CN116610837A - Construction method and construction system for graph database for factory production - Google Patents
Construction method and construction system for graph database for factory production Download PDFInfo
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- 238000010276 construction Methods 0.000 title claims abstract description 95
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- G—PHYSICS
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- G06F16/901—Indexing; Data structures therefor; Storage structures
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Abstract
The invention relates to a construction method of a graph database for factory production, which constructs a graph database comprising a plurality of event data sets by graph database software for first original industrial data related to factory production, wherein one event data set is one node in the graph database and comprises object information, event information, time information and link information, and the one event data set shows the following relation: and an object shown by the object information, wherein in a certain time period shown by the time information, a certain event shown by the event information is implemented, the plurality of event data sets are mutually linked through a link key of the graph database, and the information shown by the link key comprises a front-back sequence among a plurality of events aiming at certain link information.
Description
Technical Field
The present invention relates to a construction method and a construction system for a graph database for factory production, a construction method and a construction system for a database for factory production, a bottleneck analysis method and a bottleneck analysis system for factory production, a computer device, and a computer readable medium.
Background
In the field of industrial manufacturing, a manufacturer generally makes products related to an order in a certain period of time according to the order required by a customer through each production device of each production line in a factory. In the production process, a plurality of production processes are generally involved, and each production facility consumes resources such as electricity, gas, or water.
In addition, because of the large number of variables involved in the production process, it is often considered a chaotic system, and a simple model cannot be used to describe the entire production process. Conventionally, in order to observe and monitor a production process, various data (for example, a production process name, a produced order number, product information, consumed power, gas, water, and other resource information) generated by the production equipment during operation of the production equipment are generally collected and recorded for each production equipment, and stored in a form of a table.
Disclosure of Invention
However, the number of production facilities and production processes involved in the production process is large, and the time interval for data acquisition is short (in order to observe and monitor the production process more accurately), so that the amount of table data acquired for each production facility is large. In addition, in the case of production analysis, a series of production steps related to a plurality of production facilities may be analyzed, and conventionally, a plurality of tables collected for each production facility are generally connected (join) using conventional database software such as SQL. When a plurality of tables collected for each production device are connected for use, the production management system may be blocked or crashed due to huge amount of operation data, so that the production efficiency is seriously affected.
The present invention has been made to solve the above-mentioned problems, and an object of the present invention is to provide a method and a system for constructing a graph database for factory production, in which a graph database including a plurality of event data sets is constructed by using graph database software, and the plurality of event data sets are linked to each other by a link key of the graph database, so that when a series of production processes related to a plurality of production facilities are analyzed, a plurality of event data sets corresponding to the series of production processes can be quickly retrieved, the amount of computation can be significantly reduced, the jam and breakdown of a production management system can be reduced, the stability of the production management system can be greatly improved, and the system work efficiency can be improved.
Technical proposal for solving the technical problems
In order to solve the above-described problems, in a method for constructing a graph database for plant production according to a first aspect of the present invention, a graph database including a plurality of event data sets, one of which is a node in the graph database including object information, event information, time information, link information, is constructed by graph database software for first raw industrial data related to plant production, one of which shows the following relationship: and an object shown by the object information, wherein in a certain time period shown by the time information, a certain event shown by the event information is implemented, the plurality of event data sets are mutually linked through a link key of the graph database, and the information shown by the link key comprises a front-back sequence among a plurality of events aiming at certain link information.
Preferably, in the method for constructing a graph database for factory production according to the first aspect of the present invention, the link information includes at least one of order information and product information.
Preferably, in the method for constructing a graph database for factory production according to the second aspect of the present invention, when the link information is the order information, the information indicated by the link key further includes a front-to-back order among a plurality of orders for a certain piece of the object information.
Preferably, in the method for constructing a map database for plant production according to the first to third aspects of the present invention, a newly constructed event data group and an existing event data group may be linked by the link key.
Preferably, in the method for constructing a graph database for factory production according to the first to third aspects of the present invention, the object information includes at least one of information of a factory, information of a production line in the factory, and information of equipment on the production line.
Preferably, in the sixth aspect of the present invention, in the graph database construction method for factory production according to the first to third aspects of the present invention, the event information includes production process information and production volume information.
Preferably, in the seventh aspect of the present invention, in the method for constructing a graph database for factory production according to the first to third aspects of the present invention, the unit of time information includes at least one of month, week, day, hour, minute, and second.
In order to solve the above-described problems, a computer device according to an eighth aspect of the present invention includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method of constructing a graph database for factory production according to the first to seventh aspects of the present invention when the computer program is executed.
In order to solve the above-described problems, a computer-readable medium according to a ninth aspect of the present invention has stored thereon a computer program which, when executed by a processor, implements the construction methods of the graph database for factory production according to the first to seventh aspects of the present invention.
In order to solve the above-described problems, a tenth aspect of the present invention relates to a system for constructing a graph database for factory production, comprising: a graph database construction unit that constructs, by graph database software, a graph database including a plurality of event data sets, one of which is a node in the graph database including object information, event information, time information, link information, one of which shows the following relationship, with respect to first raw industrial data related to plant production: and an object shown by the object information, wherein in a certain time period shown by the time information, a certain event shown by the event information is implemented, the plurality of event data sets are mutually linked through a link key of the graph database, and the information shown by the link key comprises a front-back sequence among a plurality of events aiming at certain link information.
In order to solve the above-described problems, a method for constructing a database for factory production according to an eleventh aspect of the present invention includes the steps of: a first step in which, for first raw industrial data related to plant production, a graph database including a plurality of event data sets, one of which is a node in the graph database including object information, event information, time information, link information, is constructed by graph database software, one of which shows the following relationship: an object indicated by the object information, in which an event indicated by the event information is implemented within a period indicated by the time information, the plurality of event data groups being linked to each other by a link key of the graph database, the information indicated by the link key including a front-to-back order among a plurality of events for the certain link information; a second step in which, for second raw industrial data related to factory production, a time-series database including a plurality of time-series data sets including object information, element information, time information is constructed by time-series database software, the time-series data sets showing the following relationship: an object shown by the object information has a certain element value shown by the element information in a certain time period shown by the time information; and a third step of linking the event data group and the time-series data group by a processing function, the parameters of the processing function including the object information and the time information.
Preferably, in the method for constructing a graph database for factory production according to the eleventh aspect of the present invention, the link information includes at least one of order information and product information.
Preferably, in the thirteenth aspect of the present invention, in the construction of a database for factory production according to the twelfth aspect of the present invention, the information shown by the link key further includes a front-to-back order between a plurality of orders for a certain piece of the object information.
Preferably, in a fourteenth aspect of the present invention, in the construction method of a database for factory production according to the eleventh to thirteenth aspects of the present invention, a newly constructed event data group and an existing event data group may be linked by the link key.
Preferably, in a fifteenth aspect of the present invention, in the method for constructing a database for factory production according to the eleventh to thirteenth aspects of the present invention, the object information includes at least one of information of a factory, information of a production line in the factory, and information of equipment on the production line.
Preferably, in a sixteenth aspect of the present invention, in the method for constructing a database for factory production according to the eleventh to thirteenth aspects of the present invention, the event information includes production process information and production volume information.
Preferably, in a seventeenth aspect of the present invention, in the method for constructing a database for factory production according to the eleventh to thirteenth aspects of the present invention, the unit of time information includes at least one of month, week, day, hour, minute, and second.
Preferably, in the eighteenth aspect of the present invention, in the method for constructing a database for factory production according to the eleventh to thirteenth aspects of the present invention, the element information includes at least one of electric power, gas, water, unit productivity cost, unit electric power cost, and fee.
In order to solve the above-described problems, a nineteenth aspect of the present invention relates to a computer apparatus comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method of constructing a graph database for factory production according to the eleventh to eighteenth aspects of the present invention when the computer program is executed.
In order to solve the above-described problems, a computer-readable medium according to a twentieth aspect of the present invention has stored thereon a computer program which, when executed by a processor, implements the construction method of a graph database for factory production according to the eleventh to eighteenth aspects of the present invention.
To solve the above-described problems, a twenty-first aspect of the present invention relates to a database construction system for factory production, comprising: a graph database construction unit that constructs, by graph database software, a graph database including a plurality of event data sets, one of which is a node in the graph database including object information, event information, time information, link information, one of which shows the following relationship, with respect to first raw industrial data related to plant production: an object indicated by the object information, in which an event indicated by the event information is implemented within a period indicated by the time information, the plurality of event data groups being linked to each other by a link key of the graph database, the information indicated by the link key including a front-to-back order among a plurality of events for the certain link information; a time-series database construction unit that constructs, by time-series database software, a time-series database including a plurality of time-series data groups including object information, element information, and time information, the time-series data groups showing the following relationships, with respect to second raw industrial data related to factory production: an object shown by the object information has a certain element value shown by the element information in a certain time period shown by the time information; and a linking unit that links the event data group and the time-series data group by a processing function, parameters of which include the object information and the time information.
In order to solve the above problems, a bottleneck analysis method for factory production according to a twenty-second aspect of the present invention comprises the steps of: a first step in which, for first raw industrial data related to plant production, a graph database including a plurality of event data sets, one of which is a node in the graph database including object information, event information, time information, link information, is constructed by graph database software, one of which shows the following relationship: an object indicated by the object information, in which an event indicated by the event information is implemented within a period indicated by the time information, the plurality of event data groups being linked to each other by a link key of the graph database, the information indicated by the link key including a front-to-back order among a plurality of events for the certain link information; a second step in which, for second raw industrial data related to factory production, a time-series database including a plurality of time-series data sets including object information, element information, time information is constructed by time-series database software, the time-series data sets showing the following relationship: an object shown by the object information has a certain element value shown by the element information in a certain time period shown by the time information; a third step of linking the event data group and the time-series data group by a processing function, the parameters of the processing function including the object information and the time information; a fourth step of calculating a plurality of pieces of representative information for a preselected object for a preset time period based on the object information, the event information, the time information, and the element information obtained by linking the event data group to the time-series data group by the processing function, the parameters of the processing function including the object information and the time information, in the event data group; a fifth step of estimating a plurality of bottleneck candidates for the preselected object based on the result obtained in the fourth step; and a sixth step of determining a bottleneck for the pre-selected object from the plurality of bottleneck candidates by a manual determination method or an AI automatic determination method.
Preferably, in a twenty-third aspect of the present invention, in the bottleneck analysis method for factory production according to the twenty-second aspect of the present invention, the link information includes at least one of order information and product information.
Preferably, in the bottleneck analysis method for factory production according to the twenty-fourth aspect of the present invention, in the case where the link information is the order information, the information indicated by the link key further includes a front-back order among a plurality of orders for a certain piece of the object information.
Preferably, in a twenty-fifth aspect of the present invention, in the bottleneck analysis method for plant production according to the twenty-second to twenty-fourth aspects of the present invention, the newly constructed event data set and the existing event data set may be linked by the link key.
Preferably, in a twenty-sixth aspect of the present invention, in the bottleneck analysis method for plant production according to the twenty-second to twenty-fourth aspects of the present invention, the object information includes at least one of information of a plant, information of a production line in the plant, and information of equipment on the production line.
Preferably, in a twenty-seventh aspect of the present invention, in the bottleneck analysis method for factory production according to the twenty-second to twenty-fourth aspects of the present invention, the event information includes production process information and throughput information.
Preferably, in a twenty-eighth aspect of the present invention, in the bottleneck analysis method for plant production according to the twenty-second to twenty-fourth aspects of the present invention, the unit of time information includes at least one of month, week, day, hour, minute, and second.
Preferably, in the bottleneck analysis method for plant production according to the twenty-second to twenty-fourth aspects of the present invention, the element information includes at least one of production time, electric power, gas, water, cost per unit productivity, cost per unit electric power, and fee.
Preferably, in the bottleneck analysis method for plant production according to the twenty-second to twenty-fourth aspects of the present invention, the representative information is wasteful information including at least one of human wasteful information, equipment wasteful information, and unit cost wasteful information.
Preferably, in the bottleneck analysis method for factory production according to the thirty-first aspect of the present invention, the manual waste information includes at least one of management waste, action waste, grouping waste, automatic replacement waste, and measurement adjustment waste.
Preferably, in the bottleneck analysis method for plant production according to the thirty-second aspect of the present invention, the equipment waste information includes at least one of shutdown waste, failure waste, change-over adjustment waste, tool replacement waste, start-up waste, pause waste, speed drop waste, bad correction waste, and other stop waste.
Preferably, in the bottleneck analysis method for factory production according to the thirty-third aspect of the present invention, the unit cost waste information includes at least one of energy waste, mold jig waste, and yield waste.
Preferably, in a thirty-fourth aspect of the present invention, in the bottleneck analysis method for plant production according to the twenty-second aspect of the present invention, the manual judgment method performs logical judgment by a tree condition filtering structure.
Preferably, in a thirty-fifth aspect of the present invention, in the bottleneck analysis method for plant production according to the twenty-second aspect of the present invention, the AI automatic decision method establishes an AI model using a decision tree algorithm by machine learning a tree-like condition filtering structure, and makes an automatic decision using the AI model.
In order to solve the above-described problems, a computer device according to a thirty-sixth aspect of the present invention includes: a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the bottleneck analysis method for factory production according to the twenty-second to thirty-fifth aspects when executing the computer program.
In order to solve the above-described problems, a computer-readable medium according to a thirty-seventh aspect of the present invention has stored thereon a computer program which, when executed by a processor, implements the bottleneck analysis method for factory production according to the twenty-second to thirty-fifth aspects.
In order to solve the above-mentioned problems, a bottleneck analysis system for factory production according to a thirty-eighth aspect of the present invention comprises: a graph database construction unit that constructs, by graph database software, a graph database including a plurality of event data sets, one of which is a node in the graph database including object information, event information, time information, link information, one of which shows the following relationship, with respect to first raw industrial data related to plant production: an object indicated by the object information, in which an event indicated by the event information is implemented within a period indicated by the time information, the plurality of event data groups being linked to each other by a link key of the graph database, the information indicated by the link key including a front-to-back order among a plurality of events for the certain link information; a time-series database construction unit that constructs, by time-series database software, a time-series database including a plurality of time-series data groups including object information, element information, and time information, the time-series data groups showing the following relationships, with respect to second raw industrial data related to factory production: an object shown by the object information has a certain element value shown by the element information in a certain time period shown by the time information; a linking unit that links the event data group and the time series data group through a processing function, parameters of the processing function including the object information and the time information; a calculation unit that calculates a plurality of pieces of representative information for a preselected object for a preset time period based on the object information, the event information, the time information, and the element information obtained by linking the event data group to the time-series data group by the processing function, the parameters of the processing function including the object information and the time information, in the event data group; an estimating unit that estimates a plurality of bottleneck candidates for the preselected object based on a result obtained by the calculating unit; and a determination unit that determines a bottleneck for the pre-selected object from the plurality of bottleneck candidates by a manual determination method or an AI automatic determination method.
Effects of the invention
According to the construction method and the construction system for the graph database for factory production, the graph database comprising a plurality of event data sets is constructed by utilizing graph database software, the event data sets are mutually linked through the link key of the graph database, and the information shown by the link key comprises the front-back sequence among a plurality of events aiming at certain link information, so that when a series of production processes related to a plurality of production devices are analyzed, the event data sets corresponding to the series of production processes can be quickly searched and called, the operation amount is obviously reduced, the jam and breakdown of a production management system can be reduced, the stability of the production management system is greatly improved, and the working efficiency of the system is improved.
In addition, according to the construction method and the construction system of the graph database for factory production, the plurality of event data sets are linked through the link key of the graph database, the information shown by the link key further comprises the front-back sequence among the plurality of orders aiming at certain object information, so that when a series of orders about certain production equipment are analyzed, the plurality of event data sets corresponding to the series of orders can be quickly inquired and called, the operation amount is obviously reduced, the jam and breakdown of a production management system can be reduced, the stability of the production management system is greatly improved, and the working efficiency of the system is improved.
In addition, according to the construction method and construction system of the graph database for factory production of the present invention, since the newly constructed event data set and the existing event data set can be linked by the link key, the updating of the graph database becomes more flexible, for example, when the production process changes in the factory production process, such as increase or decrease, etc., the change in the factory production process can be better adapted.
In addition, according to the construction method and construction system of the database for factory production of the present invention, the event data set including the event information of the production process is stored by using the graph database, the time series data set including the element information related to the consumed power, gas or water resource information is stored by using the time series database, and the event data set and the time series data set are linked by the processing function, so that when the element information in a series of production processes related to a plurality of production devices is analyzed, a plurality of event data sets corresponding to the series of production processes and the time series data set linked by the processing function can be quickly searched and fetched, the operation amount is obviously reduced, the jam and breakdown of the production management system can be reduced, the stability of the production management system is greatly improved, and the working efficiency of the system is improved. In addition, the time series database has the advantages of high compression rate and high read-write speed, so the time series database has great advantages for storing the element information with huge data volume related to the consumed power, gas or water and other resource information and with short acquisition time interval.
Further, according to the bottleneck analysis method and bottleneck analysis system for factory production of the present invention, by storing event data sets including event information of production processes and the like by using a graph database, storing time-series data sets including element information related to resource information of consumed power, gas, water or the like by using a time-series database, and linking the event data sets with the time-series data sets by a processing function, it is possible to quickly search and retrieve a plurality of event data sets corresponding to a series of production processes and a time-series data set linked by the processing function when element information in the series of production processes related to a plurality of production facilities is analyzed to determine a bottleneck point, and it is possible to efficiently and quickly determine a bottleneck point.
Drawings
Fig. 1 is a schematic diagram showing a construction method of a graph database for factory production according to an embodiment of the present invention.
FIG. 2A is a table showing one example of a plurality of event data sets constructed using the construction method of the graph database for factory production according to the embodiment of the present invention; fig. 2B is a diagram showing another example of a plurality of event data sets constructed using the construction method of the map database for factory production according to the embodiment of the present invention in a tabular form.
Fig. 3 is a schematic view showing that a plurality of nodes as a plurality of event data groups constructed using the construction method of a graph database for factory production according to the embodiment of the present invention corresponding to fig. 2A are linked to each other by a link key representing the former and latter processes.
Fig. 4 is a diagram showing constituent information of an event data group as the node N204 in the diagram of fig. 3.
Fig. 5 is a diagram showing constituent information of an event data group as the node N203 in the diagram of fig. 3.
Fig. 6 is a diagram showing the configuration information of the event data group as the node N205 in the diagram of fig. 3.
Fig. 7 is a schematic diagram showing that a plurality of nodes corresponding to fig. 2A and 2B as a plurality of event data groups on the basis of the schematic diagram of fig. 3 are further linked to each other by a link key representing front and rear orders.
Fig. 8 is a diagram showing constituent information of an event data group as the node N204 in the diagram of fig. 7.
Fig. 9 is a diagram showing the configuration information of the event data group as the node N104 in the diagram of fig. 7.
Fig. 10 is a diagram showing constituent information of an event data group as the node N304 in the diagram of fig. 7.
FIG. 11 is a block diagram illustrating a system for building a graph database for factory production in accordance with an embodiment of the present invention.
Fig. 12A is a flowchart showing a method of constructing a database including a graph database and a time series database for factory production according to an embodiment of the present invention.
Fig. 12B is a more detailed flowchart for step ST301 in the construction method of the database including the graph database and the time series database for factory production according to the embodiment of the present invention.
Fig. 13 is a table format showing one example of a plurality of time-series data sets constructed using the construction method of the database for factory production according to the embodiment of the present invention.
Fig. 14A is a block diagram illustrating a database construction system including a graph database and a time series database for factory production according to an embodiment of the present invention.
Fig. 14B is a more detailed block diagram for the link unit 3001 in the construction system of databases including the graph database and the time series database for factory production, which shows an embodiment of the present invention.
Fig. 15 is a flowchart showing a bottleneck analysis method of bottleneck analysis of a database including a graph database and a time series database constructed using the construction method of a database for factory production according to the embodiment of the present invention.
Fig. 16A and 16B are partial examples showing, in tabular form, a plurality of data groups for deriving representative information (wasteful information) obtained from a database including a graph database and a time series database constructed by the construction method of a database for factory production according to the embodiment of the present invention.
FIG. 17 is a graph showing the resulting product yield and N based on a plurality of data sets for deriving representative information (waste information) such as those shown in FIGS. 16A and 16B 2 Graph of consumption.
FIG. 18 is a graph showing the product yield and N from FIG. 17 2 The graph of consumption yields a schematic diagram of waste 1 to waste 4 as bottleneck candidates.
FIG. 19 is a graph showing N obtained for waste 1 through waste 4 as bottleneck candidates in FIG. 18 2 Consumption amount table.
Fig. 20 is a block diagram illustrating a bottleneck analysis system for factory production using an embodiment of the present invention.
Detailed Description
Hereinafter, preferred embodiments of a map database construction method and system for factory production, a bottleneck analysis method for factory production, and a bottleneck analysis system according to the present invention will be described with reference to the drawings. In the drawings, the same or corresponding portions are denoted by the same reference numerals.
< construction method of map database for factory production and embodiment of construction System >
Fig. 1 is a schematic diagram showing a construction method of a graph database for factory production according to an embodiment of the present invention.
As a graph database construction method of the present invention, in step ST101 of fig. 1, a graph database including a plurality of event data groups is constructed by graph database software for first raw industrial data related to factory production.
Wherein the first raw industrial data is obtained, for example, from a production management system of the plant. For example, various sensors are provided on each production facility of the plant line, and the sensors can collect various data generated in the production process performed by each production facility. Here, as one example, the first raw industrial data needs to include at least the following information: the number of the production equipment, the name of the production process executed by the production equipment, the start time and the end time of the production process, the order number of the products produced by the production process, and the number of the products produced between the start time and the end time of the production process. Of course, the first raw industrial data may also include other information.
Fig. 2A is a diagram showing in tabular form one example of a plurality of event data sets constructed using the construction method of the map database for factory production according to the embodiment of the present invention. As shown in fig. 2A, one event data set corresponds to one node, and for example, the event data set as the node N201 includes object information of the production equipment number "PM0010", event information of the production number "1" and the production process name "order number collation (s-face)", time information of the process start time "2021-06-04 16:01:10" and the process end time "2021-06-04 16:10:10", and link information of the order number "002". Further, the event data group as the node N201 shows the following relationship: production equipment denoted by production equipment number "PM0010" performs a production process in which the number of production steps is "1" and the production step name is "order number check (s-side)" between the process start time "2021-06-04 16:01:10" and the process end time "2021-06-04 16:10:10". The nodes N201 to N210 will be described in detail below.
Further, fig. 2B is another example showing in tabular form a plurality of event data sets constructed using the construction method of the map database for factory production of the present invention. As shown in fig. 2B, one event data set also corresponds to one node, and for example, the event data set as the node N104 includes object information, event information, time information, and link information, wherein the object information is the production equipment number "PM0040", the event information is the production number "2" and the production process name "reflow (s-plane)", the time information is the process start time "2021-06-04 14:11:10" and the process end time "2021-06-04 14:30:10", and the link information is the order number "001". Further, as the event data group of the node N104, the following relationship is shown: production equipment denoted by production equipment number "PM0040" performs a production process having a production number of "2" and a production process name of "reflow (s-side)" between a process start time "2021-06-04 14:11:10" and a process end time "2021-06-04 14:30:10".
In fig. 2A and 2B, the event data set of the node N204 is identical in structure, and this will be described in detail below.
Note that the event data set shown in fig. 2A and 2B is only one example, and the kinds of object information, event information, time information, and link information are not particularly limited. For example, the object information may be, in addition to the production equipment number, information of a factory, information of a production line in the factory, other information of equipment on the production line, and the like. The event information may be other information about the production amount, other information about the production process, or the like, in addition to the production number and the production process name. The link information may be product information, other information about orders, and the like, in addition to order numbers. The unit of time information may be at least one of month, week, day, hour, minute, and second.
Fig. 3 is a schematic view showing that a plurality of nodes as a plurality of event data groups constructed using the construction method of a graph database for factory production according to the embodiment of the present invention corresponding to fig. 2A are linked to each other by a link key representing the former and latter processes.
As shown in fig. 3, the event data sets of the nodes N201 to N210 are linked to each other by a link key indicating the preceding and following steps, and as an example, information indicated by the link key (nextproc) is the preceding and following sequence among a plurality of production steps (that is, order number check (s-face), laser printing (s-face), preliminary product check (s-face), reflow soldering (s-face), order number check (c-face), laser printing (c-face), printing check (c-face), preliminary product check (c-face), reflow soldering (c-face), and visual check) for a certain order number (002).
That is, for a certain order number 002, a production process of order number checking (s-face) is performed at node N201, a production process of laser printing (s-face) is performed at node N202, a production process of preliminary product checking (s-face) is performed at node N203, a production process of reflow (s-face) is performed at node N204, a production process of order number checking (c-face) is performed at node N205, a production process of laser printing (c-face) is performed at node N206, a production process of printing checking (c-face) is performed at node N207, a production process of preliminary product checking (c-face) is performed at node N208, a production process of reflow (c-face) is performed at node N209, and a production process of visual checking is performed at node N210.
Fig. 4 is a diagram showing constituent information of an event data group as the node N204 in the diagram of fig. 3.
The event data set as the node N204 includes object information, event information, time information, and link information, wherein the object information is the production equipment number "PM0040", the event information is the production number "1" and the production process name "reflow (s-plane)", the time information is the process start time "2021-06-04 16:41:10" and the process end time "2021-06-04 17:00:10", and the link information is the order number "002". Further, the event data group as the node N204 shows the following relationship: production equipment denoted by production equipment number "PM0040" performs a production process having a production number of "1" and a production process name of "reflow (s-plane)" between a process start time "2021-06-04 16:41:10" and a process end time "2021-06-04 17:00:10".
Fig. 5 is a diagram showing constituent information of an event data group as the node N203 in the diagram of fig. 3.
The node N203 is a node immediately before the node 204, and the event data set as the node N203 includes object information, event information, time information, and link information, wherein the object information is the production equipment number "PM0030", the event information is the production number "1" and the production process name "preliminary product inspection (s-face)", the time information is the process start time "2021-06-04 16:31:10" and the process end time "2021-06-04 16:40:10", and the link information is the order number "002". Further, the event data group as the node N203 shows the following relationship: production facilities denoted by production facility number "PM0030" perform a production process having a production number of "1" and a production process name of "Primary product inspection (s-side)" between a process start time "2021-06-04 16:31:10" and a process end time "2021-06-04 16:40:10".
Fig. 6 is a diagram showing the configuration information of the event data group as the node N205 in the diagram of fig. 3.
The node N205 is a node subsequent to the node 204, and the event data set as the node N205 includes object information, event information, time information, and link information, wherein the object information is the production equipment number "PM0010", the event information is the production number "1" and the production process name "order number check (c-plane)", the time information is the process start time "2021-06-04 17:01:10" and the process end time "2021-06-04 17:10:10", and the link information is the order number "002". Further, the event data group as the node N205 shows the following relationship: production equipment denoted by production equipment number "PM0010" performs a production process in which the number of production steps is "1" and the production step name is "order number check (c-plane)" between the process start time "2021-06-04 17:01:10" and the process end time "2021-06-04 17:10:10".
Thus, by linking the plurality of nodes (N201 to N210) to each other by using a link key (nextproc) indicating the preceding and following steps, when analyzing a series of production steps related to a plurality of production facilities, it is possible to quickly search and retrieve a plurality of event data sets corresponding to the series of production steps; for example, all production processes (N204, N209) executed by a certain production device (PM 0040) for a certain order can be quickly queried and called, so that the operation amount can be obviously reduced, the jam and breakdown of the production management system can be reduced, the stability of the production management system can be greatly improved, and the working efficiency of the system can be improved.
Fig. 7 is a schematic diagram showing that a plurality of nodes corresponding to fig. 2A and 2B as a plurality of event data groups on the basis of the schematic diagram of fig. 3 are further linked to each other by a link key representing front and rear orders.
As shown in fig. 7, the node N104 and the node N304 are further added on the basis of fig. 3, and the respective event data sets as the respective nodes N104, N204, N304 are linked to each other by the link key of the graph database, and as an example, the information shown by the link key (nextpo) is the order between the plurality of orders (001, 002, 003) for a certain production facility number PM 0040.
That is, for a certain production facility number PM0040, the production process of reflow (s-plane) in the order of order number 001 is performed at node N104, then the production process of reflow (s-plane) in the order of order number 002 is performed at node N204, and then the production process of reflow (s-plane) in the order of order number 003 is performed at node N304.
Fig. 8 is a diagram showing constituent information of an event data group as the node N204 in the diagram of fig. 7.
The event data set as the node N204 is the same as the node N204 in fig. 4, and includes object information, event information, time information, and link information, wherein the object information is the production equipment number "PM0040", the event information is the production number "1" and the production process name "reflow (s-plane)", the time information is the process start time "2021-06-04 16:41:10" and the process end time "2021-06-04 17:00:10", and the link information is the order number "002". Further, the event data group as the node N204 shows the following relationship: production equipment denoted by production equipment number "PM0040" performs a production process having a production number of "1" and a production process name of "reflow (s-plane)" between a process start time "2021-06-04 16:41:10" and a process end time "2021-06-04 17:00:10".
Fig. 9 is a diagram showing the configuration information of the event data group as the node N104 in the diagram of fig. 7.
The event data set as the node N104 includes object information, event information, time information, and link information, wherein the object information is the production equipment number "PM0040", the event information is the production number "2" and the production process name "reflow (s-plane)", the time information is the process start time "2021-06-04 14:11:10" and the process end time "2021-06-04 14:30:10", and the link information is the order number "001". Further, as the event data group of the node N104, the following relationship is shown: production equipment denoted by production equipment number "PM0040" performs a production process having a production number of "2" and a production process name of "reflow (s-side)" between a process start time "2021-06-04 14:11:10" and a process end time "2021-06-04 14:30:10".
Fig. 10 is a diagram showing constituent information of an event data group as the node N304 in the diagram of fig. 7.
The event data set as the node N304 includes object information, event information, time information, and link information, wherein the object information is the production equipment number "PM0040", the event information is the production number "3" and the production process name "reflow (s-plane)", the time information is the process start time "2021-06-04 19:11:10" and the process end time "2021-06-04 19:30:10", and the link information is the order number "003". Further, the event data group as the node N304 shows the following relationship: production equipment denoted by production equipment number "PM0040" performs a production process having a production number of "3" and a production process name of "reflow (s-side)" between process start time "2021-06-04 19:11:10" and process end time "2021-06-04 19:30:10".
Thus, by linking a plurality of nodes (N104-N304) to each other by using a link key (next po) indicating the preceding and following orders, when a series of production processes related to the plurality of orders are analyzed, a plurality of event data sets corresponding to the series of production processes can be quickly searched and retrieved; for example, the production processes (N104-N304) of all orders executed by a certain production device (PM 0040) can be quickly inquired and called, so that the operation amount can be obviously reduced, the clamping and breakdown of the production management system can be reduced, the stability of the production management system can be greatly improved, and the working efficiency of the system can be improved.
In addition, when a production process is added in a factory production process, a new node corresponding to the production process can be easily added to the graph databases shown in fig. 3 and 7, and the new node is linked with the existing node by a link key.
Thus, the newly constructed event data set and the existing event data set can be linked through the link key, so that the updating of the graph database can be more flexible, and the change in the factory production process can be better adapted when the production process changes, such as increase or decrease in the factory production process.
The Graph Database software (Graph Database) used in the method for constructing a Graph Database for factory production according to the embodiment of the present invention may be, for example, a FlockDB or Neo4j, allegroGrap, and is not particularly limited.
Further, fig. 11 is a block diagram showing a construction system of a graph database for factory production according to an embodiment of the present invention.
The construction method of the graph database for factory production according to the embodiment of the present invention may be further configured as a modular construction system of the graph database for factory production, the construction system including: a graph database construction unit 1001 that constructs, by graph database software, a graph database including a plurality of event data sets, one event data set being one node in the graph database including object information, event information, time information, link information, for first raw industrial data related to plant production, the one event data set showing the following relationship: and an object shown by the object information, wherein in a certain time period shown by the time information, a certain event shown by the event information is implemented, the plurality of event data sets are mutually linked through a link key of the graph database, and the information shown by the link key comprises a front-back sequence among a plurality of events aiming at certain link information.
< method for constructing database including graph database and time-series database for factory production and embodiment of construction System)
Fig. 12A is a flowchart showing a method of constructing a database including a graph database and a time series database for factory production according to an embodiment of the present invention.
First, similarly to < embodiment of the construction method and construction system of the graph database for factory production >, in step ST101, the graph database including a plurality of event data groups is constructed by the graph database software for the first raw industrial data related to factory production as described above.
Up to this point, since the construction method of the map database and the embodiment of the construction system for factory production are similar to those described above, detailed description thereof will be omitted.
On the other hand, in step ST201, for the second raw industrial data related to the factory production, a time-series database including a plurality of time-series data groups is constructed by time-series database software.
Here, as one example, the second raw industrial data needs to include at least the following information: production facility number, element information (e.g., N 2 Consumption). Of course, the second raw industrial data may also include other information.
Step ST201 and step ST101 may be performed simultaneously, may be performed sequentially, and may be performed sequentially in any order.
Fig. 13 is a table format showing one example of a plurality of time-series data sets constructed using the construction method of the database for factory production according to the embodiment of the present invention.
For example, one time-series data set of the first line includes object information, time information, and element information, wherein the object information is the production equipment number "PM0040", the time information is the measurement start time "2021-06-04:07:40:00" and the measurement end time "2021-06-04:07:45:00", and the element information is the average N per minute measured between the above-mentioned measurement start time and measurement end time 2 Consumption amount. Furthermore, one time-series data set of the first row shows the following relationship: production equipment represented by production equipment number "PM0040", an average N per minute between measurement start time "2021-06-04 07:40:00" and measurement end time "2021-06-04 07:45:00" 2 The consumption was 2.5 liters.
Further, for example, one time-series data set of the second line includes object information, time information, and element information, wherein the object information is the production equipment number "PM0040", the time information is the measurement start time "2021-06-04 07:45:00" and the measurement end time "2021-06-04 07:50:00", and the element information is the average N per minute measured between the above-mentioned measurement start time and measurement end time 2 Consumption amount. Furthermore, one time-series data set of the second row shows the following relationship: production plant denoted by production plant number "PM0040" at measurement start time "2021-06-04 07:45:between 00 "and the end time of measurement" 2021-06-04 07:50:00", average N per minute 2 The consumption was 9.5 liters.
The time series data sets of the other rows are similar to the time series data sets of the first row and the second row, and are not described herein again, and refer to fig. 13 specifically.
In the present embodiment, the element information is N 2 Besides the consumption amount, the element information may be production time, electric power, gas, water, unit productivity cost, unit electric power cost, fee, and the like.
In addition, in the present embodiment, N of the time-series data group 2 The consumption measurement interval is 5 minutes, but is not limited thereto, and for example, the measurement interval may be 1 minute or other time intervals.
After the completion of the construction of the graph database composed of event data sets and the time-series database composed of time-series data sets, the event data sets are linked with the time-series data sets by the processing function whose parameters include the object information (e.g., production equipment number) and the time information in step ST 301.
Fig. 12B is a more detailed flowchart for step ST301 in the construction method of the database including the graph database and the time series database for factory production according to the embodiment of the present invention.
As shown in fig. 12B, step ST301 includes step ST301A and step ST301B. In step ST301A, a certain event data group may be queried from the graph database constructed in step ST101 using the object information and the time information as parameters, and related information (object information, time information, event information, link information) of the queried event data group may be retrieved from the graph database; in step ST301B, a certain time-series data set may be queried from the time-series database constructed in step ST201 using the object information and the time information as parameters, and the queried related information (object information, time information, element information) of the time-series data set may be retrieved from the time-series database. Further, step ST301A of acquiring an event data group and step ST301B of acquiring a time-series data group are linked to each other by parameters (object information and time information), and the related information of the event data group acquired in step ST301A and the related information of the time-series data group acquired in step ST301B may be used in combination. The above-described process may be described as "linking the event data group with the time-series data group by the processing function whose parameters are the object information and the time information" (i.e., step ST 301).
Therefore, when the event data group including event information such as production processes is stored by using the graph database, the time sequence data group including element information related to consumed power, gas or water is stored by using the time sequence database, and the event data group and the time sequence data group are linked through the processing function, a plurality of event data groups corresponding to a series of production processes related to a plurality of production devices and the time sequence data group linked through the processing function can be quickly inquired and fetched when the element information in the series of production processes related to the plurality of production devices is analyzed, the operation amount is obviously reduced, the clamping and breakdown of a production management system can be reduced, the stability of the production management system is greatly improved, and the working efficiency of the system is improved. In addition, the time series database has the advantages of high compression rate and high read-write speed, so the time series database has great advantages for storing the element information with huge data volume related to the consumed power, gas or water and other resource information and with short acquisition time interval.
The time-series database software (Time Series Database) used in the method for constructing a database for factory production according to the embodiment of the present invention may be OpenTSDB, druid, influxDB, berringei, or the like, and is not particularly limited. The processing functions used in the method for constructing a database for factory production according to the embodiment of the present invention may be implemented by software such as Java or Python, and are not particularly limited.
Further, fig. 14A is a block diagram showing a construction system of a database including a graph database and a time series database for factory production according to an embodiment of the present invention.
The construction method of the database for factory production according to the embodiment of the present invention may also be constructed as a modular construction system of the database for factory production, the construction system including: a graph database construction unit 1001 that constructs, by graph database software, a graph database including a plurality of event data sets, one event data set being one node in the graph database including object information, event information, time information, link information, for first raw industrial data related to plant production, the one event data set showing the following relationship: an object indicated by the object information, in which an event indicated by the event information is implemented within a period indicated by the time information, the plurality of event data groups being linked to each other by a link key of the graph database, the information indicated by the link key including a front-to-back order among a plurality of events for the certain link information; a time-series database construction unit 2001 which constructs, for the second raw industrial data related to the plant production, a time-series database including a plurality of time-series data groups including object information, element information, time information by time-series database software, the time-series data groups showing the following relationship: an object shown by the object information has a certain element value shown by the element information in a certain time period shown by the time information; and a linking unit 3001 that links the event data group and the time series data group by a processing function whose parameters include the object information and the time information.
Fig. 14B is a more detailed block diagram for the link unit 3001 in the construction system of databases including the graph database and the time series database for factory production, which shows an embodiment of the present invention.
As shown in fig. 14B, the link unit 3001 includes a graph database link 3001A and a time-series database link 3001B. The graph database linking unit 3001A may query a graph database constructed by the graph database construction unit 1001 for a certain event data group using the object information and the time information as parameters, and retrieve related information (object information, time information, event information, link information) of the queried event data group from the graph database; the time-series database linking unit 3001B may query a time-series database constructed by the time-series database construction unit 2001 for a certain time-series data group using the object information and the time information as parameters, and retrieve the related information (object information, time information, element information) of the queried time-series data group from the time-series database. Further, the graph database link part 3001A that acquires the event data group and the time series database link part 3001B that acquires the time series data group are linked to each other by parameters (object information and time information), and the related information of the event data group acquired by the graph database link part 3001A and the related information of the time series data group acquired by the time series database link part 3001B may be used in combination. The above-described process may be described as "linking the event data group with the time-series data group by a processing function whose parameters are object information and time information" (i.e., the linking unit 3001).
< example of bottleneck analysis method for factory production >
Fig. 15 is a flowchart showing a bottleneck analysis method of bottleneck analysis of a database including a graph database and a time series database constructed using the construction method of a database for factory production according to the embodiment of the present invention.
First, similarly to < embodiment of the construction method and construction system of the graph database including the graph database and the time series database for the factory production >, in step ST101, the graph database including the plurality of event data groups is constructed by the graph database software for the first raw industrial data related to the factory production. On the other hand, in step ST201, for the second raw industrial data related to the factory production, a time-series database including a plurality of time-series data groups is constructed by time-series database software. After the completion of the construction of the graph database composed of event data sets and the time-series database composed of time-series data sets, the event data sets are linked with the time-series data sets by the processing function whose parameters include the object information (e.g., production equipment number) and the time information in step ST 301. Here, a more detailed description about step ST301 may refer to the above-described related description about fig. 12B.
Step ST201 and step ST101 may be performed simultaneously, may be performed sequentially, and may be performed sequentially in any order.
Up to this point, since the construction method and the construction system of the database including the graph database and the time series database are similar to those described above for the factory production, detailed description thereof will be omitted.
Then, in step ST401, a plurality of pieces of representative information for a preselected object are calculated for a preset period of time based on the object information, the event information, the time information, and the element information obtained by linking the event data group to the time-series data group by the processing function, the parameters of the processing function including the object information and the time information. Then, in step ST501, a plurality of bottleneck candidates for the preselected object are estimated based on the result obtained in step ST 401. Next, step ST401 and step ST501 will be specifically described.
Fig. 16A and 16B are partial examples showing, in tabular form, a plurality of data groups for deriving representative information (wasteful information) obtained from a database including a graph database and a time series database constructed by the construction method of a database for factory production according to the embodiment of the present invention. FIG. 17 is a graph showing the resulting product yield and N based on a plurality of data sets for deriving representative information (waste information) such as those shown in FIGS. 16A and 16B 2 Graph of consumption.
Specifically, suppose that a reflow soldering apparatus with production facility number PM0040 needs to be analyzed 24 "a.m. from 6:00 a.m. to 24" a.m. on day 4 of 2021: 00 Nitrogen (N) consumed during operation 2 ) Is a waste bottleneck of (a).
Conventionally, reflow soldering equipment with equipment number PM0040 is produced at regular intervalsThe interval (e.g., 1 second) records all data detected by the plurality of sensors as a table, since 24 pm from 6:00 am: 00 for a total of 18 hours, there will be 18 x 3600=64800 tables, then all 64800 tables are connected (join) using conventional SQL database software, from which the required information about product yield and N is obtained 2 Consumption data and find that the reflow soldering apparatus is not producing a product (i.e., product yield is 0) but is still consuming N 2 The time period is listed as a waste candidate.
However, the above conventional practice has the following problems: because of the huge data volume of the connected (join) tables (64800 tables in the above example) using conventional SQL database software, the computer system may get stuck and crashed, which seriously affects the working efficiency.
In contrast, in the bottleneck analysis method for factory production of the present invention, the event data set including event information such as production process and throughput is stored by using a map database having a feature of being able to be quickly searched and retrieved; a time-series database with high compression rate and fast read-write speed is utilized to store a time-series data group comprising element information related to consumed power, gas or water and other resource information; and linking the event data set with the time series data set by a processing function whose parameters are object information and time information.
Thus, it is possible to link the graph database based on the object information (production equipment number) and the time information, and to quickly query and call event information (throughput) and corresponding time information of all production processes performed by a specific object (production equipment) through the link key of the graph database. For example, taking fig. 7 of the present invention as an example, the time information (process start time and process end time) and event information (production number) of a plurality of production processes N204, N209 in the same order number 002 and a plurality of production processes N104, N304 in different order numbers 001, 003 for the production equipment number PM0040 can be quickly searched and called by the link key.
And, can be based on the object information (production equipment number) andlinking time information to time series database, querying and calling N consumed when specific object (production equipment) executes production procedure 2 Consumption (element information) and corresponding time information. For example, taking fig. 13 of the present invention as an example, 24 PM from 6:00 am on day 4 of month 4 of 2021 for production facility number PM0040 can be queried and invoked: all measurement intervals (time information) of 00 and corresponding average N 2 Consumption (element information).
By the above function, the data set table shown in FIG. 16A and FIG. 16B can be obtained in which the average product yield is mainly obtained by linking the graph database, and the average N 2 The consumption is mainly obtained by linking a time series database. In addition, in the tables shown in fig. 16A and 16B, although only 07 is shown: 40: 00-10: 00:00, but in practice 2021, 6, 4, from 6:00 am to 24: data for all time periods of 00. Due to the excessive data amount, only 07 is shown in the tables of fig. 16A and 16B for simplicity of explanation: 40: 00-10: 00: 00.
2021, 6, 4 days, from 6:00 am to 24 "evening", according to the tables shown in fig. 16A and 16B: 00, can obtain the product yield and N shown in FIG. 17 2 Graph of consumption.
Further, from the product yield and N shown in FIG. 17 2 The graph of consumption can be further obtained as a bottleneck candidate diagram of waste 1 to waste 4 shown in FIG. 18 and N obtained for waste 1 to waste 4 shown in FIG. 19 2 Consumption amount table.
In fig. 18, in the period 07:40:00 to 09:00:00, the production yield of the production apparatus producing apparatus No. PM0040 is 0, but N 2 The consumption is 775, and therefore this time period 07:40:00 to 09:00:00 is set to waste 1. In the period 17:10:00 to 17:40:00, the production yield of the production apparatus producing apparatus No. PM0040 is 0, but N 2 The consumption is 275, so this time period 17:10:00 to 17:40:00 is set to waste 2. The production yield of the production facility producing the facility number PM0040 in the time period 19:00:00 to 19:10:00 is0, but N 2 The consumption is 95, and therefore this period of time 19:00:00 to 19:10:00 is set to be waste 3. In the period 19:50:00 to 21:50:00, the production yield of the production apparatus producing apparatus No. PM0040 is 0, but N 2 The consumption is 1085, thus setting this period of time 19:50:00 to 21:50:00 to waste 4.
Wherein, wasteful N 2 The consumption amounts 775, 275, 95, 1085 can be regarded as representative information, and waste 1 to waste 4 can be regarded as bottleneck candidates.
Further, in the above-described embodiment, the wasteful information as the representative information is wasteful N 2 Consumption, but the invention is not limited thereto. The waste information may also be manual waste information, equipment waste information, and unit cost waste information. Wherein the manual waste information comprises at least one of management waste, action waste, grouping waste, automatic replacement waste and measurement adjustment waste. The equipment waste information comprises at least one of shutdown waste, fault waste, change type adjustment waste, cutter replacement waste, starting waste, pause waste, speed reduction waste, bad correction waste and other stopping waste. The unit cost waste information comprises at least one of energy waste, mold jig waste and yield waste.
Finally, in step ST601, a bottleneck (for example, waste 4) for a preselected object is determined from among a plurality of bottleneck candidates (waste 1 to waste 4) by a manual determination method or an AI automatic determination method.
Here, as the manual judgment method, for example, logical judgment can be performed by a tree condition filtering structure. In the present invention, the manual judgment method is not limited to this, and other manual judgment methods may be used.
As the AI automatic determination method, for example, an AI model using a decision tree algorithm can be established by machine learning a tree condition filtering structure, and automatic determination can be performed using the AI model. In the present invention, the AI automatic determination method is not limited to this, and other AI automatic determination methods may be used.
Thus, according to the bottleneck analysis method for factory production of the present invention, by storing the event data set including the event information of the production process and the like by using the graph database, storing the time-series data set including the element information related to the consumed power, gas, water or the like by using the time-series database, and linking the event data set and the time-series data set by the processing function, it is possible to quickly retrieve the plurality of event data sets corresponding to the series of production processes and the time-series data set linked by the processing function when the element information in the series of production processes related to the plurality of production facilities is analyzed to determine the bottleneck point, and it is possible to efficiently and quickly determine the bottleneck point.
Further, fig. 20 is a block diagram showing a bottleneck analysis system for factory production using an embodiment of the present invention.
The bottleneck analysis method for factory production according to the embodiment of the present invention may be further configured as a modular bottleneck analysis system for factory production, the bottleneck analysis system including: a graph database construction unit 1001 that constructs, by graph database software, a graph database including a plurality of event data sets, one event data set being one node in the graph database including object information, event information, time information, link information, for first raw industrial data related to plant production, the one event data set showing the following relationship: an object indicated by the object information, in which an event indicated by the event information is implemented within a period indicated by the time information, the plurality of event data groups being linked to each other by a link key of the graph database, the information indicated by the link key including a front-to-back order among a plurality of events for the certain link information; a time-series database construction unit 2001 which constructs, for the second raw industrial data related to the plant production, a time-series database including a plurality of time-series data groups including object information, element information, time information by time-series database software, the time-series data groups showing the following relationship: an object shown by the object information has a certain element value shown by the element information in a certain time period shown by the time information; a linking unit 3001 that links the event data group and the time series data group by a processing function, the parameters of the processing function including the object information and the time information; a calculation unit 4001 that calculates a plurality of pieces of representative information for a preselected object for a preset time period based on object information, event information, time information, and element information obtained by linking the event data group to the time-series data group by a processing function, parameters of the processing function including the object information and the time information, in the event data group; an estimating unit 5001 that estimates a plurality of bottleneck candidates for a preselected object based on a result obtained by the calculating unit; and a determination unit 6001 that determines a bottleneck for a pre-selected object from the plurality of bottleneck candidates by a manual determination method or an AI automatic determination method. Here, a more detailed description about the link unit 3001 may refer to the related description about fig. 14B described above.
The description has been made above of the case where the construction method and construction system of the graph database for factory production, the construction method and construction system of the database for factory production, the bottleneck analysis method and bottleneck analysis system for factory production, of the present invention are implemented by software, but the present invention is not limited to this. The present invention may also be realized in hardware, or a combination of software and hardware.
Further, a program for executing the map database construction method for factory production, the bottleneck analysis method for factory production of the present invention may also be stored in various computer-readable media and loaded into, for example, a CPU or the like as needed for execution. The computer readable medium is not particularly limited, and for example, an optical disk such as an HDD, a CD-ROM, or a CD-R, MO, MD, DVD, an IC card, a floppy disk, a semiconductor memory such as a mask ROM, EPROM, EEPROM, or a flash ROM may be used.
Furthermore, the computer device of the present invention may be configured to include: the system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the construction method of the graph database for factory production, the construction method of the database for factory production and the bottleneck analysis method for factory production when executing the computer program.
It should be noted that all aspects of the embodiments disclosed herein are merely examples and are not intended to be limiting. The scope of the present invention is indicated by the claims rather than the above-described embodiments, and all modifications and variations that come within the meaning and range of equivalency of the claims are intended to be embraced therein.
Industrial applicability
As described above, the construction method and construction system of the graph database for factory production, the construction method and construction system of the database for factory production, the bottleneck analysis method and bottleneck analysis system for factory production of the present invention can be applied to various fields of industrial manufacturing.
Description of the reference numerals
1001. Graph database construction unit
2001. Time series database construction unit
3001. Linking unit
4001. Arithmetic unit
5001. Estimation unit
6001. And a determination unit.
Claims (38)
1. A method for constructing a graph database for factory production is characterized in that,
for a first raw industrial data related to the production of a plant, constructing a graph database comprising a plurality of event data sets by graph database software,
one of the event data sets is a node in the graph database including object information, event information, time information, link information,
One of the event data sets shows the following relationship: an object indicated by the object information, an event indicated by the event information being implemented within a period of time indicated by the time information,
the plurality of event data groups are linked to each other by a link key of the graph database, and the information shown by the link key includes a front-to-back order between a plurality of events for certain link information.
2. The method for constructing a map database for factory production according to claim 1, wherein,
the link information includes at least one of order information and product information.
3. The method for constructing a map database for factory production according to claim 2, wherein,
in the case where the link information is the order information,
the information shown by the link key further includes a front-to-back order among a plurality of orders for a certain one of the object information.
4. A method for constructing a map database for factory production according to any one of claim 1 to 3,
the newly constructed event data set and the existing event data set may be linked by the link key.
5. A method for constructing a map database for factory production according to any one of claim 1 to 3,
the object information includes at least one of information of a factory, information of a production line in the factory, and information of equipment on the production line.
6. A method for constructing a map database for factory production according to any one of claim 1 to 3,
the event information includes production process information and throughput information.
7. A method for constructing a map database for factory production according to any one of claim 1 to 3,
the unit of time information includes at least one of month, week, day, hour, minute, and second.
8. A computer device, comprising: memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of constructing a graph database for factory production according to any one of claims 1 to 7 when executing the computer program.
9. A computer readable medium having stored thereon a computer program which, when executed by a processor, implements the method of constructing a graph database for factory production of any one of claims 1 to 7.
10. A system for building a graph database for factory production, comprising:
a map database construction unit that constructs a map database including a plurality of event data groups by means of map database software for first raw industrial data related to factory production,
one of the event data sets is a node in the graph database including object information, event information, time information, link information,
one of the event data sets shows the following relationship: an object indicated by the object information, an event indicated by the event information being implemented within a period of time indicated by the time information,
the plurality of event data groups are linked to each other by a link key of the graph database, and the information shown by the link key includes a front-to-back order between a plurality of events for certain link information.
11. A method of constructing a database for factory production, comprising the steps of:
a first step in which, for first raw industrial data related to plant production, a graph database including a plurality of event data sets, one of which is a node in the graph database including object information, event information, time information, link information, is constructed by graph database software, one of which shows the following relationship: an object indicated by the object information, in which an event indicated by the event information is implemented within a period indicated by the time information, the plurality of event data groups being linked to each other by a link key of the graph database, the information indicated by the link key including a front-to-back order among a plurality of events for the certain link information;
A second step in which, for second raw industrial data related to factory production, a time-series database including a plurality of time-series data sets including object information, element information, time information is constructed by time-series database software, the time-series data sets showing the following relationship: an object shown by the object information has a certain element value shown by the element information in a certain time period shown by the time information; and
and a third step of linking the event data group and the time series data group by a processing function, wherein parameters of the processing function comprise the object information and the time information.
12. The method for constructing a database for factory production according to claim 11, wherein,
the link information includes at least one of order information and product information.
13. The method for constructing a database for factory production according to claim 12, wherein,
in the case where the link information is the order information,
the information shown by the link key further includes a front-to-back order among a plurality of orders for a certain one of the object information.
14. A method for constructing a database for factory production according to any one of claim 11 to 13,
the newly constructed event data set and the existing event data set may be linked by the link key.
15. A method for constructing a database for factory production according to any one of claim 11 to 13,
the object information includes at least one of information of a factory, information of a production line in the factory, and information of equipment on the production line.
16. A method for constructing a database for factory production according to any one of claim 11 to 13,
the event information includes production process information and throughput information.
17. A method for constructing a database for factory production according to any one of claim 11 to 13,
the unit of time information includes at least one of month, week, day, hour, minute, and second.
18. A method for constructing a database for factory production according to any one of claim 11 to 13,
the element information includes at least one of electricity, gas, water, unit productivity cost, unit electricity cost, and fee.
19. A computer device, comprising: memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of constructing a database for factory production according to any one of claims 11 to 18 when the computer program is executed.
20. A computer readable medium having stored thereon a computer program which, when executed by a processor, implements the method of constructing a database for factory production according to any one of claims 11 to 18.
21. A system for building a database for factory production, comprising:
a graph database construction unit that constructs, by graph database software, a graph database including a plurality of event data sets, one of which is a node in the graph database including object information, event information, time information, link information, one of which shows the following relationship, with respect to first raw industrial data related to plant production: an object indicated by the object information, in which an event indicated by the event information is implemented within a period indicated by the time information, the plurality of event data groups being linked to each other by a link key of the graph database, the information indicated by the link key including a front-to-back order among a plurality of events for the certain link information;
A time-series database construction unit that constructs, by time-series database software, a time-series database including a plurality of time-series data groups including object information, element information, and time information, the time-series data groups showing the following relationships, with respect to second raw industrial data related to factory production: an object shown by the object information has a certain element value shown by the element information in a certain time period shown by the time information; and
and a linking unit that links the event data group and the time-series data group by a processing function, the parameters of the processing function including the object information and the time information.
22. A bottleneck analysis method for factory production, comprising the steps of:
a first step in which, for first raw industrial data related to plant production, a graph database including a plurality of event data sets, one of which is a node in the graph database including object information, event information, time information, link information, is constructed by graph database software, one of which shows the following relationship: an object indicated by the object information, in which an event indicated by the event information is implemented within a period indicated by the time information, the plurality of event data groups being linked to each other by a link key of the graph database, the information indicated by the link key including a front-to-back order among a plurality of events for the certain link information;
A second step in which, for second raw industrial data related to factory production, a time-series database including a plurality of time-series data sets including object information, element information, time information is constructed by time-series database software, the time-series data sets showing the following relationship: an object shown by the object information has a certain element value shown by the element information in a certain time period shown by the time information;
a third step of linking the event data group and the time-series data group by a processing function, the parameters of the processing function including the object information and the time information;
a fourth step of calculating a plurality of pieces of representative information for a preselected object for a preset time period based on the object information, the event information, the time information, and the element information obtained by linking the event data group to the time-series data group by the processing function, the parameters of the processing function including the object information and the time information, in the event data group;
A fifth step of estimating a plurality of bottleneck candidates for the preselected object based on the result obtained in the fourth step; and
and a sixth step of determining a bottleneck for the pre-selected object from the plurality of bottleneck candidates by a manual determination method or an AI automatic determination method.
23. The bottleneck analysis method for industrial production of claim 22, wherein,
the link information includes at least one of order information and product information.
24. The bottleneck analysis method for industrial production of claim 23, wherein,
in the case where the link information is the order information,
the information shown by the link key further includes a front-to-back order among a plurality of orders for a certain one of the object information.
25. The bottleneck analysis method as claimed in any one of claims 22 to 24, wherein,
the newly constructed event data set and the existing event data set may be linked by the link key.
26. The bottleneck analysis method as claimed in any one of claims 22 to 24, wherein,
the object information includes at least one of information of a factory, information of a production line in the factory, and information of equipment on the production line.
27. The bottleneck analysis method for industrial production as claimed in any one of claims 22 to 24, wherein,
the event information includes production process information and throughput information.
28. The bottleneck analysis method for industrial production as claimed in any one of claims 22 to 24, wherein,
the unit of time information includes at least one of month, week, day, hour, minute, and second.
29. The bottleneck analysis method for industrial production as claimed in any one of claims 22 to 24, wherein,
the element information includes at least one of electricity, gas, water, unit productivity cost, unit electricity cost, and fee.
30. The bottleneck analysis method for industrial production as claimed in any one of claims 22 to 24, wherein,
the representative information is wasteful information,
the wastage information includes at least one of manual wastage information, equipment wastage information, and unit cost wastage information.
31. The bottleneck analysis method for industrial production of claim 30, wherein,
the manual waste information includes at least one of management waste, action waste, grouping waste, automatic replacement waste, and measurement adjustment waste.
32. The bottleneck analysis method for industrial production of claim 30, wherein,
the equipment waste information comprises at least one of shutdown waste, fault waste, change type adjustment waste, cutter replacement waste, starting waste, pause waste, speed reduction waste, bad correction waste and other stopping waste.
33. The bottleneck analysis method for industrial production of claim 30, wherein,
the unit cost waste information comprises at least one of energy waste, mold jig waste and yield waste.
34. The bottleneck analysis method for industrial production of claim 22, wherein,
the manual judgment method carries out logic judgment through a tree condition filtering structure.
35. The bottleneck analysis method for industrial production of claim 22, wherein,
the AI automatic judging method establishes an AI model using a decision tree algorithm by machine learning a tree condition filtering structure, and utilizes the AI model to automatically judge.
36. A computer device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the bottleneck analysis method for factory production according to any one of claims 22 to 35 when executing the computer program.
37. A computer readable medium having stored thereon a computer program which, when executed by a processor, implements the bottleneck analysis method for factory production of any one of claims 22 to 35.
38. A bottleneck analysis system for factory production, comprising:
a graph database construction unit that constructs, by graph database software, a graph database including a plurality of event data sets, one of which is a node in the graph database including object information, event information, time information, link information, one of which shows the following relationship, with respect to first raw industrial data related to plant production: an object indicated by the object information, in which an event indicated by the event information is implemented within a period indicated by the time information, the plurality of event data groups being linked to each other by a link key of the graph database, the information indicated by the link key including a front-to-back order among a plurality of events for the certain link information;
a time-series database construction unit that constructs, by time-series database software, a time-series database including a plurality of time-series data groups including object information, element information, and time information, the time-series data groups showing the following relationships, with respect to second raw industrial data related to factory production: an object shown by the object information has a certain element value shown by the element information in a certain time period shown by the time information;
A linking unit that links the event data group and the time series data group through a processing function, parameters of the processing function including the object information and the time information;
a calculation unit that calculates a plurality of pieces of representative information for a preselected object for a preset time period based on the object information, the event information, the time information, and the element information obtained by linking the event data group to the time-series data group by the processing function, the parameters of the processing function including the object information and the time information, in the event data group;
an estimating unit that estimates a plurality of bottleneck candidates for the preselected object based on a result obtained by the calculating unit; and
and a determination unit that determines a bottleneck for the pre-selected object from the plurality of bottleneck candidates by a manual determination method or an AI automatic determination method.
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