Hospital information data exhibits various types and quality issues, including incompleteness and irregularity, complicating backup strategies. Existing methods face challenges such as inadequate multi-objective optimization, limited global search capabilities, and difficulties in balancing efficiency and cost. To address these issues, we propose an intelligent backup method for hospital information data based on an improved Harris Hawk Algorithm (HHA). This method formulates a comprehensive backup strategy fitness function using multi-objective optimization theory, considering key indicators like data integrity, recovery ability, efficiency, security, and cost-effectiveness. The enhanced HHA employs logistic chaotic mapping and elite hierarchy to diversify population initialization, improving global search and population diversity. Additionally, we introduce an adaptive escape energy decreasing strategy and nonlinear jump strength update to enhance exploration and prevent local optima entrapment. Experimental results demonstrate that this method ensures high-quality data backup, significantly boosts backup efficiency, and reduces costs, offering a reliable solution for the secure backup of hospital information data.

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