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In the logistics and warehousing industries, determining the volumes and quantities of materials and their inbound and outbound status is vital for the management of production inventory.
Traditionally, this is done manually. For example, in coal warehousing and production, the volume of coal is typically measured with a total station held manually by a human operator. However, such conventional solution comes with constant challenges, such as a lack of technical means, significant margins of error, low efficiency, and monitoring difficulties.
Inside a typical coal warehouse
In static scenarios such as those involving coal, rapid scanning is less of a priority. Therefore, users can maximize non-repetitive scanning by prolonging the scan duration of a Livox LiDAR, which delivers greater point cloud density and thus more granular data for clients.
It is this feature that has enabled our partners SF Technology and Wuyi Yuntong to address pain points in the coal industry, by developing intelligent measurement systems that generate accurate data and real-time updates.
Intelligent Inventory System | SF Technology
The accurate measurement of large bulk volumes has been a thorny issue in the coal industry for years. A coal pile usually weighs at least 10,000 tonnes, which means that a 1% margin of error in its measurement can lead to losses in the tens of millions of yuan. Evidently, improving the accuracy of bulk measurement is crucial to inventory management and the calculation of material consumption.
The traditional method of measuring large coal heaps involves the use of a total station and RTK spot measurement, along with manual extraction of features. In these instances, tall coal piles are usually difficult for human workers to climb up, thus making feature extraction incredibly challenging. Meanwhile, such operations are inefficient due to the low data density generated by spot measurement, which reduces the accuracy of coal volume measurements.
In response to this pain point, SF Technology developed an intelligent inventory system using multiple sensors such as Livox LiDARs and cameras. The system makes full use of LiDARs to accurately collect distance data for 3D modeling of targets, as well as AI algorithms to automatically calculate coal volumes and update them in real time. This enables users to control warehouse conditions easily and directly through the web interface.
The system can perform a range of functions such as completing a designated inventory plan, periodic data collection, automatic 3D point cloud generation, auto-splicing to create whole volume data, auto-calculation of bulk volumes in defined areas, and auto-generation of computation reports. As a result, it brings greater efficiency and accuracy to the automation of inventory management.
3D warehouse view
Real-time point cloud image of coal heaps
Warehouse Inventory Management | Wuyi Yuntong
Traditional measurement methods usually involve manual and regular stocktaking. Due to their limited detection range and measurement angles, data cannot be updated in real time effectively.
The solution adopted by Wuyi Yuntong utilizes LiDARs and gimbals, which assist operators in selecting the optimal roof equipment with a wider and more flexible and comprehensive coverage of a warehouse. By splicing multi-point cloud files, the entire inventory of a warehouse can be displayed in 3D, which enables dynamic and real-time exchange of information on its items. At the same time, the volumes of materials can be measured and computed intelligently and in real time by segmenting their point clouds through positioning and virtual fencing technology. With these features, users can gather and integrate comprehensive and dynamic data, to manage safety control as well as entry and exit of goods flexibly and precisely.
3D modeling of a coal warehouse
The above two solutions utilize point clouds from LiDARs to upgrade non-structured image data to structured 3D data. On top of delivering cost-efficient, rigorous, and precise volumetric measurements, they provide an effective monitoring function for avoiding risks of theft, and expand the scope of surveillance by increasing data dimensions. As a result, the solutions greatly enhance the real-time nature and deterrent effect of data monitoring, therefore marking a significant step forward in resolving the pain points of the coal industry.
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在物流、仓储等工业行业中，获取物品体积数量、掌握物品出入库情况对生产库存管理具有重要意义。
以煤炭仓储及生产领域煤炭体积测量为例，为了解煤炭出入库情况，通常依靠人力手持全站仪进行人工煤炭体积监测。然而这一传统解决方案始终面对着技术手段缺乏、测量误差大、人工管理效率低下及监管困难等难题。
常见煤炭仓库场景
在煤炭或类似的静态场景中，由于场景本身对响应时间要求不高，通过使用Livox 激光雷达并延长激光雷达的扫描时间，利用时间换取空间充分发挥非重复扫描的优势，即可获得稠密点云密度，为客户需求提供更精细化的数据。
我们的合作伙伴顺丰科技及物易云通正是利用了这一特征，针对煤炭行业痛点开发了数据精准、可实时更新的智能量方测量系统。
智能盘库系统 | 顺丰科技
堆体测量，尤其是大型堆体体积测量如何提高准确性，是困扰煤炭行业多年的难题。正常的大型堆体一般有万吨以上，即使测量误差可控制在1% 以内，其价值损失也高达千万元以上。可见提高大型堆体体积测量的准确性，对企业加强物资管理、核算物料消耗具有重要现实意义。
传统大型煤炭矿石堆体积测量方式通常采用全站仪、RTK单点测量方式，且需要人工对特征点进行精准提取。然而由于大型煤炭矿石堆通常不便攀爬，精准特征点的提取因而异常困难；同时单点测量方式测量数据密度小，也无法精准获取煤炭矿石堆的体积，导致作业效率极低。
针对这一行业痛点，顺丰科技利用Livox 激光雷达、摄像头等多传感器开发了一套智能盘库系统。该系统充分利用激光雷达对距离信息的精准采集实现对目标物体的3D建模，利用AI算法自动测算煤炭体积并实时更新，在WEB端即可直观地管控仓库情况。
该系统可直接完成指定盘库计划、定时采集数据、自动三维点云生成、自动拼接形成整体体积数据、设定区域堆体体积自动计算、计算报告自动生成等功能，高效率、高精度地实现了库存管理自动化。
仓库三维可视图
煤炭堆实时点云图
仓库库存管理 | 物易云通
传统测量方法通常需要人工进行定期盘点以估算库存情况；同时由于探测距离及角度有限，数据单一难以进行实时同步。
物易云通通过激光雷达与云台的结合，可选取合适的仓顶灵活架设设备，覆盖范围更广，为仓库全场景提供灵活、全方位的布置方案。通过对多点云文件拼接，可将整个仓库库存情况进行三维展示，全方位实施平仓/仓库物品堆积的动态实时信息交互；同时结合定位及虚拟围栏技术，将点云进行分割管理，智能、实时进行体积量方的动态测量和计算；实现了全时空动态信息采集与融合，达到灵活、精准的仓库物品主动安全控制和进出协同管理。
煤炭仓库三维建模图示
以上两种方案，皆利用激光雷达点云把非结构化的影像数据升级为结构化的三维数据，在高性价比、科学、精确地完成量方测量的同时，不仅提供了传统视觉监管的基础功能规避偷盗风险；还通过数据的升维及增维，拓展了监管边界，极大地加强了传统煤炭行业中数据监管的实时性及震慑性，对解决行业痛点具有突破性意义。
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Posted on 2022-11-7 17:42:24