As a shipping hub responsible for 90% of global trade, container logistics play a crucial role in promoting international trade and economic development. According to the "Waterway Transport Market Development in 2021 and Market Outlook in 2022" report issued by the Ministry of Transport of China, "based on third-party institution statistics, the global total of container ships reached 5,515, and 24.97 million TEU, with a year-on-year growth of 4.1% in transport capacity scale. In 2021, the average value of the China Containerized Freight Index (CCFI) was 2,616 points, with a year-on-year growth of 165.7%. Furthermore, several international organizations and shipping enterprises forecast that the growth rate of international container shipping demand will be 4-6% in 2022, ensuring that the container transport market will continue to maintain a high level."

With the increasing demand for transportation capacity, the need for upgrading container logistics scenarios through digital intelligence is also rising. Presently, the container logistics industry faces challenges such as rising labor costs, heavy labor intensity, poor working environments, and labor shortages. Additionally, due to the overlapping hinterland, homogenized competition of goods types in container logistics, and cost efficiency have become the common pursuit of the container logistics industry. To resolve these issues, the container logistics field must revamp its old systems during the digital intelligence development process and address data silo problems. Simultaneously, it needs to dynamically modify task instructions according to actual needs and address emergencies, making flexible and timely decisions.

This process involves a large amount of data and operation, necessitating robust computing support. With the evolving artificial intelligence industry, exploring large-scale computing-based "large models" has consistently been the "ultimate goal" of global artificial intelligence technology research. Amid this wave, Westwell launched "TerminalGPT, a new intelligent robot expert for container logistics" to enable the digital transformation of container logistics.

01. GPT Helps Container Logistics Scenarios Break the Boundaries of Operation and Energy Efficiency

In complex container logistics operation scenarios, various emergency situations can significantly affect operational efficiency and present a considerable challenge to operators' experience and capabilities. Traditional technology's value in this field is limited. With the vigorous development of AIGC technology, new breakthroughs are expected in areas such as predictive alarms, intelligent recommendations, and assisted decision-making.

The rapid development of AIGC stems from the resonance of data, algorithms, and computing, subsequently assisting systems in achieving an "essential breakthrough" in data or content generation. In 2021, Gartner published 12 key strategic technology trends for 2022, with AIGC at the forefront of these strategies. Gartner predicts that 20% of content will be created by AIGC by 2023, and AIGC will generate 10% of all data (currently less than 1%) by 2025. According to Sequoia's 2022 report "Generative AI: A Creative New World," AIGC startups and business implementation programs will continue to grow in the next 2-3 years, generating trillions of dollars in the economy.

As an implementation method for AIGC, the GPT model generates high-quality, low-cost natural language text through pre-training and fine-tuning. The GPT model is arguably one of the core technologies of AIGC. The transformation toward digital intelligence is a current trend that is also prevalent in the field of container logistics. In the container logistics platform construction process, the GPT model will deeply integrate into the front-line production link, effectively improving data processing capabilities and levels, thus enabling container logistics enterprises to enhance quality and efficiency.

02. Westwell Practice: Terminal GPT--New Intelligent Robot Expert of Container Logistics

TerminalGPT is learning to become the "AI intelligent expert in the field of large logistics" through continuous development based on large-scale model technology, combined with the experiential knowledge and operational habits of large logistics scenarios. This autonomous learning results in the prediction of "intelligent recommendations" and "overall planning" in various scenarios. TerminalGPT connects various intelligent and information systems, such as WellOcean (intelligent solutions for container logistics scenarios) and Qomolo (new energy autonomous driving and intelligent commercial vehicles), to enable all systems to become more "intelligent" by effectively breaking through multi-dimensional operational barriers like vehicles, people, space, and energy in production scenarios such as vehicles, ports, and factories. The goal is to make container logistics scenarios more efficient, safer, and lower in carbon emissions.

In the actual operation process, TerminalGPT can recommend key intelligent operations, assisting individuals in making optimal decisions and improving the execution efficiency of production factors for a more efficient production operation. For example, a crane operator guided by TerminalGPT can improve loading and unloading efficiency and quickly gain 20 years of operation experience. The WellFMS fleet management and dispatch system developed by Westwell can make more advanced predictions and intelligent recommendations in the future by leveraging TerminalGPT's situational awareness capabilities and further realizing the "intelligent upgrading" of ports. Front-line operational planners can use TerminalGPT's proactive warnings to anticipate and respond to operational risks in advance. TerminalGPT can also assist the TOS terminal operating system in rehearsals and planning assistance through autonomous learning. All of these efforts significantly enhance the interaction between operators and production systems, improve the work experience of front-line employees, and break through the boundaries of operational efficiency and energy efficiency in logistics scenarios.