Against the backdrop of the transformation of engineering applications towards intelligence, efficiency, and precision, traditional computer technology and engineering algorithms suffer from problems such as a lack of integration and insufficient adaptability. This leads to low efficiency in processing engineering tasks, excessive resource consumption, and difficulty in meeting the actual needs of complex engineering scenarios. This paper focuses on this core issue and follows a research approach of "background analysis—problem decomposition—solution design—experimental verification." First, it reviews the current status and core requirements of the integration of computer technology and algorithms for engineering applications. Then, it designs a targeted integration architecture, clarifies the functions and interaction mechanisms of each module, optimizes key technologies in the integration process, and finally verifies the feasibility and superiority of the integration design through experimental testing. Experiments were conducted on three typical engineering scenarios (mechanical manufacturing scheduling, engineering data monitoring, and intelligent construction control). The results show that the proposed integration design (Scheme 4) achieves optimal performance in all three indicators: task completion efficiency reaches 98%, which is 21%, 13%, and 8% higher than Schemes 1, 2, and 3, respectively. It can effectively adapt to the actual needs of complex engineering applications and provide technical support for the intelligent upgrading of the engineering field.
Huang et al. (Thu,) studied this question.