2021-05-21

ThingWork
ThingWorx 是第一个用来创建和运行互联世界中的应用程序的平台, 减少了为智能互联产品创建创新型应用程序所需的时间、成本和风险。ThingWorx 是唯一一家能够让企业快速创建和运行互联应用程序以捕捉新出现的、无处不在的智能物体带来的商机的平台。利用 ThingWorx,公司可以经济高效地从其智能互联产品中创造价值,并提供端到端解决方案。

 

The Power of Now

The Time is Now.

Recent years have seen an explosion of the Industrial Internet of Things (IIoT) as enterprises of all shapes and sizes have been implementing new, innovative, smart solutions in pursuit of digital transformation.

After years of slowing productivity and fragmented demand across many industrial segments, innovation is long overdue. Significant forces, such as increased globalization and commoditization, have compelled industrial enterprises to explore new service-oriented, revenue-boosting business models and to reallocate resources to digital technologies and solutions.

What we're seeing now is the Fourth Industrial Revolution or Industry 4.0 which has become the fastest growing market for IoT. But—as with nearly any period of great structural change—the race to digital transformation is about more than just bragging rights. Industrial enterprises that embed IIoT into their businesses will discover:
 

  • Operational efficiencies that reduce costs
  • New, ongoing sources of revenue
  • Enhanced relationships with customers
  • Accelerated time to market

Companies that don't define an IIoT strategy risk a competitive disadvantage before even reaching the starting line. And so, for industrial enterprises that have yet to act or have been sidelined by obstacles—this is the moment of truth. Act now, or be left behind.

Fortunately, with the incredible advancement of IIoT and growth fueled by Industry 4.0, there has been no better time than now for the availability of innovative, state-of-the art technologies, resources, and opportunities for digital transformation.
 

Top Use Cases for IIoT in Industrial Manufacturing:

  • Remote asset monitoring
  • Remote maintenance/service
  • Predictive maintenance and asset management
  • Optimized equipment effectiveness
2021-03-26 20:54:33,596 - Model - INFO - Epoch 1 (1/200): 2021-03-26 20:57:40,380 - Model - INFO - Train Instance Accuracy: 0.571037 2021-03-26 20:58:16,623 - Model - INFO - Test Instance Accuracy: 0.718528, Class Accuracy: 0.627357 2021-03-26 20:58:16,623 - Model - INFO - Best Instance Accuracy: 0.718528, Class Accuracy: 0.627357 2021-03-26 20:58:16,623 - Model - INFO - Save model... 2021-03-26 20:58:16,623 - Model - INFO - Saving at log/classification/pointnet2_msg_normals/checkpoints/best_model.pth 2021-03-26 20:58:16,698 - Model - INFO - Epoch 2 (2/200): 2021-03-26 21:01:26,685 - Model - INFO - Train Instance Accuracy: 0.727947 2021-03-26 21:02:03,642 - Model - INFO - Test Instance Accuracy: 0.790858, Class Accuracy: 0.702316 2021-03-26 21:02:03,642 - Model - INFO - Best Instance Accuracy: 0.790858, Class Accuracy: 0.702316 2021-03-26 21:02:03,642 - Model - INFO - Save model... 2021-03-26 21:02:03,643 - Model - INFO - Saving at log/classification/pointnet2_msg_normals/checkpoints/best_model.pth 2021-03-26 21:02:03,746 - Model - INFO - Epoch 3 (3/200): 2021-03-26 21:05:15,349 - Model - INFO - Train Instance Accuracy: 0.781606 2021-03-26 21:05:51,538 - Model - INFO - Test Instance Accuracy: 0.803641, Class Accuracy: 0.738575 2021-03-26 21:05:51,538 - Model - INFO - Best Instance Accuracy: 0.803641, Class Accuracy: 0.738575 2021-03-26 21:05:51,539 - Model - INFO - Save model... 2021-03-26 21:05:51,539 - Model - INFO - Saving at log/classification/pointnet2_msg_normals/checkpoints/best_model.pth 我有类似于这样的一段txt文件,请你帮我写一段代码来可视化这些训练结果
02-06
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