KEYWORDS: Data storage, Data modeling, Data communications, Design, Clouds, Databases, Computer security, Internet of things, Operating systems, Fiber optic communications
Traditional terminal devices generally adopt a process oriented integrated software architecture design scheme, with high coupling between software and hardware modules in various business units. As more and more business modules are connected to intelligent terminals, the software complexity of intelligent terminals also increases exponentially. The complexity of processing new business functions is high, and the development of business software can only be carried out by terminal hardware vendors, making it difficult to meet the needs of flexible business adjustments and rapid installation and deployment. This article designs a software APP based method and system for various data collection needs in intelligent terminals. The hardware of intelligent terminals adopts a modular, scalable, and low-power design, which is adaptable to complex operating environments and has high reliability and stability; By using Docker technology to achieve software and hardware decoupling, APPs communicate through the MQTT protocol, solving the problem of data sharing between different containers and multiple applications within the terminal, and improving the data collection performance of intelligent terminals.
KEYWORDS: Data modeling, Power consumption, Machine learning, Education and training, Cloud computing, Neural networks, Data storage, Data communications, Data privacy, Computer security
There is a serious data island problem for many power data sets at present, and centralized storage of large amounts of data will cause the privacy disclosure of the original data owner, and also face security and regulatory requirements. This patent proposes a power consumption prediction model and method based on federated learning, which includes cloud computing center, central node, edge computing node and communication network. The cloud computing center builds an improved LSTM based power consumption prediction model and initializes it. The central node publishes prediction tasks and cloud computing publishes initialization models to edge computing nodes. After receiving the prediction tasks and initialization models, edge computing nodes use local load data to perform local training. After training, the central node receives the training parameters of each edge computing node, The root mean square error and average relative error of the parameters are calculated. If the error is less than the set accuracy threshold, the new model will be sent to the edge node to complete the model training. If the error is greater than the set accuracy threshold, the local model update parameters of the edge node will continue to perform the training task and perform the iterative training task, which improves the accuracy of power consumption prediction, and isolates the data to ensure that the data will not be leaked to the outside, further meeting the needs of user privacy protection and data security.
With the rapid progress of the distribution network construction, a large number of intelligent terminals need to be connected to the distribution network, which brings hidden dangers to the power grid security. Intelligent distribution master stations and terminals using the existing SM2 algorithm can obtain shared keys on insecure communication channels. However, due to the high concurrency of the main station and the limited computing power and storage capacity of the terminal, the simple use of the original SM2 algorithm cannot guarantee the fast and secure bidirectional identity authentication of the smart distribution network. Considering the high concurrency of the main station and the limited computing power and storage capacity, this paper proposes the distribution terminal security protection method, security chip and system deployed in the intelligent distribution terminal, add encryption authentication unit in the terminal security chip, adopt improved authentication key negotiation based on SM2, combine SM1 data based on decryption technology, realize secure bidirectional communication between the distribution main station and the distribution terminal.
This paper proposes an optimization method for distributed energy access to distribution network based on chaotic genetic simulated annealing algorithm, and establishes a joint optimization with the total network loss of the power grid system, the minimum node voltage, the average voltage deviation, and the user power purchase cost as the objective function. model, to realize the location selection and constant volume of distributed energy, and to use the weighted method to realize the simplification of the objective function. The chaotic genetic simulated annealing algorithm is used to solve the optimization model, and the simulation comparison is made based on the IEEE-69 node system and the particle swarm algorithm , the results show that the proposed method has higher effectiveness and reliability.
In order to improve the data processing capability of the power acquisition system, the electric information acquisition system can be better adapted to different communication environments, in this paper, a multi-mode communication mode with strong adaptability is proposed. In the upstream channel includes GPRS, optical fiber communication, 3G wireless private network, 3G wireless public network, 4G wireless public network, 4G wireless private network and other existing communication mode, and in the downstream channel contains a low-voltage narrow-band PLC, low-voltage broadband PLC, wireless sensor networks and RS485 and other communication modes. Through the analytic hierarchy process to achieve the switch between the various communication modes to adapt to different environments.
With the massive access of distributed power sources, the traditional passive distribution network is gradually transformed into an active distribution network, which has changed the structure of the original distribution network and its operation and control strategies. The self-recovery of the distribution network based on distributed power has become an extremely important development trend in the new situation. In order to improve the reliability of power supply and realize the seamless conversion between island mode and grid-connected mode, the subnet division of distributed power generation network was studied. When optimizing the results of subnet division, an improved wolf pack algorithm was adopted. Based on the nature of subnet division, the algorithm was adaptively improved, so that the algorithm can be applied to the discrete function domain; at the same time, the wolf pack algorithm is introduced. The information transmission mechanism of the gray wolf, through the information transmission between the gray wolf and the local optimal wolf and the global optimal wolf, updates the position of the gray wolf to jump out of the local optimal, and improves the accuracy and reliability of the results.
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