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 real-time and accurate fuel consumption monitoring and analysis system through multi-source data fusion technology, providing a data-driven optimization scheme for mines[3]. 2. RESEARCH STATUS OF INFLUENCING FACTORS OF FUEL CONSUMPTION Current research focus on three core influencing factors: At the working condition level, fuel consumption under heavy-load conditions accounts for 60%-75% of total energy consumption, while there are significant differences in energy consumption under no-load, idle speed and other working conditions[4][5]; At the road surface level, tests have verified a positive correlation between slope and fuel consumption: the fuel consumption increases by an average of 8%-12% for every 1% increase in slope[6]. Additionally, the roughness of mine roads in mines will increase tire rolling resistance by more than 30%, thereby increasing fuel consumption[7]; At the driving behavior level, bad habits such as sudden acceleration and sudden deceleration will increase fuel consumption, and standardized driving can reduce fuel consumption by about 8%[8]. Existing studies have shortcomings such as single data collection dimension, lack of consideration of multi-factor coupling effects, insufficient accuracy of quantitative models, and lack of closed-loop active optimization schemes[9][10]. Therefore, this study focuses on multisource data fusion technology to construct a full-dimensional fuel consumption monitoring and analysis system, providing an accurate and efficient energy-saving optimization scheme for openpit mines[11][12]. 3. DESIGN OF MULTI-SOURCE DATA COLLECTION AND FUSION SYSTEM With the goal of "accurately capturing factors affecting fuel consumption and realizing indepth collaboration of multi-source data", we build an interface with the data-collection module and the dispatching system to get the multi-dimensional data such as fuel flow, road network characteristics and operation conditions, that provide comprehensive and reliable data support for the subsequent analysis of fuel consumption influence mechanisms, construction of an energy efficiency system, and formulation of optimization strategies. 3.1 Fuel Consumption Data Collection
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