260 ANALYSIS AND OPTIMIZATION OF TRUCK FUEL CONSUMPTION IN OPEN-PIT BASED ON MULTI-SOURCE DATA FUSION Genlai Shuai, Zhenhuan Li，Hongli Li Dandong Dongfang Measurement & Control Technology Co., Ltd. E-mail：shuaigenlai@dfmc.cc ABSTRACT In open-pit mining, fuel costs of large mining trucks represent a core component of operational expenses, making refined management and control crucial for cost reduction and efficiency improvement. Traditional fuel statistical methods suffer from limitations such as insufficient data precision and inaccurate identification of influencing factors. This study proposes an integrated analysis and management approach for mining truck fuel consumption by leveraging multi-source data fusion. By installing high-precision flow sensors in the fuel pipelines of mining trucks, real-time data acquisition at 1000 milliseconds intervals can be achieved. Combined with the dispatching system and digital maps, this approach accurately quantifies both instantaneous and cumulative fuel consumption during travelling, waiting, hauling and tipping. Furthermore, by integrating data from vibration sensors and inclinometers, the study elucidates the impact mechanisms of road roughness and slope on fuel efficiency. Based on this monitoring platform, an energy efficiency benchmarking system encompassing different truck types and drivers under the same operating conditions can be established. Through comparative analysis, the most fuel-efficient truck models and driving habits are identified. Ultimately, the research findings have formed a closed-loop fuel cost control strategy across four dimensions: proactive road maintenance, optimized route planning, promotion of best driving practices, and data-driven decision-making for future fleet procurement. This provides a robust theoretical and practical foundation for mining enterprises to achieve precise energy savings and asset optimization. KEYWORDS Open-pit mine; Mining trucks; Fuel consumption; Multi-source data fusion; Flow sensor; Energy consumption optimization; Cost control. 1. INTRODUCTION In open-pit mining, the mining truck, as core transportation equipment, fuel consumption accounts for 30%-45% of the mine's operating costs. Affected by the coupling of multiple factors such as working conditions, road conditions and driving behavior, relevant research has become a core direction for energy conservation and cost reduction in mines[1]. Although traditional fuel management can monitor fuel quantity and identify oil leakage or theft, it has problems such as data lag, inability to capture energy consumption fluctuations under instantaneous working conditions, and vague attribution[2]. This study constructs a realtime and accurate fuel consumption monitoring and analysis system through multi-source data fusion technology, providing a data-driven optimization scheme for mines[3].
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