Design and Implementation of Personnel Management System Based on MFC
DOI:
https://doi.org/10.53469/jtpms.2025.05(02).03Keywords:
Management system, MFC, C++, MySQLAbstract
Personnel management is the foundation and essential aspect of managing a company or department. In the era of informatization, the elimination of paper-based personnel list management has become inevitable, and the informatization of personnel management has become an inevitable trend. The personnel management system will also become one of the essential systems for companies. There are various types of personnel management systems in the current market with complete functions, but they are not practical for some small businesses or departments, and the cost is relatively high. The personnel management system designed and implemented in this article is based on the MFC library, using a local MySQL database and a locally built server, with low cost being the biggest advantage. Due to both the database and server being local, there is no need for network transmission, and security is greatly guaranteed.
References
Lyu, T., Gu, D., Chen, P., Jiang, Y., Zhang, Z., Pang, H., ... & Dong, Y. (2024). Optimized CNNs for Rapid 3D Point Cloud Object Recognition. arXiv preprint arXiv:2412.02855.
Yin, Y., Xu, G., Xie, Y., Luo, Y., Wei, Z., & Li, Z. (2024). Utilizing Deep Learning for Crystal System Classification in Lithium - Ion Batteries. Journal of Theory and Practice of Engineering Science, 4(03), 199–206. https://doi.org/10.53469/jtpes.2024.04(03).19.
Liu, H., Li, N., Zhao, S., Xue, P., Zhu, C., & He, Y. (2024). The impact of supply chain and digitization on the development of environmental technologies: Unveiling the role of inflation and consumption in G7 nations. Energy Economics, 108165.
Huang, S., Liang, Y., Shen, F., & Gao, F. (2024, July). Research on Federated Learning's Contribution to Trustworthy and Responsible Artificial Intelligence. In Proceedings of the 2024 3rd International Symposium on Robotics, Artificial Intelligence and Information Engineering (pp. 125-129).
Yang, Z., Zhang, W., Lin, X., Zhang, Y., & Li, S. (2023, April). HGMatch: A Match-by-Hyperedge Approach for Subgraph Matching on Hypergraphs. In 2023 IEEE 39th International Conference on Data Engineering (ICDE) (pp. 2063-2076). IEEE.
Chen, Y., Tahir, A., Yan, F. Y., & Mittal, R. (2023, December). Octopus: In-Network Content Adaptation to Control Congestion on 5G Links. In 2023 IEEE/ACM Symposium on Edge Computing (SEC) (pp. 199-214). IEEE.
Peng, Q., Planche, B., Gao, Z., Zheng, M., Choudhuri, A., Chen, T., ... & Wu, Z. (2024). 3d vision-language gaussian splatting. arXiv preprint arXiv:2410.07577.
Bi, S., & Lian, Y. (2024). Advanced portfolio management in finance using deep learning and artificial intelligence techniques: Enhancing investment strategies through machine learning models. Journal of Artificial Intelligence Research, 4(1), 233-298.
Zhou, Y., Wang, Z., Zheng, S., Zhou, L., Dai, L., Luo, H., ... & Sui, M. (2024). Optimization of automated garbage recognition model based on resnet-50 and weakly supervised cnn for sustainable urban development. Alexandria Engineering Journal, 108, 415-427.
Peng, C., Zhang, Y., & Jiang, L. (2025). Integrating IoT data and reinforcement learning for adaptive macroeconomic policy optimization. Alexandria Engineering Journal, 119, 222-231.
Fan, Y., Wang, Y., Liu, L., Tang, X., Sun, N., & Yu, Z. (2025). Research on the Online Update Method for Retrieval-Augmented Generation (RAG) Model with Incremental Learning. arXiv preprint arXiv:2501.07063.
Xu, Y., Shan, X., Lin, Y. S., & Wang, J. (2025). AI-Enhanced Tools for Cross-Cultural Game Design: Supporting Online Character Conceptualization and Collaborative Sketching. In International Conference on Human-Computer Interaction (pp. 429-446). Springer, Cham.
Tian, Q., Wang, Z., & Cui, X. (2024). Improved Unet brain tumor image segmentation based on GSConv module and ECA attention mechanism. arXiv preprint arXiv:2409.13626.
