262 truck (such as driving, parking, loading and unloading preparation, etc.), providing an important basis for subsequent accurate division of working conditions and fuel consumption attribution analysis. 3.3. Dispatching System Data Interface Two types of core data are obtained from the dispatching system: One is the mine road network data, including the road map topology, the location, length and width of each road section, the design slope, and the location and area information of key nodes (loading points, unloading points); The other is the mining truck production data, including the time nodes of no-load, heavy load, waiting for loading, waiting for unloading, loading and unloading, mining trucks load, mining trucks number, driver ID and other wholeprocess operation information. 3.4. GPS/Beidou Positioning Data Collection Through the high-precision GPS/Beidou positioning module, the longitude, latitude, elevation, speed, direction and time stamp are collected. The position data is matched with the road map to the design slope of section and whether in the loading/unloading area and so on; And the date such as the speed, acceleration, angular acceleration, movement distance, lifting height and other characteristics are also collected by simple calculations. At the same time, a dual-axis inclinometer is installed in the middle of the mining truck to collect the X-axis (driving direction) and Y-axis (lateral) inclination angles, making up for the deviation between the design slope and the actual slope and the change of the slope of the section. 3.5. Road Roughness Rating An industrial-grade three-axis vibration sensor (sampling frequency 500Hz) is installed on the frame under the cab to collect lateral and vertical vibration acceleration signals. A two-step preprocessing is carried out: first, the parking state data is filtered based on GPS speed to eliminate the interference of loading and unloading vibration; second, a Butterworth low-pass filter (cut-off frequency 50Hz) is used to filter the high-frequency vibration of the engine and tires, focusing on the 0-50Hz low-frequency band. The root mean square (RMS) of vibration acceleration is used as the core index, supplemented by peak factor and kurtosis parameters, to construct a multi-characteristic rating system and improve the accuracy of roughness evaluation. 3.6. Weather Factor Interface Weather indirectly changes the fuel consumption by affecting road conditions and engine environment, so a weather factor collection module is added. By building a weather station in the mining area or establishing a data interface with weather service, the weather data such as rain/snow or fog, ambient temperature, humidity and wind speed are collected. The weather is classified into different impact levels to provide a basis for fair energy efficiency under different weather conditions. 4. CONSTRUCTION OF ENERGY EFFICIENCY MODEL BASED ON MULTI-SOURCE DATA 4.1. Selection Idea of Energy Efficiency Model During the mine transportation operation, fuel consumption is affected by multiple factors such as mining trucks operation status, load change, road conditions, environmental factors and driving behavior. There is a significant non-linear coupling relationship between the influencing factors, and it also has obvious time-series characteristics. Traditional linear regression models or static machine learning models are often difficult to fully describe the dynamic law of fuel consumption with the evolution of working conditions in such complex scenarios. Therefore, on the basis of comprehensively comparing common modeling methods
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