KR20180025757A - Smart factory flatform for processing mass data of continuous process in a real time - Google Patents

Abstract

A smart factory platform for processing the mass data of a continuous process in real time according to the present invention includes an interface part for preprocessing collection data to process the collection data collected from a continuous process including a first process and a second process connected to the first process, a real-time processing part for mapping the process identifier of a process where the collection data is generated to the preprocessed data, and aligning the mapping data mapped for a linkage process between the collection data collected in the first process and the collection data collected in the second process, and a mass data processing part for storing aligned alignment data based on the process identifier. It is possible to perform linkage based on the correlation of data generated for each process.

Description

TECHNICAL FIELD [0001] The present invention relates to a smart factory platform for processing large-capacity data for a continuous process in real time.

The present invention relates to a smart factory platform, and more particularly, to a smart factory platform for processing large-capacity data for a continuous process in real time.

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, there is a demand for a system capable of processing a large amount of data in real time in a continuous process in which the production method is applied, and processing data generated by each process in a linked manner.

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.

The present invention has been devised to overcome the above-mentioned problems of the prior art, and it is an object of the present invention to enable correlation processing based on correlation of data generated for each process by analyzing and processing data generated in a continuous process.

Another object of the present invention is to standardize various types of data generated in a continuous process so that a large amount of data can be processed in real time.

In addition, the present invention aims at preventing data loss even when a memory failure occurs in some memory and real-time processing performance of data by implementing a plurality of memories to be clustered.

It is another object of the present invention to improve the real-time processing performance of data by dispersing and parallel processing the processing steps for monitoring abnormalities of the equipment or quality of materials by a plurality of real-time processing servers.

In order to achieve the above object, a smart factory platform for real-time processing large-capacity data for a continuous process according to the present invention comprises a first process, a second process connected to a first process, An interface unit for pre-processing the collected data, a step for mapping the process identifier of the process in which the collected data is generated to the preprocessed data, and the step of mapping the collected process data collected in the first process and the collected data collected in the second process A real-time processing unit for sorting the mapped mapping data for association processing, and a large-capacity data processing unit for storing sorted alignment data based on the process identifier.

According to the present invention as described above, the following effects can be obtained.

According to the present invention, by analyzing and processing data generated in a continuous process, it is possible to perform linkage processing based on the correlation of data generated for each process.

In addition, according to the present invention, a large amount of data can be processed in real time by standardizing various types of data generated in a continuous process.

In addition, according to the present invention, a plurality of memories are clustered so that data loss can be prevented even in the event of a failure of some memories and real-time processing performance of data can be ensured.

In addition, according to the present invention, a plurality of real-time processing servers can improve the real-time processing performance of data by dispersing and parallel processing processing processes for monitoring abnormalities in equipment or quality of materials.

FIG. 1 is a diagram illustrating a whole smart factory structure including a smart factory platform for real-time processing of large-capacity data for a continuous process according to an embodiment of the present invention.
FIG. 2 and FIG. 3 are diagrams showing the configuration of the interface device of FIG. 1 in detail.
FIG. 4 and FIG. 5 are views showing the configuration of the real time processing apparatus of FIG. 1 in detail.
6 is a diagram illustrating an example in which a plurality of tasks are distributedly parallel-processed through a plurality of real-time processing units according to an embodiment of the present invention.
7 is a diagram specifically showing a configuration of the mass data processing apparatus of FIG.
FIG. 8 is a diagram illustrating a process of processing large-capacity data for a continuous process in real time through a smart factory platform for real-time processing of large-capacity data for a continuous process according to an embodiment of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS The advantages and features of the present invention, and the manner of achieving them, will be apparent from and elucidated with reference to the embodiments described hereinafter in conjunction with the accompanying drawings. The present invention may, however, be embodied in many different forms and should not be construed as being limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Is provided to fully convey the scope of the invention to those skilled in the art, and the invention is only defined by the scope of the claims.

The first, second, etc. are used to describe various components, but these components are not limited by these terms. These terms are used only to distinguish one component from another. Therefore, the first component mentioned below may be the second component within the technical spirit of the present invention.

It is to be understood that each of the features of the various embodiments of the present invention may be combined or combined with each other, partially or wholly, technically various interlocking and driving, and that the embodiments may be practiced independently of each other, It is possible.

FIG. 1 is a diagram showing a whole smart factory structure including a smart factory platform for real-time processing of large-capacity data for a continuous process according to the present invention.

As shown in FIG. 1, a smart factory structure including a smart factory platform for real-time processing of large-capacity data for a continuous process according to the present invention includes a data collection device 1, a network 2, a smart factory platform 1000), and an application unit 3.

The data collecting apparatus 1 includes various instruments, sensors, actuators, and the like for collecting micro data in the course of each step constituting the continuous process. For example, a steel process corresponding to a continuous process may be composed of various processes such as a steelmaking process, a steelmaking process, a performance process, and a rolling process.

The data collecting apparatus 1 may include a P / C, a PLC (Programmable Logic Controller), and a DCS (Distributed Control System) for integrating or controlling the data collected by the measuring instrument, the sensor, and the actuator. In this case, the microdata refers to raw data that is not processed as data itself collected through various sensors and the like.

The network 2 includes a network cable, a gateway, a router, a wireless AP, and the like for transmitting a large amount of data collected by the data collecting apparatus 1 to the smart factory platform 1000. However, no.

The smart factory platform 1000 receives and processes a large amount of micro data collected by the data collection device 1 from the network 2. [ Specifically, the smart factory platform 1000 preprocesses and arranges data generated for each process so that the data generated in the continuous process can be processed in cooperation. In addition, the smart factory platform 1000 can determine on-line whether facilities or materials are abnormal in real time. In addition, the smart factory platform 1000 may store the corresponding data in a big data storage unit for future analysis, and may provide an inquiry and analysis service for the stored data.

The smart factory platform 1000 includes an interface device 100, a real-time processing device 200, and a mass data processing device 300.

The interface device 100 provides connection means for connecting with heterogeneous devices through various protocols. In addition, the interface device 100 performs a preprocessing operation for standardization of the data collected by the data collection device 1 for the linkage processing of the collected data collected from the continuous process.

The real-time processing device 200 is a system for supporting real-time decision making of a work site, and processes standardized data in the interface device 100 on a plant or material basis for the linkage processing of collected data collected from a continuous process.

The mass data processing apparatus 300 stores the data received from the real-time processing apparatus 200 in the big data storage space. Also, the mass data processing apparatus 300 manages the data to prevent data loss and provides a query function for historical data.

Operations of the more detailed interface device 100, the real-time processing device 200, and the mass data processing device 300 will be described later.

The smart factory platform 1000 may further include at least one of a service device 400, a management device 500 and a security device 600. [

The service device 400 has a structure for reusing standardized processing processes and business standards as services.

The service device 400 reposits business know-how and facilitates linkage between planning, execution and control through a service-to-service connection defined as a functional unit. In addition, the service device 400 may invoke and execute an analysis model that includes a quality determination model or an anomaly prediction model for a material or product to produce an analysis result.

Since the analysis model is previously stored in the big data storage unit (not shown), when the execution call event for the analysis model is inputted, the service apparatus 400 extracts the data required for the analysis model from the big data storage unit or the analysis apparatus 200 And provides the result.

That is, the service device 400 receives and analyzes data distributed and parallel-processed in real time through the real-time processing unit, or when the processed data is stored in the big data storage unit of the large capacity data processing apparatus 300, The data can be analyzed.

The management device 500 manages configuration files for configurations for management of individual configurations belonging to the smart factory platform 1000 and UI / UX management data, individual monitoring of each configuration, linkage between predetermined setting values Information management, processing performance of the entire system, and integrated monitoring information.

The security device 600 performs authentication, authorization and access control for the engineer, and manages security for the data itself and security for the transmission path.

The application unit 3 refers to a business unit system that processes and provides screens and data necessary for the engineer based on the smart factory platform 1000.

Hereinafter, each configuration included in the smart factory platform 1000 will be described in more detail.

FIG. 2 and FIG. 3 are diagrams showing the configuration of the interface device of FIG. 1 in detail.

As shown in FIGS. 2 and 3, the interface device 100 includes an interface unit 110 and a queue storage unit 120.

Specifically, the interface unit 110 parses the specialized layout of the collected microdata and parses it into meaningful data units, for linkage processing of the collected data collected from the continuous process.

In addition, the interface unit 110 normalizes items that are not standardized in the parsed data. In addition, the interface unit 110 converts the unit and the number of digits of the measurement values included in the data, converts it into a data structure that can be used immediately, and stores the data structure in the queue storage unit 120, which is temporary storage of microdata for professional storage.

To this end, the interface unit 110 may include a receiving unit 111, a parsing unit 112, a normalizing unit 113, a filtering unit 114, and a transmitting unit 115.

The receiving unit 111 receives the collected data collected by the sensors constituting the data collecting apparatus 1 using one or more communication methods. For example, the receiving unit 111 may use various communication methods such as iBA, OLE for Process Control (OPC), TCP / IP, and the like. That is, the communication methods used by the respective gateways and the like constituting the network 2 may vary according to the types of sensors included in the data collecting apparatus 1, and therefore the receiving unit 111 according to the embodiment of the present invention And can support all communication methods capable of communicating with various networks 2.

The parsing unit 112 parses the collected data received through the receiving unit 111 in a meaningful unit according to a preset message layout and transfers the parsed data to the standardizing unit 113. [

Specifically, the collected data collected by the data collection device 1 has a structure in which a group ID including a plurality of item IDs, a collected time, and a plurality of measured values are repeated. In this case, the item ID is used to identify the measured property, which means a property of the equipment, material, or product during the continuous process, and may be temperature, humidity, or flatness. It can be a representative value of several items grouped by location or each process in the factory. However, the data structure in the embodiment of the present invention is not limited to this, and may include the collected time in the group ID itself.

As described above, the smart factory platform receives the data of the form in which the group ID, the collected time, and the plurality of measured values are repeated without any distinction from the data collection device 1, Since it is necessary to separately analyze the group ID, the item ID, and the plurality of measurement values that are not standardized, the real-time processing device 200 May be degraded.

Thus, the parsing unit 112 of the interface unit 110 parses the collected data based on the preset layout, for the linkage processing of the collected data collected from the continuous process.

Specifically, the parsing unit 112 parses the data collected for each group ID, and matches a plurality of item IDs and a plurality of measured values included in the group ID, respectively, to obtain a single item ID, a collected time, and a single measured value And can transfer the data to the standardization unit 113. [

The parsing unit 112 can parse the collected data by referring to the message layout storing unit in which the data defining the layout for the microdata specialization is defined and the message layout storing unit can be included in the interface unit 110 or another device . However, the present invention is not limited to this, so the information about the message layout may be included in the parsing unit 112.

The normalization unit 113 normalizes the non-standardized data transmitted from the parsing unit 112. For example, the standardization unit 113 may convert an item ID into a standard item ID, or a unit of a measurement value and a unit of a measurement value, for each data having a single item ID, a collected time, The number of digits can be standardized.

Specifically, even though each sensor or actuator included in the data collection device 1 measures the same property, each item ID may have a different format depending on the characteristics of the company in which they are manufactured and the factory in which they are manufactured. Therefore, in the embodiment of the present invention, the standardization unit 113 can change the item ID included in each data item to the standard item ID so that the data measuring the same attribute has the same ID.

As described above, in the embodiment of the present invention, the standardization unit 113 prepares the measurement values measured for the same property to have the same standard item ID, so that the collected data collected from the continuous process based on each standard item ID It can be processed in conjunction.

In addition, the format of the measurement values included in the collected data collected through the data collection device 1 differs depending on the type of the data collection device 1 such as a sensor or an actuator. As a result, in order to process the collected data of the different types collected from the continuous process, it is necessary to additionally perform a process of converting data units and length according to the type of each device. Therefore, It is impossible to perform the link processing in real time. Therefore, in the embodiment of the present invention, the standardization unit 113 can standardize the data so that the analysis apparatus 200 can process a large amount of data in real time.

The standardization unit 113 can standardize the item ID, the unit of measurement value, and the number of digits by referring to the standard conversion reference storage unit included in the interface unit 110 or another apparatus in a different configuration, but the present invention is not limited thereto The standardization unit 113 may include information on the standard conversion standard for standardization.

The filtering unit 114 determines whether to store the standardized data in the queue storage unit 120 in the normalization unit 113 according to a predetermined filtering criterion.

For example, the class is previously set according to the type of collected data, and the filtering unit 114 can determine whether to store data in the queue storage unit 120 according to the class. In this case, the class may be determined based on the importance level based on the standard item ID of the standardized data, but is not limited thereto.

The filtering unit 114 may filter the standardized data by referring to a filtering reference storage unit storing a reference for filtering data that needs to be stored in the queue storage unit 120. The filtering reference storage unit may include, Part 110 or other device. However, since the present invention is not limited to this, information on the filtering criterion may be stored in the filtering unit 114.

The transmission unit 115 stores the filtered data through the filtering unit 114 in the queue 121 of the queue storage unit 120. [

In one example, the transmission unit 115 may store the filtered data in the queue 121 of the queue storage unit 120 for each group ID or standard item ID.

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 addition, in the embodiment of the present invention, a plurality of queue storage units 120 may be configured. In this case, when a plurality of queue storage units 120 store standardized collection data in one of the queue storage units 120 And to receive and store the collected collected data in real time.

In addition, the transmission unit 115 can stop storing the collected data when the operation mode determined based on the number of the normally operated queue storage units 120 among the plurality of the queue storage units 120 is the standby mode, and the reception unit 111 ) Can also stop receiving the collected data.

That is, when the queue storage unit 120 abnormally operates and the collected data is not continuously stored in real time, when the collected data is continuously received from the data collection apparatus 1, The failure of the collected data processing operation of the interface unit 110 occurs.

Accordingly, in the embodiment of the present invention, when the operation mode is the standby mode, the interface unit 110 prevents the occurrence of a failure by stopping the reception and storage of the collected data, and when the queue storage unit 120 operates normally, Lt; RTI ID = 0.0 > and / or < / RTI >

The interface unit 110 according to the embodiment of the present invention includes at least one of a micro data merging unit 116, a message layout storing unit 117, a standard conversion reference storing unit 118, and a filtering reference storing unit 119 And may further include one.

The microdata merge unit 116 merges the data received through the receiving unit 111 and transfers the merged data to the parsing unit 112. [

For example, the microdata merge unit 116 may merge data received via the receiving unit 111 at constant time intervals (e.g., 0.1 second, 1 second, 1 minute, etc.) merge) to the parsing unit 112.

That is, due to the nature of the continuous process, the data can be sent to the parsing unit 112 in a very short period (e.g. 5 ms to 20 ms) Can degrade the real-time processing performance.

Therefore, the microdata merge unit 116 directly transfers the data required for real-time monitoring to the parsing unit 112 without merging, merges the remaining data at predetermined time intervals, . In this case, whether or not it is necessary for real-time monitoring can be set according to the importance of data. For example, data collected from facilities or materials that require immediate action in case of an abnormality can be set as data necessary for real-time monitoring. It is not.

The parsing unit 112 refers to the message layout storage unit 117 and stores the data in the form of a binary or a separate data transmission standard, It can be used as a basis for interpreting data.

Standard item IDs obtained by standardizing the item IDs of various sensors constituting the data collecting apparatus 1 and standard units and digits according to each standard item ID are stored in the standard conversion reference storage unit 118, 113 can refer to the standard conversion reference storage unit 118 to change the item ID of the parsed data to the standard item ID and use it as a basis for unifying the unit and the number of digits.

The filtering reference storage 119 stores a reference for filtering data that needs to be stored in the queue storage unit 120 among the standardized data. The filtering unit 114 refers to the filtering reference storage unit 119 The data to be stored in the queue storage unit 120 among the standardized data can be used as a basis for filtering.

The queue storage unit 120 includes a queue 121 as an area for temporarily storing data processed by the interface unit 110 before real-time processing.

The queue 121 is a storage for storing data processed by the interface unit 110 for a predetermined time. The queue 121 may store data on a disk basis instead of a memory in order to prevent data loss. In addition, the space for storing data in the queue 121 can be divided into topics, and it is also possible to process data in parallel by dividing a plurality of partitions within the same topic.

A unique ID may be assigned to the queue 121 according to data read by a fetch performing unit of a real-time processing server described later. The data fetch address can be managed by the unique ID, and the data is sequentially read Data can be stored and provided in the form of a write queue.

Although FIG. 2 illustrates preprocessing of data collected through one interface unit 110 and one queue storage unit 120, the present invention is not limited thereto.

For example, as shown in FIG. 3, the interface device 100 may include a plurality of interface units 110 and a plurality of queue storage units 120.

At this time, the plurality of interface units 110 can be expanded in a form added to the size of the data collection device included in the data collection device 1 and the physical location of the factory. In addition, each of the interface units 110 may be provided in a redundant structure for high availability (HA).

In other words, each of the interface units 110 is provided as an operation server and a backup server, so that when a normal operation server is operated and a failure occurs in the operation server, the backup server is automatically activated and the operation of the interface unit 110 is not interrupted Can be continuously implemented.

Since the plurality of queue storage units 120 have a clustering structure, when data is stored in one queue storage unit 120, data is copied to another queue storage unit 120, Even if a failure occurs in the queue storage unit 120, the service can be continued by referring to the other queue storage unit 120. [ Accordingly, even when some of the queue storage units 120 fail, the data can be processed and stored through the remaining queue storage units 120 without data loss.

When the standardization of the collected microdata is completed, the interface unit 110 selects one of the plurality of queue storage units 120 and stores the standardized data. In this case, a criterion for selecting the queue storage unit 120 for storing data may be selected from among various rules. For example, a method of selecting or sequentially selecting the queue storage unit 120 having the lowest load, It is possible to previously store and select the queue storage unit 120 to be stored for each collected sensor.

FIG. 4 and FIG. 5 are views showing the configuration of the real time processing apparatus of FIG. 1 in detail.

As shown in FIGS. 4 and 5, the real-time processing apparatus 200 includes a real-time processing unit 210 and a memory unit 220.

Specifically, the real-time processing unit 210 processes the microdata so that the microdata can be mapped on a facility or material basis and linkage analysis can be performed between areas such as operation-equipment-quality. In addition, the real-time processing unit 210 arranges the collected data preprocessed in the interface device 100 for the link processing of the collected data collected from the continuous process. In addition, the real-time processing unit 210 can estimate a missing value occurring at a point where no data is collected because there is no sensor, or in the middle of a collection period.

To this end, the real-time processing unit 210 includes a fetch performing unit 211, a loading performing unit 212, a process mapping performing unit 213, a data correcting performing unit 215, .

The fetch performing unit 211 fetches the data stored in the queue 121 of the interface device 100 and stores the fetched data in the collected data storing unit 221 of the memory unit 220. For example, the fetch performing unit 211 may read the data stored in the queue 121 for each standard item ID, and store the read data in the collected data storage unit 221 of the memory unit 220. That is, as described above, the interface unit 110 can store the standardized standardized data in the queue 121 by the group ID or the standard item ID for the linkage processing of the collected data collected from the continuous process, so that the fetch performing unit 211 May fetch the standardized data stored in the queue 121 for each group ID or standard item ID.

At this time, the fetch performing unit 211 can read the next data of the previously read data by storing the position information of the previous inquiry of the data to the queue 121 included in the queue storage unit 120 .

The loading performing unit 212 roams the collected data stored in the collected data storing unit 221 and transfers it to the process mapping performing unit 213. [ For example, the loading performing unit 212 may load the standardized data by the standard item ID and transmit the same to the process mapping performing unit 213. [

The process mapping performing unit 213 maps the process identifier for identifying the process in which the corresponding data is performed to the standardized collected data received from the loading performing unit 212 and supplies the mapped standardized data to the data correction performing unit 215, .

Specifically, the process mapping performing unit 213 can map at least one of the equipment identifier of the equipment performing the specific process and the material identifier of the material processed through the equipment performing the process to standardized data, An execution unit 213a and a material mapping execution unit 213b.

The equipment mapping performing unit 213a maps the equipment identifier to which the corresponding data is collected to the received standardized data, and transmits the mapping data to which the equipment identifier is mapped to the material mapping performing unit 213b.

Specifically, the equipment mapping performing unit 213a can extract the equipment identifier of the facility where the collected data is generated based on the time at which the standardized data was collected and the attribute information of the sensor that collected the standardized data. At this time, the attribute information of the sensor may include the standard item ID as the information for identifying the measured attribute as described above, but the present invention is not limited thereto.

That is, as described above, the standardized data by the interface device 100 includes a single item having a standard item ID, a collected time, and a standardized unit and a number of digits. Therefore, the equipment mapping performing unit 213a can confirm the facility that operated at the relevant time based on the collected time information. Since the attribute information of the sensor includes the equipment identifier information of the facility measured by the sensor, it is possible to extract and map the equipment identifier of the facility measured by the sensor having the standard item ID at a specific time.

As a result, the mapping data to which the equipment identifier is mapped will include the standard item ID, the collected time, the equipment identifier, and the single measurement value.

The material mapping performing unit 213b may map the material identifier of the material processed through the equipment corresponding to the equipment identifier to the data to which the equipment identifier is mapped.

Specifically, the material mapping performing unit 213b may extract the material identifier of the material processed in the equipment corresponding to the mapped equipment identifier according to the work instruction information performed in each process.

In this case, the material means an extract originating from the same source. A first material is produced through a facility for carrying out the first process, wherein a first material identifier is assigned in advance to the first material, and a second process is performed And a second material identifier is assigned to the second material in advance.

Thus, as described above, the data to which the equipment identifier is mapped by the equipment mapping performing unit 213a includes a single measurement value having the standard item ID, the collected time, the equipment identifier and the standardized unit and the number of digits, The material mapping unit 213b can extract and map the material identifier of the processed material through the operation performed in the equipment corresponding to the mapped equipment identifier.

As a result, the mapping data to which the material identifier is mapped will include the standard item ID, the collected time, the equipment identifier, the material identifier, and the single measurement value.

In this way, the real-time processing unit 210 according to the embodiment of the present invention further maps the facility identifier and the material identifier to the standardized data, thereby determining whether the material is collected data Can be confirmed.

As described above, since the data is standardized while passing through the interface device 100, the real-time processing unit 210 can map the equipment identifier and the material identifier to the standardized data having a predetermined structure, so that the large- Can be processed.

On the other hand, the collected data collected through the data collecting apparatus 1 includes not only collected data collected while the material is processed through the facility, but also collection data collected by detecting the equipment itself that is not processing the material .

In the case of such collected data, since there is no material identifier to be further mapped to the collected collection data to which the equipment identifier is mapped, the material mapping performing unit 213b is configured to compare the no-load data, which is mapped only with the equipment identifier, The load data in which all the identifiers are mapped can be separated and stored in the sorting data storage unit 222. [

The data correction performing unit 215 corrects the mapping data by predicting the missing data when there is missing data among the mapping data to which the process identifier is mapped.

For example, the data correction performing unit 215 predicts missing data using the mapping data corresponding to the area closest to the area corresponding to the data where the omission occurred, or the mapping data corresponding to the time before and after the omission occurred .

In addition, the data correction performing unit 215 may match the time at which the collected data with the equipment identifier and material identifier mapped before the missing data prediction matches the predetermined collection period. In other words, since the collected data should be generated at a predetermined collection period through the data collection device 1, there may be cases where the collected data does not exist. Therefore, the data correction execution unit 215 may match the collected time to the collection period.

For example, if the collection period is 20 ms and the collected data is stored, the data collection device 1 must generate the collected data every 20 ms. Therefore, the collected data at 15:01:01 and 0005 ms is 15:01:11 0000 ms , And the collected data at 15:01:11 0050 ms can be matched to the collection period by changing the collected time at 15:01:01 0040 ms.

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 correction performing unit 215 can predict missing data by utilizing the mapping data at the closest position to the area where the missing data has been collected, or the mapping data of the collection time adjacent to the time at which the missing occurred .

The collation performing unit 216 sorts the mapping data for the cooperative processing of the collected data collected from the continuous process.

As in the previous embodiment, the collation performing unit 216 may receive collection data including a standard item ID, a collected time, a facility identifier, a material identifier, and a single measurement, May sequentially sort the mapping data to which the same material identifier is mapped according to the collected time. That is, the collation performing unit 216 may arrange the mapping data in the material unit having the same material identifier for the linkage processing between the collected data collected in the continuous process.

The collation performing unit 216 may then sort the time-ordered mapping data on the material corresponding to the same material identifier based on the location at which the collection data was collected.

In one example, the collation performing unit 216 may use at least one of the length of the material, the speed of movement of the material, and the collection period of the collection data to determine the collection location of the collection data arranged on the material over time . And the data alignment performing unit 216 may calculate the measurement values at the reference points based on the distance between the reference points set at predetermined intervals on the material and each collecting position.

That is, the collection position at which the collection data is collected for each cycle on the material may be determined based on the total length of the material by multiplying the movement speed of the material by the collection period of the collection data, And may be respectively aligned with data measured at a predetermined position in the longitudinal direction of the material.

The data sorting performing unit 216 then calculates the distance between the reference points set at predetermined intervals on each material and the respective collecting positions of the collecting data collected from the first process and the second process, And the reference data at each reference point can be generated based on the calculated measurement value.

In addition, the collation performing unit 216 may sequentially sort the collection data arranged in accordance with the time and the reference data at the reference points in one direction on the material, and store the sorted data in the sorting data storage Section 222 of FIG. At this time, one direction for aligning the reference data may be at least one of a longitudinal direction of the material, a width direction of the material, and a thickness direction of the material, but the present invention is not limited thereto.

That is, 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 compared with 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 in each process in association with each other.

Therefore, in the embodiment of the present invention, in order to process the collected data collected from the first process and the second process, a reference point is set on the basis of one direction of the material passing through each process, The measured values at the respective reference points corresponding to the materials in the first process and the second process can be linked and managed. Hereinafter, an example in which the reference data is aligned in the longitudinal direction on the material will be described.

Specifically, the first reference data at a first reference point having a predetermined interval in the longitudinal direction of the material in the first process and the second reference data at a second reference point at a predetermined interval in the longitudinal direction of the material in the second process The data is linked on the basis of the first material identifier included in the first reference data and the second material identifier contained in the second reference data on the material family diagram in which the material identifier is mapped for each material for the linkage processing of the collected data .

That is, the material linkage information may include the material identifiers allocated to the same material generated while sequentially passing through the first process and the second process in the form of a pedigree diagram, so that each material connected to the tree by referring to the material linkage information The collection data to which the identifier is mapped can be associated with each other.

The material linkage information may be stored in the material linkage information storage unit 225 of the memory server 200, but the present invention is not limited thereto.

The collation performing unit 216 may store the collected data and the reference data aligned in the longitudinal direction of the material in the sorting data storing unit 222 of the memory unit 220 as described above.

In one example, the data sorting performing unit 216 stores the sorted data in the same space of the sorting data storing unit 222 for each sorted data having material identifiers associated with each other based on the material association information, It is possible to use collectively collected collected data together in the same storage space.

In addition, the data sorting performing unit 216 performs a sorting operation on the first sorting data sorted in time according to the mapping data, the second sorting data sorted based on the collecting position, and the reference data sorted in the reference points And may be separately stored in the data storage unit 222.

That is, the data sorting performing unit 216 separately stores the first sorting data and the second sorting data corresponding to the actually measured values separately on the basis of the time and the collecting position, The reference data corresponding to the virtual value calculated based on the data may be separately used, but the present invention is not limited thereto.

In addition, the data sorting performing unit 216 may store an event indicating completion of sorting in the completion event storing unit 223 so that the sorting result can be used in the mass data processing apparatus 300. [

As such, the real-time processing unit 210 may map the process identifier to the collected data, such as a facility identifier or a material identifier, and arrange the mapping data so that the collected data is processed in association.

The real-time processing unit 210 may further include at least one of the equipment malfunction detection unit 217 and the malfunction detection unit 218.

The equipment abnormality detection performing unit 217 receives the data to which the equipment identifier is mapped from the equipment mapping performing unit 213a, and determines whether or not the equipment abnormality is present according to a predetermined equipment abnormality judgment criterion. The equipment malfunction detection performing unit 217 stores the malfunction detection result in the malfunction detection result storage unit 224 of the memory unit 220 when an abnormality occurs in the specific equipment.

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 detection performing unit 218 loads the sorted collection data from the alignment data storage unit 222 and judges whether the quality is abnormal according to a predetermined quality abnormality criterion. The quality anomaly detection unit 218 stores the determination result in the anomaly detection result storage unit 224 of the memory unit 220 when an abnormality occurs in the quality of a specific material.

At this time, macro data for use as a reference of the quality abnormality determination formula is generated through operations such as averaging and error prediction of the collected collected data as an example of the quality abnormality criterion for judging the quality abnormality, May be input to the quality abnormality determination equation and the quality abnormality may be determined according to the result, but the present invention is not limited thereto.

As described above, in the embodiment of the present invention, the real-time processing unit 210 arranges the collected data for the linkage processing of the collected data, and monitors whether the quality of the equipment or the material occurs in real time based on the collected data, Equipment failure can be predicted in advance.

The real-time processing unit 210 further includes at least one of a facility information storage unit 219a, a work instruction information storage unit 219b, a sensor attribute information storage unit 219c, and a quality determination model storage unit 219d can do.

The facility information storage unit 219a stores information on which plant or which position the facility is located, maintenance history information on the facility, equipment information to be mapped to data, The equipment abnormality judgment criterion is stored.

The work instruction information storage unit 219b stores instruction information for a work in the MES (Manufacturing Execution System) system, material identifier information generated in a specific facility as the work, a quality index for performing the work, and material information And a quality abnormality judgment criterion for judging whether the quality of the material is abnormal or not is stored for each material.

In the sensor attribute information storage unit 219c, information such as the type, unit, collection period, data of the equipment identifier, factory / process, and the like collected by the sensor are stored. have.

Since the quality abnormality judgment storage 219d stores the quality abnormality judgment reference in advance, the quality abnormality detection execution unit 218 can judge the quality abnormality with reference to the quality judgment model storage 219d.

The memory unit 220 stores various data generated by the real-time processing unit 210. The memory unit 220 may include a collection data storage unit 221, an alignment data storage unit 222, a completion event storage unit 223, and an anomaly detection result storage unit 224.

The collected data storage unit 221 stores the original data read from the queue 121 through the fetch performing unit 211 before the mapping, sorting, and prediction are performed for each standard item ID, The unit 212 loads the standardized data from the collected data storage unit 221 and transfers it to the equipment mapping performing unit 213a.

The alignment data storage unit 222 stores the facility information and the material information in the alignment data storage unit 222 by the facility mapping performing unit 213a and the material mapping performing unit 213b described above so as to determine the quality of the facility or completed material through each data Is stored in a state in which the data is aligned in the longitudinal direction of the material. In the sorting data storage unit 222, collected data in which both the equipment identifier and the material identifier are mapped and collected data in which only the equipment identifier is mapped is separately stored.

As described above, the completion event storage unit 223 stores data in which the missing data are sorted according to the collection period and the missing data is stored in the sort data storage unit 222, so that the sorting result can be used in the mass data processing apparatus 300 An event indicating completion of sorting is stored. Accordingly, the mass data processing apparatus 300 monitors the completion event storage unit 223 and extracts the aligned data from the sorting data storage unit 222 when a new completion event occurs.

Specifically, the completion event includes the event transmission date and time, data collection date and time, key information for reading data in the sorting data storage unit 222, partition and directory information for storing an event, and the like. Accordingly, when a new completion event is obtained in the completion event storage unit 223, the mass data processing apparatus 300 uses the key information included in the completion event to store data corresponding to the completion event in the sorting data storage 222 ) And the partition and the directory of the storage device (e.g.

The abnormality detection result storage unit 224 stores the abnormality detection result of the specific facility detected by the equipment abnormality detection execution unit 217 and the abnormality detection result of the specific material detected by the quality abnormality detection execution unit 218. [

Therefore, the engineer can access the abnormality detection result storage unit 224 through a separate anomaly detection system (not shown) built on the outside of the smart factory platform according to the embodiment of the present invention, It is possible to confirm whether or not an abnormality has occurred in the quality of the material.

However, the present invention is not limited to this, and it is also possible to directly transmit an abnormality to the abnormality detection monitoring system when an abnormality occurs in the quality of a specific facility or a specific material, thereby allowing the engineer to immediately check for an abnormality.

In addition, the memory unit 220 may further include a material linkage information storage unit 225.

Since the material linkage information storage unit 225 stores the material linkage information in which the material identifiers that can be given to the same material while passing through the first process and the second process are connected in a tree form, The collected data in the first process and the second process can be linked and processed based on the material identifier connected in a tree form after the collected data is aligned in the longitudinal direction of the material.

In FIG. 4, standardized data is mapped and arranged through one real-time processing unit 210 and one memory unit 220, but the present invention is not limited thereto.

5, the real-time processing apparatus 200 includes a real-time processing unit 210 including a plurality of real-time processing units 210a, 210b, and 210c, and a plurality of memory units 220 .

In this case, the plurality of real-time processing units 210a, 210b, and 210c may be configured in a clustering structure. In FIG. 5, three real-time processing units are illustrated, but they may be further expanded according to data processing capabilities.

That is, since the plurality of real-time processing units 210a, 210b, and 210c have a clustering structure, when a failure occurs in a specific real-time processing unit, a task that is being operated by another real-time processing unit moves and data processing is continued, have.

In particular, although a plurality of execution units are described as separate components in the above description, the present invention is not limited thereto, and thus each of the execution units is not physically distinctive configuration but means each task executed in the real time processing unit 210 .

Accordingly, the plurality of real-time processing units 210a, 210b, and 210c can perform parallel processing by distributing tasks performed by a plurality of execution units in real time.

More specifically, the plurality of real-time processing units 210a, 210b, and 210c can perform overloading by performing all the tasks through a single real-time processing unit by distributing and parallelly executing the tasks described above.

That is, the plurality of real-time processing units 210a, 210b, and 210c includes a fetch performing unit 211, a loading performing unit 212, a facility mapping performing unit 213a, a material mapping performing unit 213b, And performs the parallel processing of the tasks of at least one of the execution units of the plurality of execution units 218, 215, the data alignment unit 216, the equipment abnormality detection execution unit 217, and the quality abnormality detection execution unit 218, (220), it is possible to process a large amount of data transmitted from the interface device (100) in real time.

The data processed by the plurality of real-time processing units 210a, 210b and 210c is stored in the plurality of memory units 220. In order to increase processing performance and ensure availability in case of failure, A plurality of queue storage units 120 may be provided and may have a clustering structure.

That is, when data is stored in one memory unit 220, data is copied to another memory unit 220, so that even if a failure occurs in one memory unit 220, the service can be continued with reference to another memory unit 220 have. Accordingly, even when some memory units 220 fail, data can be processed and stored through the remaining memory units 220 without data loss.

The plurality of memory units 220 may include a collection data storage unit 221, an alignment data storage unit 222, a completion event storage unit 223, an anomaly detection result storage unit 224, (225), respectively.

In addition, the instance of each memory unit 220 is configured in a single thread type to separate the instance and the port for each write and read, and when an instance of another memory unit 220 fails in a specific instance, Or as a slave to ensure continuity of data processing. That is, each memory unit 220 may include a master instance and a slave instance, respectively.

At this time, if the aligned data aligned in the data alignment unit 216 is written in the master instance of the first memory unit 220, the sorting data recorded in the master instance of the first memory unit 220 is replicated, May be recorded in the slave instance of the part (220).

When the sorting data is written in the master instance of the second memory unit 220, the sorting data recorded in the master instance of the second memory unit 220 may be copied and recorded in the slave instance of the first memory unit 220 have.

6 is a diagram illustrating an example in which a plurality of tasks are distributedly parallel-processed through a plurality of real-time processing units according to an embodiment of the present invention.

As described above, the real-time processing unit 210 according to the embodiment of the present invention may include a plurality of real-time processing units 210a, 210b, 210c, and a plurality of real-time processing units 210a, 210b, It is possible to process a large amount of data transferred from the interface device 100 in real time by distributing and parallel processing various tasks such as fetching, loading, facility mapping, material mapping, data correction, and data sorting.

For example, as shown in FIG. 6, the real-time processing unit 210a may access the queue storage unit 120 and fetch stored collection data. The real time processing unit 210a stores the fetched collected data in the memory unit 220. Since the plurality of memory units 220 are provided in a clustering structure as described above, Can also be replicated.

The real-time processing unit 210b can access the other memory unit 220 and load the stored data. The real-time processing unit 210b may map at least one of the equipment number and material number to the corresponding data.

Then, the real-time processing unit 210c can correct the missing data from the collected data to which the equipment number and the material number are mapped. Then, the real-time processing unit 210c may sort the corrected data in chronological order and arrange the collected data in chronological order in one direction on the material. In addition, the real-time processing unit 210c may store the sorted collected data in another memory unit 220. [

At this time, the plurality of memory units 220 may be respectively provided in a redundant structure for high availability (HA). In other words, each of the memory units 220 is provided as a master instance and a slave instance, and when a master instance is normally operated, if a failure occurs in the master instance, the slave instance is automatically activated so that various functions of the real- But can be implemented continuously. In particular, when the master instance is operated in any one of the plurality of memory units 220, the remaining memory unit 220 may operate the slave instance, but the present invention is not limited thereto.

7 is a diagram specifically showing a configuration of the mass data processing apparatus of FIG.

As shown in FIG. 7, the mass data processing apparatus 300 stores the sorting data sorted by the real-time processing device 200 in the big data storage space. For example, the mass data processing apparatus 300 may store the sorting data in the big data storage space based on the process identifier included in the sorting data. Also, the mass data processing apparatus 300 can manage data loss and provide a query function for historical data. To this end, the mass data processing apparatus 300 may include a mass data processing unit 310, a big data storage unit 320, and a query processing unit 330.

Specifically, the large-capacity data processing unit 310 performs at least one of the alignment data generated by the real-time processing unit 210 and the anomaly detection result data in a distributed parallel processing, and includes a completion event receiving unit 311, an alignment data fetching unit 312 A memory queue 313, a file creation unit 314, and an anomaly detection data reception unit 315. [0154]

The completion event receiving unit 311 monitors the completion event storage unit 223 included in the memory unit 220. [ The completion event receiving unit 311 transfers the completion event to the sorting data fetching unit 312 when a new completion event is stored in the completion event storing unit 220. [

When the completion event is received from the completion event receiving unit 311, the sorting data fetching unit 312 reads the sorting data corresponding to the completion event from the sorting data storing unit 222 included in the memory unit 220, (313).

In one embodiment, the sorting data fetch unit 312 uses the key information included in the completion event to check which partitions and directories of the sorting data storage unit 222 store data corresponding to the completion event , And the data stored in the sorting data storage unit 222 can be read.

As described above, since the sorted data having the material identifiers associated with each other are stored in the same space of the sorting data storing unit 222, the sorting data fetching unit 312 can collect the collected data Can be fetched easily.

The memory queue 313 temporarily stores the data read by the sorting data fetching unit 312 on the memory before storing the data in the big data storing unit 320. [ In one embodiment, the memory queue 313 may be implemented on a file basis if the amount of data is not large and loss prevention is required.

The file creation unit 314 creates the collected data stored in the memory queue 313 as a physical file and stores the same in the big data storage unit 320. [ In one embodiment, the file creation unit 314 may change or compress the file format when storing the file in the big data store 320. [

In addition, since the collected data to be processed in association with each other can be simultaneously fetched from the sorting data storing unit 222, the file generating unit 314 generates collected data in which the material identifiers associated with each other are mapped to one file, It is also possible to do.

In addition, as described above, since the data to which the process identifier is mapped through the real-time processing unit 210 includes the load data to which the material identifier is mapped and the no-load data to which the material identifier is not mapped, The load data and the no-load data can be stored in different storage spaces (tables).

In addition, the file generation unit 314 may divide the sorting data sorted by the real-time processing unit 210 into a predetermined number of units and store the sorted data in the big data storage unit 320.

The anomaly detection data receiving unit 315 monitors the anomaly detection result storage unit 224 included in the real-time processing device 200. The abnormality detection data receiving unit 315 stores the new abnormality detection result in the memory queue 313.

The big data storage unit 320 stores the file generated by the file creation unit 314. [ In one embodiment, the big data store 320 may be implemented on a Distributed File System basis. For example, the big data storage 320 may be implemented as a Hadoop-based distributed file system.

According to this embodiment, the big data storage 320 is composed of a master node 320a and a data node 320b. The master node 320a stores a large amount of files generated by the large capacity data processing apparatus 300 in the data nodes 320b and generates a job for inquiring data stored in the data nodes 320b And manages the metadata.

Here, the job means a unit for processing a query received from the query processing unit 330 to inquire data stored in the data node 320b. For example, when a query for querying data for one table recorded in the data node 320b is executed, if ten data nodes 320b are selected as a result of querying the data of the table, A job for retrieving and retrieving data for each data node 320b and a job for consolidating data acquired for each data node 320b are executed for the data nodes 320b.

Metadata includes a location of a file stored in the data node 320b, a file name, a block ID where a file is stored, and a storage location of a server. For example, when a file is created in the file creation unit 314, the location and file name of the file are stored in the metadata. If the file is larger than the block size and divided into five blocks and stored in three different servers, The ID and the storage location of each server are additionally stored in the metadata.

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 data node 320b.

A large amount of files generated by the mass data processing apparatus 300 are stored in the data node 320b. In one embodiment, the data nodes 320b may be implemented in a plurality of ways, and each data node 320b includes a historical data store 322 and a model store 324.

The historical data storage unit 322 included in each data node 320b stores not only the file generated by the file creation unit 314 but also the large amount of micro data collected in real time by the data collection device 1 do. In one embodiment, the files generated by the file creation unit 314 may be stored in a separate relational database (RDB).

The model storage unit 324 stores a quality judgment model and an ideal prediction model necessary for judging the quality of a material or a product. Then, the service apparatus 400 can refer to the model storage unit 324 and determine the quality of the material or the product.

The query processing unit 330 includes a query receiving unit 332, a query execution unit 336, and a query result transmitting unit 338. The query receiving unit 332 queries the data stored in the big data storing unit 320 and returns the data. The query processing unit 330 may further include a query scheduling unit 334.

Specifically, the query receiving unit 332 receives the query from the engineer and interprets the received query syntax.

The query execution unit 336 transfers the query received through the query receiving unit 332 to the big data storage unit 320 so that the query is executed and the query execution result is obtained from the big data storage unit 320. [

The query result transmission unit 338 transmits the data obtained from the big data storage unit 320 to the engineer who requested the query.

Meanwhile, when the query received through the query receiving unit 332 is composed of a plurality of sub-queries, the query scheduling unit 334 classifies the received queries into the respective sub-queries and transmits them to the query execution unit 336 do.

FIG. 8 is a diagram illustrating a process of processing large-capacity data for a continuous process in real time through a smart factory platform for real-time processing of large-capacity data for a continuous process according to an embodiment of the present invention.

As shown in FIG. 8, the interface apparatus 100 receives data collected through the data collecting apparatus 1 (S10), and collects the collected data collected from the first and second processes (S20), converts the parsed collected data into a standardized format (S30), and stores the standardized collected data in the queue storage (S40).

As described above, in the embodiment of the present invention, pre-processing is performed in advance through the process of parsing and standardizing the collected data in the interface device 100, thereby enabling linkage processing of collected data collected from the first process and the second process .

Next, the real-time processing apparatus 200 fetches the normalized data stored in the queue storage unit, maps the equipment to the fetched data (S110), and maps the material (S120). Next, the missing data in the mapping data is predicted and corrected (S130), and the mapping data is aligned (S140).

At this time, the real-time processing device 200 can process a large amount of data in real time by performing the distributed parallel processing of the tasks of the above-described steps S110 to S140 through a plurality of real-time processing units.

As described above, in the embodiment of the present invention, the collection data is arranged in the longitudinal direction of the material, so that the process of association with the collected data collected from the first process and the second process is enabled.

Next, the data stored in the large-capacity data processing apparatus 300 real-time processing apparatus 200 is transferred to the big data storage unit (S210) so that real-time processed data can be stored for a long period of time.

Next, the service apparatus 400 calls a quality judgment model and an anomaly prediction model for determining the quality of the material or product stored in the model storage unit (S310), executes the analysis model (S320) The quality of the material or product corresponding to the data or the previously processed data is determined and the abnormality is analyzed and the analysis result is transmitted (S330).

As described above, the smart factory platform for real-time processing of large-capacity data for a continuous process according to an embodiment of the present invention standardizes microdata that have not been standardized through an interface device so that collected data generated in different formats in a continuous process It can be processed in conjunction.

In addition, it is possible to map the equipment and materials to the collected data through the real-time processing server of the analyzing apparatus, and sort the collected data based on the mapping information, thereby calculating information for the collected data generated in the continuous process to be processed.

While the present invention has been described in connection with what is presently considered to be practical exemplary embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, have. Therefore, the embodiments disclosed in the present invention are intended to illustrate rather than limit the scope of the present invention, and the scope of the technical idea of the present invention is not limited by these embodiments. Therefore, it should be understood that the above-described embodiments are illustrative in all aspects and not restrictive. The scope of protection of the present invention should be construed according to the claims, and all technical ideas within the scope of equivalents should be interpreted as being included in the scope of the present invention.

1: data collecting device 2: network
3: Application part 1000: Smart Factory platform
100: Interface device 110: Interface part
111: Receiving unit 112: Parsing unit
113: Standardization unit 114: Filtering unit
115: transfer unit 116: microdata merge unit
117: Message layout storage unit 118: Standard conversion reference storage unit
119: Filtering reference storage unit 120: Queue storage unit
121: queue 200: real-time processing device
210: real-time processing unit 211: fetch performing unit
212: loading performing unit 213: process mapping performing unit
213a: equipment mapping performing unit 214: material mapping performing unit
215: Data correction performing unit 216: Data alignment performing unit
217: equipment abnormality detection performing unit 218: quality abnormality detection performing unit
219a: equipment information storage unit 219b: work instruction information storage unit
219c: Sensor attribute information storage unit 219d: Quality judgment model storage unit
220: memory unit 221: collected data storage unit
222: Sort data storage unit 223: Completion event storage unit
224: abnormality detection result storage unit 225: material association information storage unit
300: Large capacity data processing device 310: Large capacity data processing part
311: Completion event receiving unit 312: Alignment data fetch unit
313: memory queue 314: file creation unit
315: abnormality detection data receiving unit 320: big data storage unit
320a: master node 320b: data node
330: Query processing unit 332: Query receiving unit
334: Query Scheduling Unit 336: Query Execution Unit
338: Query result transmission unit 400: Service device
500: Management device 600: Security device

Claims (25)

An interface unit for preprocessing the collected data for an association process of collected data collected from a continuous process including a first process and a second process connected to the first process;
And mapping the process identifier of the process in which the collected data is generated to the preprocessed data, and mapping the mapped mapping data for association processing between the collected data collected in the first process and the collected data collected in the second process A real time processing unit; And
And a mass data processor for storing the sorted alignment data based on the process identifier. The smart factory platform for real-time processing large-capacity data for a continuous process. The method according to claim 1,
Wherein the process identifier comprises at least one of a facility identifier of each facility performing the process and a material identifier of material processed through the facility,
Wherein the real time processing unit maps at least one of a facility identifier of each facility where the collected data is generated and a material identifier of the material processed through the facility to the preprocessed data, Smart Factory Platform for. The method according to claim 1,
The interface unit includes:
And standardizing at least one of an item ID and a measurement value for identifying the measured attribute, and standardizing the collected data. The smart factory platform for processing large-capacity data for a continuous process in real time. The method according to claim 1,
Wherein the collected data includes an item ID for identifying a measured attribute, a collected time, and a measured value, wherein the first item ID included in the collected data collected in the first process and the collected item ID collected in the second process The second item IDs included in the data have different formats from each other,
Wherein the interface unit stores the standardized data generated by standardizing the first item ID and the second item ID by the item ID, in real time. The method according to claim 1,
Wherein the collected data includes an item ID for identifying a measured attribute, a collected time, and a measured value, wherein the first measured value included in the collected data collected in the first process and the collected value collected in the second process The second measured values included in the data have different units and digits,
Wherein the interface unit stores the standardized data generated by standardizing the first measured value and the second measured value for each item ID, and processes the real-time large-capacity data for the continuous process. The method according to claim 1,
Wherein the collected data includes a group ID in which a plurality of item IDs for identifying measured attributes are grouped,
Wherein the interface unit stores the preprocessed data for each group ID. The smart factory platform for processing large-capacity data for a continuous process in real time. The method of claim 3,
Wherein the process identifier includes an equipment identifier of each facility performing the process,
Wherein the real-time processing unit maps the extracted facility identifier to the standardized data based on attribute information including a time at which the collected data is collected and an item ID of a sensor that collected the collected data. A smart factory platform for processing large amounts of data in real time. 8. The method according to claim 3 or 7,
Wherein the process identifier comprises a material identifier of material processed through the facility performing the process,
Wherein the real-time processing unit maps the material identifier extracted on the basis of the work instruction information performed in each step to the standardized data, in order to process large-capacity data for the continuous process in real time. The method according to claim 1,
The real-
Mapping a first material identifier to first preprocessed data of the first process, mapping a second material identifier to second preprocessed data of the second process,
The first mapping data in which the first material identifier is mapped and the second mapping data in which the second material identifier is mapped are processed in association with each other in accordance with the material system diagram to which the material identifier is mapped for each material, A smart factory platform for real-time processing of large-scale data for process. The method according to claim 1,
The real-
Wherein the mapping data is arranged in a material unit for cooperative processing between the collected data collected in the first process and the collected data collected in the second process. platform. The method according to claim 1,
The real-
Wherein the first sorting unit sequentially sorts the mapping data in which the same material identifier is mapped, according to the collection time, in the real time. The method according to claim 2 or 11,
The real-
Wherein the mapping data is second sorted based on a collecting position at which the collected data is collected on the material processed in the process. The method according to claim 2 or 11,
The real-
Determining a collection position at which the collection data is collected using at least one of a length of the material, a moving speed of the material, and a collection period of the collection data, and based on the determined collection position, And a smart factory platform for processing large-capacity data for a continuous process in real time. The method according to claim 2 or 11,
The real-
Determining a collection position at which the collection data is collected using at least one of a length of the material, a moving speed of the material, and a collection period of the collection data,
And second sorting the mapping data based on the reference data at the reference points calculated based on the reference points set at predetermined intervals on the material and the distance between the collecting positions. Smart Factory Platform for real-time processing. The method according to claim 2 or 11,
The real-
Aligning the mapping data and the reference data in one direction on the material,
Wherein the reference data is determined based on a collection point of the collected data and a reference point of the material. The method according to claim 2 or 11,
Wherein the real-time processing unit arranges the mapping data and reference data in one direction on 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. 15. The method of claim 14,
The real-
Wherein the first sorting data, the second sorting data, and the reference data at the reference points are separately stored, wherein the first sorting data in which the mapping data are sorted in time, the second sorting data in which the mapping data are sorted based on the collecting position, A smart factory platform for processing data in real time. 15. The method of claim 14,
The first reference data for the material processed in the first process and the second reference data for the material processed in the second process are included in the first material identifier included in the first reference data and the second reference data included in the second reference data Based on the second material identifier included in the first material identifier. The smart factory platform for real-time processing large-volume data for a continuous process. The method according to claim 1,
Further comprising first and second memory units including a master instance and a slave instance storing the sorting data,
When the sorting data is stored in the master instance of the first memory unit, the sorting data stored in the master instance of the first memory unit is replicated to the slave instance of the second memory unit,
And sorting data stored in the master instance of the second memory unit is replicated to the slave instance of the first memory unit when the sorted data is stored in the master instance of the second memory unit. Smart Factory Platform for. 3. The method of claim 2,
The large-capacity data processing unit includes:
Wherein the first alignment data and the second alignment data are associated using a material identifier included in the first alignment data in the first process and a material identifier included in the second alignment data in the second process. A smart factory platform for processing large amounts of data in real time. The method according to claim 1,
Wherein the real-time processing unit comprises a plurality of execution units, and the plurality of real-time processing units are provided with a plurality of execution units for performing mapping of the process identifiers and mapping of the mapping data. Smart Factory platform for processing. The method according to claim 1,
Wherein the collected data includes a group ID including a plurality of item IDs for identifying the measured attributes, a collected time, and a plurality of measured values repeatedly arranged,
Wherein the interface unit converts each of the plurality of item IDs included in the group ID and the plurality of measured values into data having a single item ID, collected time, and a single measured value, respectively A smart factory platform for processing large amounts of data in real time. 3. The method of claim 2,
The large-capacity data processing unit includes:
Wherein the load data to which the material identifier is mapped and the no-load data to which the material identifier is not mapped are separated from each other and stored in different tables. Factory Platform. 3. The method of claim 2,
The large-capacity data processing unit includes:
Wherein the sorting data is divided and stored in units of a predetermined number, and a smart factory platform for processing large-capacity data for a continuous process in real time. The method according to claim 1,
Further comprising a plurality of queue storage units for receiving and storing the collected collected data in real time when the preprocessed collected data is stored in any one of the queue storage units,
Wherein the interface unit stops receiving and storing the collected data when the operation mode determined based on the number of normally operating queue storage units among the plurality of queue storage units is the standby mode. A smart factory platform for real-time processing.

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