JP2023012636A - Data extraction apparatus, data extraction system, data extraction method, and data extraction program - Google Patents

Abstract

To reduce the number of man-hour for extracting data related to a work content which changes in a time sequence.SOLUTION: A data extraction apparatus having a processor and a storage device which cooperate with a memory in order to execute a program, holds management data related to metadata in the storage device, the metadata being added to time-sequential sensor data collected from a sensor installed in a work environment based on a condition of the work environment; and the processor newly forms an entry of the management data related to the metadata which is added to the sensor data based on a new condition in the work environment based on an existing entry of the management data.SELECTED DRAWING: Figure 3

Description

The present invention relates to a data extraction device, data extraction system, data extraction method, and data extraction program.

With the development of the Internet of Things (IoT), services have emerged that provide value-added solutions by analyzing data collected from devices such as sensors.

A factory is an example of an IoT application. When IoT is applied to a factory, sensor data is collected from sensors attached to manufacturing equipment and workers in the factory, and the state and efficiency of the manufacturing process in the factory are visualized and analyzed to detect abnormalities at the manufacturing site. , improve production plans and manufacturing processes, improve workers' production technology, etc.

A specific example of a solution that applies IoT to a factory is a solution that analyzes the state and efficiency of the factory production line by associating sensor data collected from factory equipment with configuration data of the production line. Here, the configuration data of the production line refers to the connection order of devices installed in the production line, the types of devices to be used, the products to be produced, and the like. Conventionally, the task of associating sensor data with configuration data of a production line is dependent on the person, and since a person browses and selects all the data, an increase in man-hours for associating the data becomes a problem.

For example, when an abnormality in factory equipment or a worker is detected from sensor data, in order to analyze and deal with the abnormality, not only the relevant equipment and process, but also the relationship with the previous and subsequent processes, and the information of the worker of the relevant equipment Is required. The configuration of the production line changes according to the products to be produced, and the workers also change according to the work schedule. A technical issue is to reduce the man-hours for associating data following these changes and changes.

To address this technical problem, Patent Document 1 discloses an information processing system that utilizes device data recorded in a database according to data definitions that can be updated at any time. The version number of the device data definition is managed in association with the time, and the data definition corresponding to the specified period is provided.

Japanese Patent Application Laid-Open No. 2010-212301

However, the data definition described in Patent Literature 1 is information on the position and state of the main body of the apparatus, and there is no disclosure of a mechanism for managing information whose relationship changes in time series, such as peripheral devices and workers. not

In addition, in Patent Document 1, the version number is positioned only by the time, but in the above-mentioned solution for analyzing the state and efficiency of the production line in the factory, not only changes in the configuration of the work environment but also changes in the work content Therefore, the necessary data analysis cannot be performed only by managing the time-series changes in the data definition of the device.

SUMMARY OF THE INVENTION The present invention has been made in view of the above problems, and an object of the present invention is to reduce man-hours for extracting data related to work content that changes in time series.

A representative example of the invention disclosed in the present invention is as follows. That is, a data extraction device having a processor and a storage device that execute a program in cooperation with a memory, wherein time-series sensor data collected from sensors installed in a work environment is processed based on the conditions of the work environment. Management data about metadata to be added is held in the storage device, and the processor manages the metadata about the metadata to be added to the sensor data based on new conditions of the work environment based on existing entries in the management data. It is characterized by newly creating an entry of management data.

According to the present invention, generation management of metadata based on time and work content makes it easier for a data analyst to select relevant data from a wide variety of large amounts of operation data using time and work content as triggers. It is possible to reduce man-hours for extracting data related to work content that changes over time. In addition, based on the similarity of past metadata to the corresponding work content, past metadata can be used to automatically create new generation metadata, further reducing man-hours. Problems, configurations, and effects other than those described above will be clarified by the following description of the embodiments.

FIG. 1 is an explanatory diagram showing an example of a working environment. FIG. 2 is a block diagram showing a hardware configuration example of the metadata extraction device. FIG. 3 is a block diagram showing a configuration example of a metadata extraction system. FIG. 4 is an explanatory diagram showing an example of work environment configuration information. FIG. 5 is an explanatory diagram showing an example of an input screen of the configuration data input unit. FIG. 6 is an explanatory diagram of an example of configuration data. FIG. 7 is an explanatory diagram showing an example of a graph format database of configuration data. FIG. 8 is an explanatory diagram showing an example of data collection/processing in the data processing device. FIG. 9 is an explanatory diagram showing an example of metadata extraction rules. FIG. 10 is an explanatory diagram showing an example of generation management data. FIG. 11 is an explanatory diagram showing an example of a metadata management method. FIG. 12 is a flowchart illustrating an example of metadata extraction processing. FIG. 13 is an explanatory diagram showing an example of automatic registration of metadata. FIG. 14 is an explanatory diagram showing an example of a metadata creation screen. FIG. 15 is an explanatory diagram showing an example of a screen for manually registering a metadata extraction method. FIG. 16 is an explanatory diagram showing an example of a confirmation screen of the metadata extraction unit. FIG. 17 is an explanatory diagram showing an example of result display on the confirmation screen. FIG. 18 is an explanatory diagram of an example of metadata. FIG. 19 is a flowchart illustrating an example of metadata addition processing. FIG. 20 is an explanatory diagram showing an example of a logic setting screen for data collection processing. FIG. 21 is an explanatory diagram of an example of conversion logic. FIG. 22 is an explanatory diagram of an example of a metadata acquisition table. FIG. 23 is an explanatory diagram showing an example of processing for acquiring metadata from the metadata extraction device. FIG. 24 is an explanatory diagram of an example of metadata search processing and addition processing. FIG. 25 is an explanatory diagram of an example of updating configuration data. FIG. 26 is a flowchart illustrating an example of metadata update processing. FIG. 27 is an explanatory diagram of an example of updating generation management data. FIG. 28 is an explanatory diagram of an example of a packet ID table. FIG. 29 is a flowchart illustrating an example of processing for detecting data that fails to switch metadata. FIG. 30 is an explanatory diagram showing the concept of packet ID acquisition in the process of detecting metadata switching omission data. FIG. 31 is an explanatory diagram of an example of a collected data table. FIG. 32 is a flowchart illustrating an example of processing for updating collected data that has been omitted due to switching of metadata. FIG. 33 is an explanatory diagram of an example of update of collected data that is omitted due to switching of metadata. FIG. 34 is an explanatory diagram of an example of updating the collected data table.

An embodiment of the present invention will be described below with reference to the drawings. However, the present invention should not be construed as being limited to the descriptions of the embodiments shown below. Those skilled in the art will easily understand that the specific configuration can be changed without departing from the idea or gist of the present invention.

In the following description, the same reference numerals are given to the same or similar configurations or functions, and overlapping descriptions are omitted. Also, even if different subnumbers are attached to the same code, if they are not distinguished, the code without subnumbers is used.

<Working environment example of the embodiment>
FIG. 1 is an explanatory diagram showing an example of a working environment. Work environment 100 has two production lines 181-1, 181-2. Equipment 131 is arranged as a manufacturing device in each production line 181 . Four units of equipment 131-1, equipment 131-2, equipment 131-3, and equipment 131-4 are arranged on the production line 181-1 to manufacture a product 191-1. Four units of equipment 131-5, equipment 131-6, equipment 131-7, and equipment 131-8 are arranged on the production line 181-2 to manufacture a product 191-2.

The device 131 has a built-in sensor for measuring temperature, current, etc., and has a function of outputting the value of the sensor as sensor data together with identification information and time information of the device.

Eight workers 141 - 1 to 141 - 8 are arranged in the work environment 100 to operate and check each device 131 .

The device 131 is connected to a PLC 151 (Programmable Logic Controller) via an in-factory network 171 .

The PLC 151 is a device that controls and manages the equipment 131, and has a role of collecting sensor data from sensors built into the equipment 131 in this embodiment. The PLC 151 is connected to the IoT gateway 161 via another factory network 172 .

The IoT gateway 161 transmits information such as sensor data acquired from the connected PLC 151 to a server constructed on a network 173 outside the factory such as the Internet. The IoT gateway 161 transfers the data received from the server built on the network 173 outside the factory to the PLC 151 . In this embodiment, the IoT gateway 161 has a role of collecting and aggregating data of the PLC 151 and transmitting it to the metadata extraction device 101 .

<Hardware Configuration of Metadata Extraction Device 101>
FIG. 2 is a block diagram showing a hardware configuration example of the metadata extraction device 101. As shown in FIG. The metadata extraction device 101 is provided as an enhancement function of the data processing device 102, for example. The metadata extraction device 101 has a processor 201 , a storage device 202 , an input device 203 , an output device 204 and a communication interface (communication IF) 205 . Processor 201 , storage device 202 , input device 203 , output device 204 and communication IF 205 are connected by bus 206 . A processor 201 controls the metadata extraction device 101 . A storage device 202 serves as a work area for the processor 201 . Also, the storage device 202 is a non-temporary or temporary recording medium that stores various programs and data. Examples of the storage device 202 include ROM (Read Only Memory), RAM (Random Access Memory), HDD (Hard Disk Drive), and flash memory. The input device 203 inputs data. The input device 203 includes, for example, a keyboard, mouse, touch panel, numeric keypad, and scanner. The output device 204 outputs data. Output devices 204 include, for example, displays and printers. Communication IF 205 connects to a network and transmits and receives data.

<Configuration example of metadata extraction system>
FIG. 3 is a block diagram showing a configuration example of the metadata extraction system 200. As shown in FIG. The metadata extraction system 200 has a metadata extraction device 101 , a data processing device 102 , a device 131 , a PLC 151 and an IoT gateway 161 . The data processing device is also called a data processing infrastructure.

The IoT gateway 161 is connected to the data processing device 102 and transmits sensor data received from one or more PLCs 151 to the data processing device 102 . The PLC 151 transmits sensor data received from one or more devices 131 to the IoT gateway 161 . The device 131 measures the state of the device and transmits it to the PLC 151 as sensor data. There are multiple types of sensor data transmitted by the device 131, such as temperature, humidity, vibration, current, and position. Further, the sensor data output by the device 131 includes attached information such as the device identifier of the device 131 and time information indicating the measurement time of the sensor data.

The metadata extraction device 101 has a configuration data input unit 311 , a configuration data storage unit 312 , a metadata extraction unit 313 and a data display unit 314 . The configuration data input unit 311 and the data display unit 314 are, specifically, functions realized by causing the processor 201 to execute a program stored in the storage device 202 shown in FIG. 2, for example. Specifically, the configuration data storage unit 312 and the metadata extraction unit 313 use, for example, data stored in the storage device 202 shown in FIG. This function is realized by

The configuration data input unit 311 takes in the work environment configuration information 301 indicating the types of the devices 131 in the work environment 100, the connection relationships between the devices 131, the correspondence relationships between the devices 131 and the workers 141, and the like. It has a function of inputting as data 411 .

Further, the configuration data input unit 311 has a configuration input screen 401, which is a screen for inputting the configuration of the work environment by the work environment manager 331. The configuration data input unit 311 takes in the configuration of the work environment input by the work environment manager 331, and inputs the configuration data. It has a function of inputting to the storage unit 312 as the configuration data 411 .

<Work environment configuration information 301>
FIG. 4 is an explanatory diagram showing an example of the work environment configuration information 301. As shown in FIG. FIG. 4 shows work environment configuration information 301 of the production line 181-1. The example shown in FIG. 4 is work environment configuration information 301 written in JSON (Javascript (registered trademark) Object Notation) data format. In the working environment configuration information 301, the manufacturing model (“production_model”) manufactured in the working environment 100 is at the top, and information (“line”) of the production line 181-1 is provided under the production model. describes a hierarchical structure having information (“iot_gateway”) of the IoT gateway 161-1, and information (“plc”) of the PLCs 151-1 and 151-2 under the IoT gateway 161-1.

The working environment configuration information 301 includes information (“equipment”) of the devices 131-1 and 31-2 under the PLC 151-1, and information of the device 131-3 (“equipment”) under the PLC 151-2. structure is described.

An identifier (“id”) is essential as information for each element of the work environment configuration information 301 . Other information may be added arbitrarily.

In FIG. 4, the JSON data format is used, but other data formats may be used. For example, a similar expression is possible in the XML (eXtensible Markup Language) format, or a unique data format may be defined.

<Configuration input screen 401 of configuration data input unit 311>
FIG. 5 is an explanatory diagram showing an example of the configuration input screen 401 of the configuration data input unit 311. As shown in FIG. The configuration input screen 401 is used by the work environment manager 331 to input the configuration of the work environment. The configuration input screen 401 has first to fourteenth input fields 501 to 514 and a registration button 515 . By pressing the registration button 515 , the information entered in the first input field 501 to the fourteenth input field 514 is registered in the work environment configuration information 301 .

Identification information of the production line 181 can be entered in the first entry field 501 . Identification information of the product 191 can be entered in the second entry field 502 . Identification information of the IoT gateway 161 can be entered in the third entry field 503 . Identification information of the production line 181 can be entered in the fourth entry field 504 .

Identification information of the PLC 151 can be entered in the fifth entry field 505 . Identification information of the IoT gateway 161 can be entered in the sixth entry field 506 . Identification information of the device 131 can be entered in the seventh entry field 507 . Identification information of the PLC 151 can be entered in the eighth entry field 508 . In the ninth input field 509, the type of process can be input. Identification information of the device 131 can be entered in the tenth entry field 510 . Identification information of the worker 141 can be entered in the eleventh entry field 511 . The type of process can be entered in the twelfth entry field 512 . In the thirteenth input field 513, the type of process can be input. In the fourteenth input field 514, the type of process can be input.

In the configuration input screen 401, the elements of the identification information entered in the second input field 502, the fourth input field 504, the sixth input field 506, and the twelfth input field 512 on the right side are positioned high in the work environment configuration information 301. do. The relationship between the manufacturing model and the production line 181 is registered in the work environment configuration information 301 using the first input field 501 and the second input field 502 . The relationship between the manufacturing model and the production line 181 is registered in the working environment configuration information 301 using the third input field 503 and the fourth input field 504 .

The relationship between the IoT gateway 161 and the PLC 151 is registered in the working environment configuration information 301 by the fifth input field 505 and the sixth input field 506 . The relationship between the PLC 151 and the device 131 is registered in the working environment configuration information 301 by the seventh input field 507 and the eighth input field 508 . The relationship between the device 131 and the type of process is registered in the work environment configuration information 301 by using the ninth input field 509 and the tenth input field 510 . An eleventh entry field 511 and a twelfth entry field 512 are used to register the relationship between the process and the worker 141 in the working environment configuration information 301 . A thirteenth entry field 513 and a fourteenth entry field 514 are used to register the adjacency relationship between processes in the work environment configuration information 301 .

The screen configuration of FIG. 5 is merely an example, and another screen configuration may be used. For example, instead of input fields, a screen configuration may be used in which a pull-down box is used for selection, or a screen configuration for reading a JSON format file as shown in FIG. 4 may be used.

<Configuration data 411>
FIG. 6 is an explanatory diagram showing an example of the configuration data 411. As shown in FIG. The configuration data 411 is represented by, for example, a table having configuration data creation time 601 and graph format database ID 602 as fields. A configuration data creation time 601 is a predetermined time when the graph format database ID 602 is saved. The configuration data creation time 601 may be set at regular intervals or at the time of configuration change. In this way, the graph-format database ID 602, that is, the work environment configuration information 301 is registered regularly or irregularly.

A graph-format database ID 602 is an identifier of a graph-format database indicating the work environment configuration information 301 . An entry is added to the table of the configuration data 411 each time the metadata extraction device 101 creates or updates the configuration data 411 with the input of the work environment configuration information 301 and the worker configuration information 302 as a trigger. By referring to the graph format database ID 602 of the configuration data 411, the contents of the working environment configuration information 301 can be confirmed as shown in FIG. 7, which will be described later.

<Graph Format Database of Configuration Data 411>
FIG. 7 is an explanatory diagram showing an example of a graph-format database of the configuration data 411. As shown in FIG. The configuration data 411 expresses the contents of the work environment configuration information 301 in graph form data. In the graph format data 701, the work environment configuration information 301 is expressed by linking the product 191, production line 181, IoT gateway 161, PLC 151, equipment 131, worker 141, and process with arrows.

<Examples of data collection methods and metadata>
FIG. 8 is an explanatory diagram showing an example of data collection/processing in the data processing device 102. As shown in FIG. First, the IoT gateway 161 transmits first data 801 composed of a packet ID, an input data ID, and binary data.

Next, the data collection unit 321 of the data processing device 102 receives the first data 801 as binary data from the IoT gateway 161 . The data collection unit 321 transmits the received first data 801 to the data processing unit 322 . The data processing unit 322 converts the first data 801 into the second data 802 according to the conversion logic stored in the conversion logic 451 . Specifically, for example, binary data (first data 801) is converted into character string data (second data 802) including a time stamp, device ID, temperature, and current. The data processing section 322 transmits the second data 802 to the metadata adding section 323 . The method by which the data processing unit 322 converts the first data 801 is stored in the conversion logic 451 of the data processing unit 322 . Conversion logic 451 will be described later.

Next, metadata adding section 323 adds supplementary data necessary for data analysis to received second data 802 to generate third data 803 . In this embodiment, this supplementary data is defined as metadata. For example, the third data 803 includes character string data (second data 802) including timestamp, device ID, temperature, humidity, and current, and data item name 1 (line ID) and data item name 2 (line ID) as metadata. assigned process ID), data item name 3 (previous process ID), data item name 4 (post-process ID), and data item name 5 (worker ID) are added. The third data 803 is transmitted as processed data 804 from the metadata addition unit 323 and stored in the collected data storage unit 324 . The metadata added by the metadata adding section 323 is stored in the metadata 481 of the metadata managing section 326 . A description of the metadata will be given later.

This embodiment discloses metadata extraction for creating metadata used in the data processing device 102 from the configuration data 411 held by the configuration data storage unit 312 of the metadata extraction device 101 .

Returning to FIG. 3, functions and methods for metadata extraction are described. The metadata extraction unit 313 has a function of creating metadata to be used in the data processing apparatus 102 from the configuration data 411 held by the configuration data storage unit 312. The metadata extraction unit 421 and the metadata extraction rule 422 , generation management data 423 , and metadata output section 424 .

The metadata creating unit 421 uses the configuration data 411 in the configuration data storage unit 312 to create metadata indicating information necessary for analyzing data collected by the data processing device 102 . The metadata extraction rule 422 stores metadata extraction rules, which are search conditions for the configuration data 411 and are used by the metadata creation unit 421 to create metadata.

In addition, the metadata creating unit 421 assigns a version number to the metadata in order to manage the generation of the metadata. Information on the version number of metadata is stored in the generation management data 423 .

The metadata output unit 424 has a function of outputting metadata added to data collected by the data processing device 102 to the data processing device 102 . The metadata output by the metadata output unit 424 is created by the metadata creating unit 421 .

<Metadata extraction rule 422>
FIG. 9 is an explanatory diagram showing an example of the metadata extraction rule 422. As shown in FIG. The metadata extraction rule 422 is represented by a table having an extraction rule ID 901 and a search condition 902 as fields. The extraction rule ID 901 is an identifier that indicates an extraction rule. A search condition 902 indicates a search condition for obtaining necessary data from the configuration data 411 of the configuration data storage unit 312 in order to create metadata.

A search condition 902 is expressed by a combination of a search key 9021 and a search value 9022 . A search key 9021 indicates a data type used to search for necessary data from the configuration data 411 . The search value 9022 indicates the data type of data acquired from the configuration data 411 . Multiple search values 9022 may be specified for one search key 9021 .

A plurality of search conditions 902 may be specified for one extraction rule ID 901 . For example, for an entry with an extraction rule ID 901 of R02, the line ID, process-in-charge ID, previous-process ID, and post-process ID are acquired using the device ID as a search key. Then, using the acquired process ID in charge as the next search key, searching for the worker ID is registered as a search condition.

In the example of FIG. 9, three types of metadata are defined. The first is a metadata definition for specifying a specific device ID and acquiring the ID of the line connected to the specified device ID and the ID of the process in charge. Here, the connection relationship indicates, for example, a one-hop connection relationship in the graph-format database of the configuration data 411 . For example, this metadata is used during normal operation of a work environment such as a factory. The second method is to specify a specific device ID, acquire the line ID and the process ID in charge that are connected to the specified device ID, and also acquire the process ID of the previous process of the process in charge and the process ID of the next process, It is a metadata definition for acquiring the ID of the worker who is in charge of the ID of the process in charge using the acquired ID of the process in charge as a search key. For example, when an abnormality occurs in the work environment such as a factory, this metadata is used to understand the detailed situation of the work environment. The third is a metadata definition for designating a specific device ID and acquiring a line ID that has a connection relationship with the designated device ID. This metadata is used, for example, during commissioning of the equipment before it is introduced into the production line.

The required data differs depending on the work environment situation and the purpose of data analysis, and the data analysis time and data accumulation resource usage efficiency can be improved by collecting only the necessary data. Define and use multiple metadata as shown.

<Example of metadata generation management method>
FIG. 10 is an explanatory diagram showing an example of the generation management data 423. As shown in FIG. Generation management data 423 includes, for example, generation management data ID 1001, registration method 1002, configuration data creation time 601, configuration data condition 1003, event condition 1004, version number 1005, and extraction rule ID 901 as fields. represented by a table with
The generation management data ID 1001 is an identifier that indicates generation management data. The registration method 1002 indicates whether the metadata to be the generation management data was created manually by the user or was created automatically. The configuration data creation time 601 indicates the creation time of the configuration data used by the generation management data. A configuration data condition 1003 indicates a condition for limiting the extraction range of metadata targeted by the generation management data ID 1001 . The event condition 1004 indicates event information using generation management data, and can be arbitrarily defined by the user of the metadata extraction device 101 . An example of a method of defining the event condition 1004 is "tag=value", which will be described below with "tag=value". A version number 1005 indicates the version number of the generation management data. The extraction rule ID 901 indicates an extraction rule ID corresponding to generation management data.

In the example of FIG. 10 , one configuration data condition 1003 is defined for one generation management data ID 1001 , but multiple configuration data conditions 1003 may be defined for one generation management data ID 1001 . Similarly, although one event condition 1004 is defined for one generation management data ID 1001 in the example of FIG. 10, multiple event conditions 1004 may be defined for one generation management data ID 1001 .

<How to manage metadata>
The concept of metadata in this embodiment will be described. FIG. 11 is an explanatory diagram of a metadata management method according to this embodiment. In this embodiment, metadata is managed based on two elements: time and work content. In this embodiment, work content is defined as an event. An example of an event is, for example, information indicating which product is being manufactured on a production line. Another example is the operation status of a production line, including the status of normal operation, the status of occurrence of an abnormality, and the status of trial operation.

In FIG. 11, at time t1, metadata during normal operation of the production line that manufactures product P01 is created, and version number Ver. Give 1.0. At time t2, the same configuration data as version number Ver. 1.0 is used, and the abnormal metadata is changed to version number Ver. 1.1 is given and created. Furthermore, by using the same configuration data as version number Ver. 1. Create by giving 2.

Next, consider creating metadata for the production line that manufactures the product P02 at times t3 and t4. Only by changing the product number, the method of creating metadata is the same as in the case of manufacturing P01, so metadata can be created based on the metadata of the production line that manufactures product P01. In this embodiment, the Ver. 1.0, 1.1, and 1.2, the metadata Ver. Automatically create 1.0, 1.1, 1.2.

Next, at times t5 and t6, the environment of the production line for manufacturing the product P01 changes, and the creation of metadata is considered. The method for creating metadata is Ver. 1.0, 1.1, and 1.2, and only re-searches the configuration data. 1.0, 1.1, and 1.2, the metadata Ver. 2.0, 2.1, 2.2 are automatically created.

As described above, in this embodiment, metadata is managed based on the two elements of time and event, and when creating new metadata, if past metadata can be used, past metadata is We show how to use it and automatically create metadata.

<Metadata extraction processing>
FIG. 12 is a flowchart showing an example of metadata extraction processing executed by the metadata extraction unit 313. As shown in FIG. First, the current time, configuration data conditions, and event conditions are entered (step S1201).

Next, the metadata extraction unit 313 refers to the generation management data 423 and searches for an entry that meets the conditions input in step S1201 (step S1202).

If the generation management data 423 contains one or more similar entries containing the search key of the configuration data condition or the tag name of the event condition that matches the condition input in step S1201, the metadata extraction unit 313 detects the entry (step S1203: YES), the process moves to step S1204. The metadata extraction unit 313 selects an entry containing the largest number of configuration data condition values and event condition values input in step S1201, acquires an extraction rule ID (step S1204), and extracts an entry similar to the generation management data 423 (step S1204). A new entry to which the condition input in step S1201 is added is registered (step S1205). When registering, set the registration method to "automatic registration".

If there is no similar entry containing the search key of the configuration data condition and the tag name of the event condition that match the search condition (step S1203: NO), the metadata extraction unit 313 extracts the metadata input by the user to create the metadata. and the version number are received (step S1206). The metadata extraction unit 313 then registers the new entry of the condition input in step S1201 in the metadata extraction rule 422, and acquires a new extraction rule ID (step S1207). The metadata extraction unit 313 then automatically creates metadata and registers a new entry in the generation management data 423 (step S1208). When registering, set the registration method to "manual registration".

<Automatic Registration of Metadata>
FIG. 13 is an explanatory diagram showing an example of automatic metadata registration performed in steps S1202, S1203, S1204, and S1205 of FIG. FIG. 13 shows an example of inputting "product ID=P02" as the configuration data condition and "operating state=normal time" as the input in step S1201 of FIG. In step S1202, when the generation management data 423 is referred to, there are three entries including the configuration data condition search key "product ID" and the event condition tag name "operating state": generation management data ID=V0001, V0002, V0003. Since it exists, step S1203 becomes YES. Next, in step S1204, it is checked whether or not the search value "P02" of the configuration data condition and the value "normal time" of the event condition are included. Therefore, the record whose extraction rule ID 901, which is the metadata ID of the entry of V0001, is R01 is acquired.

Then, in step S1205, as a new entry, the generation management data ID 1001 is set to "V0004", the value of the registration method 1002 is set to "automatic registration", and the value of the configuration data creation time 601 is set to the configuration data of the latest entry in the configuration data 411. With the creation time 601 as the value, the configuration data condition 1003 is "product ID=P02" input in step S1201 of FIG. 12, and the event condition 1004 is "operating state=normal 1.0 since the version number 1005 is newly registered, and the extraction rule ID 901 is registered as "R01", which is the extraction rule ID of the entry of V0001.

In the example of FIG. 13, in step S1204, there was only one entry containing the most values of the configuration data conditions and event conditions input in step S1201. One entry is selected based on conditions such as the largest number of event conditions, the latest configuration data creation time, and the latest version number.

<Example of metadata creation screen>
FIG. 14 is an explanatory diagram showing an example of the metadata creation screen 431. As shown in FIG. The metadata creation screen 431 is used by the metadata developer 341 and has, for example, a configuration data condition input field 1401 , an event condition input field 1402 , and a create button 1403 . A configuration data condition input field 1401 inputs a search key 14011 and a search value 14012 . A tag 14021 and a tag value 14022 are entered in the event condition input field.

When a create button 1403 is pressed, metadata extraction processing shown in the flowchart of FIG. 12 is performed. When performing manual registration shown in steps S1206 to S1208 in the flowchart of FIG. 12, a screen is displayed for the metadata developer 341 to enter the metadata extraction method.

<Screen for manually registering the metadata extraction method>
FIG. 15 is an explanatory diagram showing an example of a screen for manually registering a metadata extraction method by the metadata developer 341. As shown in FIG. The manual registration screen 432 has, for example, an input field 1501 for the primary condition of the metadata extraction condition, an input field 1502 for the subordinate condition of the metadata extraction condition, an input field 1503 for the version number, and a register button 1504 .

A search key 15011 and a search value 15012 are entered in the main condition input field 1501 . Multiple items can be entered in the search value 15012 by pressing an add button 15013 . In FIG. 15, four search values 15012-1 to 15012-4 are input.

In the subordinate condition input field 1502, one of the search values 15012 of the main condition input field 1501 is obtained as a search key 15021, and the obtained search key 15021 and its search value 15022 are input as search conditions. Multiple items can be entered in the search value 15022 by pressing an add button 15023 . Further, by pressing an add button 15024, a plurality of subordinate condition input fields 1502 can be input.

A version number input field 1503 is used to input a version number 15031 when registering in the generation management data 423 . When a registration button 1504 is pressed, manual registration shown in steps S1206 to S1208 in the flowchart of FIG. 12 is performed. The screen configuration of FIG. 15 is only an example, and another screen configuration may be used. For example, instead of input fields, a screen configuration may be selected using a pull-down box, or a screen configuration for reading a JSON format file or a CSV format file. It's okay.

<Confirmation screen 433>
FIG. 16 is an explanatory diagram showing an example of the confirmation screen 433 of the metadata extraction unit 313. As shown in FIG. The confirmation screen 433 of the metadata extraction unit 313 has, for example, a configuration condition input field 1601, an event condition input field 1602, a version condition input field 1603, and a display button 1604. FIG. A search key 16011 and a search value 16012 are entered in the configuration condition input field 1601 . An event condition input field 1602 inputs a tag 16021 and a tag value 16022 . In the version condition input field 1603, either a radio button 16031 for searching for the latest version or a radio button 16032 for specifying the version number is pressed. When specifying the version number, enter the version number in the version number input field 16033 .

When the display button 1604 is pressed, the generation management data 423 is referred to based on the entered configuration conditions, event conditions, and version conditions, and the corresponding entry is obtained. Regarding the version condition, when retrieving the latest version, the metadata ID of the entry with the latest version number is obtained from the entries that match the configuration condition and event condition. If the version number is specified, an entry that matches the version number entered in the version number entry field 16033 is acquired. The screen configuration in FIG. 16 is only an example, and other screen configurations may be used. For example, instead of input fields, a screen configuration may be selected using pull-down boxes, or input fields and pull-down boxes may be used instead of radio buttons. It is also possible to configure the screen to

<Result display when the display button 1604 of the confirmation screen 433 is pressed>
FIG. 17 is an explanatory diagram showing an example of result display when the display button 1604 on the confirmation screen 433 in FIG. 16 is pressed. Metadata is extracted from the configuration data 411 based on the search method (search condition 902) corresponding to the extraction rule ID 901 of the acquired entry of the generation management data 423, and is displayed on the screen 1701 as character string data such as JSON format. indicate. The display example is merely an example, and other display methods may be used, such as CSV file format or table format. From the result display in FIG. 17, it can be confirmed which metadata is extracted for the specified configuration data condition, event condition, and version condition.

According to this embodiment, while managing configuration data of the work environment whose relationship changes in time series, it follows not only changes in the work environment but also changes in the viewpoint of analysis (addition of variations of viewpoint axis values, etc.), Generation management data 423 related to metadata to be added can be automatically created.

Metadata addition processing in data collection and processing will be described below with reference to FIGS. 18 to 24 as Example 1 of the present embodiment.

<Metadata 481 of Example 1>
FIG. 18 is an explanatory diagram showing an example of the metadata 481. As shown in FIG. Metadata is represented by a table having, for example, a metadata ID 1800 , configuration data creation time 1801 , registration time 1802 , version 1803 and metadata body 1804 . The metadata ID 1800 is an identifier of metadata. The configuration data creation time 1801 indicates the creation time of the configuration data on which the metadata is based. The registration time 1802 indicates the time when metadata information was registered in the metadata 481 . A version 1803 indicates version information of the metadata.

Metadata body 1804 indicates the contents of metadata, and has, for example, search key 18041 and output metadata value 18042 . A search key 18041 indicates a field name and its value used as a metadata search condition. The output metadata value 18042 indicates the content of metadata to be output.

The example of FIG. 18 shows an example of two types of metadata with metadata IDs M0001 and M0002. The metadata with the metadata ID=M0001 has the search key 18041 as the device ID, and the output metadata values corresponding to the value of the device ID are the line ID, process ID in charge, previous process ID, post-process ID, and worker ID. Output the value of 5 items. Metadata with metadata ID=M0002 uses the PLC ID as the search key 18041, and outputs the values of the three items of device ID, line ID, and product ID as output metadata values.

<Metadata addition processing of the first embodiment>
FIG. 19 is a flowchart illustrating an example of metadata addition processing. First, the data processing developer 351 shown in FIG. 3 inputs data conversion logic to the data conversion logic setting unit 325 of the data processing device 102 (step S1901).

Next, the data conversion logic setting unit 325 transmits the configuration data condition and the event condition to the metadata output unit 424 of the metadata extraction device 101 to request metadata output (step S1902).

Next, the metadata output unit 424 of the metadata extraction device 101 refers to the generation management data 423 using the received configuration data conditions and event conditions and the current time as search conditions, and extracts the corresponding extraction rule ID 901 and version number 1005. is obtained (step S1903). In step S1903, the metadata output unit 424 refers to the generation management data 423, the configuration data creation time 601 is the closest before the current time of the search condition, and the configuration data condition 1003 and the event condition 1004 are the configuration data conditions of the search condition. and the extraction rule ID 901 and version number 1005 of the entry that matches the event condition.

Next, the metadata output unit 424 refers to the metadata extraction rule 422 using the extraction rule ID 901 acquired in step S1903 as a search condition, and acquires the search key 9021 and the search value 9022 (step S1904).

Subsequently, the metadata output unit 424 inputs the current time, the configuration data condition, the search key 9021 and the search value 9022 obtained from the metadata extraction rule 422, and acquires the metadata from the configuration data 411 at the corresponding creation time. and transmits it to the metadata management unit 326 of the data processing device 102 (step S1905). The creation time of the configuration data 411 corresponding to the current time is the closest creation time of the configuration data 411 before the current time.

The metadata management unit 326 of the data processing device 102 registers the metadata received from the metadata output unit 424 in the metadata 481, and notifies the metadata ID of the registered metadata to the data conversion logic setting unit 325 (step S1906).

The data conversion logic setting unit 325 pairs the input data ID and the search key input by the data processing developer 351 in step S1901 described above with the metadata ID received from the metadata management unit 326 in step S1906 described above, and sets the metadata. It is registered in the metadata acquisition table 461 of the data addition unit 323 (step S1907).

The data conversion logic setting unit 325 registers the data processing logic input by the data processing developer 351 in step S1901 in the conversion logic 451 of the data processing unit 322, and starts data collection and processing (step S1908). .

<Logic setting screen 471>
FIG. 20 is an explanatory diagram showing an example of the data collection processing logic setting screen 471 used in step S1901 of FIG.

In the example of FIG. 20, four input screens of input data ID setting 2001, data processing setting 2002, metadata setting 2003, and data output setting 2004 are provided.

In input data ID setting 2001, which is the first input screen, a data ID, which is an identifier of data to be received by the data processing apparatus 102, is input.

A data processing setting 2002, which is the second setting screen, describes a data processing method and specific processing contents. FIG. 20 shows a method of converting binary data into a character string (JSON) as an example of a processing method. As input items, byte string information indicating which byte group of binary data constitutes one piece of data, a field name indicating the content of the byte string data, and a data format are entered.

Although FIG. 20 describes a method of converting binary data into a character string (JSON), another processing method may be used. For example, a processing method that converts binary data into comma-separated data may be employed. In this case, examples of input items include byte information indicating data delimiters and the format of each data.

In metadata setting 2003, which is the third setting screen, a search key and a search value for configuration data conditions are entered as metadata acquisition conditions. Also, as the event condition, the tag indicating the event and the tag value are input. This tag and tag value correspond to the event condition of the generation management data 423 in FIG.

Data output setting 2004, which is the fourth setting screen, sets the type of collected data in the collected data storage unit 324, which is the output destination of the third data 803 in FIG. FIG. 20 shows an RDB (Relational Database) as an example of data output settings. In RDB, enter the name of the table that stores the data.

Although an example of RDB is shown in FIG. 20, another data output destination may be specified. For example, a method of saving as a file may be specified. In this case, examples of setting items include the directory of the file saving destination, the saving file name, and the like.

<Conversion logic 451>
FIG. 21 is an explanatory diagram showing an example of the conversion logic 451. As shown in FIG. Conversion logic 451 is represented, for example, by a table having input data ID 2101 and logic 2102 as fields. The input data ID 2101 is an identifier of input data. The input data ID 2101 is information entered in the input data ID setting 2001 on the logic setting screen 471 of FIG.

Logic 2102 is information indicating a method for converting input data, for example, information indicating a method for converting binary data into a character string (JSON). Logic 2102 is represented by byte string 21021, field name 21022, and format type 21023, for example.

The byte string 21021 indicates byte string information for indicating which byte group of binary data constitutes one piece of data. The field name 21022 indicates the contents of the byte string data. The format type 21023 indicates the format type of data. Information of the logic 2102 is information input in the data processing setting 2002 of the logic setting screen 471 of FIG.

<Metadata acquisition table 461>
FIG. 22 is an explanatory diagram showing an example of the metadata acquisition table 461. As shown in FIG. The metadata acquisition table 461 is represented by a table having an input data ID 2101, a search key 2201, and a metadata ID 2203, for example. The input data ID 2101 is an identifier of input data. The input data ID 2101 is information entered in the input data ID setting 2001 on the logic setting screen 471 of FIG.

A search key 2201 is information used as a search condition when searching for metadata. A search key 2201 is information input in the metadata setting 2003 of the logic setting screen 471 in FIG. A metadata ID 2202 is an identifier of metadata.

The input data ID 2101 and the metadata ID 2202 have a correspondence relationship of m:n (m and n are positive integers). With the metadata acquisition table 461, it is possible to identify the input data affected by the metadata update.

<Metadata Acquisition Processing of Data Processing Device 102>
FIG. 23 is an explanatory diagram showing an example of processing for acquiring metadata from the metadata extraction device 101 executed by the data processing device 102. As shown in FIG. The data conversion logic setting unit 325 transmits the configuration data condition “product ID=P01” and the event condition “operating condition=normal time” to the metadata output unit 424 of the metadata extraction device 101 to request metadata output. (Step S2301).

Next, the metadata output unit 424 of the metadata extraction device 101 extracts generation management data 423 using the received configuration data condition “product ID=P01”, the event condition “operating condition=normal time”, and the current time as search conditions. (step S2302) to obtain the corresponding extraction rule ID "R01" and version number "Ver.2.0" (step S2303).

Next, the metadata output unit 424 refers to the metadata extraction rule 422 using the acquired metadata ID “R01” as a search condition (step S2304), and searches for a search key “device ID” and a search value “line ID, person in charge”. process ID" is acquired (step S2305).

Subsequently, the metadata output unit 424 outputs the current time, the configuration data condition “product ID=P01”, the search key “apparatus ID” obtained from the metadata extraction rule 422, and the search value “line ID, process ID in charge”. is input (step S2306), and the main body of metadata is obtained from the configuration data 411 at the relevant time (step S2307).

Finally, the metadata output unit 424 transmits the acquired metadata main body, configuration data creation time, and version number "Ver.2.0" to the metadata management unit 326 of the data processing device 102 (step S2308). .

<Metadata search processing and addition processing>
FIG. 24 is an explanatory diagram of an example of metadata search processing and addition processing. The metadata addition unit 323 performs the metadata search processing and addition processing.

First, the metadata adding unit 323 receives the second data 802 and refers to the input data ID included in the second data 802 . In FIG. 24, "D0001" is acquired as the value of the input data ID.

Next, the metadata adding section 323 refers to the metadata acquisition table 461 managed by the metadata adding section 323 using the input data ID=D0001 as a search condition. In FIG. 24, "M0001" is acquired as the metadata ID corresponding to the input data ID=D0001, and "apparatus ID" is acquired as the search key corresponding to the input data ID=D0001. Then, the metadata adding unit 323 acquires the value of the search key “device ID” acquired as the search key corresponding to the input data ID=D0001 from the received second data 802 . In FIG. 24, “E02” is acquired as the device ID value included in the second data 802 .

The metadata adding unit 323 refers to the metadata in the metadata managing unit 326 using the acquired metadata ID=M0001 and device ID=E02 as search conditions (step S2301). Each table in FIG. 24 has omissions. In FIG. 24, as metadata corresponding to metadata ID=M0001 and device ID=E02, (line ID=L01, assigned process ID=O12, previous process ID=O01, post-process ID=O04, worker ID=H02 ) are acquired (step S2402).

Metadata management unit 326 adds the metadata acquired in step S2402 to second data 802 and outputs it as third data 803 .

<Effect in Example 1>
The effect of the invention in this embodiment will be described. In the past, it was necessary to manually describe and set metadata one by one. On the other hand, in this embodiment, generation management of metadata is performed by chronological order and event, and necessary metadata is automatically output only by screen input of simple search conditions as manual work, reducing the time required for manual work. can. Furthermore, if past metadata and events are similar, new generation metadata can be automatically created based on past metadata, further reducing man-hours.

In this embodiment, the metadata is acquired at the current time, but the metadata may be acquired by specifying a time period in the past. For example, when inputting data for a certain period of time in the past to the data processing apparatus 102, the metadata adding unit 323 obtains metadata corresponding to the certain period of time, and extracts the metadata from a set of the input data ID, the time stamp, and the metadata ID. Decide which metadata to add. This method is effective when processing data for a certain period of time in the past as batch processing.

Metadata update processing when updating the configuration data 411 in real time will be described below as a second embodiment.

<Updating the configuration data 411 of the second embodiment>
FIG. 25 is an explanatory diagram showing an example of updating the configuration data 411. As shown in FIG. The configuration data input unit 311 transmits the configuration change time and configuration change content of the work environment 100 to the configuration data storage unit 312 (step S2501).

The configuration data storage unit 312 updates the configuration data 411 . In the example of FIG. 25, the device E04 is replaced with the device E09, and the process O14 is replaced with the process O31.

<Metadata update processing>
FIG. 26 is a flowchart illustrating an example of metadata update processing. First, the configuration data input unit 311 acquires configuration change notification including configuration change time and configuration change content of the work environment 100 from the management system of the work environment 100 (step S2601).

Next, the configuration data input unit 311 transfers the configuration change notification to the configuration data storage unit 312 (step S2602). Next, the configuration data storage unit 312 updates the configuration data 411 based on the configuration change notification received from the configuration data input unit 311, and extracts update items (step S2603). The configuration data storage unit 312 then transmits the configuration change time and the extracted update items to the metadata extraction unit 313 (step S2604).

Next, the metadata extraction unit 313 refers to the generation management data 423 (step S2605), checks the search conditions for all entries, and confirms whether there is an entry related to the update item in the search conditions (steps S2606 to S2606). S2608). If there is an entry related to the update item in the search condition (step S2608: Yes), the update item is duplicated and newly registered in the generation management data 423 (step S2609).

Next, the metadata extraction unit 313 acquires the newly registered metadata from the configuration data 411 and transmits it to the metadata management unit 326 (step S2610). Finally, the metadata management unit 326 of the data processing device 102 registers the metadata received from the metadata extraction unit 313 in the metadata 481 (step S2611).

<Example of updating generation management data 423 accompanying updating of configuration data 411>
FIG. 27 is an explanatory diagram showing an example of updating the generation management data 423 accompanying updating of the configuration data 411 of FIG. FIG. 27 corresponds to the processing of steps S2606 to S2608 in the flowchart of FIG. In FIG. 27, since the device and process related to product P01 have been changed, the three entries V0001 to V0003 of the generation management data ID whose configuration data condition is "device ID=product P01" are to be updated. .

The metadata extracting unit 313 duplicates the three entries with generation management data IDs V0001 to V0003, sets the generation management data IDs 1001 to V0005, V0006, and V0007, adds 1 to the version number 1005, and sets Ver. 2.0, Ver. 2.1, Ver. 2.2, the configuration data creation time 601 is set to the configuration change time in step S2501 of FIG.

<Packet ID table 441>
FIG. 28 is an explanatory diagram showing an example of the packet ID table 441. As shown in FIG. Packet ID table 441 has packet ID 2801, time stamp 2802, and input data ID 2101, for example.

Packet ID 2801 indicates the identifier of the input data packet. A packet ID 2801 is an identifier that the IoT gateway 161 assigns to each piece of data to be transmitted. The time stamp 2802 records the time when the data collection unit 321 received the packet.

<Metadata switching omission detection processing>
FIG. 29 is a flow chart showing an example of processing for detecting data that fails to switch metadata, which is executed by the data processing apparatus 102 .

First, the metadata management unit 326 transmits the configuration data creation time 1801 and registration time 1802 of metadata newly registered by metadata update to the data conversion logic setting unit 325 (step S2901).

Next, the data conversion logic setting unit 325 refers to the packet ID table 441 and acquires the packet ID 2801 included in "switching period=registration time-configuration data creation time" (step S2902).

Finally, the data conversion logic setting unit 325 generates a switching omission packet ID list in which the packet ID 2801 acquired in step S2902 is described (step S2903).

<Method of generating a switching omission packet list>
FIG. 30 is an explanatory diagram showing the concept of obtaining the packet ID 2801 in step S2902 of FIG. In FIG. 30, the configuration data creation time 1801 of the metadata newly registered in the metadata 481 is set to "2021/4/1 13:00:00.600" and the registration time 1802 is set to "2021/2021" by the metadata update process of FIG. /4/1 13:00:01.300".

In the example of FIG. 30, first data 801-3 and 801 of time stamps after "2021/4/1 13:00:00.600" and before "2021/4/1 13:00:01.300" -4, 801-5 will be leaked from switching, so the switching leakage packet ID including P0003, P0004, P0005, which are the packet IDs of the first data 801-3, 801-4, 801-5 Generate a list.

As an example of a method of using the switching leakage packet ID list, a process in which the data processing device 102 uses the switching leakage packet ID list to update collected data stored with old metadata information will be described.

<Collected data table 491>
FIG. 31 is an explanatory diagram showing an example of the collected data table 491 used by the collected data accumulation unit 324 to store collected data.

Collected data table 491 has packet ID 2801, time stamp 2802, input data ID 2101, device ID 3101, temperature 3102, current 3103, line ID 3104, assigned process ID 3105, previous process ID 3106, post process ID 3107, and worker ID 3108, for example.

Packet ID 2801 indicates the identifier of the input data packet. A packet ID 2801 is an identifier given to each piece of data transmitted by the IoT gateway 161 . The time stamp 2802 records the time when the data collection unit 321 received the packet.

A device ID 3101 , a temperature 3102 , a current 3103 , a line ID 3104 , a responsible process ID 3105 , a pre-process ID 3106 , a post-process ID 3107 , and a worker ID 3108 are data groups stored in the third data 803 .

The data processing device 102 extracts the data of the packet IDs listed in the switching omission packet ID list from the collected data table 491, and rewrites the data with new metadata.

<Switch omission data update processing>
FIG. 32 is a flowchart showing an example of a process of updating collected data that has been omitted from metadata switching executed by the data processing apparatus 102 .

First, the data collecting unit 321 transmits the switching omission packet ID list generated by the procedure shown in FIG. 30 to the collected data storage unit 324 (step S3201).

Next, the collected data accumulation unit 324 acquires the data of the target packet IDs indicated by the switching omission packet ID list received from the data collection unit 321, and transmits the data to the metadata addition unit 323 (step S3202).

The metadata adding unit 323 acquires the input data ID from the data received from the collected data storage unit 324, refers to the metadata acquisition table 461 using the input data ID as a search condition, and acquires the corresponding metadata search condition. , to the metadata management unit 326 (step S3203).

The metadata management unit 326 acquires metadata corresponding to the metadata search condition received from the metadata addition unit 323 from the metadata 481, and transmits the metadata to the metadata addition unit 323 (step S3204).

Then, the metadata adding unit 323 changes the metadata of the data of the target packet ID received from the metadata managing unit 326 to the metadata acquired from the metadata managing unit 326, and transmits the metadata to the collected data storage unit 324 ( step S3205).

The collected data storage unit 324 updates the data in the collected data table 491 with the data received from the metadata adding unit 323 (step S3206).

With the above processing, the data processing apparatus 102 can automatically update data that has been omitted due to switching of metadata.

<Updating collected data that has been omitted from switching metadata>
FIG. 33 is an explanatory diagram showing an example of update of collected data that is omitted due to switching of metadata in the data processing apparatus 102. In FIG.

First, the data collecting unit 321 transmits the switching omission packet ID list 3301 to the collected data storage unit 324 (step S3201). In the example of FIG. 33, the switching leakage packet ID list 3301 includes the packet IDs “P0003, P0004, P0005” of the packets that are the target of metadata switching leakage in the example shown in FIG.

Next, the collected data accumulation unit 324 acquires the data corresponding to the packet IDs “P0003, P0004, P0005” described in the switching omission packet ID list 3301 from the collected data table 491, and stores the data as the update target packet 3302 in the metadata. It is transmitted to the data adding unit 323 (step S3202). The data format of the update target packet 3302 is, for example, the same data format as the data format of the third data 803 .

Subsequently, the metadata adding unit 323 refers to the content of the data of the received packet to be updated 3302, and transmits the metadata search condition 1901 of the packet to be updated 3302 to the metadata managing unit 326 (step S3203). The metadata 1902 of the update target packet 3302 is acquired from the management unit 326 (step S3204). This processing is similar to the metadata search processing and addition processing in FIG.

Next, the metadata adding unit 323 updates the content of the received update target packet 3302 with the metadata acquired from the metadata managing unit 326 to create an updated packet 3303 . The metadata adding unit 323 then transmits the created post-update packet 3303 to the collected data storage unit 324 (step S3205). The data format of the post-update packet 3303 is, for example, the same data format as the data format of the third data 803 .

The example of FIG. 33 shows an example of updating the metadata of a packet with a packet ID of P0003 based on the configuration data update example shown in FIG. is updated to

The collected data accumulation unit 324 updates the data of the collected data table 491 with the data of the received post-update packet 3303 (step S3206).

<Example of updating the collected data table 491>
FIG. 34 is an explanatory diagram showing an example of updating the collected data table 491 in the example of FIG. FIG. 34 shows an example of updating data with packet IDs P0003, P0004, and P0005 shown in FIG. 30, based on the example of updating configuration data shown in FIG. In this example, the post-process ID 3107 of the data of P0003 and the pre-process ID 3106 of the data of P0004 are updated from "O14" to "O31". There is no change in the data of P0005.

As described above, even if it takes time to reflect the update of the metadata and data of switching omission occurs, by using the information of the packet ID table 441 of the data collection unit 321, the omission of switching can be automatically performed. Metadata updates of the data can be retroactively performed at a later time.

<Effect in Example 2>
In the future, it is expected that the work environment and layout will be changed seamlessly, and the composition of the work environment and the work content will be changed in real time. At that time, it is necessary to change data collection and processing in real time, but it is conceivable that data that cannot follow the changes will be generated. This processing is effective in such a case.

As an effect of the invention in this embodiment, conventionally, before and after a change in the configuration of the work environment or work content, there is data that cannot be determined whether it is associated with new or old metadata, and a worker with field knowledge can confirm the metadata. Work was required, but the present invention can eliminate the work.

In addition, there is no need to update all the metadata every time the configuration of the working environment or the content of work is changed, and the calculation load on the data processing device 102 can be reduced.

It should be noted that the present invention is not limited to the embodiments described above, but includes various modifications and equivalent configurations within the scope of the appended claims. For example, the above-described embodiments have been described in detail to facilitate understanding of the present invention, and the present invention is not necessarily limited to those having all the described configurations. Also, part of the configuration of one embodiment may be replaced with the configuration of another embodiment. Moreover, the configuration of another embodiment may be added to the configuration of one embodiment. Moreover, other configurations may be added, deleted, or replaced with respect to a part of the configuration of each embodiment. Also, various processing functions may be appropriately integrated and distributed for processing efficiency and implementation efficiency. Similarly, storage areas for storing various data may be appropriately integrated and distributed for processing efficiency and implementation efficiency.

As described above, each configuration, function, processing unit, processing means, etc. described above may be realized by hardware, for example, by designing a part or all of them with an integrated circuit, and the processor realizes each function. It may be realized by software by interpreting and executing a program to execute.

Information such as programs, tables, files, etc. that realize each function is stored in storage devices such as memory, hard disk, SSD (Solid State Drive), or IC (Integrated Circuit) card, SD card, DVD (Digital Versatile Disc) recording Can be stored on media. Alternatively, information such as programs, tables, and files that implement each function may be acquired from an external computer having a non-temporary storage device through communication via the communication IF 205 . Alternatively, information such as programs, tables, files, etc. that implement each function may be recorded on a non-temporary recording medium and read by a medium reader.

In addition, the control lines and information lines indicate those considered necessary for explanation, and do not necessarily indicate all the control lines and information lines necessary for mounting. In practice, it can be considered that almost all configurations are interconnected.

100: work environment, 101: metadata extraction device, 102: data processing device, 131: equipment, 141: operator, 161: IoT gateway, 181: production line, 200: metadata extraction system, 301: work environment configuration information , 311: configuration data input unit, 312: configuration data storage unit, 313: metadata extraction unit, 314: data display unit, 321: data collection unit, 322: data processing unit, 323: metadata addition unit, 324: collection Data storage unit 325: Data conversion logic setting unit 326: Metadata management unit 401: Configuration input screen 411: Configuration data 421: Metadata creation unit 422: Metadata extraction rule 423: Generation management data 424: metadata output unit, 431: creation screen, 432: manual registration screen, 433: confirmation screen, 441: packet ID table, 451: conversion logic, 461: metadata acquisition table, 471: logic setting screen, 481: metadata Data, 491: Collected data table

Claims (13)

A data extraction apparatus having a processor and a storage device that cooperate with a memory to execute a program,
holding in the storage device management data relating to metadata to be added to time-series sensor data collected from sensors installed in the work environment based on conditions of the work environment;
The processor
A data extracting device that newly creates an entry of the management data related to metadata to be added to the sensor data based on a new condition of the working environment based on an existing entry of the management data. The data extraction device of claim 1,
the management data includes a version number, managing generations of entries of the management data based on the version number;
The processor
When an existing generation entry of the management data similar to the new condition exists, the management of metadata to be added to the sensor data based on the new condition based on the existing generation entry. A data extractor characterized by creating a new generation of entries for data. A data extraction device according to claim 2,
The management data includes configuration data conditions related to the configuration of the work environment and event conditions indicating work content in the work environment,
The processor
The new generation entry is created based on an existing generation entry of the management data containing a configuration data condition or event condition similar to the configuration data condition or event condition included in the new condition. data extractor. A data extraction device according to claim 2,
holding configuration data indicating the configuration of the working environment in the storage device;
the management data includes an identifier indicating a metadata extraction method;
The processor
obtaining the identifier from the management data based on the current time and conditions of the working environment in which the sensor data is generated;
A data extracting device that extracts metadata to be added to the sensor data from the configuration data based on a method of extracting the metadata that corresponds to the acquired identifier. A data extraction device according to claim 4,
the management data includes an event condition indicating work content in the work environment;
The processor
A data extracting device that acquires the identifier from an entry of the management data that includes the specified event condition. A data extraction device according to claim 5,
the management data includes a configuration data condition indicating the configuration of the working environment;
The processor
A data extracting device that obtains the identifier from an entry of the management data that includes the designated time and the configuration data condition. A data extraction device according to claim 2,
The management data includes configuration data conditions indicating the configuration of the work environment and event conditions indicating work content in the work environment,
The processor
Based on an existing generation entry of the management data, a new generation entry of the management data relating to metadata to be added to the sensor data is created in accordance with an update of the configuration data condition or the event condition. data extractor. A data extraction device according to claim 6,
The processor
displaying a screen for accepting specification of the configuration data condition, the event condition, and the version number on a display device;
Acquiring the identifier from the entry of the management data corresponding to the received designation, extracting the metadata from the configuration data based on the extraction method of the metadata corresponding to the acquired identifier, and displaying it on the screen in a predetermined format. A data extraction device characterized by: a data extraction device according to any one of claims 1 to 8;
a data processing device that adds the metadata to the sensor data based on the management data created by the data extraction device;
The processor
determining whether or not the metadata to be added to the sensor data is updated according to the update of the configuration of the work environment; and if the metadata is updated, the update time of the metadata and the updated metadata are A data extraction system characterized by notifying a data processing device. A data extraction system according to claim 9, comprising:
The data processing device is
managing the correspondence relationship between the sensor data and metadata added to the sensor data;
A data extraction system, wherein the sensor data corresponding to the updated metadata notified from the data extraction device is specified based on the correspondence relationship. A data extraction system according to claim 9, comprising:
The data processing device is
Identifying the sensor data collected from the sensor during a period from the time when the configuration of the work environment is updated to the time when metadata is added to the sensor data based on the updated configuration of the work environment. death,
A data extraction system, wherein the identified sensor data is reattached with metadata based on the updated work environment configuration. A data extraction method executed by a data extraction device,
The data extraction device is
a storage device holding management data related to metadata to be added to time-series sensor data collected from sensors installed in a work environment based on conditions of the work environment;
A data extraction method, comprising: creating a new management data entry related to metadata to be added to the sensor data based on a new condition of the work environment, based on an existing entry of the management data. to the computer,
holding, in a storage device, management data relating to metadata added to time-series sensor data collected from sensors installed in a work environment based on conditions of the work environment;
each process of newly creating an entry of the management data related to metadata to be added to the sensor data based on a new condition of the working environment based on the existing entry of the management data. Data extraction program.

Images