矿山数据压缩采集与重建方法研究
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摘要
近年兴起的矿山物联网技术作为下一代矿山信息化建设发展的主要方向受到高度重视,然而矿山物联网背景下海量信息的获取受制于井下的特殊环境以及从源端到井下环网间的线路带宽,存在传输瓶颈问题.在线路改造成本高,施工难度大的限制下,对传感器数据的压缩采集成了降低带宽需求的唯一选择.而近年来备受关注的压缩感知理论开创性地指出对具有可稀疏表示的信号,能够利用少量的线性观测值表示并通过非线性方法重建,恰好能满足矿山物联网的应用需求.
论文对压缩观测矩阵重建算法及其在矿山监控信息采集中的实际应用等方面进行了深入研究.论文的主要研究工作和创新成果包括:
(1)对感知矿山和压缩感知的研究与应用现状进行了综述.提出了论文的主要研究思路和研究内容.重点展开压缩感知理论及压缩感知理论在实践中的应用两方面的研究.
(2)理论研究主要结果：
本文利用混沌的伪随机特性,提出了一种混沌观测矩阵,充分利用混沌序列的高阶不相关性来产生观测矩阵,使得观测矩阵兼备随机矩阵的随机分布特征和伪随机可控的特征,从而能有效降低重建复杂度且有助于增加采集信息安全性.
提出了基于残差收敛的正交追踪(StORCP)算法,算法每次对剩余原子的残差在最优选择基上进行投影,寻找最小的投影值原子作为新的备选原子,从而逐次快速逼近最优解.该算法在信号重构误差,重构概率和重构时间方面都优于OMP,StOMP, ROMP等具有代表性的贪婪算法.
(3)在压缩感知理论应用研究主要成果:
提出一种能适应信息特征变化的自适应观测矩阵—基于系数贡献度的自适应观测(CCBAM)矩阵和增强的CCBAM (E-CCBAM)算法,将非线性运算简化为线性运算,分别将计算复杂度从O(MN)降低为O(N+2k),(k《N)和O (N+2k+w),(k《N,w《N).
利用CCBAM研究结果,提出基于关注度的多尺度1-bitCS算法,有效解决了1-bitCS算法存在的因过载量化失真而丢失敏感信息的问题.上述应用研究成果已被成功应用于淮北矿业集团26个煤矿的安全监控信息压缩采集中,取得良好的应用效果,相关科研项目通过安徽省科技厅组织的鉴定.
As an emerging technology and the major research area of next generation ofmine informatization, mine of things (MoT) technology is attracting much attention inthe field. Nevertheless, confined by the specific environment under the shaft andbandwidth between the source and underground ring network, the massiveinformation acquisition under the background of MoT has the transmission bottleneckissue. Under the restriction of high renovation costs and high construction difficulty,sensor data compressed sensing becomes the only method to reduce the bandwidthrequirement. The recent highly concerned Compressed Sensing (CS) theoryinitiatively indicated that the sparsely projectable signal could be non-linearlyreconstructed by utilizing few linear measured values. This theory exactly satisfies theapplication demands of MoT by overcoming the problems such as high costs, lowefficiency and resources waste of data storage and transmission.
This thesis deeply researched in some aspects such as compressed measurementmatrix algorithm and its application in acquisition of coalmine monitoringinformation, and achieved certain innovative results. The major research works andthe innovations are as followed:
(1) The present situation researches and applications on Perception Mine and CSwere summarized as well as the main research thinking and content were presented,furthermore, CS theory and its applications were the research focus in this thesis.
(2) The theoretical research are as followed:
By utilizing pseudo-random characteristic of chaos, this thesis put forward achaos measurement matrix and generated measurement matrix by taking fulladvantage of high order irrelevance of chaos sequence taken. This measurementmatrix has both random distribution characteristic of stochastic matrix andpseudorandom controllable characteristic; thereby the reconstruction complexitycould be reduced effectively as well as the security of information acquisition couldbe enhanced.
This thesis proposed a algorithm named StORCP, which projected the residualof remaining atom on the purpose of finding the minimum projection valued atom asthe new alternative atom, so as to approximate the solution successively and rapidly.This algorithm is superior to the representative greedy algorithms as OMP, StOMPand ROMP in the aspects of signal reconstruction errors, reconstruction time and reconstruction probability.
(3) In application aspect of CS theory, several application researches are asfollowed:
Coefficient Contribution based Adaptive Measurement(CCBAM) algorithm andEnhanced CCBAM(E-CCBAM) algorithm are proposed, which convert the non-linearreconstruction to linear reconstruction so that the calculation complexity could begreatly reduced. The2algorithms decrease the calculation complexity from O (MN)to O(N+2k),(k《N)and O(N+2k+w),(k《N,w《N).respectively.
Aiming at one-dimensional data compressed sensing of coalmine, Combinedwith CCBAM measurement research results, this thesis introduced prior informationof coalmine production into1-bit compressed sensing and presented attention degreebased multi-scale1-bit CS, which effectively solved the problem of sensitiveinformation loss caused by overload quantization distortion.
The research result mentioned above has been successfully implemented andperforms well in security monitoring information compressed sensing in26coalminerelative projects of Huaibei Mining Group and has passed the appraisal made byScience and Technology Department of Anhui Province.
论文对压缩观测矩阵重建算法及其在矿山监控信息采集中的实际应用等方面进行了深入研究.论文的主要研究工作和创新成果包括:
(1)对感知矿山和压缩感知的研究与应用现状进行了综述.提出了论文的主要研究思路和研究内容.重点展开压缩感知理论及压缩感知理论在实践中的应用两方面的研究.
(2)理论研究主要结果：
本文利用混沌的伪随机特性,提出了一种混沌观测矩阵,充分利用混沌序列的高阶不相关性来产生观测矩阵,使得观测矩阵兼备随机矩阵的随机分布特征和伪随机可控的特征,从而能有效降低重建复杂度且有助于增加采集信息安全性.
提出了基于残差收敛的正交追踪(StORCP)算法,算法每次对剩余原子的残差在最优选择基上进行投影,寻找最小的投影值原子作为新的备选原子,从而逐次快速逼近最优解.该算法在信号重构误差,重构概率和重构时间方面都优于OMP,StOMP, ROMP等具有代表性的贪婪算法.
(3)在压缩感知理论应用研究主要成果:
提出一种能适应信息特征变化的自适应观测矩阵—基于系数贡献度的自适应观测(CCBAM)矩阵和增强的CCBAM (E-CCBAM)算法,将非线性运算简化为线性运算,分别将计算复杂度从O(MN)降低为O(N+2k),(k《N)和O (N+2k+w),(k《N,w《N).
利用CCBAM研究结果,提出基于关注度的多尺度1-bitCS算法,有效解决了1-bitCS算法存在的因过载量化失真而丢失敏感信息的问题.上述应用研究成果已被成功应用于淮北矿业集团26个煤矿的安全监控信息压缩采集中,取得良好的应用效果,相关科研项目通过安徽省科技厅组织的鉴定.
As an emerging technology and the major research area of next generation ofmine informatization, mine of things (MoT) technology is attracting much attention inthe field. Nevertheless, confined by the specific environment under the shaft andbandwidth between the source and underground ring network, the massiveinformation acquisition under the background of MoT has the transmission bottleneckissue. Under the restriction of high renovation costs and high construction difficulty,sensor data compressed sensing becomes the only method to reduce the bandwidthrequirement. The recent highly concerned Compressed Sensing (CS) theoryinitiatively indicated that the sparsely projectable signal could be non-linearlyreconstructed by utilizing few linear measured values. This theory exactly satisfies theapplication demands of MoT by overcoming the problems such as high costs, lowefficiency and resources waste of data storage and transmission.
This thesis deeply researched in some aspects such as compressed measurementmatrix algorithm and its application in acquisition of coalmine monitoringinformation, and achieved certain innovative results. The major research works andthe innovations are as followed:
(1) The present situation researches and applications on Perception Mine and CSwere summarized as well as the main research thinking and content were presented,furthermore, CS theory and its applications were the research focus in this thesis.
(2) The theoretical research are as followed:
By utilizing pseudo-random characteristic of chaos, this thesis put forward achaos measurement matrix and generated measurement matrix by taking fulladvantage of high order irrelevance of chaos sequence taken. This measurementmatrix has both random distribution characteristic of stochastic matrix andpseudorandom controllable characteristic; thereby the reconstruction complexitycould be reduced effectively as well as the security of information acquisition couldbe enhanced.
This thesis proposed a algorithm named StORCP, which projected the residualof remaining atom on the purpose of finding the minimum projection valued atom asthe new alternative atom, so as to approximate the solution successively and rapidly.This algorithm is superior to the representative greedy algorithms as OMP, StOMPand ROMP in the aspects of signal reconstruction errors, reconstruction time and reconstruction probability.
(3) In application aspect of CS theory, several application researches are asfollowed:
Coefficient Contribution based Adaptive Measurement(CCBAM) algorithm andEnhanced CCBAM(E-CCBAM) algorithm are proposed, which convert the non-linearreconstruction to linear reconstruction so that the calculation complexity could begreatly reduced. The2algorithms decrease the calculation complexity from O (MN)to O(N+2k),(k《N)and O(N+2k+w),(k《N,w《N).respectively.
Aiming at one-dimensional data compressed sensing of coalmine, Combinedwith CCBAM measurement research results, this thesis introduced prior informationof coalmine production into1-bit compressed sensing and presented attention degreebased multi-scale1-bit CS, which effectively solved the problem of sensitiveinformation loss caused by overload quantization distortion.
The research result mentioned above has been successfully implemented andperforms well in security monitoring information compressed sensing in26coalminerelative projects of Huaibei Mining Group and has passed the appraisal made byScience and Technology Department of Anhui Province.
引文
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