The era of intelligence has driven computing power resources to become highly flexible and adjustable. They have also made the bidirectional collaborative optimization of computing and electricity into a new method of economic optimization in comprehensive energy systems. The explosive growth in computational demand has led to a shortage of computational resources. It also brings the challenges of high energy consumption and carbon emissions for data centers， where the annual electricity consumption can reach billions of kilowatt-hours. When the cost of computing resources is high and the stability of power grid operations is affected， there is an urgent need to explore bidirectional collaborative technologies between computing and power nodes with adjustable resources to reduce the energy cost and enhance the stability and economic efficiency of power grid operation. This study constructs a bidirectional collaborative scheduling architecture for adjustable resources in computing and power nodes， and quantitatively models the diverse adjustable resources within them. Considering the matching and real-time adjustment characteristics between computing tasks and resources， a dual-layer two-stage collaborative optimization scheduling model is proposed by scheduling computing tasks under the computing node and adjustable loads under the power node. Through numerical examples， it was verified that the bidirectional collaborative optimization of adjustable resources for computing and power nodes is feasible and effective. Under the setting of an overall adjustable resource of approximately 2300 MW for power nodes， the cost reduction provided by computing nodes of 50 MW can account for 4.71% of the power node operation， while reducing its daily operating costs by approximately 0.70%.