## 5 Summary  
As one of the advanced large-size six-degree-of-freedom measurement technologies, the laser tracking attitude dynamic measurement system has shown a broad application prospect in the field of high-end intelligent manufacturing. With the deepening of the transformation of intelligent manufacturing mode, it is especially important to study and optimize the real-time dynamic measurement technology. In this paper, a multi-source information fusion attitude angle dynamic measurement method based on a backpropagation algorithm is proposed with laser tracking equipment as the base station. The laser tracking attitude dynamic measurement mathematical model is established, the network structure and algorithm flow are designed, and the attitude angle prediction of the target object is realized through model training. The optimal network structure is 6-11-3. For the problem that the target value is difficult to select in the process of backpropagation algorithm data fusion, this paper adopts the method based on the neighborhood search to realize the determination of the optimal training target value. The MSE of the neighborhood search method has decreased to 0.196, which is a 34% reduction compared to the IMU determination method; meanwhile, the R has increased from 0.901 to 0.929. The neighborhood search method can effectively improve the performance of the model when the true value cannot be determined. Finally, the experimental platform is set up, with the total station as the base station and the precision rotary table as the carrier. The effectiveness of the research method of this paper is verified, and the method adopts the backpropagation algorithm proposed in this paper for the fusion within the range of 8 m and 0-50\({}^{\circ}\) of angle measurement, and the absolute value of the angle measurement error can be less than 0.4\({}^{\circ}\). The research method in this paper provides a new idea for the attitude dynamic measurement method based on multi-source information fusion. However, the attitude measurement method based on the BP neural network in this study has achieved significant results. In order to further improve the performance and adaptability of the system, future work will explore the introduction of reinforcement learning into the optical tracking system to achieve more efficient and accurate attitude measurement. In addition, there are problems of insufficient data volume and unbalanced distribution in the data collection process, which affect the generalization ability of the model. In future research, we will focus on optimizing the model structure. By introducing reinforcement learning, we expect to significantly improve the precision and speed of attitude measurement in the optical tracking system. Especially when dealing with fast-moving and severely occluded targets, the reinforcement learning model will be able to more effectively adjust the tracking parameters and improve the robustness of the measurement. Through these future works, we believe that we can elevate the attitude measurement method based on the BP neural network to a new level and provide more powerful technical support for related applications. Conceptualization, Z.Z. ([PERSON]) and Z.X. ; methodology, Z.X. and Z.Z. ([PERSON]); software, H.Z., Z.G. and Z.X. ; validation, Z.X., W.Z. and Z.Z. ([PERSON]); formal analysis, Z.X. and Z.Z. ([PERSON]); investigation, H.Z. ; resources, Z.Z. ([PERSON]), Z.G. and Z.X. ; data curation, Z.Z. ([PERSON]) and H.Z. ; writing--original draft preparation, Z.Z. ([PERSON]) and Z.X. ; writing--review and editing, Z.Z. ([PERSON]) and Z.X. ; visualization, W.Z. ; supervision, X.L. and Z.Z. ([PERSON]); project administration, X.L. and Z.Z. ([PERSON]); funding acquisition, Z.Z. ([PERSON]) and Z.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript.