Aeronautical data analysis method and device based on big data and storage medium

A data analysis and big data technology, applied in the aviation field, can solve problems such as inaccuracy and inaccurate flights, and achieve the effects of reasonable and accurate flight adjustment, increased revenue, and improved reliability.

Inactive Publication Date: 2019-03-08
XIAMEN KUAISHANGTONG INFORMATION TECH CO LTD
View PDF3 Cites 7 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to overcome the inaccurate problem of using artificial judgment on aviation data experience in the prior art to adjust flights, etc., to propose a method, equipment and storage medium for aviation data analysis based on big data, and to provide the obtained data analysis results to Flight adjustments provide specific data references, and airlines make flight adjustments based on the results more reasonable and accurate, which can avoid inaccurate flight adjustments due to human experience judgments

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Aeronautical data analysis method and device based on big data and storage medium
  • Aeronautical data analysis method and device based on big data and storage medium
  • Aeronautical data analysis method and device based on big data and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0045] Embodiment 1 of the present invention provides a kind of aviation data analysis method based on big data, as attached figure 1 shown, including the following steps:

[0046] Step S1, constructing the flight system model;

[0047] The flight system model adopts a K-means clustering algorithm, and the K-means clustering algorithm is as follows:

[0048] 1) Randomly select K data from N data as the centroid, where N and K are both positive integers, and N≥K;

[0049] 2) measure the distance to each centroid for each remaining data, and classify it into the nearest centroid;

[0050] 3) Recalculate the centroid of each class that has been obtained;

[0051] 4) Iterate 2 to 3 steps until the new centroid is equal to the original centroid or less than the specified threshold, and the K-means clustering algorithm ends;

[0052] Step S2, obtaining the airline's route past raw data; the route past raw data includes one or more of the following: "passengerMeter" passenger kilom...

Embodiment 2

[0070] In order to further ensure the efficiency of data calculation, before entering the K-means clustering model, some processing is performed on the data. The main function of the processing is dimensionality reduction. Before the past data is imported into the flight system model, PCA is performed on the preprocessed airline's past data to reduce the dimension to two-dimensional variables. Specifically, the data is clustered and analyzed according to the route groupby, and the average value of each data is calculated, and then Z-Score normalization is performed, and PCA (Principal Component Analysis) is used to reduce the dimensionality into a two-dimensional variable, and enter the KMeans model. After the model algorithm, Get results for three flight categories. Wherein, the steps of the big data-based aviation data analysis method in this embodiment are similar to those in Embodiment 1, and will not be repeated here in this embodiment.

[0071] The flight system model i...

Embodiment 3

[0084] A device, the device includes a memory and a processor connected to the memory, a processing system that can run on the processor is stored in the memory, and the processing system is executed by the processor based on big data The steps of the aviation data analysis method.

[0085] Wherein, the steps of the big data-based aviation data analysis method in this embodiment are the same as those in Embodiment 1, and will not be repeated here in this embodiment.

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses an aeronautical data analysis method and device based on big data and a storage medium. The method comprises the following steps of constructing a flight system model, whereinthe flight system model adopts a K-means clustering algorithm; acquiring the original data on airline routes; preprocessing the route passing original data, wherein the preprocessing comprises importing the preprocessed airline passing data into the flight system model for training to obtain the flight K classification result. The airline past data is analyzed by the K-means clustering algorithm,the airline past data can be provide with concrete data reference for flight adjustment, the method of the invention is more reasonable, avoids the inaccurate problem of adjusting flight according toartificial experience judgment, improves resource utilization rate, and is beneficial to improve airline income.

Description

technical field [0001] The present invention relates to the field of aviation, in particular to a big data-based aviation data analysis method, device and storage medium. Background technique [0002] Generally, airline flights can be divided into three types: golden flights, public flights, and low-quality flights according to punctuality, occupancy rate, and return rate. As a commercial company, airlines hope to increase golden flights and reduce Low-quality flights to achieve the purpose of maximizing service and revenue. The current commonly used method is generally to adjust parameters such as flight time, frequency, and aircraft type based on human judgment based on experience, which is not accurate enough. Contents of the invention [0003] The purpose of the present invention is to overcome the inaccurate problem of using artificial judgment on aviation data experience in the prior art to adjust flights, etc., to propose a method, equipment and storage medium for ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06F16/28
Inventor 刘晓葳肖龙源蔡振华李稀敏谭玉坤
Owner XIAMEN KUAISHANGTONG INFORMATION TECH CO LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap