The invention discloses a hydraulic
machine intelligent optimization control method based on
big data analysis, and relates to the technical field of hydraulic
machine intelligent control, and the method comprises the following steps: carrying out the real-
time frequency domain analysis of pressure, flow and
energy consumption signals during the operation of a hydraulic
machine, constructing a
load spectrum analysis module, extracting main frequency fluctuation characteristics, and building a corresponding spectrum template; and performing sliding
cross matching on the control threshold change curve of the energy-saving
mode switching and the
frequency spectrum template, identifying a periodic
coupling risk section, and labeling and recording the periodic
coupling risk section. According to the invention, through
frequency domain analysis and a self-adaptive
lag mechanism, frequency
resonance type oscillation in energy-saving control is effectively suppressed, and the operation stability and the response reliability of the
system are improved; meanwhile, a strategy self-learning
mechanism based on
resonance labels and energy efficiency feedback is introduced, parameters and control strategies are dynamically optimized and judged, the adaptability and long-term robustness of the
system to complex working conditions are enhanced, and intelligentization and high efficiency of energy-saving control of the
hydraulic equipment are achieved.