真空绝热板真空度原位监测与衰减预测模型及其在建筑节能调控中的应用

doi:10.13385/j.cnki.vacuum.2026.03.14

摘要/Abstract

摘要： 为解决建筑节能领域中真空绝热板（VIP）在长期服役下因真空维持失效导致的绝热性能劣化问题,本文研究了真空绝热板真空维持状态的原位监测技术,并据此建立了建筑节能动态调控策略。搭建了多模态真空状态监测平台,实时原位采集VIP芯材真空维持状态的关键参数;结合改进粒子滤波算法,构建了真空衰减动力学模型,动态预测VIP在不同严苛气候条件下真空维持能力的退化轨迹;开发了基于深度强化学习的建筑围护结构动态优化系统,耦合BIM模型与实时真空状态监测数据,自适应调节建筑能源管理系统运行参数。研究表明：该研究实现对服役中VIP真空维持状态关键参数的原位、连续、精准监测;所构建的真空衰减预测模型为评估VIP长期服役可靠性提供了新方法;基于真空状态实时感知的优化系统能有效补偿真空衰减导致的绝热损失,显著降低建筑能耗。

关键词: 真空绝热板, 真空度监测, 气体渗透率, 原位监测, 粒子群优化算法

Abstract: Aiming at the problem of insulation performance degradation caused by vacuum maintenance failure of vacuum insulation panels (VIP) in the field of building energy conservation under long-term service, this paper investigated in-situ monitoring of vacuum maintenance status for VIP and dynamic control of building energy conservation. Built a multimodal vacuum status monitoring platform to real-time collect key parameters of VIP core material vacuum maintenance status in situ; combining the improved particle filter algorithm, a vacuum attenuation dynamic model was constructed to dynamically predict the degradation trajectory of VIP's vacuum maintenance ability under different harsh climate conditions; developed a dynamic optimization system for building envelope structures based on deep reinforcement learning, coupled with BIM models and real-time vacuum monitoring data, to adaptively adjust the operating parameters of the building energy management system.The result shows that the research realizes the in-situ, continuous and accurate monitoring of the key parameters of VIP vacuum maintenance state in service. The vacuum decay prediction model provides a new method for evaluating the long-term service reliability of VIP. The optimization system based on real-time perception of vacuum state can effectively compensate the adiabatic loss caused by vacuum attenuation and significantly reduce building energy consumption.

Key words: vacuum insulation board, vacuum monitoring, gas permeability, in situ monitoring, particle swarm optimization

中图分类号: TB79

唐通, 宗飞超, 杨伟, 高学杨. 真空绝热板真空度原位监测与衰减预测模型及其在建筑节能调控中的应用[J]. 真空, 2026, 63(3): 97-103.

TANG Tong, ZONG Feichao, YANG Wei, GAO Xueyang. In-situ Monitoring and Attenuation Prediction Model for Vacuum Degree of Vacuum Insulation Panel and Its Application in Building Energy Saving Regulation[J]. VACUUM, 2026, 63(3): 97-103.

参考文献

[1] 左志宇,牟晋东,毛罕平,等.基于深度强化学习的温室环境协调控制系统设计[J].农机化研究,2025,47(5):22-27.
[2] Chen S J, Shi M X, Chen Z F,et al.Research on thermal insulation performance and application simulation of high-temperature vacuum insulation panel[J].Journal of Porous Materials,2025, 32(1):217-227.
[3] IBADOV N, AKGÜN F M, SMAIL ÜNCÜ İ S, et al. Real-time service life estimation of vacuum insulated panels via embedded sensing and machine learning models[J].BUILDINGS,2025, 15(16):2879.
[4] 陈玉霞,闫林君.混凝土夹心保温墙体的保温层厚度优化[J].兰州大学学报(自然科学版),2024,60(5):705-710.
[5] 黑玉林,陈照峰,吕岩,等.PCM+VIP耦合的疫苗冷链物流箱保温性能研究[J].制冷与空调,2024,24(12):100-105.
[6] 赵洪凯,王宗灿,徐岩.单向切割真空绝热板热桥效应研究[J].真空科学与技术学报,2023,43(9):805-811.
[7] 司秉卉,张林瀛,李艳霞,等.建筑性能优化设计中优化算法的最佳参数设置研究[J].建筑科学,2023,39(6):226-237.
[8] 王晓波,张学勇,杨佳,等.内置真空绝热板复合保温系统热工及节能特性研究[J].新型建筑材料,2024,51(1):140-144.
[9] 王伟龙,陈兴涛,左长安,等.超低能耗建筑的真空绝热板复合高性能有机保温板系统构造设计研究[J].新型建筑材料,2023,50(10):132-134.
[10] 刘震. 某办公建筑围护结构节能改造效果分析[J].建筑技术,2023,54(2):163-166.
[11] 石文喆,李冰洁,尤培培,等.基于深度强化学习的建筑能源系统优化策略[J].中国电力,2023,56(6):114-122.
[12] 王磊,胡国,吴海,等.基于分层深度强化学习的分布式能源系统多能协同优化方法[J].电力系统自动化,2024,48(1):67-76.
[13] 庞嘉炜,高建强,陈光奇,等.薄壁构件的氦质谱正压-真空检漏试验研究[J].真空,2025,62(1):15-20.
[14] 李刚,李尚科,陈英瑜,等.核电厂安全壳氦质谱检漏方法的可行性分析[J].科技创新与应用,2024,14(5):20-23.
[15] 雷晓庆,邵奇,陈岚.氦质谱检测法考察定量压力气雾剂包装密封完整性[J].药物分析志,2024,44(2):298-306.
[16] 侯慧,何梓姻,陈跃,等.基于深度强化学习区间多目标优化的智能建筑低碳优化调度[J].电力系统自动化,2023,47(21):47-57.
[17] 蔺伟山,王小君,孙庆凯,等.计及安全约束的综合能源系统深度强化学习优化调度策略研究[J].电网技术,2023,47(5):1970-1983.
[18] 李萌,韦冠一,方随,等.基于静态气体质谱仪的Xe稳定同位素高精度分析[J].同位素,2023,36(3):351-357.
[19] 胡静,黄诗惠.气味评价与气体预浓缩仪-气相色谱质谱联用识别包装材料中的异味物质[J].广州化工,2024,52(19):126-128,132.
[20] 武超,胡石林,任英,等.高分辨率气体同位素质谱法测定氢同位素丰度[J].质谱学报,2024,45(5):647-655.
[21] 杨挺,刘豪,王静,等.基于深度强化学习的园区综合能源系统低碳经济调度[J].电网技术,2024,48(9):3604-3613.
[22] 陈实,朱亚斌,刘艺洪,等.基于世界模型深度强化学习的含风电电力系统低碳经济调度[J].电网技术,2024,48(8):3143-3154.
[23] ZOTOVA I, KIRILOVS E, ZIEMELE L,et al.Comparative analysis of reaction to fire and flammability of hemp shives insulation boards with incorporated microencapsulated phase change materials[J]. Journal of Renewable Materials, 2024,12(3):603-613.
[24] YOUSUF S, KADRI M B.Improving the position accuracy and computational efficiency of UAV terrain aided navigation using a two-stage hybrid fuzzyparticle filtering method[J]. Computers, Materials & Continua, 2025,82(1): 1193-1210.
[25] ZHENG Z, ZHONG X, SUN Q Q, et al.Investigation on power frequency following current for vacuum circuit breaker interrupting shunt reactor in offshore wind farms[J]. IEEE Transactions on Power Delivery, 2024,39(1): 306-316.
[26] SINGH S, KHOSLA A, KAPOOR R,et al.Visual-thermal fusion-based bbject tracking via a granular computing backed particle filtering[J]. IETE journal of research, 2023,69(10): 6693-6708.
[27] 赵利军,白硕,李欣刚,等.改进BIM模型的高层办公建筑节能优化仿真[J].计算机仿真,2024,41(8):281-285.
[28] VIVEK C K, SARGUNAN K,VASA J S S K, et al. Applying ANN-PSO algorithm to maximize the compressive strength and split tensile strength of blended self curing concrete on the impact of supplementary cementitious materials[J]. International Journal on Interactive Design and Manufacturing IJIDeM, 2024,18(4):1997-2006.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed