Last week, two significant developments took place in the industry: PERA Global unveiled an industrial large model at a simulation technology conference and secured the highest level of national certification. Almost simultaneously, Qingluan Fuxing announced its digital twin solution—featuring “virtual simulation rehearsals combined with AI-driven prediction and early warning”—scheduled for official launch on August 1. These two news items point to the same signal: AI-powered simulation has moved from concept to reality.
Signal 1: Simulation software has been upgraded—evolving from a “calculator” into a “strategic advisor.”
Let’s first clarify the fundamental nature of this matter.
How did traditional simulation software work?
Engineers would build a model, set parameters, and click “Calculate”; the software would then churn out a mass of curves and data. If you could interpret the results, you were competent; if not, you either adjusted the parameters and tried again or called in an expert.
In essence, simulation software was a passive, responsive computational tool—it answered only what you asked.
Yet, the truly difficult challenges in engineering often lie not in *how* to calculate, but in *what* to calculate: When might equipment failure risks arise? How will a new set of process parameters perform under real-world operating conditions? Which part of the equipment is on the verge of failure? These are questions that cannot be answered simply by running a simulation once. PERA Global’s industrial large model achieved something pivotal: it integrated simulation’s computational power with AI’s predictive capabilities, transforming the software from a passive calculator into an active inferential tool. It can predict—based on historical data—which parameter combinations are prone to risk; it can make inferences even with incomplete data; and it can pinpoint where your design might harbor hidden flaws. It does not replace simulation; rather, it gives simulation a pair of eyes.
Signal 2: “Simulation plus early warning” is becoming the standard approach for digital twins.
Another noteworthy development from last week: Qingluan Fuxing announced the launch of its industrial digital twin solution on August 1st. Their product strategy follows a clear, two-step approach.
Step one involves using virtual simulation for pre-execution runs: whether for designs, manufacturing processes, or construction plans, the workflow is first simulated within the digital twin to identify potential flaws in advance. Step two utilizes AI for predictive warnings: during actual operation, the system integrates real-time data, with AI continuously monitoring for anomalies and alerting users to potential issues before they occur. Combined, these two steps represent what the industry currently calls “Video Twin + AI“—leveraging real-time video, digital models, and AI analysis to achieve synchronized perception and intelligent decision-making across the physical and digital worlds. Real-world applications are already in place: factories use twin systems to monitor production lines and detect equipment anomalies early, reducing unplanned downtime by over 40%; subway operators use AI to predict wear cycles for tracks and rolling stock, cutting maintenance costs by approximately 30%; and water conservancy projects employ digital twin platforms to simulate flood progression, reducing warning response times from hours to minutes. These are not merely concepts found in slide decks, but systems already in operation. These capabilities are transforming from mere “technical possibilities” into a “quantifiable market.”
Signal 3: A market size of 21.4 billion, with industrial manufacturing accounting for over 40%—real capital is entering the market.
Having discussed products and use cases, let’s turn to the data. By 2025, China’s digital twin market is projected to reach 21.4 billion yuan, with the industrial manufacturing sector accounting for over 40% of that total. What does this figure signify? It indicates that the combination of simulation and AI has moved beyond the realm of academic research; it is now a solution that enterprises are willing to invest real money in. For context, the market size was merely a few billion yuan in 2019; it has multiplied several times over in just five or six years—a growth rate far outpacing that of most other industrial software sectors. The driving force behind this is straightforward: industrial systems are becoming increasingly complex, making it impossible to manage them relying solely on human experience. With vast numbers of devices, high data density, and obscure failure modes, human memory and intuition simply cannot cope with such constantly evolving, complex systems. Simulation combined with AI offers the ability to understand system dynamics through data and to anticipate unforeseen issues using models. The fact that enterprises are willing to pay for this capability demonstrates that its value has been proven.
Three signals pointing to the same trend.
Consider these two news items from last week within the context of industry trends: Simulation software is evolving from mere “engineering tools” into “knowledgeable AI assistants”; digital twins are shifting from “visual displays” to “operational systems capable of rehearsal and early warning”; and the market is moving from the “incubation phase” into a “period of rapid growth.”
Fundamentally, these three developments are facets of the same phenomenon: industrial intelligence is transitioning from concept to large-scale implementation. For Jiangsu Rongyi Technology, this trend is not a matter of choice but a mandatory challenge. The core capabilities we have cultivated in engineering equipment simulation—such as force feedback, digital twins, and intelligent assessment—are extending from ground equipment training into the aerospace sector; we are simultaneously addressing needs in areas like counter-UAV training, low-Earth orbit (LEO) satellite communication simulation, and big data closed-loop systems.
For industry professionals, this implies two things:
First, your competitors may already be utilizing AI-driven simulation. Not everyone is waiting for the wind to pick up; some are already creating the wind.
Second, the value proposition of simulation is shifting. In the past, value lay in “people who knew how to use simulation tools”; in the future, value will lie in “people who know how to collaborate with AI, ask the right questions, and interpret AI’s assessments.”
This transformation is already underway, and simulation training offers the best pathway for operators to adapt to this new reality in advance.
Post time: Aug-10-2026