KEYWORDS: Industry, Performance modeling, Data modeling, Education and training, Industrial applications, Instrument modeling, Model based design, Artificial intelligence
With the rapid development of artificial intelligence technology, large language models are increasingly applied in the power industry, providing strong support for the intelligent management of power systems. This paper proposes an evaluation system for large language models in the power domain, aiming to evaluate their performance and limitations in solving specific tasks within the power industry. Through testing on the evaluation dataset, we analyzed the performance of large language models in both general domain and the power industry. The experimental results show that domain-adapted power large language model should balance both power industry-specific and general domain capabilities during evaluation. This research has significant theoretical and practical value for the evaluation of large language models in other industries.
This paper mainly proposes a message push method suitable for iOS mobile terminals. In this method, APNS is used for offline iOS terminal message push and MQTT is used for online iOS terminal message push. This method can reduce the number of messages sent through APNS. It can also solve the disability of storage-and-forward and the dissatisfaction of delivery rate under large amount of concurrent messages or over extent of message length. At the same time, different push strategies can be selected according to the online state of the target terminals. The experimental results about bandwidth and message length show that when the concurrency of pushing messages is large or network resources are insufficient, this strategy can dynamically balance the load or expand the bandwidth of network devices and servers to increase throughput. This strengthens the data processing capability for message push and improve resource utilization.
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