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摘要: 為解決特種變壓器生產車間實時化管控弱、調度決策能力差、過程監控不直觀、運行態勢不明朗的問題，從特種變壓器車間的數字孿生建模、知識信息融合、運行可靠性分析和可視化表達等方面入手，探討了一種特種變壓器車間數字孿生系統的新型體系架構；闡述了基于“5維度-4視角”模型、高性能算力孿生引擎及沉浸式可視化技術的數字孿生建模表達方法；研究了數字孿生車間指標體系的構建方法以及車間知識信息與數字孿生的融合方法；基于層次分析理論和關聯熵方法，構建了一種基于復合物元信息熵的車間數字孿生系統可靠性分析模型；最后，以某特種變壓器生產車間為應用案例，基于上述方法和模型，結合實際生產過程開發了原型數字孿生系統，實現了特種變壓器車間的多維多尺度實時智能管控，驗證了該方法的合理性和有效性.Abstract: To address the weak real-time control, poor decision-making ability, nonintuitive process monitoring, and unclear operation situation problems in a special transformer production workshop, a new digital twin system architecture is discussed for the workshop, focusing on digital twin modeling, knowledge information fusion, operation reliability analysis, and visual representation. The modeling and expression of the digital twin were based on the “Five dimensions–Four perspectives” engine, where the “Five dimensions” refer to physical entities, virtual objects, twin data, connection mapping, and services and the “Four perspectives” refer to geometric, physical, behavioral, and rule models, with high-performance computing power and immersive visualization technology. This paper describes the method of 3D modeling, data mapping, computing power development, and visual expression for the digital twin. A deep fusion method between knowledge and digital twin was studied based on the principle of tree growth. This model includes two key parts. First, a design of the index system having a hierarchical structure based on the principle of tree growth was proposed. Indices were divided into four levels—workshop, production line, station, and equipment levels—based on the workshop scale, vertically constituting the main branches of the index tree model. From the perspective of the life cycle of production activities, indices were divided into planning parameters, process parameters, work order parameters, quality parameters, equipment parameters, and so on. Thus, a complete index system of the digital twin workshop was established. Furthermore, the photosynthesis and the transport of organic products in the tree were simulated by formally describing the deep fusion mechanism of knowledge and digital twin in transformer workshops, and diverse intelligent computing units were built to mine knowledge and identify information from disorganized workshop operation big data. Finally, a multilevel and complex model was constructed to integrate the knowledge and digital twin of a special transformer workshop based on the computation model, and the real-time monitoring and control of the workshop were realized. An operational reliability analysis model of the digital twin system for special transformer workshops based on composite matter element information entropy was also proposed, which combines the analytical hierarchy process and the correlation entropy method. A composite matter element model was systematically built based on the historical operational data of the transformer workshop, which can be used for the real-time monitoring of the operational reliability and the evolution of any trends in the operational parameters. Thus, a prototype digital twin system was developed, and its rationality and effectiveness were verified using the special transformer workshop as the application case. The research results have an outstanding reference for the construction of digital twin systems and the intelligent management of transformer workshops.
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Key words:
- digital twin /
- special transformer /
- construction method /
- digital workshop /
- knowledge fusion
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圖 1 特種變壓器生產車間數字孿生體系架構
Figure 1. Digital twin system architecture of the special transformer workshop
圖 2 特種變壓器生產車間數字孿生的建模和開發原理
Figure 2. Principle of the modeling and development of the digital twin in special transformer workshop
圖 4 仿生樹木生長的數字孿生車間指標體系形式化建模原理
Figure 4. Formal modeling principle of the digital twin workshop index system by imitating the growth of tree
圖 6 仿生樹木光合作用及其有機產物匯聚輸送的知識信息與數字孿生融合機制
Figure 6. Fusion of the knowledge and digital twin by imitating tree photosynthesis and transport of organic products
圖 7 特種變壓器生產車間知識信息和數字孿生融合模型的構建
Figure 7. Fusion model of the knowledge information and digital twin in special transformer workshop
圖 9 特種變壓器數字孿生車間多維多尺度實時監控
Figure 9. Multidimensional and multiscale, real-time monitoring of the special transformer digital twin workshop
圖 10 特種變壓器數字孿生車間深度沉浸式可視化3D漫游
Figure 10. Deep immersion visualization: 3D walkthrough of special transformer digital twin workshop
表 1 某月特種變壓器車間的運行指標歷史數據
Table 1. Historical data of special transformer workshop for a month
Time S1/% S2/min S3/% S4/% S5/min S6/h S7/% S8/% S9 S10/% T1 85 350 92 65 60 100 93 70 3 100 T2 90 340 96 67 60 100 95 98 5 99 T3 95 340 95 66 55 100 95 100 4 100 … … … … … … … … … … … Ti 95 335 97 70 57 101 96 100 3 100 … … … … … … … … … … … T30 96 330 97 71 50 101 95 100 3 100 
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表 2 復合物元信息熵權重
Table 2. Information entropy weight of the composite matter element
Index Subjective weight Correlation entropy
weight $ \omega '' $Comprehensive
weight $ \omega $S1 14.028 8.572 11.98 S2 6.245 8.765 5.45 S3 10.95 7.499 8.18 S4 12.481 13.081 16.27 S5 9.394 27.682 25.91 S6 7.819 6.513 5.07 S7 9.391 6.881 6.44 S8 12.485 6.513 8.1 S9 9.388 8.364 7.82 S10 7.819 6.129 4.77
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