-
摘要: 为提高鲜活农产品运输车辆的通行效率，减少收费站稽查人员劳动量，提出了一种基于激光测距系统的货运车辆快速检测方法；分析了鲜活农产品承运车型的特点，通过车辆流向归纳，确定了常见的鲜活农产品承运车型；引入车辆外廓尺寸激光测距系统，建立了货物密度数据库作为判定基础；通过分析免征通行费的合理装载要求，结合货物密度的概率分布，以8×4载货车为分析对象，分析了货物密度上限与下限阈值的取值范围，提出了违规装载的货物密度判定模型；根据货车轴组间距的规律，运用力矩平衡原理确定靠近货厢最前端轴组的理论质量，提出了轴组判定模型；利用收费站实测数据，验证了货物密度判定模型和轴组判定模型的可行性，分析了计算误差，并对比了该检测方法的稽查优势。研究结果表明：鲜活农产品的主要承运货车车型有5种；货物密度判定模型测算的体积偏差率、密度偏差率分别为±3.75%、±4.94%，明显小于判定逻辑中合理区间、警告区间公差的50%；轴组判定模型对激光测距系统的依赖性较大；依托2种模型构建的快速检测方法是人工稽查效率的5.97倍，能够有效提高车道通行效率；被调查车道内的开厢查验率为16.62%，能够减少货车开厢查验频次，节省稽查人员劳动量和岗位配置；虽然增加的选择和输入参数过程平均耗时0.41 min，但仅为人工稽查耗时的15.70%。Abstract: To improve the traffic efficiency of trucks loaded with fresh agricultural products (FAPs) and reduce the workload of inspectors at toll stations, a rapid check model for trucks loaded with FAPs based on a laser ranging system was proposed. The characteristics of main types of trucks loaded with FAPs was analysed, and common types of trucks loaded with FAPs were determined by traveling route induction. The laser ranging system of carriage outline was introduced and the cargo density database was established as the basis of judgment. According to the required loading limits of tolling for free, and considering the probability distribution of cargo densities, the upper and lower thresholds of the cargo density were determined, and a cargo density judging model for unreasonable loading was developed for the case of an 8×4 truck. Based on the features of space between truck axles, the moment balance principal (MBP) was used to deduce the theoretical weight of the assembled axles, which were close to the front of the carriage. The assembled axles judging model was then developed based on the MBP. The feasibilities of the cargo density judging model and assembled-axles judging model were verified by the measuring data of toll stations, the computational errors of the two models were analyzed, and the advantages of the rapid check model were compared with those of the manual model. Analysis results show that there are five main types of trucks in FAP transport, The deviation rates of volume and density measured by the cargo density judging model are ±3.75% and ±4.94%, respectively, which is considerably less than 50% of the tolerance of a reasonable range or warning range in the judging logic. The dependence of the cargo density judging model based on the laser ranging system is less than that of the assembled axles judging model. The efficiency of the rapid check model based on the two models is 5.97 times higher than that of the manual inspection, which can effectively improve the efficiency of the lane. The inspection rate of all the investigated trucks is 16.62%, which can reduce the inspection frequency and economize labor and employment. Although the average time of the added selection and input parameter process is 0.41 min, it is only 15.70% of the time of the manual inspection. 4 tabs, 3 figs, 30 refs.
-
表 1 鲜活农产品货车的运输状态
Table 1. Transportation status of trucks loaded fresh agricultural product
货车车型 序号 车流量比重/% 穿越 进省 出省 省内 4×2载货车 
1 20.36 58.80 57.83 84.97 6×2载货车 
2 5.23 4.87 6.46 1.48 8×4载货车 
3 22.31 15.96 18.98 2.70 6轴半挂列车 
4 16.70 6.85 4.86 0.43 
5 33.38 9.59 7.22 0.51 其他车型 6 2.02 3.93 4.65 9.91 
下载: 导出CSV
表 2 不同货厢尾板、侧壁板和底板的平均厚度
Table 2. Average thicknesses of tailboard, wall and floor of different carriages
货车厢型 l0/mm w0/mm h0/mm 厢式 85.0 84.6 151.0 栅栏式 43.5 43.0 110.0
下载: 导出CSV
表 3 被检测系统显示为怀疑的8×4货车
Table 3. 8×4 loaded trucks doubted by detection system
货车编号 货物品类 M/t V/m3 [ρd, ρu]/(kg·m-3) m20/t l2/m lr/m l/m m2/t 1 苹果 30.67 58 [237, 275] 3.10 5.28 3.36 9.62 8.23 2 苹果 32.50 64 [237, 275] 3.14 5.18 3.64 9.65 8.78 3 辣椒 30.08 62 [171, 209] 3.22 5.28 3.71 9.66 7.36 4 西瓜 30.05 66 [342, 418] 3.32 5.18 3.66 9.66 11.20 5 结球甘蓝 32.12 71 [180, 220] 3.25 5.38 3.18 9.62 9.11 6 大蒜 29.82 56 [216, 264] 3.01 5.28 3.04 9.40 6.98 7 鸡蛋 28.74 66 [310, 352] 2.98 5.18 3.55 9.59 8.91 8 蜜桃 30.88 56 [264, 290] 3.18 5.28 3.49 9.66 9.07 9 马铃薯 28.46 57 [234, 286] 3.03 5.28 3.59 9.63 7.27 10 胡萝卜 30.76 58 [267, 292] 3.18 5.18 3.65 9.63 8.10
下载: 导出CSV
表 4 快速检测方法的优势
Table 4. Advantages of rapid check method
调查内容 人工检测方法平均值 基于激光测距的快速检测方法平均值 每车道配备人力/人 2 1 每车检测时间/min 2.61 0.41 开厢查验率/% 100.00 16.62 车道通行效率/(veh·min-1) 0.32 1.91
下载: 导出CSV
-
[1] 陈荫三, 肖润谋, 闫晟煜, 等. 2019中国高速公路运输量统计调查分析报告[M]. 北京: 人民交通出版社股份有限公司, 2020.CHEN Yin-san, XIAO Run-mou, YAN Sheng-yu, et al. Statistics and Analysis Report of Freeways of China in 2019[M]. Beijing: China Communications Press Co., Ltd., 2020. (in Chinese) [2] 刘哲. X射线透射绿色通道检测系统的研究[D]. 杭州: 浙江大学, 2015.LIU Zhe. Research of X-ray transmission green channel detection system[D]. Hangzhou: Zhejiang University, 2015. (in Chinese) [3] 史源平, 于京生, 孙宏强, 等. 绿色通道货物检测系统[J]. 核电子学与探测技术, 2011, 31(10): 1178-1180, 1188. doi: 10.3969/j.issn.0258-0934.2011.10.026SHI Yuan-ping, YU Jing-sheng, SUN Hong-qiang, et al. Green channel cargo inspection system[J]. Nuclear Electronics and Detection Technology, 2011, 31(10): 1178-1180, 1188. (in Chinese) doi: 10.3969/j.issn.0258-0934.2011.10.026 [4] CHISM W. Z-scanning laser photoreflectance as a tool for characterization of electronic transport properties[J]. Journal of Applied Physics, 2018, 124(22): 225701. doi: 10.1063/1.5050633 [5] LIU Xi, HE Ren. Application of laser sensor in urban vehicle type detection[J]. Tehnicki Vjesnik, 2018, 25(2): 622-626. http://www.researchgate.net/publication/324924950_Application_of_Laser_Sensor_in_Urban_Vehicle_Type_Detection [6] 李晓宇. 绿色通道车辆驾驶室主动避让及车长动态测量方法研究[D]. 长春: 吉林大学, 2016.LI Xiao-yu. Research on active avoidance of vehicle cab system and dynamic measurement of vehicle on expressway[D]. Changchun: Jilin University, 2016. (in Chinese) [7] 申长春. 基于BP神经网络Bagging集成的高速公路绿通车稽查模型研究[D]. 西安: 长安大学, 2018.SHEN Chang-chun. Research on inspection model of freeway green channel vehicle based on BP neural network ensemble with bagging[D]. Xi'an: Chang'an University, 2018. (in Chinese) [8] 朱志强. 基于压电传感的激光测距相结合的车型识别系统[D]. 天津: 天津大学, 2013.ZHU Zhi-qiang. Vehicle identification system based on piezoelectricity sensor and laser ranging[D]. Tianjin: Tianjin University, 2013. (in Chinese) [9] 李琳. 鲜活农产品流通模式与流通效率研究[D]. 青岛: 中国海洋大学, 2011.LI Lin. Study on circulation pattern and efficiency of fresh agricultural products[D]. Qingdao: Ocean University of China, 2011. (in Chinese) [10] 张萌, 曲建华, 方增利, 等. 河南省高速公路鲜活农产品运输"绿色通道"固定验货点布设规划研究[J]. 综合运输, 2016, 38(5): 29-34. https://cdmd.cnki.com.cn/Article/CDMD-10466-1016308526.htmZHANG Meng, QU Jian-hua, FANG Zeng-li, et al. Fixed-point inspection layout study for green channel of fresh farm products transport by highway in Henan Province[J]. China Transportation Review, 2016, 38(5): 29-34. (in Chinese) https://cdmd.cnki.com.cn/Article/CDMD-10466-1016308526.htm [11] 邵宝峰. 基于点激光和双目视觉的货物尺寸测量的研究[D]. 大连: 大连理工大学, 2016.SHAO Bao-feng. Research of dimensional measurement of cargo based on point laser and binocular vision[D]. Dalian: Dalian University of Technology, 2016. (in Chinese) [12] 闫晟煜, 肖润谋. 基于收费数据的高速公路运输量指标特征分析[J]. 交通运输工程学报, 2018, 18(6): 112-120. https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC201806017.htmYAN Sheng-yu, XIAO Run-mou. Index characteristics of expressway transportation volume based on toll collection data[J]. Journal of Traffic and Transportation Engineering, 2018, 18(6): 112-120. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC201806017.htm [13] 张大印. 自动驾驶车载激光雷达关键技术研究[D]. 成都: 电子科技大学, 2019.ZHANG Da-yin. Research on key technologies of autopilot vehicle lidar[D]. Chengdu: University of Electronic Science and Technology of China, 2019. (in Chinese) [14] 潘斯航. GB 1589—2016限制条件下高速公路超限货车分布特性分析研究[D]. 西安: 长安大学, 2017.PAN Si-hang. Analysis and research on the distribution characteristic of the expressway overload truck with the limit value of GB 1589—2016[D]. Xi'an: Chang'an University, 2017. (in Chinese) [15] 肖润谋, 陈李军, 韩海伟. 高速公路货车不规范过秤模拟试验及质量恢复方法[J]. 中国公路学报, 2011, 24(4): 87-93. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201104015.htmXIAO Run-mou, CHEN Li-jun, HAN Hai-wei. Simulation test and mass recovery method of passing mass platform non-normative of trucks on expressway[J]. China Journal of Highway and Transport, 2011, 24(4): 87-93. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201104015.htm [16] 张翔, 韩佳伟, 杨信廷, 等. 不同构造冷藏车厢体的冷却性能模拟与对比[J]. 制冷学报, 2018, 39(2): 89-98. https://www.cnki.com.cn/Article/CJFDTOTAL-ZLXB201802012.htmZHANG Xiang, HAN Jia-wei, YANG Xin-ting, et al. Simulation and comparison of cooling performances of refrigerated vehicles with different structures[J]. Journal of Refrigeration, 2018, 39(2): 89-98. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZLXB201802012.htm [17] 朱向雷. 载货汽车空载行驶性能研究[D]. 西安: 长安大学, 2007.ZHU Xiang-lei. Study on the running performance of no-load autotruck[D]. Xi'an: Chang'an University, 2007. (in Chinese) [18] 闫晟煜, 肖润谋, 潘斯航. 贯彻GB 1589—2016对高速公路货车的影响[J]. 综合运输, 2017, 39(2): 45-48. https://www.cnki.com.cn/Article/CJFDTOTAL-YSZH201702010.htmYAN Sheng-yu, XIAO Run-mou, PAN Si-hang. Effect on truck on freeway after carrying out GB 1589—2016[J]. China Transportation Review, 2017, 39(2): 45-48. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSZH201702010.htm [19] SHIKHALIEV P M. Large-scale MV CT for cargo imaging: a feasibility study[J]. Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2018, 904: 35-43. http://www.sciencedirect.com/science/article/pii/S0168900218308490 [20] TRZCINSKI G, MOSKALIK T, WOJTAN R. Total weight and axle loads of truck units in the transport of timber depending on the timber cargo[J]. Forests, 2018, 9: 164-176. doi: 10.3390/f9040164 [21] MIAO Rui, WU Yi-zhou, SUN Gong-qing, et al. The influence of measurement error on calculation of process capability indices and proportion of conformance[J]. International Journal of Reliability and Safety, 2016, 10(3): 195-209. doi: 10.1504/IJRS.2016.081602 [22] 李琦, 姜桂艳. 城市快速路车辆检测器数据质量评价与控制方法[J]. 交通运输工程学报, 2013, 13(2): 120-126. https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC201302017.htmLI Qi, JIANG Gui-yan. Quality evaluation and control method of vehicle detector data on urban expressway[J]. Journal of Traffic and Transportation Engineering, 2013, 13(2): 120-126. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC201302017.htm [23] 祝志文, 黄炎, 向泽. 货运繁重公路的车辆荷载谱和疲劳车辆模型[J]. 交通运输工程学报, 2017, 17(3): 13-24. https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC201703002.htmZHU Zhi-wen, HUANG Yan, XIANG Ze. Vehicle loading spectrum and fatigue truck models of heavy cargo highway[J]. Journal of Traffic and Transportation Engineering, 2017, 17(3): 13-24. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC201703002.htm [24] DIAO Yun-feng, CHENG Wen-ming, DU Run, et al. Vision-based detection of container lock holes using a modified local sliding window method[J]. EURASIP Journal on Image and Video Processing, 2019, 69(6): 1-8. doi: 10.1186/s13640-019-0472-1/fulltext.html [25] ZHOU Fu-qiang, LI Juan, LI Xiao-song, et al. Freight car target detection in a complex background based on convolutional neural networks[J]. Proceeding of the Institution of Mechanical Engineering, Part F: Journal of Rail and Rapid Transit, 2019, 233(3): 298-311. http://www.researchgate.net/publication/327501244_Freight_car_target_detection_in_a_complex_background_based_on_convolutional_neural_networks [26] MI Chao, WANG Jun, MI Wei-jian, et al. Research on regional clustering and two-stage SVM method for container truck recognition[J]. Discrete and Continuous Dynamical Systems Series, 2019, 12(4/5): 1117-1133. http://www.researchgate.net/publication/329212507_Research_on_regional_clustering_and_two-stage_SVM_method_for_container_truck_recognition [27] 刘军, 肖新平, 郭金海, 等. 单增序列灰色GM(1, 1)模型解之间的误差分析[J]. 系统工程理论与实践, 2014, 34(12): 3182-3189. https://www.cnki.com.cn/Article/CJFDTOTAL-XTLL201412018.htmLIU Jun, XIAO Xin-ping, GUO Jin-hai, et al. Error analysis between the solutions of GM(1, 1) grey models with monotone increasing sequences[J]. Systems Engineering— Theory and Practice, 2014, 34(12): 3182-3189. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XTLL201412018.htm [28] 姚杰. 广东省高速公路整车式计重改造研究[D]. 广州: 华南理工大学, 2016.YAO Jie. Research on weight-reformation of whole vehicle in Guangdong Province[D]. Guangzhou: South China University of Technology, 2016. (in Chinese) [29] 秦进, 贺钰昕. 定量化交通网络效率评价方法比较[J]. 交通运输工程学报, 2018, 18(2): 111-119. https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC201802015.htmQIN Jin, HE Yu-xin. Evaluation methods comparison of quantitative transportation network efficiency[J]. Journal of Traffic and Transportation Engineering, 2018, 18(2): 111-119. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC201802015.htm [30] 刘芳华, 贺岩, 罗远, 等. 基于光子计数激光雷达的运动小目标测距测速系统[J]. 中国激光, 2021, 48(13): 1310001. https://www.cnki.com.cn/Article/CJFDTOTAL-JJZZ202113022.htmLIU Fang-hua, HE Yan, LUO Yuan, et al. Moving target distance and velocity measurement system based on photo-counting lidar[J]. Chinese Journal of Lasers, 2021, 48(13): 1310001. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JJZZ202113022.htm -
点击查看大图
图(3) / 表(4)
计量
- 文章访问数: 526
- HTML全文浏览量: 191
- PDF下载量: 29
- 被引次数: 0
