基于对象-属性双维度的民用机场核心业务流智慧化程度评价方法

doi:10.13229/j.cnki.jdxbgxb.20221262

摘要/Abstract

摘要：

为解决机场在进行智慧化程度评价过程中由于建设内容体量庞大，业务错综复杂，无法做到业务流科学划分并有效评价的难题，本文从面向任务对象角度以及面向功能属性的角度建立了基于对象-属性双维度的机场核心业务流评价体系，并提出了90项基础指标。在此基础上，通过优劣解距法对禄口机场的双维度指标层进行了分值计算。研究结果表明：双维度评价指标体系的建立有助于厘清机场业务流种类多元且流向繁杂的问题，方便后续机场进行智慧化程度评价；从基于任务对象维度的指标层分值可以看出，旅客流的隔离区外智慧化建设程度最高为73.76分。以功能属性维度划分的各指标中，感知类的数量/重量指标评分最高为79.68分，高出平均值21.4%；预测类的时间类指标评分最高为80.26分，高出平均值9.1%；决策类的事件类指标评分最高为76.30分，高出平均值10.2%。预测类和决策类的相关指标智慧化建设程度相对均衡，而感知类指标最低分为54.36分，最高分为79.68分，各业务智慧化建设程度相差较大。

关键词: 航空工程, 对象-属性双维度, 核心业务流, 智能化程度评价

Abstract:

In order to solve the difficult problem of not being able to scientifically classify and effectively evaluate the business flow due to the huge construction content and complicated business during the evaluation of the degree of intelligence of airports， this study establishes the evaluation system of airport core business flow based on object-attribute double dimensions from the perspective of task object-oriented and functional attribute-oriented， and proposes 90 basic indicators. On this basis， the scores of the indicator layer of the two dimensions of Lukou Airport were calculated by the superior and inferior solution distance method. The research results show that： The establishment of the two-dimensional evaluation index system helps to clarify the problem of multiple types of business flows and complicated flows in smart airports， and facilitates the evaluation of the degree of smartness in subsequent airports； The index layer score based on the task object dimension shows that the degree of intelligent construction outside the segregated area for passenger flow is the highest at 73.76. In the evaluation of each indicator divided by functional attribute dimension， the quantity/weight indicator of the perception category has the highest rating of 79.68， 21.4% higher than the average value； the time indicator of the prediction category has the highest rating of 80.26， 9.1% higher than the average value； and the event indicator of the decision-making category has the highest rating of 76.30， 10.2% higher than the average value. The degree of intelligent construction of the related indicators in the prediction and decision-making categories is relatively balanced， while the lowest score of the perception category indicator is 54.36 and the highest score is 79.68， and there is a big difference in the degree of intelligent construction of each business.

Key words: aeronautical engineering, object-attribute bi-dimensionality, core business flows, evaluation of the degree of intelligence

中图分类号:

U414

张锐,黄卫,马涛. 基于对象-属性双维度的民用机场核心业务流智慧化程度评价方法[J]. 吉林大学学报(工学版), 2024, 54(5): 1267-1276.

Rui ZHANG,Wei HUANG,Tao MA. Evaluation method of intelligent degree of core business flow in civil airports based on object⁃attribute dual dimension[J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(5): 1267-1276.

图/表 8

图1

图2

表1

表2

表3

表4

表5

表6

参考文献 23

1	芮海田, 吴群琪. 高铁运输与民航运输选择下的中长距离出行决策行为[J]. 中国公路学报, 2016, 29 (3): 134-141.
	Rui Hai-tian, Wu Qun-qi. Medium-and-long distance travel mode decision between high-speed rail and civil aviation[J]. China Journal of Highway and Transport, 2016, 29(3): 134-141.
2	余朝军, 江驹, 徐海燕, 等. 基于改进遗传算法的航班-登机口分配多目标优化[J]. 交通运输工程学报, 2020, 20(2): 121-130.
	Yu Chao-jun, Jiang Ju, Xu Hai-yan, et al. Multi-objective optimization off light-gate assignment based on improved genetic algorithm[J]. Journal of Traffic and Transportation Engineering, 2020, 20(2): 121-130.
3	Khadonova S V, Ufimtsev A V, Dymkova S S. Digital smart airport system based on innovative navigation and information technologies[C]∥International Conference on Engineering Management of Communication and Technology, Vienna, Austria, 2020: 1-6.
4	杨光. 基于数字化转型的A集团智慧机场建设研究[D]. 济南: 山东大学管理学院, 2023.
	Yang Guang. Research on the construction of group A´s smart airport in the context of digital transformation[D]. Ji´nan: School of Management, Shandong University, 2023.
5	张名芳, 付锐, 郭应时, 等. 基于三维不规则点云的地面分割算法[J]. 吉林大学学报: 工学版, 2017, 47(5): 1387-1394.
	Zhang Ming-fang, Fu Rui, Guo Ying-shi, et al. Road segmentation method based on irregular three dimensional point cloud[J]. Journal of Jilin University (Engineering and Technology Edition), 2017, 47(5): 1387-1394.
6	袁义, 李国祥, 王继军. 基于Markov随机场模型的数字X光图像自适应增强算法[J]. 吉林大学学报: 理学版, 2023, 61 (2): 377-383.
	Yuan Yi, Li Guo-xiang, Wang Ji-jun. Adaptive enhancement algorithm of digital X-ray image based on Markov random fiedl model[J]. Journal of Jilin University (Science Edition), 2023, 61(2): 377-383.
7	刘柳, 冯卫星. 基于NNBR模型的隧道盾构施工地表沉降实测与计算分析[J]. 吉林大学学报: 工学版, 2021, 51(1): 245-251.
	Liu Liu, Feng Wei-xing. Field measurement and calculation analysis of tunnel shield tunnel construction based on NNBR model[J]. Journal of Jilin University (Engineering and Technology Edition), 2021, 51(1): 245-251.
8	Lykou G, Anagnostopoulou A, Gritzalis D. Smart airport cybersecurity: threat mitigation and cyber resilience controls[J]. Sensors, 2018, 19(1): 190119.
9	Nagy E, Csiszár C. Revealing influencing factors of check-in time[J]. Acta Polytechnica Hungarica, 2017, 14(4): 225-243.
10	李国锋. “共享”理念与人文机场建设[J]. 空运商务, 2021(6): 31-34.
	Li Guo-feng. The concept of "sharing" and the construction of humanistic airport[J]. Air Freight Business, 2021(6): 31-34.
11	王同琛, 王丽欣, 刘恒飞. 从北京大兴国际机场谈GIS的发展前景[J]. 测绘与空间地理信息, 2019, 42(12): 149-151.
	Wang Tong-chen, Wang Li-xin, Liu Heng-fei. Talking about the development prospect of GIS from Beijing Daxing International Airport[J]. Geomatics ＆ Spatial Information Technology, 2019, 42(12): 149-151.
12	赵鸿铎, 李琛琛, 刘诗福, 等. 机场智慧飞行区内涵分级与评价[J]. 同济大学学报: 自然科学版, 2019, 47(8): 1137-1142.
	Zhao Hong-duo, Li Chen-chen, Liu Shi-fu, et al. Concept intelligence rating and evaluation of smart airfield in airport[J]. Journal of Tongji University (Natural Science), 2019, 47(8): 1137-1142.
13	金雷, 王银银, 傅惠, 等. 基于模糊层次分析法的机场陆侧智慧交通系统感知水平评价[J]. 科学技术与工程, 2022, 22(8): 3365-3372.
	Jin Lei, Wang Yin-yin, Fu Hui, et al. Perception level evaluation of airport land-side intelligent traffic system based on fuzzy analytic hierarchy process[J]. Science Technology and Engineering, 2022, 22(8): 3365-3372.
14	Dissakoon C, Sajjakaj J, Vatan S R. Measurement model of passengers' expectations of airport service quality[J]. International Journal of Transportation Science and Technology, 2021, 10(4): 342-352.
15	Lupo T. Fuzzy servperf model combined with ELECTRE III to comparatively evaluate service quality of international airports in Sicily[J]. Journal of Air Transport Management, 2015, 42: 249-259.
16	Göçmen E. Smart airport: evaluation of performance standards and technologies for a smart logistics zone[J]. Transportation Research Record, 2021, 2675(7): 480-490.
17	王红岩, 许雅玺. 基于层次分析法的机场服务质量评价[J]. 科技和产业, 2015, 15(6): 64-67.
	Wang Hong-yan, Xu Ya-xi. Airport service quality evaluation based on analytic hierarchy process[J]. Science Technology and Industry, 2015, 15(6): 64-67.
18	Santhiapillai F P, Ratnayake R M. Exploring knowledge work waste in public emergency services using the AHP algorithm[J]. International Journal of Lean Six Sigma, 2023(14): 1431-1455.
19	刘玲. 基于 Topsis 思想的内容推荐算法研究[J]. 数学的实践与认识, 2012, 24(16): 113-119.
	Liu Ling. Research on content recommendation algorithm based on Topsis thought[J]. Mathematics in Practice and Theory, 2012, 24(16): 113-119.
20	Wang K N, He K N, Gao S J, et al. Analysis and control strategy of residual chlorine concentration in swimming pool based on sine and cosine function temperature prediction model[J]. Academic Journal of Mathematical Sciences, 2023, 4(4): 74-82.
21	Yuan Z K, Wang J L, Zhao L Z, et al. An MTRC-AHP compensation algorithm for Bi-ISAR imaging of space targets[J]. IEEE Sensors, 2020, 20(5): 2356-2367.
22	Masoud I, Behrang B. An enhanced AHP-TOPSIS-based load balancing algorithm for switch migration in software-defined networks[J]. The Journal of Supercomputing, 2020, 77(1): 1-34.
23	Wang R T, Ho C T, Feng C M, et al. A comparative analysis of the operational performance of Taiwan's major airports[J]. Journal of Air Transport Management, 2004, 10(5): 353-360.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

航班流	分值
滑入	48.36
过站	49.52
滑出	56.98

旅客流	分值
到达航站楼	57.02
隔离区外	53.18
值机/托运行李	40.52
安检/边检	56.23
登机	49.68
隔离区外	73.76

行李流	分值
值机/托运行李	58.36
分拣	62.13
到达站房	56.67
装车	58.95
装备装机	64.12
装机	63.29

预测	分值
时间类	80.26
预警类	78.38
时间发生类	70.15
数量类	65.38

决策	分值
事件类	76.30
优化类	70.36
调度类	65.89
特殊类	73.27
堆垛分布类	60.23