[Press Release]
SHINDEV’s latest in-depth research indicates that, empowered by digital twin architectures, “AI + Big Data” is rapidly penetrating critical infrastructure domains—including railway operations and maintenance (O&M), power grid monitoring, smart cities, and aerospace—while forming increasingly complete and replicable commercialization models in China. These models are effectively filling market gaps in intelligent infrastructure O&M. As generative AI has surged since 2023, more industries are embedding AI into products and service workflows. In this context, “AI + Big Data” has become a practical and effective pathway for real-world and information-world interaction, collaboration, and optimization, with long-term and sustained market growth potential.
SHINDEV believes the sector presents meaningful entry barriers for new participants: beyond sustained R&D investment, market trust must be earned through engineering validation, compliance certifications, and scalable deployments. Meanwhile, policy tailwinds, the acceleration of “new infrastructure” build-out, and broader industrial digitalization are creating structural opportunities for technology providers. At the same time, data security, public–private coordination, and complex deployment environments remain critical challenges. Companies that actively explore innovative business models and build end-to-end solution capabilities are more likely to capture emerging commercial and investment opportunities.
The digital economy—built on digital, network, and information technologies—has become a key engine of economic development. Gartner projects that by 2025 the global digital economy will reach USD 41 trillion, accounting for 24% of global GDP. China’s digital economy has also made notable progress: according to publicly available figures, China’s digital economy reached RMB 45.5 trillion in 2021, ranking second globally and giving rise to 97 new “digital occupations.”
SHINDEV notes that the digital economy is not only about data growth, but also about the evolution of an integrated digital technology stack—AI, 5G, communications, and computing. Future competitiveness will depend on converting technology tailwinds into measurable industrial efficiency and scalable commercial loops.
(1) Artificial Intelligence: Rapid scaling and continuous application penetration
AI has been elevated to a national strategic priority and is increasingly integrated with industry applications, systems, and supporting software. Through intelligent scenarios, AI enables more personalized, precise, and automated services—streamlining processes, improving productivity, and reducing costs. Driven by policy support, rising demand, and technology iteration, China’s AI market has expanded rapidly: by service-provider revenue, it grew from RMB 86.1 billion (2018) to RMB 345.05 billion (2022), a 41.5% CAGR. It is projected to grow from RMB 500.63 billion (2023) to RMB 1,506.56 billion (2027), sustaining a 31.7% CAGR from 2023 to 2027.
(2) Big Data: From resource advantage to production factor, accelerating value creation
China is a major data-resource holder. In 2020, government policy formally recognized data as a production factor alongside land, labor, capital, and technology, highlighting its strategic importance. China’s big data market has continued to expand: by service-provider revenue, it reached RMB 100.94 billion in 2022, with a 17.8% CAGR from 2018 to 2022. With governments and large enterprises placing greater emphasis on data assets and increasing budgets, the market is expected to maintain solid growth over the next five years.
The concept of digital twins was introduced by Professor Michael Grieves (University of Michigan) in 2003. A digital twin creates a real-time mapping of the physical world in digital space. SHINDEV finds that, when combined with AI and big data, digital twins are forming actionable value loops across several high-impact scenarios:
1) Railway O&M: AI + machine vision boosts efficiency and enables “inspect-and-repair” workflows
With the maturation of AI, big data, cloud computing, and machine vision, digital-twin-enabled railway O&M monitoring solutions are developing rapidly. By classifying and analyzing massive high-frequency monitoring data, images, and video streams, deep learning systems can identify risks around traction power and surrounding environments, supporting faster response and maintenance planning, and reducing operational incidents.
On the demand side, rail and urban transit mileage continues to expand in China, driving rising O&M needs. However, intelligent inspection and O&M penetration remains relatively low (urban rail penetration is estimated at ~20%–30%). As the industry shifts toward a “construction + operations” balance, demand for intelligent monitoring systems across traction power, track engineering, and rolling stock is expected to rise quickly.
2) Power grid monitoring: condition assessment and intelligent O&M as the core, with expanding software investment
AI and digital twins in the grid domain support platform construction, state monitoring and assessment, intelligent O&M, and emergency control—leveraging data integration, analytics, knowledge graphs, deep reinforcement learning, and computer vision.
From an investment perspective, China’s smart grid (excluding generation) is projected to grow from approximately RMB 83.0 billion (2022) to RMB 102.1 billion (2027), implying a ~4.2% CAGR. Software-related investments are projected to rise from RMB 27.7 billion (2022) to nearly RMB 42.0 billion (2027). Demand for online monitoring, analytics, operational assessment, cybersecurity, forecasting, and business-layer applications will continue to support market expansion.
3) Smart cities: holistic sensing + digital-space modeling enables the shift from “monitoring” to “control”
In smart city development, digital twins can integrate GIS with multi-source sensing (video surveillance, millimeter-wave radar, etc.) to build digital representations of traffic systems. Real-time analytics and tracking can address inefficiencies such as wasted capacity, rigid signal control, unpredictable accidents, and slow emergency response. With expanding device diversity and explosive data growth, surveillance intelligence is becoming essential—enabling real-time anomaly extraction, filtering, and alerts, and transforming traditional passive monitoring.
4) Aerospace: mission visualization and decision loops powered by digital twins
Digital-twin-enabled 3D real-time visualization can construct virtual models based on sensor data, presenting orbit, position, attitude, and payload information—supporting efficient and intuitive decision-making for ground control and mission execution.
5) Broader expansion: from digital twin workshops to next-generation intelligent manufacturing
“AI + Digital Twin” is not only a research hotspot but increasingly integral to industrial digitalization. Digital twin workshops are viewed as a future operating paradigm and are expected to support the evolution of industrial internet and service-oriented manufacturing models.
SHINDEV emphasizes that complex scenarios such as railway catenary systems and power grids require interdisciplinary integration and long-cycle engineering capabilities. End-to-end delivery typically involves IoT sensing (drones, fixed cameras, inspection robots, satellites), data preprocessing, model training, and operational deployment. Industry leaders often accumulate data assets and algorithmic know-how through years of projects. New entrants therefore face longer timelines for R&D, certification, and customer trust-building—making technical barriers substantial. Continuous policy support for railway innovation, smart grids, and urban digitalization further strengthens market tailwinds.
Challenges: increasing pressure on data security and privacy compliance; insufficient public–private coordination and data-sharing mechanisms in some regions.
Opportunities: new infrastructure provides a foundational stack; advances such as edge computing improve efficiency and latency; deeper urbanization supports smart city expansion into county-level and lower-tier markets, opening incremental growth.
Conclusion
SHINDEV believes “AI + Big Data + Digital Twin” is moving from “technically feasible” to “scalable and replicable.” Policy guidance and industrial digitalization will continue to release demand, offering substantial room for solution providers. Looking ahead, digital empowerment of traditional industries and continued new infrastructure investment are expected to accelerate commercialization and broaden adoption of intelligent infrastructure O&M solutions.
