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 Pass in recent years
and the experience of optimizing the existing models in the
industry for decades, Metal Pass has declared to the industry
that as long as there is sufficient data in quality and
quantity, Metal Pass 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 Pass's core advantage --- high-precision data model.
Specifically, Metal Pass's core technical advantage lies in 30
years of intelligent modeling experience and high-precision
defect model. Metal Pass'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
Biz discuss, Planning, Maturity,
Consulting Area,
Predict Maint.
Defect
Early Warning, Models, Manufacturing, Li-Battery, Steel
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Profile of the
key consultant.
 
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