Vertical Large Model
What the human eye can distinguish, AI can recognize.
An A I camera module is installed on the dial allowing the camera to take photos of the dial. The camera module transmits the image back to a GPU server, which in turn uses, METER MIND multimodal vertical scaled large model to converts the readings in the photos into recognition data.
Defining a New Paradigm for AI Recognition
Meter-agnostic&fully compatible
Cold start with the full sample coverage: Identifies all new meter types without prior training, retrofitting old residential areas is “plug-and-play.”
AI semantic parsing: Automatically identifies background, camera offset, and recognition accuracy logic, eliminating manual parameter configuration.
Continuous Evolution Capability: The more samples are there , the higher accuracy is, with continuous, autonomous upgrades and optimizations.
High Robustness · Clear Vision
Comprehensive environmental interference resistance: Unaffected by glare, fog, limescale, scratches, bubbles, and more extreme environments.
Instant diagnosis: Automatically tags abnormal images such as those are blurry or partially occluded, generate fault warning reports, and replaces manual on-site verification.
Low cost · Lightweight
Cost reduction: Easy installation without shutting off the water or meter replacement , reducing labor costs for meter reading and operational expenses.
Flexible deployment: One-click switching between local , public (or private) cloud, and edge devices. Government and enterprise customers can customize privatized solutions as needed.
The industry pain points we’ve addressed
Technical level
Address the issue of electromechanical conversion error in traditional pulse-type smart meters (leading to unreliable trade settlement data).
Address the low recognition rate of traditional camera direct reading technology, caused by limitations of meter types and conditions.
Address the Metaverse's Demand for the Authenticity of Trusted Data
Management level
Provide time-sensitive evidence of water volumes, and the disputed water volumes can be traced.
Address the predicament of traditional mechanical water meters on-site, which cannot be intelligently upgraded due to environmental and cost constraints.
Reduce equipment upgrade costs during the mandatory six-year periodic metering rotation.
Core architecture breakthrough
A vertical multimodal large model that redefines the underlying logic of recognition.
To objectively and comprehensively evaluate the practical capabilities of current mainstream multimodal large models and specialized algorithms in the field of instrument recognition, we selected one industry-specific engine, six general-purpose multimodal large models (three domestic and three international), and one instrument-recognition-specific API interface for a comparative evaluation. The data in the table shows that Hangzhou Meter’s multimodal vertical large model demonstrates an absolute leading advantage , while general-purpose large models generally reveal shortcomings in both stability and accuracy.
Supports tens of billions of parameters
Designed with a parameter scale of tens of billions, this specialized model—built with high-cost training and optimization using a GPU cluster—is tailored specifically for water meter measurement scenarios. Compared to general-purpose multimodal models such as GPT-5 and Gemini-2.5-pro, it addresses the core challenges in water meter recognition in a targeted manner.
METER MIND 4.0
The industry’s first vertical large-model AI recognition algorithm
A joint research and development team of expert doctoral researchers jointly trained by the Chinese Academy of Sciences and MIT.
Won global championships at CVPR/ICCV + achieved a breakthrough in the top-tier ECCV journal.
Company Address: Room 1203, Rongxin Center, Shangcheng District, Hangzhou City, Zhejiang Province
Company email: maitu@meter-mind.com
Working hours: Monday to Friday, 9:00 a.m. to 6:00 p.m.
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