Research On Information Processing Method Of Mine Safety Monitoring System Based On Internet Of Things Sensor

By targeting at issues such as backward technologies, low functionality, monitoring gaps and inability in joint actions and controls which are existing in coal mine’s safety monitoring systems, the paper focuses on research of key technologies for perception of internet of things at coal mines by applying intelligentized perception technology of internet of things into such monitoring systems, with "perception" as the breakthrough point. The key technologies are distributed information fusion perception algorithm for perception of safety conditions at coal mines and fault diagnosis perception algorithm for perception of health status of sensor nodes. The paper has accomplished the following achievements:(1) It has defined concept of perception domain of internet of things and completed designs from many aspects, topology structure of perception layer, routing and aggregation mechanism and intermediate components and so on:An open and cluster-based distributed perception architecture, distributed star wireless sensor network (DSWSN) was built; Improvements based on LEACH and PEGASIS protocols to develop an efficient routing and aggregation mechanism were made, which can improve quality of services (QoS) and satisfy reliability and real-time requirements of communications; A cluster-based coordinating multifunctional structure of intermediate component systems aiming at integrating cluster layers and resource management layer with mobile Agent technology was built, which can sufficiently support development of application programs and utilize application programs to represent formal transformations for realization of coordination and unification of many types of application forms; and a deployment way of cloud data service platform (PaaS service) for data storage and transmission was developed, and an efficient safety monitoring and perception platform of internet of things for coal mines was constructed.(2) The paper has developed an information fusion strategy for perception of safety conditions at coal mines based on analyses of complicated underground mine environmental conditions and has adopted dynamic amplitude limiting filtering algorithm which integrates confidence distance measure and timestamp of data collection in data pre-processing module, to eliminate negligence and errors. Optimal weighting algorithm is used to make data level fusion to optimize post-fusion values and obtain more accurate site monitoring information by relying on estimates of self-related or mutually related sensor variances and finding out corresponding weight number of each sensor in a self-adapting way by utilizing measured values of each sensor under the optimal conditions of minimal total mean square error and satisfying unbiasedness. A fuzzy rough-gray correlation (FR-GC) based algorithmic model has been established in decision level fusion algorithm, in which no additional information needs to be provided in advance and data’s indiscernibility relation is used to extract potential information hidden inside data, which guarantees objectivity of analyses. At the same time, analyses of system features have been made by using gray correlation of coal mine’s environmental feature vectors and standard feature vectors, and coal mine’s environmental safety has been considered in an all-round way, and in the end, judgment about system safety was made according to the correlation. Tests indicate this algorithm is characterized with rationality in weight distribution, stability in absolute errors, soundness in dynamic response characteristics, high speed in convergence speed and ability to effectively remove disturbing data. It can improve affinity between samples to be decided and identification mode with the stronger complementary relation between fuzzy rough model and gray correlation analyses, which highlights quantitative degrees, has higher perception distinction degrees, reduces affect from objective factors and increases decision-making objectivities.(3) Four fault modes of sensors in the monitoring system have been analyzed, based on which, a sensor fault diagnosis strategy has been established. For example, by targeting at the four common latent soft faults of constant value faults, drifting type faults, biased faults and periodic faults from which gas sensor node suffers, the paper proposes a sensor fault diagnosis method, which adopts wavelet analysis and FRBF neural network as the basis and makes mode pattern classification and identification with wavelet packet adopting Hamming window added Shannon as mother wavelet to decompose and extract characteristic energy spectrum, and with FRBF neural network optimized by expanded Kalman filtering algorithm (EKF). Sensor output signals can be decomposed with wavelet packets, and cut with cost function based local discriminant bases (LDB) algorithm to obtain optimum characteristic energy spectrum, which will be used as characteristic vector after processed to train EKF-FRBF neutral network. Then parameter augmentation and statistical dynamics method, and EKF parameter estimation with regulated factors can be used to identify fault type of sensor node. Tests indicate the identification accuracy of this method is over95%, and both its false alarm rate and missing alarm rate are apparently lower than others. So this method can be adopted effectively in on-line fault diagnoses of sensor nodes in the system of things of internet.(4) It analyzed functions and features of intelligent mobile Sink nodes in DSWSN system and completed design and development of Sink nodes by making gradual progress in the three aspects of simulation design, hardware design and software design. Tests demonstrate this Sink node can well perform processing and transmission of monitoring data and achieve correct perception of safety status and node health state at coal mines. With its advantages in simple circuits, complete functions and advanced technical performances, this is satisfactory design for aggregation nodes of things of internet, and can be used to build a more densely-distributed and more efficient things of internet for safe production at coal mines.Close cooperation between information fusion and fault diagnosis perception algorithms has achieved information complementation and coordinated perception, which can significantly reduce uncertainty and unreliability of monitoring systems, and false alarms and conflicts caused by information limits of single sensor, and promote real-time safety monitoring and early safety warning ability of coal mines. So it can provide a strong guarantee for safe production at coal mines. The research done by the author can give full play to advantages in applying perception technology of things of internet in underground sections of coal mines and can provide a brand-new comprehensive informationalized platform for boosting production efficiency and safety management performances at coal mines.