This research has developed an industrial robot which is driven by generative AI for intelligent IIoT environments, and thus it evaluates its effectiveness in the work of monitoring, fault handling assistance, and scheduling support. One closed-loop structural frame is built through the integration of sensor flow data, PLC/SCADA signals, incident recording documents, one vectorized document store, one knowledge graph, digital twin statuses, tool invocation, and human-participated middle inspection. The experiment platform is constituted by two CNC units, two robot work stations, one conveyor unit, and one compressor/environment node. Through a 30-day observation time frame, the system has collected 51,840 time-ordered data records, 60 alert events, 60 upkeep records, 420 working personnel test searches, and 54 arrangement cases. The results obtained by us indicate that the bot which we put forward has obtained 93.3% total accuracy, 92.9% task finishing rate, 93.8% tool calling success rate, and 90.8% complex query accuracy, meanwhile it lets the hallucination rate be maintained at 2.9%. In the evaluation of industrial value, the system that we put forward made the time of alarm explanation reduce by 54.8%, made MTTR decrease by 35.2%, made machine stop time lower by 34.1%, made the effective maintenance adoption rate rise to 95.0%, made total working time reduce by 17.2%, and made the number of coordination work cut by 45.1% when it is compared with the rule-based basic method. These result points out that the put forward industrial robot can promote both conversation reliability and working efficiency in intelligent IIoT situation. However, the method still depends on high-quality industrial knowledge resources, and further work is needed in edge deployment efficiency, long-term robustness, and security governance.
