JP2005352704A - Integrated management system of production information and its program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a unified analysis environment as much as possible. <P>SOLUTION: An integrated DB 50 consists of an event actual achievement DB 51, a trend actual achievement DB 52 or the like in a state in which event data and trend data are related to each other. A DB storage processor 64 by object creates a DB 40 by object in which data for each variety of objects (for example, by lot/patch, by fixed period lot/patch, by fixed period collection/period, etc.) are classified, extracted and organized on the basis of the integrated DB 50. A data collection/calculation processor 63 acquires data matching with a retrieval condition on the basis of the DB 40 by object to display it onto a display in a form such as a table, a graph and a Gantt chart. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to a system, method, program, and the like for performing integrated management of information in a production system.

Conventionally, various techniques have been proposed in relation to production system control and management in factories and the like, and in particular, in relation to food factories, for example, the technique described in Patent Document 1 has been proposed. The invention described in Patent Document 1 intends to manage HACCP (hazard analysis / critical control point monitoring) management by an ASP (application service provider). That is, the management company's server receives various requests related to food sanitation management from the computer in the food manufacturing factory, various performance data, and information selected from the group of various instructions via the communication line. To the server in the factory, information selected from the group of various applications relating to food hygiene management, various instructions and various treatment instructions is sent out through the communication line.
JP 2002-49660 A

  No matter what the product is, there is no doubt that there is no defective product. However, in the case of food, if food poisoning or the like is caused by a defective product, there is a possibility that even the survival of the company will be jeopardized. Therefore, it goes without saying that efforts are made to prevent defective products from being produced, but there is a need for a mechanism that allows early detection and cause investigation when a problem still occurs.

  In particular, as a feature of the production form of batch plant type (or continuous process type) food production, mixing, lamination, separation, distribution, etc. of raw materials or intermediate products occur in the process (process) of raw material input to processing to commercialization. Is mentioned. For this reason, the relationship between the process, the raw material and the product, and the intermediate product and the product are complicated, which is a big wall for identifying the true cause.

  From the above, it is a big issue to understand the process in the manufacturing process, the execution event and its context, and to create a mechanism that can collect and analyze manufacturing activity information and quality information using this as a key.

  An object of the present invention is to create and manage integrated data of trend data and event data, and divide the integrated data by lot / batch, by time, by hierarchy, or obtain an aggregate value and give identifiers to these. It is to provide a production information integrated management system that stores and manages and provides a unified analysis environment as much as possible, including an analysis support environment, and its program.

  The production information integrated management system of the present invention includes data collection means for collecting trend data and event data from a control device, integrated data generation / storage means for storing the collected trend data and event data in association with each other, and the integration Purpose-specific database generation / storage means for generating and storing various purpose-specific databases obtained by classifying the integrated data stored in the data generation / storage means according to various purposes, and the various data according to arbitrary search conditions Display control means for acquiring and displaying data that matches the search condition from the purpose-specific database.

  Trend data is sensor data along the time axis, etc., and as it is, it cannot be classified and organized from various viewpoints, but it can be correlated with event data managed in lots and batches, for example. It becomes possible. In addition, by creating a database that is classified and organized by purpose, searching becomes easy.

  For example, the event data is data according to a hierarchical structure in which processes are subdivided step by step, and the integrated data generation / storage means cuts out a portion corresponding to each hierarchy of the collected event data from the trend data. The association may be performed.

  The hierarchical structure is compliant with, for example, S88, whereby data can be classified and stored in accordance with a specific hierarchical structure, and only a desired portion can be quickly searched and displayed.

  For example, the extraction is to extract the trend data between the start time and the end time of each layer, and the integrated data generation / storage means obtains the total / maximum / minimum / average of the extracted trend data. In this case, the association may be performed.

For example, the display control means may display the acquired data as a table, a graph, a Gantt chart, or a correlation diagram.
For example, the purpose-specific database is obtained by classifying the integrated data by lot / batch, or by calculating the aggregated value from the integrated data and classifying by lot / batch, or by integrating the integrated data in a time-sharing manner, An identifier is assigned to each aggregate value group, and the display control means may perform the search using a lot / batch identifier or an identifier for each aggregate value group.

  According to the production information integrated management system and the like of the present invention, integrated data of trend data and event data is created and managed, and this integrated data is divided by lot / batch, by time, by hierarchy, or a total value is obtained. These can be stored and managed by providing identifiers, and an analysis environment that is as uniform as possible can be provided, including an analysis support environment.

Embodiments of the present invention will be described below with reference to the drawings.
FIG. 1 is a schematic configuration diagram of the production management system of this example.
The production management system shown in the figure is roughly divided into a MES (Manufacturing Execution System) and a control device such as a DCS / PLC.

  In the MES, a manufacturing instruction is given to the DCS / PLC via the control system I / F (interface) 9 while performing progress management by the manufacturing planning device 1, the instruction expanding device 3, the manufacturing instruction device 5, and the progress management device 7. The configuration for sending is an existing configuration and will not be described in particular. In the following description, DB is a database, DCS is a distributed control system, and PLC is a programmable controller.

  The feature of the production management system of this example is the production information integrated management system 10. The production information integrated management system 10 includes a DB unified management device 11 and a general-purpose performance analysis device 15. The DB integrated management device 11 reads trend data and event data from the DCS / PLC via the control system I / F 9 and associates them with each other and stores them in the manufacturing performance DB (integrated DB) 12. Further, the data stored in the manufacturing performance DB (integrated DB) 12 is classified and organized by purpose and stored in the purpose-specific DB 13. The general-purpose performance analysis device 15 performs general-purpose / dedicated graph display, performance analysis, overall progress management, and the like using data stored in the purpose-specific DB 13.

The functions of the production information integrated management system 10 are schematically as follows.
1. Integrated database (1) Integrated storage and management of multiple types of data such as trend data and event data.

System management function: Login authentication (security) management function, database management function, logging management function.
(2) Data can be collected and edited at batch (including time lot) / lot / time / day / week / month / year levels. It can be extracted by process / equipment / item unit.

  When various data such as trend data and event data collected via the control system I / F 9 are stored in the integrated DB 12, an identification code called a data item ID is assigned and stored and managed in association with this. This data item ID is managed as common information in the purpose-specific DB creation and results analysis processing.

・ By lot batch: Event results, trend results, trend summary data linked to lots / batch ・ By regular lot batch: Result data by summing up lots / batch results by time limit (eg daily unit) or by time unit Lots / batch results (round lots, etc.)
・ Periodic period: Results collected by time limit (assuming data not linked to lots and batches)

2. Analysis / display function (1) A display in which a plurality of types of data such as trend data and event data are linked is possible, and an analysis / display environment that is easier to understand for the user is provided.
(2) Provide a unified analysis environment as much as possible, including an analysis support environment.

The above is the general function of the production information integrated management system 10.
Note that the control system I / F 9 has, for example, a DCS multi-vendor compatible, OPC standard interface, and enables flexible cooperation. The progress management device 7 has an event record collection / inter-process interlock management / progress management function in cooperation with the manufacturing instruction function, and realizes a central command function of the MES.

  Hereinafter, the above-described process of “reading trend data and event data from the DCS / PLC via the control system I / F 9 and storing them in the manufacturing result DB (integrated DB) 12” will be described.

Before that, first, the concept of the event performance model related to event data in this example will be described with reference to FIG.
As shown in FIG. 2, the event data of this example is based on S88, which is an international standard for batch control models and terminology defined by the ISA (International Society for Measurement and Control). Manage by hierarchy.
Procedure: Data for identifying the highest hierarchical performance model in S88. In S88, it often means an item.
Unit: Data for identifying the second-layer performance model in S88. In S88, it often means an apparatus (equipment).
Operation: Data for identifying the third layer performance model in S88. S88 often means a process.
Phase: Data for identifying the fourth layer performance model in S88. S88 often means a small process.

  In the example shown in FIG. 2, the procedure hierarchy is a process of charging device → mixing → mixing device → transfer to storage → storage device. Taking the charging device as an example, the unit hierarchy is a process of raw material processing device → charging device → filtering device → boiling device. Taking the raw material processing apparatus as an example, the operation hierarchy consists of operations 1 and 2, and in the phase hierarchy, operation 1 consists of valve opening and heating on, and operation 2 consists of stirring on and cooling on. Event result data is collected in each of the unit hierarchy, operation hierarchy, and phase hierarchy processes.

FIG. 3 is a conceptual diagram for explaining an association process between event data and trend data.
FIG. 4 is a diagram showing the flow of the association process between event data and trend data.

  First, event data usually consists of start time, end time, process name (equipment), value, etc., trend data is collected from collection time, data type (equipment name / sensor name, etc.), value (raw data), etc. However, in this example, as shown in FIG. 3, for example, the event data includes a start time, an end time, a lot ID, a batch ID, and the S88 hierarchical ID (procedure ID, unit ID, operation ID, phase ID). . On the other hand, the trend data is the normal data, and in the example shown in the figure, data whose data type (equipment name, sensor name, etc.) is the raw material processing apparatus temperature is shown. That is, it is data collected from a temperature sensor installed in the raw material processing apparatus. As in this example, the trend data is only data that is continuous in time series, and it is not known which part corresponds to which event.

  Therefore, as shown in FIG. 3, the result data classified by hierarchy which linked | related event data and trend data are produced. The creation method uses the start time and end time of the event data to cut out trend data (raw data) between the start and end times. Then, for example, the cut trend data (raw data) may be associated with the event data as it is, or the maximum value, the minimum value, the average value, etc. are obtained from the raw data, and these are associated with the event data. May be. As shown in the figure, the result data by level includes the result occurrence time (start time, end time), lot ID, batch ID, and S88 hierarchy ID (procedure ID, unit ID, operation ID, phase ID) as shown in the figure. It consists of raw data or maximum / minimum / average values.

  As described above, trend data associated with each event can be stored. For example, in the example shown in the figure, trend data linked to the “operation 1” event, trend data linked to the “valve open” event in the phase hierarchy, and the like can be generated and stored. When it is desired to display the raw material processing apparatus temperature of “Operation 1” or “Valve Open”, the corresponding trend data can be easily retrieved and acquired and displayed.

The association shown in FIG. 3 is realized by the processing flow shown in FIG. 4, for example.
First, event data 31 passed from the DCS / PLC via the control system I / F 9 is temporarily stored in a storage unit such as a hard disk (DB copy or file copy). It is determined whether or not (step S1). If the tightening conditions are met, the process of creating the above-mentioned result data classified by hierarchy (also referred to as trend cooperation process) is executed while managing the tightening state for each hierarchy (step S2) (step S3). . As a result, firstly, the layer-by-tier result data 34 in which the trend data remains as raw data is created. Further, by executing a totaling process for calculating the total value, maximum value, minimum value, average value, and the like from the raw data based on the hierarchical performance data 34 (step S4), the hierarchical total data 35 is obtained. create.

  At this time, an arbitrary data item ID is assigned to “total value, maximum value, minimum value, average value, etc.” (may be for raw data, but will be omitted here). When there are multiple types of raw data, a data item ID is assigned to each. For example, when there are two types of raw material processing apparatus temperatures, point A temperature and point B temperature, the data item ID = 'A1' with respect to the “total value, maximum value, minimum value, average value, etc.” of the point A temperature And the data item ID = 'A2' is assigned to the “total value, maximum value, minimum value, average value, etc.” of the point B temperature, and so on. Thus, if the data item ID is used, the “total value, maximum value, minimum value, average value, etc.” of the desired data can be easily acquired and displayed.

  In the process of step S2, if the received event data 31 is for the event occurrence, it is stored in the tightening management table 33 for storing the tightening state for each counting unit. In this case, there is data of the start time of the data item of the event data 31, but there is no data of the end time. When the event data 31 at the end of the same event is received (there is end time data), the event data stored in the closing management table 33 is updated (end time data is added). For example, in the example of FIG. 3, if event data at the occurrence of the event of the phase “valve opening” is received, and then event data at the end of the event is received, and if this meets the tightening condition, step S3 By executing the above process, the result data classified by hierarchy as the trend data linked to the “valve open” event and the aggregated data thereof are created. The tightening condition is “in the hierarchical structure, the upper layer is tightened after the start / end of the lower layer is finished (tightened)”. For example, “operation 1” is not tightened even after “valve opening” is finished, but “operation 1” is tightened after “heating on” is finished. Similarly, at the stage where “operation 1” and “operation 2” are closed, the “raw material processing apparatus” is closed.

  The hierarchical management in step S2 is performed with reference to the hierarchical management master. The hierarchy management master is a procedure master, unit master, operation master, phase master, event master, TAG master, device master, or the like. The procedure master stores a procedure ID which is an identifier (ID) for identifying each procedure. The unit master stores an upper hierarchy (procedure ID) to which each unit ID belongs. The operation master stores an upper hierarchy (procedure ID and unit ID) to which each operation ID belongs. The phase master stores the upper hierarchy (procedure ID, unit ID, and operation ID) to which each phase ID belongs. The TAG master stores an event TAG and data TAG. The event TAG is a DCS address for acquiring state change (state change) data. Data TAG is a DCS address for acquiring the current value.

FIG. 5 shows a combined image of event data and trend data.
In the example shown in the figure, it is assumed that the temperature change at the time of heat treatment in a certain facility as shown on the right side of the figure is to be displayed. Here, a state in which trend data between the start time and end time of the event TAG is linked by the event TAG and the trend TAG is shown. The trend TAG is the data item ID, and the event TAG is an event ID described later.

  Next, the creation and display control processing of the purpose-specific DB in which the data stored in the integrated DB is classified and organized by various purposes will be described with reference to FIG. This can also be said to construct an environment that supports performance analysis (various displays) described later.

  In FIG. 6, the integrated DB 50 is an event record DB 51, a trend record DB 52, or the like in a state where the event data and trend data are linked. Based on these and the data item definition 62, the purpose-specific DB storage processing unit 64 creates the purpose-specific DB 40.

In the illustrated example, the integrated DB 50 further includes a discrete DB 53.
The discrete DB 53 stores fixed period discrete data and indefinite period discrete data. Examples of periodic periodic data include, for example, the amount of raw materials and packaging materials received (when regularly received), quality check data (concentration, density, particle size) (when periodically checked), raw materials and packaging materials Inventory quantity (when regularly checking inventory), product shipment quantity (when regularly shipping), and the like. Examples of indefinite periodic discrete data include, for example, the amount of raw materials and packaging materials received (if irregularly arrived), quality check data (concentration, density, particle size) (if irregularly checked), raw materials, This includes the amount of packaging material stock (when irregularly checking inventory), the amount of product shipped (when irregularly shipping), and the like.

  In the illustrated example, four types of purpose-specific DBs 40 are created for each purpose. That is, the DB 41 by lot / batch, the DB 42 by regular lot / batch, the DB 43 by periodic collection / period, and the progress DB 44.

  First, regarding creation of the batch / batch-specific DB 41, the purpose-specific DB storage processing unit 72 collects event data and trend data related to a specific lot / batch (for example, designated by the user) by searching the integrated DB 50. And stored in the batch / batch DB 41. However, since the event data is originally classified and managed by lot / batch as described above, there is no problem. However, since the trend data has no information on the lot / batch, the corresponding data cannot be searched as it is. Therefore, although not shown in FIG. 6, in practice, the above-described hierarchical performance data 34 or hierarchical aggregation data 35 is searched to obtain the corresponding data. However, in the above-mentioned tier-specific result data 34, classification and management are performed not only by the lot ID and batch ID but also by the S88 hierarchical ID. However, the present invention is not limited to this example, and classification and management is performed by lot ID and batch ID. In this case, as described above, data search is performed using the designated lot ID and batch ID.

Next, regarding the regular lot / batch-specific DB 42, the purpose-specific DB storage processing unit 72 performs the following two types of data collection processing.
(A) For each lot / batch, time-division data is further cut out and stored in the regular lot / batch DB 42.
(B) The volume, number of times, etc. are calculated every predetermined period (for example, every hour, every day, every week).

  In the case of (A), the equipment ID associated with the designated lot ID and batch ID is acquired from the progress data or the like. Then, a collection item is searched from the event ID and equipment ID, and trend results are collected and aggregated. Link lot / batch information to the collected trend data. It becomes possible to search and reference trend data related to lots and batches using lots and batches as keys. Data such as time zone lots that are managed for each time zone within a lot / batch are also targeted.

  In the case of (B), a lot / batch is retrieved from the event record DB 51 by time designation, and the corresponding record data is collected and further tabulated. For example, various data related to the prefecture for manufacturing in units of one day / one week / one month is totaled (total production, total usage of raw materials and packaging materials, etc.). Display the total results. The collection target is data of already completed lots, batches, and events.

  Next, with respect to the regular period collection / period-specific DB 43, data is collected in a predetermined time unit such as day, week, month, year, etc., and the trend data is mainly targeted, and the time is specified for the collected data. Aggregate, and store / manage the equipment ID as a key item.

The progress DB 44 stores progress data and the like.
Examples of data structures of the purpose-specific DB 40, the integrated DB 50, and the data item definition master 62 related to the lot / batch-specific DB 41, the regular lot / batch-specific DB 42, the periodic collection / period-specific DB 43, and the progress DB 44 are shown in FIG. As shown in FIG.

FIG. 7 shows a data configuration example of the lot-batch DB 41, the integrated DB 50 related to the creation thereof, and the data item definition master 62.
First, regarding the integrated DB 50, the event record header in the event record data is the event number. Is a procedure ID, device ID, event ID, start time, end time, fastening state, lot ID, batch ID, and the like. And event no. There are event performance data and event performance trend data associated with the. These are event numbers. In accordance with the data item ID, the actual value and each total value are stored.

  The purpose-specific DB storage processing unit 64 refers to the lot / batch collection definition master and the purpose-specific data item master of the data item definition master 62 from the event result data, and determines the lot / batch event header, the lot / batch event. Data, event data by lot / batch, and event trend data by lot / batch are created and stored in the DB 41 by lot / batch.

  The lot / batch collection definition master stores device IDs for data item IDs, procedure IDs, and event IDs. The purpose-specific data item master stores a unit, a collection destination, a collection data item ID, a collection cycle, a batch level, and the like for the data item ID and the collection type. The lot / batch event header stores a device ID, an end time, and a tightening state with respect to the procedure ID, lot ID, batch ID, event ID, and start time. In the event data by lot / batch, the device ID and the actual value are stored for the data item ID, lot ID, batch ID, procedure ID, event ID, and start time. Lot / batch event data aggregation stores device IDs, totals, and event counts for data item IDs, lot IDs, batch IDs, procedure IDs, and event IDs. The number of valid trend data is stored.

  On the other hand, in the integrated DB 50, the trend performance data stores, for example, a plurality of data that is temporally continuous with respect to the data item ID, the collection date and time, and the collection cycle. Since the corresponding procedure ID, lot ID, batch ID, event ID, etc. can be known from the data item ID or the like, trend data by lot / batch as shown in the figure can be created.

FIG. 8 shows a data configuration example of the regular lot / batch-specific DB 42, the integrated DB 50 related to the creation thereof, and the data item definition master 62.
In FIG. 8, the event record header and event record data of the integrated DB 50 are substantially the same as those in FIG. In the regular lot / batch-specific DB 41, data obtained by aggregating event performance data in predetermined time units is created and stored as shown in the figure. In the example shown in the figure, the total values are in units of hours, days, and months. For this creation, the periodic lot / batch collection definition master, purpose-specific data item master, and periodic collection type master of the data item definition master 62 are referred to. The purpose-specific data item master is the same as in FIG. The periodic lot / batch collection definition master stores the device ID for the data item ID and the periodic collection type. The regular collection type master stores the regular collection date and time and the previous regular collection date and time for the regular collection type.

FIG. 9 shows a data configuration example of the fixed period collection / period-specific DB 43, the integrated DB 50 related to the creation thereof, and the data item definition master 62.
In this case, the integrated DB 50 creates and stores data obtained by tabulating trend performance data in a predetermined time unit. In the example shown in the figure, the total value is in units of hours, days, and months. For this creation, the periodic lot / batch collection definition master, the purpose-specific data item master, and the periodic collection type master of the data item definition master 62 are substantially the same as in FIG. The added data is created and stored in the DB 43 by periodic collection / period. For the master, an inter-item calculation ID and an arithmetic expression corresponding to the ID are stored as compared with FIG. 8, and the inter-item calculation can be performed using the ID.

The progress data shown in FIG. 10 is as illustrated.
FIG. 11 shows a graph creation flow.
In FIG. 11, the integrated DB 50 and the purpose-specific DB 40 are created by the processing described above. The master DB 70 corresponds mainly to the purpose-specific DB reference master 61 shown in FIG. 6, and is a graph definition master 71, graph detail definition master 72, purpose-specific DB conversion master 73, product type definition master 74, equipment definition master 75. The data item definition master 76 and the like.

  Although the graph definition master 71 and the graph detailed definition master 72 are distinguished from each other here, the graph detailed definition master 72 is included in the graph definition master 71 without distinction in the following description.

An example of the graph definition master 71 is shown in FIG.
Further, only the purpose-specific DB conversion master 73 corresponds to the data item definition master 62, not the purpose-specific DB reference master 61, and specific examples thereof are shown in FIGS.

  An example of the product type definition master 74, the equipment definition master 75, and the TAG definition master 76 is shown in FIGS. 13A, 13B, and 13C, respectively. In the example of the product type definition master 74 shown in FIG. 13 (a), item name, item type, and unit price data are stored using the item ID as a key item. In the example of the equipment definition master 75 shown in FIG. 13B, the equipment name is stored using the equipment ID as a key item. The data item definition master 76 shown in FIG. 13C stores data item name, data type, unit ID, data type, and collection system ID using the data item ID as a key item.

  Returning to the description of the flow in FIG. In FIG. 11, the process (steps S11 to S15) for creating the various purpose-specific DBs 40 based on the various DBs and the process for displaying various graphs (steps S21 to S28) based on the created purpose-specific DBs 40 are performed. It is shown.

First, the processing of steps S11 to S15 will be described.
First, data stored in the purpose-specific DB conversion master 73, the equipment definition master 75, and the data item definition master 76 are read (steps S11 and S12). Further, data stored in the event record DB 51 and the trend record DB 52 of the integrated DB 50 are collected (step S13). Based on the acquired various data, predetermined calculation processing is executed to create a purpose-specific DB (steps S14 and S15).

  Next, the processing of steps S21 to S28 will be described. The processes in steps S21 to S28 are processes executed by the Web server while referring to the master DB 70 and the purpose-specific DB 40 in accordance with requests and settings from the Web client.

  First, when a user selects a desired form in the Web client (step S31), the selection result is notified to the Web server, and the Web server reads data (graph definition) stored in the graph definition master 71 (step S21). ) Create a search screen and display it on the display of the Web client. An example of this search / display screen is shown in FIG.

FIG. 14 shows an example (part 1) of the search / display screen.
In the illustrated search / display screen, search conditions include graph type, display unit, item by item, item, date, and the like. Regarding the graph type, hourly report, daily report, lot report, batch report, weekly report, monthly report, annual report, arbitrary period report, etc. are displayed as options in the combo box, and the user arbitrarily selects from these. Further, regarding the display unit, options corresponding to the selected graph type are displayed in the combo box, and the user arbitrarily selects from these. For example, when the daily report is selected, the hour, day, batch, and lot report are displayed in the combo box when the day, batch, and lot report are selected. The check box for each item, if checked, the graph displayed as the search result is displayed along the time axis with the data divided for each item, and if not checked, the time in the item Displayed in order. For items, when a graph displayed as a search result is event data, a list of related item names is displayed in a combo box. In addition, a date to be displayed is input as the date according to the selected graph type. For example, if it is a daily report, the date is entered.

  When the above search conditions are input (step S32) and the “display” button is operated, a display request including the input search conditions is sent to the Web server. The Web server reads the detailed graph definition and the data item definition from the master DB 70 (steps S23 and S24), and further collects data that matches the search conditions from the purpose-specific DB 40 (step S25). Then, a predetermined calculation process is executed to create a graph page (steps S26 and S27), and the created graph page is returned to the Web client (step S28).

The Web client performs graph creation processing based on the received graph page, and displays a graph as shown in FIG. 14, for example.
The example of FIG. 14 is an example of basic graph display, but other processing graph display described below can be performed. Examples of the processing graph display function include display by behavior analysis, lot flow, correlation analysis, and the like.

  In behavior analysis, trend data (raw data) for each batch is displayed in the same apparatus (same process). FIG. 15 shows a search / display screen for this purpose. When a behavior analysis command is sent from a web client, the web server acquires screen definition information for behavior analysis from various screen definition information prepared in advance, and returns this to the web client. The search / display screen of FIG. 15 is displayed.

  The user inputs a desired search condition on the search / display screen. That is, the user first selects a desired device group from the device group list by using the combo box, and selects a desired device name from the device name list belonging to the selected device group. Subsequently, since a list of display data related to the device having the selected device name is displayed in the list box, desired display data is selected. Examples of the display data list include, for example, steam inlet temperature, steam outlet temperature, steam inlet pressure, steam outlet pressure, and the like. That is, a list of data names of data collected from various sensors provided in the selected device is displayed.

  Then, by pressing the “batch search” button, the batches that match the above selected conditions are searched, and the batch names are displayed in a list box. The trend data (raw data) of the batch is displayed in a graph as shown.

Next, the lot flow displays trend data at a plurality of facilities in the same procedure. An example of a lot flow search / display screen is shown in FIG.
The screen shown in FIG. 16 is a screen displayed after designating an arbitrary plurality of facilities (equipment name; facility 1 to facility 4) on a screen (not shown). This equipment name is obtained by searching from the machining graph equipment definition shown in FIG. 12 and the equipment definition master shown in FIG. In addition, designation | designated of this some installation may be arbitrary, and you may make it acquire the mutually related installation using the link between processes mentioned later.

  In FIG. 16, the user first selects a data name to be displayed in a graph for each of the facilities 1 to 4 from the list displayed in the combo box. In the illustrated example, the point A temperature is selected for the facility 1, the point B temperature is selected for the facility 2, the flow rate is selected for the facility 3, and the pressure is selected for the facility 4. Further, as an optional additional search condition, the user selects a desired batch from the batch list. Alternatively, specify the time zone. When the time zone is specified, the corresponding batch is searched based on the specified time zone. When the “display” button is operated, corresponding data is acquired using the input data name and batch as a search key, and is displayed in a graph as illustrated.

  Finally, correlation analysis displays a correlation diagram between two data. An example of the correlation search / display screen is shown in FIG. On the illustrated screen, the user first selects a desired data group. That is, first, a desired equipment is selected, and a desired equipment is selected from a list of equipment related to the selected equipment. As a result, various display data names related to the selected device are acquired and displayed in a list in the “X-axis” and “Y-axis” combo boxes shown in the figure. That is, in the illustrated “X-axis” and “Y-axis” combo boxes, a list of display data candidates to be displayed on the X-axis / Y-axis of the correlation diagram is displayed. Thus, the user selects a desired display data name to be displayed on the X axis and Y axis of the correlation diagram. In the illustrated example, the pressure in the hook is selected for the X axis, and the steam pressure is selected for the Y axis. Further, a list of batches that can be displayed in a graph is displayed in the “batch selection” list box shown in the figure, and the user selects a desired batch name. Then, after specifying the plot interval and the display position, when the “display” button is operated, the correlation diagram is displayed in a graph as shown in the figure.

  FIG. 18 is an example of a screen data item ID definition screen. The purpose of this screen is to narrow down the data items to be displayed on the screen from the data items stored in the purpose-specific DB.

First, in the “procedure selection” combo box shown in the figure, a procedure of data to be selected is specified (for example, a list of preparation / preparation / storage / all is displayed, and it is specified from this). Then, a list of hierarchical models conforming to S88 related to the selected procedure is displayed, and the user arbitrarily selects from among them. When the “select” button is operated, the data items corresponding to the selection result are displayed in the “data item list” shown in the figure. When the user selects an arbitrary data item and operates the “detail display” button, the details of the data item are displayed on the right side of the figure, so that the user can confirm the contents.
After confirmation, a desired data item is selected with the illustrated arrow keys (↑, ↓) and registered in the screen data item definition DB.

The lot flow search in FIG. 16 is performed based on the inter-process link information. This will be described below.
By recording as performance information, link between processes is realized.

  That is, as illustrated in FIG. 19, the master DB of this example includes an inter-process link master 71, an inter-process link data master 72, a batch event master 73, an event performance header 81, event performance data 82, a process. The inter-link record header 83 and the inter-process link record data 84 are set.

  These pieces of information are constructed / set on general-purpose databases such as the data set master 74, the data set item master 75, and the data item master 76 controlled by the general-purpose database program 22b. The inter-process link processing program 22c And is set / controlled by the traceability program 22d as described later.

  As shown in FIG. 19, an inter-process link ID 171a, receiving procedure ID 171b, receiving device ID 171c, receiving event as key items are included in the inter-process link master 171 that defines the basic link relationship between the payer and the receiver. Information of ID 171d, payment procedure ID 171e, payment device ID 171f, and payment event ID 171g is set. Each link relation is identified by the inter-process link ID 171a.

  The batch event master 173 in which the definition of the batch event is stored includes information on a procedure ID 173a, device ID 173b and event ID 173c as key items, a data set ID 173d, an item operation ID 173e, a batch level 173f, and a parent event ID 173g. Is set.

  In the event result header 181, event number 181a, procedure ID 181b, device ID 181c, event ID 181d, start time 181e, end time 181f, tightening state 181g, lot number 181h, and batch number 181i are set as key items. ing.

  In the event record data 182, information such as an event number 182 a and a data item ID 182 b as key items and a record value 182 c are set. The actual value 182c includes names or amounts of raw materials, intermediate products, intermediate raw materials, and final products, actual values of various measurement data, and the like.

  The inter-process link record header 183 in which the lot / batch association information is set includes an inter-process link ID 183a, a reception end time 183b, a reception lot number 183c, a reception batch number 183d, a payment end time 183e, a payment lot number 183f, Each information such as the payment batch number 183g and the link state 183h is stored.

  The inter-process link performance data 184 in which the actual value of receipt / payment is stored stores inter-process link ID 184a, receipt end time 184b, data item ID 184c, receipt performance value 184d, and payment performance value 184e. .

  FIG. 20 shows an example of creating inter-process link record data. If there is a payment event A and a payment event B in the preceding process and there is a receiving event B in the subsequent process, in case C1 in which the payment event A and the receiving event A are linked, the inter-process link definition is the table T1-1. Thus, the actual value becomes the table T1-2.

Further, in the case C2 in which the payment event B and the receiving event A are linked, the inter-process link definition is the table T2-1 and the actual value is the table T2-2.
In addition, in a continuous manufacturing system such as a food manufacturing process for beverages and the like, as shown in FIG. 21, a complicated processing path such as multipath, merging, repeating, and branching is configured between a plurality of facilities 1 to 8. Therefore, if performance data is managed in units of individual facilities (procedures), it is not possible to display across procedures.

FIG. 22 is a hardware configuration diagram of a computer such as a personal computer / server that implements the production information integrated management system 10.
A computer 100 shown in the figure includes a CPU 101, a memory 102, an input unit 103, an output unit 104, a storage unit 105, a recording medium drive unit 106, and a network connection unit 107, which are connected to a bus 108. It has become. The configuration shown in the figure is an example, and the present invention is not limited to this.

The CPU 101 is a central processing unit that controls the entire computer 100.
The memory 102 is a memory such as a RAM that temporarily stores a program or data stored in the storage unit 105 (or the portable recording medium 109) during program execution, data update, or the like. The CPU 101 executes the various processes described above using the program / data read out to the memory 102.

The input unit 103 is, for example, a keyboard, a mouse, a touch panel, or the like. The output unit 104 is, for example, a display.
The network connection unit 107 is configured to connect to any network such as a LAN, the Internet, or a serial bus and perform command / data transmission / reception with another information processing apparatus. In the above embodiment, for example, control It is connected to the system I / F 9 to transmit / receive commands / data and the like to / from each DCS / PLC.

  The storage unit 105 is, for example, a hard disk or the like, and stores a program for causing the computer 100 to execute the various processes and various data used for the program process.

  Alternatively, these programs / data may be stored in the portable recording medium 109. In this case, the program / data stored in the portable recording medium 109 is read by the recording medium driving unit 106. The portable recording medium 109 is, for example, an FD (flexible disk) 109a, a CD-ROM 109b, a DVD, a magneto-optical disk, or the like.

  Alternatively, the program / data may be downloaded from another apparatus via a network connected by the network connection unit 107. Or you may download further what was memorize | stored in the other external apparatus via the internet.

  In addition, the present invention can be configured not only as a portable storage medium recording a program for realizing the various processes of the present invention on a computer, but also as the program itself.

It is a schematic block diagram of a production management system. It is a figure for demonstrating the event performance model regarding the event data of this example. It is a conceptual diagram for demonstrating the correlation process of event data and trend data. It is a figure which shows the flow of an association process of event data and trend data. The combined image of event data and trend data is shown. It is a figure for demonstrating creation and display control processing of DB according to purpose. It is various data structural examples regarding DB classified by lot and batch. It is various data structural examples regarding DB by periodical lot and batch. It is an example of various data structures regarding fixed period collection and DB according to a period. It is an example of various data structures concerning progress DB. It is a graph creation flow. It is an example of a graph definition master. (A)-(c) is a figure which shows an example of a kind definition master, an equipment definition master, and a TAG definition master. FIG. 10 illustrates an example of a search screen (part 1); It is FIG. (2) which shows an example of a search screen. FIG. 10 illustrates an example of a search screen (part 3); It is FIG. (4) which shows an example of a search screen. It is an example of a screen data item ID definition screen. It is an example of the master concerning the link between processes. It is an example of creation of inter-process link performance data. It is a figure which shows the complexity of the process in a batch type manufacturing system like a foodstuff manufacturing process. It is a computer hardware block diagram.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Manufacturing plan apparatus 2 Manufacturing plan constraint condition 3 Order expansion apparatus 4 Manufacturing recipe 5 Manufacturing order apparatus 6 Order reservation 7 Progress management apparatus 8 Transition rule 9 Control system I / F
10 Production Information Integrated Management System 11 DB Integrated Management Device 12 Manufacturing Performance DB (Integrated DB)
13 DB by purpose
15 General-purpose results analyzer 31 Event data 32 Trend data 33 Closing management table 34 Result data 35 by layer 35 Total data 40 by layer DB for each purpose
41 DB by lot / batch
42 DB by regular lot / batch
43 Periodical collection / DB by period
44 Progress DB
50 Integrated DB
51 Event DB
52 Trend DB
53 Discrete DB
61 DB reference master by purpose 62 Data item definition master 63 Data collection / arithmetic processing unit 64 DB storage processing unit by purpose 71 Graph definition master 72 Detailed graph definition master 73 DB conversion master by purpose 74 Model definition master 75 Equipment definition master 76 Data Item definition master 100 Computer 101 CPU
DESCRIPTION OF SYMBOLS 102 Memory 103 Input part 104 Output part 105 Storage part 106 Recording medium drive part 107 Network connection part 108 Bus 109 Portable recording medium 109a FD (flexible disk)
109b CD-ROM

Claims (6)

Data collection means for collecting trend data and event data from the control device;
Integrated data generation / storage means for storing the collected trend data and event data in association with each other;
Purpose-specific database generation / storage means for generating and storing various purpose-specific databases obtained by classifying the integrated data stored in the integrated data generation / storage means according to various purposes;
Display control means for acquiring and displaying data matching the search condition from the database for various purposes according to an arbitrary search condition;
A production information integrated management system characterized by comprising:   The event data is data according to a hierarchical structure in which processes are subdivided step by step, and the integrated data generation / storage means cuts out a portion corresponding to each hierarchy of the collected event data from the trend data, and The production information integrated management system according to claim 1, wherein the association is performed. The cutout is to cut out trend data between the start time and end time of each hierarchy,
3. The integrated production information management system according to claim 1, wherein the integrated data generation / storage means obtains the total, maximum, minimum, and average of the extracted trend data and performs the association as well.   The production information integrated management system according to claim 1, wherein the display control unit displays the acquired data as a table, a graph, a Gantt chart, or a correlation diagram. The purpose-specific database includes the integrated data classified by lot / batch, the aggregated data obtained from the integrated data and classified by lot / batch, or the integrated data totaled in a time-sharing manner. An identifier is assigned to each value group,
The production information integrated management system according to any one of claims 1 to 4, wherein the display control means performs the search using an identifier of a lot / batch or an identifier for each of the total value groups. On the computer,
A function to collect trend data and event data from the control unit;
A function of storing the collected trend data and event data in association with each other;
A function of generating and storing a database for various purposes in which the stored integrated data is classified according to various purposes;
In accordance with an arbitrary search condition, a function for acquiring and displaying data that matches the search condition from the database for various purposes, and
A program to realize