In order to improve the effect of smart home control and management, a new smart home control and management method based on big data analysis is designed. The basic hardware of smart home control and management is designed, including smoke sensor hardware, temperature and humidity sensor hardware, and infrared sensor hardware, so as to collect smart home data and realize data visualization and buzzer alarm. The collected data are transmitted through the indoor wireless network of smart home gateway equipment, and the data distributed cache architecture based on big data analysis is used to store smart home data. Based on the relevant data, the hybrid particle swarm optimization algorithm is used to schedule the control and management tasks of smart home to complete the control and management of smart home. The experimental results show that the device control and scenario management effect of this method is better, and the communication performance is superior and has high practical application value.

Technology has boosted electric power consumption both locally and worldwide, resulting in a substantial increase in demand for electric power. A multiobjective smart house human-computer interaction load control method is suggested to meet the goal of lowering power usage and pricing in smart home load control. It presents a model that includes marginal costs and establishes an electricity price model that takes the load rate into account. It identifies switch appliances and temperature control appliances and gathers human activities, indoor and outdoor temperature, and light intensity using intelligent equipment to create a multiparameter comfort model. It creates a multiobjective model of comfort and electricity price with the purpose of reducing electricity price and multiparameter comfort, and it improves particle swarm performance by using a distance ratio based on fitness value. The optimization algorithm solves the model, determines the best smart home human-computer interaction load control scheme, creates the smart home remote control system’s functional modules, and optimizes the multiobjective smart home human-computer interaction load control algorithm using a frequency-duration parameter tracking learning model. The testing findings suggest that the proposed algorithm can cut power prices in a reasonable manner, as well as regulating 40% reduced electricity usage and load in smart homes.