摘要：【中文】本发明公开了一种存储系统性能预测的方法，包括以下步骤：收集预定时长的性能数据作为历史性能数据；将性能数据函数按预定周期展开为傅里叶级数，基于所述历史性能数据，利用最大似然估计法计算得到所述傅里叶级数的系数矩阵，并基于所述系数矩阵得到周期分量预测值；基于所述历史性能数据得到性能增长方程；以及基于所述周期分量预测值和所述性能增长方程，得到性能预测值。本发明还公开了一种计算机设备和可读存储介质。本发明提出的网络性能诊断的方法、设备及介质实现快速对存储性能数据进行实时预测，能够较好的拟合历史数据的周期特性，得到准确的预测值，指导存储系统合理规划高负载业务，提高系统的可靠性。 【EN】The invention discloses a method for predicting the performance of a storage system, which comprises the following steps: collecting performance data of a predetermined time as historical performance data; expanding a performance data function into Fourier series according to a preset period, calculating by using a maximum likelihood estimation method based on the historical performance data to obtain a coefficient matrix of the Fourier series, and obtaining a period component predicted value based on the coefficient matrix; obtaining a performance growth equation based on the historical performance data; and obtaining a performance predicted value based on the period component predicted value and the performance growth equation. The invention also discloses a computer device and a readable storage medium. The method, the equipment and the medium for diagnosing the network performance can realize the real-time prediction of the storage performance data, can better fit the periodic characteristics of the historical data, obtain accurate predicted values, guide a storage system to reasonably plan high-load services and improve the reliability of the system.