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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2022, Vol. 56 Issue (8): 1568-1577    DOI: 10.3785/j.issn.1008-973X.2022.08.011
    
Evaluation of aircraft cockpit form based on hesitant fuzzy sets
Yan-hao CHEN(),Sui-huai YU,Jian-jie CHU*(),Wen-zhe CUN
Key Laboratory of Industrial Design and Ergonomics, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi’an 710072, China
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Abstract  

A comprehensive evaluation method of aircraft cockpit design form based on hesitant fuzzy set was proposed, in order to solve the problems of difficult description of cognitive preferences and hesitation in decision making of pilots in the process of aircraft cockpit form design. The attribute feature system of the cognitive demand of cockpit design form was established by analyzing the cognitive demand of cockpit design form. The evaluation indexes affecting the cognitive preference of cockpit design form were obtained by using the rough number evaluation model, the features lines of cockpit key component design form were extracted, the core features of cockpit key component design form were obtained, and further combined with the cognitive preference evaluation index of cockpit design form, the comprehensive decision evaluation value was obtained. The cognitive entropy theory was used to modify the evaluation index weights and construct the set hesitant fuzzy evaluation model, so as to obtain the cockpit design form priority ranking under compound cognitive preference. Results show that the proposed method has good reliability and can effectively solve the hesitation of evaluation information, which can better achieve the accurate evaluation of the cognitive preference of aircraft cockpit design form.

Key words： aircraft cockpit form      cognitive demand      cognitive preference      hesitant fuzzy set      comprehensive evaluation     
Received: 12 June 2021      Published: 30 August 2022
CLC:  TB 47  
Fund:  国防科技基础加强计划技术领域基金资助项目(2020-JCJQ-JJ-439)；高等学校学科创新引智计划资助项目(B13044)
Corresponding Authors: Jian-jie CHU     E-mail: chenyhmail@163.com;cjj@nwpu.edu.cn
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Yan-hao CHEN
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Cite this article:

Yan-hao CHEN,Sui-huai YU,Jian-jie CHU,Wen-zhe CUN. Evaluation of aircraft cockpit form based on hesitant fuzzy sets. Journal of ZheJiang University (Engineering Science), 2022, 56(8): 1568-1577.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2022.08.011     OR     https://www.zjujournals.com/eng/Y2022/V56/I8/1568


基于犹豫模糊集的飞机驾驶舱形态评价

为了解决飞机驾驶舱形态设计过程中，飞行员存在的认知偏好描述困难及决策犹豫的问题，提出基于犹豫模糊集的飞机驾驶舱设计形态综合评价方法. 通过对驾驶舱设计形态的认知需求分析，建立了驾驶舱设计形态认知需求的属性特征体系；利用粗数评价模型得到影响驾驶舱设计形态认知偏好的评价指标，提取驾驶舱关键部件设计形态特征线，获取了驾驶舱关键部件设计形态的核心特征，进一步结合驾驶舱设计形态的认知偏好评价指标，获取综合决策评价值；采用认知熵理论修正评价指标权重，构建集结犹豫模糊评价模型，从而得到复合认知偏好下的驾驶舱设计形态优先级排序. 与5种同类评价算法进行比较及一致性检验，结果表明本研究所提方法具有良好的可靠性且能有效解决评价信息的犹豫性，能够较好地实现对飞机驾驶舱设计形态认知偏好的精确评估.


关键词： 飞机驾驶舱形态,  认知需求,  认知偏好,  犹豫模糊集,  综合评价 
Fig.1 Hierarchical structure of cognitive demand attributes
需求类型 属性特征 特征定义
本能需求 安全防护感 驾驶舱形态具备安全可靠和基础保障
操纵空间感 驾驶舱形态操纵空间在肢体可达域范围
轮廓间距感 驾驶舱形态设计元件接口间距合理
总体布局感 驾驶舱形态机载系统设施布置准确
感官需求 部件协调感 驾驶舱形态部件，定位协调舒适
符号统一感 驾驶舱形态符号整体清晰统一
标识易读感 驾驶舱形态元素能够准确辨识
区域比例感 驾驶舱形态部件尺寸比例恰当
整体对称感 驾驶舱形态整体对称美观
感性需求 简洁感 驾驶舱形态简洁规整
科技感 驾驶舱形态创新集成
精密感 驾驶舱形态精准周密
智能感 驾驶舱形态感知决策
现代感 驾驶舱形态前瞻开放
Tab.1 Attribute features of cognitive demand of cockpit design form
Fig.2 Selection of evaluation index of cockpit cognitive preferences
Fig.3 Hesitant fuzzy comprehensive evaluation model
Fig.4 Cockpit form evaluation scheme library
特征属性A 分值
D1 D2 D3 … D15 D16
A1 3 7 9 … 7 5
A2 1 5 5 … 5 3
A3 1 3 5 … 3 3
A4 3 5 7 … 7 5
$\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $
A13 7 7 9 … 5 7
A14 3 5 5 … 5 3
Tab.2 Evaluation results of attribute feature of cognitive demands of cockpit form design
本能需求 属性 D1 D2 D3 … D15 D16 均值
A1 粗数 [3.0,5.5] [5.3,7.3] [5.5,9.0] … [5.3,7.3] [4.1,6.3] [4.4,6.6]
边界 2.5 2.0 3.5 2.0 2.2 2.2
A2 粗数 [1.0,3.4] [3.1,5.3] [3.1,5.3] … [3.1,5.3] [2.2,4.2] [2.3,4.5]
边界 2.4 2.2 2.2 2.2 2.0 2.2
A3 粗数 [1.0,3.0] [2.2,3.9] [2.7,5.5] … [2.2,3.9] [2.2,3.9] [2.0,4.1]
边界 2.0 1.7 2.8 1.7 1.7 2.1
A4 粗数 [3.0,5.4] [4.2,6.2] [5.1,7.3] … [5.1,7.3] [4.2,6.2] [4.3,6.5]
边界 2.4 2.0 2.2 2.2 2.0 2.2
Tab.3 Rough number of instinctive demand features based on cockpit design form
感官需求 属性 D1 D2 D3 … D15 D16 均值
A5 粗数 [4.5,5.8] [5.3,7.0] [5.3,7.0] … [5.3,7.0] [4.5,5.8] [4.5,6.1]
边界 1.3 1.7 1.7 1.7 1.3 1.6
A6 粗数 [2.3,4.0] [3.0,5.4] [3.3,7.0] … [2.3,4.0] [2.3,4.0] [2.2,4.4]
边界 1.7 2.4 3.7 1.7 1.7 2.2
A7 粗数 [1.0,3.1] [2.5,3.6] [3.1,5.0] … [2.5,3.6] [2.5,3.6] [2.4,3.9]
边界 2.1 1.1 1.9 1.1 1.1 1.5
A8 粗数 [4.6,6.0] [5.4,7.3] [5.4,7.3] … [4.6,6.0] [4.6,6.0] [4.7,6.5]
边界 1.4 1.9 1.9 1.4 1.4 1.8
A9 粗数 [2.2,3.7] [2.2,3.7] [2.9,5.0] … [2.2,3.7] [2.2,3.7] [2.0,3.8]
边界 1.5 1.5 2.1 1.5 1.5 1.8
Tab.4 Rough number of sensory demand features based on cockpit design form
感性需求 D1 D2 D3 … D15 D16 均值
A10 粗数 [2.4,3.9] [3.4,5.0] [3.4,5.0] … [3.4,5.0] [1,3.4.0] [2.5,4.2]
边界 1.5 1.6 1.6 1.6 2.4 1.7
A11 粗数 [5.4,9.0] [4.5,5.9] [4.5,5.9] … [4.9,8.0] [4.5,5.9] [4.4,6.5]
边界 3.6 1.4 1.4 3.1 1.4 2.1
A12 粗数 [2.3,4.4] [3.3,5.6] [3.8,7.0] … [3.3,5.6] [2.3,4.4] [2.6,5.0]
边界 2.1 2.3 3.2 2.3 2.1 2.4
A13 粗数 [5.9,7.7] [5.9,7.7] [6.5,9.0] … [5.0,6.5] [5.9,7.7] [5.6,7.4]
边界 1.8 1.8 2.5 1.5 1.8 1.8
A14 粗数 [2.8,3.9] [3.5,5.3] [3.5,5.3] … [3.5,5.3] [2.8,3.9] [3.0,4.5]
边界 1.1 1.8 1.8 1.8 1.1 1.5
Tab.5 Rough number of perceptual demand features based on cockpit design form
Fig.5 Rough lower boundary and rough boundary results
Fig.6 Degree table of fuzzy evaluation term set
方案U A1 A4 A5 A8 A11 A12
u1 {0.38, 0.44, 0.53} {0.56, 0.63, 0.68} {0.38, 0.45, 0.52} {0.17, 0.20, 0.29} {0.33, 0.38, 0.47} {0.23, 0.28, 0.37}
u2 {0.47, 0.55, 0.60} {0.43, 0.49, 0.56} {0.25, 0.30, 0.38} {0.32, 0.37, 0.47} {0.18, 0.25, 0.35} {0.18, 0.23, 0.33}
u3 {0.17, 0.20, 0.28} {0.17, 0.21, 0.28} {0.22, 0.27, 0.36} {0.20, 0.25, 0.33} {0.16, 0.20, 0.28} {0.44, 0.48, 0.55}
u4 {0.18, 0.22, 0.31} {0.38, 0.48, 0.56} {0.36, 0.41, 0.49} {0.25, 0.30, 0.40} {0.34, 0.41, 0.49} {0.17, 0.22, 0.32}
$\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $
u29 {0.53, 0.62, 0.66} {0.52, 0.59, 0.65} {0.60, 0.68, 0.74} {0.67, 0.76, 0.80} {0.63, 0.72, 0.76} {0.63, 0.71, 0.77}
u30 {0.66, 0.73, 0.77} {0.65, 0.74, 0.78} {0.64, 0.73, 0.78} {0.55, 0.65, 0.70} {0.53, 0.61, 0.67} {0.46, 0.53, 0.59}
Tab.6 Hesitant fuzzy decision matrix
方案 s+ s? si
u1 0.83 0.95 0.54
u2 0.80 0.97 0.55
u3 0.72 0.99 0.58
u4 0.80 0.98 0.55
$\vdots $ $\vdots $ $\vdots $ $\vdots $
u29 0.98 0.80 0.45
u30 0.97 0.82 0.46
Tab.7 Comprehensive similarity results of evaluation scheme
Fig.7 Distribution of pros and cons of evaluation plan
模型 Nc
u1 u2 u3 u4 $ \cdots $ u29 u30
灰色关联法 23 26 30 28 $ \cdots $ 4 5
TOPSIS 23 26 30 28 $ \cdots $ 4 5
熵权TOPSIS 21 26 30 27 $ \cdots $ 4 5
RSR 24 27 30 28 $ \cdots $ 4 5
VIKOR 27 25 30 28 $ \cdots $ 3 5
Tab.8 Sort results of different evaluation models
Fig.8 Scattered point distribution of various evaluation rankings
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