Human fatigue monitoring system based on semantic network

A semantic network and fatigue monitoring technology, applied in special data processing applications, complex mathematical operations, instruments, etc., can solve problems such as poor versatility, and achieve robust and universal effects

Inactive Publication Date: 2018-07-13
JIANGSU UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In order to solve the defect that the existing fatigue monitoring means and methods are not universal, the present invention provides a human fatigue monitoring system based on semantic network

Method used

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  • Human fatigue monitoring system based on semantic network
  • Human fatigue monitoring system based on semantic network
  • Human fatigue monitoring system based on semantic network

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Embodiment Construction

[0025] The present invention will be further described below in conjunction with the drawings and embodiments.

[0026] This embodiment proposes a human fatigue monitoring system based on a semantic network, including all direct and indirect influencing factors that cause human fatigue. Such as figure 1 As shown, it includes a multi-source information module 101, a multi-source information module 102, a multi-source information module 103, a multi-source information module 104, a multi-source information module 105, a semantic network 2 and a fatigue prediction module 3. Source information one module 101, multi-source information second module 102, multi-source information three module 103, multi-source information four module 104, and multi-source information five module 105 respectively receive various sensors that affect sleep quality 12, work environment 14, work Condition 16, physical condition 18 and circadian 20 signals, and through the semantic network 2 V={V 1 ,V 2 ,...,...

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Abstract

The invention discloses a human fatigue monitoring system based on a semantic network. The system comprises all direct influence factors and indirect influence factors causing human fatigue, assumingthat all the direct and indirect influence factors causing the human fatigue are already obtained through all sensors, all the direct factors and indirect factors causing the human fatigue are firstlyclassified, the semantic network representing knowledge in artificial intelligence is used for modeling the relation of all the classified factors, then through a linked probability distribution function and a Bayes equation in the semantic network, solving is conducted, and finally, the human fatigue degree is monitored. The system has robustness and universality.

Description

Technical field [0001] The invention relates to a fatigue monitoring system, in particular to a human fatigue monitoring system based on a semantic network. Background technique [0002] Semantic Network is a mathematical model proposed by Quilian in 1968 when studying human associative memory, and it is a method of knowledge representation. Quilian believes that memory is realized by the connection between concepts. In 1972, artificial intelligence experts Simmon and Slocum first applied the semantic network to a natural language understanding system. The semantic network is a directed network graph that expresses knowledge through concepts and their semantic relations. Among them, the nodes of the directed graph represent various things, concepts, attributes, etc.; arcs represent various semantic relationships between nodes, and indicate a certain semantic relationship between the nodes that it connects. The semantic network can be used to represent complex concepts, things ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G06F17/18
CPCG06F17/18G06F30/20
Inventor 于冬梅李玎
Owner JIANGSU UNIV OF TECH
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