To address the problems of over-maintenance and delayed maintenance in traditional maintenance management models following the deployment of large-scale high-tech equipment in military forces， which make it difficult to detect equipment faults in a timely manner and seriously affect the generation of equipment combat effectiveness and the accomplishment of military missions， this paper proposes an improved grey GM（1，1） model based on an HHO-PSO hybrid optimization algorithm that integrates Harris Hawks optimization and particle swarm optimization. By synergizing the global exploration capability of HHO and the local feature extraction capability of PSO， the algorithm optimizes parameters and establishes model adaptation using root mean square error （RMSE） as the convergence criterion， thus effectively improving prediction accuracy. The experimental results indicate that the improved model demonstrates good performance in equipment fault prediction， with low errors and high accuracy， and can provide technical support for early fault warning of equipment.

Traditional interference waveform generation methods require prior acquisition of parameter characteristics of the target signal， making it difficult to achieve favorable results when confronted with the complex and diverse communication signal patterns nowadays. To address this issue， this paper proposes a communication interference generation technology based on lightweight deep convolutional generative adversarial networks （DCGAN）. The DCGAN network is utilized to perform unsupervised learning on the features of the target signal without prior information， thereby generating interference waveforms that are highly similar to the target signal. Meanwhile， lightweight processing is applied to the network to reduce the number of network parameters. In this paper， simulation experiments are conducted on communication signals of various modulation types. The experimental results show that the lightweight DCGAN network reduces the number of parameters by approximately 38% without compromising the quality of the generated interference waveforms. When the jamming-to-signal ratio （JSR） is above 0 dB， the bit error ratio （BER） of the communication system affected by the generated interference waveforms rapidly approaches 0.5， which is far superior to the interference effect of traditional interference waveforms.