Operation Prediction Model of Power Maintenance Robot Based on Taylor Linear Fitting

This study presents a comprehensive model to optimize the procurement strategy of power substation maintenance robots, addressing the need for cost-effective operations in the electric power industry. The research scope encompasses multiple constraints, including robot attrition rates and fluctuating market conditions. Using a robust data set obtained from various public databases, the study employed linear fitting and greedy algorithm techniques for predictive analysis. The model, validated through mathematical induction and optimized for real-world applicability, demonstrates a significant correlation between total operational costs and robot attrition rates, highlighting the importance of strategic procurement and maintenance. The findings reveal the potential for substantial cost savings and improved operational efficiency, with implications for wider application in similar resource optimization challenges in the electric power sector and beyond. The study's limitations and potential for future research lie in its applicability to other types of maintenance robots and varying market conditions.