The invention discloses a low-energy-consumption task scheduling strategy for CPU-GPU heterogeneity, and provides an 
ant colony task scheduling 
algorithm capable of simultaneously paying attention toreal-time constraints and 
system energy consumption aiming at the characteristics of a heterogeneous multi-
core system and the problems that a traditional 
ant colony 
algorithm only optimizes a singletarget and is too slow in convergence speed. The method comprises the following steps: firstly, providing guidance information in a 
pheromone initialization process according to the energy consumptionof a task on a heterogeneous core; accelerating 
algorithm convergence speed, then, the cores are screened through task real-time constraint conditions; the method comprises the steps of obtaining a heterogeneous core of a task, selecting an appropriate execution core according to the calculation 
energy consumption of the task on the heterogeneous core, the inter-core communication 
energy consumption of different tasks and the 
pheromone content, continuously searching a scheduling scheme with lower energy consumption through multiple iterations of an 
ant colony algorithm, and adjusting the 
pheromone content according to the obtained result to further accelerate the convergence speed of the algorithm. A final task scheduling scheme is obtained after a plurality of iterations, so that the energy consumption of the 
system can be optimized under the condition that the real-time constraint of the task is met.