Traffic light adjustment method for low-altitude mixed take-off and landing fields based on meteorological data

Abstract

This invention relates to low-altitude airspace management, specifically to a traffic light adjustment method for mixed take-off and landing fields of low-altitude aircraft based on meteorological data. It integrates ADS-B trajectory data, radar data, meteorological data, and airspace status information to construct an airspace congestion calculation model, outputting aircraft density, conflict hotspots, and service load indices in real time. The airspace congestion calculation results are mapped to a BeiDou three-dimensional grid space to generate a multi-scale dynamic heat map, supporting historical traffic flow retrospectives and aircraft type proportion analysis for any airspace profile. Based on the heat map and its association with meteorological data and airspace status information, a closed-loop decision chain optimization mechanism of "real-time perception—trend prediction—resource allocation" is established. Traffic light timing is adjusted in real time according to the decision chain instructions. The technical solution provided by this invention effectively overcomes the shortcomings of existing technologies in accurately and in real-time adjusting traffic light timing under dynamic airspace load environments.

Description

TECHNICAL FIELD

[0001] The present application relates to low-altitude airspace management, in particular to a red-green light adjustment method for a low-altitude aircraft mixed take-off and landing field based on meteorological data. BACKGROUND

[0002] With the continuous promotion of low-altitude airspace opening and the rapid growth of low-altitude aircraft such as unmanned aerial vehicles and general aircraft, the density and complexity of low-altitude flight activities have significantly increased, and higher requirements have been put forward for the operation efficiency and safety control of mixed take-off and landing fields. The traditional low-altitude traffic management mainly relies on manual scheduling and fixed signal timing, which is difficult to adapt to real-time needs in a dynamic airspace load environment, especially under complex weather conditions (such as low visibility, strong convection, wind shear, etc.). Aircraft take-off conflicts, uneven allocation of airspace resources, and other issues frequently occur, leading to flight delays, increased operational risks, and even accidents.

[0003] In the prior art, the signal control of the low-altitude aircraft mixed take-off and landing field mainly has the following limitations:

[0004] 1) Data island and perception lag: Airspace state monitoring usually independently relies on a single data source such as ADS-B (Automatic Dependent Surveillance-Broadcast), radar, or weather station, lacks fusion analysis of multi-source heterogeneous data, resulting in one-sided evaluation of airspace congestion, and inability to capture conflict hotspots and traffic mutations in real time;

[0005] 2) Insufficient visualization and traceability: Airspace congestion is usually presented as a two-dimensional plane heat map, lacking multi-scale dynamic mapping in three-dimensional grid space, making it difficult to support historical traffic backtracking and model share analysis in complex terrain (such as mountainous areas, urban canyons, etc.) or three-dimensional intersection airspace, limiting the refinement of scheduling strategies;

[0006] 3) Broken decision chain and extensive resource allocation: The existing system lacks a closed-loop decision-making mechanism of "perception-prediction-allocation", meteorological data is only used for post-risk warning, and cannot be used to optimize red-green light timing and runway resource allocation in advance through trend prediction, resulting in lagging control measures relative to risk evolution;

[0007] 4) Static timing and dynamic demand mismatch: The existing red-green light timing scheme mainly relies on manual scheduling and fixed signal timing, without considering meteorological risk changes (such as the need for take-off and landing interval adjustment due to sudden wind) and historical traffic periodic fluctuations, making it difficult to adapt to dynamic airspace load. SUMMARY

[0008] (I) Technical problems solved

[0009] In view of the above-mentioned shortcomings of the existing technology, the present invention provides a traffic light adjustment method for low-altitude aircraft mixed take-off and landing fields based on meteorological data, which can effectively overcome the shortcomings of the existing technology in that it is difficult to perform accurate and real-time traffic light timing under dynamic airspace load environment.

[0010] (II) Technical Solution

[0011] To achieve the above objectives, the present invention provides the following technical solution:

[0012] The traffic light adjustment method for mixed takeoff and landing fields for low-altitude aircraft based on meteorological data includes the following steps:

[0013] S1. Integrate ADS-B trajectory data, radar data, meteorological data, and airspace status information to construct an airspace busyness calculation model and output aircraft density, conflict hotspots, and service load index in real time.

[0014] S2. Map the airspace congestion calculation results to the BeiDou three-dimensional grid space to generate a multi-scale dynamic heat map, and support historical traffic backtracking and aircraft type ratio analysis for any airspace profile.

[0015] S3. Based on the correlation of heat map with meteorological data and airspace status information, establish a closed-loop decision chain optimization mechanism of "real-time perception - trend prediction - resource allocation";

[0016] S4. Adjust traffic light timings in real time according to the instructions of the decision chain.

[0017] Preferably, S1 integrates ADS-B trajectory data, radar data, meteorological data, and airspace status information to construct an airspace congestion calculation model, and outputs in real time aircraft density, conflict hotspots, and service load indices, including:

[0018] 1) Calculate aircraft density: Introduce grid radius and indicator function to discretize the aircraft distribution from continuous space into statistics within three-dimensional grid cells, thus solving the density calculation error caused by radar data due to scanning interval;

[0019] 2) Detecting conflict hotspots: The Sigmoid function is introduced to map the distance between aircraft to the probability of conflict, avoiding the abrupt changes of the traditional threshold method and better reflecting the ambiguity of the actual flight safety boundary; the aircraft type weight is introduced to quantify the difference in conflict risk of different aircraft type combinations, and solve the conflict assessment bias caused by the inconsistency of the dynamic characteristics of aircraft types in the take-off and landing field.

[0020] 3) Calculate the service load index: weighted aggregation of aircraft density, conflict probability and weather risk, breaking through the limitation of traditional traffic statistics that only rely on density or conflict as a single dimension;

[0021] The ADS-B trajectory data includes longitude, latitude, altitude, and speed; the radar data includes position and speed; the meteorological data includes wind speed, visibility, and precipitation intensity; and the airspace status information includes air traffic control instructions and takeoff and landing plans.

[0022] Spatiotemporal alignment of ADS-B trajectory data and radar data was performed, outliers were removed, and meteorological data was interpolated into the airspace according to three-dimensional grid cells.

[0023] Preferably, the density of the aircraft is calculated using the following formula:

[0024] ;

[0025] in, Let j be the number of aircraft per unit volume within a three-dimensional mesh cell. This is an indicator function; it returns 1 if the condition is met, and 0 otherwise. i,j Let r be the distance between aircraft i and 3D mesh element j. j Let V be the radius of the 3D mesh element j, n be the total number of aircraft, and V be the radius of the mesh element j. j Let be the volume of the three-dimensional mesh element j.

[0026] Preferably, the following formula is used to detect conflict hotspots:

[0027] ;

[0028] Among them, P j Let j be the collision probability of a 3D mesh element. For the Sigmoid function, For aircraft k and aircraft Minimum safe distance between , All of these are safety distance threshold parameters. For aircraft model m of aircraft k k With aircraft Model The weighting of aircraft type pairs is as follows: fixed-wing aircraft type pairs have a weighting of 1.5, rotorcraft type pairs have a weighting of 0.8, and mixed-type aircraft type pairs have a weighting of 1.

[0029] Preferably, the service load index is calculated using the following formula:

[0030] ;

[0031] Among them, L j R is the service load index of 3D mesh cell j. j The meteorological risk value of a three-dimensional grid cell j is calculated by weighted summation of wind speed, visibility, and precipitation intensity, R. maxThis represents the historical extreme weather risk value. , , All are weighting coefficients.

[0032] Preferably, in S2, the airspace congestion calculation results are mapped to the BeiDou three-dimensional grid space to generate a multi-scale dynamic heat map, and support historical traffic backtracking and aircraft type ratio analysis for any airspace profile, including:

[0033] S21. Using the BeiDou coordinate system as a reference, the airspace is divided into multiple three-dimensional grid units to form the BeiDou three-dimensional grid space, and the airspace busyness calculation results are mapped to the corresponding three-dimensional grid units.

[0034] S22. Calculate thermal values: Introduce height layer attenuation parameters to quantify the weakening effect of the high-altitude airspace on the ground take-off and landing field, and solve the problem of false high-altitude flow density caused by ignoring height in traditional two-dimensional heat maps; Introduce time weight parameters to distinguish heat maps of different time levels, balance real-time performance and computational efficiency, and meet the needs of different scenarios.

[0035] S23. Updates minute-level heatmaps every minute, aggregates hourly heatmaps every hour, and stores heatmap time series. Supports historical traffic backtracking and aircraft type ratio analysis for any airspace profile.

[0036] Preferably, the thermal value is calculated using the following formula:

[0037] ;

[0038] in, Let be the thermal value of the three-dimensional mesh element j at time t. Let j be the service load index of the three-dimensional grid cell j at time t. The maximum service load index for all 3D mesh cells at time t. For height layer attenuation parameters, This is the time weighting parameter.

[0039] Preferably, in S3, a closed-loop decision-making chain optimization mechanism of "real-time perception—trend prediction—resource allocation" is established based on heat map-linked meteorological data and airspace status information, including:

[0040] S31. Calculate the predicted service load index: Quantify the amplification effect of the rate of weather deterioration on airspace load by using the rate of change of meteorological risk values, thus solving the problem of traditional prediction models ignoring dynamic meteorological changes; introduce Fourier series terms to fit historical flow periodic fluctuations to improve the dynamic adaptability of trend prediction; finally, construct a trend prediction model to calculate the predicted service load index:

[0041] ;

[0042] in, For the three-dimensional mesh element j at time... The predicted service load index Let be the rate of change of meteorological risk value of 3D grid cell j at time t. , , These represent the three-dimensional mesh element j at time t, Meteorological risk value, For the Fourier series terms, A m f m , These are all periodic flow fluctuation parameters, obtained by fitting historical flow data. m is the index of the periodic flow fluctuation component, and M is the total number of periodic flow fluctuation components. , , All are adjustable parameters;

[0043] S32. If the calculated predicted service load index is greater than the preset threshold, then the traffic light timing adjustment will be triggered.

[0044] Preferably, in S4, the traffic light timing is adjusted in real time according to the decision chain instructions, including:

[0045] S41. Calculate the adjusted green light duration: Adjust the green light duration by using the predicted service load index of priority and non-priority directions to avoid the rigidity of traditional fixed timing and achieve precise matching of resource allocation with actual demand; introduce an adjustment coefficient to limit the change range of a single timing and prevent aircraft scheduling chaos caused by frequent switching of red and green lights due to sudden load changes.

[0046] S42. Update the traffic light timing in real time based on the adjusted green light duration;

[0047] S43. Collect the actual adjustment effects and feed them back to the trend prediction model for parameter optimization.

[0048] Preferably, the adjusted green light duration is calculated using the following formula:

[0049] ;

[0050] Among them, T green The adjusted green light duration is T0, which is the base green light duration. , These are the predicted service load indices for priority and non-priority directions, respectively. This is for adjusting the coefficient.

[0051] (III) Beneficial Effects

[0052] Compared with existing technologies, the traffic light adjustment method for mixed take-off and landing fields of low-altitude aircraft based on meteorological data provided by this invention has the following beneficial effects:

[0053] 1) Accurately grasp the airspace situation to support scientific decision-making.

[0054] By integrating multiple data sources to construct an airspace congestion calculation model, key indicators are output in real time, and the results are mapped to generate multi-scale dynamic heat maps. This supports historical traffic backtracking and aircraft type ratio analysis, enabling managers to accurately, comprehensively, and dynamically grasp the real-time status and historical patterns of airspace. It breaks through the limitations of traditional information and provides a strong basis for formulating scientific and reasonable low-altitude airspace management strategies and traffic light timing adjustment schemes, thereby improving the scientificity and accuracy of decision-making.

[0055] 2) Intelligent prediction and allocation to improve operational efficiency

[0056] Based on the correlation of heat map with meteorological data and airspace status information, a closed-loop decision chain optimization mechanism of "real-time perception - trend prediction - resource allocation" is established. This mechanism can not only perceive the airspace situation in real time, but also predict future trends, adjust traffic light timing in advance, optimize the take-off and landing sequence and rhythm of aircraft, realize the efficient use of airspace resources, reduce aircraft waiting time, and improve the overall operational efficiency of the low-altitude aircraft mixed take-off and landing field.

[0057] 3) Adapt flexibly to dynamic changes and ensure operational safety

[0058] Taking into full account the dynamic changes of multiple factors such as meteorological data and airspace status, as well as the periodic fluctuations of historical traffic flow, the timing of traffic lights is adjusted in real time. When faced with complex and ever-changing weather conditions and sudden airspace situations, it can react quickly, flexibly adjust the rules for aircraft take-off and landing, avoid conflicts and dangerous situations caused by environmental changes, effectively ensure the safe operation of the mixed take-off and landing field for low-altitude aircraft, and reduce safety risks. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0060] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0062] The following describes the specific process of the traffic light adjustment method for low-altitude aircraft mixed take-off and landing fields based on meteorological data provided by this invention, using specific examples (e.g.) Figure 1 (As shown).

[0063] I. Integrating ADS-B trajectory data, radar data, meteorological data, and airspace status information, a model for calculating airspace busyness is constructed, outputting real-time aircraft density, conflict hotspots, and service load indices, specifically including:

[0064] 1) Calculate aircraft density: Introduce grid radius and indicator function to discretize the aircraft distribution from continuous space into statistics within three-dimensional grid cells, thus solving the density calculation error caused by radar data due to scanning interval;

[0065] The density of an aircraft is calculated using the following formula:

[0066] ;

[0067] in, Let j be the number of aircraft per unit volume within a three-dimensional mesh cell. This is an indicator function; it returns 1 if the condition is met, and 0 otherwise. i,j Let r be the distance between aircraft i and 3D mesh element j. j Let V be the radius of the 3D mesh element j, n be the total number of aircraft, and V be the radius of the mesh element j. j Let j be the volume of the three-dimensional mesh element.

[0068] 2) Detecting conflict hotspots: The Sigmoid function is introduced to map the distance between aircraft to the probability of conflict, avoiding the abrupt changes of the traditional threshold method and better reflecting the ambiguity of the actual flight safety boundary; the aircraft type weight is introduced to quantify the difference in conflict risk of different aircraft type combinations, and solve the conflict assessment bias caused by the inconsistency of the dynamic characteristics of aircraft types in the take-off and landing field.

[0069] The following formula is used to detect conflict hotspots:

[0070] ;

[0071] Among them, P j Let j be the collision probability of a 3D mesh element. For the Sigmoid function, For aircraft k and aircraft Minimum safe distance between , All of these are safety distance threshold parameters. For aircraft model m of aircraft k k With aircraft Model The weighting of the aircraft type pairs is as follows: fixed-wing aircraft type pairs have a weighting of 1.5, rotorcraft type pairs have a weighting of 0.8, and mixed-use aircraft type pairs have a weighting of 1.

[0072] 3) Calculate the service load index: weighted aggregation of aircraft density, conflict probability and weather risk, breaking through the limitation of traditional traffic statistics that only rely on density or conflict as a single dimension;

[0073] The service load index is calculated using the following formula:

[0074] ;

[0075] Among them, L j R is the service load index of 3D mesh cell j. j The meteorological risk value of a three-dimensional grid cell j is calculated by weighted summation of wind speed, visibility, and precipitation intensity, R. max This represents the historical extreme weather risk value. , , All are weighting coefficients;

[0076] The ADS-B trajectory data includes longitude, latitude, altitude, and speed; the radar data includes position and speed; the meteorological data includes wind speed, visibility, and precipitation intensity; and the airspace status information includes air traffic control instructions and takeoff and landing plans.

[0077] Spatiotemporal alignment of ADS-B trajectory data and radar data was performed, outliers were removed, and meteorological data was interpolated into the airspace according to three-dimensional grid cells.

[0078] II. Mapping the airspace congestion calculation results to the BeiDou 3D grid space to generate multi-scale dynamic heat maps, and supporting historical traffic backtracking and aircraft type proportion analysis for arbitrary airspace profiles, specifically including:

[0079] 1) Using the BeiDou coordinate system as a reference, the airspace is divided into multiple three-dimensional grid units to form the BeiDou three-dimensional grid space, and the airspace busyness calculation results are mapped to the corresponding three-dimensional grid units;

[0080] 2) Calculate heat values: Introduce an altitude layer attenuation parameter to quantify the weakening effect of the upper-level airspace on the ground take-off and landing field, and solve the problem of false high-altitude flow density caused by ignoring altitude in traditional two-dimensional heat maps; introduce a time weight parameter to distinguish heat maps of different time levels, balance real-time performance and computational efficiency, and meet the needs of different scenarios.

[0081] The thermal value is calculated using the following formula:

[0082] ;

[0083] in, Let be the thermal value of the three-dimensional mesh element j at time t. Let j be the service load index of the three-dimensional grid cell j at time t. The maximum service load index for all 3D mesh cells at time t. For height layer attenuation parameters, For time weighting parameters;

[0084] 3) Update minute-level heatmaps every minute, aggregate hourly heatmaps every hour, and store heatmap time series. Supports historical traffic backtracking and aircraft type ratio analysis for any airspace profile.

[0085] The above technical solution integrates multiple data sources to construct an airspace congestion calculation model, outputs key indicators in real time, and maps the results to generate multi-scale dynamic heat maps. It supports historical traffic backtracking and aircraft type ratio analysis, enabling managers to accurately, comprehensively, and dynamically grasp the real-time status and historical patterns of the airspace. This breaks through the limitations of traditional information and provides a strong basis for formulating scientific and reasonable low-altitude airspace management strategies and traffic light timing adjustment schemes, thereby improving the scientific nature and accuracy of decision-making.

[0086] III. Based on heatmap-linked meteorological data and airspace status information, establish a closed-loop decision-making chain optimization mechanism of "real-time perception—trend prediction—resource allocation," specifically including:

[0087] 1) Calculate the predicted service load index: Quantify the amplification effect of the rate of weather deterioration on airspace load by using the rate of change of meteorological risk values, thus addressing the problem of traditional prediction models neglecting dynamic meteorological changes; introduce Fourier series terms to fit historical flow periodic fluctuations, improving the dynamic adaptability of trend prediction; finally, construct a trend prediction model to calculate the predicted service load index:

[0088] ;

[0089] in, For the three-dimensional mesh element j at time... The predicted service load index Let be the rate of change of meteorological risk value of 3D grid cell j at time t. , , These represent the three-dimensional mesh element j at time t, Meteorological risk value, For the Fourier series terms, A m f m , These are all periodic flow fluctuation parameters, obtained by fitting historical flow data. m is the index of the periodic flow fluctuation component, and M is the total number of periodic flow fluctuation components. , , All are adjustable parameters;

[0090] 2) If the calculated predicted service load index is greater than the preset threshold, traffic light timing adjustment will be triggered.

[0091] IV. Adjust traffic light timings in real time according to the decision chain instructions, specifically including:

[0092] 1) Calculate the adjusted green light duration: Adjust the green light duration by using the predicted service load index of priority and non-priority directions to avoid the rigidity of traditional fixed timing and achieve precise matching of resource allocation with actual demand; introduce an adjustment coefficient to limit the change range of a single timing and prevent aircraft scheduling chaos caused by frequent switching of red and green lights due to sudden load changes.

[0093] The adjusted green light duration is calculated using the following formula:

[0094] ;

[0095] Among them, T green The adjusted green light duration is T0, which is the base green light duration. , These are the predicted service load indices for priority and non-priority directions, respectively. For adjustment coefficients;

[0096] 2) Update traffic light timings in real time based on the adjusted green light duration;

[0097] 3) Collect actual adjustment effects and feed them back to the trend prediction model for parameter optimization.

[0098] The above technical solution establishes a closed-loop decision-making chain optimization mechanism based on heat map-linked meteorological data and airspace status information, which is "real-time perception - trend prediction - resource allocation". This mechanism can not only perceive the airspace situation in real time, but also predict future trends, adjust traffic light timing in advance, optimize the take-off and landing sequence and rhythm of aircraft, realize the efficient use of airspace resources, reduce aircraft waiting time, and improve the overall operational efficiency of low-altitude mixed take-off and landing fields.

[0099] Furthermore, the aforementioned technical solution fully considers the dynamic changes of multiple factors such as meteorological data and airspace status, as well as the periodic fluctuations of historical traffic flow, and adjusts the timing of traffic lights in real time. When faced with complex and ever-changing weather conditions and sudden airspace situations, it can react quickly, flexibly adjust the rules for aircraft take-off and landing, avoid conflicts and dangerous situations caused by environmental changes, effectively ensure the safe operation of mixed take-off and landing fields for low-altitude aircraft, and reduce safety risks.

[0100] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A traffic light adjustment method for low-altitude aircraft hybrid landing sites based on meteorological data, characterized by: The method comprises the following steps: S1, fusing ADS-B trajectory data, radar data, weather data and airspace state information, constructing an airspace congestion calculation model, and outputting aircraft density, conflict hotspots and service load index in real time; S2, mapping the airspace congestion calculation results to the Beidou three-dimensional grid space to generate a multi-scale dynamic heat map, and supporting historical traffic backtracking and aircraft model proportion analysis of any airspace profile; S3, based on the heat map, associating weather data and airspace state information, establishing a closed-loop decision chain optimization mechanism of "real-time sensing-trend prediction-resource allocation", including: S31, calculating and predicting the service load index: quantifying the amplification effect of weather deterioration speed on airspace load through the change rate of weather risk value, solving the problem of ignoring weather dynamic changes in traditional prediction models; introducing Fourier series terms to fit the periodic fluctuations of historical traffic, improving the dynamic adaptability of trend prediction; finally, constructing a trend prediction model to calculate and predict the service load index: ； wherein, is the predicted service load index of the three-dimensional grid cell j at time , is the rate of change of the meteorological risk value of the three-dimensional grid cell j at time t, , , are the meteorological risk values of the three-dimensional grid cell j at time t, , is the Fourier series term, A m , f m , are periodic flow fluctuation parameters fitted by historical flow data, m is the serial number of the periodic flow fluctuation component, and M is the total number of periodic flow fluctuation components, , , are adjustment parameters; S32, if the calculated predicted service load index is greater than the preset threshold, triggering the red-green light timing adjustment; S4, adjusting the red-green light timing in real time according to the decision chain instructions.

2. The method of claim 1, wherein the method is a method of adjusting a traffic light of a low altitude aircraft hybrid landing strip based on weather data, the method comprising: In S1, the ADS-B trajectory data, radar data, weather data and airspace state information are fused to construct an airspace congestion calculation model, and aircraft density, conflict hotspots and service load index are output in real time, including: 1) Calculate the aircraft density: introduce grid radius and indicator function to discretize the aircraft distribution from continuous space to statistical quantity in three-dimensional grid cells, solve the density calculation error caused by scanning interval of radar data; 2) Detect conflict hotspots: introduce Sigmoid function to map aircraft spacing to conflict probability, avoid the abruptness of traditional threshold method, and better conform to the fuzziness of actual flight safety boundary; introduce aircraft model weight to quantify the conflict risk difference of different aircraft combinations, solve the conflict assessment deviation caused by inconsistent dynamic characteristics of take-off and landing fields; 3) Calculate the service load index: weight and aggregate aircraft density, conflict probability and weather risk, break through the single-dimensional limitation of traditional traffic statistics relying on density or conflict; Wherein, the ADS-B trajectory data includes longitude, latitude, height and speed, the radar data includes position and speed, the weather data includes wind speed, visibility and precipitation intensity, and the airspace state information includes control instructions and take-off and landing plan; The ADS-B trajectory data and radar data are spatio-temporally aligned, outliers are removed, and weather data is interpolated to the airspace according to three-dimensional grid cells.

3. The method of claim 2, wherein the method further comprises: determining a wind speed and a wind direction at the low altitude aircraft hybrid landing strip based on the weather data; and determining a wind speed and a wind direction at the low altitude aircraft hybrid landing strip based on the weather data. The aircraft density is calculated by the following formula: ； where, is the number of vehicles within a unit volume within three-dimensional grid cell j, is an indicator function that is 1 if the condition is met and 0 otherwise, d i,j is the distance between vehicle i and three-dimensional grid cell j, r j is the radius of three-dimensional grid cell j, n is the total number of vehicles, V j is the volume of three-dimensional grid cell j.

4. The method of claim 3, wherein the method is characterized by: The conflict hotspots are detected by the following formula: ； where P j is the conflict probability of the three-dimensional grid cell j, is the Sigmoid function, is the minimum safety distance between the aircraft k and the aircraft , , are safety distance threshold parameters, is the aircraft type pair weight between the aircraft type m k of the aircraft k and the aircraft type of the aircraft, The aircraft type pair weight between fixed-wing and fixed-wing is 1.5, the aircraft type pair weight between rotorcraft and rotorcraft is 0.8, and the aircraft type pair weight of mixed types is 1.

5. The method of claim 4, wherein the method further comprises: The service load index is calculated by the following formula: ； wherein, L j is the service load index of the three-dimensional grid unit j, R j is the meteorological risk value of the three-dimensional grid unit j, which is calculated by weighted summation of wind speed, visibility and precipitation intensity, R max is the historical extreme meteorological risk value, , , are all weight coefficients.

6. The method of claim 1, wherein the method is a method of adjusting a traffic light of a low altitude aircraft hybrid landing strip based on weather data, the method comprising: In S2, the airspace congestion calculation results are mapped to the Beidou three-dimensional grid space to generate a multi-scale dynamic heat map, and support historical traffic backtracking and aircraft model proportion analysis of any airspace profile, including: S21, divide the airspace into multiple three-dimensional grid cells based on the Beidou coordinate system to form a Beidou three-dimensional grid space, and map the airspace congestion calculation results to the corresponding three-dimensional grid cells; S22, calculate the thermal value: introduce the height layer attenuation parameter to quantify the influence of high-altitude airspace on the ground take-off and landing field and weaken the effect, solve the problem of false density of high-altitude traffic caused by ignoring height in traditional two-dimensional thermal map; Introducing time weight parameter to distinguish thermal map of different time level, balance real-time and calculation efficiency, meet different scene demand; S23, update the minute-level thermal map every minute, aggregate the hour-level thermal map every hour, and store the thermal map time sequence to support historical traffic backtracking and aircraft model proportion analysis of any airspace profile.

7. The method of claim 6, wherein the method further comprises: determining a weather condition of the low altitude aircraft hybrid landing strip; and adjusting the red light based on the weather condition. The thermal value is calculated by the following formula: ； wherein, is the thermodynamic value of the three-dimensional grid cell j at time t, is the service load index of the three-dimensional grid cell j at time t, is the maximum service load index of all three-dimensional grid cells at time t, is the altitude layer attenuation parameter, is the time weight parameter.

8. The method for adjusting the traffic light of the low altitude aircraft hybrid landing strip based on meteorological data according to claim 1, characterized in that: In S4, according to the decision chain instruction, the timing of the traffic light is adjusted in real time, including: S41, calculate the adjusted green light duration: adjust the green light duration through the predicted service load index of the priority direction and the non-priority direction, avoid the rigidity of traditional fixed timing, realize the precise matching of resource allocation and actual demand; Introducing adjustment coefficient to limit the change range of single timing, prevent the frequent switching of traffic light caused by load mutation and cause the confusion of aircraft scheduling; S42, update the timing of the traffic light in real time according to the adjusted green light duration; S43, collect the actual adjustment effect and feed back to the trend prediction model for parameter optimization.

9. The method of claim 8, wherein the method further comprises: determining a weather condition of the low altitude aircraft hybrid landing strip; and adjusting the red light based on the weather condition. The adjusted green light duration is calculated by the following formula: ； Wherein, T green is the adjusted green light duration, T0 is the basic green light duration, , are the predicted service load indexes of the priority direction and the non-priority direction, respectively, is the adjustment coefficient.

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