Large Coal Model
Large Coal Model
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- Customer Pain Points
- Programmatic Architecture
- Program Value
- Implementation Process
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Coal sources are diversified and procurement decisions are complex
In the face of multiple suppliers, different quality grades of coal and fluctuating market prices, enterprise decision-makers need to comprehensively consider multiple factors such as cost, quality and delivery time, and the procurement decision-making process is relatively complex.
Artificial decision making is not conducive to scientific, objective and timely decision making
Traditional and current business personnel are mostly based on experience, partial lag market information and short-term market trend when proposing thermal coal purchase demand, and there are deficiencies in objectivity, comprehensiveness, timeliness and long-term trend judgment.
Universal large-scale models are common, and vertical fields are lacking.
The mainstream large models represented by ChatGPT and Wenxin Yiyan mainly focus on general content, and the depth of vertical domain information is not enough. In the field of coal mining demand, decision-making relies heavily on industry in-depth data and real-time information, requiring R & D investment by enterprises that have both in-depth knowledge of the industry and sufficient talents and data reserves.
Programmatic Architecture
Relying on the AI+IoT platform capabilities of Zhaomei, build PaaS platforms and multi-scenario SaaS applications that meet coal mining enterprises
Improve decision efficiency and accuracy
The large model of coal mining demand can analyze and process a large amount of data, and quickly generate accurate decision-making suggestions. Compared with traditional artificial analysis methods, artificial intelligence can deal with complex data relationships more efficiently, reduce human error and subjectivity, and improve the accuracy and efficiency of decision-making.
Optimize cost control
Coal mining and sales is an important cost in power plant operation. Through comprehensive analysis of market conditions and supplier information by artificial intelligence, more accurate procurement timing and supplier selection can be realized, thus reducing procurement costs, optimizing inventory management, reducing capital occupation and financial costs.
Dealing with market uncertainty
The coal market is affected by many factors, such as large price fluctuation and high market uncertainty. Artificial intelligence can predict market trends through analysis of historical data and market information, provide more accurate market analysis and decision support for power plants, and reduce market risks.
Improve supply chain synergy efficiency
Coal mining and marketing decisions need to be closely coordinated with other links in the supply chain, including transportation and inventory management. Artificial intelligence can realize real-time sharing and analysis of data in all links of the supply chain, optimize the overall efficiency and synergy of the supply chain, and improve the operational efficiency of power plants.
data-driven decision making
The large model decision-making of coal mining demand is based on the analysis and model prediction of a large number of data, which can avoid the interference of artificial subjective factors and realize data-driven decision-making. In this way, suppliers, market conditions and other factors can be evaluated more objectively, and the scientificity and reliability of decision-making can be improved.
On-site visits to investigate needs
Provide customized solutions
Sign the contract and make the down payment
Installation, deployment, commissioning, training
Customer acceptance, payment of final payment
