Abstract: In the wireless sensor network system, there is a large amount of real-time data transmission between the master node and the slave node. In order to ensure the accuracy of data transmission, the clocks of all nodes on the sensor network should be synchronized. Among them, accurate measurement of clock deviation and clock drift is the key to realize master-slave clock synchronization. Aiming at the clock drift and clock deviation in the actual working environment, a dual-compensation clock synchronization algorithm based on the improved particle swarm algorithm to optimize PID is proposed. This algorithm uses the Kalman filter to simultaneously estimate the clock deviation and clock drift, and uses the estimated value to compensate and correct the slave node clock. On the basis of the update equation of the particle swarm algorithm, environmental factors are introduced to make the particles adapt to the dynamic environment of constantly changing clocks. Experiments show that the introduction of an improved dual-compensation clock synchronization algorithm for particle swarm optimization PID in wireless sensor network systems reduces the impact of traditional particle swarms falling into local optimal values in dynamic environments, and enhances the global optimization capability of particle swarm optimization, improve the accuracy of clock synchronization and eliminate the influence of clock instability from the node on clock synchronization.