What will be the effect of using ChatGPT in the power industry? On September 7th, the reporter saw at the Shenzhen Power Supply Bureau of China Southern Power Grid (hereinafter referred to as the Shenzhen Power Supply Bureau) that a staff member lightly clicked the mouse, and a hidden danger image of a crane arm adjacent to the transmission line immediately appeared on a construction site; Continuing to click on the image, the system will provide text like ChatGPT, describing the hidden danger. This is the first multimodal pre trained large model in the power industry launched by Shenzhen Power Supply Bureau - "Zhurong 2.0". It endows traditional power grid AI technology with logical reasoning and textual expression capabilities similar to ChatGPT, improving the efficiency of power grid safety hazard warning by six times. The power inspection system will infer that "traditional power grid AI technology identifies objects through representations such as color, shape, and texture, but it is difficult to determine the degree of danger that the object poses to power facilities. For objects with similar shapes, traditional power grid AI technology is also difficult to accurately distinguish them." Zhang Yunxiang, a technical expert at Shenzhen Power Supply Bureau, introduced, For example, in traditional mode, although the system can filter out alarm screens with bright lights near the line, it still requires inspection personnel to further determine whether the light is caused by a fire or emitted by street lights. If the light is emitted by street lights, the alarm is invalid. In response to the existing problems, Shenzhen Power Supply Bureau released the first AI pre training model based on Ascension Ecology in the power industry, "Zhurong", early last year, laying the foundation for the transformation and upgrading of artificial intelligence in the power grid. At the end of last year, the bureau upgraded "Zhurong" to version 2.0 using a new artificial intelligence model similar to ChatGPT, enabling the power inspection system not only to read, record and analyze, but also to understand, reason, and express. Zhurong 2.0 "is a dual modal model that utilizes image and language models to extract image and text features and fuse them, resulting in better performance than traditional single modal image models. The addition of language models enhances the logical reasoning ability of image models, thereby improving the accuracy of judgments. Taking the scene of mountain fire monitoring in the power grid as an example, at night, the image forms of lighting and fire are very similar, and it is difficult to distinguish the two solely based on image models, which can easily lead to a large number of false positives; Now with the support of language models, the system will interpret the overall image. For example, when the model discovers that the light is neatly arranged and on one side of the road, it can be inferred that it is light, not mountain fire. Nowadays, the inspection system can transmit alarm information through images and text, and accurately describe hidden dangers and their degree of danger to power grid equipment, without the need for further manual investigation. This greatly improves the effectiveness of alarms and adds a strong line of defense for the safe operation of the power grid, "said Zhang Yunxiang. The potential for further promotion and application is enormous. Currently, single modal models are difficult to achieve good results in many scenarios in the power grid industry. For example, when climbing a ladder, it is necessary to check whether the operator is wearing a safety rope and whether someone is helping to support the ladder. The use of image models alone cannot make logical judgments on multiple simultaneous actions mentioned above. It is necessary to overlay language models and compare them with safety standards for comprehensive evaluation