Capacity flow matching method, storage medium and related device

Abstract

The invention relates to the technical field of air traffic management, and discloses a capacitive flow matching method, a storage medium and a related device. According to the method, the static capacity of an airport is analyzed according to airport airspace basic data, original meteorological data are input into a meteorological prediction model, and attenuation influence indexes of various meteorological types in each time period in the future are predicted. And then calculating a comprehensive weather attenuation influence index of each time period in the future according to the attenuation influence index of each weather type of each time period, and calculating a predicted dynamic capacity of each time period in the future according to the comprehensive weather attenuation influence index and the static capacity of the airport. And finally, comparing the predicted dynamic capacity with the planned flow, and adjusting the sortie of the excess area, so that the adjusted and optimized planned flow in each time period does not exceed the corresponding predicted dynamic capacity, and forming a flow adjustment suggestion and a capacity flow matching analysis report. According to the method, the accuracy of airport capacity change prediction is improved by comprehensively considering the influence of various weather types.

Description

Technical Field

[0001] This invention relates to the field of air traffic management technology, specifically to a flow matching method, storage medium, and related devices. Background Technology

[0002] With the rapid growth in demand for civil aviation transportation, flight irregularities have become increasingly prominent, with weather-related factors accounting for approximately 50%. Severe convection, typhoons, low clouds, and low visibility are the main factors affecting airport operations. The International Civil Aviation Organization's (ICAO) concept of capacity-to-flow matching requires that flight flow demand be commensurate with capacity levels to avoid over-capacity operations.

[0003] Current technologies for airport capacity forecasting mostly use static capacity values, failing to fully consider the dynamic impact of complex weather conditions. Capacity flow matching analysis is largely based on macroscopic traffic statistics, resulting in coarse granularity that cannot meet the needs of refined management. While some technologies consider weather factors, they suffer from the following shortcomings: they only address a single weather type and do not integrate multi-source meteorological data; the capacity forecasting models lack stability and have not established a quantitative relationship between weather impact and capacity decay; and capacity flow matching lacks a dynamic capacity adjustment mechanism, making it difficult to cope with traffic surges / drops. Therefore, there is an urgent need for a technical solution that can integrate multi-source data, accurately predict dynamic capacity, and achieve refined capacity flow matching. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, the present invention aims to provide a capacity matching method that can comprehensively consider the impact of various weather types and accurately predict future capacity dynamics based on the quantitative relationship between weather impact and capacity decay, thereby avoiding airport over-capacity operation.

[0005] To solve the above problems, the technical solution adopted by the present invention is as follows: a current matching method, comprising the following steps: Acquire raw meteorological data, flight operation data, and basic airport airspace data, and perform standardized processing and integrity verification on the acquired data; Analyze the static capacity of the airport based on basic airspace data; Raw meteorological data is input into a meteorological forecasting model to predict the attenuation impact index of various meteorological types in future time periods. Calculate the comprehensive meteorological attenuation impact index for future periods based on the attenuation impact index of each meteorological type in each period; The predicted dynamic capacity for each future time period is calculated based on the comprehensive meteorological attenuation impact index and the airport's static capacity. Based on flight operation data, the planned traffic flow for each future time period is obtained, and the capacity flow matching index for each future time period is calculated based on the planned traffic flow and predicted dynamic capacity for each time period. Based on the capacity matching indicators for each future time period, the planned traffic for each future time period is adjusted according to the preset traffic recovery rules to ensure that the traffic for all future time periods does not exceed the corresponding predicted dynamic capacity, thus forming a traffic adjustment suggestion. A capacity and flow matching analysis report is generated based on capacity and flow matching indicators and flow adjustment suggestions.

[0006] Compared to existing technologies, the advantages of this invention are as follows: This method uses a meteorological forecasting model trained on historical meteorological data to predict the attenuation impact index of various meteorological types for different time periods in the future. Based on the comprehensive meteorological attenuation impact index of various meteorological types, the static capacity of the airport is corrected, and the predicted dynamic capacity under the predicted future meteorological environment is estimated. This allows for an accurate comparison of whether planned traffic exceeds the predicted dynamic capacity for the corresponding time period, providing recommendations for capacity matching and preventing airport over-capacity operation. Because the predicted dynamic capacity considers the impact of multiple meteorological types, the prediction of airport capacity changes is more accurate, thus providing more reliable support for the formulation of airport traffic management strategies.

[0007] In the above-mentioned capacity matching method, the step of inputting raw meteorological data into a meteorological prediction model to predict the attenuation impact index of various meteorological types in future time periods includes a meteorological prediction model comprising a parameter calculation model, a prediction model, and an attenuation index calculation model. The parameter calculation model is used to calculate meteorological diagnostic parameters based on the raw meteorological data. The prediction model is used to predict the meteorological level parameters of each meteorological type in future time periods based on the meteorological diagnostic parameters. The attenuation index calculation model is used to calculate the attenuation impact index of each meteorological type in each time period based on the mapping model between meteorological level and capacity attenuation index and the meteorological level parameters in future time periods.

[0008] The above-mentioned flow matching method includes the attenuation impact index, which includes thunderstorm index, rainfall index, strong wind index, low visibility index and low cloud index. The comprehensive meteorological attenuation impact index is the weighted sum of the attenuation impact indices of various meteorological types in the same period.

[0009] The above-mentioned flow matching method uses raw meteorological data including numerical weather prediction data, MDRS meteorological probability forecast data, and surface meteorological observation data.

[0010] A storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, it implements the above-described flow matching method.

[0011] An electronic device includes a memory and a processor, the memory and the processor being communicatively connected via a data bus, wherein when the processor calls and executes a computer program in the memory, the above-described capacity matching method is implemented.

[0012] A capacity matching system includes: an acquisition module for acquiring raw meteorological data, flight operation data, and airport airspace basic data, and performing standardization processing and integrity verification on the acquired data; a static capacity analysis module for analyzing the static capacity of the airport based on the airport airspace basic data; an attenuation impact prediction module for predicting the attenuation impact index of various meteorological types in future time periods using a meteorological prediction model and the raw meteorological data, and calculating the comprehensive meteorological attenuation impact index for future time periods based on the attenuation impact index of each meteorological type in each time period; a dynamic capacity prediction module for calculating the predicted dynamic capacity for future time periods based on the comprehensive meteorological attenuation impact index and the airport's static capacity; and a capacity matching analysis module for obtaining the planned flow for future time periods based on flight operation data, and generating a capacity matching analysis report based on the predicted dynamic capacity and planned flow for future time periods.

[0013] The aforementioned flow matching system also includes a data storage module, which is used to persistently store the standardized raw meteorological data and flight operation data, as well as the attenuation impact index and the flow matching analysis report.

[0014] The aforementioned capacity matching system includes a decay impact prediction module comprising a diagnostic parameter calculation module, a prediction module, and a decay index calculation module. The diagnostic parameter calculation module calculates meteorological diagnostic parameters based on the raw meteorological data. The prediction module predicts meteorological level parameters for each meteorological type in future time periods based on the meteorological diagnostic parameters. The decay index calculation module calculates the decay impact index for each meteorological type in each time period based on the mapping model between meteorological level and capacity decay index and the meteorological level parameters in future time periods. It then calculates a weighted sum of the decay impact indices for each future time period based on preset weights for each meteorological type to obtain the comprehensive decay impact index for each future time period.

[0015] The aforementioned capacity matching system includes a capacity matching analysis module comprising a planned flow extraction module, a capacity matching index calculation module, a flow recovery module, and an analysis module. The planned flow extraction module extracts planned flow for each future time period from the flight operation data. The capacity matching index calculation module calculates the capacity matching index for each future time period based on the predicted dynamic capacity and the planned flow for each time period. The flow recovery module adjusts the planned flow according to preset flow recovery rules based on the predicted channel capacity and planned flow for each future time period, ensuring that the flow for all future time periods does not exceed the corresponding predicted dynamic capacity, thus generating a flow adjustment suggestion. The analysis module generates the matching analysis report based on the capacity matching index and the flow adjustment suggestion.

[0016] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments. Attached Figure Description

[0017] Figure 1 This is a flowchart of the flow matching method according to an embodiment of the present invention.

[0018] Figure 2 This is a schematic block diagram of an electronic device according to an embodiment of the present invention.

[0019] Figure 3 This is a flowchart of the flow matching system according to an embodiment of the present invention. Detailed Implementation

[0020] The embodiments of the present invention are described in detail below, with reference to... Figure 1 The present invention provides a current matching method, comprising the following steps: Acquire raw meteorological data, flight operation data, and basic airport airspace data, and perform standardized processing and integrity verification on the acquired data; Analyze the static capacity of the airport based on basic airspace data; Raw meteorological data is input into a meteorological forecasting model to predict the attenuation impact index of various meteorological types in future time periods. Calculate the comprehensive meteorological attenuation impact index for future periods based on the attenuation impact index of each meteorological type in each period; The predicted dynamic capacity for each future time period is calculated based on the comprehensive meteorological attenuation impact index and the airport's static capacity. Based on flight operation data, the planned traffic flow for each future time period is obtained, and the capacity flow matching index for each future time period is calculated based on the planned traffic flow and predicted dynamic capacity for each time period. Based on the capacity matching indicators for each future time period, the planned traffic for each future time period is adjusted according to the preset traffic recovery rules to ensure that the traffic for all future time periods does not exceed the corresponding predicted dynamic capacity, thus forming a traffic adjustment suggestion. A capacity and flow matching analysis report is generated based on capacity and flow matching indicators and flow adjustment suggestions.

[0021] This method uses a meteorological forecasting model trained on historical meteorological data around the airport to predict the attenuation impact index of various weather types in future time periods. It then corrects the airport's static capacity based on the comprehensive attenuation impact index of various weather types, thereby estimating the airport's predicted dynamic capacity under the predicted future meteorological environment. This allows for a precise comparison of planned traffic flow with the predicted dynamic capacity for the corresponding time period, providing recommendations for capacity matching and preventing airport over-operation. Because the correction of the airport's static capacity considers the impact of various weather types, it can more accurately predict future airport capacity changes compared to considering only a single weather type, thus providing more reliable support for the formulation of airport traffic management strategies.

[0022] Understandably, the meteorological forecasting model is a machine learning model trained using training samples consisting of historical meteorological data and flight operation data from the airport's surrounding area over the past few years. Based on the raw meteorological data actually acquired from the airport, the model predicts changes in meteorological data for each time period within the next 24 hours. It then determines the meteorological level parameters for each meteorological type in the future based on the predicted meteorological data for each time period, such as thunderstorm probability, CAPE threshold, rainfall, wind speed, visibility, and cloud base height. Furthermore, it fits the mapping relationship between various meteorological level parameters and airport capacity decay based on historical meteorological data and corresponding flight operation data, obtaining a mapping model between meteorological level and capacity decay index. Therefore, based on the predicted meteorological level parameters for each time period within the next 24 hours, combined with the mapping model, the impact index of the meteorological type on airport capacity decay for each future time period can be calculated.

[0023] Specifically, weather forecasting models include parameter calculation models, forecasting models, and attenuation index calculation models. The parameter calculation model is used to calculate meteorological diagnostic parameters based on raw meteorological data to improve the forecasting accuracy of the forecasting model. These meteorological diagnostic parameters include wind direction and speed, temperature, humidity, air pressure, southwest polar current index, warm advection distance, temperature advection index, isobaric field index, height inversion layer index, relative vorticity, relative divergence, convective effective potential energy (CAPE), K-index (KI), Schaeffler index (SI), lifting index (LI), and severe weather threat index (SWEAT). It is understood that the parameter calculation model can be constructed from empirical functions or models of the above parameters. For example, convective effective potential energy can be obtained by generating a parcel profile using the parcel_profile function and then combining it with the parcel profile line. The specific calculation methods are common knowledge in this field and will not be elaborated here.

[0024] The predictive model is used to predict meteorological grade parameters for various weather types over a future period, such as 24 hours, based on meteorological diagnostic parameters. In this embodiment, weather types include thunderstorms, rainfall, strong winds, haze, and low clouds; correspondingly, meteorological grade parameters include thunderstorm probability, CAPE value, rainfall, wind speed, visibility, and cloud base height. The predictive model can be trained using real-time weather records corresponding to meteorological data from the airport over the past few years, or it can utilize large AI models published by meteorological bureaus, such as the "Fengqing" short-term forecast model published by the China Meteorological Administration.

[0025] The attenuation index calculation model is used to calculate the attenuation impact index of each weather type based on the mapping model between weather levels and capacity attenuation index and the predicted weather level parameters for future time periods. The mapping model between weather levels and capacity attenuation index is obtained through correlation analysis of historical weather data and flight operation data of the airport in recent years. The impact of weather level parameters of each weather type on airport capacity is quantified through regression analysis, global sensitivity analysis, or random forest, obtaining the mapping relationship between weather level parameters of various weather types and their attenuation impact index on airport capacity, and obtaining the thunderstorm index, rainfall index, strong wind index, low visibility index, and low cloud index. In this embodiment, the comprehensive attenuation impact index is the weighted sum of the thunderstorm index, rainfall index, strong wind index, low visibility index, and low cloud index. The weights of each weather type are set based on experience obtained from the analysis of historical weather data. In this embodiment, the impact index = thunderstorm index × 0.3 + rainfall index × 0.2 + strong wind index × 0.2 + low visibility index × 0.2 + low cloud index × 0.1.

[0026] In this embodiment, to further improve the accuracy of capacity change prediction, the system integrates raw meteorological data from multiple sources, including numerical weather prediction data, MDRS probabilistic weather forecast data, and surface meteorological observation data. For duplicate items in these data, the confidence level of each duplicate item is set based on experience to control the influence of data from different sources on the prediction results, thereby further improving the accuracy of dynamic capacity prediction. In this embodiment, the numerical weather prediction data includes Global Forecast System (GFS) data and European Centre for Medium-Range Weather Forecasts (ECMWF) data. In this embodiment, the preprocessing process includes converting the acquired data into a preset standard format for storage and interpolating missing values ​​using an interpolation algorithm to ensure that data from different sources are aligned in time. After acquiring the data, the received raw meteorological data is subjected to MD5 verification. If the MD5 verification fails, the data is considered incomplete and invalid, and the data is deleted. Simultaneously, the system requests data again from the corresponding server.

[0027] In this embodiment, the capacity matching index includes whether the planned flow in each future time period exceeds the corresponding predicted dynamic capacity (excess situation), the margin value of the predicted dynamic capacity exceeding the planned flow, and the flow change rate of the planned flow before and after the adjustment of adjacent time periods. The flow recovery rule is as follows: for time periods with excess flow, based on the amount of excess, the flights with later planned departure times are evenly distributed to the next three consecutive time periods, and processed one time period at a time, until the planned flow in all time periods within the modified next 24 hours does not exceed the corresponding predicted dynamic capacity. According to the aforementioned traffic recovery rules, the system adjusts the planned traffic for each time period based on the overcapacity situation in future time periods, obtains traffic adjustment suggestions, and displays the planned traffic and predicted dynamic capacity before and after optimization for each time period in the capacity matching analysis report in the form of bar charts. The original planned traffic exceeding the corresponding predicted dynamic capacity is marked on the chart with prominent colors such as red. Time periods with a traffic change rate greater than 2.5 relative to the previous time period are marked as surges, and time periods with a traffic change rate less than -2.5 relative to the previous time period are marked as drops, in order to further facilitate users' decision-making in airport traffic management. The traffic change rate is calculated as (current time period traffic - previous time period traffic) / previous time period traffic.

[0028] In some embodiments, after generating the capacity-flow matching analysis report, the meteorological level parameters of various meteorological types for each time period, the predicted dynamic capacity for each time period, and the capacity-flow matching analysis report can be stored in the database for persistent storage to facilitate subsequent backtracking and querying.

[0029] In some embodiments, to further improve the accuracy of dynamic capacity prediction for future periods, a clustering algorithm can be used to cluster meteorological data from the past three years into 5-7 typical meteorological scenarios. The mapping relationship between different meteorological scenarios and capacity attenuation coefficients is then fitted based on corresponding flight operation data to obtain a scenario-attenuation coefficient mapping model. During the prediction process, the similarity between the predicted meteorological parameters for each period and various meteorological scenarios is calculated, and the scenario capacity correction coefficient is calculated using similarity as a weight. Finally, the static capacity is corrected using the scenario capacity correction coefficient, or the predicted dynamic capacity is further corrected to obtain the final predicted dynamic capacity. Specifically, DBSCAN clustering analysis can be used to cluster historical meteorological data into 5 typical meteorological scenarios: clear weather (attenuation coefficient 0.0); thunderstorms (attenuation coefficient 0.2); low cloud / fog weather (attenuation coefficient 0.3); mixed weather (attenuation coefficient 0.4); and severe convective weather (attenuation coefficient 0.6). When the similarity between the predicted meteorological data for a certain period and thunderstorm weather is 0.7, and the similarity between the predicted data and low cloud and fog weather is 0.3, then the scene capacity correction coefficient = 0.2 × 0.7 + 0.3 × 0.3 = 0.23. The predicted dynamic capacity can be calculated directly through the scene capacity correction coefficient, or the predicted dynamic capacity can be further corrected to further improve the accuracy of capacity prediction.

[0030] In some embodiments, in order to further improve the accuracy of capacity change prediction, the system can also record the actual flow rate of each time period into the database, establish a prediction accuracy evaluation model based on the actual flow rate and the corresponding predicted dynamic capacity, evaluate the accuracy of the predicted dynamic capacity of each time period, and adjust the weight of the influence attenuation index of each weather type according to the accuracy of the predicted dynamic capacity through a weight adjustment algorithm, so that the optimized predicted dynamic capacity can better match reality.

[0031] Based on the same inventive concept, embodiments of the present invention also provide a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the above-described flow matching method.

[0032] In some possible implementations, various aspects of the current matching method provided by the present invention can also be implemented in the form of a program product, which includes program code that, when the program product is run on a device, causes the control device to perform the steps in the current matching method according to the various exemplary embodiments of the present application described above.

[0033] By designing and programming the processor, the code corresponding to the current matching method described in the foregoing embodiments can be embedded into the chip, enabling the chip to execute the steps of the current matching method shown in the embodiments of the present invention during operation. How to design and program the processor is a technique well-known to those skilled in the art and will not be elaborated upon here.

[0034] Based on the same inventive concept, referring to Figure 2 The present invention also provides an electronic device for implementing the above-described current matching method, including a memory and a controller. The memory and the controller are connected via a data bus. The controller can implement the above-described current matching method by calling and executing a computer program in the memory.

[0035] In one possible design, the processor may include one or more processing units. The processor and memory may be implemented on the same chip or on separate chips. The processor may be a general-purpose processor, such as a central processing unit (CPU), digital signal processor, application-specific integrated circuit, field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the current matching method disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules in the processor.

[0036] Memory, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. Memory can include at least one type of storage medium, such as flash memory, hard disk, multimedia card, card-type memory, random access memory (RAM), static random access memory (SRAM), programmable read-only memory (PROM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), magnetic memory, magnetic disk, optical disk, etc. Memory is any other medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer, but is not limited to this. The memory in the embodiments of this application can also be a circuit or any other device capable of implementing storage functions for storing program instructions and / or data.

[0037] Reference Figure 3 Based on the same inventive concept, embodiments of the present invention also provide a capacity flow matching system, including an acquisition module, a static capacity analysis module, an attenuation impact prediction module, a dynamic capacity prediction module, and a capacity flow matching analysis module. The acquisition module acquires raw meteorological data, flight operation data, and airport airspace basic data, and performs standardization processing and integrity verification on the acquired data. The static capacity analysis module analyzes the static capacity of the airport based on the airport airspace basic data. The attenuation impact prediction module predicts the attenuation impact index of various meteorological types for future periods using meteorological prediction models and raw meteorological data, and calculates the comprehensive meteorological attenuation impact index for future periods based on the attenuation impact index of each meteorological type for each period. The dynamic capacity prediction module calculates the predicted dynamic capacity for future periods based on the comprehensive meteorological attenuation impact index and the airport's static capacity. The capacity flow matching analysis module obtains the planned flow for future periods based on flight operation data, and generates a capacity flow matching analysis report based on the predicted dynamic capacity and planned flow for future periods.

[0038] Understandably, referring to Figure 3 In some embodiments, a data storage module is also included, which is used to persistently store the standardized raw meteorological data and flight operation data, as well as the attenuation impact index and capacity matching analysis report.

[0039] In some embodiments, the attenuation impact prediction module includes a diagnostic parameter calculation module, a prediction module, and an attenuation index calculation module. The diagnostic parameter calculation module calculates meteorological diagnostic parameters based on raw meteorological data. The prediction module predicts the meteorological grade parameters for each meteorological type in future time periods based on the meteorological diagnostic parameters. The attenuation index calculation module calculates the attenuation impact index for each meteorological type in each time period based on the mapping model between meteorological grades and capacity attenuation indices and the meteorological grade parameters for each meteorological type in future time periods. It then calculates a weighted sum of the attenuation impact indices for each meteorological type in future time periods based on preset weights for each meteorological type, obtaining the comprehensive attenuation impact index for each future time period.

[0040] In some embodiments, the capacity-flow matching analysis module includes a planned flow extraction module, a capacity-flow matching index calculation module, a flow recovery module, and an analysis module. The planned flow extraction module is used to statistically analyze the arrival and departure volumes for each hour in flight operation data, thereby calculating the planned flow for each time period. The capacity-flow matching index calculation module is used to calculate the capacity-flow matching index for future time periods based on the predicted dynamic capacity and planned flow for each time period. The flow recovery module is used to adjust and optimize the planned flow according to preset flow recovery rules based on the predicted channel capacity and planned flow for future time periods, ensuring that the flow for all future time periods does not exceed the corresponding predicted dynamic capacity, and generating flow adjustment recommendations. The analysis module is used to generate a matching analysis report based on the capacity-flow matching index and flow adjustment recommendations.

[0041] In some embodiments, the attenuation impact prediction module is further configured to calculate the similarity between the predicted meteorological data for each future time period and typical meteorological scenarios obtained by clustering historical meteorological data of the airport, and to calculate the weighted sum of the attenuation coefficients of various scenarios using the similarity as a weight, thereby obtaining the scenario capacity correction coefficient. Finally, the predicted dynamic capacity is calculated using the scenario capacity correction coefficient and the airport's static capacity, or the predicted dynamic capacity obtained by calculating the attenuation impact index of various meteorological types is further corrected to obtain the final predicted dynamic capacity.

[0042] In some embodiments, the system further includes a weight correction module. The data storage module is also used to record the actual flow rates for each time period into the database, and to correct the weights of the impact attenuation index of each weather type based on the difference between the actual flow rates and the corresponding predicted dynamic capacity, so that the optimized predicted dynamic capacity value can better reflect the actual situation.

[0043] It should be noted that in the description of this invention, "several" means one or more, "more than" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. If "first" or "second" is mentioned, it is only for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0044] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0045] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0046] These computer program instructions can also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0047] In the description of this invention, unless otherwise explicitly defined, terms such as "set up," "install," and "connect" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0048] The above embodiments are merely preferred embodiments of the present invention and should not be construed as limiting the scope of protection of the present invention. Any non-substantial changes and substitutions made by those skilled in the art based on the present invention shall fall within the scope of protection claimed by the present invention.

Claims

1. A current matching method, characterized in that, Includes the following steps: Acquire raw meteorological data, flight operation data, and basic airport airspace data, and perform standardized processing and integrity verification on the acquired data; Analyze the static capacity of the airport based on basic airspace data; Raw meteorological data is input into a meteorological forecasting model to predict the attenuation impact index of various meteorological types in future time periods. Calculate the comprehensive meteorological attenuation impact index for future periods based on the attenuation impact index of each meteorological type in each period; The predicted dynamic capacity for each future time period is calculated based on the comprehensive meteorological attenuation impact index and the airport's static capacity. Based on flight operation data, the planned traffic flow for each future time period is obtained, and the capacity flow matching index for each future time period is calculated based on the planned traffic flow and predicted dynamic capacity for each time period. Based on the capacity matching indicators for each future time period, the planned traffic for each future time period is adjusted according to the preset traffic recovery rules to ensure that the traffic for all future time periods does not exceed the corresponding predicted dynamic capacity, thus forming a traffic adjustment suggestion. A capacity and flow matching analysis report is generated based on capacity and flow matching indicators and flow adjustment suggestions.

2. The capacity matching method according to claim 1, characterized in that, In the step of inputting raw meteorological data into a meteorological prediction model to predict the attenuation impact index of various meteorological types in future time periods, the meteorological prediction model includes a parameter calculation model, a prediction model, and an attenuation index calculation model. The parameter calculation model is used to calculate meteorological diagnostic parameters based on the raw meteorological data. The prediction model is used to predict the meteorological level parameters of each meteorological type in future time periods based on the meteorological diagnostic parameters. The attenuation index calculation model is used to calculate the attenuation impact index of each meteorological type in each time period based on the mapping model between meteorological level and capacity attenuation index and the meteorological level parameters in future time periods.

3. The current matching method according to claim 2, characterized in that, The attenuation impact index includes thunderstorm index, rainfall index, strong wind index, low visibility index, and low cloud index. The comprehensive meteorological attenuation impact index is the weighted sum of the attenuation impact indices of each meteorological type in the same period.

4. The current matching method according to claim 1, characterized in that, The raw meteorological data includes numerical weather prediction data, MDRS meteorological probability forecast data, and surface meteorological observation data.

5. A storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the flow matching method according to any one of claims 1 to 4.

6. An electronic device, characterized in that, It includes a memory and a processor, the memory and the processor being communicatively connected via a data bus, and when the processor calls and executes a computer program in the memory, it implements the current matching method according to any one of claims 1 to 4.

7. A current matching system, characterized in that, include: The acquisition module is used to acquire raw meteorological data, flight operation data, and basic airport airspace data, and to perform standardization processing and integrity verification on the acquired data; The static capacity analysis module is used to analyze the static capacity of the airport based on the airport airspace basic data. The attenuation impact prediction module is used to predict the attenuation impact index of various meteorological types in future time periods through meteorological prediction models and the raw meteorological data, and to calculate the comprehensive meteorological attenuation impact index for future time periods based on the attenuation impact index of each meteorological type in each time period. The dynamic capacity prediction module is used to calculate the predicted dynamic capacity for each future time period based on the comprehensive meteorological attenuation impact index and the airport's static capacity. The capacity matching analysis module is used to obtain the planned traffic flow for each future time period based on flight operation data, and to generate a capacity matching analysis report based on the predicted dynamic capacity and planned traffic flow for each future time period.

8. The current matching system according to claim 7, characterized in that, It also includes a data storage module, which is used to persistently store the standardized raw meteorological data and the flight operation data, as well as the attenuation impact index and the capacity flow matching analysis report.

9. The current matching system according to claim 7, characterized in that, The attenuation impact prediction module includes a diagnostic parameter calculation module, a prediction module, and an attenuation index calculation module. The diagnostic parameter calculation module is used to calculate meteorological diagnostic parameters based on the raw meteorological data. The prediction module is used to predict the meteorological level parameters of each meteorological type in future time periods based on the meteorological diagnostic parameters. The attenuation index calculation module is used to calculate the attenuation impact index of each meteorological type in each time period based on the mapping model between meteorological level and capacity attenuation index and the meteorological level parameters in future time periods. Based on the preset weights of each meteorological type, the module calculates the weighted sum of the attenuation impact indices in future time periods to obtain the comprehensive attenuation impact index for future time periods.

10. The current matching system according to claim 7, characterized in that, The capacity matching analysis module includes a planned flow extraction module, a capacity matching index calculation module, a flow recovery module, and an analysis module. The planned flow extraction module is used to extract planned flow for each future time period from the flight operation data. The capacity matching index calculation module is used to calculate the capacity matching index for each future time period based on the predicted dynamic capacity and the planned flow for each time period. The flow recovery module is used to adjust the planned flow according to the predicted channel capacity and planned flow for each future time period, according to preset flow recovery rules, so that the flow for all future time periods does not exceed the corresponding predicted dynamic capacity, and forms a flow adjustment suggestion. The analysis module is used to generate the matching analysis report based on the capacity matching index and the flow adjustment suggestion.

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