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LIN X K , LIN J N , SU Z Y , et al. Research on key technologies of power internet of things based on artificial intelligence technology [J ] . IOP Conference Series:Earth and Environmental Science , 2021 , 714 ( 4 ): 042067 . DOI: 10.1088/1755-1315/714/4/042067 http://doi.org/10.1088/1755-1315/714/4/042067 With the development of science and technology, the power industry has combined many new technologies driven by science and technology to improve its own operating quality. The Internet of Things is a new technology that can significantly improve the power system. This article aims to study the key technologies of power Internet of Things based on artificial intelligence technology. This paper takes the research of intelligent meter reading system as an example, and conducts experimental analysis based on artificial intelligence algorithms. Using artificial algorithms in the spider web network topology, as the network increases, the communication paths that can be selected between nodes also increase, and the full-end reliability of the network topology also increases. The collection node is responsible for collecting temperature and voltage values through artificial intelligence technology, and then transmits the collected values to the coordinator node responsible for collecting information, and sends the collected information to the PC through the serial port, and uses the serial port debugging tool to display collected data. Experimental data shows that signal attenuation gradually increases with distance, and distance and obstacles make it impossible to send data accurately. So you can add routing nodes to the network, add collectors and concentrators, and then add an enhanced RF (Radio Frequency Identification) generator to the antenna design. The experimental results show that in the case of two indoor walls and D≤20m, the signal strength is in the range of -82dBm to -94dBm, which can ensure the normal communication of data. If D≥25m, data communication cannot be guaranteed. By combining power and the Internet of Things, power-based Internet of Things can be built to comprehensively enhance power operations.

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ZHAO X , JI Y X , LUO X B , et al. Computation method for the optimal burst height of proximity fuze based on improved particle swarm optimization algorithm [J ] . Acta Armamentarii , 2021 , 42 ( 5 ): 924 - 929 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2021.05.004 http://doi.org/10.3969/j.issn.1000-1093.2021.05.004 The timely detonation of warhead with proximity fuze is related to whether it can effectively attack and destroy a target. The optimal coordination relationship of fuze and warhead is proposed. A mathematical model of warhead power is established. The relationship between related parameters is analyzed using the particle swarm optimization algorithm, and the inertia weight is dynamically updated using the Euler distance method. An improved particle swarm optimization algorithm was presented and applied to the mathematical model to obtain the calculation model of warhead power in fuze-warhead coordination. Through simulation experiments, the relationship among lethal area, burst height and falling angle was obtained, the best power conditions for fuze-warfare coordination were determined, and the maximum lethal area, the corresponding interval of 90% lethal area and the corresponding burst height were calculated. The results show that the proposed method has higher calculation accuracy of the maximum lethal area than the traditional method, and has faster convergence speed than the particle swarm optimization algorithm, which can better meet the speed calculation requirements for the range test and tactical fied firing.

杜广泽 , 张旭东 , 邹渊 , 等 . 非结构道路场景下轮式无人车辆避障算法 [J ] . 兵工学报 , 2020 , 41 ( 10 ): 2096 - 2105 . DOI: 10.3969/j.issn.1000-1093.2020.10.020 http://doi.org/10.3969/j.issn.1000-1093.2020.10.020 针对非结构化道路下轮式无人车辆的避障问题，提出一种基于模型预测控制的避障算法。在没有离线地图和参考轨迹的非结构化场景下，通过优化方法直接获得车辆运动的轨迹、前轮转角与参考纵向加速度，同时满足车辆运动约束与避障功能。利用车辆感知信息，根据非结构化道路场景将避障问题划分为分段最优控制问题；明确最优控制问题各段的车辆约束与评价指标，并将约束转换为与车辆横纵向控制相关的形式，进而对最优控制问题进行求解。实车测试结果表明，该算法在非结构道路中实现了轨迹规划，规划结果满足车辆约束并使得车辆实现避障。

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