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Most of the existing new power system terminals use private protocols of third-party vendors， which often proves to be challenging to analyze. The Modbus protocol commonly used in existing new power system terminals is analyzed in reverse. Based on the characteristics of protocol hierarchy， the valid fields of the unknown protocol are extracted from the header fields of network messages， and the protocol fields are divided into words based on the statistical N-gram algorithm， and a Modbus protocol state machine is constructed using protocol keywords. Further， by protocol source code compilation and instrumentation， the Modbus server-side program is fuzzy tested by generating random test cases， and the test crash results are analyzed. The experimental results show that the scheme is capable of fast reverse identification and fuzzy testing of private protocols from third-party vendors and holds substantial practical value.

The external wireless terminals of the new power system are susceptible to be attacked through internal network penetration triggered by physical contact. The traditional device security test has little effect on improving the security performance of the access devices and is prone to produce a high false positive rate （FPR）. A wireless access security testing system based on deep neural networks （DNN） is proposed. The system adopts a stacked sparse autoencoder （SSAE） to realize the feature dimensionality reduction of the test dataset and selects the appropriate feature dimensions for training. The selected features are used as the input layer of the DNN to construct highly efficient test cases， as well as to monitor and discover the abnormal states. The experiment results show that the system has an accuracy rate of 90% and can efficiently detect anomalies in the access environment of wireless power terminals.

The effects of different preparation techniques (aqueous enzymatic extraction, aqueous extraction, cold pressing, hot pressing and organic solvent extraction) on the physical and chemical properties, nutritional components and oxidation stability of tiger nut oil from the cultivar ‘Zhongyousha 1’ grown in Xinjiang were investigated. The results showed that organic solvent extraction gave the highest extraction rate (88.31%), and there were significant differences in the physicochemical properties of tiger nut oils prepared by different processes (P < 0.05). Oleic acid accounted for 76.04% of the total fatty acids in the oil prepared by aqueous extraction, which was 1.72–2.34 percentage points higher than that obtained with other preparation processes (P < 0.05). Different preparation techniques had significant effects on the lipid concomitant content and oxidation stability of tiger nut oil (P < 0.05). The oil obtained by organic solvent extraction had the highest contents of tocopherol (370.67 mg/kg), total phenol (168.59 mg/100 g) and phytosterol (271.26 mg/100 g), while the content of squalene was highest (162.04 mg/kg) in the oil obtained by aqueous extraction. The cold pressed oil had the highest oxidation stability index (24.15 h) indicating good oxidation stability. Correlation analysis indicated that the oxidation stability of tiger nut oil was positively correlated with unsaturated fatty acid and oleic acid (r = 0.72, r = 0.48), and the antioxidant capacity (1,1-diphenyl-2-picrylhydrazyl and 2,2’-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) free radical scavenging rate) of polar components was significantly positively correlated with total phenol content (r = 0.91, r = 0.96, P < 0.05). The results of principal component analysis (PCA) revealed that the cold pressed oil was similar to the oil obtained aqueous enzymatic extraction, while the oils prepared by organic solvent extraction, hot pressing and aqueous extraction were differentiated well. These results can provide a theoretical basis for the development of high-quality tiger nut oil.

The moving targets in coal mine underground monitoring videos often have significant scale changes and deformations. This results in low accuracy of target tracking algorithms based on computer vision. Moreover, the massive amount of video data makes it difficult for centralized cloud-based data processing methods to meet the real-time requirements of target tracking. In order to solve the above problems, a scale-adaptive target tracking method for coal mine underground based on cloud-edge collaboration is proposed. A scale-adaptive target tracking algorithm based on depth estimation is designed. The scale-adaptive target tracking is achieved by constructing a depth-scale estimation model, which uses target depth values to estimate scale values. The problem of low tracking accuracy caused by target scale change and deformation is solved. An intelligent monitoring system architecture based on cloud-edge collaboration is designed. The sub-modules of the scale-adaptive target tracking algorithm, which are divided into fine granularity, are deployed at the edge and cloud of the system according to the required computing resources. The algorithm's operational efficiency is improved through distributed parallel processing at the edge and cloud, solving the problem of poor real-time performance in the centralized data processing. The scale-adaptive target tracking method based on cloud-edge collaboration is applied in coal mine underground video sequences. The tracking performance and real-time performance are verified experimentally. The results show that compared with three classic target tracking algorithms, namely kernel correlation filter (KCF), discriminant scale space tracking (DSST) algorithm, and scale adaptive multiple feature (SAMF) algorithm, the scale-adaptive target tracking algorithm based on depth estimation has higher tracking precision and success rate when there are significant scale changes and deformations in coal mine underground targets. Compared with traditional cloud computing processing methods, the deployment method of scale-adaptive target tracking algorithm based on cloud-edge collaboration reduces the total delay of the algorithm by 32.55%. It effectively improves the real-time performance of target tracking of intelligent monitoring system in coal mine underground.