With the continuous progress of artificial intelligence technology, intelligent Q&A systems have become one of the most important ways for people to obtain information. However, traditional Q&A systems retrieve answers only through keyword matching and are unable to perform deep semantic understanding and reasoning, thus affecting the accuracy and reliability of the system. Therefore, this paper proposes an intelligent Q&A reasoning algorithm based on natural language processing technology, aiming to improve the Q&A capability of the system. This paper focuses on the application of intelligent Q&A reasoning algorithm based on natural language processing technology in the field of intelligent training for power information communication. An intelligent Q&A reasoning algorithm is designed by combining neural network technology. The experimental results show that the algorithm performs well in entity extraction and entity-relationship graph design, and achieves good accuracy in question-answer reasoning. The algorithm has the potential and advantage of providing an efficient and intelligent solution for intelligent training in power information and communication.

At present, thanks to the development of related technologies such as computers and communications, the existing ubiquitous power Internet of Things related technologies have made considerable progress, such as widely supporting distributed energy access, energy optimization configuration, energy interconnection and sharing, and energy supply and demand balance. At the same time, the ubiquitous power Internet of Things is increasingly carrying differentiated energy services internally and externally. It is urgent to use virtualization technology to realize the concept of virtual operators, and provide and deploy services in the form of virtual service resource leasing to meet business needs. The operation requirements of ubiquitous power Internet of Things are not only isolated from each other but also integrated with each other. Existing energy operation management and control technology research is mostly carried out for decentralized networks. However, due to the heterogeneous, decentralized, and obvious differentiation of distributed energy, it is difficult to effectively organize and control the business, and it has not yet obtained better applications. Moreover, the ubiquitous power Internet of Things is a fusion model formed by the "cloud, management, edge, and end" ICT business function chain. Currently, there is still a lack of research on the operation and control technology for its unique resource chain mode.

In the regional distribution network, microgrid is often used to build local energy system to realize regional autonomy in the process of power generation, transmission, and consumption. Applying blockchain technology in microgrid can meet the needs of security and privacy in energy transactions, and can conduct secure point-to-point transactions between anonymous entities. However, blockchain nodes will generate numerous computing-intensive tasks in the process of mining, and cause high delay in energy transaction. Therefore, we take advantage of mobile edge computing (MEC) technology and propose an edge-terminal collaborative mining task processing framework to increase the computing ability of the blockchain system. This framework includes three working modes: local computing, user collaboration and edge node collaboration. Particularly, the trust value of collaborative user nodes is considered to avoid security threats caused by malicious nodes. Furthermore, we establish a delay-and-throughput-based blockchain computing task offloading model, and use asynchronous advantage actor-critic (A3C) algorithm to jointly optimize offloading decision, transmission power allocation, block interval and size configuration. Simulation results show that, compared with Only-MEC and Fixed-BlockSize algorithms, the proposed algorithm can reduce the average delay by 1.7% and 2.5%, and improve the average transaction throughput by 12.1% and 28.5% respectively.

Abstract The traditional network operation and maintenance technology is that the administrator passively waits for the network fault to report and then carries on the emergency treatment. This method lacks active mechanism and has many disadvantages in dealing with network communication failure. The low timeliness of operation and maintenance reduces the reliability of network communication operation and maintenance to a certain extent, so it is particularly urgent to explore the research of network communication operation and maintenance technology driven by big data. This article describes the difference between several different transmission modes in the field of optical fiber communication, and discusses the related factors that restrict the reliability and timeliness of the optical fiber transmission network. On this basis, this paper analyses the possibility of integration of big data technology and the optical fiber network, and lists the typical application of big data technology in the optical fiber network, which provides a new thinking direction for the performance monitoring and operation maintenance of the optical fiber network.

The essence of optical fiber communication transmission technology is to use light as the carrier of information, and to achieve the purpose of information transmission through the optical fiber network. The communication mode based on optical fiber communication technology has been widely recognized in the field of industrial production and telecommunication transmission due to its good information transmission performance. However, with the expansion of the construction scale of optical fiber communication network and the improvement of user coverage, the disadvantages of the management and maintenance of the traditional optical fiber communication network are becoming more and more obvious. Therefore, this paper describes the characteristics of the optical fiber communication technology, and analyses the existing problems in the optical communication system and their causes. On this basis, this paper analyses the automatic operation and maintenance technology of optical fiber communication system. The analysis results in this paper provide some technical support for the deep integration of computer control technology and optical fiber communication automatic maintenance system.

NFV separates network functions from hardware-dependent middle boxes, which can significantly reduce costs and improve network management flexibility. It has been widely used in operator networks. However, due to traffic fluctuation in the network, using virtual network functions to provide flexible services is still challenging. In addition, most VNF scaling methods are passive in nature, which may cause high latency and fail to meet the QoS requirements of services. Therefore, this paper first proposes a GRU-based traffic prediction model and scales in/out VNF instances in advance based on the prediction result. Then we design a VNF buffering mechanism to avoid frequently releasing and creating VNF instances. Furthermore, based on the scaling results of VNF, we apply a DRL algorithm called A3C to train the agent and then obtain the optimal strategy of deploying new instances. Simulation results show that compared with other methods, the proposed proactive method can respond to traffic fluctuation in advance and reduce the total operating costs.

Unified power flow controller (UPFC) combines the advantages of FACTS devices of series and parallel, which is an important direction for the development of intelligent substations in the future. However, a large number of MMC submodules have become potential fault points of UPFC system. The rapid judgment of fault state and accurate positioning of fault sub-modules are the primary conditions to ensure the safe and stable operation of UPFC system, which can effectively reduce the mobile operation and maintenance time of power engineering operation and maintenance personnel, and guarantee the safety of operation and maintenance. Therefore, this paper proposes a fault diagnosis technology of intelligent substation UPFC system based on abnormal sub-module port voltage (SPVE). Experimental results show that the SPVE method has advantages in identification accuracy and rapidity.