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针对基于可信计算的网格行为信任模型,运用波动信息能量变换给出了根据实体的交互经验和交互时间计算历史信任值和直接信任值的方法;用Hausdorff距离构造了函数相关程度的算法,进而给出了自我信任值的计算方法。通过采用门限值作为平均值、自我信任值为偏差量的正态分布函数构造了推荐信任值的更新函数,给出了域间评估流程图,并用一个有2 000个实体的区域网格进行域内数据更新实验。分析结果说明了各评估算法的合理性和有效性。
Aiming at the trust behavior model of grid based on trusted computing, the method of calculating historical trust value and direct trust value based on the interaction experience and interaction time of the entity is given by using the volatility information energy transformation. The Hausdorff distance is used to construct the function correlation degree algorithm, And then gives the calculation method of self-trust value. By using the threshold value as the average value and the self-confidence value as the normal distribution function of the deviation, an update function of the recommended trust value is constructed. An inter-domain assessment flow chart is given and a regional grid with 2,000 entities is used Domain data update experiment. The analysis results show the rationality and effectiveness of each evaluation algorithm.