preset echo stabilization characteristic quantity, the asymmetric attenuation coefficient.

[0091] It should be noted that membership degree is the core of fuzzy set, used to represent the degree to which an element belongs to the wind turbine clutter set (value [0,1]). It is a preset threshold (e.g., 0.3). It is the attenuation coefficient (e.g., 0.5): when (Extremely stable), membership degree is 1 (completely belongs to the clutter set); when Membership degree follows The value decreases exponentially as it increases, reflecting ambiguity (not black and white, but a gradual transition).

[0092] Avoid the problem of applying a hard threshold across the board. For example, Value = 0.3 (equal to) When ), the membership degree = 1. Value = 0.4 (slightly greater than) When the membership degree is approximately 0.82, When the value is 0.6, the membership degree is approximately 0.45, which distinguishes between stable and unstable echoes while preserving the rationality of the transition range, conforming to the gradual characteristics of actual echoes.

[0093] Echo stability characteristic threshold This value is used to determine whether the echo is stable in time. If it exceeds this value, it is considered unstable. It is usually set based on historical data or experimental experience. For example, it is set to 0.2 to 0.5, which means that if the echo stability characteristic is lower than this value, it is considered to have strong time stability and may be wind turbine noise.

[0094] Echo stability characteristic quantity asymmetric attenuation coefficient This is used to control the membership decay rate after a stable characteristic exceeds a threshold. It is usually adjusted according to the sensitivity of distinguishing clutter from meteorological echoes in practical applications, for example, by taking a value of 0.5 to 1.0.

[0095] The formula for calculating the membership degree of the echo morphology consistency feature is as follows:

[0096] ;

[0097] in, This indicates the membership degree of the echo morphology consistency characteristic. This represents the preset threshold for echo pattern consistency characteristics. This represents the asymmetric adjustment coefficient of the preset echo pattern consistency characteristic quantity.

[0098] Echo morphology consistency feature threshold This value is used to determine whether the echoes are spatially consistent. If the value is lower than this value, it is considered inconsistent. It is usually set to 0.7 to 0.9, which means that if the spatial consistency of the echoes is poor (small value), it is more likely to be wind turbine noise.

[0099] echo shape consistent characteristic quantity asymmetric adjustment coefficient This is used to control the rate at which the membership degree decays after the morphologically consistent feature exceeds a threshold. It is usually small, such as 0.1 to 0.3, so that the membership degree drops rapidly once the threshold is exceeded.

[0100] Step S5: Based on the membership degree of the echo stability feature quantity and the membership degree of the echo morphology consistency feature quantity, calculate the fuzzy comprehensive judgment quantity of the echo observation sub-unit at the data acquisition time point, preset the threshold, and analyze and identify wind turbine clutter.

[0101] Specifically, based on the membership degree of echo stability characteristics Membership degree of features consistent with echo morphology Calculate the data collection time point Lower echo observation subunit The fuzzy comprehensive decision quantity is calculated using the following formula:

[0102] ;

[0103] in, Indicates the data collection time point Lower echo observation subunit Fuzzy comprehensive decision quantity, and These represent the preset membership degrees of the echo stabilization characteristic. Membership degree of features consistent with echo morphology Influence factors;

[0104] Echo stability characteristic membership degree influence factor This is used to assign weights to time stability features in fuzzy comprehensive decision-making; the membership degree influence factor of the echo morphology consistency feature quantity. , used to assign weights to spatial consistency features in fuzzy comprehensive determination;

[0105] generally If time stability is more important, then Larger values ​​(e.g., 0.6–0.8); if spatial consistency is more important, then... The weights are relatively large; the optimal weights can be determined through experiments or principal component analysis.

[0106] It should be noted that the membership of the two core dimensions of time stability and spatial isolation is weighted and integrated to avoid the one-sidedness of a single dimension feature—such as the echo time stability of a stationary building ( High) but not isolated in space ( Low), comprehensive judgment value This will reduce the risk of misjudgment; the timing of rapidly moving meteorological echoes is unstable. Low) but spatially continuous ( (High) will also be correctly judged, solving the problem of missed judgment in traditional single-dimensional methods.

[0107] Based on data collection time points Lower echo observation subunit Fuzzy comprehensive decision quantity The preset wind turbine noise judgment threshold range is used, and the fuzzy comprehensive judgment quantity is... If the noise exists within the wind turbine clutter determination threshold range, then the echo observation subunit is determined. The corresponding echo characteristics are wind turbine clutter, if the fuzzy comprehensive decision quantity If the echo observation subunit does not exist within the wind turbine clutter determination threshold range, then the echo observation subunit is determined. The corresponding echo characteristics are meteorological echoes;

[0108] The fuzzy comprehensive judgment value is acquired in real time at each data acquisition time point to perform dynamic identification of wind turbine clutter.

[0109] Please see Figure 2 In this second embodiment: a weather radar wind turbine clutter identification system based on fuzzy reasoning is provided. The system includes: a unit division and data acquisition module, a discreteness calculation and stable feature quantity calculation module, a deviation quantity calculation and consistency feature quantity calculation module, a membership degree calculation module, and a fuzzy comprehensive judgment quantity calculation and analysis identification module.

[0110] The unit division and data acquisition module divides the monitoring area of ​​the target weather radar into several echo observation sub-units; constructs a radar echo data acquisition device and a data acquisition time series, and acquires the reflectivity factor, radial velocity, spectral width and differential reflectivity of the echo observation sub-unit at each data acquisition time point;

[0111] The discreteness calculation and stable characteristic quantity calculation module calculates the discreteness of the reflectivity factor and radial velocity of the echo observation sub-unit on the data acquisition time series, respectively; and constructs the echo stable characteristic quantity of the echo observation sub-unit on the data acquisition time series.

[0112] The deviation calculation and consistency feature calculation module: constructs an echo neighborhood set, calculates the spectral width consistency deviation and differential reflectivity consistency deviation between the echo observation sub-unit and the echo observation sub-unit in the echo neighborhood set; and calculates the echo morphology consistency feature of the echo observation sub-unit at the data acquisition time point.

[0113] The membership calculation module calculates the membership of the echo stability feature and the echo morphology consistency feature of the echo observation sub-unit at the data acquisition time series, respectively.

[0114] The fuzzy comprehensive judgment quantity calculation and analysis identification module calculates the fuzzy comprehensive judgment quantity of the echo observation subunit at the data acquisition time point based on the membership degree of the echo stability feature quantity and the membership degree of the echo shape consistency feature quantity, presets a threshold, and analyzes and identifies wind turbine clutter.

[0115] Furthermore, the discreteness calculation and stable feature quantity calculation module includes a discreteness calculation unit and a stable feature quantity calculation unit;

[0116] The discreteness calculation unit calculates the discreteness of the reflectivity factor and radial velocity of the echo observation subunit acquired at the data acquisition time point, respectively, on the data acquisition time series.

[0117] The stable characteristic calculation unit: Based on the reflectivity factor and the dispersion of radial velocity on the data acquisition time series, it constructs the echo stable characteristic of the echo observation subunit on the data acquisition time series.

[0118] Furthermore, the deviation calculation and consistency feature calculation module includes a deviation calculation unit and a consistency feature calculation unit;

[0119] The deviation calculation unit: taking the echo observation sub-unit as the center, selects the echo observation sub-unit adjacent to the echo observation sub-unit from the echo observation sub-unit set, and constructs the echo neighborhood set; based on the spectral width and differential reflectance of the echo observation sub-unit acquired at the data acquisition time point, it calculates the spectral width consistency deviation and differential reflectance consistency deviation between the echo observation sub-unit and the echo observation sub-units in the echo neighborhood set.

[0120] The consistency feature calculation unit calculates the echo morphology consistency features of the echo observation subunit at the data acquisition time point based on the spectral width consistency deviation and differential reflectivity consistency deviation between the echo observation subunit and the echo observation subunit in the echo neighborhood set.

[0121] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0122] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A weather radar wind turbine clutter identification method based on fuzzy reasoning, characterized in that, The method includes the following steps: Step S1: Divide the monitoring area of ​​the target weather radar into several echo observation sub-units; construct a radar echo data acquisition device and a data acquisition time series, and collect the reflectivity factor, radial velocity, spectral width and differential reflectivity of the echo observation sub-unit at each data acquisition time point; Step S2: Calculate the reflectivity factor and radial velocity of the echo observation sub-unit on the data acquisition time series respectively; construct the echo stability characteristic of the echo observation sub-unit on the data acquisition time series; Step S3: Construct an echo neighborhood set, calculate the spectral width consistency deviation and differential reflectivity consistency deviation between the echo observation sub-unit and the echo observation sub-unit in the echo neighborhood set; calculate the echo morphology consistency characteristics of the echo observation sub-unit at the data acquisition time point. Step S4: Based on the echo stability feature of the echo observation sub-unit on the data acquisition time series and the echo morphology consistency feature of the echo observation sub-unit at the data acquisition time point, calculate the membership degree of the echo stability feature and the echo morphology consistency feature respectively. Step S5: Based on the membership degree of the echo stability feature quantity and the membership degree of the echo morphology consistency feature quantity, calculate the fuzzy comprehensive judgment quantity of the echo observation sub-unit at the data acquisition time point, preset the threshold, and analyze and identify wind turbine clutter.

2. The weather radar wind turbine clutter identification method based on fuzzy reasoning according to claim 1, characterized in that, The specific implementation process of step S1 includes: The monitoring area of ​​the target weather radar is obtained from the weather radar data storage system. Based on the scanning azimuth and range gate of the target weather radar, the monitoring area is divided into several echo observation sub-units of the target weather radar according to spatial units, and a set of echo observation sub-units is constructed, denoted as . ,in, This represents the echo observation sub-unit corresponding to the m-th range gate and the n-th scanning azimuth angle, where M represents the total number of range gates and N represents the total number of scanning azimuth angles. A radar echo data acquisition device is constructed, comprising a reflectivity acquisition unit, a Doppler velocity acquisition unit, a spectral width acquisition unit, and a polarization parameter acquisition unit. The reflectivity acquisition unit is used to acquire the reflectivity factor of the echo observation sub-unit; the Doppler velocity acquisition unit is used to acquire the radial velocity of the echo observation sub-unit; the spectral width acquisition unit is used to acquire the spectral width of the echo observation sub-unit; and the polarization parameter acquisition unit is used to acquire the differential reflectivity of the echo observation sub-unit.

3. The weather radar wind turbine clutter identification method based on fuzzy reasoning according to claim 2, characterized in that, The specific implementation process of step S1 also includes: The preset data collection time series is denoted as... ,in, This represents the t-th data acquisition time point, where T represents the total number of data acquisition time points. At each data acquisition time point, the reflectivity factor, radial velocity, spectral width, and differential reflectivity of the echo observation sub-unit are collected. The data acquisition time points are then... Echo observation subunit acquired from the bottom The reflectivity factor, radial velocity, spectral width, and differential reflectivity are denoted as... and .

4. The weather radar wind turbine clutter identification method based on fuzzy reasoning according to claim 3, characterized in that, The specific implementation process of step S2 includes: Based on data collection time points Echo observation subunit acquired from the bottom reflectivity factor and radial velocity Calculate the reflectivity factor separately. and radial velocity The dispersion of the data acquisition time series is calculated using the following formula: ； in, and Reflectivity factors and radial velocity The dispersion over the data acquisition time series, where T represents the total number of data acquisition time points. This represents the mean reflectance factor across all data acquisition time points. This represents the average radial velocity across all data acquisition time points. Based on reflectivity factor and radial velocity Based on the dispersion of the data acquisition time series, an echo observation sub-unit is constructed on the data acquisition time series. The echo stability characteristic is calculated using the following formula: ； in, Represents the echo observation sub-unit on the data acquisition time series. The echo stability characteristic quantity, This represents the maximum reflectivity factor in all echo observation sub-units at all data acquisition time points. This represents the maximum radial velocity value in all echo observation sub-units at all data acquisition time points.

5. The weather radar wind turbine clutter identification method based on fuzzy reasoning according to claim 4, characterized in that, The specific implementation process of step S3 includes: With echo observation subunit Centered on the echo observation sub-unit set Selecting and echoing observation sub-units Adjacent echo observation sub-units are identified, and an echo neighborhood set is constructed, denoted as... ; Based on data collection time points Echo observation subunit acquired from the bottom spectral width and differential reflectivity Calculate the echo observation sub-unit With echo neighborhood set The formulas for calculating the spectral width consistency deviation and differential reflectivity consistency deviation of the echo observation sub-unit are as follows: ； in, and These represent echo observation sub-units. With echo neighborhood set The spectral width consistency deviation and differential reflectivity consistency deviation of the echo observation sub-unit in the data. Represents the echo neighborhood set The number of echo observation sub-units in the middle, Let represent the echo observation sub-cell corresponding to the p-th range gate and the q-th scan azimuth angle, and . and These represent the data collection time points. Echo observation subunit acquired from the bottom Spectral width and differential reflectance; Based on echo observation subunit With echo neighborhood set The spectral width consistency deviation and differential reflectivity consistency deviation of the echo observation sub-unit are calculated at the data acquisition time point. The echo observation subunit below The echo shape consistency characteristic is calculated using the following formula: ； in, Indicates the data collection time point Lower echo observation subunit The echo morphology is consistent with the characteristic quantity. This represents the maximum spectral width across all echo observation sub-units at all data acquisition time points. This represents the maximum differential reflectivity of all echo observation sub-units at all data acquisition time points.

6. The weather radar wind turbine clutter identification method based on fuzzy reasoning according to claim 5, characterized in that, The specific implementation process of step S4 includes: Based on the echo observation subunit of the data acquisition time series echo stability characteristic and data collection time point Lower echo observation subunit echo morphology consistent characteristic Calculate the echo stability characteristic respectively Characteristic quantities consistent with echo morphology The membership degree is as follows: The formula for calculating the membership degree of the echo stability characteristic is as follows: ； in, Indicates the membership degree of the echo stability characteristic. This represents the preset threshold for echo stability characteristics. This represents the preset echo stabilization characteristic quantity, the asymmetric attenuation coefficient. The formula for calculating the membership degree of the echo morphology consistency feature is as follows: ； in, This indicates the membership degree of the echo morphology consistency characteristic. This represents the preset threshold for echo pattern consistency characteristics. This represents the asymmetric adjustment coefficient of the preset echo pattern consistency characteristic quantity.

7. The weather radar wind turbine clutter identification method based on fuzzy reasoning according to claim 6, characterized in that, The specific implementation process of step S5 includes: Based on the membership degree of echo stability characteristic Membership degree of features consistent with echo morphology Calculate the data collection time point Lower echo observation subunit The fuzzy comprehensive decision quantity is calculated using the following formula: ； in, Indicates the data collection time point Lower echo observation subunit Fuzzy comprehensive decision quantity, and These represent the preset membership degrees of the echo stabilization characteristic. Membership degree of features consistent with echo morphology Influence factors; Based on data collection time points Lower echo observation subunit Fuzzy comprehensive decision quantity The preset wind turbine noise judgment threshold range is used, and the fuzzy comprehensive judgment quantity is... If the noise exists within the wind turbine clutter determination threshold range, then the echo observation subunit is determined. The corresponding echo characteristics are wind turbine clutter, if the fuzzy comprehensive decision quantity If the echo observation subunit does not exist within the wind turbine clutter determination threshold range, then the echo observation subunit is determined. The corresponding echo characteristics are meteorological echoes; The fuzzy comprehensive judgment value is acquired in real time at each data acquisition time point to perform dynamic identification of wind turbine clutter.

8. A weather radar wind turbine clutter identification system based on fuzzy reasoning, executing the weather radar wind turbine clutter identification method based on fuzzy reasoning as described in any one of claims 1-7, characterized in that, The system includes: a unit partitioning and data acquisition module, a discreteness calculation and stable feature quantity calculation module, a deviation quantity calculation and consistency feature quantity calculation module, a membership degree calculation module, and a fuzzy comprehensive decision quantity calculation, analysis, and identification module; The unit division and data acquisition module divides the monitoring area of ​​the target weather radar into several echo observation sub-units; constructs a radar echo data acquisition device and a data acquisition time series, and acquires the reflectivity factor, radial velocity, spectral width and differential reflectivity of the echo observation sub-unit at each data acquisition time point; The discreteness calculation and stable characteristic quantity calculation module calculates the discreteness of the reflectivity factor and radial velocity of the echo observation sub-unit on the data acquisition time series, respectively; and constructs the echo stable characteristic quantity of the echo observation sub-unit on the data acquisition time series. The deviation calculation and consistency feature calculation module: constructs an echo neighborhood set, calculates the spectral width consistency deviation and differential reflectivity consistency deviation between the echo observation sub-unit and the echo observation sub-unit in the echo neighborhood set; and calculates the echo morphology consistency feature of the echo observation sub-unit at the data acquisition time point. The membership calculation module calculates the membership of the echo stability feature and the echo morphology consistency feature of the echo observation sub-unit at the data acquisition time series, respectively. The fuzzy comprehensive judgment quantity calculation and analysis identification module calculates the fuzzy comprehensive judgment quantity of the echo observation subunit at the data acquisition time point based on the membership degree of the echo stability feature quantity and the membership degree of the echo shape consistency feature quantity, presets a threshold, and analyzes and identifies wind turbine clutter.

9. A weather radar wind turbine clutter identification system based on fuzzy reasoning according to claim 8, characterized in that: The discreteness calculation and stable feature quantity calculation module includes a discreteness calculation unit and a stable feature quantity calculation unit; The discreteness calculation unit calculates the discreteness of the reflectivity factor and radial velocity of the echo observation subunit acquired at the data acquisition time point, respectively, on the data acquisition time series. The stable characteristic calculation unit: Based on the reflectivity factor and the dispersion of radial velocity on the data acquisition time series, it constructs the echo stable characteristic of the echo observation subunit on the data acquisition time series.

10. A weather radar wind turbine clutter identification system based on fuzzy reasoning according to claim 9, characterized in that: The deviation calculation and consistency feature calculation module includes a deviation calculation unit and a consistency feature calculation unit; The deviation calculation unit: taking the echo observation sub-unit as the center, selects the echo observation sub-unit adjacent to the echo observation sub-unit from the echo observation sub-unit set, and constructs the echo neighborhood set; based on the spectral width and differential reflectance of the echo observation sub-unit acquired at the data acquisition time point, it calculates the spectral width consistency deviation and differential reflectance consistency deviation between the echo observation sub-unit and the echo observation sub-units in the echo neighborhood set. The consistency feature calculation unit calculates the echo morphology consistency features of the echo observation subunit at the data acquisition time point based on the spectral width consistency deviation and differential reflectivity consistency deviation between the echo observation subunit and the echo observation subunit in the echo neighborhood set.

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