Computing Power Network （CPN） is a new network system that solves the contradiction between computing power supply and demand， network transmission problems， and the issue of universal access to computing resources. According to the supply capacity of computing power resource providers and the dynamic resource requirements of application demanders， the computing， storage， network and other multi-dimensional resources of the underlying computing power infrastructure in the region are integrated to provide users with personalized computing power resource services and realize efficient management and on-demand allocation of computing power resources. To enhance the utilization and reliability of CPN resource matching and scheduling， a highly reliable matching method was proposed， namely Resource Measurement and Rescheduling Matching Method （RMRMM）. To achieve high-utilization resource scheduling， RMRMM designed a resource measurement matching scheme based on entropy weighted Technique for Order Preference by Similarity to Ideal Solution （entropy weighted TOPSIS） method and Deep Reinforcement Learning （DRL）， comprehensively measured the Structural Feature Value （SFV）， computing power， storage capacity， and network communication capacity of the node， and narrowed the resource matching range to improve the matching accuracy and resource utilization. Additionally， RMRMM considered the failure of nodes due to attacks， and designed a rescheduling module based on the Adaptive Large Neighborhood Search （ALNS） algorithm. When matches failed， nodes and tasks were rescheduled to improve the acceptance rate of tasks and enhance the overall reliability. Simulation experimental results on OMNet++ platform demonstrate that average BandWidth （BW） utilization， average Random Access Memory （RAM） utilization， average STORAGE utilization， and task request reception rate of RMRMM reach 69.7%， 66.4%， 68.5%， and 75.5%， respectively. Both resource utilization and request reception rate of RMRMM outperform other matching strategies， improving the efficiency and reliability of RMRMM.