According to Ping'an Hubei, large language models are accelerating their penetration into vertical industries and have shown concrete practical value in grassroots social governance and smart policing. On May 14, 2026, the Qianjiang City Public Security Bureau of Hubei Province cracked a case of diesel theft at a construction site. When the frontline officers faced a "zero clue" scene with no surveillance video or eyewitnesses, they used ByteDance's AI large model tool "Doubao" and successfully broke the case.

The police input the key wheelbase data of 1440mm left at the crime scene into Doubao. The model quickly and accurately retrieved and deduced highly matching suspect vehicle models such as Wuling Hongguang and Chang'an Kuayue Star, guiding the investigation. Subsequently, the police used this key clue to retrieve surrounding checkpoint surveillance footage, quickly identified the suspect vehicle, and arrested the suspect Dou. This led to the dismantling of an underground diesel sales and reselling den.

This typical case not only demonstrates the response speed of general large models in multimodal retrieval and vertical domain knowledge base queries, but also marks that AI tools are moving from simple productivity assistance to the deep waters of "AI + policing." In the past, the comparison of non-standard industrial data highly relied on manual experience or multi-system joint checks. However, large models, with their strong semantic understanding and generalized reasoning capabilities, can instantly fill the gap in grassroots technical means.

From a technical application trend perspective, the successful practice of large language models like Doubao in the public security field indicates that AI technology inclusiveness is extending to long-tail application scenarios. In the future, it is expected to further promote the intelligent upgrading and efficiency reorganization of grassroots law enforcement processes.