Data processing device, data processing system, management system, and data processing method

Abstract

A data processing device (22) comprises: a hierarchical data processing unit (221) that acquires data from a common database unit (21) including, for each data type of a unit that cannot be obtained by data processing, one or more databases managed in a format corresponding to a category of data, hierarchically combines, for each application, data processing that utilizes the data, and processes the data into data utilized by the applications; and a data processing management unit (222) that manages the hierarchical data processing in the hierarchical data processing unit (221).

Description

Data processing apparatus, data processing system, management system, and data processing method

[0001] The present disclosure relates to a data processing apparatus, a data processing system, a management system, and a data processing method for performing data processing.

[0002] Conventionally, various applications have utilized data stored in a database to provide various services. Regarding how an application utilizes data stored in a database, for example, in Patent Document 1, when creating various business applications in a distributed system, in order to cope with differences in databases of partner systems, build-in processing for the differences is excluded from the application, and by maintaining consistency with the operation details, constraints, etc. of the system other than the functions of the application, a technique for performing necessary processing is disclosed.

[0003] Japanese Patent Application Laid-Open No. 2015-79431

[0004] However, according to the above conventional technology, data stored in databases of different systems is utilized. Therefore, when performing data processing using data stored in a plurality of databases, depending on the data format of each data, build-in processing for linking the data is required, and there is a problem that it is difficult to efficiently utilize the data.

[0005] The present disclosure has been made in view of the above, and an object thereof is to obtain a data processing apparatus capable of efficiently utilizing data.

[0006] In order to solve the above-described problems and achieve the object, the data processing apparatus of the present disclosure acquires data from a common database unit having one or more databases managed in a manner corresponding to the category of data for each data type in a unit that cannot be obtained by data processing, hierarchically combines data processing for using the data for each application, and includes a hierarchical data processing unit that processes the data into data used by the application, and a data processing management unit that manages the hierarchical data processing in the hierarchical data processing unit.

[0007] The data processing device disclosed herein has the effect of enabling efficient use of data.

[0008] Figure showing an example configuration of a management system including the data processing system according to Embodiment 1. Figure showing an example configuration of the data processing system according to Embodiment 1. Figure showing an example of the case where the data processing system according to Embodiment 1 manages the data processing flow with tags. Figure showing a flowchart showing the operation of the data processing system according to Embodiment 1. Figure showing an example of the hardware configuration that realizes the data processing system according to Embodiment 1. Figure showing an example of data stored in the data processing system according to Embodiment 2. Figure showing an example of data stored in the data processing system according to Embodiment 7. Figure showing an example of data stored in the data processing system according to Embodiment 10.

[0009] The data processing device, data processing system, management system, and data processing method according to embodiments of this disclosure will be described in detail below with reference to the drawings.

[0010] Embodiment 1. Figure 1 is a diagram showing an example configuration of a management system 60 including a data processing system 20 according to Embodiment 1. The management system 60 comprises a collection device 10, a data processing system 20, a cloud server 30, a server 40, and an application terminal 50.

[0011] The data collection device 10 collects various data, such as the operating status of production equipment and the production status of products, from production equipment on a production line (not shown). The data collection device 10 is a PLC (Programmable Logic Controller) 11 such as a sequencer, a mobile terminal 12, a personal computer 13, etc., but is not limited to these. Furthermore, the data collected by the data collection device 10 is not limited to data such as the operating status of production equipment on a production line and the production status of products.

[0012] The data processing system 20 acquires the data collected by the collection device 10 from the collection device 10. In the management system 60, the collection device 10 may output data and the data processing system 20 may acquire the data, or the data processing system 20 may read the data from the collection device 10 and acquire it. In the following description, the acquisition of data by the data processing system 20 from the collection device 10 may be expressed as the data processing system 20 collecting data from the collection device 10. The data processing system 20 stores the data collected from production equipment, etc., in one or more databases with standardized database configurations, etc., according to the data type, such as work time, traceability, troubleshooting, etc. The data processing system 20 processes the stored data into data that can be used by the application terminal 50, i.e., performs data processing. At this time, the data processing system 20 performs data processing hierarchically using the data in the standardized database for each application.

[0013] The data processing system 20 transfers the stored data to the cloud server 30 at a predetermined interval. The data processing system 20 may also transfer the stored data to the cloud server 30 at times other than the predetermined interval. The data processing system 20 may transfer the stored data to the cloud server 30 in order from oldest to newest, or in order from least frequently used data to least old data. The data processing system 20 also outputs the processed data to the application terminal 50 via the server 40. The detailed configuration and operation of the data processing system 20 will be described later. In the following description, the database may be referred to as DB (DataBase).

[0014] The cloud server 30 stores the data transferred from the data processing system 20. The cloud server 30 may also output the stored data to the application terminal 50 via the server 40 in response to a request from the application terminal 50. Although the data flow is omitted in Figure 1, the cloud server 30 may also output the stored data to the data processing system 20 in response to a request from the data processing system 20, etc.

[0015] In the management system 60, when the data processing system 20 transfers data to the cloud server 30, it may immediately delete the data transferred to the cloud server 30, or it may retain the data for a specified period before deleting it. The purpose of the data processing system 20 transferring data to the cloud server 30 is not only to move unnecessary data that is no longer used for data processing from the data processing system 20 to the cloud server 30, but also to shorten the data retrieval time in the data processing system 20. Generally, the time required to read necessary data from the DB increases as the amount of data stored in the DB increases, because it takes time to search for the desired data. Therefore, the data processing system 20 may continue to retain the data if it can process the data within a specified period, i.e., if it can maintain real-time performance. If the data processing system 20 cannot process the data within a specified period, i.e., if it cannot maintain real-time performance, it will transfer some of the data to the cloud server 30 to reduce the amount of data to be retained, so that it can process the data within a specified period, i.e., ensure real-time performance.

[0016] Server 40 relays communication between the data processing system 20 and the application terminal 50, and communication between the cloud server 30 and the application terminal 50. Note that the management system 60 may not include server 40 if the data processing system 20 and the cloud server 30 can communicate directly with the application terminal 50.

[0017] The application terminal 50 utilizes data processed by the data processing system 20. The application terminal 50 is a mobile terminal 51, a personal computer 52, etc., but is not limited to these. The application terminal 50 is a terminal device that executes applications installed on a PLC such as a sequencer, a management terminal that manages the operating status of production equipment, or the production status of products. The application terminal 50 may display or output the analysis results after executing the application and analyzing the data, or it may display or output the results after executing the application, allowing the user of the application terminal 50 to determine the operating status of production equipment, the production status of products, etc. Based on the data processed by the data processing system 20, the application terminal 50 can also acquire the data that forms the basis of the data processed by the data processing system 20 from the cloud server 30 via the server 40.

[0018] In the management system 60, the data processing system 20 stores the data collected by the data collection device 10 in a standardized database. Here, the format of the data collected by the data collection device 10 corresponds to the configuration of the database provided by the data processing system 20, which is the storage destination. In the management system 60, the configuration of the database provided by the data processing system 20 is standardized, and the format of the collected data is also standardized. Standardization of the data format means that the data is unified into a format that can be processed by each data processing unit of the hierarchical data processing unit 221, which will be described later, provided by the data processing system 20. In other words, the data collected by the data collection device 10 and managed by the data processing system 20 has its format and other elements systematically unified so that it can be processed by the data processing system 20. As a result, the management system 60 can perform data collection and data processing using the same method for production equipment on production lines at multiple locations. Multiple locations may be multiple factories in Japan, multiple factories overseas, or one or more factories in Japan and one or more factories overseas.

[0019] In the management system 60, data is standardized, making it easy to use on the application terminal 50. However, considering use on the application terminal 50, the types of data stored in the database of the data processing system 20 are insufficient. The types of data are linked by data items. Therefore, the data processing system 20 adds data items when each data processing unit of the hierarchical data processing unit 221 (described later) processes the data to accommodate use on the application terminal 50.

[0020] In the management system 60, the data collection device 10 collects the collected data as "correct time." Therefore, each device in the data collection device 10 is synchronized with the standard time. Each device in the data collection device 10 can be associated with the database of the data processing system 20 by recording data together with, for example, the time of an edge terminal synchronized with the standard time. Synchronization with the standard time may be achieved by time synchronization with a specific device or by accumulation over an interval.

[0021] Next, the detailed configuration and operation of the data processing system 20 will be described. Figure 2 is a diagram showing an example configuration of the data processing system 20 according to Embodiment 1. The data processing system 20 comprises a common DB unit 21 and a data processing device 22. For the sake of simplicity, the explanation will be given using the example shown in Figure 2, where the data processing system 20 can communicate with the application terminal 50 without going through the server 40.

[0022] The common DB unit 21 has one or more databases and manages data in a format corresponding to the category of the collected data. The common DB unit 21 has a database for each data type. In this embodiment, the common DB unit 21 includes a master management unit 211, a work completion DB 212, a traceability DB 213, and a troubleshooting DB 214.

[0023] The master management unit 211 manages each database within the common database unit 21, and standardizes and manages the names of data items using corresponding tables. In the example in Figure 2, each database refers to the work completion database 212, the traceability database 213, and the troubleshooting database 214. The master management unit 211 manages the names of data items as, for example, a general name + sequential number. General names include, for example, screw serial number, screw tightening torque value, part serial number, measured value, and standard value. This allows the management system 60 to manage all production lines with the same approach using SCADA (Supervisory Control And Data Acquisition), etc.

[0024] Specifically, the master management unit 211 manages the writing of data to the work completion DB 212, traceability DB 213, and troubleshooting DB 214, and the reading of data from the work completion DB 212, traceability DB 213, and troubleshooting DB 214. For example, the master management unit 211 writes the data collected by the collection device 10 to the work completion DB 212, traceability DB 213, and troubleshooting DB 214. The master management unit 211 also reads data from the work completion DB 212, traceability DB 213, and troubleshooting DB 214 and outputs it to the data processing device 22. Furthermore, the master management unit 211 transfers data from the work completion DB 212, traceability DB 213, and troubleshooting DB 214 to the cloud server 30. The master management unit 211 may retrieve data from the cloud server 30 and rewrite the data to the work completion DB 212, the traceability DB 213, or the troubleshooting DB 214.

[0025] The work completion DB 212, traceability DB 213, and troubleshooting DB 214 are standardized databases in terms of their configuration and the data structure they store. Here, the data types are work completion, traceability, and troubleshooting. Work completion is a data type related to time, traceability is a data type related to work content, and troubleshooting is a data type related to problems. In the example in Figure 2, it can be said that the common DB unit 21 has databases focused on work time, work content, and work problems. Here, the data types corresponding to the databases held by the common DB unit 21 are data types that represent the smallest unit from which the hierarchical data processing unit 221 cannot process the data to generate the desired data, and are prime number-like data types. In the data processing system 20, the databases are set up so that the requirements of the hierarchical data processing unit 221 correspond to the data types.

[0026] A prime number-like data type refers to a unit of data that cannot be obtained through the data processing of the hierarchical data processing unit 221. A prime number-like data type is a data type that is divided according to the concept of prime numbers. A prime number is a natural number that is divisible only by 1 and itself, and a desired natural number can be calculated by appropriately combining prime numbers. A prime number-like data type is the smallest unit of data type that serves as the starting point for calculations, and a desired data type can be calculated by appropriately combining prime number-like data types. While prime numbers are natural numbers and are absolute, prime number-like data types are relative.

[0027] For example, let's explain using the relationship between "voltage," "current," and "power," specifically the case where the data type collected by the data collection device 10 is "current." In this case, "current" is a prime number data type, while "voltage" and "power" are not. This is because "voltage" and "power" can be calculated from "current" using the resistance value, which is a characteristic of the material. Also, if the desired data type is "energy," then "time" is required for its calculation. Therefore, in this case, "time" is a prime number data type. As another example, in the relationship between "voltage," "current," and "power," if the data type collected by the data collection device 10 is "voltage," then "voltage" is a prime number data type, while "current" and "power" are not. This is because "current" and "power" can be calculated from "voltage." When considering "current" and "voltage," they are usually mutually calculable, so neither one is a prime number data type.

[0028] In this embodiment, prime number data types represent data types that cannot be obtained by the data processing of the hierarchical data processing unit 221. For example, if the hierarchical data processing unit 221 does not have a data processing unit that generates "current," but has a data processing unit that uses "current" to generate "voltage," then "current" is a prime number data type. Prime number data types are selected as management items that serve as the starting point for calculations of data processing (for example, calculation formulas of each data processing unit) that the hierarchical data processing unit 221 combines. More specifically, prime number data types are selected as management items that serve as the starting point for calculations in the relationship between the data types collected by the collection device 10, the data processing (for example, calculation formulas of each data processing unit) that the hierarchical data processing unit 221 combines, and the data used by the application.

[0029] Furthermore, the data collected by the data collection device 10 is associated with an equipment ID (IDentifier), which is identification information for identifying the production equipment or other source of data. The common DB unit 21 acquires the equipment ID information along with the data from the data collection device 10, and manages the equipment ID as a data management item, i.e., a category. A category includes, for example, specific content belonging to the data type (measured value, situation, etc.) and the identification ID. This allows the management system 60 to identify the production equipment or other source of data, making it easier to perform the desired data analysis. Preferably, the data category includes at least one of the following when the product was produced: work time, work content, and work troubles. In this embodiment, the data category includes the work time, work content, and work troubles when the product was produced.

[0030] The data processing device 22 hierarchically combines multiple data processing operations, each of which includes at least one operation that utilizes data managed by the common DB unit 21 for each application that uses the data, to process the data managed by the common DB unit 21 into data that can be used by applications. The data collected by the common DB unit 21 is data that cannot be obtained by the data processing of the data processing device 22. The data processing device 22 comprises a hierarchical data processing unit 221 and a data processing management unit 222. The proportion of data managed by the common DB unit 21 that cannot be obtained by the data processing of the data processing device 22 is preferably 50% or more, more preferably 70% or more, even more preferably 90% or more, and particularly preferably 100%.

[0031] The hierarchical data processing unit 221 acquires data from each DB of the common DB unit 21. The data acquired by the hierarchical data processing unit 221 from each DB of the common DB unit 21 is data stored in the DB of the common DB unit 21 and is data unified in a format that can be processed by the hierarchical data processing unit 221 of the data processing device 22. The hierarchical data processing unit 221 comprises multiple data processing units that perform defined data processing. In Figure 2, the data processing units of the hierarchical data processing unit 221 are specifically exemplified as primary use data processing unit 221-1a, primary use data processing unit 221-1b, primary use data processing unit 221-1c, secondary use data processing unit 221-2a, and secondary use data processing unit 221-2b, but are not limited to these. The hierarchical data processing unit 221 may have two or fewer primary use data processing units or four or more primary use data processing units, or one or three or more secondary use data processing units. Furthermore, the hierarchical data processing unit 221 may include a third-level or higher data processing unit, such as a third-level data processing unit or a fourth-level data processing unit, which are not shown in the figures.

[0032] In the data processing device 22, the hierarchical data processing unit 221, in the example shown in Figure 2, has a hierarchical arrangement of multiple data processing units that perform data processing. The hierarchical data processing unit 221 may be configured so that the multiple data processing units are connected to the common DB unit 21. Alternatively, the hierarchical data processing unit 221 may be configured so that the multiple data processing units are connected to the application terminal 50 that executes the application. Alternatively, the hierarchical data processing unit 221 may be configured so that the higher-level data processing units are connected to the common DB unit 21, and the remaining lower-level data processing units are connected to the application terminal 50. The data processing device 22 can arrange the multiple data processing units at any desired hierarchical level. For example, by providing data processing units on the data processing system 20 side, the data processing load on the application terminal 50 side can be reduced, and by providing data processing units on the application terminal 50 side, the data processing load on the data processing system 20 side can be reduced. By distributing the data processing units provided on the application terminal 50 and the data processing system 20, the overall data processing load of the system can be optimized. The hierarchical data processing unit 221 has data processing units arranged hierarchically, and is configured to generate data used by the application terminal 50 by sharing the workload among all data processing units, thereby optimizing the data processing load. Thus, in the management system 60, at least some of the data processing units of the hierarchical data processing unit 221 of the data processing device 22 of the data processing system 20 may be provided on the application terminal 50.

[0033] The data processing management unit 222 manages the flow of data processed by the hierarchical data processing unit 221 for each application, that is, it sets the flow of data processing. It can also be said that the data processing management unit 222 manages the input and output of each data processing unit of the hierarchical data processing unit 221. For example, for application A, the data processing management unit 222 manages the flow in which the secondary data processing unit 221-2a processes the data using the results processed by the primary data processing unit 221-1a and the primary data processing unit 221-1b. Similarly, for application B, the data processing management unit 222 manages the flow in which the secondary data processing unit 221-2b processes the data using the results processed by the primary data processing unit 221-1b and the primary data processing unit 221-1c. Furthermore, the data processing management unit 222 manages the flow of data processing for application C using the results processed by the primary data processing unit 221-1a, which are then processed by the secondary data processing unit 221-2a and the secondary data processing unit 221-2b.

[0034] The data processing management unit 222 controls the transmission of the results obtained from data processing in the hierarchical data processing unit 221, i.e., data, to the application terminal 50. This allows the management system 60 to monitor the production equipment on the production line, the productivity of personnel, the quality of the products being produced, etc., at the application terminal 50. The data processing management unit 222 may transmit data to the application terminal 50 at a predetermined interval, or it may transmit data to the application terminal 50 in response to a request from the application terminal 50. The data processing management unit 222 manages the transmission of data to the application terminal 50, i.e., data distribution. In this way, the data processing management unit 222 manages the input of data to a data processing unit or the results of data processing by a preceding data processing unit, and manages the output of the results of data processing from a data processing unit to a subsequent data processing unit or the application terminal 50.

[0035] Furthermore, if the higher-level data processing unit of the hierarchical data processing unit 221 is connected to the common DB unit 21, and the remaining lower-level data processing unit is connected to the application terminal 50, the data processing management unit 222 may be located on the common DB unit 21 side, on the application terminal 50 side, or on both the common DB unit 21 side and the application terminal 50 side.

[0036] In the data processing system 20, the hierarchical data processing unit 221 of the data processing device 22 is equipped with data processing units such as a primary data processing unit and a secondary data processing unit in a hierarchical structure. This makes it easy to reuse the data processing units required for each application in the data processing system 20, and to process the necessary data.

[0037] As described above, in the hierarchical data processing unit 221, if the higher-level data processing unit among the multiple data processing units is connected to the common DB unit 21, and the remaining lower-level data processing unit among the multiple data processing units is connected to the application terminal 50, the data processing system 20 can easily handle the process by having the application terminal 50 perform additional data processing on the data that has been processed, i.e., modified, on the common DB unit 21 side.

[0038] Thus, in the data processing device 22, the hierarchical data processing unit 221 acquires data from a common DB unit 21 which has one or more DBs that manage data in a format corresponding to the category of the collected data, and combines multiple data processing operations hierarchically, each of which includes at least one data processing operation that uses the data managed in the common DB unit 21 for each application that uses the data, to process the data managed in the common DB unit 21 into data that can be used by the application. The data processing management unit 222 manages the hierarchical data processing in the hierarchical data processing unit 221.

[0039] At this time, the data collected by the common DB unit 21 and retrieved from the common DB unit 21 by the data processing unit 22 is data that cannot be obtained by the data processing of the hierarchical data processing unit 221. The data collected by the common DB unit 21 and retrieved from the common DB unit 21 by the data processing unit 22 can also be said to be data that cannot be recursively retrieved by the data processing of the hierarchical data processing unit 221. Regarding the operation of the hierarchical data processing unit 221, it can be said that the hierarchical data processing unit 221 retrieves data from the common DB unit 21, which has one or more DBs for each data type that cannot be obtained by data processing, which are managed in a format according to the data category, and then combines data processing that uses the data in a hierarchical manner for each application, and processes the data into data that the application can use.

[0040] Here, we will describe each data processing unit of the hierarchical data processing unit 221. In the hierarchical data processing unit 221, each data processing unit is configured by loading data processing content, such as calculation formulas, into memory. Specifically, the data processing content is a data processing program. In the hierarchical data processing unit 221, the primary data processing unit basically uses data stored in the common DB unit 21, but secondary data processing units at lower levels than the primary data processing unit often refer to the results of data processing obtained from higher-level data processing units. As a result, the hierarchical data processing unit 221 can hierarchically utilize the results of data processing by each data processing unit.

[0041] In the hierarchical data processing unit 221, when performing hierarchical data processing, the subsequent data processing unit that performs data processing following the preceding data processing unit may obtain the information necessary for data processing from the preceding data processing unit either in the state of the data processing result or in the state of the data processing function expression. For example, consider a case where the primary data processing unit 221-1a obtains data x1 and x2 from the common DB unit 21 and performs data processing y1 = x1 + x2, the primary data processing unit 221-1b obtains data x3 and x4 from the common DB unit 21 and performs data processing y2 = x3 - x4, and the secondary data processing unit 221-2a performs data processing z1 = y2 / y1. In this case, the secondary data processing unit 221-2a may obtain "y1" from the primary data processing unit 221-1a and "y2" from the primary data processing unit 221-1b as described above, and perform data processing for z1 = y2 / y1, or it may obtain "x1 + x2" from the primary data processing unit 221-1a and "x3 - x4" from the primary data processing unit 221-1b, and perform data processing for z1 = (x3 - x4) / (x1 + x2).

[0042] The transfer of data processing results between data processing units may be pre-configured through initial settings, or the data processing management unit 222 may configure and modify these settings. Furthermore, in the hierarchical data processing unit 221, when each data processing unit outputs data processing results to multiple lower-level data processing units, it is not necessary to perform data processing each time a result is output. Instead, it may retain the result of the initial calculation or the function expression used at the time of the initial calculation, and output the information it holds to multiple lower-level data processing units. By preparing the calculation result or the function expression used at the time of calculation in advance, each data processing unit can reduce the increase in load when performing data processing.

[0043] In the data processing device 22, the data processing management unit 222 manages the data processing flow of each data processing unit as described above, by visualizing and managing the data processing as, for example, tags. That is, the data processing management unit 222 manages each data processing unit of the hierarchical data processing unit 221 using hierarchical tags. The name of the tag is, for example, a management item of the common DB unit 21. The data processing management unit 222 can create new management item tags, for example, virtual management item tags, by hierarchically combining tags with management items. As a result, the data processing system 20 can manage data with multiple management items, including virtual management items. Application terminals 50 and the like can efficiently utilize data by collecting data from the data processing system 20 via management item tags or virtual management item tags. The management item tags are visually operable. The data processing system 20 can perform desired data processing, i.e., data calculations, by combining tags.

[0044] FIG. 3 is a diagram showing an example in which the data processing system 20 according to Embodiment 1 manages the flow of data processing with tags. In the data processing system 20, the data processing management unit 222 of the data processing device 22 manages, for example, the data stored in the work completion DB 212 of the common DB unit 21 as shown in FIG. 3(a), such as management item (1), management item (2), management item (3), and so on. Further, when the data processing management unit 222 of the data processing device 22 performs data processing to add the data of management item (1) and management item (2) stored in the work completion DB 212 by the primary use data processing unit 221-1a of the data processing device 22, as shown in FIG. 3(b), it manages the addition result of the data of management item (1) and management item (3) as a virtual management item (1). Also, in FIG. 3, the result of the data processing of the primary use data processing unit 221-1b, which is not shown, is set as a virtual management item (2). At this time, when the secondary use data processing unit 221-2a of the data processing device 22 performs data processing to add the data of the virtual management item (1) of the primary use data processing unit 221-1a and the data of the virtual management item (2) of the primary use data processing unit 221-1b, as shown in FIG. 3(c), the data processing management unit 222 of the data processing device 22 manages the addition result of the data of the virtual management item (1) and the virtual management item (2) as a virtual management item a.

[0045] As described above, the data processing management unit 222 of the data processing device 22 can manage the relationship of data processing by a plurality of data processing units visually and hierarchically with tags as virtual management items. Regarding the management items related only to the common DB unit 21 as shown in FIG. 3(a), the master management unit 211 of the common DB unit 21 may manage them with tags. Also, regarding the contents as shown in FIGS. 3(b) and 3(c), the master management unit 211 of the common DB unit 21 may have a function of managing them with tags. That is, in the data processing system 20, the master management unit 211 of the common DB unit 21 may have the same function as the data processing management unit 222 of the data processing device 22 and manage the data processing in the hierarchical data processing unit 221.

[0046] Figure 4 is a flowchart showing the operation of the data processing system 20 according to Embodiment 1. In the data processing system 20, the common DB unit 21 has one or more DBs, acquires data from the collection device 10 (step S1), and manages the data in a format corresponding to the category of the acquired data (step S2). The data processing device 22 hierarchically combines multiple data processing operations, each of which uses data managed by the common DB unit 21, to process the data managed by the common DB unit 21 into data that can be used by the application, i.e., the application terminal 50 (step S3).

[0047] Next, the hardware configuration for realizing the data processing system 20 of Embodiment 1 will be described. In the data processing system 20, the work completion DB 212, the traceability DB 213, and the troubleshooting DB 214 are realized by DBs, i.e., memory. The hierarchical data processing unit 221 is realized by memory that holds the contents of data processing, i.e., function expressions, data processing results, etc. The master management unit 211 and the data processing management unit 222 are realized by processing circuits. The processing circuits are realized by a processor and memory. Figure 5 is a diagram showing an example of the hardware configuration for realizing the data processing system 20 according to Embodiment 1. The processing circuit 90 is realized by a processor 91 and memory 92. In other words, even including the work completion DB 212, the traceability DB 213, the troubleshooting DB 214, the hierarchical data processing unit 221, etc., the data processing system 20 of Embodiment 1 is realized by a processor 91 and memory 92.

[0048] The processor 91 is a CPU (Central Processing Unit). The processor 91 may also be an arithmetic unit, a microprocessor, a microcomputer, or a DSP (Digital Signal Processor). The memory 92 is a volatile or non-volatile semiconductor memory such as, for example, a RAM (Random Access Memory), a ROM (Read Only Memory), a flash memory, an EPROM (Erasable Programmable Read Only Memory), or an EEPROM (registered trademark) (Electrically Erasable Programmable Read Only Memory).

[0049] A program for operating as the master management unit 211 and the data processing management unit 222 is stored in the memory 92. By reading and executing this program by the processor 91, it is possible to realize the master management unit 211 and the data processing management unit 222. Note that the program for operating as the master management unit 211 and the data processing management unit 222 stored in the memory 92 may be in a form provided to a user or the like in a state written on a storage medium such as a CD (Compact Disc)-ROM or a DVD (Digital Versatile Disc)-ROM, or may be in a form provided via a network. Also, the processor 91 outputs data such as an arithmetic result to the volatile memory of the memory 92. Or, the processor 91 stores the data by outputting the data such as an arithmetic result to an auxiliary storage device via the volatile memory of the memory 92.

[0050] Figure 5 shows an example of hardware when the data processing system 20 is implemented using a general-purpose processor 91 and memory 92. However, the data processing system 20 may also be implemented using a dedicated processing circuit instead of the processor 91 and memory 92. Here, the dedicated processing circuit may be a single circuit, a composite circuit, an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or a circuit combining these. Alternatively, part of the data processing system 20 may be implemented using the processor 91 and memory 92, and the remainder implemented using the dedicated processing circuit.

[0051] As described above, according to this embodiment, in the data processing system 20, the common DB unit 21 has one or more DBs and manages the data in a format corresponding to the category of the collected data. The data processing device 22 hierarchically combines multiple data processing operations, each of which uses data managed by the common DB unit 21, to process the data managed by the common DB unit 21 into data that can be used by the application. At this time, the data collected by the common DB unit 21 is data that cannot be obtained by the data processing of the data processing device 22. As a result, the data processing system 20 can reduce the number of data management items managed by the DB of the common DB unit 21, and can build a system in which data can be commonly used by many application terminals 50, thus reducing application development costs, maintenance costs, etc. Furthermore, by standardizing the data management part, such as the DB configuration, the data processing system 20 can also standardize the input part for data collection and the output part for data utilization, enabling optimized data utilization for the entire system. Furthermore, in the data processing system 20, the data processing device 22 can efficiently utilize data by processing it, that is, by manipulating the data to obtain the necessary data.

[0052] In this embodiment, the common DB unit 21 of the data processing system 20 was described as having three DBs, but it is not limited to this. The common DB unit 21 may have one DB, and data similar to the data stored in the work completion DB 212, traceability DB 213, and troubleshooting DB 214 may be stored in that single DB.

[0053] Furthermore, in this embodiment, the data processing device 22 of the data processing system 20 included a hierarchical data processing device 221 comprising multiple data processing devices, but is not limited to this. The hierarchical data processing device 221 may comprise one data processing device, and hierarchical data processing may be performed by having one data processing device perform multiple data processing operations. Alternatively, the hierarchical data processing device 221 may comprise fewer data processing devices than the number of data processing operations performed by the hierarchical data processing device 221, with some data processing devices performing one data processing operation and the remaining data processing devices performing multiple data processing operations.

[0054] Embodiment 2. In the following embodiments, specific examples of data utilization in the data processing system 20 will be described. Embodiment 2 will describe the first example of data utilization.

[0055] As a first example of its application, the data processing system 20 calculates the trouble time—the time it takes to recover from a problem on the production line—through calculation rather than directly acquiring it. Generally, on the edge side, for example, a PLC can store trouble time data by calculating the trouble time from the occurrence to recovery using a timer. However, this method requires a program to perform the above operation on all units of the target production line, production equipment, etc., which results in a significant amount of man-hours. Furthermore, if the power to the production line, production equipment, etc., is turned off, i.e., restarted, to recover from a problem, the operation is reset, and there is a possibility that accurate data cannot be acquired.

[0056] Therefore, the data processing system 20 uses the collected data to calculate the trouble time as the difference between the time the trouble occurred and the time the next work was completed. For example, when calculating the trouble time for trouble A on production line A, the data processing device 22 reads the time the trouble occurred for trouble A on production line A stored in the troubleshooting DB 214, as shown in Figure 6. Figure 6 is a diagram showing an example of data stored in the data processing system 20 according to Embodiment 2. In the example in Figure 6, specific examples of data stored in the work completion DB 212 and the troubleshooting DB 214 are shown.

[0057] In addition, the data processing device 22 reads the work completion time for production line A stored in the work completion DB 212. In this case, the data processing device 22 reads the closest time after the trouble occurrence time of trouble A as the work completion time. Then, the data processing device 22 calculates the trouble duration from the difference between the read trouble occurrence time and the work completion time. The data processing device 22 may also calculate the trouble duration by subtracting the work time from the difference between the read trouble occurrence time and the work completion time. If the work completion DB 212 also acquires and stores the work start time as well as the work completion time, the data processing device 22 may also calculate the trouble duration from the difference between the read trouble occurrence time and the work start time. The data processing device 22 can obtain the necessary data by calculation by utilizing the data of each item in multiple DBs.

[0058] In this way, the common DB unit 21 collects and manages the time of occurrence of troubles that occur on the target production line, and the completion time of work for each product of each model produced on the target production line. The data processing unit 22 reads the time of occurrence of the desired trouble and the work completion time closest to the time of occurrence of the trouble for the same production line as the desired trouble as data from the common DB unit 21. As data processing, the data processing unit 22 uses the read time of occurrence and work completion time to calculate the trouble time, which indicates the period from when the desired trouble occurred until it was recovered.

[0059] Alternatively, the common DB unit 21 collects and manages the time of the trouble and the start time of work for each product of each model produced on the target production line. The data processing unit 22 reads the time of the trouble for the desired trouble and the start time of work closest to the time of the trouble for the same production line as the desired trouble as data from the common DB unit 21. As data processing, the data processing unit 22 uses the read time of the trouble and the start time of work to calculate the trouble time, which indicates the period from when the desired trouble occurred until it was recovered.

[0060] Embodiment 3. Embodiment 3 describes a second example of application.

[0061] As a second application example, the data processing system 20 calculates the cycle time of products being manufactured on the production line without directly acquiring the data. Generally, on the edge side, for example, a PLC can store cycle time data by calculating the time from the start to the end of the work using a timer. However, this method requires a program that performs the above operation on all units of the target production line, resulting in a significant amount of man-hours.

[0062] Therefore, the data processing system 20 uses the collected data to calculate the cycle time as the difference between the end time of work for one product and the end time of work for the next product, and adopts the mode within a specified period as the cycle time. For example, as shown in Figure 6, the data processing device 22 reads the end time of work from the work completion DB 212 and calculates the cycle time using the method described above.

[0063] In this way, the common DB unit 21 collects and manages the completion times for each product of each model produced on the target production line. The data processing unit 22 reads the completion times for the desired product model as data from the common DB unit 21. As part of the data processing, the data processing unit 22 uses the read completion times to calculate the difference between the completion time of a product of the desired model and the completion time of the next product as the cycle time for the desired model. The mode cycle time among the cycle times of multiple products of the desired model calculated within a specified period is adopted as the cycle time for the desired model.

[0064] Embodiment 4. Embodiment 4 describes a third application example.

[0065] As a third application example, the data processing system 20 calculates the setup time by calculation rather than directly acquiring it. Generally, on the edge side, for example, a PLC can store setup time data by calculating the time from the start to the completion of the setup change using a timer. However, with the above method, a program is required to perform the above operation on all units of the target production line, production equipment, etc., which results in a great deal of manpower.

[0066] Therefore, the data processing system 20 uses the collected data to calculate the setup changeover time from the difference between the end of work for the product before the model name change and the end of work for the product after the model name change. The data processing system 20 may also calculate the setup changeover time by subtracting the work time from the difference between the end of work for the product before the model name change and the end of work for the product after the model name change. If the data processing device 22 has acquired and stored not only the end of work but also the start of work in the work completion DB 212, it may also calculate the setup changeover time from the difference between the read end of work for the product before the model name change and the start of work for the product after the model name change. For example, as shown in Figure 6, the data processing device 22 selects model a and model b from the work completion DB 212, as shown in the shaded area, as models that will undergo a model changeover. The data processing device 22 reads the end time of the work that occurs when the machine type changes, and calculates the setup changeover time from the difference between the read end times. Alternatively, the data processing device 22 reads the end time of the work that occurs when the machine type changes, and calculates the setup changeover time by subtracting the work time from the difference between the read end times. Alternatively, the data processing device 22 reads the end time of the work before the machine type changes and the start time of the work after the machine type changes, and calculates the setup changeover time from the difference between the read start time and end time.

[0067] In this way, the common DB unit 21 collects and manages the completion times for each product of each model produced on the target production line. The data processing unit 22 reads from the common DB unit 21 as data the completion times for the products of the desired model, and the completion times for products of different models that were produced on the same production line as the products of the desired model before the products of the desired model were produced. As part of the data processing, the data processing unit 22 uses the read completion times to calculate the setup time when producing the products of the desired model.

[0068] Alternatively, the common DB unit 21 collects and manages the work completion time and the work start time for each product of each model produced on the target production line. The data processing unit 22 reads from the common DB unit 21 as data the work start time for the desired product model and the work completion time for products of a different model that were produced on the same production line as the desired product model before the desired product model was produced. As data processing, the data processing unit 22 uses the read work completion time and work start time to calculate the setup time when producing the desired product model.

[0069] Embodiment 5. Embodiment 5 describes a fourth example of application.

[0070] As a fourth application example, the data processing system 20 calculates the start-up time by calculation rather than directly acquiring it. Generally, on the edge side, for example, a PLC can store start-up time data by calculating the time from the start of work to the end of work using a timer after the start button is pressed. However, with the above method, a program is required to perform the above operation on all units of the target production line, production equipment, etc., and the worker also needs to press the start button, resulting in a great deal of manpower.

[0071] Therefore, the data processing system 20 uses the collected data to calculate the start time as the difference between the start time of the work and the end time of the first product's work. The first product is the first product produced after a certain event. For example, as shown in Figure 6, the data processing device 22 reads the start time and end time of the work from the work completion DB 212 and calculates the start time from the difference between the read start time and end time of the work.

[0072] Furthermore, the work completion DB 212 of the common DB unit 21 does not need to store all work start times in conjunction with the work completion times. For example, in the production of a certain product model, if the work for the next product starts at the same time as the work for the previous product is completed, the work completion time of the previous product can be considered as the work start time of the next product. In such a case, only the work start time of the first product will be stored in the work completion DB 212. When only the work start time of the first product is stored in the work completion DB 212, the data processing device 22 can calculate the start time of each first product by reading the time in which the work start time and work completion time are stored as a set from the work completion DB 212.

[0073] In this way, the common DB unit 21 collects and manages the end time of work for each product of each model produced on the target production line, and the start time of work for each product of each model produced on the target production line. The data processing unit 22 reads the start time of work for the desired product model and the end time of work for the desired product model, which is managed together with the start time of work for the desired product model, as data from the common DB unit 21. As data processing, the data processing unit 22 uses the read end time and start time of work to calculate the first production start time, which indicates the period until the first product is produced when producing the desired product model.

[0074] Embodiment 6. Embodiment 6 describes a fifth example of application.

[0075] As a fifth application example, the data processing system 20 calculates the trouble initiation time and trouble resolution time by calculation, rather than directly acquiring them. Here, the trouble time described in the first application example of Embodiment 2 can be divided into two time periods. These two time periods are the time from the occurrence of the trouble to the resolution of the trouble, and the time spent actually dealing with the trouble. To reduce the former, measures are needed to notify the worker dealing with the trouble as early as possible, and to reduce the latter, measures are needed to reduce the time it takes to actually resolve and recover from the trouble, so the response methods are different. Therefore, in order to reduce trouble time, it is necessary to understand each of these time periods. However, until now, data has not been collected by dividing trouble time into the time from the occurrence of the trouble to the resolution of the trouble, and the time spent actually dealing with the trouble. It is also possible to use a timer, but this would require a great deal of manpower. Furthermore, if the power to the production line, production equipment, etc. is turned off, i.e., restarted, in order to recover from a trouble, the operation will be reset, and it may not be possible to acquire accurate data.

[0076] Therefore, the data processing system 20 uses the collected data to calculate the two times mentioned above. Specifically, the data processing system 20 obtains the time the trouble occurred and the time the trouble recovery started. The data processing device 22 calculates the difference between the time the trouble occurred and the time the trouble recovery started as the trouble initiation time, and calculates the difference between the time the trouble recovery started and the time the work on the next product was completed as the trouble handling time.

[0077] For example, as shown in Figure 6, the data processing device 22 reads the trouble occurrence time and trouble recovery start time for trouble A from the troubleshooting DB 214, and calculates the trouble initiation time from the difference between the trouble occurrence time and the trouble recovery start time. The data processing device 22 also reads the trouble recovery start time corresponding to trouble A from the troubleshooting DB 214, and reads the closest work completion time after the acquired trouble recovery start time for model a corresponding to production line A, which is the production line where trouble A occurred, from the work completion DB 212. The data processing device 22 calculates the trouble handling time from the difference between the trouble recovery start time and the work completion time of the next product. The data processing device 22 may also calculate the trouble handling time by subtracting the work time from the difference between the trouble recovery start time and the work completion time of the next product. If the work completion DB 212 acquires and stores not only the work completion time but also the work start time, the data processing device 22 may calculate the trouble handling time from the difference between the read trouble recovery start time and the work start time.

[0078] In this way, the common DB unit 21 collects and manages the trouble occurrence time for troubles that occur on the target production line, the trouble recovery start time which is the start time of recovery work for troubles that occur on the target production line, and the work completion time for each product of each model produced on the target production line. The data processing unit 22 reads the trouble occurrence time for the desired trouble, the trouble recovery start time which is managed together with the trouble occurrence time, and the work completion time that is closest to the trouble recovery start time after the trouble recovery start time for the production line where the desired trouble occurred, as data from the common DB unit 21. As data processing, the data processing unit 22 calculates the trouble initiation time, which is the period from when the desired trouble occurs until the recovery of the desired trouble begins, using the read trouble occurrence time and trouble recovery start time, and further calculates the trouble handling time, which is the period of time spent dealing with the recovery of the desired trouble, using the read trouble recovery start time and work completion time.

[0079] Alternatively, the common DB unit 21 collects and manages the trouble occurrence time, the trouble recovery start time, and the work start time for each product of each model produced on the target production line. The data processing unit 22 reads the trouble occurrence time for the desired trouble, the trouble recovery start time which is managed together with the trouble occurrence time, and the work start time that is closest to the trouble recovery start time after the trouble recovery start time for the production line where the desired trouble occurred, as data from the common DB unit 21. As data processing, the data processing unit 22 calculates the trouble initiation time, which indicates the period from when the desired trouble occurred until the recovery of the desired trouble began, using the read trouble occurrence time and trouble recovery start time, and further calculates the trouble handling time, which indicates the period during which the recovery of the desired trouble was dealt with, using the read trouble recovery start time and work start time.

[0080] Embodiment 7. Embodiment 7 describes the sixth application example.

[0081] As a sixth application example, the data processing system 20 determines the operating status of the production line. Generally, monitoring devices can display the status of the production line by receiving signals from the production line at regular intervals, such as "in operation" or "in automatic operation." However, the above method requires constant exchange of signals indicating the status of the production line. Furthermore, discrepancies may occur between the actual operating status of the production line and the signals indicating the status of the production line, such as when a part runs out even during automatic operation, or when an operator is on break even during operation.

[0082] Therefore, in this embodiment, the data processing system 20 assumes that data is collected in real time, that the time is perfectly synchronized between the equipment from which data is collected, and that traceability information is available. Figure 7 is a diagram showing an example of data stored in the data processing system 20 according to Embodiment 7. In the example in Figure 7, an example of data stored in the work completion DB 212 and the traceability DB 213 is shown.

[0083] For example, the data processing device 22 reads the work completion time from the work completion DB 212 and calculates the difference between the work completion time and the current time. The data processing device 22 determines that the system is operational if the difference is less than or equal to a defined first threshold, for example, 3 minutes. If the difference is greater than the first threshold and less than or equal to a defined second threshold greater than the first threshold, for example, 60 minutes, it determines that the system is temporarily stopped. If the difference is greater than the second threshold, it determines that the production line is stopped. The first threshold of 3 minutes and the second threshold of 60 minutes are just examples and are not limited to these. The first and second thresholds may be different for each model, taking into account the time required to produce one product of the model produced on the production line, or they may be common values ​​regardless of the model. The data processing management unit 222 may also change the first and second thresholds in response to an operation from the operator.

[0084] Furthermore, the traceability information is not limited to data stored in the traceability DB 213; any data with continuity, that is, data updated at specified time intervals and presented as a continuous record, is acceptable. This allows the data processing device 22 to determine the operating status of the production line based on the difference between the work completion time and the current time.

[0085] In this way, the common DB unit 21 collects and manages the end times of work for each product of each model produced on the target production line. The data processing unit 22 reads the end times of work for the desired production line as data from the common DB unit 21. As part of the data processing, the data processing unit 22 calculates the difference between the read end times of work and the current time. If the difference is less than or equal to a defined first threshold, the desired production line is determined to be operating normally. If the difference is greater than the first threshold but less than or equal to a defined second threshold that is greater than the first threshold, the desired production line is determined to be "temporarily stopped". If the difference is greater than the second threshold, the desired production line is determined to be "stopped". "Temporarily stopped" is a shorter stop than "stopped", for example, a short stop. A "choko-tei" (short stop) in automated equipment refers to a state where, during automated or semi-automated operations such as transporting, processing, assembly, inspection, and measurement of workpieces (raw materials, workpieces, processing tools, etc.), an abnormal condition occurs in the workpiece or equipment part, causing the equipment's operational functions to temporarily stop. It is a temporary stop that is not serious.

[0086] Embodiment 8. Embodiment 8 describes the seventh application example.

[0087] As a seventh application example, the data processing system 20 calculates the material change time when changing materials used in a production line. Generally, on the edge side, for example, a PLC can store material change time data by calculating the time from the start to the completion of material change using a timer after the start button is pressed. However, with the above method, a program is required to perform the above operation on all units of the target production line, and the operator also needs to press the start button, resulting in a significant amount of man-hours.

[0088] Therefore, the data processing system 20 uses the collected data and material lot information to calculate the material exchange time by subtracting the work completion time from the difference between the work completion time of the final product in a lot and the work completion time of the first product in the next lot. For example, as shown in the thick-bordered area of ​​Figure 7, the data processing device 22 obtains information on model a, which changes from lot number 1 to lot number 2, from the traceability DB 213 as the lot number where the lot number change occurs, and also obtains the work completion time corresponding to the obtained model a from the work completion DB 212. The data processing device 22 uses the obtained work completion time to calculate the material exchange time from the difference between the work completion time of the final product in a lot and the work completion time of the first product in the next lot. Alternatively, the data processing device 22 may use the obtained work completion time to calculate the material exchange time by subtracting the work time from the difference between the work completion time of the final product in a lot and the work completion time of the first product in the next lot. If the data processing device 22 has acquired and stored not only the work completion time but also the work start time in the work completion DB 212, it may calculate the material exchange time from the difference between the work completion time of the last product of the retrieved lot and the work start time of the first product of the next lot.

[0089] In this way, the common DB unit 21 collects and manages traceability information, including lot information which is information about the materials used in a particular model of product, and the end-of-work times for each product of each model produced on the target production line. The data processing unit 22 reads the end-of-work times for consecutive products of the desired model that correspond to the lot information before and after the change in lot information when there is a change in the lot information of the material for the desired model. As part of the data processing, the data processing unit 22 calculates the material change time using the read end-of-work times.

[0090] Alternatively, the common DB unit 21 collects and manages lot information, work completion times, and work start times for each product of each model produced on the target production line. The data processing unit 22 reads data from the common DB unit 21, including the work completion time for the product of the desired model before the change in lot information when there is a change in the material lot information for the desired model, and the work start time for the product of the desired model after the change in lot information. As part of the data processing, the data processing unit 22 calculates the material exchange time using the read work completion time and work start time.

[0091] Embodiment 9. Embodiment 9 describes the eighth example of application.

[0092] As an eighth example of its use, the data processing system 20 monitors changes in the specification values ​​of materials and other items indicated by traceability information. Here, upper and lower limits are assumed as specification values, but depending on the item being monitored, only the upper limit or only the lower limit may be targeted. Generally, even if the lot of material used in the production line changes and the specification values ​​change, production flow continues as is, and workers or others had to periodically check the materials.

[0093] Therefore, the data processing system 20 is configured to compare the latest standard value data with the previous standard value data at predetermined intervals and to notify if there is a discrepancy. In this embodiment, the data processing system 20 assumes that data is collected in real time, that the time is perfectly synchronized between the equipment from which data is collected, and that traceability information is available. For example, as shown in the shaded area of ​​Figure 7, the data processing device 22 can obtain standard values ​​from the traceability DB 213 and monitor changes in standard values ​​based on the obtained standard values.

[0094] In this way, the common DB unit 21 collects and manages lot information, which is information about the materials used in a particular product model, and specification values ​​for those materials, as traceability information. The data processing unit 22 acquires specification values ​​as data from the common DB unit 21 at a specified interval. As part of the data processing, the data processing unit 22 calculates the difference between the previously acquired specification value and the latest specification value for the same material, and determines whether or not the specification value has changed based on the difference. The previously acquired specification value and the latest specification value may be the specification value acquired earlier and the specification value acquired later.

[0095] Embodiment 10. Embodiment 10 describes the ninth application example.

[0096] As a ninth application example, the data processing system 20 monitors the occurrence of defects in jig pallets used on the production line. In the following explanation, jig pallets may be simply referred to as pallets. Generally, there is no method for aggregating troubles caused by jig pallets used for transporting products on the production line.

[0097] Therefore, the data processing system 20 can quantitatively identify jig pallets that frequently experience problems by collecting information on the numbers of jig pallets when a problem occurs on the production line. For example, as shown in Figure 8, the data processing device 22 obtains model a (a2) and model c (c1) from the troubleshooting DB 214 as information on the model in which the problem occurred. Figure 8 is a diagram showing an example of data stored in the data processing system 20 according to Embodiment 10. In the example in Figure 8, an example of data stored in the traceability DB 213 and the troubleshooting DB 214 is shown. The data processing device 22 obtains information on the numbers of jig pallets used by the model obtained from the troubleshooting DB 214, that is, the numbers of jig pallets corresponding to the obtained model, from the traceability DB 213. By aggregating the obtained jig pallet numbers, the data processing device 22 can identify jig pallets that frequently experience problems, as described above.

[0098] In this way, the common DB unit 21 collects and manages the production line where the trouble occurred, the model that was being produced on the production line where the trouble occurred, and the pallet number, which indicates the identification information of the pallet used on the production line as traceability information. The data processing unit 22 obtains the production line where the trouble occurred, the model that was being produced on the production line where the trouble occurred, and the pallet number of the pallet that was being used when the model was being produced on the production line where the trouble occurred, as data from the common DB unit 21, and as data processing, identifies pallets that frequently experience trouble from the obtained pallet numbers.

[0099] Furthermore, as a ninth example of its use, the data processing system 20 can also determine the number of jig pallets used in the production line. Generally, the only way to determine the number of jig pallets flowing through a production line was for workers to count them visually.

[0100] Therefore, when the data processing system 20 collects information on the numbers of the jig pallets when a problem occurs on the production line, it can determine the number of jig pallets flowing through the production line without having to visually count them by aggregating the pallet numbers, that is, by counting the types of pallet numbers. For example, the data processing device 22 can determine the number of jig pallets by obtaining the pallet numbers from the traceability DB 213 and counting them as described above.

[0101] In this way, the common DB unit 21 collects and manages pallet numbers, which indicate the identification information of pallets used in the target production line, as traceability information. The data processing device 22 obtains the pallet numbers of the pallets used in the target production line as data from the common DB unit 21, and as data processing, aggregates the number of pallets used for each production line based on the type of pallet number obtained.

[0102] Embodiment 11. Embodiment 11 describes the tenth application example.

[0103] As a tenth application example, the data processing system 20 prioritizes improvements to multiple locations on a production line when there are several locations that are suspected to be potential sources of trouble. Generally, when there are multiple locations on a production line that are candidates for improvement, cycle time delay and trouble time can be used to determine the priority of improvements. However, with the above method, cycle time delay and trouble time are aggregated separately because they are of different dimensions, making it difficult to determine which one is affecting the productivity of the production line.

[0104] Therefore, the data processing system 20 aggregates cycle time delay and trouble time using a defined loss rate per unit of time, i.e., a common indicator, and compares them on the same level to prioritize improvements. For example, the data processing device 22 calculates cycle time using the method shown in Embodiment 3 (Second Application Example), and calculates a defined loss rate per unit of time based on the multiple calculated cycle times. The data processing device 22 also acquires information on troubles that occurred on the production line from the troubleshooting DB 214, calculates trouble handling time using the method shown in Embodiment 6 (Fifth Application Example), and in addition calculates work time based on the work completion time acquired from the work completion DB 212, and uses the calculated trouble handling time and work time to calculate a defined loss rate per unit of time. As a result, the data processing device 22 can judge the quality of cycle time delay and trouble time based on the loss rate per unit of time, which is a common criterion for judgment.

[0105] As described above, while the data processing system 20 performs the operations described in Embodiment 6 (Fifth Application Example), the data processing device 22 reads the work completion time for a desired product model as data from the common DB unit 21. As data processing, it uses the read work completion time to calculate the difference between the work completion time of a desired product and the work completion time of the next product as the cycle time for the desired product. It adopts the most mode cycle time among the cycle times of multiple desired products calculated within a specified period as the cycle time for the desired product, and calculates a first loss rate per hour defined based on the cycle time. The data processing device 22 also calculates the work time per hour defined using the work completion time, or using the work completion time and work start time, and calculates a second loss rate per hour defined using the work time and troubleshooting time. Based on the first loss rate and the second loss rate, the data processing device 22 determines the priority of production lines that require improvement.

[0106] The configurations shown in the above embodiments are merely examples, and it is possible to combine them with other known technologies, combine different embodiments, and omit or modify parts of the configuration without departing from the gist of the invention.

[0107] 10 Data collection device, 11 PLC, 12, 51 Mobile terminal, 13, 52 Personal computer, 20 Data processing system, 21 Common DB section, 22 Data processing device, 30 Cloud server, 40 Server, 50 Application terminal, 60 Management system, 90 Processing circuit, 91 Processor, 92 Memory, 211 Master management section, 212 Work completion DB, 213 Traceability DB, 214 Troubleshooting DB, 221 Hierarchical data processing section, 221-1a, 221-1b, 221-1c Primary use data processing section, 221-2a, 221-2b Secondary use data processing section, 222 Data processing management section.

Claims

1. A data processing device comprising: a common database unit having one or more databases for each data type that cannot be obtained by data processing, which manage data in a format according to the data category; a hierarchical data processing unit that combines data processing that uses the data in a hierarchical manner for each application and processes the data into data that the application can use; and a data processing management unit that manages the hierarchical data processing in the hierarchical data processing unit.

2. The data processing device according to claim 1, characterized in that the data categories include working time, work content, and work troubles when the product was produced.

3. The data processing apparatus according to claim 1 or 2, characterized in that the data stored in the database of the common database unit is data unified in a format that enables data processing by the hierarchical data processing unit.

4. The data processing device according to any one of claims 1 to 3, characterized in that the hierarchical data processing device has a plurality of data processing devices arranged hierarchically, and the plurality of data processing devices are connected to the common database unit, or the plurality of data processing devices are connected to an application terminal that executes the application, or the data processing device of the upper hierarchy among the plurality of data processing devices is connected to the common database unit, and the remaining lower hierarchy among the plurality of data processing devices is connected to the application terminal.

5. The data processing apparatus according to claim 4, characterized in that the data processing management unit manages the relationships between the data processing by the plurality of data processing units as virtual management items, visually and hierarchically using tags.

6. The data processing apparatus according to any one of claims 1 to 5, characterized in that the data collected by and obtained from the common database unit is data that cannot be recursively obtained by the data processing of the hierarchical data processing unit.

7. A data processing system comprising: a common database unit having one or more databases for each data type that cannot be obtained by data processing, which manage data in a format corresponding to the data category; and a data processing device according to any one of claims 1 to 6.

8. The data processing system according to claim 7, characterized in that the common database unit collects and manages the time of occurrence of troubles that occur on the target production line and the completion time of work for each product of each model produced on the target production line, the data processing unit reads from the common database unit as data the time of occurrence of a desired trouble and the completion time of work closest to the time of occurrence of the trouble for the same production line as the desired trouble, and calculates a trouble time indicating the period from the time of occurrence of the desired trouble until recovery using the read trouble time and completion time of work, or, the common database unit collects and manages the time of occurrence of a trouble and the start time of work for each product of each model produced on the target production line, the data processing unit reads from the common database unit as data the time of occurrence of a desired trouble and the start time of work closest to the time of occurrence of the trouble for the same production line as the desired trouble, and calculates a trouble time indicating the period from the time of occurrence of the desired trouble until recovery using the read trouble time and start time of work.

9. The data processing system according to claim 7 or 8, characterized in that the common database unit collects and manages the end times of work for each product of each model produced on the target production line, the data processing device reads the end times of work for a product of a desired model as data from the common database unit, and as data processing, uses the read end times of work to calculate the difference between the end time of work for a product of the desired model and the end time of work for the next product as the cycle time for the desired model, and adopts the most mode cycle time among the cycle times of multiple products of the desired model calculated within a specified period as the cycle time for the desired model.

10. The common database unit collects and manages the end-of-work times for each product of each model produced on the target production line; the data processing unit reads the end-of-work times for products of a desired model and the end-of-work times for products of a different model that were produced on the same production line as the desired model before the desired model was produced, and as data processing, calculates the setup time for producing the desired model using the read-out end-of-work times; or, the common database unit collects and manages the end-of-work times and the start-of-work times for each product of each model produced on the target production line; the data processing unit reads the start-of-work times for products of a desired model and the end-of-work times for products of a different model that were produced on the same production line as the desired model before the desired model was produced, and as data processing, calculates the setup time for producing the desired model using the read-out end-of-work times and start-of-work times. A data processing system according to any one of claims 7 to 9.

11. The data processing system according to any one of claims 7 to 10, characterized in that the common database unit collects and manages the end time of work for each product of each model produced on the target production line, and the start time of work for each product of each model produced on the target production line; the data processing device reads the start time of work for a product of a desired model and the end time of work for a product of the desired model, which is managed together with the start time of work, from the common database unit as data; and as data processing, calculates the start time of the first product, which indicates the period until the first product is produced when producing a product of the desired model, using the read end time of work and start time of work.

12. The common database unit collects and manages the trouble occurrence time for troubles that occur on the target production line, the trouble recovery start time which is the start time of recovery work for troubles that occur on the target production line, and the work completion time for each product of each model produced on the target production line. The data processing device reads the trouble occurrence time for a desired trouble, the trouble recovery start time which is managed together with the trouble occurrence time, and the work completion time which is closest to the trouble recovery start time after the trouble recovery start time for the production line where the desired trouble occurred, as data from the common database unit, and as data processing, calculates the trouble initiation time which indicates the period from when the desired trouble occurs until the recovery of the desired trouble begins, using the read trouble occurrence time and trouble recovery start time, and further calculates the trouble handling time which indicates the period during which the recovery of the desired trouble was dealt with, using the read trouble recovery start time and work completion time. Or, the common database unit collects and manages the trouble occurrence time, the trouble recovery start time, and the work start time for each product of each model produced on the target production line. The data processing device reads from the common database unit as data the time of occurrence of a desired trouble, the time of start of trouble recovery which is managed together with the time of occurrence of the trouble, and the time of start of work which is closest to the time of start of trouble recovery after the time of start of trouble recovery for the production line in which the desired trouble occurred, and as data processing, calculates a trouble initiation time, which is the period from when the desired trouble occurred until the start of recovery of the desired trouble, using the read time of occurrence of the trouble and the time of start of trouble recovery, and further calculates a trouble handling time, which is the period of time spent dealing with the recovery of the desired trouble, using the read time of start of trouble recovery and the time of start of work, as the data processing device according to any one of 7 to 11.

13. The data processing system according to any one of claims 7 to 12, characterized in that the common database unit collects and manages the end times of work for each product of each model produced on the target production line, the data processing unit reads the end times of work for a desired production line as data from the common database unit, calculates the difference between the read end times of work and the current time as data processing, determines that the desired production line is operating normally if the difference is less than or equal to a defined first threshold, determines that the desired production line is temporarily stopped if the difference is greater than the first threshold and less than or equal to a defined second threshold that is greater than the first threshold, and determines that the desired production line is stopped if the difference is greater than the second threshold.

14. The common database unit collects and manages lot information, which is information about the materials used in a product of a certain model, and the end time of work for each product of each model produced on the target production line, as traceability information; the data processing unit reads the end times of work for consecutive products of the desired model that correspond to the time before and after the change in lot information when there is a change in the lot information of the material for the desired model, and calculates the material replacement time using the read end times of work; or, the common database unit collects and manages the lot information, the end times of work, and the start times of work for each product of each model produced on the target production line; the data processing unit reads the end times of work for products of the desired model that correspond to the time before the change in lot information when there is a change in the lot information of the material for the desired model, and the start times of work for products of the desired model that correspond to the time after the change in lot information, and calculates the material replacement time using the read end times of work and start times of work. A data processing system according to any one of claims 7 to 13.

15. The data processing system according to any one of 7 to 14, characterized in that the common database unit collects and manages lot information, which is information about the materials used in a product of a certain model, and specification values ​​for said materials as traceability information, and the data processing device acquires the specification values ​​from the common database unit as data at a specified period, calculates the difference between the earlier specification value and the later specification value for the same material as data processing, and determines whether or not the specification value has changed based on the difference.

16. The data processing system according to any one of 7 to 15, characterized in that the common database unit collects and manages the production line in which a problem occurred when a problem occurs in the target production line, the model that was being produced on the production line in which the problem occurred, and the pallet number indicating the identification information of the pallet used on the target production line as traceability information; the data processing device obtains the production line in which the problem occurred, the model that was being produced on the production line in which the problem occurred, and the pallet number of the pallet that was being used when the model was being produced on the production line in which the problem occurred as data from the common database unit, and identifies pallets that frequently experience problems from the obtained pallet numbers.

17. The data processing system according to any one of 7 to 16, characterized in that the common database unit collects and manages pallet numbers indicating identification information of pallets used in the target production line as traceability information, the data processing unit obtains the pallet numbers of the pallets used in the target production line as data from the common database unit, and, as data processing, aggregates the number of pallets used for each production line from the type of pallet number obtained.

18. The data processing device reads the work completion time for a product of a desired model as data from the common database unit, and as data processing, calculates the difference between the work completion time of a product of the desired model and the work completion time of the next product using the read work completion time, adopts the most mode cycle time among the cycle times of multiple products of the desired model calculated within a specified period as the cycle time of the desired model, calculates a first loss rate per hour defined based on the cycle time, calculates the work time per hour defined using the work completion time, or using the work completion time and the work start time, calculates a second loss rate per hour defined using the work time and the trouble-shooting time, and determines the priority of production lines requiring improvement based on the first loss rate and the second loss rate.

19. A management system comprising: a data processing system according to any one of claims 7 to 18; and an application terminal that utilizes the data processed by the data processing system.

20. The management system according to claim 19, comprising a collection device for collecting data managed in a database having a common database unit of the data processing system, wherein the data processing system acquires the data collected by the collection device from the collection device.

21. The management system according to claim 19 or 20, characterized in that at least some of the data processing units among the plurality of data processing units of the hierarchical data processing unit of the data processing device provided in the data processing system are provided in the application terminal.

22. A data processing method comprising: a data management step in which a common database unit has one or more databases and manages the collected data in a format corresponding to the category of the collected data; and a data processing step in which a data processing device hierarchically combines a plurality of data processing steps, each of which uses the data managed by the common database unit for each application that uses the data, and processes the data managed by the common database unit into data that can be used by the application, wherein the data collected by the common database unit is data that cannot be obtained by the data processing of the data processing device.

23. The data processing method according to 22, characterized in that the categories of the data are working time, work content, and work troubles when the product was produced.

24. The data processing method according to 22 or 23, characterized in that the data stored in the database of the common database unit is data unified in a format that allows data processing by the data processing device.

25. The data processing method according to any one of 22 to 24, characterized in that the data processing device has a plurality of data processing units arranged hierarchically, and the plurality of data processing units are connected to the common database unit, or the plurality of data processing units are connected to an application terminal that executes the application, or the data processing unit of the upper hierarchy of the plurality of data processing units is connected to the common database unit, and the remaining lower hierarchy of the plurality of data processing units is connected to the application terminal.

26. The data processing method according to 25, characterized in that, in the data processing step, the data processing device manages the relationships between the data processing by the plurality of data processing units as virtual management items, visually and hierarchically using tags.

27. The data processing method according to any one of 22 to 26, characterized in that the data collected in the common database unit is data that cannot be recursively obtained by the data processing of the data processing device.

28. In the data management step, the common database unit collects and manages the time of occurrence of troubles that occur on the target production line and the completion time of work for each product of each model produced on the target production line; In the data processing step, the data processing device reads from the common database unit as data the time of occurrence of the desired trouble and the completion time of work closest to the time of occurrence of the trouble for the same production line as the desired trouble, and as data processing, calculates the trouble time, which indicates the period from the occurrence of the desired trouble to its recovery, using the read time of occurrence and completion time of work; or, in the data management step, the common database unit collects and manages the time of occurrence of the trouble and the start time of work for each product of each model produced on the target production line; The data processing method according to any one of 22 to 27, characterized in that, in the data processing step, the data processing device reads from the common database unit as data the time of occurrence of the desired trouble and the start time of work that is closest to the time of occurrence of the trouble after the time of occurrence of the trouble for the same production line as the desired trouble, and as data processing, calculates a trouble time indicating the period from the time of occurrence of the desired trouble until recovery using the read-out time of occurrence of the trouble and the start time of work.

29. The data processing method according to any one of 22 to 28, characterized in that, in the data management step, the common database unit collects and manages the end times of work for each product of each model produced on the target production line; in the data processing step, the data processing device reads the end times of work for products of a desired model as data from the common database unit; and as data processing, uses the read end times of work to calculate the difference between the end time of work for a product of the desired model and the end time of work for the next product as the cycle time of the desired model; and adopts the most mode cycle time among the cycle times of multiple products of the desired model calculated within a specified period as the cycle time of the desired model.

30. In the data management step, the common database unit collects and manages the end time of work for each product of each model produced on the target production line; In the data processing step, the data processing device reads from the common database unit as data the end time of work for the product of the desired model, and the end time of work for products of a different model that were produced on the same production line as the product of the desired model before the product of the desired model was produced, and as data processing, calculates the setup time for producing the product of the desired model using the read end time of work; or, in the data management step, the common database unit collects and manages the end time of work and the start time of work for each product of each model produced on the target production line; The data processing method according to any one of 22 to 29, characterized in that, in the data processing step, the data processing device reads from the common database unit as data the start time of work for a product of a desired model, and the end time of work for a product of a different model that was produced on the same production line as the product of the desired model before the product of the desired model was produced, and as data processing, calculates the setup time for producing the product of the desired model using the read-out end time and the start time of work.

31. The data processing method according to any one of 22 to 30, characterized in that, in the data management step, the common database unit collects and manages the end time of work for each product of each model produced on the target production line, and the start time of work for each product of each model produced on the target production line; and in the data processing step, the data processing device reads from the common database unit as data the start time of work for a product of a desired model, and the end time of work for a product of the desired model, which is managed together with the start time of work; and as data processing, calculates the start time of the first product, which indicates the period until the first product is produced when producing a product of the desired model, using the read end time of work and start time of work.

32. In the data management step, the common database unit collects and manages the trouble occurrence time of a trouble that occurred on the target production line, the trouble recovery start time which is the start time of recovery work for the trouble that occurred on the target production line, and the work completion time for each product of each model produced on the target production line. In the data processing step, the data processing device reads from the common database unit as data the trouble occurrence time for the desired trouble, the trouble recovery start time which is managed together with the trouble occurrence time, and the work completion time which is closest to the trouble recovery start time after the trouble recovery start time for the production line where the desired trouble occurred. As data processing, it calculates the trouble initiation time, which is the period from when the desired trouble occurred until the recovery of the desired trouble began, using the read trouble occurrence time and trouble recovery start time, and further calculates the trouble handling time, which is the period during which the recovery of the desired trouble was dealt with, using the read trouble recovery start time and work completion time, or The data processing method according to any one of claims 22 to 31, characterized in that, in the data management step, the common database unit collects and manages the trouble occurrence time, the trouble recovery start time, and the work start time for each product of each model produced on the target production line; in the data processing step, the data processing device reads from the common database unit as data the trouble occurrence time for the desired trouble, the trouble recovery start time which is managed together with the trouble occurrence time, and the work start time which is closest to the trouble recovery start time after the trouble recovery start time for the production line where the desired trouble occurred; and as data processing, calculates a trouble initiation time, which indicates the period from when the desired trouble occurred until work to recover from the desired trouble started, using the read trouble occurrence time and trouble recovery start time; and further calculates a trouble handling time, which indicates the period during which work to recover from the desired trouble was dealt with, using the read trouble recovery start time and work start time.

33. The data processing method according to any one of 22 to 32, characterized in that, in the data management step, the common database unit collects and manages the end time of work for each product of each model produced on the target production line; in the data processing step, the data processing device reads the end time of work for the desired production line as data from the common database unit, calculates the difference between the read end time of work and the current time as data processing, determines that the desired production line is operating normally if the difference is less than or equal to a defined first threshold, determines that the desired production line is temporarily stopped if the difference is greater than the first threshold and less than or equal to a defined second threshold that is greater than the first threshold, and determines that the desired production line is stopped if the difference is greater than the second threshold.

34. In the data management step, the common database unit collects and manages lot information, which is information about the materials used in a product of a certain model, and the end time of work for each product of each model produced on the target production line, as traceability information; In the data processing step, the data processing device reads from the common database unit as data the end times of work for consecutive products of the desired model that correspond to the time before and after the change in lot information when there is a change in the lot information of the material for the desired model, and as data processing, calculates the material exchange time using the read end times of work; or, in the data management step, the common database unit collects and manages the lot information, the end time of work, and the start time of work for each product of each model produced on the target production line. The data processing method according to any one of 22 to 33, characterized in that, in the data processing step, the data processing device reads from the common database unit as data the work completion time of the product of the desired model that corresponds to the lot information before the change in lot information when there is a change in the lot information of the material for the desired model, and the work start time of the product of the desired model that corresponds to the change in lot information after the change in lot information, and as data processing, calculates the material replacement time using the read work completion time and work start time.

35. The data processing method according to any one of 22 to 34, characterized in that, in the data management step, the common database unit collects and manages lot information, which is information about the materials used in a product of a certain model, and specification values ​​for the materials, as traceability information; and in the data processing step, the data processing device obtains the specification values ​​from the common database unit as data at a predetermined period; and as data processing, calculates the difference between the earlier specification value and the later specification value for the same material; and determines whether or not the specification value has changed based on the difference.

36. The data processing method according to any one of 22 to 35, characterized in that, in the data management step, the common database unit collects and manages the production line where the trouble occurred, the model that was being produced on the production line where the trouble occurred, and the pallet number indicating the identification information of the pallet used on the production line as traceability information; and in the data processing step, the data processing device obtains from the common database unit as data the production line where the trouble occurred, the model that was being produced on the production line where the trouble occurred, and the pallet number of the pallet that was being used when the model was being produced on the production line where the trouble occurred, and as data processing, identifies pallets that frequently experience trouble from the obtained pallet number.

37. The data processing method according to any one of 22 to 36, characterized in that, in the data management step, the common database unit collects and manages pallet numbers indicating identification information of pallets used in the target production line as traceability information, and in the data processing step, the data processing device obtains the pallet numbers of the pallets used in the target production line as data from the common database unit, and as data processing, aggregates the number of pallets used for each production line from the type of pallet number obtained.

38. The data processing method according to 32, characterized in that, in the data processing step, the data processing device reads the work completion time for a product of a desired model as data from the common database unit, calculates the difference between the work completion time of a product of the desired model and the work completion time of the next product using the read work completion time as the cycle time of the desired model, adopts the most mode cycle time among the cycle times of multiple products of the desired model calculated within a specified period as the cycle time of the desired model, calculates a first loss rate per hour defined based on the cycle time, calculates the defined work time per hour using the work completion time, or using the work completion time and the work start time, calculates a second loss rate per hour defined using the work time and the trouble-shooting time, and determines the priority of production lines requiring improvement based on the first loss rate and the second loss rate.

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