A Load Forecasting Method for Units in Nonlinear Vertically Structured Human-Machine System

Abstract

The invention relates to a load forecasting method of a nonlinear longitudinal structured man-machine system unit, comprising the following steps: 1) quantifying the task load of the unit members according to the basic work load function and elements; 2) making decision and control channels according to the basic information, Quantify the channel load of crew members; 3) Introduce virtual workload and establish the unit workload evaluation mechanism of the human-machine intelligent system; 4) Add the switching jump constraint of the automatic equipment opening state to the unit workload evaluation mechanism of the human-machine intelligent system to realize the evaluation Quasi-Markov simplex estimation of the discontinuous parameters of the automation level of the human-machine system in the mechanism; 5) Quantitatively predict the workload of crew members in a relatively recursive manner through the workload evaluation mechanism of the human-machine intelligent system unit. Compared with the prior art, the invention has the advantages of improving accuracy and robustness, considering coupling characteristics, being objective and accurate, adapting to various unit configurations, and having a wide application range.

Description

technical field [0001] The invention relates to the field of aviation technology, in particular to a method for predicting the workload of a human-machine intelligent system crew based on a nonlinear longitudinal structural creep dynamic topology. Background technique [0002] Factors that affect or reflect the flight crew workload include the automation mode of the human-machine system, the tasks undertaken by the crew, and the physical and psychological resources required to complete the tasks. These factors in human-machine system function assignment and mission scenarios are a complex set of latent variables that cannot be directly observed but interact with each other and further constrain flight performance and even safety. With the maturity of airborne automation and intelligent technology, the flight system gradually integrates more and more automation and intelligent equipment and functions. Human-machine integration is the key to improving human efficiency and sys...

AI Technical Summary

Problems solved by technology

In step 3), the NASA-TLX scale method needs to weight factors such as mental demand, physical demand, time demand, effort level, performance level, and frustration level. The SWAT scale method needs to divide time, pressure, and effort into three factors. There are three levels, and the Cooper-Harper grading method needs to divide the difficulty of aircraft driving into ten levels; these methods are based on the assumption that the pilot's workload is directly related to the quality of control. The assumptions are unreasonable; in addition, these methods are difficult to quantify objectively, and cannot be applied to the entire process of aircraft design, airworthiness compliance verification, and aviation operations

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