Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Genetic algorithm back propagation (GABP) neural network based controller workload prediction method and system

A BP neural network and workload technology, applied in the aviation field, can solve problems such as low prediction reliability, insufficient comprehensiveness, and data limitations

Inactive Publication Date: 2015-12-16
CHENGDU CIVIL AVIATION AIR TRAFFIC CONTROL SCI & TECH +1
View PDF5 Cites 10 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] However, the data referenced by this method is relatively limited, the index dimension is single, not comprehensive and comprehensive, there are certain limitations, and the prediction reliability is not high

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
  • Genetic algorithm back propagation (GABP) neural network based controller workload prediction method and system
  • Genetic algorithm back propagation (GABP) neural network based controller workload prediction method and system
  • Genetic algorithm back propagation (GABP) neural network based controller workload prediction method and system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0088] figure 1 It is a flow chart of a method for forecasting a controller's workload in an embodiment of the present invention. As shown in the figure, the method includes steps:

[0089] S1: Determine the topological structure of the BP neural network, collect samples of air traffic flow situation indicators at different time periods, and establish a sample set;

[0090] S2: Use the genetic algorithm to optimize the weight and threshold of the BP neural network, perform network training, and output the optimized BP neural network;

[0091]S3: According to the input real-time data of the traffic flow situation index of the sector, the sector performance comprehensive index is predicted through the BP neural network optimized in step S2.

[0092] Because the present invention adopts the quantitative analysis method, through the uninterrupted detection and calculation and analysis of massive operating data, the accurate air traffic flow situation index data in the future peri...

Embodiment 2

[0173] Such as Figure 4 Shown is a schematic diagram of a controller workload prediction system according to Embodiment 2 of the present invention. In combination with Embodiment 1, it can be known that the controller workload prediction system 100 includes:

[0174] BP neural network module 1: used to determine the topological structure of the BP neural network, optimize the weight and threshold of the BP neural network by genetic algorithm, and train the BP neural network according to the sample data;

[0175] Detection module 2: Input the real-time data of the sector traffic flow situation index number to detect and output the controller workload index; wherein, the BP neural network module 1 is coupled with the detection module 2.

[0176] Due to the use of quantitative analysis methods, the system calculates accurate air traffic flow situation index data in the future period through continuous detection and calculation analysis of massive operating data, and relies on hist...

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 a controller workload prediction method and system. The method comprises a first step of determining a back propagation (BP) neutral network topology structure, collecting sector traffic flow state index samples of different times, and building a sample set; a second step of optimizing a weight and a threshold of a BP neutral network by use of a genetic algorithm, performing network training, and outputting an optimized BP neutral network; a third step of predicting the index of the controller workload through the optimized BP neutral network of the second step according to the input real-time data of the sector traffic flow state index; and a fourth step of responding to a warning if a result of predicted index of the controller workload meets a preset condition. Through adoption of the method, the reliability of the prediction result of the controller workload can be improved, actual demands of an air traffic control unit on real-time prediction and warning for the controller workload can be met, and the method can provide data support for improvement of operation management level and optimization of the controlled airspace structure.

Description

technical field [0001] The invention relates to the field of aviation, in particular to a method and system for predicting the workload of controllers. Background technique [0002] With the development of the air transport industry, in order to ensure the safety and order of various flight activities, the air traffic control service came into being and was continuously developed and improved until it became mature in the 1980s. The main content of modern air traffic control services is: Air traffic controllers (referred to as "controllers", the same below) rely on modern communication, navigation, and surveillance technologies to manage and control aircraft under their jurisdiction, coordinate and guide their movement paths and Mode to prevent collisions between aircraft in the air and collisions between aircraft and obstacles in the maneuvering area of ​​the airport, and maintain and speed up the orderly flow of air traffic. The main job of the controller is to closely mo...

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): G06F19/00G06N3/02
Inventor 裴锡凯张建平丁鹏欣程延松周自力吴振亚
Owner CHENGDU CIVIL AVIATION AIR TRAFFIC CONTROL SCI & TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com