An Entropy Weighting-improved TOPSIS Method for Sector Complexity Evaluation
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摘要: 扇区复杂度评估是开展空域规划、制定空域资源调配策略的关键和基础，为解决复杂度评估多因素耦合影响及现有评估方法简单和主观性问题，研究提出基于熵权改进-TOPSIS法的扇区复杂度评估方法。针对扇区运行特点，从空域静态结构、交通运行状态、动态限制情况3个维度，新构了包括航路结构、航空器潜在冲突、恶劣天气等8个具体量化指标，形成了全面反映空域扇区复杂度的多维评估指标体系。运用高斯分布异常值检测处理方法，消除了极端异常值对评估的影响，结合熵权法客观权重计算的优势，建立了更贴近扇区实际运行情况、符合主观认知的基于熵权改进-TOPSIS法的扇区复杂度评估方法。本研究以北京区域扇区为例进行验证分析，以均衡扇区复杂度为标准，分别从多扇区间横向对比和单扇区不同时段对比2个场景对扇区复杂度进行评估分析，并与专家评估结果进行了对比验证。结果表明：2个场景的复杂度均处于不均衡状态，说明当前北京区域空域资源分配在时间和空间这2个维度均存在一定的失衡，依据评估结果，可针对性的开展空域结构调整与交通流优化工作。相较于专家经验评估结果，本方法评估结果与专家主观认知吻合度高达80%，验证了本文方法的可行性、准确性和有效性，相较于专家评估方法，本文方法具有可量化、客观性强、计算便捷等优势。
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关键词:
- 空域规划 /
- 扇区复杂度 /
- 复杂度评估模型 /
- 熵权改进-TOPSIS法 /
- 复杂度评估指标体系
Abstract: Sector complexity evaluation is the key and foundation for airspace planning and airspace resource allocation strategy. To solve the problems of multi-factor coupling and the issue of simplicity and subjectivity of existing evaluation methods, a sector complexity evaluation method based on entropy weight improvement-TOPSIS method is proposed. According to the operation characteristics of airspace sector, eight specific quantitative indicators are developed from three aspects of airspace static structure, traffic operation state and dynamic restriction, including route structure, aircraft potential conflict and bad weather, to establish a multidimensional evaluation index system that comprehensively reflects the complexity of airspace sector. A Gaussian distribution outlier detection method is used to eliminate the influence of extreme outliers on the evaluation. Combined with objective weight calculation of entropy weight method, a sector complexity evaluation method is proposed based on entropy weighting improved TOPSIS method, which is closer to the actual operation of the sector and conforms to the subjective cognition. This study takes Beijing regional sectors as an example for verification analysis, based on the standard of balanced sector complexity, the sector complexity is evaluated and analyzed from two scenarios: horizontal comparison between multiple sectors and comparison of single sector at different time periods, and compared with the expert evaluation results. The results show that the complexity of the two scenarios is unbalanced, indicating that the current allocation of airspace resources in Beijing area is unbalanced in both time and space. According to the evaluation results, airspace structure adjustment and traffic flow optimization can be carried out. Compared with the evaluation results of expert experience, the evaluation results of this method agree with the subjective cognition of experts as high as 80%, which verifies the feasibility, accuracy and effectiveness of this method. This method outperforms the expert evaluation method with the advantages of quantification, strong objectivity and convenient calculation. -
图 3 航路及航路交叉点示意图
Figure 3. Schematic diagram of air route and intersection of air routes
图 4 航路及航路交叉角示意图
Figure 4. Schematic diagram of air route and air route intersection angle
图 7 某典型日31个扇区复杂度指标对比图
Figure 7. Comparison chart of complexity index of 31 sectors of a typical day
图 8 典型日北京10扇复杂度指标值小时对比图
Figure 8. Comparison chart of complexity index values in Beijing 10 sector of a typical day
图 9 熵权-改进TOPSIS方法计算结果
Figure 9. Calculation results of entropy weight-improved TOPSIS meth
表 1 航空器机型分类表
Table 1. Aircraft type classification table
MTOW/kg 航空器类型 包括机型 13 600 重型机（H） A322、A333、A342、A343、A344、A345、等 7 000~13 600 中型机（M） B733、B735、B736、B737、B738、B739、等 ＜7 000 轻型机（L） B350、BE10、BE20、BE30等 
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表 2 原始航路数据示例
Table 2. Original route data example
航线代号 该段航路起点报告点 起点经度 起点纬度 该段航路终点报告点 终点经度 终点纬度 磁航向角（/°） 航段距离/km A1 BUNTA E1092342 N165000 P97 E1100716 N171803 56 93 A1 P97 E1100716 N171803 LENKO E1101800 N172457 56 23 A1 LENKO E1101800 N172457 IKELA E1121442 N183942 56 248 A202 SIKOU E1113000 N205036 ISBIG E1105430 N203839 250 65 A202 ISBIG E1105430 N203839 SAMAS E1102942 N203018 251 46
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表 3 部分飞行计划数据匹配雷达航迹数据汇总表
Table 3. Some flight plan data match radar track data summary table
航班号 机型 入扇时间 出扇时间 高度/m 实际过点时间 航路点7 航路点7时间 航路点8 航路点8时间 航路点9 航路点9时间 CSN6256 A321 2021-07-16 19:10:00 2021-07-16 19:22:00 10 088.88 2021-07-16 19:10:10 IGPAS 2021-07-16 19:08:00 OSRUR 2021-07-16 19:11:00 TMR 2021-07-16 19:12:00 CSN6256 A321 2021-07-16 19:10:00 2021-07-16 19:22:00 10 088.88 2021-07-16 19:10:20 IGPAS 2021-07-16 19:08:00 OSRUR 2021-07-16 19:11:00 TMR 2021-07-16 19:12:00 CSN6256 A321 2021-07-16 19:10:00 2021-07-16 19:22:00 10 088.88 2021-07-16 19:10:30 IGPAS 2021-07-16 19:08:00 OSRUR 2021-07-16 19:11:00 TMR 2021-07-16 19:12:00 $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$ CSN6256 A321 2021-07-16 19:10:00 2021-07-16 19:22:00 10 088.88 2021-07-16 19:15:10 IGPAS 2021-07-16 19:08:00 OSRUR 2021-07-16 19:11:00 TMR 2021-07-16 19:12:00 CSN6256 A321 2021-07-16 19:10:00 2021-07-16 19:22:00 10 088.88 2021-07-16 19:15:20 IGPAS 2021-07-16 19:08:00 OSRUR 2021-07-16 19:11:00 TMR 2021-07-16 19:12:00 $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$
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表 4 部分气象与航迹数据融合表
Table 4. Fusion table of partial meteorological and track data
Lon/（°） Lat/（°） Height/km 时间 经度（/°） 纬度（/°） 时间 雷达反射率R /dBZ 116.61 39.49 7.8 20210715230909 116.59 39.40 0716000000 29 116.70 39.47 7.8 20210715231049 116.69 39.40 0716000000 49 117.08 39.48 7.8 20210715234543 117.00 39.40 0716000000 40 113.75 36.20 7.8 20210715234727 113.69 36.20 0716000000 12 117.61 38.90 9.8 20210715230334 117.59 38.90 0716000000 30 117.74 38.80 9.8 20210715230224 117.69 38.80 0716000000 17 116.74 39.49 7.8 20210715230925 116.69 39.40 0716000000 45 116.41 39.07 8.4 20210715234737 116.40 39.10 0716000000 41
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表 5 某典型日扇区复杂度指标量化结果汇总表
Table 5. Summary of quantization results of sector complexity index of a typical day
扇区名称 $F_1^i$ $F_2^i$ $F_3^i$ $F_4^i$ $F_5^i$ $F_6^i$ $F_7^i$ $F_8^i$ ACC01 0.818 3.312 3.148 0.181 0.782 0.279 0.206 1.000 ACC02 0.740 0.238 1.221 1.063 0.988 0.844 0.213 0.002 ACC03 0.856 0.217 1.079 2.198 3.465 2.472 0.474 0.003 $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$ ACC30 0.933 0.189 0.869 3.448 0.527 0.968 0.519 0.001 ACC31 0.744 0.347 0.869 2.496 0.580 0.966 2.145 0.001
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表 6 2个场景的扇区复杂度排名结果
Table 6. Sector complexity ranking results of two scenes
分类 专家结果 本文方法 复杂度按从高到低排序（扇） 复杂度按从低到高排序（扇） 复杂度按从高到低排序（扇） 复杂度按从低到高排序（扇） 多扇区 23 05 19 05 12 04 23 11 19 11 06 04 06 18 12 22 26 08 26 02 28 15 17 08 25 02 03 20 03 14 28 13 10 13 01 21 09 20 25 14 北京10个扇区不同时段 20：00—21：00 04：00—05：00 22：00—23：00 04：00—05：00 16：00—17：00 00：00—01：00 15：00—16：00 03：00—04：00 15：00—16：00 05：00—06：00 20：00—21：00 02：00—03：00 18：00—19：00 01：00—02：00 16：00—17：00 00：00—01：00 19：00—20：00 02：00—03：00 23：00—24：00 05：00—06：00 13：00—14：00 03：00—04：00 18：00—19：00 11：00—12：00 17：00—18：00 06：00—07：00 17：00—18：00 09：00—10：00 14：00—15：00 07：00—08：00 13：00—14：00 21：00—22：00 22：00—23：00 21：00—22：00 12：00—13：00 01：00—02：00 12：00—13：00 08：00—09：00 10：00—11：00 06：00—07：00
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