多循环嵌套云制造服务生态系统学习演化建模

doi:10.16182/j.issn1004731x.joss.25-0993

系统仿真学报 ›› 2026, Vol. 38 ›› Issue (8): 2265-2282.doi: 10.16182/j.issn1004731x.joss.25-0993

• 论文 • 上一篇

多循环嵌套云制造服务生态系统学习演化建模

李芳¹^,², 周德雨³, 王刚¹^,², 刘光军⁴, 胡淇⁴

^1.西安财经大学信息学院，陕西西安 710100
^2.智财协同可信计算陕西省高等学校重点实验室，陕西西安 710100
^3.山东大学软件学院，山东济南 250101
^4.西安文理学院信息工程学院，陕西西安 710065

收稿日期:2025-10-15 修回日期:2026-01-05 出版日期:2026-08-28 发布日期:2026-08-31
通讯作者: 周德雨
第一作者简介:李芳(1986-)，女，讲师，博士，研究方向为云计算、计算实验、区块链等。
基金资助:
国家自然科学基金(62472306);中国(西安)丝绸之路研究院(2019HZ03)

Learning Evolution Modeling of Multi-cycle Nested Cloud Manufacturing Service Ecosystem

Li Fang¹^,², Zhou Deyu³, Wang Gang¹^,², Liu Guangjun⁴, Hu Qi⁴

^1.School of Information, Xi'an University of Finance and Economics, Xi'an 710100, China
^2.Key Laboratory of Intelligent Finance Collaboration and Trusted Computing, Shaanxi Provincial Institutions of Higher Education, Xi'an 710100, China
^3.School of Software, Shandong University, Jinan 250101, China
^4.School of Information Engineering, Xi'an University, Xi'an 710065, China

Received:2025-10-15 Revised:2026-01-05 Online:2026-08-28 Published:2026-08-31
Contact: Zhou Deyu

摘要/Abstract

摘要：

针对现有学习演化模型缺乏对多制造单元间协同治理机制导致的企业个体适应性变化与系统整体演化趋势之间的综合考虑，提出多循环嵌套云制造服务生态系统学习演化模型。微观上，在个体层通过PREA(planning-readiness-execution-assessment)循环和OODA循环嵌套实现企业内部多制造单元的自适应联动决策；宏观上，实现个体层、组织层和社会层的闭环仿真，为数字化和智能化的云制造系统发展提供可解释的决策支持工具。通过分析计算实验在不同策略和订单分配比例下企业的存活数量、聚集程度和整体资本验证了模型的有效性。

关键词: 基于主体的建模方法, 云边端架构, 云制造, 计算实验

Abstract:

In view of the lack of comprehensive consideration of the individual adaptability changes of enterprises caused by the collaborative governance mechanism among multiple manufacturing units and the overall evolution trend of the system in existing learning evolution models, this proposed a learning evolution model of multi-cycle nested cloud manufacturing service ecosystem. At the micro level, the adaptive linkage decision-making among multiple manufacturing units within the enterprise was achieved in the individual layer through the nesting of planning-readiness-execution-assessment (PREA) loops and OODA loops; at the macro level, the closed-loop simulation of the individual layer, organizational layer, and social layer was achieved to provide an interpretable decision support tool for the development of digital and intelligent cloud manufacturing systems. The effectiveness of the model was verified by analyzing the survival quantity, aggregation degree, and overall capital of enterprises under different strategies and order allocation ratios in computational experiments.

Key words: agent-based modeling method, cloud-edge-end architecture, cloud manufacturing, computational experiment

中图分类号:

TP391.9

李芳,周德雨,王刚等 . 多循环嵌套云制造服务生态系统学习演化建模[J]. 系统仿真学报, 2026, 38(8): 2265-2282.

Li Fang,Zhou Deyu,Wang Gang,et al . Learning Evolution Modeling of Multi-cycle Nested Cloud Manufacturing Service Ecosystem[J]. Journal of System Simulation, 2026, 38(8): 2265-2282.

图/表 15

表1

图1

图2

图3

图4

图5

表2

表3

实验参数设置

参数	实验设置
环境大小	180×100
企业Agent的初始数量	50
企业Agent初始能量值	[100, 200]内随机取值
企业Agent视野范围	10
每步消耗能量值	2
每日消耗能量值	1
订单最大等待时间	30
单个订单收益能量值	[20, 50]内随机取值
订单1密集区域坐标和半径	(90, 62)和18
订单2密集区域坐标和半径	(41, 26)和18
订单3密集区域坐标和半径	(150, 50)和18
繁殖阈值	300
初始订单数 $N$	90
订单波动 $M$	20
工序总数 $J$	[5, 10]内随机取值
每个机器加工单个工件的时间/s	1
同一工厂内机器转移时间/s	0.5
不一工厂内机器转移时间/s	1
迭代次数 $T$	100
学习率	0.001
折扣因子	0.99
贪心因子	0.1
经验池大小	128
批次大小	16
种群大小	50
交叉概率	0.9
变异概率	0.1

表3

图6

图7

图8

图9

图10

图11

图12

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项目	SLE^[22]	SLE2^[23]	本文模型
个体学习模式	OODA循环	OODA循环	PREA和OODA循环嵌套
个体学习技术	遗传算法	遗传算法、强化学习	遗传算法、深度强化学习
组织拓扑结构	随机分布	层级结构	层级结构
组织演化规则	邀请邻居并学习	群体智能的交互模式	基于战略定位、商业模式等策略的演化博弈
社会层面演化	学习精英经验	社会交流和分层机制	基于精英文化(例如策略)组织间演化博弈

属性	现实模型	计算模型
数量	不同类型企业的数量	不同类型企业Agent的数量
获取订单	企业的市场竞争力决定了获得订单的数量	企业Agent的视野
处理订单	企业竞争力决定了其处理订单的数量	企业Agent处理订单的时间
运营成本	企业的生产成本、管理成本等运营成本	企业Agent的移动能量消耗和日常消耗等
能量值	决定企业生存时间的资本	企业Agent的能量值(在消耗中减少同时处理完订单会增加)
繁殖	新企业涌入市场或者企业扩大规模	当企业Agent的能量值超过繁殖阈值时繁殖新子代企业Agent
死亡	企业在竞争中倒闭或缩小规模	企业Agent能量值为0时会死亡

多循环嵌套云制造服务生态系统学习演化建模

Learning Evolution Modeling of Multi-cycle Nested Cloud Manufacturing Service Ecosystem

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