Intelligent identification method for civil aviation medium or heavy rainfall intensity based on phased array radar

Abstract

The invention discloses a civil aviation medium or heavy rainfall intensity intelligent identification method based on a phased array radar, and relates to the technical field of civil aviation meteorological monitoring and alarm, and the method comprises the following steps: S100, obtaining historical data of a phased array radar storm identification characteristic quantity, and corresponding automatic station rainfall historical data; s200, analyzing the distribution characteristics and the characteristic distinction degree of the storm identification characteristic quantity under different precipitation amounts, and taking the distribution characteristics and the characteristic distinction degree as the characteristic basis of a fuzzy logic algorithm; s300, analyzing the relationship between the precipitation of the automatic station and the storm identification characteristic quantity, and establishing a fuzzy logic model through a fuzzy logic algorithm; and S400, identifying the precipitation amount in real time by using a fuzzy logic model, and calculating the precipitation intensity. The method has the advantages that intelligent recognition of heavy rain storms, moderate rain storms and light rain storms is achieved by introducing the storm recognition characteristic quantity, the recognition effect of the algorithm is comprehensively evaluated through a quantitative test method, and a reliable basis is provided for follow-up optimization of the algorithm.

Description

Technical Field

[0001] The present invention relates to the field of civil aviation meteorological monitoring and warning technology, in particular to a method for intelligently identifying medium or heavy precipitation intensity in civil aviation based on phased array radar. Background Art

[0002] Precipitation is one of the most common weather factors affecting civil aviation flight activities, and its intensity directly determines whether flights can take off and land normally. Different from the precipitation level classification standard proposed by the National Meteorological Administration, precipitation in civil aviation is divided into three levels according to intensity: small (weak), medium, and large (strong). At present, the judgment of precipitation intensity in civil aviation is mainly based on manual observation, supplemented by instrument measurement data. However, due to the large scope of airports, the coverage of manual vision and automatic weather observation system (AWOS) is small, and there are large blind spots, resulting in insufficient judgment of the precipitation intensity of the entire field, which cannot meet the urgent needs of aviation users for refined meteorological services. The C-band Doppler radar, which is widely used at present, has a wide spatial coverage range, but its temporal and spatial resolution is low. In this regard, phased array radar, with its higher temporal and spatial resolution, has shown significant advantages in the monitoring and early warning of local short-term heavy precipitation. It can realize continuous, real-time and comprehensive monitoring of the precipitation process, which is an important supplement to conventional detection methods and has broad application prospects.

[0003] In recent years, fuzzy logic has shown higher flexibility and adaptability in dealing with uncertainty and fuzziness, and has been widely used in solving a variety of problems, such as aircraft icing prediction, hail weather radar identification, ground object echo identification, etc. Therefore, by combining the advantages of fuzzy logic algorithm and phased array radar's high temporal and spatial resolution, a set of intelligent identification algorithms for medium or heavy precipitation intensity that meet the actual business operation needs of civil aviation is invented, which can improve the monitoring and early warning capabilities of civil aviation for heavy precipitation weather. Summary of the invention

[0004] The purpose of this section is to provide a method for intelligent identification of moderate or heavy precipitation intensity in civil aviation based on phased array radar, which can improve the monitoring and early warning capabilities of civil aviation for heavy precipitation weather.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: a method for intelligently identifying medium or heavy precipitation intensity in civil aviation based on phased array radar, comprising the following steps: S100, acquiring historical data of storm identification feature quantities of phased array radar and corresponding historical data of precipitation at automatic stations; S200, analyzing the distribution characteristics and feature discrimination of storm identification feature quantities under different precipitation amounts as feature basis of fuzzy logic algorithm; S300, analyzing the relationship between automatic station precipitation and storm identification feature quantities, and establishing a fuzzy logic model through fuzzy logic algorithm; S400, using the fuzzy logic model to identify precipitation in real time and calculate precipitation intensity.

[0006] As a preferred solution of the method for intelligently identifying medium or heavy precipitation intensity in civil aviation based on phased array radar described in the present invention, wherein: in S100, the storm identification feature quantity includes the three-minute cumulative amount of instantaneous rainfall intensity, the near-ground dBZ intensity and the near-ground KDP intensity, dBZ is the unit used to measure the radar reflectivity factor, and KDP is the differential propagation phase shift rate.

[0007] As a preferred solution of the method for intelligently identifying medium or heavy precipitation intensity in civil aviation based on phased array radar described in the present invention, wherein: in S100, combined echo data is obtained, and the storm body contour is preliminarily divided using thresholds of 30, 40 and 50 dBZ, and the storm identification feature quantities between each threshold and the corresponding automatic station precipitation are counted.

[0008] As a preferred embodiment of the method for intelligently identifying medium or heavy precipitation intensity in civil aviation based on phased array radar described in the present invention, in which: in S200, three precipitation intensity intervals a, b, and c are set, and rough screening and classification are performed according to the automatic station precipitation intensity threshold of 0.4 / 1.3 (mm / 3min). The precipitation intensity in interval a is set as a heavy rain storm, the precipitation intensity in interval b is set as a moderate rain storm, and the precipitation intensity in interval c is set as a light rain storm. The distribution characteristics refer to the numerical statistics of the storm identification feature quantity under different precipitation intensities, and the feature discrimination refers to the degree of difference in the same storm identification feature quantity under different precipitation intensities.

[0009] As a preferred embodiment of the method for intelligently identifying medium or heavy precipitation intensity in civil aviation based on phased array radar described in the present invention, in which: in S200, the specific steps are as follows: S201, respectively compare the numerical differences of the three-minute cumulative amount of instantaneous rainfall intensity, near-ground dBZ intensity and near-ground KDP intensity under three precipitation intensities: heavy rain storm, moderate rain storm and light rain storm; S202, screen out the features whose numerical differences of the identification feature quantities of the same storm under different precipitation intensities meet the preset values; S203, assign feature weights according to the size of the numerical differences, and set the recognition probability threshold.

[0010] As a preferred embodiment of the intelligent identification method for medium or heavy precipitation intensity in civil aviation based on phased array radar described in the present invention, in S203, the characteristic weight of the three-minute cumulative amount of instantaneous rainfall intensity is set to 4, the characteristic weight of the near-ground dBZ intensity is set to 1, and the characteristic weight of the near-ground KDP intensity is set to 4.

[0011] As a preferred embodiment of the method for intelligently identifying medium or heavy precipitation intensity in civil aviation based on phased array radar described in the present invention, in S203, the heavy rain storm identification probability threshold is set to 0.6, the light rain storm identification probability threshold is set to 0.6, and events other than heavy rain storms and light rain storms are set to moderate rain storms.

[0012] As a preferred embodiment of the intelligent identification method of medium or heavy precipitation intensity in civil aviation based on phased array radar described in the present invention, wherein: in S300, the specific steps are as follows: S301, obtaining the precipitation intensity at various locations within a 3km radius of the automatic station at the same time in a storm case, comparing the precipitation intensity at various locations with the precipitation intensity detected by the automatic station, and if there is at least one place with the same precipitation intensity, it is regarded as a detection hit; S302, performing detection statistics on multiple storm cases, and calculating the three indicators of recognition hit rate, missed alarm rate, and false alarm rate under different precipitation intensities; S303, eliminating the false alarm data caused by the advance amount of storm alarm, and recalculating the false alarm rate; S304, establishing a fuzzy logic model according to the feature weights of each storm identification feature quantity and the storm identification probability threshold.

[0013] As a preferred solution of the method for intelligently identifying medium or heavy precipitation intensity in civil aviation based on phased array radar described in the present invention, in S400, the echo data of the phased array radar is calculated through a fuzzy logic algorithm to accurately identify the precipitation amount and precipitation intensity level, and the calculation results are uploaded to the cloud for storage in real time to ensure data security, and a storm warning is issued.

[0014] Beneficial effects of the present invention:

[0015] 1. By introducing storm identification characteristics, the intelligent identification of heavy rain, moderate rain and light rain storms is realized, and the recognition effect of the algorithm is comprehensively evaluated through quantitative test methods, which provides a reliable basis for the subsequent optimization of the algorithm. The algorithm can improve flight safety and efficiency, reduce the risk of flight delays and cancellations due to precipitation, and has broad application prospects in the civil aviation field, meeting the actual business operation needs of civil aviation.

[0016] 2. Compared with the existing storm identification algorithms, the present invention can provide timely and comprehensive precipitation intensity judgment, provide a reference for the monitoring and early warning of heavy precipitation weather, help control departments and pilots make decisions, thereby ensuring safe, efficient and smooth air traffic, and promoting high-quality development of civil aviation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. The drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creativity and labor. Among them:

[0018] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0019] Figure 2It is a distribution characteristic diagram of the three-minute cumulative amount of instantaneous rainfall intensity under different precipitation intensities of the present invention;

[0020] Figure 3 This is a distribution characteristic diagram of the near-ground dBZ intensity under different precipitation intensities of the present invention;

[0021] Figure 4 This is a distribution characteristic diagram of the near-ground KDP intensity under different precipitation intensities of the present invention. DETAILED DESCRIPTION

[0022] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below with reference to the accompanying drawings.

[0023] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from this description. The "embodiment" referred to here refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention.

[0024] Example

[0025] Reference Figure 1 to Figure 4 The present embodiment provides a method for intelligently identifying medium or heavy precipitation intensity in civil aviation based on phased array radar, which specifically includes the following steps: S100, obtaining historical data of storm identification feature quantities of phased array radar and corresponding historical data of precipitation at automatic stations; S200, analyzing the distribution characteristics and feature discrimination of storm identification feature quantities under different precipitation amounts as feature basis of fuzzy logic algorithm; S300, analyzing the relationship between automatic station precipitation and storm identification feature quantities, and establishing a fuzzy logic model through fuzzy logic algorithm; S400, using the fuzzy logic model to identify precipitation in real time and calculate precipitation intensity.

[0026] In S100 , the storm identification feature quantities include the three-minute cumulative amount of instantaneous rainfall intensity, the near-ground dBZ intensity, and the near-ground KDP intensity.

[0027] dBZ is the unit of weather radar reflectivity factor, which is an indicator of the ability of precipitation particles (such as raindrops, ice crystals, snowflakes, etc.) to reflect radar waves. It is essentially a logarithmic scale used to represent the magnitude of radar reflectivity. The reflectivity factor is related to factors such as the size, shape, quantity and phase state of precipitation particles. KDP is the differential propagation phase shift rate, which is a physical quantity obtained by precipitation particles (non-spherical particles, such as raindrops) causing radar electromagnetic waves to produce differential propagation phase shifts during propagation. KDP is mainly used to describe the shape and orientation information of precipitation particles. For oblate spherical raindrops, they have a certain orientation during their falling process. KDP can better reflect the impact of such asymmetric particles on radar wave propagation and is more sensitive to changes in raindrop size.

[0028] In S100, the combined echo data is obtained, and the storm body contour is preliminarily divided using thresholds of 30, 40 and 50 dBZ, and the storm identification feature quantities between the thresholds and the corresponding automatic station precipitation are counted.

[0029] In S200, three precipitation intensity intervals a, b, and c are set, and according to the rough screening classification of the automatic station precipitation intensity threshold of 0.4 / 1.3 (mm / 3min), the precipitation intensity in interval a is set as a heavy rain storm, the precipitation intensity in interval b is set as a moderate rain storm, and the precipitation intensity in interval c is set as a light rain storm. The distribution characteristics refer to the numerical statistics of the storm identification feature quantity under different precipitation intensities, and the feature discrimination refers to the degree of difference in the same storm identification feature quantity under different precipitation intensities.

[0030] In S200, the specific steps are as follows: S201, respectively compare the numerical differences of the three-minute cumulative amount of instantaneous rainfall intensity, the near-ground dBZ intensity and the near-ground KDP intensity under the three precipitation intensities of heavy rain storm, moderate rain storm and light rain storm; S202, filter out the features whose numerical differences of the identification feature quantities of the same storm under different precipitation intensities meet the preset values; S203, assign feature weights according to the size of the numerical differences, and set the recognition probability threshold.

[0031] According to statistical data, the three-minute cumulative amount of instantaneous rainfall intensity has relatively concentrated distribution characteristics under the two precipitation intensities of moderate rain storm and light rain storm, but the distinction between the two is not high. The distribution characteristics under heavy rain storm show two peaks. The peak area of ​​the latter segment has a higher distinction compared with moderate rain storm and light rain storm. The latter segment data can be used as the characteristic basis for identifying heavy rain. The near-ground dBZ intensity has a low distinction under different precipitation intensities and cannot be used as an effective identification feature. The distribution characteristics of the near-ground KDP intensity under different precipitation intensities are similar to the three-minute cumulative amount of instantaneous rainfall intensity. The latter segment data can also be used as the characteristic basis for identifying heavy rain.

[0032] In S203 , the feature weight of the three-minute cumulative instantaneous rainfall intensity is set to 4, the feature weight of the near-ground dBZ intensity is set to 1, and the feature weight of the near-ground KDP intensity is set to 4.

[0033] In S203, the heavy rain storm identification probability threshold is set to 0.6, the light rain storm identification probability threshold is set to 0.6, and events other than the heavy rain storm and the light rain storm are set as moderate rain storms.

[0034] Under the same precipitation intensity, if the precipitation intensity in the parent storm is greater than or equal to the precipitation intensity of the child storm, the child storm will be merged into the parent storm.

[0035] In S300, the specific steps are as follows: S301, obtain the precipitation intensity at various locations within a 3km radius of the automatic station at the same time in a storm case, compare the precipitation intensity at various locations with the precipitation intensity detected by the automatic station, and if there is at least one place with the same precipitation intensity, it is regarded as a detection hit; S302, perform detection statistics on multiple storm cases, and calculate the three indicators of recognition hit rate, missed alarm rate, and false alarm rate under different precipitation intensities; S303, eliminate the false alarm data caused by the advance amount of storm alarm, and recalculate the false alarm rate; S304, establish a fuzzy logic model based on the feature weights of each storm identification feature quantity and the storm identification probability threshold.

[0036] According to statistics, the recognition hit rate of heavy rain storms is 61.4%, the recognition hit rate of light rain storms is 73.0%, and the false alarm rates of heavy rain storm and light rain storm identification are 43.3% and 56.9%, respectively.

[0037] False alarms exist in all cases. Most of the false alarms are caused by the lead time of the storm warning. When the algorithm is in progress and identifying heavy rain storms, the lead time of the storm has been considered in the design of features and the selection of thresholds. When the real-time record of rainfall at the automatic station changes, the heavy rain alarm has been reported at this time, so it will be recorded as a false alarm. This is also the advantage of this algorithm.

[0038] In the S400, the echo data of the phased array radar is calculated through fuzzy logic algorithms to accurately identify the precipitation amount and precipitation intensity level, and the calculation results are uploaded to the cloud for storage in real time to ensure data security and issue storm warnings.

[0039] Importantly, although only a few embodiments are described in detail in this disclosure, it should be readily understood by those who refer to this disclosure that many modifications are possible without substantially departing from the subject matter described in this application, such as the size, structure, shape and proportion of the various elements, as well as temperature, pressure, mounting arrangements, use of materials, color, directional changes, etc.; for example, an element shown as integrally formed may be composed of multiple parts or elements, and the position of the element may be inverted or otherwise changed; therefore, all such modifications should be included within the scope of the present invention, and other substitutions, modifications, changes and omissions may be made in the design, operating conditions and arrangement of the exemplary embodiments without departing from the scope of the present invention.

Claims

1. A method for intelligently identifying moderate or heavy precipitation intensity in civil aviation based on phased array radar, characterized in that: The following steps are involved: S100, obtaining historical data of storm identification feature quantities of phased array radar and corresponding historical data of precipitation at automatic stations; S200, analyzing the distribution characteristics and characteristic discrimination of storm identification characteristic quantities under different precipitation amounts as characteristic basis of fuzzy logic algorithm; S300, analyzing the relationship between the precipitation at the automatic station and the storm identification characteristic quantity, and establishing a fuzzy logic model through a fuzzy logic algorithm; S400, using a fuzzy logic model to identify precipitation in real time and calculate precipitation intensity.

2. The method for intelligently identifying moderate or heavy precipitation intensity in civil aviation based on phased array radar according to claim 1, characterized in that: In S100, storm identification feature quantities include the three-minute cumulative instantaneous rainfall intensity, near-ground dBZ intensity, and near-ground KDP intensity. dBZ is a unit used to measure radar reflectivity factor, and KDP is differential propagation phase shift rate.

3. The method for intelligently identifying moderate or heavy precipitation intensity in civil aviation based on phased array radar as claimed in claim 2, characterized in that: In S100, the combined echo data is obtained, and the storm body contour is preliminarily divided using thresholds of 30, 40 and 50 dBZ, and the storm identification feature quantities between the thresholds and the corresponding automatic station precipitation are counted.

4. The method for intelligently identifying moderate or heavy precipitation intensity in civil aviation based on phased array radar according to claim 1 or 3, characterized in that: In S200, three precipitation intensity intervals a, b, and c are set, and according to the rough screening classification of the automatic station precipitation intensity threshold of 0.4 / 1.3 (mm / 3min), the precipitation intensity in interval a is set as a heavy rain storm, the precipitation intensity in interval b is set as a moderate rain storm, and the precipitation intensity in interval c is set as a light rain storm. The distribution characteristics refer to the numerical statistics of the storm identification feature quantity under different precipitation intensities, and the feature discrimination refers to the degree of difference in the same storm identification feature quantity under different precipitation intensities.

5. The method for intelligently identifying moderate or heavy precipitation intensity in civil aviation based on phased array radar according to claim 4, characterized in that: In S200, the specific steps are as follows: S201. Compare the numerical differences of the three-minute cumulative instantaneous rainfall intensity, the near-surface dBZ intensity and the near-surface KDP intensity under the three precipitation intensities of heavy rain storm, moderate rain storm and light rain storm respectively; S202, screening out features whose numerical differences of the same storm identification feature quantity under different precipitation intensities meet the preset values; S203: assign feature weights according to the numerical difference and set a recognition probability threshold.

6. The method for intelligently identifying moderate or heavy precipitation intensity in civil aviation based on phased array radar as claimed in claim 5, characterized in that: In S203 , the feature weight of the three-minute cumulative instantaneous rainfall intensity is set to 4, the feature weight of the near-ground dBZ intensity is set to 1, and the feature weight of the near-ground KDP intensity is set to 4.

7. The method for intelligently identifying moderate or heavy precipitation intensity in civil aviation based on phased array radar as claimed in claim 5, characterized in that: In S203, the heavy rain storm identification probability threshold is set to 0.6, the light rain storm identification probability threshold is set to 0.6, and events other than the heavy rain storm and the light rain storm are set as moderate rain storms.

8. The method for intelligently identifying moderate or heavy precipitation intensity in civil aviation based on phased array radar according to claim 1, characterized in that: In S300, the specific steps are as follows: S301, obtaining the precipitation intensity at various locations within a 3km radius of the automatic station at the same time in a storm case, and comparing the precipitation intensity at various locations with the precipitation intensity detected by the automatic station. If there is at least one location with the same precipitation intensity, it is considered a detection hit; S302, performing detection statistics on multiple storm cases, and calculating three indicators of recognition hit rate, missed alarm rate, and false alarm rate under different precipitation intensities; S303, eliminating the false alarm data caused by the advance amount of the storm alarm, and recalculating the false alarm rate; S304: Establish a fuzzy logic model according to the feature weights of each storm identification feature quantity and the storm identification probability threshold.

9. The method for intelligently identifying moderate or heavy precipitation intensity in civil aviation based on phased array radar according to claim 1, characterized in that: In the S400, the echo data of the phased array radar is calculated through fuzzy logic algorithms to accurately identify the precipitation amount and precipitation intensity level, and the calculation results are uploaded to the cloud for storage in real time to ensure data security and issue storm warnings.

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