Industrial AI Analytics System Vertical Solution

Not at all. The 27 core sub-models of FEXLINK industrial AI come pre-trained on real industrial data from 100+ categories of smart hardware, 100,000+ devices, and 200+ enterprise customers, plus over a million manually labeled samples — customers can use them from day one. During operation, the system supports online learning: O&M staff label AI warnings as correct or incorrect, and the models iterate and improve automatically (enabled only with customer authorization), becoming more accurate with use. FEXLINK also offers custom AI model services, fine-tuning models for specific equipment or special scenarios (e.g., a particular motor type or a dedicated model for a specific VFD brand), typically delivered within 2-4 weeks.

FEXLINK industrial AI is designed from the ground up for reliability: ① Dual-engine cross-validation: the Qianzhi rule engine and the Wanxiang AI models analyze independently — a high-confidence alarm is issued only when both reach the same conclusion; disagreements are automatically downgraded and routed for manual review. ② White-box explanation: every AI warning states its triggering cause (e.g., leakage current up 23% within 15 days + temperature above the 85th percentile of peer devices + a match to Clause 3 of the GB 14287 temperature-rise rule) plus a SHAP feature-weight contribution chart, making the decision process 100% transparent. ③ Graded accountability: AI confidence spans 5 levels; only the top 2 levels are recommended for automated control execution, while the remaining 3 are advisory alerts only, with final decisions confirmed by humans. The system follows the principle of shared responsibility in industrial AI, and customers retain full control throughout.

The core differences are industry depth and data security. ① Industry fine-tuning: Tianyan large model V3.0 undergoes secondary pre-training and RLHF fine-tuning on millions of FEXLINK industrial corpus entries (national-standard texts, fault cases, O&M manuals, technical documents, and alarm records), so its answer accuracy in specialized domains such as electrical safety, smart lightning protection, digital energy, and virtual power plants far exceeds that of general-purpose large models. ② Knowledge-graph grounding: every answer strictly cites national-standard clauses, product parameters, and historical cases from the Qianzhi knowledge graph, so all claims are sourced and traceable — avoiding the hallucinations typical of general large models. ③ Private deployment: it can be fully deployed on a customer's private cloud or on-premises servers, keeping all business data off the public internet and meeting China's MLPS compliance and industrial data-security requirements. ④ Taiyi Intelligent Control integration: the large model doesn't just answer questions — it can invoke the Taiyi control engine to generate O&M recommendations, issue control commands, and produce diagnostic reports, moving from Q&A to execution.

Fully supported. FEXLINK industrial AI uses a cloud-training + edge-deployment architecture: ① the cloud handles model training, hyperparameter tuning, and major version iterations (using de-identified industrial datasets); ② the edge side (high-end CW gateways with a built-in NPU neural accelerator) runs lightweight AI models with real-time inference executed entirely locally — raw data never leaves the premises, and only AI inference results and alarm summaries are reported upstream (customers can disable cloud reporting entirely). Typical design: high-frequency workloads such as arc-fault recognition, SPD leakage-current trend detection, and electrical hazard grading run at the edge, while models requiring external data — PV generation forecasting, load forecasting — run in the cloud, with flexible combinations.