For Electric Power Disaster Early Warning Scenarios: A Large Model and Lightweight Models Joint Deployment Scheme Based on Limited Spectrum Resources

CHEN Lei; HUANG Zaichao; LIU Chuan; ZHANG Weiwei

doi:10.11999/JEIT250321

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CHEN Lei, HUANG Zaichao, LIU Chuan, ZHANG Weiwei. For Electric Power Disaster Early Warning Scenarios: A Large Model and Lightweight Models Joint Deployment Scheme Based on Limited Spectrum Resources[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250321

Citation:

CHEN Lei, HUANG Zaichao, LIU Chuan, ZHANG Weiwei. For Electric Power Disaster Early Warning Scenarios: A Large Model and Lightweight Models Joint Deployment Scheme Based on Limited Spectrum Resources[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250321

Citation:

CHEN Lei, HUANG Zaichao, LIU Chuan, ZHANG Weiwei. For Electric Power Disaster Early Warning Scenarios: A Large Model and Lightweight Models Joint Deployment Scheme Based on Limited Spectrum Resources[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250321

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For Electric Power Disaster Early Warning Scenarios: A Large Model and Lightweight Models Joint Deployment Scheme Based on Limited Spectrum Resources

doi: 10.11999/JEIT250321 cstr: 32379.14.JEIT250321

1.
China Electric Power Research Institute, Nanjing 210003, China
2.
State Grid Shandong Electric Power Research Institute, Jinan 250003, China

Funds: Science and Technology Project of the State Grid Corporation of China (5700-202441329A-2-1-ZX)

Received Date: 2025-04-27
Rev Recd Date: 2025-10-22

Available Online: 2025-10-27

Abstract

Abstract

Objective Traditional approaches to electric power disaster early warning rely on dedicated, scenario-specific systems, leading to redundant data collection and high development costs. To enhance accuracy and reduce costs, comprehensive early warning frameworks based on Artificial Intelligence (AI) large models have become an important research direction. However, large models are typically deployed in the cloud, and limited wireless spectrum resources constrain the uploading of complete data streams. Deploying lightweight models at terminal devices through substantial model compression can alleviate spectrum limitations but inevitably compromises model performance. Methods To address these limitations, this study proposes a cloud–terminal collaborative joint deployment scheme integrating large and lightweight models. In this framework, a high-precision large model is deployed in the cloud to process complex tasks, whereas lightweight models are deployed at terminal devices to handle simple tasks. Task offloading decisions are governed by a confidence threshold that dynamically determines whether computation occurs locally or in the cloud. A power-domain Non-Orthogonal Multiple Access (NOMA) technique is incorporated to allow multiple terminals to share identical time–frequency resources, thereby improving system detection accuracy by increasing the proportion of tasks processed in the cloud. Additionally, for scenarios considering (i) only uplink shared-channel bandwidth constraints and (ii) both terminal access collision constraints and shared-channel bandwidth constraints, corresponding algorithms are developed to determine the maximum number of terminals supported under a given bandwidth and to identify the optimal confidence threshold that maximizes detection accuracy. Results and Discussions (1) As shown in Figures 3(a) and 3(b), when the uplink shared-channel bandwidth $ W $ increases, the number of supported terminals rises for both the proposed scheme and the orthogonal multiple access (OMA)-based scheme. This occurs because a larger $ W $ enables more terminals with low-confidence detection results to upload raw data to the cloud for further processing, thereby enhancing detection accuracy and reducing the missed detection rate. (2) In contrast, the number of supported terminals $ M $ in the pure on-device processing scheme remains constant with varying $ W $, as this scheme relies entirely on the lightweight model deployed at the terminal and is therefore independent of bandwidth. (3) Compared with the OMA-based and pure on-device schemes, the proposed approach markedly increases the number of supported terminals, confirming that non-orthogonal reuse of time–frequency resources and cloud–terminal collaborative deployment of large and lightweight models are key to improving system performance. (4) As shown in Table 3, an increase in the number of preambles reduces the probability of terminal access collisions, allowing more terminals to successfully transmit raw data to the cloud for detection. Therefore, the missed detection rate decreases, and overall detection accuracy improves. Conclusions For electric power disaster early warning scenarios, this study integrates power-domain NOMA and proposes a cloud–terminal collaborative deployment scheme combining a large model with lightweight models. By dynamically determining whether tasks are processed locally by a lightweight model or in the cloud by a large model, the system achieves optimized detection accuracy and a reduced missed detection rate. Numerical results indicate that, under given uplink shared-channel bandwidth, minimum detection accuracy, and maximum missed detection rate, the introduction of power-domain NOMA effectively increases the number of supported terminals. Furthermore, when both terminal access collision constraints and shared-channel bandwidth constraints are considered, optimizing the confidence threshold to regulate the number of terminals transmitting data to the cloud further enhances detection accuracy and reduces the missed detection rate.

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References(20)

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