Route planning is one example of AI application. Traditional rout planning algorithms and tools can no longer keep up with the logistics issues that are presented by today’s complex and dynamic rapid changes. New mindset, new algorithm and new technology should be applied. Furthermore, mainstream map services today are controlled by Internet giants as part of their ecologies. That said, they are not neutral, and they are more of a consumer product instead of industrial. The logistics industry needs map service that provides high accuracy, stability and real-time map service. So we have been investing a lot of efforts in studying smart logistics map. By combining technologies like GIS and reinforcement learning etc., we explore and study route planning tools that are more suitable and can help optimize the cost and effectiveness.

Another example of AI application is digital smart management. Logistics companies have many spaces, facilities, and operators. In general, the operation is managed and operated by humans, and through human observation. We are studying to utilize technologies like machine learning etc. to automatically identify the objects in a space and enable the system to learn from extraordinary managers and operators. Gradually we can achieve assistive decision-making and decision-making automation.

The last application I want to talk about is the waybill recognition. Little might people know that it’s hard for machines to identify Chinese characters while English and numbers are easier. We use image recognition, address base, and convolutional neural network to increase the accuracy of the recognition, drastically lowering the work load and mistake rate.

There are many other applications. AI has a vast application scene in the logistics industry. We will constantly explore, study and build a smart logistics brain at Shunfeng.

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