This invention relates to the field of
automatic control and
processing technology, specifically to an
adaptive control method for crushing construction
solid waste. The method includes real-time acquisition of particle size,
rebar ratio,
moisture content, strength, and feed rate via a multi-source
sensor array; generation of operating condition vectors via a cross-
modal fusion network; and training of a
convolutional neural network to output
rotor speed, cavity clearance,
hydraulic pressure, screen aperture size, and
shape memory alloy deformation. This application also relates to
adaptive control equipment for crushing construction
solid waste. The controller of this application synchronously drives a variable frequency motor,
hydraulic cylinder, variable screen, and Ni-Ti
shape memory alloy actuator, completing reversible geometric adjustment within 10 seconds. This enables rapid switching between coarse and fine crushing, incremental updates via online particle size detection and
energy consumption metering feedback network, forming a
perception-decision-execution
closed loop. This significantly reduces
energy consumption, substantially improves particle size qualification rate, extends liner life, reduces the need for manual intervention, enhances overall
operational stability and intelligence, and reduces dust emissions.