The field of software engineering has been significantly influenced by the rapid development of large language models (LLMs). These models, which are pre-trained with a vast amount of code from open-source repositories, are capable of efficiently accomplishing tasks such as code generation and code completion. However, a large number of codes in the open-source software repositories are constrained by open-source licenses, bringing potential open-source license violation risks to the large models. This study focuses on the license violation risks between code generated by LLMs and open-source repositories. A detection framework that supports the tracing of the source of code generated by large models and the identification of copyright infringement issues is developed based on code clone technology. For 135 000 Python codes generated by 9 mainstream code large models, the source is traced and the open-source license compatibility is detected in the open-source community by this framework. Through practical investigation of three research questions, the impact of large model code generation on the open-source software ecosystem is explored: (1) To what extent is the code generated by large models cloned from open-source software repositories? (2) Is there a risk of open-source license violations in the code generated by large models? (3) Is there a risk of open-source license violations in the large model-generated code included in real open-source software? The experimental results indicate that among the 43 130 and 65 900 python codes longer than six lines generated by using functional descriptions and method signatures, 68.5% and 60.9% of the codes respectively are traced to have cloned open-source code segments. The CodeParrot and CodeGen series models have the highest clone ratios, while GPT-3.5-Turbo has the lowest. Besides, 92.7% of the codes generated by using functional descriptions lack license declaration. By comparing with the licenses of the traced codes, 81.8% of the codes have open-source license violation risks. Furthermore, among 229 codes generated by LLMs collected from GitHub, 136 codes are traced to have open-source code segments, among which 38 are of Type1 and Type2 clone types, and 30 have open-source license violation risks. These issues are reported to the developers in the form of problem reports. Up to now, feedback has been received from eight developers.