Design and implementation of intelligent management system for wharf coal yard based on big data analysis

Aiming at the problems of low efficiency, obvious potential safety hazard and high cost of traditional wharf coal yard management, this paper puts forward a design and implementation scheme of intelligent management system of wharf coal yard based on big data analysis. The system adopts hierarchical architecture design, covering four levels: data acquisition, storage, analysis and application. By deploying various sensors and network equipment, the data acquisition layer efficiently collects data such as temperature, dust concentration and equipment running status of coal yard by using tools such as Apache Kafka and Fluentd. The data storage layer combines HDFS, MongoDB and MySQL to realize the efficient storage and management of large-scale data. In the data analysis layer, Apache Flink is used for real-time stream processing to monitor equipment status and detect anomalies, Apache Spark is used for off-line batch mining data patterns, and a machine learning model is built to predict equipment failures with the help of Scikit-Learn and TensorFlow. The application layer provides real-time monitoring and early warning, intelligent scheduling, decision support and other functional modules. Through the realization of key technologies, such as data acquisition and transmission based on Python and Kafka, real-time stream processing based on Apache Flink and intelligent scheduling based on genetic algorithm, the system can effectively improve the operation efficiency of the dock coal yard, reduce security risks and optimize resource allocation. The system performance test results show that the system response time and error rate are kept within a reasonable range when there are less than 100 concurrent users.