One of the necessary requirements of coal mine safety mining is to survey hidden disaster causing factors. Hidden disaster causing factors include geological structures such as scouring zones and faults. Channel wave seismic exploration is one of the most promising geophysical methods for exploring the interior of coal seams. In this paper, CSP is used to extract the features of channel wave signals and BP neural network is used to classify. The classification results are more than 95%, which proves its effectiveness. First, the channel wave signal is studied theoretically. Combined with COMSOL Multiphysics software to simulation modeling goaf, wash zone, collapse column and fault in coal measure strata are established. The Ricker wavelet is used as the source to transmit and the channel wave signal data set is received at the geophone. Then the spatial feature of the signal is extracted by CSP algorithm, and the extracted data is sent to BP neural network for training. The results show that this method can extract the characteristics of channel wave signals and the prediction error is small, which improves the accuracy of classification.

With the rapid development of social economy and the acceleration of urbanization, energy consumption has increased rapidly, and power resources have become a scarce resource. In order to actively respond to the national green lighting strategy, this paper uses wireless sensor network and Internet of Things technology to design a smart street light management system, and incorporate urban street lights into the new urban Internet of Things system. The system collects the data of the surrounding environment of the street lamp in real time through the street lamp controller with multiple types of sensors installed in the street lamp pole, receives the data of the street lamp node through the LoRa network, and then uploads the data to the cloud monitoring platform through the NB-IoT technology. The city street lamp information is effectively collected and stored; at the same time, combined with WEBGIS technology, the current, voltage, power and other street lamp status data are visually displayed in the electronic map. For abnormal data, the system analyzes, locates and alarms, so as to realize the information interaction between the street light and the cloud platform, the precise control of the street light, and the intelligent monitoring and management. The experimental results show that the system has good stability, can effectively save energy and reduce consumption, and save the costs of management and operation and maintenance.