Extensive data resources are critical for broadening the application scope and improving the performance of large language models (LLMs). Local deployment of LLMs enables users to leverage the advanced capabilities of LLMs while maintaining secure access to local data. Therefore, local LLMs deliver more domain-specific services than general LLMs. However, concerns over data security and the absence of economic incentives restrict cross-industry collaborative utilization of data resources. To address this challenge, an interdisciplinary framework integrating artificial intelligence, economic theory, and data security technologies is required to explore the data trading mechanism for local LLMs. The key aspects of a data market framework, economic evaluation for data pricing, and trusted data trading are analyzed to support efficient utilization of cross-industry data resources. By constructing a data trading mechanism, industry-specific local LLMs can be further enhanced through cross-industry knowledge integration, thereby improving productivity across sectors.