Abstract: An optimization method for turbines of a thermal power unit based on sparse big data mining in the technical field of turbine optimization. The optimization method comprises: acquiring historical operating data of a unit; selecting a set of controllable operating parameters highly correlated with the heat rate of turbines as optimized parameters; constructing a new sparse data-oriented pattern-growth association rule mining algorithm based on binary matrixes and the hyperlink technique; proposing a load balancing strategy based on matrix operation on an Apache Spark platform, and parallelly implementing the global calculation-balanced pattern-growth association rule mining algorithm; and discretizing the historical operating data by clustering, parallelly mining the discretized historical operating data to obtain an association rule, and performing de-discretization to obtain target values of the optimized parameters of the turbines under boundary conditions.

Abstract: Disclosed are a method and device for evaluating damage caused by secondary stress to a vacuum vessel, a terminal device, and a medium, to perform following steps: obtaining secondary stress of a vacuum vessel that passes a primary-stress failure evaluation; obtaining structural damage parameters of the vacuum vessel when determining, based on evaluation parameters for the primary-stress failure evaluation of the vacuum vessel and the obtained secondary stress, that the vacuum vessel meets a precondition for a progressive deformation; and determining, based on the obtained structural damage parameters, whether the vacuum vessel meeting the precondition for the progressive deformation experiences structural damage due to the progressive deformation. In this way, a vacuum vessel of a nuclear fusion reactor can be evaluated based on damage caused by the secondary stress.