Universal method and system for flow-oriented calculation of sector flow in aviation industry

Abstract

The invention relates to the technical field of sector flow calculation, in particular to a universal method and system for flow calculation of sector flow in the aviation industry, and the method comprises the following steps: data collection, data preprocessing, flight path point space attribution calculation, sector flow statistics, and data storage and display. The method and the system have the advantages that the method and the system are high in real-time performance, millisecond-level processing of track point data is realized by the aid of streaming computing technologies, and data processing time intervals are greatly shortened; high efficiency: through distributed architecture and memory calculation, the processing efficiency of massive track point data is improved, and a high-concurrency scene can be coped with; the accuracy is good; an advanced geographic space algorithm is adopted, so that accurate space affiliation calculation of track points and high-precision statistics of sector flow are realized; and the system architecture is flexible, can be expanded according to service requirements, and is suitable for application scenarios with different scales and complexities.

Description

Technical Field

[0001] The present invention relates to the technical field of sector flow calculation, and in particular to a general method and system for stream calculation of sector flow in the aviation industry. Background Art

[0002] Air traffic management (ATM) is one of the key areas to ensure air traffic safety and efficiency. With the continuous growth of global air transport demand and the increasing density of air traffic, airspace management faces huge challenges. In this context, effectively monitoring and managing the flow of aircraft in various airspace sectors has become an important task to ensure flight safety and optimize airspace resource allocation.

[0003] Traditional sector traffic monitoring methods mainly rely on timed sampling and offline processing, and their core processes include track point data collection, data storage, subsequent analysis and manual processing. Although these methods can meet basic needs to a certain extent, their limitations are also very obvious:

[0004] Poor timeliness: Traditional methods usually adopt batch processing mode to perform intermittent sampling and offline analysis of track point data, which leads to time lag in traffic statistics and cannot reflect the dynamic situation of air traffic in real time.

[0005] Low computational efficiency: Faced with an increasing number of flights and complex airspace structures, the processing efficiency of traditional methods is difficult to meet actual needs, especially during peak flight periods, when data processing bottlenecks are particularly prominent.

[0006] Limited accuracy: Due to reliance on fixed sampling periods and offline analysis, traditional methods have deficiencies in the spatial attribution calculation of track points and the accuracy of traffic statistics, making it difficult to provide high-precision real-time data support. Summary of the invention

[0007] The purpose of the present invention is to provide a general method and system for streaming sector traffic in the aviation industry, aiming to achieve real-time processing of track point data and accurate statistics of sector traffic. By introducing advanced streaming computing framework and efficient geospatial algorithm, this method provides an efficient, real-time and accurate solution for air traffic management, which helps to improve aviation safety and airspace resource utilization efficiency.

[0008] To achieve the above object, the present invention provides the following technical solution: a general method for stream computing sector traffic in the aviation industry, the method comprising the following steps:

[0009] Data collection: real-time collection of track point data from multiple data sources;

[0010] Data preprocessing: format conversion and data cleaning of the collected track point data;

[0011] Track point spatial attribution calculation: Use geographic information system (GIS) technology to map each track point to the corresponding aviation sector;

[0012] Sector traffic statistics: real-time statistics of traffic information for each aviation sector, including the number of flights and flight altitude distribution indicators;

[0013] Data storage and display: The calculation results are stored in the database, and the sector traffic situation is dynamically displayed through visualization tools.

[0014] Preferably, the data preprocessing step includes:

[0015] Format conversion: convert track point data from different sources into a unified format;

[0016] Data cleaning: Remove duplicate, erroneous or missing data.

[0017] Preferably, the track point spatial attribution calculation step includes:

[0018] Use Geographic Information System (GIS) technology to calculate the spatial position of track points;

[0019] The spatial index algorithm is used to map the track points to the corresponding aviation sectors.

[0020] Preferably, the data storage step includes:

[0021] Store the computation results in an efficient distributed database.

[0022] Preferably, the data display step includes:

[0023] Use visualization tools to dynamically display sector traffic information and support multi-dimensional query and analysis.

[0024] A general system for streaming sector traffic in the aviation industry, the system comprising:

[0025] Data collection module: collect track point data from different data sources in real time;

[0026] Data preprocessing module: Apache Flink reads the track point data in MQ in real time and preprocesses the data;

[0027] Streaming calculation module: calculates the sector range that the track point data falls into;

[0028] Storage and display module: provides a database for the computing module to write the latest calculation results in real time.

[0029] Preferably, the data collection module collects track point data in real time from different data sources, and the data source includes an Oracle / MySQL relational database, wherein the relational database needs to enable binlog for the data collection tool to collect to MQ; the real-time location information of the hardware is sent to MQ.

[0030] Preferably, in the data preprocessing module, Apache Flink reads the track point data in MQ in real time, performs format conversion, denoising and data cleaning on the data to ensure data quality; the processed data is sent to the downstream Kafka topic.

[0031] Preferably, in the streaming computing module, after the Apache Flink task is started, the sector data is first loaded, the track point data after consumption processing by Kafka is connected, the sector range in which the track point data falls is calculated, the sector flow is increased by 1, the result data is cached in the memory, and a timer is set in the Apache Flink operator to write the cached result data to the external database at regular intervals.

[0032] Preferably, the storage and display module provides a database for the calculation module to write the latest calculation results in real time. The BI system or large-screen system provides data application and display, reads the calculation results from the database, displays them on the front-end page, and automatically refreshes the data at regular intervals.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] The general method and system for streaming calculation of sector traffic in the aviation industry proposed in the present invention have strong real-time performance: using streaming computing technology, millisecond-level processing of track point data is achieved, which greatly shortens the time interval for data processing; high efficiency: through distributed architecture and memory computing, the processing efficiency of massive track point data is improved, and it can cope with high-concurrency scenarios; good accuracy: using advanced geospatial algorithms, accurate spatial attribution calculation of track points and high-precision statistics of sector traffic are achieved; strong scalability: the system architecture is flexible and can be expanded according to business needs to adapt to application scenarios of different scales and complexities. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0036] In order to make the purpose and technical solution of the present invention clearly and completely described, and the advantages more clearly understood, the embodiments of the present invention are further described in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are part of the embodiments of the present invention, rather than all of the embodiments, and are only used to explain the embodiments of the present invention, and are not used to limit the embodiments of the present invention. All other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] For example, see Figure 1 The present invention provides a technical solution: a general method for streaming sector traffic in the aviation industry, the method comprising the following steps:

[0038] Data collection: real-time collection of track point data from multiple data sources;

[0039] Data preprocessing: format conversion and data cleaning of the collected track point data; including:

[0040] Format conversion: convert track point data from different sources into a unified format;

[0041] Data cleaning: Remove duplicate, erroneous or missing data.

[0042] Track point spatial attribution calculation: Use geographic information system (GIS) technology to map each track point to the corresponding aviation sector; including:

[0043] Use Geographic Information System (GIS) technology to calculate the spatial position of track points;

[0044] The spatial index algorithm is used to map the track points to the corresponding aviation sectors.

[0045] Sector traffic statistics: real-time statistics of traffic information for each aviation sector, including the number of flights and flight altitude distribution indicators;

[0046] Data storage and display: The calculation results are stored in the database, and the sector traffic situation is dynamically displayed through visualization tools; the data storage step includes: storing the calculation results in an efficient distributed database; the display step includes: using visualization tools to dynamically display sector traffic information and support multi-dimensional query and analysis.

[0047] Embodiment 2, based on embodiment 1, proposes a general system for stream computing sector traffic in the aviation industry, the system comprising:

[0048] Data collection module: collects track point data in real time from different data sources, including relational databases such as Oracle / MySQL. Binlog needs to be enabled in the relational database for data collection tools to collect data to MQ; real-time hardware location information sent to MQ, etc.

[0049] Data preprocessing module: Apache Flink reads the track point data in MQ in real time, converts the data format (such as longitude and latitude coordinate system conversion), performs denoising and data cleaning to ensure data quality. The processed data is sent to the downstream Kafka topic.

[0050] Stream computing module: After the Apache Flink task is started, it first loads the sector data, connects to the track point data after consumption and processing by Kafka, calculates which sector range the track point data falls into, adds 1 to the sector flow, and caches the result data into memory. A timer is set in the Apache Flink operator to write the cached result data to the external database at a regular interval.

[0051] Storage and display module: This module provides a database for the calculation module to write the latest calculation results in real time. The BI system or large screen system provides data application and display, reads the calculation results from the database, displays them on the front-end page, and automatically refreshes the data at regular intervals.

[0052] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A general method for streaming sector traffic in the aviation industry, characterized by: The method comprises the following steps: Data collection: real-time collection of track point data from multiple data sources; Data preprocessing: format conversion and data cleaning of the collected track point data; Track point spatial attribution calculation: Use geographic information system (GIS) technology to map each track point to the corresponding aviation sector; Sector traffic statistics: real-time statistics of traffic information for each aviation sector, including the number of flights and flight altitude distribution indicators; Data storage and display: The calculation results are stored in the database, and the sector traffic situation is dynamically displayed through visualization tools.

2. According to claim 1, a general method for streaming sector traffic in the aviation industry is characterized by: The data preprocessing steps include: Format conversion: convert track point data from different sources into a unified format; Data cleaning: Remove duplicate, erroneous or missing data.

3. According to claim 1, a general method for streaming sector traffic in the aviation industry is characterized by: The spatial attribution calculation steps of track points include: Use Geographic Information System (GIS) technology to calculate the spatial position of track points; The spatial index algorithm is used to map the track points to the corresponding aviation sectors.

4. According to claim 1, a general method for streaming sector traffic in the aviation industry is characterized by: The data storage steps include: Store the computation results in an efficient distributed database.

5. According to claim 1, a general method for streaming sector traffic in the aviation industry is characterized by: The data display steps include: Use visualization tools to dynamically display sector traffic information and support multi-dimensional query and analysis.

6. A general system for calculating sector traffic in the aviation industry by streaming according to the general method for calculating sector traffic in the aviation industry by streaming according to any one of claims 1 to 5, characterized in that: The system comprises: Data collection module: collect track point data from different data sources in real time; Data preprocessing module: Apache Flink reads the track point data in MQ in real time and preprocesses the data; Streaming calculation module: calculates the sector range that the track point data falls into; Storage and display module: provides a database for the computing module to write the latest calculation results in real time.

7. A universal system for streaming sector traffic in the aviation industry according to claim 6, characterized in that: The data collection module collects track point data in real time from different data sources, including Oracle / MySQL relational databases. Binlog needs to be enabled in the relational database for data collection tools to collect data to MQ. Real-time hardware location information sent to MQ.

8. A universal system for streaming sector traffic in the aviation industry according to claim 6, characterized in that: In the data preprocessing module, Apache Flink reads the track point data in MQ in real time, converts the data format, performs denoising and data cleaning to ensure data quality; the processed data is sent to the downstream Kafka topic.

9. A universal system for streaming sector traffic in the aviation industry according to claim 6, characterized in that: In the streaming computing module, after the Apache Flink task is started, the sector data is first loaded, the track point data after consumption and processing by Kafka is connected, the sector range in which the track point data falls is calculated, the sector flow is increased by 1, the result data is cached in the memory, and a timer is set in the Apache Flink operator to write the cached result data to the external database at a regular interval.

10. A universal system for streaming sector traffic in the aviation industry according to claim 6, characterized in that: The storage and display module provides a database for the calculation module to write the latest calculation results in real time. The BI system or large-screen system provides data application and display, reads the calculation results from the database, displays them on the front-end page, and automatically refreshes the data at a fixed time.

Images