KR20180026301A - Big Data Analysis System for Smart Factory - Google Patents
Big Data Analysis System for Smart Factory Download PDFInfo
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- KR20180026301A KR20180026301A KR1020160113506A KR20160113506A KR20180026301A KR 20180026301 A KR20180026301 A KR 20180026301A KR 1020160113506 A KR1020160113506 A KR 1020160113506A KR 20160113506 A KR20160113506 A KR 20160113506A KR 20180026301 A KR20180026301 A KR 20180026301A
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- 238000007405 data analysis Methods 0.000 title claims abstract description 34
- 238000012545 processing Methods 0.000 claims abstract description 135
- 238000000034 method Methods 0.000 claims abstract description 120
- 230000008569 process Effects 0.000 claims abstract description 95
- 238000010924 continuous production Methods 0.000 claims abstract description 40
- 239000000463 material Substances 0.000 claims description 116
- 238000003860 storage Methods 0.000 claims description 84
- 238000013507 mapping Methods 0.000 claims description 72
- 238000004458 analytical method Methods 0.000 claims description 10
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- 238000013500 data storage Methods 0.000 abstract description 45
- 230000005856 abnormality Effects 0.000 description 36
- 238000001514 detection method Methods 0.000 description 26
- 238000010586 diagram Methods 0.000 description 18
- 238000001914 filtration Methods 0.000 description 16
- 238000013480 data collection Methods 0.000 description 14
- 238000005259 measurement Methods 0.000 description 12
- 238000012937 correction Methods 0.000 description 11
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 10
- 229910000831 Steel Inorganic materials 0.000 description 10
- 239000010959 steel Substances 0.000 description 10
- 238000004519 manufacturing process Methods 0.000 description 9
- 238000007726 management method Methods 0.000 description 6
- 238000009628 steelmaking Methods 0.000 description 6
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- 238000006243 chemical reaction Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 5
- 229910052742 iron Inorganic materials 0.000 description 5
- 238000012544 monitoring process Methods 0.000 description 5
- 238000004891 communication Methods 0.000 description 4
- 239000000284 extract Substances 0.000 description 4
- 238000012367 process mapping Methods 0.000 description 4
- 230000002159 abnormal effect Effects 0.000 description 3
- 239000012535 impurity Substances 0.000 description 3
- 239000002994 raw material Substances 0.000 description 3
- 238000005096 rolling process Methods 0.000 description 3
- 238000013506 data mapping Methods 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 238000013475 authorization Methods 0.000 description 1
- 238000007664 blowing Methods 0.000 description 1
- 238000005266 casting Methods 0.000 description 1
- 239000003610 charcoal Substances 0.000 description 1
- 239000003245 coal Substances 0.000 description 1
- 238000009749 continuous casting Methods 0.000 description 1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2219—Large Object storage; Management thereof
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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- Manufacturing & Machinery (AREA)
- Quality & Reliability (AREA)
- Automation & Control Theory (AREA)
- Testing And Monitoring For Control Systems (AREA)
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Abstract
Description
BACKGROUND OF THE
A plurality of processes for producing the finished product using the raw material are successively performed and the processes of the respective processes are mixed with each other or the state of the output of the specific process is changed and supplied to the subsequent process, The production method is called continuous process production method. The steel industry, the energy industry, the paper industry, or the oil refining industry are representative industries to which the continuous process production method is applied.
For example, the steel industry is made up of a plurality of processes such as a milling process, a steelmaking process, a performance process, and a rolling process. The ironmaking process is a process of producing molten iron (iron charcoal), which iron ores are melted by the heat that comes from burning the coal into the blast furnace. The steelmaking process is a process to remove impurities from the molten metal. The molten steel and the molten iron are put together in the converter, and the impurities are removed by blowing oxygen. The casting process is a process in which liquid iron is solidified, and molten steel from which impurities have been removed is injected into a mold and cooled and solidified while passing through a continuous casting machine to be made of an intermediate material such as slab, bloom, or billet. The rolling process is a process of making steel into steel or wire, and slabs, blooms, or billets produced in the performance process are passed through rolls to produce steel sheets by stretching or thinning.
In the case of this type of continuous process production, unlike in the industry where a single process production method is applied, raw material or intermediate material moves at high speed, so data collection cycle is short and data amount is large, and noise, And so on. Therefore, there is a tendency that measurement errors occur frequently, and the intermediate materials are mixed with each other or the position of the material moves according to the working method.
Accordingly, a system capable of processing a large amount of data in real time and analyzing the relationship between data generated by each process is required in an industry in which a continuous process is applied to a production method.
However, a general factory data processing system disclosed in, for example, Korean Patent Laid-Open Publication No. 10-2015-0033847 (titled " Digital Factory Production Capacity Management System Reflecting Real Time Factory Situation ", published on May 20, Processing system) is for processing and analyzing data generated in a single process, it is not possible to process a large amount of data generated in a continuous process in real time, but also to analyze the relationship between data generated in each process There is a problem that it can not be done.
It is a technical feature of the present invention to provide a big data analysis system for a smart factory that can store a large amount of data collected in a continuous process in a big data store based on a distributed file system .
Another object of the present invention is to provide a big data analysis system for a smart factory that can store data collected in a continuous process into load data and no load data and store the same in a distributed file system.
It is another technical object of the present invention to provide a big data analysis system for a smart factory that can divide and store data collected in a continuous process by a predetermined number of units.
Another object of the present invention is to provide a big data analysis system for a smart factory capable of processing data collected in a continuous process into a file in parallel.
In order to achieve the above object, a big data analysis system for a smart factory according to an embodiment of the present invention includes a big data analysis system for a smart factory for processing collected data collected in a continuous process in which a plurality of processes are connected A first sorting data fetching unit for reading the load data for each process among the data collected in the continuous process; A file generation unit for generating the load data as a file; And a big data storage in which files generated by the file generation unit are stored.
According to the present invention, since a large amount of data collected in a continuous process is stored in a big data store based on a distributed file system, microdata currently collected can be processed in real time.
In addition, according to the present invention, since the data collected in the continuous process is divided into load data and no-load data and is stored in the distributed file system, the file retrieving speed can be improved and there is no need to scan no- The query execution time can be shortened.
In addition, according to the present invention, since data collected in a continuous process is divided and stored in units of a predetermined number, an effect of preventing out-of-memory generation of a memory queue in which data generated in a continuous process is temporarily stored have.
Further, according to the present invention, by implementing a plurality of file generation units that process data collected in a continuous process into files, the file creation job can be processed in parallel and the processing speed can be further improved.
1 is a diagram illustrating a smart factory architecture including a distributed parallel processing system for processing data for a continuous process in real time according to an embodiment of the present invention.
2 is a block diagram illustrating a configuration of an interface system according to an embodiment of the present invention.
3 is a diagram illustrating a configuration of an interface system including a plurality of interface processing units and a plurality of queue storage units.
FIG. 4 is a block diagram of a distributed parallel processing system according to an embodiment of the present invention. Referring to FIG.
5 is a diagram illustrating a configuration of a distributed parallel processing system including a plurality of real-time processing units and a plurality of memory units.
6 is a conceptual diagram illustrating a distributed parallel processing method of data mapping and sorting operations.
FIG. 7 is a diagram specifically illustrating a configuration of a big data analysis system according to an embodiment of the present invention.
FIG. 8 is a diagram specifically illustrating a configuration of a big data analysis system according to another embodiment of the present invention.
9 is a diagram showing an example of load data and no-load data.
Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
The meaning of the terms described herein should be understood as follows.
The word " first, "" second," and the like, used to distinguish one element from another, are to be understood to include plural representations unless the context clearly dictates otherwise. The scope of the right should not be limited by these terms.
It should be understood that the terms "comprises" or "having" does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or combinations thereof.
It should be understood that the term "at least one" includes all possible combinations from one or more related items. For example, the meaning of "at least one of the first item, the second item and the third item" means not only the first item, the second item or the third item, but also the second item and the second item among the first item, Means any combination of items that can be presented from more than one.
FIG. 1 is a diagram illustrating a smart factory architecture including a distributed parallel processing system that processes data for a continuous process in real time according to an embodiment of the present invention.
1, the smart factory architecture according to the present invention is composed of layers such as a
The
Since the steel manufacturing process is composed of various processes such as a steelmaking process, a steelmaking process, a performance process, and a rolling process, the data collecting device 10 can perform various processes such as a steelmaking process, a steelmaking process, a performance process, And collects generated micro data. Here, micro data refers to raw data as data collected through various sensors and the like. Hereinafter, microdata will be referred to as collected data for convenience of explanation.
To this end, the
The
The
In one embodiment, the
The
Hereinafter, the
2 is a diagram illustrating a configuration of an
The
The receiving
The
Specifically, the collected data collected by the
In this manner, when the collected data is composed of a group ID, a collecting time, and a plurality of measured values repeatedly without any other distinction, the distributed
Thus, the
In one embodiment, the
According to this embodiment, parsing
The
Specifically, even though each sensor or actuator included in the
In this manner, the
In addition, the format of the measurement values included in the collected data collected through the
The
The
The
The
That is, in the embodiment of the present invention, the collected data for each process having different formats are parsed and standardized and converted into a certain format, and the standardized data is stored for each group ID or standard item ID, Since the data can be checked, the collected data collected from the continuous process can be processed in real time.
In one embodiment, the
In addition, the
On the other hand, when the operation mode of the plurality of
Accordingly, in the embodiment of the present invention, when the operation mode of the
The
The collection
Accordingly, the collected
The
In the standard conversion
According to this embodiment, the above-described
The
The
The
In one embodiment, a group ID unique to each data group fetched from the
In the above-described embodiment, it is described that the collected data is preprocessed through one
According to this embodiment, the plurality of
That is, each of the
The plurality of
In addition, when standardization of collected collected data is completed, the
1, the distributed
FIG. 4 is a diagram illustrating a configuration of a distributed
The real-
4, the real-
In one embodiment, the plurality of
Hereinafter, functions of each of the plurality of
The fetch
At this time, the
The
The process
In one embodiment, the
The facility
In one embodiment, the facility
That is, as described above, the data standardized by the
As a result, the mapping result data to which the equipment identifier is mapped includes the standard item ID, the collected time, the equipment identifier, and the single measurement value.
The material
The material
For example, when a first material having a first material identifier is generated through a first facility in the first process, the material
Since the data to which the equipment identifier is mapped by the equipment
In this way, the distributed
In addition, since the collected data is standardized through the
On the other hand, the collected data collected through the
In the case of no-load data, since there is no material identifier to be further mapped to the collection data to which the equipment identifier is mapped, the material
The data
For example, when the collection period is 20 ms, the
If the collection period is 20 ms, the collected data should be transmitted in 20 ms. If some collected data is missing, the collected period of each collected data becomes longer than 20 ms. Therefore, the data
The data
In one embodiment, the data sorting performing
The data sorting performing
For the alignment of the mapping data, the
The data sorting performing
The
In the first and second steps, each collected data is collected at different collection periods and the length, width, or thickness of the material passing through the first process is different from the length, width, or thickness of the material passing through the second process It may be difficult to manage the change in the relationship of the measured values measured in a specific region of the material on each process in conjunction with each other. Therefore, in the present invention, in order to perform an association process between the collected data collected from the first process and the second process, measurement values at a plurality of reference points set on the material processed in each process are calculated, and measurement Value is used to manage the mapping data between each process.
Hereinafter, an example in which the data
The first reference points are set at predetermined intervals in the longitudinal direction of the first material processed in the first process and the second reference points are set at predetermined intervals in the length direction of the processed second material in the second process. In this case, a first material identifier corresponding to the first material is mapped to the first reference data at the first reference points, and a second material identifier corresponding to the second material is mapped to the second reference data at the second reference points. The identifier is mapped. Thus, the first reference data and the second reference data are linked based on the first material identifier and the second identifier on the material pedestal (not shown) on which the material identifier is mapped for each material.
That is, the material family diagram is connected in the form of a material seed layer tree. By referring to the material family diagram, mapping data of each process through the material identifiers assigned to the materials generated while sequentially passing through the first and second processes Can be linked to each other.
The
In one example, the
In addition, the data sorting performing
That is, the data sorting performing
In addition, the data sorting
As such, the real-
The equipment abnormality
At this time, if it is determined that an abnormality has occurred in the facility when the collected data collected for the predetermined period of time exceeds a predetermined reference value, for example, The present invention is not limited thereto.
The quality abnormality
In one embodiment, the quality anomaly
In this way, the distributed
Meanwhile, the real-
The facility
The work instruction
In the sensor attribute
Since the equipment abnormality criterion and the quality abnormality criterion are stored in advance in the quality judgment
The
The standardized data read from the
The mapping data in which at least one of the equipment identifier and the material identifier is mapped by the facility
The completion
Specifically, the completion event includes a corresponding event transmission time, a data collection time, key information for reading data from the sorting
The abnormality detection
Therefore, according to the present invention, the user accesses the anomaly detection
However, the present invention is not limited to this, and if an abnormality occurs in the quality of a specific facility or a specific material, it is also possible to directly transmit the result to the abnormality detection monitoring system so that the user can immediately check the abnormality.
In the above-described embodiment, the distributed
Hereinafter, a distributed parallel processing system according to a modified embodiment will be described with reference to FIGS. 4 and 5. FIG.
5 is a diagram schematically illustrating a configuration of a distributed parallel processing system including a plurality of real-time processing units and a plurality of memory units.
4, the distributed
One or
That is, the plurality of real-
In this case, a plurality of
In one embodiment, the plurality of real-
Although the distributed
The plurality of
That is, when data is stored in one
In one embodiment, the plurality of
In this case, when the alignment data is stored in the master instance M of the
In one embodiment, the alignment data recorded in the slave instance S can be backed up as a file in the form of a scripter for each piece of data for failure recovery. At this time, a scripter-like file means a file in which a command related to writing or reading of data is stored together with the corresponding data.
Meanwhile, when a master instance M included in the
In one embodiment, the master instance M and the slave instance S of each
The master instance M and the slave instance S included in each
Hereinafter, with reference to FIG. 6, a method of performing distributed parallel processing of data mapping and sorting operations standardized will be described as an example.
6, since the first real-
The second real-
The second real-
The third real-
The master instance M included in the
However, according to this embodiment, since the master instance M and the slave instance S are implemented as a single thread, when the master instance M of the
Thus, in a modified embodiment, the
In this case, the master instance M included in the
The master instance M included in the
The master instance M included in the third memory unit 220c operates as a pair with the second slave instances S1 included in the
Referring back to FIG. 2, the big
FIG. 7 is a block diagram illustrating a configuration of a big data analysis system according to an embodiment of the present invention.
7, the big
The large capacity
The completion
When the completion event is received from the completion
The
The
The anomaly detection
The big data storage unit 320 stores the file generated by the
According to this embodiment, the big data storage 320 is composed of a
Here, the job means a unit for processing a query received from the
Metadata includes a location of a file stored in the
The meta data is used as location information of the data when distributing the job and loading the data of a specific file when executing a job for inquiring the data stored in the
A large amount of files generated by the mass
The historical
The
The
Specifically, the
The
The query
Meanwhile, when the query received through the
The sorting
FIG. 8 is a diagram illustrating a configuration of a big data analysis system according to another embodiment of the present invention.
8, the big
The mass
The functions of the completion
The first sorting
The second sorting
The first sorting
In one embodiment, the alignment
When the completion
The first sorting
In addition, the second data fetch
In the above-described embodiment, the first
Further, in accordance with the above-described embodiment, the
The
The reason why the big
The
Referring again to FIG. 1, the
Since the analysis model is stored in advance in the
That is, the
The
The
The
It will be understood by those skilled in the art that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
It is therefore to be understood that the above-described embodiments are illustrative in all aspects and not restrictive. The scope of the present invention is defined by the appended claims rather than the detailed description and all changes or modifications derived from the meaning and scope of the claims and their equivalents are to be construed as being included within the scope of the present invention do.
1: data collecting device 2: network
3: Application System 1000: Smart Factory Platform
100: Interface system 200: Distributed parallel processing system
300: Big Data Analysis System 310: Mass Data Processing Unit
320: Data store 330: Query processor
Claims (20)
A first sorting data fetch unit for reading load data for each process among the data collected in the continuous process;
A file generation unit for generating the load data as a file; And
And a big data store in which the file generated by the file generating unit is stored.
Wherein the first sorting data fetching unit comprises:
Wherein the load data is read out from the collection data in which the equipment identifier of the facility in which the collected data is generated and the material identifier of the material generated in the facility are mapped in the load data, Analysis system.
Further comprising a second sorting data fetching unit for extracting no-load data among the data collected in the continuous process,
Wherein the second sorting data fetching unit reads, from the collected data, the collected data to which the equipment identifier of the facility in which the collected data is generated is mapped to the no-load data. .
Wherein the facility identifier is extracted based on a time at which the collected data is collected and attribute information of the sensor that collected the collected data.
Wherein the material identifier is extracted based on work instruction information for each process.
Wherein the first sorting data fetching unit comprises:
And the collection data in which the mapping data in which the equipment identifier and the material identifier are mapped are arranged in at least one of a collection time sequence and a material unit processed in the process is read out as the load data. system.
Wherein the first sorting data fetching unit comprises:
The collected data that is unit-aligned on the basis of the reference data at each reference point calculated based on the distance between the reference points set at predetermined intervals on the material and the collecting position of the collecting data is read out as the load data and,
Wherein the collecting position is determined using at least one of a length of the material processed in the process, a moving speed of the material, and a collection period of the collection data.
The first sorting data fetching unit reads the collected data in which the mapping data and the reference data are aligned in one direction on the material processed in the process into the load data,
The reference data being determined based on a collection point of the collection data and a reference point of the material,
Wherein one direction on the material is at least one of a longitudinal direction of the material, a width direction of the material, and a thickness direction of the material.
Wherein the load data is recorded in a first sorting data store configured in a queue form,
Wherein the no-load data is recorded in a second sorting data store configured in a queue form.
Further comprising a memory queue in which load data read by said first sorting data fetching unit is temporarily recorded,
Wherein the file generation unit generates load data recorded in the memory queue as a file.
Further comprising a data dividing unit for dividing the load data read by the first sorting data fetching unit into a predetermined number of units,
Wherein the data division unit records the load data divided by the predetermined number of units in the memory queue.
Wherein the file generation unit is implemented in a plurality of units and the plurality of file generation units are clustered so that the plurality of file generation units process file creation jobs of the load data in parallel. .
The big data store,
A plurality of data nodes in which files generated by the file creation unit are stored; And
And a master node for distributing and storing the file generated by the file generation unit to the plurality of data nodes.
The master node,
Wherein when a query for querying a file stored in the plurality of data nodes is received, a job, which is a unit for processing the query, is generated and managed.
The master node,
Managing metadata including location information of a file stored in the data node and a file name,
Wherein the location information of the file includes at least one of a storage location of the file, an ID of a block in which the file is stored, and a location information of a data node in which the file is stored in the data node. Big data analysis system for.
Further comprising a second sorting data fetching unit for extracting no-load data among the data collected in the continuous process,
Wherein the file generation unit generates the load data as a file and stores the file in a first table of the data node and generates the file as the file and stores the file in a second table of the data node Big data analysis system.
Further comprising a query processing unit for executing a query input from a user to query the big data store and returning a query execution result to a user.
A query scheduling unit that classifies the query into the plurality of sub queries if the query received from the user includes a plurality of sub queries;
A query execution unit for transmitting the sub-queries classified by the query scheduling unit to the big data store to execute the sub-query, and obtaining the query execution result from the big data store; And
And a query result transmission unit for returning a query execution result obtained by the query execution unit to a user.
A second sorting data fetching unit for extracting no-load data among the data collected in the continuous process; And
When the first completion event informing the generation of the load data is received, the first completion event is transmitted to the first sorting data fetching unit so that the first sorting data fetching unit reads the load data, Further comprising a completion event receiving unit for transmitting the second completion event to the second sorting data fetching unit so that the second sorting data fetching unit reads out the no-load data when a second completion event informing of generation is received Big Data Analysis System for Smart Factories.
Wherein the first completion event includes a first key corresponding to the load data and the first sorting data fetching unit is configured to extract, from the partition and the directory of the first sorting data store in which the load data is stored Reads the load data,
Wherein the second completion event includes a second key corresponding to the no-load data, and the second sorting data fetch unit uses the second key to extract a partition from the partition and the directory of the second sorting data store in which the no- And reading out the no-load data.
Priority Applications (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| KR1020160113506A KR101892351B1 (en) | 2016-09-02 | 2016-09-02 | Big Data Analysis System for Smart Factory |
| US15/693,158 US11079728B2 (en) | 2016-09-01 | 2017-08-31 | Smart factory platform for processing data obtained in continuous process |
| CN201710780183.7A CN107798460B (en) | 2016-09-01 | 2017-09-01 | Intelligent factory platform for processing data obtained in a continuous process |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| KR1020160113506A KR101892351B1 (en) | 2016-09-02 | 2016-09-02 | Big Data Analysis System for Smart Factory |
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| Publication Number | Publication Date |
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| KR20180026301A true KR20180026301A (en) | 2018-03-12 |
| KR101892351B1 KR101892351B1 (en) | 2018-08-27 |
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| KR1020160113506A KR101892351B1 (en) | 2016-09-01 | 2016-09-02 | Big Data Analysis System for Smart Factory |
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| KR20200023882A (en) * | 2018-08-27 | 2020-03-06 | 스퀘어네트 주식회사 | Processing method of process data of smart factory |
| WO2020106018A1 (en) * | 2018-11-20 | 2020-05-28 | 부산대학교 산학협력단 | Heating furnace monitoring system and method |
| KR20200059146A (en) * | 2018-11-20 | 2020-05-28 | 부산대학교 산학협력단 | System and method for monitoring heating furnaces in hot press forging factory |
| KR20200094852A (en) * | 2019-01-25 | 2020-08-10 | 전자부품연구원 | Connected car big data acquisition device, system and method |
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| KR102643413B1 (en) | 2021-12-29 | 2024-03-05 | 금오공과대학교 산학협력단 | Autonomous drone system for smart factory automation management |
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| KR20200021686A (en) | 2018-08-21 | 2020-03-02 | 유재성 | Smart factory system based on health test room for operating personnel |
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| KR20200094852A (en) * | 2019-01-25 | 2020-08-10 | 전자부품연구원 | Connected car big data acquisition device, system and method |
| US11609922B2 (en) | 2019-01-25 | 2023-03-21 | Korea Electronics Technology Institute | Connected car big data acquisition device, system and method for storing data gathered in a single platform |
| CN115913809A (en) * | 2022-09-26 | 2023-04-04 | 重庆长安汽车股份有限公司 | Data distribution communication method, system, computer device and storage medium |
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