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To solve the multi-parameter and multi-objective optimization problem in the intelligent resource allocation of textile enterprises, Multi-P-LevyFOA is proposed. Firstly, a multi-parameter and multi-objective resource allocation model for intelligent transformation of textile enterprises is established. Then, if the number of iterations is less than or equal to 2/3 of the total number of iterations, based on the random number of Levy flight, the population position is updated to expand the search range and avoid falling into local optimization. When the number of iterations is greater than 2/3 of the total number, the population position is updated with random numbers of uniform distribution, and the search range is narrowed to avoid jumping out of the optimal value range. The global and local optimization ability of the algorithm at different iteration times are analyzed, and the time complexity of Multi-P-LevyFOA algorithm is analyzed and compared with standard FOA and five improved FOA, and its convergence is proven. The performance of Multi-P-LevyFOA is compared with other four improved FOA, the influence of threshold SEP of the algorithm on the 22 benchmark functions is analyzed, and the influence law of β parameter on the optimization effect of the algorithm in Levy flight is studied. Finally, taking the intelligent resource allocation of a textile enterprise in Shaanxi province as an example, the feasibility of Multi-P-LevyFOA is verified. Experimental results show that the Multi-P-LevyFOA can effectively solve the multi-parameter and multi-objective optimization problem under appropriate βparameters, which provides a reference for the intelligent resource allocation of textile enterprises.

The key performance parameters of spinning frame are difficult to be indentified and the performance degradation model is difficult to be established. To solve the problem, a reliability evaluation method for multiple performance degradation process of spinning frame is proposed. The relationship between the performance parameters of the spinning frame was analyzed, and the subjective and objective comprehensive weight identification method was used to screen the monitored performance parameters. The parameters with comprehensive weight greater than 0.5 were selected as the characterization parameters of the performance degradation of the spinning frame. The Wiener process was used to fit the multiple parameter degradation failure track. The Copula function was used to analyze the correlation of multiple parameter degradation failure process, and the Gibbs algorithm was used to estimate the parameters. The application results show that the proposed reliability evaluation method is in line with the actual production, and has high accuracy, which is helpful to improve the reliability level of spinning frame performance.