Research on Cloud Computing-Based Editorial and Publishing Collaboration Platform with Intelligent Scheduling Optimization

To address the challenges of limited computing resources, complex data processing, and high security requirements in the editorial and publishing process, this paper proposes a collaborative platform that integrates cloud computing and intelligent scheduling optimization technologies. The platform leverages the distributed architecture and elastic resource management of cloud computing to achieve efficient collaboration in the editorial and publishing workflow, while enhancing task allocation efficiency through intelligent scheduling algorithms. The platform utilizes Principal Component Analysis (PCA) and Crow Search Algorithm (CSA) for intelligent task scheduling optimization, ensuring stable system operation in multi-task scenarios. The specific approach includes dimensionality reduction of user data to eliminate redundant information and constructing a fitness function based on the task model to achieve dynamic updates of the optimal scheduling scheme. Experimental results demonstrate that the proposed scheduling method performs excellently in terms of task execution time, cost, and service quality. This research provides a cloud computing platform with intelligent scheduling optimization for digital editorial and publishing, offering technical support for the development of information management.