WO2019000580A1 - 一种基于gpb算法的运维多模态决策系统原型 - Google Patents
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Abstract
本发明公开一种基于GPB算法的运维多模态决策系统原型,按k-1时刻分别采样传感器模型和摄像头模型;按k时刻分别采样传感器模型和摄像头模型;利用Kalman算法进行各自状态估计,和估计误差协方差阵;然后计算状态估计的合成,以及相应的协方差阵;最后是状态估计与协方差的融合输出,并构建基于GPB算法的运维多模态决策系统原型,以实现事故自动预警防范。
Description
本发明涉及一种基于GPB算法的运维多模态决策系统原型
随着云计算及虚拟化,呈现出“大规模”、“高密度”、“高能耗”、“复杂化”等特点,建设与发展新一代数据中心,提升数据中心基础设施管理将变得日趋重要,数据中心的基础架构融合管理与智能将成为数据中心发展的新趋势。
目前,运维缺乏自动化手段,被动运维,效率低下,大规模IT设施带来管理压力。需要实现数据中心的自动化监控,提高系统和环境参数的及时告警能力,提高系统和环境异常变化的响应速度和监控水平。使用传感器和摄像头等各种手段感知信息,就能实现统一的服务管理软件平台。
多源信息融合通过感知部件产生的数据来获得信息。信息融合涉及多种不同的感知器和不同的执行器,不同的感知设备会产生不同类型的数据。如何有效的融合这些多模态数据进而正确地反映运维的状态是十分重要的研究课题。
传感器子系统为环境探测装置,作用在实时地检测环境变化并为数据融合子系统提供相关数据;决策支持子系统利用数据融合的结构及时进行势态估计,该结果又为传感器管理提供重要依据;传感器管理子系统根据前面几个阶段提供的反馈信息,对传感器资源进行实时地调整和优化。
摄像头目标检测是将目标的状态作为跟踪的初始状态,同时对目标建模,获取相关特征构造目标的描述模型,然后在后续的图像中利用目标模型,采用滤波的方式估计目标的当前状态,同时利用当前状态更新目标模型。
固定模型集的最优估计是全假设估计,即考虑每一时刻系统的所有可能模式。其模型集是预先确定的,而不管模型本身是不是时变的。所以,有必要利用某些假设管理技术来建立更有效的非假设树算法,以保证剩余的假设数量在一定范围内。所谓广义伪Bayes方法(GPB),就是在时刻k,进行系统状态估计时仅考虑系统过去有限个采样时间间隔内的目标模型历史。
本发明提供了一种基于GPB算法的运维多模态决策系统原型,按k-1时刻分别采样传感器模型和摄像头模型;按k时刻分别采样传感器模型和摄像头模型;利用Kalman算法进行各自状态估计,和估计误差协方差阵;然后计算状态估计的合成,以及相应的协方差阵;最后是状态估计与协方差的融合输出,并构建基于GPB算法的运维多模态决策系统原型,以实现事故自动预警防范。
发明内容
本发明的目的在于提供一种基于GPB算法的运维多模态决策系统原型。本发明包括以下特征:
发明技术方案
1.一种基于GPB算法的运维多模态决策统架构,其具体步骤如下:
1)按k-1时刻分别采样传感器模型和摄像头模型;
2)按k时刻分别采样传感器模型和摄像头模型;
3)利用Kalman算法进行各自状态估计,和估计误差协方差阵;
4)然后计算状态估计的合成,以及相应的协方差阵;
5)最后是状态估计与协方差的融合输出。
2.基于权利要求1的架构,构建基于GPB算法的运维多模态决策系统原型。
图1是基于GPB算法的运维多模态决策系统原型图。
这种基于GPB算法的运维多模态决策系统原型,包括如下步骤:
1)按k-1时刻分别采样传感器模型和摄像头模型;
2)按k时刻分别采样传感器模型和摄像头模型;
3)利用Kalman算法进行各自状态估计,和估计误差协方差阵;
4)然后计算状态估计的合成,以及相应的协方差阵;
5)最后是状态估计与协方差的融合输出;
6)构建基于GPB算法的运维多模态决策系统原型。
Claims (2)
- 一种基于GPB算法的运维多模态决策系统架构,其具体步骤如下:1)按k-1时刻分别采样传感器模型和摄像头模型;2)按k时刻分别采样传感器模型和摄像头模型;3)利用Kalman算法进行各自状态估计,和估计误差协方差阵;4)然后计算状态估计的合成,以及相应的协方差阵;5)最后是状态估计与协方差的融合输出。
- 基于权利要求1的架构,构建基于GPB算法的运维多模态决策系统原型。
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US7719461B1 (en) * | 2008-08-05 | 2010-05-18 | Lockheed Martin Corporation | Track fusion by optimal reduced state estimation in multi-sensor environment with limited-bandwidth communication path |
CN102322861A (zh) * | 2011-05-31 | 2012-01-18 | 电子科技大学 | 一种航迹融合方法 |
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