Industrial Digital Transformation and Intelligent Connected Vehicles: Best Practices with YMatrix Hyper‑Converged Database

The piece begins with an overview of the shift toward digital‑intelligent (数智化) transformation in industry, emphasizing that digitization is the foundation and intelligence the goal, and that the value lies in cost reduction, efficiency gains, and innovative user experiences.

It then introduces intelligent connected vehicles (ICV) as a concrete example, describing how intelligence (advanced driver assistance, energy efficiency, etc.) relies on robust connectivity and data integration across the vehicle’s lifecycle.

Key challenges (pain points) are identified: lack of unified top‑level design, poor data infrastructure, fragmented business units, massive heterogeneous data volumes, and low‑efficiency glue code that hampers rapid iteration.

The proposed solution emphasizes a gradual, persistent approach: building a unified data platform that supports private‑cloud infrastructure, data standardization, and a data‑native organizational model, enabling seamless data fusion between manufacturing and service domains.

Architectural diagrams illustrate a dual‑track platform: a manufacturing big‑data platform fed by MES systems and a vehicle‑network big‑data platform fed by T‑Box telemetry, both converging on a common data foundation.

YMatrix, a self‑developed hyper‑converged database, is highlighted for its ability to handle all data types, provide real‑time and batch analytics, and embed languages such as Python, SQL, and Java directly within the database for scalable predictive‑maintenance workloads.

Performance benchmarks show YMatrix outperforming Hive (26.7× on TPCH), achieving over 1.5 million TPS on TPCB tests, and surpassing ClickHouse by more than 30 % on SSB.

The article also outlines typical AI‑driven predictive‑maintenance pipelines that combine physics‑based models with big‑data analytics, and discusses organizational roles (data owners, data‑management department, engineers, analysts, scientists) needed to sustain the transformation.

Finally, it stresses the importance of unified data standards and models, a robust data base, and a cloud‑native, scalable architecture to enable real‑time insight, cost reduction, and enhanced customer satisfaction in the era of industrial digital intelligence.