## 2 Motivation  
Power supply and grid maintenance for high-speed rail operating lines are very difficult. With the development and maturity of wireless sensor network technology, high-speed train operation provides more efficient, reliable, and low-cost solutions in the field of environmental monitoring and detection, and then implements high-tech manual maintenance. Experts have conducted a lot of in-depth research on the coordination of the routes of the plane network, but less on the coordination of the high-speed railway network. If there is a general problem with the existing routing protocols, it is their lack of applicability to monitoring regional environments. There are many types of sensors in high-speed rail systems. Various sensors vary widely in terms of latency, transfer rate, data volume, etc. Inspired by the above requirements and the existing work, this paper proposes a heterogeneous network data aggregation model and adaptive routing algorithm for high-speed railway monitoring network based on reinforcement learning. The overall framework of the backbone routing communication protocol for high-speed railway state monitoring wireless sensor network is depicted in Figure 1, consisting of four components:  
1. Communication structure and data feature analysis, encompassing the analysis of node energy information, monitoring device characteristics, monitoring functional requirements, and data features;
2. Analysis of monitoring targets and requirements, including aspects such as operational environment, track service status, and OCS (Overhead Catenary System) system status;
3. Aggregation strategy and intelligent routing modeling, constructing an adaptive multi-objective optimization model based on the lifecycle and transmission delay requirements of different network objects;
4. Adaptive routing algorithm, developing separate lifecycle and real-time evaluation models for monitoring tasks, dynamically adjusting multi-hop transmission paths to ensure the network fulfills both lifecycle and real-time requirements for various tasks simultaneously.  
Figure 1: Overall scheme of adaptive routing protocol for high-speed railway monitoring network based on reinforcement learning.  
The main contributions of this paper can be summarized as follows:  
1. A lossless data aggregation transmission model for HSR networks is proposed, which can effectively reduce the amount of data in the network and reduce the energy consumption of data transmission;
2. A Double-Q-value model based on data aggregation is proposed. Forn the two Q values, we consider the data aggregation degree, the remaining energy level, the link strength, the distance from the node to the sink, and the forwarding delay to consider the network lifetime and the real-time performance of data forwarding. The defined reward function can capture the dynamic changes in the network in real time and achieve dynamic control of the entire network with less overhead;
3. An adaptive energy-saving routing algorithm based on Double-Q-values is proposed to classify HSR network devices according to their real-time requirements and life cycle requirements. An adaptive control algorithm is adopted for different business priorities. It meets the real-time requirements of the HSR network and prolongs the network's life and improves service quality.  
The work arrangement of this paper is as follows. Section 3 presents the typical structure of the high-speed railway monitoring network as well as the analysis of node requirements and introduces the data aggregation scheme. Section 4 describes in detail the proposed demand-aware energy-saving routing algorithm based on Q-learning. Section 5 discusses and analyzes the performance metrics of the proposed routing protocol through simulation experiments. The conclusions are summarized in Section 6.