Abstract
Accurate quantitative evaluation of the supervision effect of the smart pension industry can reduce the cost of social pension. The traditional methods cannot effectively classify the regulatory risk levels of the smart pension industry. Therefore, this paper proposes a multisource information intelligent fusion algorithm based on the intelligent pension industry optimization path research. Firstly, we establish the principal model of the supervision effect system of the intelligent elderly care industry optimization path and describe the risk level of the supervision effect from different levels. We build the intelligent service platform of the intelligent elderly care training, calculate the weight vector of the supervision risk of the optimization path at all levels, and determine the attribute type of the supervision effect at all levels. Finally, we calculate the maximum influence value of the supervision effect of the intelligent elderly care industry optimization path and use this value to complete the quantitative evaluation of its supervision effect. Simulation results show that the proposed method can evaluate the regulatory effect of smart pension industry and improve the precision of the regulatory effect of smart pension industry effectively.
Highlights
Intelligent Endowment IndustryUse formula (4) to calculate the supervision success probability of the optimization path of the smart elderly care industry: Pi. where Pi represents the success probability of system supervision i, Ui represents the difficulty of using system supervision FF, Ei represents the current average exposure degree of the system supervision i, Ki represents the knowledge level, PRi represents the proficiency, REi represents the current repair degree of the system supervision i, m represents the number of system supervision, and ω, δ, c, θ, and λ, respectively, represent the weight of each factor on the success probability
E development and implementation of quality management measures in in-patient and out-patient medical rehabilitation facilities is an ongoing process and extends to other rehabilitation areas. e aging of the elderly could be reduced and better health could be maintained by physical activities such as aerobic exercises, strength training, and flexibility training
E main contributions of the paper include the following: (1) We establish the principal model of the supervision effect system of the intelligent elderly care industry optimization path and describe the risk level of the supervision effect from different levels
Summary
Use formula (4) to calculate the supervision success probability of the optimization path of the smart elderly care industry: Pi. where Pi represents the success probability of system supervision i, Ui represents the difficulty of using system supervision FF, Ei represents the current average exposure degree of the system supervision i, Ki represents the knowledge level, PRi represents the proficiency, REi represents the current repair degree of the system supervision i, m represents the number of system supervision, and ω, δ, c, θ, and λ, respectively, represent the weight of each factor on the success probability. Use formula (5) to establish the principal model of the intelligent elderly care industry optimization path supervision effect system: Ei. where F(Pi) represents the weight matrix of each risk factor and ρ(f) represents the consequences of each regulatory impact. On the basis of the principal model of the supervision effect system for the optimal path of the smart pension industry, an intelligent service platform for smart pension physical fitness training shall be established.
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