Jingnuo Data needed to turn highly specialized smelting expertise into a repeatable system because foundries relied too heavily on master workers' intuition for material ratios, quality adjustment, and process efficiency.
Reported impact
Jingnuo gave foundries a repeatable AI decision process for smelting, reducing dependence on scarce expert workers while improving raw-material efficiency, process speed, and operational transparency.
Jingnuo Data used Baidu's AI stack to build an intelligent smelting system that converts expert metallurgy knowledge into model-driven recommendations for foundries. The solution begins with a pre-charge module that evaluates inventory, raw-material prices, and target metal composition to propose an optimized material mix. During fine charging, a second model combines AI predictions with spectral-test results to recommend precise adjustments for specific industrial requirements. The system also extends beyond the furnace by pushing production information to mobile apps, SMS, and WeChat mini programs so managers can see plant conditions without waiting for manual reporting. Underneath, the project used Baidu tools to encode the experience of more than one hundred master workers and decades of operating know-how into an AI-driven decision layer. That lets younger staff execute smelting workflows with more consistency while the plant gains better material efficiency, faster processing, and more transparent production data.
Production inputs such as inventory, raw-material prices, target composition, and spectral-test results are fed into Jingnuo's AI modules. The pre-charge and fine-charge models generate recommended formulas or adjustments, and the outputs are then sent to plant staff and managers through mobile apps, SMS, and WeChat mini programs for execution and monitoring.
Fine charging linked AI recommendations with spectral-test results to support precise industrial adjustments (original: 熔炼精配模块会利用百度飞桨提供的AI模型，联动光谱检测结果，精确计算不同工业要求下的最优调整方案).
Jingnuo turned 30 years of master-worker experience into an AI model that can produce an optimal ratio in 3 seconds (original: 将30年老师傅经验转化为AI模型，实现3秒精准配料).
Raw-material savings average 15% and can reach 27% (original: 平均15%的原料节省，最大降幅达27%).
The project relied on input from more than 100 master workers (original: 精诺请教了100多位老师傅).
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
The employer profile states "50人以下", which places the company in the SMB band.
Related
Baidu AI Cloud Sample Apr 9, 2026 China Baowu Steel Group Baowu needed to move ladle status data, image data and production-process data safely to the cloud so it could detect hidden risks, optimize operations and lower storage costs. BML Full-Function AI Development Platform · Big Data Platform · IoT Core Baidu AI Cloud Sample Apr 8, 2026 SANY Heavy Machinery Co., Ltd. (三一重机) SANY needed to uphold its 'solve within one day' service standard for excavators and other machinery even when remote field engineers lacked deep experience and half of faults could not be diagnosed accurately without… Before the project, as many as 50% of problems could not be diagnosed accurately. Industrial AR Remote Assistance System · Intelligent Industrial Quality Inspection · Tiangong Edge-Cloud Integrated IoT Platform Baidu AI Cloud Sample Dec 26, 2025 China Iron and Steel Research Institute Group (中国钢研科技集团) China Iron and Steel Research needed a practical way to apply large-model and visual AI capabilities across metallurgical research and manufacturing, especially for metallography analysis, surface defect detection, and… 100 ms response time Baige AI Heterogeneous Computing Platform · Kunlun · Qianfan Large Model Platform 