A Mode of Intelligent Equipment Monitoring Optimization Based on Dynamic Programming Algorithm

Area of Science:

• Intelligent Systems and Control
• Data Science and Analytics
• Signal Processing

Background:

• Urban development and economic growth increase demand for intelligent equipment.
• Current intelligent equipment monitoring faces challenges with data accuracy due to time series algorithm limitations.
• Accurate monitoring is crucial for public services, policy-making, and research.

Purpose of the Study:

• To analyze the current state and challenges in digital intelligent equipment development and monitoring.
• To propose an optimized algorithm for intelligent equipment monitoring that improves accuracy and efficiency.
• To provide solutions for hardware, software, and algorithmic aspects of intelligent equipment.

Main Methods:

• Literature review of existing intelligent equipment monitoring technologies and algorithms.
• Development of a novel dynamic programming algorithm combined with wavelet functions.
• Simulation and validation of the proposed algorithm using MATLAB.

Main Results:

• The dynamic programming algorithm effectively removes up to 90% of redundant monitoring data.
• Wavelet functions are used to cluster distorted monitoring data, enhancing accuracy.
• The proposed algorithm achieved 95% accuracy with a computation time under 25 seconds, outperforming static algorithms.

Conclusions:

• The dynamic programming algorithm offers a significant improvement in intelligent equipment monitoring accuracy and computational efficiency.
• This approach optimizes the utilization of intelligent equipment's capabilities for enhanced monitoring.
• The findings support the development of more reliable and effective intelligent monitoring systems.