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Model Development with Existing Data based on AI Intelligent Learning

As long as there is enough data, high quality models can be established! The difficulty is to build a high-quality model based on the limited amount of data! This technology can make up for many weaknesses in the current market: (1) the waste of resources caused by traditional statistical methods and offline models. For example, offline models are difficult to achieve high accuracy and track the trend of data environment changes; (2) The current big data method requires a high amount of data.

Possible application fields of this technology: (1) in cases where it is considered that sufficient data have been collected but do not know how to use data for Smart Manufacturing, for example, many enterprises have collected enough data after two chemical standards implementation or other efforts; (2) Many industries themselves have a large amount of data, such as clothing sales data (it is very important to determine the inventory or output of this month based on the accurate forecast of next month's sales), financial industry data, and online marketing data on the Internet, etc. After the challenges of a series of high-precision model projects of Metal Data in recent years and the experience of optimizing the existing models in the industry for decades, Metal Data has declared to the industry that as long as there is sufficient data in quality and quantity, Metal Data can develop models with sufficient precision and scope of application! The main data requirements include: (1) target parameters to be studied, such as the quantity value, sales volume, production capacity, comprehensive quality value of a defect, etc; (2) The comprehensive data of each parameter affecting the target parameter is stored in the database.

Metal Data's core advantage --- high-precision data model. Specifically, Metal Data's core technical advantage lies in 30 years of intelligent modeling experience and high-precision defect model. Metal Data's intelligent modeling technology can build high-quality models as long as there is high-quality data! Specific methods:

(1) Mathematical description: establish mathematical model
(2) Physical mechanism: various mechanisms of modeling objects are considered
(3) Logical judgment: integrate multiple models (based on software) with high accuracy but narrow application range to achieve a wide application range
(4) Software integration: first, complete the intelligent Learning of each set of mathematical / physical model; Then combine multiple sets of models through logic and software; Each model is a set of software, including the above models, data collection, data and Learning hierarchy

Due to the lack of intelligent system (Level 2), MES (tertiary system) and basic automation (primary system) cannot be directly connected. Intelligent systems must be developed to be complete.

Later, you can refer to a series of project cases based on data modeling, including a series of projects in Germany, the United States, South Korea and China (in chronological order).

Planning & Consulting

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