基于關(guān)聯(lián)圖模型的信息物理融合系統(tǒng)感知數(shù)據(jù)可信性分析
doi: 10.11999/JEIT140437 cstr: 32379.14.JEIT140437
基金項目:
國家自然科學(xué)基金(51201182)和航空科學(xué)基金(20101996012)資助課題
Creditability Analysis of Sensor Data in the Cyber-physical System Based on the Relationship Diagram Model
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摘要: 針對信息物理融合系統(tǒng)(CPS)感知層數(shù)據(jù)的不確定性與隨機性，該文提出一種CPS中感知數(shù)據(jù)的可信性分析框架。摒棄以往以傳感器為中心的建模思路，該文充分考慮被監(jiān)測對象因素，建立傳感器-目標關(guān)聯(lián)圖模型，以此為基礎(chǔ)設(shè)計了傳感數(shù)據(jù)可信性推理算法。同時，為提高算法的實時性，減少傳感器-目標關(guān)聯(lián)圖的搜索空間與時間，設(shè)計了基于可信目標篩選的改進推理算法。通過實例驗證表明，該算法能實時、有效地濾掉CPS中感知數(shù)據(jù)中的虛假信息，極大提高感知數(shù)據(jù)的可信性。
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關(guān)鍵詞:
- 信息物理融合系統(tǒng) /
- 感知數(shù)據(jù) /
- 可信性分析 /
- 關(guān)聯(lián)圖
Abstract: The high uncertainty and randomness are the characteristics of the sensor data in the Cyber-Physical Systems (CPS), which make the data unreliable. A creditability analysis framework is proposed to solve those problems. Abandoning the idea that the sensor is the center in modeling, the theory takes monitoring targets into consideration and constructs the sensor-target relationship diagram, which is the base of the creditability reasoning algorithm. Meanwhile, in order to reduce the space and time of searching the relationship diagram, an improving reasoning method basing on filtering the incredible targets is designed. The examples demonstrate that the proposed algorithm can filter out the false message in the sensor data and enhances the creditability of the data in CPS.-
Key words:
- Cyber-Physical System (CPS) /
- Sensor data /
- Creditability analysis /
- Relationship diagram
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