Bird monitoring method and system for airports in rainy areas based on bird detection radar

Abstract

The invention relates to the field of radar monitoring technology, in particular to a method and system for monitoring bird conditions at an airport in a rainy area based on a bird-detecting radar. In the invention, real-time and historical meteorological data and bird-detecting radar signal data are collected through a data collection module, and the current rain intensity is determined; a background noise analysis module sets a rain intensity threshold according to the historical meteorological data, dynamically adjusts the size of a background window according to the current rain intensity, calculates the intensity weight and distance weight of each signal unit, and generates an accurate background noise estimation; a target detection module initializes primary and secondary thresholds according to the background noise estimation, performs double screening on signal units in the background window, generates a target signal unit, and dynamically modifies the threshold in real time by establishing a relationship model between the rain intensity and the screening threshold, thereby improving the accuracy and robustness of target detection, and effectively solving the problems of inaccurate background noise estimation and poor detection effect of a fixed threshold in a traditional system under different rain intensity conditions.

Description

Technical Field

[0001] The invention relates to the technical field of radar monitoring, and in particular to a method and system for monitoring bird conditions at an airport in a rainy area based on a bird-detecting radar. Background Art

[0002] The traditional bird monitoring method and system for airports in rainy areas based on bird detection radar has technical problems such as inaccurate background noise estimation and poor fixed threshold detection effect when facing different rainfall intensity conditions.

[0003] First, background noise estimation usually uses fixed thresholds or simple statistical methods, which are unstable under different meteorological conditions. In light rain or sunny conditions, the background noise level is low, which may be able to effectively distinguish between background and target signals. However, in heavy rain or other complex meteorological conditions, the background noise increases significantly, and the fixed background window size cannot adapt to this change, which easily leads to deviations in background noise estimation. This makes it difficult for the system to accurately capture the characteristics of background noise under high rain intensity conditions, thereby affecting the detection accuracy of target signals.

[0004] Secondly, traditional systems use screening thresholds to screen target signals, and the screening thresholds are often pre-set and lack an adaptive adjustment mechanism. When the rainfall intensity changes, the fixed screening threshold cannot be adjusted dynamically, resulting in the possibility of missing target signals if the screening threshold is set too high under low rainfall conditions, and the screening threshold is set too low under high rainfall conditions, which easily misjudges a large amount of background noise as multiple target signals, increasing the false alarm rate and processing complexity of the system. This setting of fixed screening thresholds and background window sizes not only reduces the robustness and accuracy of the system, but also shows obvious deficiencies in complex and changing meteorological environments.

[0005] Therefore, the traditional bird-detecting radar-based airport bird monitoring system in rainy areas is difficult to maintain an efficient and stable working state in an environment with large changes in rainfall intensity, which affects the accurate monitoring and timely processing of airport bird conditions. Summary of the invention

[0006] The purpose of the present invention is to provide a method and system for monitoring bird conditions at airports in rainy areas based on bird-detecting radar, so as to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, one of the objects of the present invention is to provide a bird monitoring system for airports in rainy areas based on bird detection radar, including a data collection module, a background noise analysis module and a target detection module, wherein:

[0008] The data collection module collects real-time meteorological data, historical meteorological data and bird-detecting radar signal data, wherein the bird-detecting radar signal data includes the echo strength of each signal unit; and determines the current rainfall intensity according to the real-time meteorological data and the echo strength of each signal unit;

[0009] The background noise analysis module sets a rain intensity threshold according to historical meteorological data; sets a background window for the bird detection radar signal data according to the rain intensity threshold and the current rain intensity, wherein the background window is composed of a plurality of signal units; calculates the intensity weight of each signal unit in the background window according to the echo intensity of each signal unit in the background window; and calculates the distance weight of each signal unit in the background window according to the distance between each signal unit in the background window; and generates a background noise estimate according to the intensity weight and the distance weight;

[0010] The target detection module initializes a screening threshold according to background noise estimation, wherein the screening threshold includes a primary threshold and a secondary threshold; performs primary screening and secondary screening on each signal unit in the background window according to the primary threshold and the secondary threshold, and generates a target signal unit; establishes a relationship model between rainfall intensity and the screening threshold according to historical meteorological data, and dynamically modifies the screening threshold according to the current rainfall intensity and the established relationship model.

[0011] As a further improvement of the present technical solution, the real-time meteorological data in the data collection module represents the meteorological data of the current time period, including rainfall data and meteorological satellite data; the historical meteorological data represents the meteorological data of the past time period, which is collected through a public database.

[0012] As a further improvement of the technical solution, the process of determining the current rainfall intensity by the background noise analysis module specifically includes:

[0013] Calculate the average value of rainfall data in real-time meteorological data as preliminary rainfall intensity; verify the rationality of preliminary rainfall intensity estimation value according to rainfall intensity provided by meteorological satellite data in real-time meteorological data; if there is any abnormality in rationality, recalculate preliminary rainfall intensity;

[0014] Bird-detecting radar signal data is used to assist in preliminary rainfall intensity estimation. By analyzing the echo intensity distribution of all signal units in the bird-detecting radar signal data and identifying the low-intensity echo area caused by rainfall, the echo intensity of the area is statistically analyzed and its average value is calculated to obtain the rainfall intensity estimation value in the bird-detecting radar signal data.

[0015] The current rainfall intensity is obtained by weighted summing of the preliminary rainfall intensity and the rainfall intensity estimation value in the bird-detecting radar signal data.

[0016] As a further improvement of the present technical solution, the background noise analysis module includes a background window setting unit, and the process of the background window setting unit setting the background window specifically includes:

[0017] The rainfall intensity in the historical meteorological data is sorted from small to large; and quantiles are selected to determine the low rainfall intensity threshold, medium rainfall intensity threshold and high rainfall intensity threshold. The quantiles include the 25th quantile, the 50th quantile and the 75th quantile, which respectively represent the values ​​at the 25th, 50th and 75th positions after sorting;

[0018] The 25th percentile is used as the low rainfall intensity threshold; the 50th percentile is used as the medium rainfall intensity threshold; and the 75th percentile is used as the high rainfall intensity threshold.

[0019] The current rainfall intensity is compared with the low rainfall intensity threshold, the medium rainfall intensity threshold and the high rainfall intensity threshold to determine the background window, where:

[0020] If the current rainfall intensity is within the low rainfall intensity threshold, the background window size is set to 3×3 signal units;

[0021] If the current rainfall intensity is within the medium rainfall intensity threshold, the background window size is set to 5×5 signal units;

[0022] If the current rainfall intensity is within the high rainfall intensity threshold, the background window size is set to 7×7 signal units.

[0023] As a further improvement of the present technical solution, the background noise analysis module includes a background noise estimation unit, and the process of the background noise estimation unit generating a background noise estimation specifically includes:

[0024] Normalize the echo intensity of each signal unit in the background window so that their range is between 0 and 1;

[0025] The inverse intensity method is used to calculate the intensity weight of each signal unit. The calculation formula is as follows:

[0026] ,in Indicates The strength weight of each signal unit; Indicates The echo strength of each signal unit; Represents a positive number to avoid the denominator being zero;

[0027] Select the signal unit at the center of the background window as the central signal unit, and use the Euclidean distance to calculate the distance between each signal unit and the central signal unit;

[0028] Use the Gaussian function to calculate the distance weight, the calculation formula is as follows:

[0029] ,in Indicates The distance weight of each signal unit; Indicates The distance between each signal unit and the central signal unit; represents a tuning parameter;

[0030] The intensity weight and distance weight Perform weighted summation to obtain the comprehensive weight ;

[0031] Use comprehensive weights The echo strength of all signal units in the background window is weighted averaged to obtain the background noise estimate , the calculation formula is as follows:

[0032] ,in Indicates the total number of signal units in the background window.

[0033] As a further improvement of the technical solution, the target detection module includes a threshold setting unit, and the process of the threshold setting unit generating the target signal unit specifically includes:

[0034] Primary Threshold By estimating the background noise Add a fixed increment Initialization is performed, and the secondary threshold By the primary threshold Add a fixed increment Initialize;

[0035] Perform primary screening on each signal unit in the background window, and the screening condition is that the echo intensity of the signal unit is greater than the primary threshold ;

[0036] Perform secondary screening on each signal unit in the primary screening results, and the screening condition is that the echo intensity of the signal unit is greater than the secondary threshold ; The signal unit in the secondary screening result is used as the target signal unit.

[0037] As a further improvement of the technical solution, the target detection module includes a dynamic modification unit, and the process of the dynamic modification unit dynamically modifying the screening threshold specifically includes:

[0038] A linear regression model is used to establish the relationship between rainfall intensity and screening threshold. The formula is as follows:

[0039] , ,in Represents the rainfall intensity data in historical meteorological data, are the linear regression model parameters, obtained by the least squares method;

[0040] According to the current rainfall intensity and the established relationship model, the screening threshold is dynamically modified by inputting the current rainfall intensity into the established linear regression model and outputting the modified primary threshold and secondary threshold. According to the modified primary threshold and secondary threshold, each signal unit in the background window is re-screened.

[0041] The second object of the present invention is to provide a method for a bird monitoring system for an airport in a rainy area based on a bird detecting radar, comprising the following method steps:

[0042] S1. Collecting real-time meteorological data, historical meteorological data and bird-detecting radar signal data, wherein the bird-detecting radar signal data includes the echo strength of each signal unit; determining the current rainfall intensity according to the real-time meteorological data and the echo strength of each signal unit;

[0043] S2. According to historical meteorological data, a rain intensity threshold is set; according to the rain intensity threshold and the current rain intensity, a background window is set for the bird detection radar signal data, wherein the background window is composed of a plurality of signal units; according to the echo intensity of each signal unit in the background window, an intensity weight of each signal unit in the background window is calculated; and according to the distance between each signal unit in the background window, a distance weight of each signal unit in the background window is calculated; and according to the intensity weight and the distance weight, a background noise estimate is generated;

[0044] S3. Initialize the screening threshold according to the background noise estimation, wherein the screening threshold includes a primary threshold and a secondary threshold; perform primary screening and secondary screening on each signal unit in the background window according to the primary threshold and the secondary threshold, and generate a target signal unit; establish a relationship model between rainfall intensity and the screening threshold according to historical meteorological data, and dynamically modify the screening threshold according to the current rainfall intensity and the established relationship model.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. The bird monitoring method and system for airports in rainy areas based on bird-detecting radar sets a rainfall threshold according to historical meteorological data, and sets a background window for the bird-detecting radar signal data according to the rainfall threshold and the current rainfall intensity. This process solves the problem of inaccurate background noise estimation under different rainfall intensity conditions. In traditional bird monitoring systems, background noise estimation often uses fixed thresholds or simple statistical methods, which are unstable under different meteorological conditions and easily lead to deviations in background noise estimation. The system can dynamically adjust the size of the background window according to the current rainfall intensity by setting the rainfall threshold, thereby more accurately capturing the background noise. Features; Specifically, the background window is set to 3×3 signal units at low rain intensity, 5×5 signal units at medium rain intensity, and 7×7 signal units at high rain intensity. This setting can ensure that the background noise estimation will not be interfered by too many background signals under different rain intensity conditions; in addition, the calculation method of the echo intensity and distance weight of each signal unit in the background window further improves the accuracy and reliability of background noise estimation; through the weighted summation of intensity weight and distance weight, the system can more accurately reflect the spatial distribution and intensity changes of background noise, thereby effectively reducing the false alarm rate and missed alarm rate, and improving the robustness and accuracy of target detection.

[0047] 2. The bird monitoring method and system for airports in rainy areas based on bird-detecting radar establishes a relationship model between rainfall intensity and screening threshold according to historical meteorological data, and dynamically modifies the threshold according to the current rainfall intensity. This process solves the problem that fixed thresholds have poor detection effects under different meteorological conditions. Traditional bird monitoring systems usually use fixed primary and secondary thresholds, which may perform well under sunny or light rain conditions, but under heavy rain or other complex meteorological conditions, it is easy to cause a large amount of background noise to be misjudged as target signals, increasing the false alarm rate of the system. The system establishes the relationship between rainfall intensity and screening threshold through a linear regression model, and can adaptively adjust the primary and secondary thresholds under different rainfall intensity conditions to ensure that background noise is not detected. The distinction between the target signal and the target signal is more accurate; specifically, the primary threshold is initialized by adding a fixed increment to the background noise estimate, and the secondary threshold is initialized by adding a fixed increment to the primary threshold. Such a double screening mechanism can effectively reduce the false alarm rate; more importantly, the dynamic modification unit calculates the modified threshold in real time according to the current rainfall intensity and the established relationship model, so that the system can flexibly adapt to various meteorological conditions and enhance its bird monitoring ability in rainy areas; this dynamic threshold adjustment method not only improves the accuracy of target detection, but also enhances the real-time and adaptability of the system, so that it can still maintain an efficient and stable working state in a complex and changeable meteorological environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1It is a schematic diagram of the overall method flow of the present invention;

[0049] Figure 2 It is a schematic diagram of the overall module unit of the present invention.

[0050] In the figure: 100, data collection module; 200, background noise analysis module; 201, background window setting unit; 202, background noise estimation unit; 300, target detection module; 301, threshold setting unit; 302, dynamic modification unit. DETAILED DESCRIPTION

[0051] The following will be combined with the accompanying drawings in the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] Next, see Figure 1-Figure 2 One of the purposes of this embodiment is to provide a bird monitoring system for airports in rainy areas based on bird detection radar, which includes a data collection module 100, a background noise analysis module 200 and a target detection module 300.

[0053] The data collection module 100 collects real-time meteorological data, historical meteorological data and bird-detecting radar signal data, wherein the bird-detecting radar signal data includes the echo strength of each signal unit; the historical meteorological data represents the meteorological data of the past time period, which is collected through a public database; the current rainfall intensity is determined based on the meteorological data and the echo strength of each signal unit; the real-time meteorological data represents the meteorological data of the current time period, specifically including:

[0054] Ground rain gauges are distributed at multiple locations around the airport. They update rainfall data once at a fixed time. In addition to ground rain gauges, meteorological satellite data is also an important data source. Meteorological satellite data covers a wider area and provides real-time rainfall intensity. Through data interpolation and fusion technology, it complements the data from ground rain gauges to improve the accuracy of rainfall intensity estimation. Precipitation data and meteorological satellite data are used together as real-time meteorological data.

[0055] Based on the real-time meteorological data and the echo strength of each signal unit, the process of determining the current rainfall intensity specifically includes:

[0056] Calculate the average value of rainfall data in real-time meteorological data as preliminary rainfall intensity; verify the rationality of preliminary rainfall intensity estimation value according to rainfall intensity provided by meteorological satellite data in real-time meteorological data; if there is any abnormality in rationality, recalculate preliminary rainfall intensity to ensure the accuracy of rainfall intensity estimation;

[0057] Bird-detecting radar signal data is used to assist in preliminary rainfall intensity estimation. By analyzing the echo intensity distribution of all signal units in the bird-detecting radar signal data and identifying the low-intensity echo area caused by rainfall, the echo intensity of the area is statistically analyzed and its average value is calculated to obtain the rainfall intensity estimation value in the bird-detecting radar signal data.

[0058] The current rainfall intensity is obtained by weighted summing of the preliminary rainfall intensity and the rainfall intensity estimation value in the bird-detecting radar signal data.

[0059] The background window setting unit 201 in the background noise analysis module 200 sets a rain intensity threshold according to historical meteorological data; and sets a background window for the bird detection radar signal data according to the rain intensity threshold and the current rain intensity, wherein the background window is composed of a plurality of signal units, and the plurality of signal units are used as background units, specifically including:

[0060] The rainfall intensity in the historical meteorological data is sorted from small to large; and quantiles are selected to determine the low rainfall intensity threshold, medium rainfall intensity threshold and high rainfall intensity threshold. The quantiles include the 25th quantile, the 50th quantile and the 75th quantile, which respectively represent the values ​​at the 25th, 50th and 75th positions after sorting;

[0061] The 25th percentile is used as the low rainfall intensity threshold; the 50th percentile is used as the medium rainfall intensity threshold; and the 75th percentile is used as the high rainfall intensity threshold.

[0062] The current rainfall intensity is compared with the low rainfall intensity threshold, the medium rainfall intensity threshold and the high rainfall intensity threshold to determine the background window, where:

[0063] If the current rainfall intensity is within the low rainfall intensity threshold, the background window size is set to 3×3 signal units;

[0064] If the current rainfall intensity is within the medium rainfall intensity threshold, the background window size is set to 5×5 signal units;

[0065] If the current rainfall intensity is within the high rainfall intensity threshold, the background window size is set to 7×7 signal units.

[0066] The background window setting unit 201 dynamically adjusts the size of the background window according to the rainfall intensity percentile in the historical meteorological data; this method of flexibly adjusting the background window according to the current rainfall intensity can more accurately capture the background noise characteristics, reduce the interference of background signals, and improve the accuracy and robustness of target detection.

[0067] The background noise estimation unit 202 in the background noise analysis module 200 calculates the intensity weight of each signal unit in the background window according to the echo intensity of each signal unit in the background window; and calculates the distance weight of each signal unit in the background window according to the distance between each signal unit in the background window; and generates a background noise estimation according to the intensity weight and the distance weight, specifically including:

[0068] Normalize the echo intensity of each signal unit in the background window so that their range is between 0 and 1;

[0069] In order to reduce the impact of strong echoes on background noise estimation, the inverse intensity method is used to calculate the intensity weight of each signal unit. The calculation formula is as follows:

[0070] ,in Indicates The strength weight of each signal unit; Indicates The echo strength of each signal unit; Represents a positive number to avoid the denominator being zero;

[0071] Select the signal unit at the center of the background window as the central signal unit, and use the Euclidean distance to calculate the distance between each signal unit and the central signal unit;

[0072] In order to reduce the impact of the signal unit far away from the center, the Gaussian function is used to calculate the distance weight. The calculation formula is as follows:

[0073] ,in Indicates The distance weight of each signal unit; Indicates The distance between each signal unit and the central signal unit; represents a tuning parameter;

[0074] The intensity weight and distance weight Perform weighted summation to obtain the comprehensive weight ;

[0075] Use comprehensive weights The echo strength of all signal units in the background window is weighted averaged to obtain the background noise estimate , the calculation formula is as follows:

[0076] ,in Indicates the total number of signal units in the background window.

[0077] The threshold setting unit 301 in the target detection module 300 initializes the screening threshold according to the background noise estimation, wherein the screening threshold includes a primary threshold and a secondary threshold; according to the primary threshold and the secondary threshold, primary screening and secondary screening are performed on each signal unit in the background window, and a target signal unit is generated; specifically, the following steps are included:

[0078] If the threshold is set too low, background noise may be misjudged as a target signal, increasing the false alarm rate; conversely, if the threshold is set too high, the actual target signal may be missed, increasing the false alarm rate; therefore, setting the threshold is the key to balancing the false alarm rate and the false alarm rate; by setting the primary threshold and the secondary threshold, multi-level screening can be performed to more accurately identify the target signal; the primary screening can preliminarily exclude most of the low-intensity background noise, and the secondary screening can further confirm which signal units are the real target signals; the primary screening can significantly reduce the number of signal units that need to be further processed, thereby reducing the computational complexity and processing time; the secondary screening is performed on less data, further improving the accuracy of detection;

[0079] Primary Threshold By estimating the background noise Add a fixed increment Initialization is performed, and the secondary threshold By the primary threshold Add a fixed increment Initialize;

[0080] Perform primary screening on each signal unit in the background window, and the screening condition is that the echo intensity of the signal unit is greater than the primary threshold ;

[0081] Perform secondary screening on each signal unit in the primary screening results, and the screening condition is that the echo intensity of the signal unit is greater than the secondary threshold ; The signal unit in the secondary screening result is used as the target signal unit.

[0082] The dynamic modification unit 302 in the target detection module 300 establishes a relationship model between rainfall intensity and the screening threshold according to historical meteorological data, and dynamically modifies the screening threshold according to the current rainfall intensity and the established relationship model, specifically including:

[0083] Since the parameters of the linear regression model have clear physical meanings, the impact of rainfall intensity on the threshold can be intuitively understood; this helps system designers and users understand the working principle of the model and make further optimization and adjustments; therefore, the linear regression model is used to establish the relationship model between rainfall intensity and screening threshold, and the formula is as follows:

[0084] , ,in Represents the rainfall intensity data in historical meteorological data, are the linear regression model parameters, obtained by the least squares method;

[0085] According to the current rainfall intensity and the established relationship model, the screening threshold is dynamically modified by inputting the current rainfall intensity into the established linear regression model and outputting the modified primary threshold and secondary threshold. According to the modified primary threshold and secondary threshold, each signal unit in the background window is re-screened.

[0086] The second purpose of this embodiment is to provide a method for a bird monitoring system for an airport in a rainy area based on a bird detection radar, including the following method steps:

[0087] S1. Collecting real-time meteorological data, historical meteorological data and bird-detecting radar signal data, wherein the bird-detecting radar signal data includes the echo strength of each signal unit; determining the current rainfall intensity according to the real-time meteorological data and the echo strength of each signal unit;

[0088] S2. According to historical meteorological data, a rain intensity threshold is set; according to the rain intensity threshold and the current rain intensity, a background window is set for the bird detection radar signal data, wherein the background window is composed of a plurality of signal units; according to the echo intensity of each signal unit in the background window, an intensity weight of each signal unit in the background window is calculated; and according to the distance between each signal unit in the background window, a distance weight of each signal unit in the background window is calculated; and according to the intensity weight and the distance weight, a background noise estimate is generated;

[0089] S3. Initialize the screening threshold according to the background noise estimation, wherein the screening threshold includes a primary threshold and a secondary threshold; perform primary screening and secondary screening on each signal unit in the background window according to the primary threshold and the secondary threshold, and generate a target signal unit; establish a relationship model between rainfall intensity and the screening threshold according to historical meteorological data, and dynamically modify the screening threshold according to the current rainfall intensity and the established relationship model.

[0090] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. The bird monitoring system for airports in rainy areas based on bird detection radar is characterized by: It comprises a data collection module (100), a background noise analysis module (200) and a target detection module (300), wherein: The data collection module (100) collects real-time meteorological data, historical meteorological data and bird-detecting radar signal data, wherein the bird-detecting radar signal data includes the echo intensity of each signal unit; and determines the current rainfall intensity according to the real-time meteorological data and the echo intensity of each signal unit; The background noise analysis module (200) sets a rain intensity threshold according to historical meteorological data; sets a background window for the bird detection radar signal data according to the rain intensity threshold and the current rain intensity, wherein the background window is composed of a plurality of signal units; calculates the intensity weight of each signal unit in the background window according to the echo intensity of each signal unit in the background window; and calculates the distance weight of each signal unit in the background window according to the distance between each signal unit in the background window; and generates a background noise estimate according to the intensity weight and the distance weight; The target detection module (300) initializes a screening threshold according to background noise estimation, wherein the screening threshold includes a primary threshold and a secondary threshold; performs primary screening and secondary screening on each signal unit in the background window according to the primary threshold and the secondary threshold, and generates a target signal unit; establishes a relationship model between rainfall intensity and the screening threshold according to historical meteorological data, and dynamically modifies the screening threshold according to the current rainfall intensity and the established relationship model.

2. The bird monitoring system for airports in rainy areas based on bird detection radar according to claim 1 is characterized in that: The real-time meteorological data in the data collection module (100) represents the meteorological data of the current time period, including rainfall data and meteorological satellite data; the historical meteorological data represents the meteorological data of the past time period, which is collected through a public database.

3. The bird monitoring system for airports in rainy areas based on bird detection radar according to claim 2 is characterized in that: The process of the background noise analysis module (200) determining the current rainfall intensity specifically includes: Calculate the average value of rainfall data in real-time meteorological data as preliminary rainfall intensity; verify the rationality of preliminary rainfall intensity estimation value according to rainfall intensity provided by meteorological satellite data in real-time meteorological data; if there is any abnormality in rationality, recalculate preliminary rainfall intensity; Bird-detecting radar signal data is used to assist in preliminary rainfall intensity estimation. By analyzing the echo intensity distribution of all signal units in the bird-detecting radar signal data and identifying the low-intensity echo area caused by rainfall, the echo intensity of the area is statistically analyzed and its average value is calculated to obtain the rainfall intensity estimation value in the bird-detecting radar signal data. The current rainfall intensity is obtained by weighted summing of the preliminary rainfall intensity and the rainfall intensity estimation value in the bird-detecting radar signal data.

4. The bird monitoring system for airports in rainy areas based on bird detection radar according to claim 3 is characterized in that: The background noise analysis module (200) comprises a background window setting unit (201), and the process of setting the background window by the background window setting unit (201) specifically comprises: The rainfall intensity in the historical meteorological data is sorted from small to large; and quantiles are selected to determine the low rainfall intensity threshold, medium rainfall intensity threshold and high rainfall intensity threshold. The quantiles include the 25th quantile, the 50th quantile and the 75th quantile, which respectively represent the values ​​at the 25th, 50th and 75th positions after sorting; The 25th percentile is used as the low rainfall intensity threshold; the 50th percentile is used as the medium rainfall intensity threshold; and the 75th percentile is used as the high rainfall intensity threshold. The current rainfall intensity is compared with the low rainfall intensity threshold, the medium rainfall intensity threshold and the high rainfall intensity threshold to determine the background window, where: If the current rainfall intensity is within the low rainfall intensity threshold, the background window size is set to 3×3 signal units; If the current rainfall intensity is within the medium rainfall intensity threshold, the background window size is set to 5×5 signal units; If the current rainfall intensity is within the high rainfall intensity threshold, the background window size is set to 7×7 signal units.

5. The bird monitoring system for airports in rainy areas based on bird detection radar according to claim 4 is characterized in that: The background noise analysis module (200) comprises a background noise estimation unit (202), and the process of generating a background noise estimation by the background noise estimation unit (202) specifically comprises: Normalize the echo intensity of each signal unit in the background window so that their range is between 0 and 1; The inverse intensity method is used to calculate the intensity weight of each signal unit. The calculation formula is as follows: ,in Indicates The strength weight of each signal unit; Indicates The echo strength of each signal unit; Represents a positive number to avoid the denominator being zero; Select the signal unit at the center of the background window as the central signal unit, and use the Euclidean distance to calculate the distance between each signal unit and the central signal unit; Use the Gaussian function to calculate the distance weight, the calculation formula is as follows: ,in Indicates The distance weight of each signal unit; Indicates The distance between each signal unit and the central signal unit; represents a tuning parameter; The intensity weight and distance weight Perform weighted summation to obtain the comprehensive weight ; Use comprehensive weights The echo strength of all signal units in the background window is weighted averaged to obtain the background noise estimate , the calculation formula is as follows: ,in Indicates the total number of signal units in the background window.

6. The bird monitoring system for airports in rainy areas based on bird detection radar according to claim 5 is characterized in that: The target detection module (300) comprises a threshold setting unit (301), and the process of generating a target signal unit by the threshold setting unit (301) specifically comprises: Primary Threshold By estimating the background noise Add a fixed increment Initialization is performed, and the secondary threshold By the primary threshold Add a fixed increment Initialize; Perform primary screening on each signal unit in the background window, and the screening condition is that the echo intensity of the signal unit is greater than the primary threshold ; Perform secondary screening on each signal unit in the primary screening results, and the screening condition is that the echo intensity of the signal unit is greater than the secondary threshold ; The signal unit in the secondary screening result is used as the target signal unit.

7. The bird monitoring system for airports in rainy areas based on bird detection radar according to claim 6 is characterized in that: The target detection module (300) comprises a dynamic modification unit (302), and the process of the dynamic modification unit (302) dynamically modifying the screening threshold specifically comprises: A linear regression model is used to establish the relationship between rainfall intensity and screening threshold. The formula is as follows: , ,in Represents the rainfall intensity data in historical meteorological data, are the linear regression model parameters, obtained by the least squares method; According to the current rainfall intensity and the established relationship model, the screening threshold is dynamically modified by inputting the current rainfall intensity into the established linear regression model and outputting the modified primary threshold and secondary threshold. According to the modified primary threshold and secondary threshold, each signal unit in the background window is re-screened.

8. A method for using the bird monitoring system for airports in rainy areas based on bird detection radar according to any one of claims 1 to 7, characterized in that: The method comprises the following steps: S1. Collecting real-time meteorological data, historical meteorological data and bird-detecting radar signal data, wherein the bird-detecting radar signal data includes the echo strength of each signal unit; determining the current rainfall intensity according to the real-time meteorological data and the echo strength of each signal unit; S2. According to historical meteorological data, a rainfall intensity threshold is set; according to the rainfall intensity threshold and the current rainfall intensity, a background window is set for the bird detection radar signal data, wherein the background window is composed of multiple signal units; according to the echo intensity of each signal unit in the background window, an intensity weight of each signal unit in the background window is calculated; and calculating the distance weight of each signal unit in the background window according to the distance between each signal unit in the background window; and generating a background noise estimate based on the intensity weight and the distance weight; S3, initializing a screening threshold according to background noise estimation, wherein the screening threshold includes a primary threshold and a secondary threshold; According to the primary threshold and the secondary threshold, each signal unit in the background window is subjected to primary screening and secondary screening, and a target signal unit is generated; according to the historical meteorological data, a relationship model between rainfall intensity and the screening threshold is established, and the screening threshold is dynamically modified according to the current rainfall intensity and the established relationship model.

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