Traffic light adjusting method for low-altitude aircraft mixed take-off and landing field based on meteorological data

Abstract

The invention relates to low-altitude airspace management, in particular to a meteorological data-based traffic light adjustment method for a low-altitude aircraft hybrid take-off and landing field, which comprises the following steps of: fusing ADS-B (Automatic Dependent Surveillance-Broadcast) trajectory data, radar data, meteorological data and airspace state information, constructing an airspace busy degree calculation model, and outputting aircraft density, conflict hotspots and service load indexes in real time; mapping an airspace busy degree calculation result to a Beidou three-dimensional grid space, generating a multi-scale dynamic thermodynamic diagram, and supporting historical flow backtracking and model proportion analysis of any airspace profile; establishing a closed-loop decision chain optimization mechanism of'real-time perception-trend pre-judgment-resource allocation 'based on the thermodynamic diagram associated meteorological data and airspace state information; according to the decision chain instruction, traffic light timing is adjusted in real time; according to the technical scheme provided by the invention, the defect that accurate and real-time traffic light timing is difficult to carry out in a dynamic airspace load environment in the prior art can be effectively overcome.

Description

Technical Field

[0001] This invention relates to low-altitude airspace management, specifically to a method for adjusting traffic lights at mixed take-off and landing fields for low-altitude aircraft based on meteorological data. Background Technology

[0002] With the continuous opening of low-altitude airspace and the rapid increase in the number of low-altitude aircraft such as drones and general aviation aircraft, the density and complexity of low-altitude flight activities have significantly increased, placing higher demands on the operational efficiency and safety management of mixed take-off and landing fields. Traditional low-altitude traffic management mainly relies on manual scheduling and fixed signal timing, which is difficult to adapt to the real-time needs of dynamic airspace load environments. Especially under complex weather conditions (such as low visibility, strong convection, wind shear, etc.), problems such as aircraft take-off and landing conflicts and uneven allocation of airspace resources frequently occur, leading to flight delays, increased operational risks, and even accidents.

[0003] In existing technologies, signal control for mixed takeoff and landing fields of low-altitude aircraft has the following main limitations: 1) Data silos and perception lag: Airspace status monitoring usually relies independently on a single data source such as ADS-B (Automatic Dependent Surveillance-Broadcast), radar or weather stations, lacking the fusion analysis of multi-source heterogeneous data, resulting in one-sided airspace busyness assessment and inability to capture conflict hotspots and traffic changes in real time. 2) Insufficient visualization and traceability capabilities: Airspace congestion is usually presented in a two-dimensional planar heat map, lacking multi-scale dynamic mapping in a three-dimensional grid space. It is difficult to support historical traffic backtracking and aircraft type ratio analysis in complex terrain (such as mountainous areas, urban canyons, etc.) or three-dimensional intersecting airspaces, which limits the fine-grained formulation of scheduling strategies. 3) Broken decision-making 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-event risk warning. It fails to optimize traffic light timing and runway resource allocation in advance through trend prediction, resulting in control measures lagging behind risk evolution. 4) Mismatch between static timing and dynamic demand: The existing traffic light timing scheme mainly relies on manual scheduling and fixed signal timing, without taking into account changes in meteorological risks (such as the need to adjust take-off and landing intervals due to sudden winds) and periodic fluctuations in historical traffic flow, making it difficult to adapt to dynamic airspace load. Summary of the Invention

[0004] (a) Technical problems to be solved 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.

[0005] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: The traffic light adjustment method for mixed takeoff and landing fields for low-altitude aircraft based on meteorological data includes the following steps: 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. 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. 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"; S4. Adjust traffic light timings in real time according to the instructions of the decision chain.

[0006] 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: 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; 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. 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; 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. The ADS-B trajectory data and radar data were spatiotemporally aligned, outliers were removed, and meteorological data were interpolated into the airspace according to three-dimensional grid cells.

[0007] Preferably, the aircraft density is calculated using the following formula: ; 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.

[0008] Preferably, the following formula is used to detect conflict hotspots: ; 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.

[0009] Preferably, the service load index is calculated using the following formula: ; 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.

[0010] 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: 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. 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. 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.

[0011] Preferably, the thermal value is calculated using the following formula: ; 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.

[0012] 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: 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: ; 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; S32. If the calculated predicted service load index is greater than the preset threshold, then the traffic light timing adjustment will be triggered.

[0013] Preferably, in S4, the traffic light timing is adjusted in real time according to the decision chain instructions, including: 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. S42. Update the traffic light timing in real time based on the adjusted green light duration; S43. Collect actual adjustment effects and feed them back to the trend prediction model for parameter optimization.

[0014] Preferably, the adjusted green light duration is calculated using the following formula: ; 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.

[0015] (III) Beneficial Effects 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: 1) Accurately grasp the airspace situation to support scientific decision-making. 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. 2) Intelligent prediction and allocation to improve operational efficiency 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. 3) Adapt flexibly to dynamic changes and ensure operational safety 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

[0016] 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.

[0017] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0018] 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.

[0019] 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).

[0020] 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: 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; The density of an aircraft is calculated using the following formula: ; 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. 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. The following formula is used to detect conflict hotspots: ; 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. 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; The service load index is calculated using the following formula: ; 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; 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. The ADS-B trajectory data and radar data were spatiotemporally aligned, outliers were removed, and meteorological data were interpolated into the airspace according to three-dimensional grid cells.

[0021] 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: 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; 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. The thermal value is calculated using the following formula: ; 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; 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.

[0022] 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.

[0023] 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: 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: ; 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; 2) If the calculated predicted service load index is greater than the preset threshold, traffic light timing adjustment will be triggered.

[0024] IV. Adjust traffic light timings in real time according to the decision chain instructions, specifically including: 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. The adjusted green light duration is calculated using the following formula: ; 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; 2) Update traffic light timings in real time based on the adjusted green light duration; 3) Collect actual adjustment effects and feed them back to the trend prediction model for parameter optimization.

[0025] 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.

[0026] 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.

[0027] 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 method for adjusting traffic lights in mixed takeoff and landing fields for low-altitude aircraft based on meteorological data, characterized in that: Includes the following steps: 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. 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. 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"; S4. Adjust traffic light timings in real time according to the instructions of the decision chain.

2. The method for adjusting traffic lights in a mixed takeoff and landing field for low-altitude aircraft based on meteorological data according to claim 1, characterized in that: S1 integrates ADS-B trajectory data, radar data, meteorological data, and airspace status information to construct an airspace congestion calculation model, outputting real-time aircraft density, conflict hotspots, and service load indices, including: 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; 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. 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; 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. The ADS-B trajectory data and radar data were spatiotemporally aligned, outliers were removed, and meteorological data were interpolated into the airspace according to three-dimensional grid cells.

3. The traffic light adjustment method for a mixed takeoff and landing field for low-altitude aircraft based on meteorological data according to claim 2, characterized in that: The density of an aircraft is calculated using the following formula: ； 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.

4. The traffic light adjustment method for low-altitude mixed take-off and landing fields based on meteorological data according to claim 3, characterized in that: The following formula is used to detect conflict hotspots: ； 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.

5. The traffic light adjustment method for a mixed takeoff and landing field for low-altitude aircraft based on meteorological data according to claim 4, characterized in that: The service load index is calculated using the following formula: ； 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.

6. The method for adjusting traffic lights in a mixed takeoff and landing field for low-altitude aircraft based on meteorological data according to claim 1, characterized in that: S2 maps airspace congestion calculation results to the BeiDou 3D grid space, generating multi-scale dynamic heatmaps, and supports historical traffic backtracking and aircraft type ratio analysis for arbitrary airspace profiles, including: 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. 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. 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.

7. The method for adjusting traffic lights in a mixed takeoff and landing field for low-altitude aircraft based on meteorological data according to claim 6, characterized in that: The thermal value is calculated using the following formula: ； 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 mesh 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.

8. The method for adjusting traffic lights in a mixed takeoff and landing field for low-altitude aircraft based on meteorological data according to claim 1, characterized in that: S3 establishes a closed-loop decision-making chain optimization mechanism based on heatmap-linked meteorological data and airspace status information, encompassing "real-time perception—trend prediction—resource allocation," including: 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: ； 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; S32. If the calculated predicted service load index is greater than the preset threshold, then the traffic light timing adjustment will be triggered.

9. The method for adjusting traffic lights in a mixed takeoff and landing field for low-altitude aircraft based on meteorological data according to claim 1, characterized in that: S4 adjusts traffic light timings in real time based on decision chain instructions, including: 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. S42. Update the traffic light timing in real time based on the adjusted green light duration; S43. Collect the actual adjustment effects and feed them back to the trend prediction model for parameter optimization.

10. The traffic light adjustment method for a mixed takeoff and landing field for low-altitude aircraft based on meteorological data according to claim 9, characterized in that: The adjusted green light duration is calculated using the following formula: ； 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.

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