称重异常站点识别技术

称重异常站点识别技术基于大规模称重数据分析与机器学习算法，精准识别公路治超站点的称重精度偏离情况。该技术利用超限检测站、不停车检测点基础信息和检测信息，以及车辆定位数据，通过数据清洗、通行链构建及EM算法计算误差分布，筛选出误差较大的异常站点，并评估其称重偏差程度。算法可有效剔除无效数据，提高称重数据的可信度，助力治超执法部门精准监管，降低非现执法行政复议风险。

The technology for identifying abnormal weighing stations, based on large-scale weighing data analysis and machine learning algorithms, accurately identifies the weighing accuracy deviations of highway overload control stations. This technology leverages basic information and detection data from over-limit detection stations and non-stop weighing points, alongside vehicle positioning data. It performs data cleaning, traffic chain construction, and error distribution calculation via the Expectation-Maximization (EM) algorithm, screens out abnormal stations with significant errors, and evaluates their weighing deviation levels. The proposed algorithm can effectively eliminate invalid data, enhance the reliability of weighing data, assist overload control law enforcement agencies in conducting precise supervision, and reduce the risk of administrative reconsideration for off-site law enforcement operations.

搜集汇总

背景与挑战

背景概述

该数据集专注于利用称重数据和机器学习算法识别公路治超站点的称重异常，覆盖湖南省范围，每月动态更新，存储大小为89 GB。数据集的应用目标是提高称重数据的可信度，助力治超执法部门精准监管，降低非现执法行政复议风险。