煤矿概率流数据挖掘方法研究
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摘要
煤矿概率流数据是指满足概率流数据模型的煤矿安全监测监控系统中的数据,煤矿概率流数据挖掘方法研究是以煤矿灾害预测为最终目标,研究煤矿概率流数据挖掘系统体系结构,以及在低时空复杂度、增量维护、自适应、概率描述等条件限制下的轻量级概率流数据挖掘方法,针对这些问题,本文的主要研究工作包括:
1.在分析煤矿概率流数据挖掘系统目标和特点的基础上,参照典型的集中式数据挖掘系统和流数据挖掘系统的构成,以煤矿灾害预测为挖掘目标,构建了适合于煤矿概率流数据环境的挖掘系统,包括系统模型、体系结构、层次结构等。
2.针对煤矿概率流数据挖掘系统中序列数据的模式表示问题,以传统时间序列中分段线性拟合方法为基础,提出基于拟合点的分段线性拟合方法,该方法不依赖于序列整体状态和专家领域知识,还达到了低时空复杂度、增量维护、自适应、概率描述等概率流数据对挖掘算法的要求。
3.在对煤矿概率流数据的模式异常检测问题分析基础上,研究了矿井灾害发生初期监测数据在形态上的异常发现问题,给出了煤矿概率流数据模式异常检测的概念,并以概率流数据之间的概率相似距离为基础,提出基于概率相似距离的模式异常检测算法,该算法对于上下文无关的模式异常具有较好的检测效果,并在窗口宽度小于30时具有较低的时空复杂度。
4.研究了煤矿概率流数据模式异常信息的传输问题,提出模式异常定向扩散算法,该算法以小部分牺牲网络延迟为代价,较大幅度降低了节点模式异常概率的漏报率,使异常信息可以完整地发送至汇聚节点,同时可以有效减少网络能量的损耗、平衡网络负载,延长整个系统的寿命,为系统的灾害异常检测提供硬件保障。
5.对煤矿概率流数据模型的煤矿灾害预测问题,以传统的趋势分析异常检测方法为基础,提出基于趋势分析的灾害异常检测算法,运用正常时期瓦斯监测数据建立灾害时期模式异常概率的预测模型,解决了灾害数据获取困难的问题;运用线性预测方法解决非线性系统的预测问题,解决了非线性方法时空复杂度较高的问题,与本文提出的模式异常检测方法相结合,可以快速、准确地实现矿井灾害的预测。
6.通过构建煤矿概率流数据挖掘系统的原型系统,实现了煤矿概率流数据的模式表示、模式异常检测、异常信息的路由选择以及灾害异常检测功能,印证了煤矿概率流数据挖掘方法的可行性和有效性,为煤矿灾害预测提出新的解决思路。
该论文有图60幅,表8个,参考文献篇153。
Mine Probabilistic Stream Data means that the data in coal mine safety monitor system is satisfied with probability data stream model. Research on mining method of Mine Probabilistic Stream Data has the goal of predicting the mine disaster, researching mining system architecture of Mine Probability Stream Data, and the lightweight minging method under the restriction of the low time and space complexity, incremental maintenance, adaptive and description of probability . To overcome these limitations, the main research work includes:
1. Under analizing the goals and the characteristics of the mine Probabilistic stream data mining system, Referring to the system architecture of centralized and streamed, aiming at the forecast of mine disaster, construction the mining system is fit for mine probabilistic data stream, including system model, architecture and hierarchical structure.
2. Researched the pattern representaion problem of mine probabilistic stream data.Referencing the Piecewise Linear Fitting of traditional time series, this paper advanced a Fitting Point based Piecewise Linear Fitting method, shorted as FP_PLF method. FP_PLF method does not rely on the factor of whole length of time series and expert's domain knowledge, also satifies the mining algorithm requirements that the low time-space complexity, incremental maintenance, adaptive and description of probability .
3. Researched the problem on finding the shape abnomal of the beginning of the mine disaster, this paper put forward the conception of pattern abnomal detection for mine probabilistic stream data .Based on the similarity distance between two probilistic stream, advancing a Probabilistic Similarity Distance based Pattern Abnomal Detection method, shorted as PSD_PAD method.The PSD_PAD method has good effections on context uncorrelated abnomal and has low time-space complexity in the condition that the window size smaller than 30.
4. Researched the problem of transmission of pattern abnomal probability in mine probabilistic stream data mining system.This paper, we put forward a Pattern Abnomal Dirrected Diffusion algorithm, shorted as PADD algorithm. The PADD alogrithm has a higher network delay,but lower missed report rate than traditional WSN ruting protocols and implements the abnomal probability completed to the server, reduce network energy waste, postpone the life of data mining system. Provide hardware protection for ultimate mining goal of the system.
5. Researched the problem of mine disaster forcasting based on the mine probabilistic stream data.In this paper, based on exception test method of trend analysis, proposed a Trend Analyze based Disaster Abnomal Detection method ,shorted as TA_DAD method. The TA_DAD method make use of monitoring gas data in the normal times to establish the forecasting model during the hazards period to solve the difficulties of data acquisition; solved the problem of predicting in nonlinear systems and the nonlinear method complexity and high complexity of the time and space problem in use of linear prediction method, combination with exception test method, you can quickly and accurately to achieve the mine disaster forecasts.
6. By the construction of prototype system of mine probabilistic stream data, this paper realized the model representation, pattern anomaly detection, anomaly information transfer and disaster anomaly detection of the mine probabilistic stream data mining system, which proved the feasibility and effectiveness of data mining method of the coal probability data stream, and put forward new ideas and new exploration for mine disaster prediction.
1.在分析煤矿概率流数据挖掘系统目标和特点的基础上,参照典型的集中式数据挖掘系统和流数据挖掘系统的构成,以煤矿灾害预测为挖掘目标,构建了适合于煤矿概率流数据环境的挖掘系统,包括系统模型、体系结构、层次结构等。
2.针对煤矿概率流数据挖掘系统中序列数据的模式表示问题,以传统时间序列中分段线性拟合方法为基础,提出基于拟合点的分段线性拟合方法,该方法不依赖于序列整体状态和专家领域知识,还达到了低时空复杂度、增量维护、自适应、概率描述等概率流数据对挖掘算法的要求。
3.在对煤矿概率流数据的模式异常检测问题分析基础上,研究了矿井灾害发生初期监测数据在形态上的异常发现问题,给出了煤矿概率流数据模式异常检测的概念,并以概率流数据之间的概率相似距离为基础,提出基于概率相似距离的模式异常检测算法,该算法对于上下文无关的模式异常具有较好的检测效果,并在窗口宽度小于30时具有较低的时空复杂度。
4.研究了煤矿概率流数据模式异常信息的传输问题,提出模式异常定向扩散算法,该算法以小部分牺牲网络延迟为代价,较大幅度降低了节点模式异常概率的漏报率,使异常信息可以完整地发送至汇聚节点,同时可以有效减少网络能量的损耗、平衡网络负载,延长整个系统的寿命,为系统的灾害异常检测提供硬件保障。
5.对煤矿概率流数据模型的煤矿灾害预测问题,以传统的趋势分析异常检测方法为基础,提出基于趋势分析的灾害异常检测算法,运用正常时期瓦斯监测数据建立灾害时期模式异常概率的预测模型,解决了灾害数据获取困难的问题;运用线性预测方法解决非线性系统的预测问题,解决了非线性方法时空复杂度较高的问题,与本文提出的模式异常检测方法相结合,可以快速、准确地实现矿井灾害的预测。
6.通过构建煤矿概率流数据挖掘系统的原型系统,实现了煤矿概率流数据的模式表示、模式异常检测、异常信息的路由选择以及灾害异常检测功能,印证了煤矿概率流数据挖掘方法的可行性和有效性,为煤矿灾害预测提出新的解决思路。
该论文有图60幅,表8个,参考文献篇153。
Mine Probabilistic Stream Data means that the data in coal mine safety monitor system is satisfied with probability data stream model. Research on mining method of Mine Probabilistic Stream Data has the goal of predicting the mine disaster, researching mining system architecture of Mine Probability Stream Data, and the lightweight minging method under the restriction of the low time and space complexity, incremental maintenance, adaptive and description of probability . To overcome these limitations, the main research work includes:
1. Under analizing the goals and the characteristics of the mine Probabilistic stream data mining system, Referring to the system architecture of centralized and streamed, aiming at the forecast of mine disaster, construction the mining system is fit for mine probabilistic data stream, including system model, architecture and hierarchical structure.
2. Researched the pattern representaion problem of mine probabilistic stream data.Referencing the Piecewise Linear Fitting of traditional time series, this paper advanced a Fitting Point based Piecewise Linear Fitting method, shorted as FP_PLF method. FP_PLF method does not rely on the factor of whole length of time series and expert's domain knowledge, also satifies the mining algorithm requirements that the low time-space complexity, incremental maintenance, adaptive and description of probability .
3. Researched the problem on finding the shape abnomal of the beginning of the mine disaster, this paper put forward the conception of pattern abnomal detection for mine probabilistic stream data .Based on the similarity distance between two probilistic stream, advancing a Probabilistic Similarity Distance based Pattern Abnomal Detection method, shorted as PSD_PAD method.The PSD_PAD method has good effections on context uncorrelated abnomal and has low time-space complexity in the condition that the window size smaller than 30.
4. Researched the problem of transmission of pattern abnomal probability in mine probabilistic stream data mining system.This paper, we put forward a Pattern Abnomal Dirrected Diffusion algorithm, shorted as PADD algorithm. The PADD alogrithm has a higher network delay,but lower missed report rate than traditional WSN ruting protocols and implements the abnomal probability completed to the server, reduce network energy waste, postpone the life of data mining system. Provide hardware protection for ultimate mining goal of the system.
5. Researched the problem of mine disaster forcasting based on the mine probabilistic stream data.In this paper, based on exception test method of trend analysis, proposed a Trend Analyze based Disaster Abnomal Detection method ,shorted as TA_DAD method. The TA_DAD method make use of monitoring gas data in the normal times to establish the forecasting model during the hazards period to solve the difficulties of data acquisition; solved the problem of predicting in nonlinear systems and the nonlinear method complexity and high complexity of the time and space problem in use of linear prediction method, combination with exception test method, you can quickly and accurately to achieve the mine disaster forecasts.
6. By the construction of prototype system of mine probabilistic stream data, this paper realized the model representation, pattern anomaly detection, anomaly information transfer and disaster anomaly detection of the mine probabilistic stream data mining system, which proved the feasibility and effectiveness of data mining method of the coal probability data stream, and put forward new ideas and new exploration for mine disaster prediction.
引文
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