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With the integration of information technology and traditional industries,it has become a trend to use computer-controlled machines instead of humans to perform repetitive,boring and even dangerous tasks.In order to effectively interact with computers in natural language,human-computer interaction and dialogue systems based on multi-turn dialogue technology have become a research hotspot in the field of artificial intelligence and natural language processing.In the grid control system,the dispatcher needs to do a large number of query operations manually.To reduce the complexity of existing dispatching system and improve the speed of emergency handling of dispatchers,multi-turn dialogue technology can be applied to realize intelligent voice query of power grid data.This paper first describes the basic architecture of the task-oriented multi-turn dialogue system,including functions and related algorithms of its three modules:natural language understanding,dialogue management,and natural language generation.Next,in order to meet the demand of power grid companies for specific scenarios such as intelligent data queries,this paper designs and implements a multi-module task-oriented multi-turn dialogue system which consists of natural language understanding module,dialogue management module,natural language generation module and knowledge base as core mo-dules.The grid dispatcher can ask the system questions and get answers in the form of natural language.This process does not require keyboard or mouse operations,which greatly improves the rapidity and convenience of the grid information query.

In the internet of vehicles systems that combining mobile edge computing (MEC) with non-orthogonal multiple access (NOMA) technology,to solve the high latency problem when user processes computationally intensive and latency-sensitive task,a strategy of task offloading,migration and cache optimization based on game theory and Q learning is proposed.Firstly,the mo-del of offloading delay,migration delay and cache delay of the internet of vehicles task based on NOMA-MEC is established.Se-condly,we use the cooperative game method to obtain the optimal user group to optimize the offloading delay.Finally,in order to avoid local optima,the Q learning algorithm is utilized to optimize the joint delay of the migration cache in the user group.The simulation results show that compared with other solutions,the proposed algorithm can effectively improve the offloading efficiency and reduce the task delay by about 22% to 43%.

A based-cluster resource allocation mechanism was proposed for suppressing interference between Femtocell and Macrocell in Macro-Femto network.The mechanism included three parts,FBS was put in disjoint clusters by graph theory and convex optimization,then a sub-channel allocation algorithm was adopted to assign sub-channel to the FUE and MUE based on fairness of rate.In the end,power allocation was performed by using distributed algorithm.Simulation results show that the proposed mechanism effectively suppresses cross-tier and co-tier interference in the Macro-Femto network,improves the average rate,meanwhile satisfies the requirements of fairness of rate,which makes users more satisfied.

A based-cluster resource allocation mechanism was proposed for suppressing interference between Femtocell and Macrocell in Macro-Femto network.The mechanism included three parts,FBS was put in disjoint clusters by graph theory and convex optimization,then a sub-channel allocation algorithm was adopted to assign sub-channel to the FUE and MUE based on fairness of rate.In the end,power allocation was performed by using distributed algorithm.Simulation results show that the proposed mechanism effectively suppresses cross-tier and co-tier interference in the Macro-Femto network,improves the average rate,meanwhile satisfies the requirements of fairness of rate,which makes users more satisfied.