Airport dominant visibility automatic measurement method and system based on three-dimensional laser radar

Abstract

The invention belongs to the technical field of aviation meteorological monitoring, and relates to an airport dominant visibility automatic measurement method and system based on a three-dimensional laser radar, and the method comprises the steps: carrying out the full-space scanning of an airport through the laser radar, so as to collect echo signals; preprocessing and calibrating the echo signal; performing atmospheric extinction coefficient inversion on the preprocessed echo signal based on an inversion algorithm to obtain an atmospheric extinction coefficient, and then converting the atmospheric extinction coefficient into meteorological visibility; carrying out quantile statistics on all meteorological visibility in a period, judging a dominant visibility value, and carrying out adaptive weighted fusion on the dominant visibility value, the visibility value measured by the point-type visibility meter and the visibility value observed manually to obtain a fused dominant visibility value. The system can break through the limitation of traditional manual and point type visibility meter observation, and realizes accurate and automatic measurement of all-region, all-weather and unattended dominant visibility of an airport.

Description

Technical Field

[0001] This invention belongs to the field of aviation meteorological monitoring technology, and relates to an automatic method and system for measuring dominant visibility at airports, particularly an automatic method and system for measuring dominant visibility at airports based on three-dimensional lidar. Background Technology

[0002] With the continued rapid development of the global air transport industry, airports are placing higher demands on the automation and intelligence of meteorological observation. Visibility, as a key meteorological element affecting the normal takeoff and landing and operational safety of airport flights, is directly related to flight safety assurance through the accuracy and real-time monitoring of its parameters.

[0003] Currently, monitoring dominant visibility at airports primarily relies on two technical approaches. One is the manual visual observation method, where observers at designated observation points at the airport visually determine and record dominant visibility. This method is characterized by its ease of operation and long history. The other is the point-based visibility meter observation method, with representative equipment including forward scattering visibility meters and atmospheric transmissometers. Forward scattering visibility meters estimate visibility by measuring the intensity of forward scattering of light of a specific wavelength by aerosols in the atmosphere, while atmospheric transmissometers infer atmospheric visibility by measuring the degree of light beam attenuation over a certain distance. These instruments are generally deployed at both ends of the airport runway and in key areas of the airport. The dominant visibility observation results are automatically generated by taking the median or weighted average of measurement data from multiple points.

[0004] However, the aforementioned existing technical solutions have revealed a series of significant technical shortcomings in practical applications. First, manual observation is limited by the observer's personal experience, subjective judgment, and ambient lighting, leading to problems such as discontinuity, poor timeliness, and large subjective errors in the observation data, especially at night or under extreme weather conditions. This fails to meet the airport's need for high-frequency, continuous, and objective visibility monitoring. Second, point-based visibility meters (such as forward scatterers and transilluminators) can only reflect the local visibility near the equipment deployment point, with limited sampling volume and insufficient representativeness. Under complex weather conditions, measurement results vary greatly between different locations, and the instruments are not sensitive to changes in low visibility, rain, or foggy weather, posing a risk of misjudgment and missed judgment. Furthermore, while merging multiple instruments to obtain the median can improve the representativeness of the observation results to some extent, it is still limited by instrument layout, sampling space, and algorithm mechanisms, making it difficult to accurately reflect the true dominant visibility across the entire airport area. Moreover, the system construction and maintenance costs are high, hindering widespread application.

[0005] Therefore, in order to address the shortcomings of the existing technologies, it is necessary to develop a new automatic method and system for measuring airport dominant visibility. Summary of the Invention

[0006] To overcome the shortcomings of existing technologies, this invention proposes an automatic measurement method and system for dominant visibility at airports based on three-dimensional lidar. This method can overcome the limitations of traditional manual and point-type visibility meter observations and achieve accurate automatic measurement of dominant visibility across the entire airport area, in all weather conditions, and without human intervention.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] An automatic method for measuring dominant visibility at airports based on three-dimensional lidar, characterized by comprising the following steps:

[0009] 1) Use lidar to scan the entire airport space to collect echo signals;

[0010] 2) Preprocess and calibrate the acquired echo signals to obtain preprocessed echo signals;

[0011] 3) Based on the inversion algorithm, the atmospheric extinction coefficient of the preprocessed echo signal is inverted to obtain the atmospheric extinction coefficient. Then, the atmospheric extinction coefficient is converted into meteorological visibility to obtain meteorological visibility data of the entire airport space.

[0012] 4) Perform quantile statistics on all meteorological visibility data within a period to determine the dominant visibility value, and then perform adaptive weighted fusion with the visibility value measured by the point visibility meter and the visibility value observed manually to obtain the fused dominant visibility value.

[0013] Preferably, step 2) specifically involves: performing multi-level denoising, background noise correction, and outlier removal on the acquired echo signal, and using a photon counting method to standardize the signal intensity of the echo signal, thereby normalizing the echo signals from different ranging and different detection channels to obtain the pre-processed echo signal.

[0014] Preferably, step 3) specifically includes:

[0015] 31) The atmospheric extinction coefficient α(r) of the preprocessed echo signal was retrieved using the Klett-Fernald inversion algorithm:

[0016]

[0017] Where α(r) is the atmospheric extinction coefficient at a distance r, and P(r) is the echo signal at a distance r from the lidar. f ) and r f The echo signal and distance to the distant reference point are α(r) and α(r), respectively. f ) represents the atmospheric extinction coefficient at the distant reference point, and ξ represents the integral variable;

[0018] 32) When the laser wavelength of the lidar matches the standard wavelength of 550nm, the atmospheric extinction coefficient α(r) is converted into meteorological visibility V(r) using Koschmieder's law:

[0019]

[0020] When the laser wavelength of the lidar does not match the standard wavelength of 550nm, an empirical correction formula is introduced to obtain the meteorological visibility V(r):

[0021]

[0022] Where λ is the operating wavelength of the lidar, in μm; and q is an empirical parameter related to the type of atmospheric aerosol.

[0023] Preferably, step 4) involves performing quantile statistics on all meteorological visibility data within a period to determine the dominant visibility value. Specifically, this involves obtaining meteorological visibility data for n azimuths or spatial points within a period, arranging all meteorological visibility data within that period in ascending order to obtain an ordered sequence {V}. (1) V (2) ,...,V (n) The median or a specified quantile of the ordered sequence is used as the dominant visibility value V. prevailing :

[0024] V prevailing =V (k) ,

[0025] Among them, V (k) The meteorological visibility data is the median or specified quantile of the ordered sequence.

[0026] Preferably, in step 4), the process of adaptively weighting and fusing the dominant visibility value with the visibility value measured by the point visibility meter and the visibility value observed manually to obtain the fused dominant visibility value specifically involves: assuming the dominant visibility value output by the lidar is V... LIDAR The visibility value measured by the point-type visibility meter is V. PT The visibility value observed manually is V. MANUAL The resulting dominant visibility value is V. final :

[0027] V final =w1V LIDAR +w2V PT +w3V MANUAL ,

[0028] Where w1, w2, and w3 are adaptive weights, satisfying w1 + w2 + w3 = 1;

[0029] The adaptive optimization process of the adaptive weights w1, w2, and w3 is achieved by minimizing the weighted mean square error objective function:

[0030]

[0031] Among them, V REF The dominant visibility value is used for reference or historical verification. It represents the mathematical expectation.

[0032] Preferably, in step 4), before determining the dominant visibility value, the ordered sequence {V} is analyzed using sliding window statistical analysis and spatial continuity constraints. (1) V (2) ,...,V (n) The system automatically eliminates or corrects instantaneous abnormal jumps, isolated extreme values, or physically unexplainable measurement results.

[0033] Preferably, it further includes:

[0034] 5) Monitor the changes and spatial distribution of the fused dominant visibility value in real time, and use sliding window difference and anomaly detection algorithms to identify sudden drops or extreme changes in the fused dominant visibility value, and generate business-oriented early warnings and decision-making suggestions.

[0035] Preferably, the generation of operational early warning and decision-making suggestions in step 5) specifically involves: assuming the fused dominant visibility value for N periods is... First-order difference and standard deviation dynamic threshold discrimination are used:

[0036] |V final (t i )-V final (t i-1 )|>λ·σ N ,

[0037] Where, σ N V is the standard deviation of the dominant visibility value after fusion over N periods, λ is the sensitivity parameter, and V final (t i V represents the dominant visibility value after fusion in the i-th period. final (t i-1 ) represents the dominant visibility value after fusion in the (i-1)th period; if the discrimination condition is met, business-oriented early warning and decision-making suggestions are generated.

[0038] Preferably, step 5) further includes: outputting the fused dominant visibility value and its spatial distribution to the user based on the user's permissions.

[0039] Furthermore, the present invention also provides an automatic airport dominant visibility measurement system based on three-dimensional lidar, characterized in that it includes:

[0040] Data acquisition module, which is used to perform full-space scanning of the airport using lidar to acquire echo signals;

[0041] The signal preprocessing module is used to preprocess and calibrate the acquired echo signal to obtain the preprocessed echo signal.

[0042] The inversion and meteorological visibility conversion module is used to invert the atmospheric extinction coefficient of the preprocessed echo signal based on the inversion algorithm to obtain the atmospheric extinction coefficient, and then convert the atmospheric extinction coefficient into meteorological visibility to obtain meteorological visibility data of the entire airport space.

[0043] The dominant visibility discrimination and fusion module performs quantile statistics on all meteorological visibility data within a period to determine the dominant visibility value. It then adaptively weights and fuses this dominant visibility value with visibility values ​​measured by a point visibility meter and manually observed visibility values ​​to obtain the fused dominant visibility value.

[0044] Compared with the prior art, the automatic measurement method and system for airport dominant visibility based on three-dimensional lidar of the present invention has one or more of the following beneficial technical effects:

[0045] 1. Improve measurement representativeness and coverage: The three-dimensional scanning lidar enables simultaneous observation of visibility across the entire airport area, including runways, taxiways, and key operating areas, accurately reflecting the overall dominant visibility level.

[0046] 2. Improve measurement accuracy and timeliness: By adopting advanced extinction coefficient inversion algorithm and multi-source data fusion technology, it can continuously output high-precision visibility data under complex weather conditions, effectively making up for the shortcomings of manual and point-type visibility meters in terms of timeliness and continuity.

[0047] 3. Enhance flight operation safety capabilities: Provide airport control, air traffic control, and pilots with real-time and reliable dominant visibility information, improve risk warning capabilities under extreme weather conditions, and reduce misjudgments and operational delays.

[0048] 4. Promote the automation and localization of meteorological observation: It can realize long-term unattended operation, remote monitoring and intelligent operation and maintenance, and adapt to the development direction of meteorological automation, smart airports and the new generation of civil aviation meteorological observation system.

[0049] 5. It has broad application value: It is not only applicable to various types of airports, but can also provide technical support for transportation hubs such as ports and highways that have high visibility requirements, and promote the upgrading of related industries. Attached Figure Description

[0050] Figure 1 This is a flowchart of the automatic measurement method for airport dominant visibility based on three-dimensional lidar of the present invention;

[0051] Figure 2 This is a schematic diagram of the automatic airport visibility measurement system based on three-dimensional lidar according to the present invention. Detailed Implementation

[0052] Before detailing any embodiment of the invention, it should be understood that the invention, in its application, is not limited to the details of the construction and arrangement of the components set forth in the following description or illustrated in the following figures. The invention can have other embodiments and can be practiced or carried out in various ways. Furthermore, it should be understood that the wording and terminology used herein are for descriptive purposes and should not be considered limiting. The use of “comprising” or “having” and variations thereof is intended to cover the items set forth below and their equivalents, as well as any additional items. Unless otherwise specified or limited, the terms “installation,” “connection,” “support,” and “linkage,” and variations thereof are used broadly and cover both direct and indirect installation, connection, support, and linking. Moreover, “connection” and “linkage” are not limited to physical or mechanical connections or links.

[0053] Furthermore, firstly, in the disclosure of this invention, the terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the above terms should not be construed as limiting this invention. Secondly, the term "a" should be understood as "at least one" or "one or more," that is, in one embodiment, the number of an element can be one, while in another embodiment, the number of the element can be multiple. The term "a" should not be construed as a limitation on the quantity.

[0054] To address the shortcomings of existing airport dominant visibility monitoring systems, such as insufficient representativeness, high subjectivity, and low automation, this invention proposes an automatic measurement method and system for airport dominant visibility based on three-dimensional lidar. It employs a high-sensitivity three-dimensional scanning lidar as the core sensor, acquiring real-time atmospheric scattering signals over a wide area of ​​the airport through multi-directional laser scanning across the entire field. Combined with an automatic extinction coefficient calculation method based on Koschmieder's law and the Klett-Fernald inversion algorithm, it achieves high-precision inversion of atmospheric visibility at different azimuths and altitudes. According to the meteorological definition of dominant visibility, it automatically extracts the quantiles of multi-directional observation data within a single period as the dominant visibility result. This data can be intelligently fused with point-based visibility meters or manual observation data, ensuring stable, continuous, and reliable dominant visibility information even under complex weather conditions. It supports unattended continuous operation, features real-time data upload, intelligent alarm, and remote management functions, and can be seamlessly integrated into the existing airport meteorological automatic observation system.

[0055] Figure 1 A flowchart of the automatic airport dominant visibility measurement method based on three-dimensional lidar of the present invention is shown. Figure 1 As shown, the automatic airport dominant visibility measurement method based on three-dimensional lidar of the present invention includes the following steps:

[0056] I. Data Collection.

[0057] In this invention, a lidar is used to perform a full-space scan of the airport to collect echo signals, that is, the echo signals after the signals emitted by the lidar are scattered by the atmosphere.

[0058] Specifically, three-dimensional scanning lidar is deployed in key areas of the airport (e.g., runways, taxiways, aprons, and surrounding airspace). Reasonable elevation and azimuth angles are set to perform multi-angle, periodic spatial scans of the runways, taxiways, aprons, and surrounding airspace. The lidar emits pulse signals and collects echo signals through its built-in high-sensitivity detector, obtaining raw data on atmospheric backscattering intensity at different distances, directions, and altitudes from the airport.

[0059] The three-dimensional scanning lidar employed in this invention uses a highly stable all-fiber laser as its emission source, with a typical operating wavelength of 1064nm. It boasts significant advantages such as high power, low noise, and zero interference with control tower and flight area equipment. The lidar periodically performs full-coverage multi-angle spatial scanning of key airport areas (such as runways, taxiways, aprons, and surrounding airspace) at preset elevation and azimuth angles. Utilizing the lidar's built-in avalanche photodiode (APD) array as a high-sensitivity detector, it effectively captures laser backscattered signals submerged in background noise, enabling long-distance, large-area, all-weather, uninterrupted data acquisition.

[0060] II. Signal Preprocessing.

[0061] The acquired echo signals are preprocessed and calibrated to obtain preprocessed echo signals.

[0062] In this invention, the acquired echo signals undergo multi-stage denoising, background noise correction, and outlier removal. A photon counting method is used to standardize the signal intensity of the echo signals, normalizing echo signals from different ranging distances and detection channels to obtain pre-processed echo signals. This eliminates the influence of environmental noise, strong light interference, and system background noise, ensuring the physical validity and numerical stability of subsequent inversion data.

[0063] Specifically, the collected echo signals are at different spatial distances r and directions. Echo intensity According to the lidar equation, the signal is expressed as:

[0064]

[0065] Where P0 is the average power of the laser pulse, c is the speed of light, τ is the laser pulse width, A is the effective optical area of ​​the receiving system, η is the optical efficiency of the receiving system, Y(r) is the overlap factor, r is the ranging distance, β(r) is the atmospheric backscattering coefficient, T(r) is the optical path transmittance (i.e., the atmospheric attenuation factor), and θ and These are the pitch angle and the azimuth angle, respectively.

[0066] To facilitate digital signal statistics, this invention employs a high-precision photon counting method to obtain the number of photons detected per unit time interval.

[0067]

[0068] Where η0 is the detector quantum efficiency, λ is the laser wavelength, h is Planck's constant, and Δt is the integration time.

[0069] III. Inversion and meteorological visibility conversion.

[0070] The atmospheric extinction coefficient is inverted based on the inversion algorithm to obtain the atmospheric extinction coefficient. Then, the atmospheric extinction coefficient is converted into meteorological visibility to obtain meteorological visibility data for the entire airport space.

[0071] In this invention, the core inversion process employs the Klett-Fernald algorithm, whose theoretical basis is that the spatial attenuation of the echo signal is affected not only by the atmospheric extinction coefficient α(r) but also by distance. To avoid the non-convergence or error accumulation problems that easily occur in conventional forward iteration in actual observations, this invention adopts a backward integral inversion method, setting the farthest effective distance of the observation area as the distant reference point, and selecting a physically reasonable boundary extinction coefficient α(r). f The inversion formula for the atmospheric extinction coefficient is:

[0072]

[0073] Where α(r) is the atmospheric extinction coefficient at a distance r from the lidar, and P(r) is the echo signal at a distance r from the lidar. f ) and r f The echo signal and distance to the distant reference point are α(r) and α(r), respectively. f ) represents the atmospheric extinction coefficient at the distant reference point, and ξ is the integral variable.

[0074] To ensure the physical validity of the inversion results, the boundary extinction coefficient, i.e., the atmospheric extinction coefficient α(r) at the distant reference point, is used. f Based on the ISO 28902-1 international standard and actual observation experience at typical airports, an adaptive selection was made. Specifically, in situations with extremely high visibility (e.g., clear skies, no aerosols), α... min =1.5×10 -3 m -1 Under extremely low visibility conditions (such as dense fog, thick smoke, etc.), α max =0.1m -1 This invention can also employ a time-varying boundary condition self-calibration algorithm to analyze the full-area data distribution characteristics of lidar in real time, adaptively correct the boundary extinction coefficient, and improve stability and robustness under complex weather changes.

[0075] After inverting the atmospheric extinction coefficient α(r), meteorological optical visibility is converted using Koschmieder's law:

[0076]

[0077] If the actual operating wavelength of the lidar deviates from the meteorological standard wavelength of 550nm, the following empirical correction formula shall be used:

[0078]

[0079] Where λ is the wavelength of the lidar (in μm), and q is an empirical correction parameter related to the aerosol type. For airport aerosol types, q is usually taken as 1.3.

[0080] Of course, this invention can also introduce partitioned multi-scale weighted smoothing and anomaly detection algorithms. Through spatial continuity constraints and dynamic filtering of historical data, it can effectively suppress misjudgments and noise caused by instantaneous disturbances or local extreme weather, ensuring that the visibility inversion results of each spatial point have high physical consistency and operational reliability.

[0081] IV. Dominant Visibility Judgment and Fusion.

[0082] Quantile statistics are performed on all meteorological visibility data within a period to determine the dominant visibility value. The dominant visibility value is then adaptively weighted and fused with the visibility values ​​measured by a point visibility meter and the visibility values ​​observed manually to obtain the fused dominant visibility value.

[0083] In this invention, a high-order statistical and adaptive fusion algorithm is used to automatically determine the dominant visibility value at the airport, ensuring that the output results are representative of the entire field, physically accurate, and operationally real-time. In meteorological terms, dominant visibility refers to the maximum horizontal distance achievable within half or more of the field of view around the observation point. This requirement necessitates a comprehensive quantile analysis of spatially distributed visibility, rather than simply taking extreme values ​​or the mean.

[0084] In the specific implementation process, the visibility data V(r) retrieved through 3D spatial inversion is first grouped by azimuth and integrated periodically. Assuming that the lidar acquires n azimuth or spatial point visibility data V(r) within one cycle, all visibility data within that cycle can be arranged in ascending order to obtain an ordered sequence {V... (1) V (2) ,...,V (n) According to the International Civil Aviation Organization (ICAO) and domestic airport dominant visibility standards, the statistical value of dominant visibility should be the median or a specified quantile of visibility within a given period. If there is obstruction at the scanning angle, assuming the actual scanning angle is Θ degrees, then the dominant visibility is taken as the $k$-th quantile, where... Therefore, the automatic determination of dominant visibility can be expressed as:

[0085] V prevailing =V (k) ,

[0086] Among them, V (k) The visibility data is based on the aforementioned quantiles.

[0087] To improve robustness to data biases caused by outliers, localized extreme weather, or equipment fluctuations, this invention introduces an adaptive weighted fusion algorithm to dynamically fuse the dominant visibility criterion from lidar with multi-source observation results from traditional point-based visibility meters and manual observations. Let V be the dominant visibility value output by the lidar. LIDARThe dominant visibility value measured by the point-type visibility meter is V. PT The dominant visibility value observed manually is V. MANUAL The final dominant visibility value V final For weighted fusion output:

[0088] V final =w1V LIDAR +w2V PT +w3V MANUAL ,

[0089] Where w1, w2, and w3 are the adaptive weights of each dominant visibility value, satisfying w1 + w2 + w3 = 1.

[0090] The weights w1, w2, and w3 can be dynamically adjusted not only based on the real-time stability and historical error statistics of each sensor or manual observation, but also according to current weather conditions, data consistency, and reliability. For example, in large-scale, clear weather or when visibility is greater than 1500m, the weight w1 of the lidar is automatically increased; during rainfall, extreme low visibility, or instrument maintenance periods, the weights w2 and w3 of the point visibility meter or manual observation are appropriately increased. The adaptive optimization process of the weights can be achieved by minimizing the weighted mean square error objective function.

[0091]

[0092] Among them, V REF For reference or historical verification of dominant visibility values, It represents the mathematical expectation.

[0093] Furthermore, this invention employs multi-scale anomaly detection and intelligent fault-tolerant algorithms. It uses sliding window statistical analysis and spatial continuity constraints on the input dominant visibility sequence to automatically eliminate or correct instantaneous anomalous jumps, isolated extreme values, or physically unexplainable measurement results. For example, if a sudden change in dominant visibility in a certain azimuth interval exceeds three standard deviations or does not conform to meteorological evolution logic, a redundancy check and data re-sampling mechanism is triggered to ensure stable, continuous, and reliable operational output.

[0094] V. Data Output and Intelligent Management.

[0095] The system monitors the changes and spatial distribution of the fused dominant visibility value in real time, and uses sliding window difference and anomaly detection algorithms to identify sudden drops or extreme changes in the fused dominant visibility value, and generates business-oriented early warnings and decision-making suggestions.

[0096] For example, the width of the sliding window is N, the step size is 2-3, and the dominant visibility value after fusion over N periods is . First-order difference and standard deviation dynamic threshold discrimination are used:

[0097] |V final (t i )-V final (t i-1 )|>λ·σ N ,

[0098] Where, σ N V is the standard deviation of the dominant visibility value after fusion over N periods, λ is the sensitivity parameter, and V final (t i V represents the dominant visibility value after fusion in the i-th period. final (t i-1 ) represents the dominant visibility value after fusion in the (i-1)th period; if the discrimination condition is met, business-oriented early warning and decision-making suggestions are generated.

[0099] Furthermore, the fused dominant visibility value and its spatial distribution can be output to the user based on their permissions. Using user permission U and data requirement level L as variables, dynamic filtering is performed as follows:

[0100] D out (U,L,t)=f(V final (t),V spatial (t),U,L),

[0101] Among them, D out (U,L,t) represents the subset of data output to user U at time t, where V final (t) represents the fused dominant visibility value at time t, V spatial (t) represents the spatially distributed visibility set, and f(·) represents the data pruning and privacy protection algorithm.

[0102] Figure 2 A schematic diagram of the automatic airport dominant visibility measurement system based on three-dimensional lidar of the present invention is shown. Figure 2 As shown, the automatic airport dominant visibility measurement system based on three-dimensional lidar of the present invention includes:

[0103] 1. Data collection module.

[0104] The data acquisition module is used to perform a full-space scan of the airport using lidar to collect echo signals;

[0105] 2. Signal preprocessing module.

[0106] The signal preprocessing module is used to preprocess and calibrate the acquired echo signal to obtain the preprocessed echo signal.

[0107] 3. Inversion and meteorological visibility conversion module.

[0108] The inversion and meteorological visibility conversion module is used to perform atmospheric extinction coefficient inversion on the preprocessed echo signal based on the inversion algorithm to obtain the atmospheric extinction coefficient, and then convert the atmospheric extinction coefficient into meteorological visibility to obtain meteorological visibility data for the entire airport space.

[0109] 4. Dominant visibility discrimination and fusion module.

[0110] The dominant visibility discrimination and fusion module is used to perform quantile statistics on all meteorological visibility data within a period, determine the dominant visibility value, and adaptively weight and fuse the dominant visibility value with the visibility value measured by the point visibility meter and the visibility value observed manually to obtain the fused dominant visibility value.

[0111] Of course, the automatic airport dominant visibility measurement system based on three-dimensional lidar of the present invention may also include:

[0112] 5. Data output and intelligent management module.

[0113] The data output and intelligent management module is used to monitor the changes and spatial distribution of the fused dominant visibility value in real time. It uses sliding window difference and anomaly detection algorithms to identify sudden drops or extreme changes in the fused dominant visibility value and generate business-oriented early warnings and decision suggestions.

[0114] Of course, the data output and intelligent management module can also be responsible for automatically generating and uploading the dominant visibility results, and providing functions such as intelligent alarms, data storage, historical query and remote management to achieve full-process automation and business application.

[0115] This invention effectively addresses the shortcomings of existing technologies in terms of representativeness, real-time performance, accuracy, and automation. By continuously scanning the entire airport area in three dimensions, it avoids the limitations of point-based instrument observations, which are confined to single points and susceptible to local interference, achieving a comprehensive and objective reflection of dominant visibility. The automated algorithm eliminates the drawbacks of subjective and discontinuous data in manual observation, significantly improving observation timeliness and accuracy, maintaining high reliability even at night and in extreme weather conditions. Furthermore, it intelligently integrates multi-source data, effectively reducing the risk of misjudgments and omissions under special weather conditions. Unmanned operation and intelligent management significantly reduce maintenance and labor costs, greatly enhancing the modernization and intelligence of airport meteorological observation, and providing solid data support for flight safety scheduling and operational assurance.

[0116] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the scope of protection of the present invention. Those skilled in the art can modify or make equivalent substitutions to the technical solutions of the present invention based on the concept of the present invention, without departing from the essence and scope of the technical solutions of the present invention.

Claims

1. An automatic method for measuring dominant visibility at airports based on three-dimensional lidar, characterized in that, Includes the following steps: 1) Use lidar to scan the entire airport space to collect echo signals; 2) Preprocess and calibrate the acquired echo signals to obtain preprocessed echo signals; 3) Based on the inversion algorithm, the atmospheric extinction coefficient of the preprocessed echo signal is inverted to obtain the atmospheric extinction coefficient. Then, the atmospheric extinction coefficient is converted into meteorological visibility to obtain meteorological visibility data of the entire airport space. 4) Perform quantile statistics on all meteorological visibility data within a period to determine the dominant visibility value, and then perform adaptive weighted fusion with the visibility value measured by the point visibility meter and the visibility value observed manually to obtain the fused dominant visibility value.

2. The automatic airport dominant visibility measurement method based on three-dimensional lidar according to claim 1, characterized in that, Step 2) specifically involves: performing multi-level denoising, background noise correction, and outlier removal on the acquired echo signal, and using a photon counting method to standardize the signal intensity of the echo signal, thereby normalizing the echo signals from different ranging and different detection channels to obtain the pre-processed echo signal.

3. The automatic airport dominant visibility measurement method based on three-dimensional lidar according to claim 1, characterized in that, Step 3) specifically includes: 31) The atmospheric extinction coefficient α(r) of the preprocessed echo signal was retrieved using the Klett-Fernald inversion algorithm: Where α(r) is the atmospheric extinction coefficient at a distance r from the lidar, and P(r) is the echo signal at a distance r from the lidar. f ) and r f The echo signal and distance to the distant reference point are α(r) and α(r), respectively. f ) represents the atmospheric extinction coefficient at the distant reference point, and ξ represents the integral variable; 32) When the laser wavelength of the lidar matches the standard wavelength of 550nm, the atmospheric extinction coefficient α(r) is converted into meteorological visibility V(r) using Koschmieder's law: When the laser wavelength of the lidar does not match the standard wavelength of 550nm, an empirical correction formula is introduced to obtain the meteorological visibility V(r): Where λ is the operating wavelength of the lidar, in μm; and q is an empirical parameter related to the type of atmospheric aerosol.

4. The automatic airport dominant visibility measurement method based on three-dimensional lidar according to claim 1, characterized in that, Step 4) involves performing quantile statistics on all meteorological visibility data within a given period to determine the dominant visibility value. Specifically, this involves obtaining meteorological visibility data from n azimuth or spatial points within a given period, arranging all meteorological visibility data within that period in ascending order to obtain an ordered sequence {V}. (1) V (2) ,...,V (n) The median or a specified quantile of the ordered sequence is used as the dominant visibility value V. prevailing : V prevailing =V (k) , Among them, V (k) The meteorological visibility data is the median or specified quantile of the ordered sequence.

5. The automatic measurement method for dominant visibility at airports based on three-dimensional lidar according to claim 4, characterized in that, Step 4) involves adaptively weighting and fusing the dominant visibility value with the visibility value measured by the point visibility meter and the visibility value observed manually to obtain the fused dominant visibility value. Specifically, let the dominant visibility value output by the lidar be V. LIDAR The visibility value measured by the point-type visibility meter is V. PT The visibility value observed manually is V. MANUAL The resulting dominant visibility value is V. final : In final =w1V LIDAR +w2V PT +w3V MANUAL , Where w1, w2, and w3 are adaptive weights, satisfying w1 + w2 + w3 = 1; The adaptive optimization process of the adaptive weights w1, w2, and w3 is achieved by minimizing the weighted mean square error objective function: Among them, V REF The dominant visibility value is used for reference or historical verification. It represents the mathematical expectation.

6. The automatic measurement method for airport dominant visibility based on three-dimensional lidar according to claim 5, characterized in that, In step 4), before determining the dominant visibility value, the ordered sequence {V} is analyzed using sliding window statistical analysis and spatial continuity constraints. (1) V (2) ,...,V (n) The system automatically eliminates or corrects instantaneous abnormal jumps, isolated extreme values, or physically unexplainable measurement results.

7. The automatic measurement method for dominant visibility at airports based on three-dimensional lidar according to any one of claims 1-6, characterized in that, Further includes: 5) Monitor the changes and spatial distribution of the fused dominant visibility value in real time, and use sliding window difference and anomaly detection algorithms to identify sudden drops or extreme changes in the fused dominant visibility value, and generate business-oriented early warnings and decision-making suggestions.

8. The automatic airport dominant visibility measurement method based on three-dimensional lidar according to claim 7, characterized in that, The generation of operational early warning and decision-making suggestions in step 5) specifically involves: assuming the dominant visibility value after the fusion of N periods is... First-order difference and standard deviation dynamic threshold discrimination are used: |V final (t i )-V final (t i-1 )|>λ·σ N , Where, σ N V is the standard deviation of the dominant visibility value after fusion over N periods, λ is the sensitivity parameter, and V final (t i V represents the dominant visibility value after fusion in the i-th period. final (t i-1 ) represents the dominant visibility value after fusion in the (i-1)th period; if the discrimination condition is met, business-oriented early warning and decision-making suggestions are generated.

9. The automatic measurement method for airport dominant visibility based on three-dimensional lidar according to claim 7, characterized in that, Step 5) further includes: outputting the fused dominant visibility value and its spatial distribution to the user based on the user's permissions.

10. An automatic airport dominant visibility measurement system based on three-dimensional lidar, characterized in that, include: Data acquisition module, which is used to perform full-space scanning of the airport using lidar to acquire echo signals; The signal preprocessing module is used to preprocess and calibrate the acquired echo signal to obtain the preprocessed echo signal. The inversion and meteorological visibility conversion module is used to invert the atmospheric extinction coefficient of the preprocessed echo signal based on the inversion algorithm to obtain the atmospheric extinction coefficient, and then convert the atmospheric extinction coefficient into meteorological visibility to obtain meteorological visibility data of the entire airport space. The dominant visibility discrimination and fusion module is used to perform quantile statistics on all meteorological visibility data within a period, determine the dominant visibility value, and adaptively weight and fuse the dominant visibility value with the visibility value measured by the point visibility meter and the visibility value observed manually to obtain the fused dominant visibility value.

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