港口起重机的平面度测定是其安全性评估的重要环节之一,针对传统测定方法在工程领域应用的局限性,提出利用近景摄影测量及计算机相关处理技术来解决港口起重机平面度测定问题.近景摄影测量可以获得待测平面大量的点云信息,运用最小二乘法拟合点云坐标形成参考平面,计算参考平面两侧的点到该平面的最大距离之和,完成平面度的计算.实验的结果表明,基于近景摄影测量的起重机平面度测定方法,可以快速精准的完成多台起重机多组数据的测量与处理,工程应用环境要求低,后期处理方便,测量稳定性好.

近景摄影测量技术作为一种新的无接触式测量手段,在现代工业测量监测领域发挥出越来越重要的作用.文中针对传统近景摄影测量技术在港口起重机械的应用场景下,双目相机的标定实现困难、精度低且不稳定的关键问题,在一种双站式的测量系统基础上,提出了一种基于分层全域扫描原理的双目相机联合标定方法.在该方法的实现过程中,通过双目相机各个主距上的特定扫描方式,将主距不同的共线方程联立求解,搜索最佳的主距组合,实现双目相机的联合标定.实验数据表明:该标定方法的结果稳定、准确,满足工作要求,可以作为港口起重机的一种高效测量手段.

起重机由于主梁发生严重变形容易引起各种安全问题,而传统的钢丝法和水准仪法检测速度和精度都不理想.为了解决此问题,文中提出基于摄影测量的起重机主梁变形检测方法,将普通数码相机标定后选择合适的位置,从左、右方两个角度进行拍摄,通过控制点解算相机的位置和姿态.两张相片前方交会即可求得起重机主梁待测点的坐标,从而计算主梁的变形.实验结果表明,该方法方便快捷、精度高,满足起重机主梁变形检测的要求.

摄影测量技术作为一种新兴的测量技术,现今被广泛用于工程测量领域.将摄影测量应用于大型工程机械的测量,特别是港口起重机械时,受限于复杂的工作环境和机器本身庞大的体型,通常无法获得良好的拍摄角度和控制点.为克服这些问题,提出一种基于系统自标定的摄影测量方法.将普通数码相机和旋转平台连接,实现相机在空间中的旋转.在实验室内确定相机的旋转参数,正式拍摄时根据相机旋转的角度解算相机在空间中的位置和姿态,无需在港口起重机械上设置控制点.实验结果表明,解算出的待测点相对误差最大为1.84％,满足测量要求.

As a non-contact measurement technology with high data acquisition efficiency, photogrammetry is an ideal choice for collecting the data needed in the safety evaluation of port hoisting machinery. However, the radius fitting result accuracy cannot meet the requirements of safety assessment due to the limitation of the port crane itself and the working environment characteristics, when the existing photogrammetry method is used to measure the rotary body structure represented by the portal crane slewing mechanism. In order to solve this problem, an iterative optimization algorithm for weighted radius prediction for the photogrammetry of the slewing mechanism of port hoisting machinery is proposed in this paper. First, the algorithm uses the generalized multi-line rendezvous model to transform the radius fitting problem into the multi-line intersection point prediction problem, which lays a theoretical basis for the subsequent algorithm implementation. Second, by introducing a weighting algorithm based on the camera optical distortion model, the algorithm optimizes the accuracy of radius fitting results. In addition, through the quantitative evaluation method of fitting accuracy based on weighted algorithm, the algorithm also establishes a set of iterative rules to balance the accuracy of measurement results and the execution efficiency of the algorithm. Finally, this paper designs theoretical verification tests and simulation engineering tests based on the characteristics of the algorithm and the engineering practice of port hoisting machinery photogrammetry. The experimental results demonstrate that the algorithm described in this paper can significantly improve the accuracy of radius fitting results when the data quantity is small and the data quality is poor compared with the traditional algorithm.

近景摄影测量作为一种新的技术手段,如今已逐渐被广泛应用于工程领域.港口起重机由于自身的庞大结构,外形尺寸的测量难度大,而起重机的安全性评估又与相关尺寸有密切联系.为解决这一问题,构建了一个无控制点的起重机近景摄影测量模型.通过设定坐标系系统,利用相关算法处理像片得到待测点坐标.实验的结果表明,基于测量系统内部坐标系的起重机械无控制点的测量方法,不需要在物方空间标定控制点,也可以获得物方点坐标,且测量精度较好.该方法是在起重机复杂的应用场景下对相关尺寸进行测量的一种新的实用手段.

输送带跑偏检测技术在矿山、港口以及电力等行业中有着举足轻重的地位,但是目前尚没有一种能够准确、实时地对输送带位置、运行状态进行检测的技术.颜色识别作为一种准确、可靠、高效的检测技术,是输送带跑偏检测的理想选择.对此,本文提出一种基于颜色检测的输送带跑偏检测技术,通过视频监控屏幕上的刻度来检测输送带跑偏程度,同时根据输送带上色带颜色变化判断输送带拉伸程度和应力状态.

As a mature technology, photogrammetry is widely applied in today's engineering measurement field. However, because of the limitation of the port condition, it is impossible to obtain photos that are taken at ideal angles and distances when photogrammetry is used to measure port hoisting machinery, and this leads to invalid measurements with low accuracy data. To solve this problem, a new algorithm is proposed in this work. First, the proposed method introduces redundant measurements through an intersection point prediction algorithm to improve the measurement data's accuracy. Second, a weighting algorithm based on the lens distortion model is then provided to further improve accuracy. Third, an iterative method is established from the threshold setting method based on the weighting algorithm. Thus, the quality of the final measurement could be controllable. Finally, an experiment is devised for the characteristics of the algorithm and the port condition. The results demonstrate that the method described in this paper significantly improves the accuracy of the measuring results of photogrammetry while photos used for the calculation were taken at unsatisfactory angles and distances caused by the limitation of the port condition.

We propose a robust and accurate camera pose determination method based on geometric optimization search using the Internet of Things (IoT). The central idea is to (1) obtain image information through Internet of Things technology, (2) obtain the first pose by minimizing the error function, and (3) use the geometric relationship and constraint condition to obtain the appropriate attitude angles as a new initial value for the next iteration calculation. The features of this method are as follows. First, this method can deal with a large amount of uncertain data, such as in the case of any shooting angle, in the case of any reference point, and in the case of a small number of feature points. Finally, because of using Internet of Things technology, our method can quickly complete data processing and transmission. Compared to state-of-the-art methods, the experimental results show that our approach performs well on both synthetic and real data and can be used to provide accurate and stable data for subsequent applications.

In view of the problems in potential energy recycling and the detection of lifting mechanism of the portal crane, the paper analyzes energy flow use and loss of the lifting mechanism during operation , describes the energy efficien-cy computational formula for the entire operating cycle of the lifting mechanism , introduces the detection method for poten-tial energy recycling , and gives specific procedures of the detection and wiring diagram .With the hoisting mechanism of MQ4040 port portal crane as the test prototype , the energy efficiencies before and after installing the energy-saving device are compared.The result shows that , after the energy recovery unit is installed , the energy efficiency index improves by 41.4%, indicating significant energy-saving effect .