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From the Office to the Production Line:
Networks connect applications and devices.
From the Campus to the Data Center:
Borders are gradually eliminated.
From Static to Dynamic:
Networks evolve through applications.
Understanding Intent
Machines Replace Humans
for Deeper Understanding of
Service Intents and Objectives
Based on Deep Learning (DL) of networks and service models, enabling the optimization of networks and parameters.
Digital Simulation
Data Computing
Compensates for Human
"Knowledge Gaps"
Based on network calculus and formal verification, realizing simulation verification prior to the physical world.
Intelligent Decision-Making
Breaks through the Limits of Human
Intelligence to Realize Dynamic
Decision-Making for
Large-Scale Networks
Based on knowledge graphs and Machine Learning (ML) algorithms, enabling fault inference and closed-loop decision-making.
Using Artificial Intelligence (AI), an Autonomous Driving Network (ADN) facilitates the constant innovation of network architecture based on knowledge and data, breaking the limits of manual processing. In addition to helping Operations and Maintenance (O&M) personnel better understand service intents and objectives through DL, ADN uses data computing to bypass "knowledge gaps," with ceaseless ML breaking the limits of what manual, experience-based decision-making can achieve. As a result, networks can become self-organizing, self-healing, self-optimizing, and autonomous, bringing intelligent connectivity within reach for enterprises and accelerating the digital transformation of industry.
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