## 1 Introduction  
With the continuous development of the industrial Internet, the number of industrial IoT devices is increasing, generating more real-time data. Traditional cloud processing methods lead to resource waste and increased pressure on network, computational, and storage resources. Additionally, device heterogeneity introduces security issues, with edge devices being particularly vulnerable to data leakage and system instability. Utilizing edge information processing capabilities can promote data exchange between edge nodes, reducing overall network burden, and employing dynamically adjustable privacy protection policies can enhance data security and system stability. However, establishing a robust cloud-edge cooperative architecture faces two main challenges: insufficient data exchange between edge nodes, and the security and timeliness of data transmission. Recent advances in intelligence techniques have significantly enhanced our ability to perceive, understand, and control the physical world, impacting production and lifestyles. Yet, their rapid deployment in critical services raises security and privacy concerns due to vulnerability to malicious attacks. Drawing inspiration from the characteristics of the natural world, intelligent computing finds widespread application today in problem-solving endeavors. By analyzing and crafting algorithms rooted in natural principles, intelligent computing offers a suite of benefits including self-learning, self-organization, and self-adaptation. Its versatility has led to its integration across various domains. Key methodologies within intelligent computing encompass evolutionary computation, fuzzy set theory, neural network computation, and swarm intelligence computation. Continuously influenced by biological intelligence, intelligent computing tackles challenges of increasing complexity. In the current research on cloud edge collaboration, how to make the connection between the edges closer and guarantee security while maintaining efficient information exchange is receiving more and more attention from scholars. In different fields, the cloud has more and more applications [1; 2; 3; 4]; the cloud can effectively help to reduce the cost of information silos from the original to modern industrial data transformation. There are more and more scholars that are beginning to study Cloud-Edge Collaborative Storage (CECS), allowing the cloud to quickly respond to requests from IoT devices and easily share IoT data with users [5; 6]. The cloud has many benefits, but despite the convenience it brings, it also raises many security concerns. Edge endpoints are vulnerable to attacks and counterfeiting, and edge data are susceptible to theft and tampering [7; 8], posing new challenges for enhanced edge security [9; 10]. To promote a robust Industrial Internet of Things (IIoT) ecosystem, it is crucial to address two main challenges: ensuring secure and timely data exchange, and establishing enterprise credibility. Current encryption frameworks focus on the cloud, where all data are processed before being distributed, but this approach can lead to inefficiencies and high costs. Edge-to-edge interactions, while faster, are prone to security risks, with edge nodes potentially becoming targets for malicious attacks. These vulnerabilities underscore the need for a collaborative architecture that balances security, efficiency, and enterprise credibility. This paper contributes to the field of industrial information security and cloud-edge collaboration with a focus on architectural innovation. The key contributions are as follows:  
* We propose a comprehensive cloud-edge collaborative architecture for industrial cooperation, enabling the seamless interaction between upstream and downstream enterprises while incorporating cloud-based oversight. This structure supports coordinated supervision and collaboration within the Industrial Internet of Things (IIoT) framework.