On June 17, 2026, the Department of Science and Technology and Information Technology of the State Administration for Market Regulation officially announced that a key breakthrough has been achieved in the construction of the knowledge base for quality and safety risk control of smart products in my country.
This is not only a technical progress at the market supervision level, but also means that more and more products around us under the banner of “smart” will be included for the first time in a new security regulatory framework specifically targeting algorithms, data and network attacks.
This breakthrough fills the gap in domestic professional security databases for smart products. Its core is a quality and safety knowledge graph that integrates more than 50,000 pieces of basic data, 16,000 nodes and 32,000 relationships.
In the past, our quality supervision of a product mainly stayed at the visible and tangible hardware level.
The traditional “man-machine-environment” analysis model focuses on checking whether the user’s operation complies with specifications, whether the physical components of the machine are aging, and whether the physical environment such as temperature and humidity is friendly.
But smart products are another story entirely.
The hardware of a smart speaker may be intact, but it may give misleading suggestions due to biased algorithmic logic; a smart connected car may have no problem with mechanical braking, but may lose control due to attacks on cloud information.
Once something goes wrong with a smart product, “the physical components are safe and the virtual world is turned upside down.”
The research team fundamentally broke the limitations of the traditional model, incorporated the “information” dimension into the quality and safety analysis system for the first time, and innovatively proposed a four-in-one analysis framework of “people-product-environment-information”.
This means that the reach of supervision has not only extended to the product itself, but also penetrated into the fields of data security, algorithm logic and network stability behind intelligent interaction.
Official information is clear that the knowledge base covers 10 typical categories of smart products, and the currently public ones can be confirmed to include smart home products.
However, the complete list of the 10 specific categories has not been announced yet.
This is not meant to be deliberately concealed. At a stage when the knowledge base has not yet been fully promoted and pilot data is still being sent back for verification, it is a prudent scientific attitude to first frame a large scope and then gradually refine specific categories.
Locking in a very specific product definition too early may lead to omissions in future technology iterations.
At present, we can at least reasonably surmise from application scenarios that products involving highly complex interactions, such as intelligent connected cars and smart wearable devices, must be among the key focuses of this framework.
The official has confirmed that Anhui Zhongjiayun is one of the pilot units.
As a pilot, it is directly experiencing the changes brought about by this “technical brain”.
For manufacturing companies such as Anhui Zhongjiayun, in the past, safety testing of smart home products may have focused more on preventing leakage, fire, and physical structural defects.
Now, relying on the risk visualization and intelligent decision-making question and answer functions of the new knowledge base, enterprises can ask questions to the system to check whether a specific design plan or software version contains a causal chain of “hazard source-safety incident”.
After the pilot, the most intuitive gain is that the speed is faster and hidden dangers are discovered earlier.
Enterprises can troubleshoot problems that were easily overlooked in the past in advance, such as the chain security reactions that may be caused by the failure of intelligent interconnection.
The establishment of this early identification and rapid response mechanism has brought about a fundamental change in the regulatory model: from the past, only post-event traceability and market recall can be achieved, to prior warning and active defense based on knowledge graphs.
At present, the official has not announced a clear national promotion timetable.
This means that there is still a way to go from the laboratory to the factory and then to thousands of households.
But the direction is extremely clear: this database should not only serve as a “physical examination”, but also serve as a “health gatekeeper” for the entire life cycle of various smart products.
For ordinary consumers, there are two things worth paying attention to: First, when our smart devices rely on more cloud services and external data, their security credentials should no longer be just a simple 3C hardware quality label. Future products are likely to have more complex software security verification standards;
Second, if the smart home product or smart car you are using has a strange malfunction that is not physically damaged, be sure to report it back to the manufacturer through official channels as soon as possible. These real negative feedback data are the most precious nutrients that feed back to the knowledge base of regulatory agencies and are used to train models and identify new “hazard sources.”
