This research focuses on the effects of microwave-assisted activated carbon fibre (ACF) (MW-ACF) treatment on sewage sludge at alkaline pH. The disintegration and biodegradability of sewage sludge were studied. It was found that the MW-ACF process at alkaline pH provided a rapid and efficient process to disrupt the microbial cells in the sludge. The results suggested that when irradiated at 800 W MW for 110 s with a dose of 1.0 g ACF/g solid concentration (SS) at pH 10.5, the MW-ACF pretreatment achieved 55% SS disintegration, 23% greater than the value of MW alone (32%). The concentration of total nitrogen, total phosphorus, supernatant soluble chemical oxygen demand, protein, and polysaccharide increased by 60%, 144%, 145%, 74%, and 77%, respectively. An increase in biogas production by 63.7% was achieved after 20 days of anaerobic digestion (AD), compared to the control. The results indicated that the MW-ACF pretreatment process at alkaline pH provides novel sludge management options in disintegration of sewage sludge for further AD.

An intelligent evaluation method of power information intrusion tolerance based on machine learning is proposed to solve the problems of selection of evaluation indexes, poor evaluation accuracy and efficiency in the evaluation method of power information intrusion tolerance. An intelligent evaluation system of power information intrusion tolerance is established, and an intelligent evaluation model is established by using random forest algorithm. The random forest algorithm determines the evaluation weight according to the quantitative value of the index, sets the value of the intrusion risk function, and obtains the intelligent evaluation results of intrusion tolerance. The method studied is applied to the comparative evaluation experiment of the survivability situation of the power information physical system. The evaluation mean square deviation of the intelligent evaluation method based on machine learning is less than 0.1, the average time is 140.72ms, and the evaluation efficiency is increased by 42.51%. At 1000min, the reliability value is still 0.46, which has practical significance.

Power information communication contains sensitive and private data, which may be illegally stolen and tampered after being leaked, posing a threat to the data security of power enterprises and users. Aiming at the problem of limited key space, a data leakage prevention method for power information communication based on chaotic mapping is proposed. The physical layer of power communication includes power data produced and distributed by power equipment, and the information layer includes operation, monitoring and dispatching data. There is an association between the two data. The dispatch and control center uses the association relationship to establish access rights for users and generate access private keys for trusted users. According to the user authority, the communication data is encrypted and decrypted by chaotic mapping, so that the two adjacent components of the reconstructed key space are relatively independent, and the properties of the chaotic sequence are maintained. The test results show that the proposed method can effectively reduce the computing and communication overhead of the power data encryption process and improve the encryption efficiency.