Abstract: A cavitation state identification method driven by vibration data of fluid machinery is disclosed in the present invention, and belongs to the field of big data learning models. According to the present invention, an adaptive neural network is trained by means of a cavitation data set to form a cavitation state identification model, such that vibration signal sequences can be collected online by all vibration sensors arranged at different positions of a target centrifugal pump, the collected vibration signal sequences are input into the cavitation state identification model obtained by training, and a current real-time cavitation intensity of the target centrifugal pump is predicted online. Moreover, the cavitation intensity predicted in the present invention can use a more detailed quantitative label, such that fine-grained prediction about a cavitation development degree is achieved.

Abstract: The present invention provides a hydraulic turbine cavitation acoustic signal identification method based on big data machine learning. According to the method, time sequence clustering based on multiple operating conditions under the multi-output condition of the hydraulic turbine set is performed by utilizing an neural network, characteristic quantities of the hydraulic turbine set under a steady condition in a healthy state is screened; a random forest algorithm is introduced to perform feature screening of multiple measuring points under steady-state operation of the hydraulic turbine set, optimal feature measuring points and optimal feature subsets are extracted, finally a health state prediction model is constructed by using gated recurrent units; whether incipient cavitation is present in the equipment is judged.

Abstract: The present invention discloses an optimization design method for structural parameters of biomass boiler economizers and belongs to the field of big data learning models. In the present invention, a sample database is established by utilizing historical operating big data of biomass boiler economizers, a heat exchanger residual self-attention convolution model is established based on a CNN and a self-attention mechanism, a plurality of target parameters to be optimized are quickly predicted through machine learning, and multi-target optimization of structural parameters to be optimized in the economizers can be performed in combination with an iterative optimization algorithm.

Abstract: The present invention discloses an optimization design method for structural parameters of biomass boiler economizers and belongs to the field of big data learning models. In the present invention, a sample database is established by utilizing historical operating big data of biomass boiler economizers, a heat exchanger residual self-attention convolution model is established based on a CNN and a self-attention mechanism, a plurality of target parameters to be optimized are quickly predicted through machine learning, and multi-target optimization of structural parameters to be optimized in the economizers can be performed in combination with an iterative optimization algorithm.

Abstract: A preparation method for an ultraviolet-responsive coumarin controlled-release and self-repairing anti-fouling paint includes: reacting double-end-group reactive polydimethylsiloxane, polyisocyanate, and an organic diluting solvent; adding a dihydroxycoumarin compound, a cross-linking agent and an organotin catalyst; adding a simple coumarin compound, and irradiating the mixture with 365 nm ultraviolet light to obtain the anti-fouling paint. An anti-fouling coat formed by the paint of the present invention has the advantages of controllable release of a coumarin green anti-fouling agent in response to external ultraviolet stimulation and self-repairing, and the problems that the release of the conventional anti-fouling agents in the anti-fouling coat is difficult to control, and that the low-surface-energy anti-fouling coat is difficult to repair after being damaged are solved.

Abstract: The present invention provides a hydraulic turbine cavitation acoustic signal identification method based on big data machine learning. According to the method, time sequence clustering based on multiple operating conditions under the multi-output condition of the hydraulic turbine set is performed by utilizing an neural network, characteristic quantities of the hydraulic turbine set under a steady condition in a healthy state is screened; a random forest algorithm is introduced to perform feature screening of multiple measuring points under steady-state operation of the hydraulic turbine set, optimal feature measuring points and optimal feature subsets are extracted, finally a health state prediction model is constructed by using gated recurrent units; whether incipient cavitation is present in the equipment is judged.