Offshore oilfield drilling platform equipment sensor data collection based on IoT deep learning

The sensor layout of offshore oilfield drilling platform equipment ignores key monitoring points or has repeated coverage, resulting in low data collection quality. In this regard, a sensor data collection method for offshore oilfield drilling platform equipment based on deep learning of the Internet of Things is studied. By calculating the node density and rationally planning the distribution of sensors, a four-layer acquisition system is designed to quantify the impact of spatial distance. The deep learning algorithm is used to determine the data distribution, process massive data and filter valuable information. In view of the data problems caused by sensor differences, internal fusion is performed to form an accurate state description, and the sensor data collection process is designed to determine the total amount of sensor node information. The experimental results show that after the application of this method, the energy consumption curve is stable and low, the integrity is high, and the wider coverage range under multiple error thresholds is better, which has significant advantages and application prospects.