Core Technology and the Device–Edge–Cloud–Intelligence Four-Layer AI Architecture
Zhisuo Zhikong has built the four-layer Device–Edge–Cloud–Intelligence AI architecture. From loom sensors to cloud-based LLMs, data at every layer fuels the intelligence of the next. The four layers work together to form a complete closed loop: data acquisition → edge preprocessing → cloud accumulation and analysis → AI decision output.
From Loom Sensors to Cloud LLMs · Four-Layer Capability Overview
Data at every layer fuels the intelligence of the next. All performance figures below are estimates for typical scenarios and are ultimately subject to on-site POC validation.
- 650 nm laser detection; non-contact, with no impact on warp yarn movement
- Random Forest edge inference with <1 s response
- Five-stage winding-displacement motion detection principle
- Next-generation roadmap: F407 + CNN (Convolutional Neural Network)
- LoRa 433 MHz + 4G dual-communication redundancy
- Edge data preprocessing and cleansing to reduce cloud load
- Local lightweight AI inference for real-time alarms without cloud dependence
- Store-and-forward: data is cached locally and auto-synced after recovery
- 天工 (Tiangong) voice interaction in MES — one spoken sentence produces a report
- AI intelligent production scheduling to optimize capacity allocation
- Quality early warning with automatic defective-product traceability
- LoomOS AI supply–demand matching + capacity forecasting + price index
- LLM Quantitative Computing + Multi-Factor AI Models
- Value Chain Propagation GNN (Graph Neural Network)
- NLP Event-Driven Analysis (News / Policy / Exchange Rates)
- A self-iterating data flywheel that gets more accurate the more it is used
Data Flywheel · A Self-Reinforcing Technical Loop
As more equipment is installed, edge AI nodes multiply and real-time production data keeps accumulating, lifting MES data quality.
LoomOS industry data keeps growing and TexOracle forecast accuracy keeps improving; predictions feed back into production decisions, amplifying customer value.
More installations → more data → better models → smarter decisions → higher customer value → more installations: a self-reinforcing growth loop.
Traditional Approach / Common Competitors / Zhisuo Solution · 10-Dimension Capability Comparison
The comparison below is based on Zhisuo Zhikong's internal simulation validation and joint estimates from pilot deployments at typical customers. Each difference is an objective statement of parameter differences; no absolute claims or disparaging terms are used. Actual selection should be based on customer operating conditions and on-site POC.
* The 10 comparison conclusions above are statements of parameter differences based on Zhisuo Zhikong's internal simulation validation and joint estimates from typical customer pilots; they do not constitute disparaging conclusions about competitors. Actual capabilities are subject to on-site POC validation, formal contract terms and acceptance criteria.
IP and Qualification Portfolio Across the Four Technology Layers
Across optical detection device structures, real-time monitoring methods, LoRa networking data acquisition, MES visualization dashboards, edge computing and other directions, the company has filed a cumulative 10 invention patent applications (all under substantive examination); 9 utility models (applications accepted); 1 software copyright registered (No. 2026SR1016542), with 2 more under review.A Jiangsu Province enterprise certified for IP management system certification (GB/T 29490-2013), and a recognized technology-based SME registered in the national database.Final qualifications and patent status are subject to the official documents of the China National Intellectual Property Administration and competent authorities at all levels.