多源信息融合下冷链配送车辆碳排放动态预测方法  

Dynamic Prediction Method for Carbon Emissions of Cold Chain Distribution Vehicle under Multi-Source Information Fusion

作　　者：杨霖 刘双印 徐龙琴[1,2] 赫敏 绳庆峰[1,3] 韩佳伟 YANG Lin;LIU Shuangyin;XU Longqin;HE Min;SHENG Qingfeng;HAN Jiawei(College of Information Science and Technology,Zhongkai University of Agriculture and Engineering,Guangzhou 510225,China;Guangzhou Key Laboratory of Agricultural Products Quality&Safety Traceability Information Technology,Zhongkai University of Agriculture and Engineering,Guangzhou 510225,China;Academy of Intelligent Agricultural Engineering Innovations,Zhongkai University of Agriculture and Engineering,Guangzhou 510225,China;Research Center of Information Technology,Beijing Academy of Agriculture and Forestry Sciences,Beijing 100097,China)

机构地区：[1]仲恺农业工程学院信息科学与技术学院,广东广州510225 [2]仲恺农业工程学院广州市农产品质量安全溯源信息技术重点实验室,广东广州510225 [3]仲恺农业工程学院智慧农业创新研究院,广东广州510225 [4]北京市农林科学院信息技术研究中心,北京100097

出　　处：《智慧农业（中英文）》2024年第4期138-148,共11页Smart Agriculture

基　　金：国家自然科学基金项目(62373390,61871475);国家重点研发计划项目(2022YFD2001804,2023YFD2001302);北京市农林科学院科研创新平台建设项目(PT2024-24)。

摘　　要：[目的/意义]冷链配送碳排放动态预测是企业碳排放精准评估及其绿色信用等级评定的重要依据。本研究面向车辆碳排放受路况信息、行驶特征、制冷参数等多因素影响,提出一种融合多源信息的冷藏车辆碳排放动态预测模型。[方法]基于道路车辆数量与像素面积占比表征路况信息,构建基于改进YOLOv8s的路况信息识别模型,并以路况信息、行驶特征(速度、加速度)、货物重量、制冷参数(温度、功率)等为输入,构建基于改进iTransformer的冷藏车辆碳排放动态预测模型。最后与其他模型展开对比分析,分别验证路况信息识别与车辆碳排放动态预测的精度。[结果]改进的YOLOv8s路况信息识别模型在精确率、召回率和平均识别精度上分别达到98.1%、95.5%和98.4%,比YOLOv8s分别提高了1.2%、3.7%和0.2%,参数量和运算量分别减少了12.5%和31.4%,检测速度提高了5.4%。改进的iTransformer模型碳排放量预测的均方误差、平均绝对误差、均方根误差和R^(2)分别为0.026 1%VOL、0.079 1%VOL、0.161 5%VOL和0.940 0,均优于其他时序预测模型。[结论]提出的多源信息融合下冷链配送碳排放预测模型可实现对冷藏车辆碳排放量的精准预测,为有效降低冷链配送碳排放与提升配送企业绿色信用等级等提供理论参考。[Objective]The dynamic prediction of carbon emission from cold chain distribution is an important basis for the accurate assessment of carbon emission and its green credit grade.Facing the fact that the carbon emission of vehicles is affected by multiple factors,such as road condition information,driving characteristics,refrigeration parameters,etc.,a dynamic prediction model of carbon emission was proposed from refrigerated vehicles that integrates multi-source information.[Methods]The backbone feature extraction network,neck feature fusion network and loss function of YOLOv8s was firstly improved.The full-dimensional dynamic convolution was introduced into the backbone feature extraction network,and the multidimensional attention mechanism was introduced to capture the contextual key information to improve the model feature extraction capability.A progressive feature pyramid network was introduced into the feature extraction network,which reduced the loss of key information by fusing features layer by layer and improved the feature fusion efficiency.The road condition information recognition model based on improved YOLOv8s was constructed to characterize the road condition information in terms of the number of road vehicles and the percentage of pixel area.Pearson's correlation coefficient was used to compare and analyze the correlation between carbon emissions of refrigerated vehicles and different influencing factors,and to verify the necessity and criticality of the selection of input parameters of the carbon emission prediction model.Then the iTransformer temporal prediction model was improved,and the external attention mechanism was introduced to enhance the feature extraction ability of iTransformer model and reduce the computational complexity.The dynamic prediction model of carbon emission of refrigerated vehicles based on the improved iTransformer was constructed by taking the road condition information,driving characteristics(speed,acceleration),cargo weight,and refrigeration parameters(temperature,po

关 键 词：冷链配送 碳排放 路况识别 时序预测 YOLOv8s iTransformer 多源信息融合 渐进特征金字塔网络 

分 类 号：S24[农业科学—农业电气化与自动化]