Data acquisition system and method for industrial machines, and information storage medium

Abstract

The present disclosure relates to a data acquisition system and method for an industrial machine and an information storage medium. There is provided a data acquisition system (1) for an industrial machine (30), the data acquisition system (1) comprising: an acquisition module (104, 202) configured to acquire acquisition data related to the industrial machine based on a predetermined acquisition setting; an acquisition setting identification module (106) configured to identify the acquisition setting based on identification information associated with the acquisition data; and an execution module (107) configured to perform parsing processing related to the acquisition data based on the identified acquisition setting.

Description

Technical Field

[0001] The present disclosure relates to a data acquisition system for an industrial machine, a data acquisition method for an industrial machine, and an information storage medium. Background Art

[0002] In WO 2015 / 068210 A, a system is described for tracking the operation of an industrial machine in time series based on predetermined acquisition settings to acquire acquisition data and upload the acquisition data to a cloud server. An analyst is requested to analyze the acquisition data uploaded from the industrial machine. Summary of the Invention

[0003] The problem to be solved by the present disclosure is to perform, for example, parsing processing corresponding to the acquisition settings on the acquisition data.

[0004] According to one aspect of the present disclosure, there is provided a data acquisition system for an industrial machine, the data acquisition system including: an acquisition module configured to acquire acquisition data related to the industrial machine based on predetermined acquisition settings; an acquisition setting identification module configured to identify the predetermined acquisition settings based on identification information associated with the acquisition data; and an execution module configured to perform parsing processing related to the acquisition data based on the identified predetermined acquisition settings.

[0005] According to the present disclosure, it is possible to perform, for example, parsing processing corresponding to the acquisition settings on the acquisition data. Brief Description of the Drawings

[0006] Figure 1 is a diagram for illustrating an example of the overall configuration of the data acquisition system.

[0007] Figure 2 is a diagram for illustrating an example of the data acquisition process in the data acquisition system according to the first embodiment of the present disclosure.

[0008] Figure 3 is a view for illustrating an example of a setting screen.

[0009] Figure 4 is a functional block diagram for illustrating an example of the functions implemented in the data acquisition system according to the first embodiment.

[0010] Figure 5 is a table for illustrating an example of data storage in a database.

[0011] Figure 6 is a diagram for illustrating an example of mutually distinguishing variable names for feedback based on a topic ID.

[0012] Figure 7This is a diagram for illustrating an example of preparing a database for each industrial machine.

[0013] Figure 8 This is a flowchart for illustrating an example of the processing performed in the data acquisition system according to the first embodiment.

[0014] Figure 9 This is a flowchart for illustrating an example of the processing performed in the data acquisition system according to the first embodiment.

[0015] Figure 10 This is a diagram for illustrating an example of the data acquisition process in the data acquisition system according to the second embodiment of the present disclosure.

[0016] Figure 11 This is a diagram for illustrating an example of the data acquisition process in the data acquisition system according to the third embodiment of the present disclosure.

[0017] Figure 12 This is a diagram for illustrating an example of the data acquisition process in the data acquisition system according to the fourth embodiment of the present disclosure.

[0018] Figure 13 This is a diagram for illustrating an example of the data acquisition process in the data acquisition system according to the fifth embodiment of the present disclosure.

[0019] Figure 14 This is a functional block diagram of a modification example of the present disclosure. Detailed Description of the Invention

[0020] [1. First Embodiment]

[0021] A description is given of an example of an embodiment of a data acquisition system according to the present disclosure. Now, a description is given of the data acquisition system according to the first embodiment of the present disclosure.

[0022] [1-1. Overall Configuration of the Data Acquisition System]

[0023] Figure 1 This is a diagram for illustrating an example of the overall configuration of the data acquisition system. As Figure 1 shown, the data acquisition system 1 includes a data acquisition device 10, an upper layer control device 20, and an industrial machine 30. Each device is connected to each other through any network, such as a field network or a general network.

[0024] The data acquisition device 10 is a device capable of acquiring various types of data in the data acquisition system 1. In the first embodiment, "acquisition" has the same meaning as "reception" or "obtaining". For example, the data acquisition device 10 is a personal computer, a server computer, a tablet terminal, or a smart phone. For example, the data acquisition device 10 includes a CPU 11, a storage unit 12, a communicator 13, an operation interface 14, and a display 15.

[0025] The CPU 11 includes at least one processor. The CPU 11 is a certain type of circuit. The storage unit 12 includes at least one of a volatile memory or a non-volatile memory. The communicator 13 includes at least one of a communication interface for wired communication or a communication interface for wireless communication. The operation interface 14 is an input device, such as a mouse or a keyboard. The display 15 is a liquid crystal display or an organic EL display.

[0026] The upper layer control device 20 is a device for controlling one or more industrial machines 30. In the first embodiment, the meaning of "control" not only includes instructing the industrial machine 30 to start a process, but also includes, in principle, not instructing the start of a process, but only sending other minimal instructions to the industrial machine 30. A configuration that only requires the industrial machine 30 to perform a certain operation based on a certain instruction issued from the upper layer control device 20 corresponds to control.

[0027] The upper layer control device 20 can control any number of industrial machines 30. When the entire data acquisition system 1 is referred to as a "unit", the upper layer control device 20 is sometimes referred to as a "unit controller". The upper layer control device 20 can be a device with other names, for example, a programmable logic controller (PLC). For example, the upper layer control device 20 includes a CPU 21, a storage unit 22, a communicator 23, and an IoT unit 24. A human machine interface (HMI) device 25 can be connected to the upper layer control device 20. The physical configuration of each of the CPU 21, the storage unit 22, and the communicator 23 can be the same as the physical configuration of the CPU 11, the storage unit 12, and the communicator 13, respectively.

[0028] The IoT unit 24 is hardware for transmitting data to another computer through a network. For example, the IoT unit 24 includes a CPU, a storage unit, and a communicator. The physical configuration of each of the CPU, the storage unit, and the communicator included in the IoT unit 24 can be the same as the physical configuration of the CPU 11, the storage unit 12, and the communicator 13, respectively. For example, data consistency can be achieved periodically or aperiodically between the CPU 21 and the IoT unit 24. When the CPU 21 has a data acquisition function, the IoT unit 24 can be omitted.

[0029] The HMI device 25 is a device for a user to set the upper layer control device 20. The HMI device 25 can be a device developed specifically for the upper layer control device 20, or can be a personal computer, a tablet terminal, or a smart phone. The HMI device 25 may be capable of setting the industrial machine 30. For example, the user connects the HMI device 25 to the upper layer control device 20 or the industrial machine 30 to perform at least one of creation of a program, setting of parameters, setting of variables, or setting of communication.

[0030] The industrial machine 30 is a machine that can be controlled by the upper layer control device 20. The industrial machine 30 is sometimes also referred to as an "equipment" or a "device". A group of multiple industrial machines 30 is sometimes referred to as a "line" or a "unit". The industrial machine 30 can be any machine, such as a PLC, a robot controller, an industrial robot, a motor controller, a servo amplifier, a motion controller, a numerical control device, or a power conversion device. For example, the industrial machine 30 includes a CPU 31, a storage unit 32, and a communicator 33. The physical configuration of each of the CPU 31, the storage unit 32, and the communicator 33 can be the same as the physical configuration of the CPU 11, the storage unit 12, and the communicator 13, respectively.

[0031] The programs and data stored in each of the storage units 12, 22, and 32 can be provided through a network. In addition, the hardware configuration of each device is not limited to the above examples, and various types of hardware can be applied. For example, it may include a reader for reading a computer-readable information storage medium (e.g., a memory card slot) and an input / output device for connecting an external device (e.g., a USB terminal). In this case, the programs and data stored in the information storage medium can be provided through the reader or the input / output device. In addition, a circuit called an "FPGA" or an "ASIC" may also be included.

[0032] [1-2. Overview of the data acquisition system]

[0033] In the data acquisition system 1, for each of a plurality of objects, at least one process is performed in a predetermined order. The object is an article to be processed. The object is also referred to as a "workpiece". The object can be any one of a final product to be produced, an intermediate product, a material, and a raw material. The object can be any type of object, such as a semiconductor device, an electrical appliance, a vehicle, food, or a commodity. The process is work performed on the object, such as machining, assembling, transporting, grasping, measuring, or inspecting. The process can be considered as an operation of the industrial machine 30.

[0034] The industrial machine 30 stores a process program in which individual behaviors in the process are defined. The industrial machine 30 determines whether the execution conditions of the process are satisfied, and when the execution conditions are satisfied, executes the process program to start the process. The execution conditions can be any conditions, and can be, for example, the condition of receiving a predetermined instruction from the upper control device 20, the condition that variables prepared for each process program reach a predetermined value, or the condition of inputting a predetermined signal from a sensor.

[0035] For example, one or more sensors are connected to the industrial machine 30. The sensors can be any type of sensors, such as vision sensors, torque sensors, motor encoders, object detection sensors, temperature sensors, or grasping sensors. The industrial machine 30 records information in time series based on an instruction from the upper control device 20, such as an image generated by a vision sensor or a physical quantity detected by a torque sensor. The data recorded in time series is hereinafter referred to as "acquisition data".

[0036] The acquisition data is the data to be acquired in the data acquisition system 1. The acquisition data is sometimes also referred to as "tracking data" or "recording data". In the first embodiment, it is assumed that the acquisition data includes information at each of a plurality of time points, but the acquisition data may only include information at a certain time point (instantaneous value). Time information (timestamp) is associated with each piece of information included in the acquisition data.

[0037] In the first embodiment, the case where time information is synchronized between the upper control device 20 and the industrial machine 30 is described, but time information synchronization is not particularly required. In addition, in the first embodiment, the case where the upper control device 20 and the industrial machine 30 have different control cycles from each other is described, but their control cycles can be the same. The time information is synchronized, but the control cycles are different from each other (control cycle out of sync), so that asynchronous data acquisition is to be performed in the data acquisition system 1.

[0038] The acquisition data can include any information, and for example includes at least one of the following information: the value of a variable stored in the industrial machine 30, the information detected by each sensor, the calculation result inside the industrial machine 30, the occurrence status of an alarm, the parameters during process execution, the inspection result of an object obtained by an inspection device, the measurement result of an object obtained by a measurement device, and the process program and firmware used in the execution of the process. The acquisition data can include, for example, the value of a variable stored in the upper control device 20.

[0039] The collected data is collected for various purposes, and these various purposes include ensuring the traceability of an object, analyzing the cause of an alarm, improving the quality of the object, and improving the operation efficiency of a unit. For example, when collecting the collected data for the purpose of ensuring the traceability of an object, at least one of the information capable of identifying the object itself or the information capable of identifying the process performed on the object may be associated with the collected data. When this information is associated with the collected data, it may be possible to use the collected data associated with the product to analyze the manufacturing state of the product after the object is made into a product and shipped. For example, even when performing small-quantity and large-quantity production of variable quantity types, the traceability of each object may be ensured.

[0040] Figure 2 is a diagram for illustrating an example of the process of data collection in the data collection system 1 according to the first embodiment. As Figure 2 shown, in the data collection system 1, the collected data is collected by performing roughly divided processes 1 to 14. Figure 2 Processes 1 to 14 are roughly divided process parts, and Figure 4 corresponding parts of the functional blocks and Figure 8 and Figure 9 corresponding parts of the flowcharts give detailed descriptions of each process part.

[0041] First, the user starts the setting tool stored in the storage unit 12 of the data collection device 10 to display a setting screen for specifying collection settings on the display 15 (Process 1). This setting tool is an application for performing collection settings. The setting tool may be a tool for setting at least one of the upper layer control device 20 or the industrial machine 30. For example, the user can use the setting tool to set the industrial machine 30 to be controlled by the upper layer control device 20, set each process performed by the industrial machine 30, create a control program, and create a process program.

[0042] Figure 3 is a view for illustrating an example of the setting screen. As Figure 3 shown, on the setting screen G, an input form F1, an input form F2, a button B1, and a button B2 are displayed. The input form F1 is used to specify the industrial machine 30 as the target of data collection. The input form F2 is used to specify the specific content of the collection settings. The button B1 is used to register the collection settings. The button B2 is used to start data collection. In the first embodiment, the situation where the specification of the collection settings and the start of data collection are performed on the same setting screen G is described, but the specification and start may be performed on different screens.

[0043] In the input form F1, the industrial machine 30 to be controlled by the upper control device 20 is displayed so that the industrial machine 30 can be selected. For example, the data acquisition device 10 or the upper control device 20 stores definition information that defines the industrial machine 30 to be controlled. This definition information includes information that can identify the industrial machine 30 to be controlled and includes at least one of, for example, a machine ID, a machine name, a communication method, or an IP address. The definition information may include the definition of variables for the control of the industrial machine 30 and may include at least one of, for example, a variable name, a register address, a data type, or a data size (number of bytes). For example, in the input form F1, a list of industrial machines 30 indicated by the definition information is displayed. The user selects from the list the industrial machine 30 for which acquisition settings are to be specified.

[0044] In the input form F2, the values of each item included in the acquisition settings are displayed. In Figure 3 the example of, the input form F2 has a table form, and values can be input into each cell of the table. The acquisition settings may include any items, such as including a machine ID, a subject ID, a setting name, a trigger, a variable name of the variable to be acquired, a schedule, and a sampling period. The acquisition settings are not limited to the examples in the first embodiment but may include the specification of other information, for example, the shaft of a motor that is the target of acquisition. In the first embodiment, parsing processing corresponding to the acquisition settings is performed, and thus a parser is also specified on the setting screen G. A detailed description of the parser and the parsing processing will be given later.

[0045] The machine ID is information that can identify the industrial machine 30 that is the target of data acquisition based on the acquisition settings. The subject ID is information that can identify the acquisition settings. In the first embodiment, multiple acquisition settings can be registered for one industrial machine 30. Each acquisition setting is identified based on the set of the machine ID and the subject ID. The setting name is the name of the acquisition setting. The trigger is the start condition of data acquisition. The variable name is the name of the variable that is the acquisition target. The schedule is the time at which data acquisition is to be performed. The sampling period is the time interval of data acquisition.

[0046] The user specifies the industrial machine 30 from the input form F1 and specifies the content of the acquisition settings in the input form F2. For the machine ID of the input form F2, the machine ID of the industrial machine 30 specified in the input form F1 can be automatically input. When the user selects the button B1, the content of the acquisition settings specified on the setting screen G is transmitted to the IoT module (processing 2) of the data acquisition device 10. The IoT module is one of the programs executed by the data acquisition device 10 and is responsible for sending and receiving data to and from the IoT unit 24 of the upper control device 20.

[0047] The IoT module registers the acquisition settings passed from the setting tool in the database DB stored in the storage unit 12 of the data acquisition device 10 (process 3). After that, when the timing for the user to start data acquisition arrives, the user selects the button B2 after specifying the acquisition settings for the data acquisition to be started. In the first embodiment, the case where the acquisition settings for the data acquisition to be started are specified by selecting a row of the input form F2 is described, but these acquisition settings can be specified by any method and can also be specified on another screen. For example, a list of the acquisition settings registered in the database DB can be displayed on another screen, and the acquisition settings can be specified from the list.

[0048] When an instruction to start data acquisition is received via the button B2 (process 4), the IoT module reads out the acquisition settings registered in the database DB (process 5). As described above, in the first embodiment, the acquisition settings are identified based on the set of the machine ID and the topic ID, and the IoT module thus reads out from the database DB the acquisition settings associated with the set of the machine ID and the topic ID specified by the user from the input form F2.

[0049] The IoT module feeds back the acquisition settings read out from the database DB to the upper layer control device 20 (process 6). This feedback is to send certain data from the data acquisition device 10 to the upper layer control device 20. As Figure 2 shown, when the IoT unit 24 of the upper layer control device 20 receives the acquisition settings to be fed back from the data acquisition device 10, the IoT unit 24 transfers the acquisition settings to the CPU 21. The set of the machine ID and the topic ID corresponding to the acquisition settings is also fed back.

[0050] In the first embodiment, it is assumed that the consistency of variables is achieved periodically or aperiodically between the CPU 21 and the IoT unit 24. It is assumed that whether the variable of the IoT unit 24 is transferred (copied) to the CPU 21 or the variable of the CPU 21 is transferred (copied) to the IoT unit 24 is determined based on the attribute of the variable. The variable corresponding to the acquisition settings to be fed back is required to be transferred from the IoT unit 24 to the CPU 21, and the corresponding attribute is thus defined accordingly. At the same time, the variable corresponding to the acquisition data is required to be transferred from the CPU 21 to the IoT unit 24, and the corresponding attribute is thus defined accordingly. Those attributes of the variables can be specified by the user from the setting tool or can be defined as default attributes.

[0051] The CPU 21 of the upper-layer control device 20 uses an application for data acquisition (hereinafter referred to as the "acquisition application") to obtain the acquisition settings fed back by the IoT unit 24, and performs the acquisition of acquisition data (process 7). The acquisition application is created by the user and is pre-stored in the storage unit 22 of the upper-layer control device 20. The acquisition application can perform various types of processing for data acquisition and perform, for example, each of the following: obtaining the fed-back acquisition settings, setting the acquisition settings to the industrial machine 30, instructing the industrial machine 30 to start acquisition, setting a trigger to the industrial machine 30, and reading out the acquisition data from the industrial machine 30.

[0052] When the CPU 21 of the upper-layer control device 20 acquires the acquisition data from the industrial machine 30 (process 8), the CPU 21 transfers the acquisition data acquired from the industrial machine 30 to the IoT unit 24 (process 9). When the CPU 21 transfers the acquisition data, the CPU 21 stores a set of the machine ID and the topic ID corresponding to the acquisition settings in the header of the packet. As a result, the data acquisition device 10 can identify the acquisition settings corresponding to the acquisition data. The IoT unit 24 of the upper-layer control device 20 transfers the acquisition data to the data acquisition device 10. The IoT module of the data acquisition device 10 requests the parser to perform a parsing process on the transferred acquisition data (process 10).

[0053] The parser is a program for analyzing the acquisition data to convert the data structure. The parser is pre-stored in the storage unit 12 of the data acquisition device 10. At least one parser is stored in the data acquisition device 10. When a common rule of the data structure can be adopted among multiple acquisition settings, a general parser common to the multiple acquisition settings can be prepared. When a parser for the data structure dedicated to each acquisition setting is required, a parser can be prepared for each acquisition setting. Similarly, a general parser common to multiple industrial machines 30 can be prepared, or a parser can be prepared for each industrial machine 30.

[0054] The parser of the data acquisition device 10 refers to the database DB and obtains the acquisition settings associated with the set of the machine ID and the topic ID included in the header of the acquisition data (process 11). The parser of the data acquisition device 10 performs a parsing process on the acquisition data based on the obtained acquisition settings (process 12). The parser of the data acquisition device 10 requests the IoT module to register the acquisition data after the parsing process in the database DB (process 13). The IoT module of the data acquisition device 10 registers the acquisition data after the parsing process in the database DB (process 14), and the registration of the acquisition data is completed.

[0055] As described above, in the data acquisition system 1 according to the first embodiment, the acquisition settings stored in the database DB are identified based on the set of the machine ID and the subject ID. Based on the identified acquisition settings, parsing processing is performed on the acquisition data acquired from the industrial machine 30. As a result, parsing processing corresponding to the acquisition settings for the acquisition data is achieved. A detailed description of such a configuration will now be given.

[0056] [1-3. Functions Implemented in the Data Acquisition System]

[0057] Figure 4 is a functional block diagram for illustrating an example of the functions implemented in the data acquisition system 1 according to the first embodiment. In the first embodiment, the functions implemented in each of the data acquisition device 10, the upper layer control device 20, and the industrial machine 30 are described.

[0058] [1-3-1. Functions Implemented in the Data Acquisition Device]

[0059] As Figure 4 shown, the data acquisition device 10 includes a data storage unit 100, a setting module 101, a receiving module 102, a storage module 103, an acquisition module 104, a parser identification module 105, an acquisition setting identification module 106, and an execution module 107. The data storage unit 100 is mainly implemented by the storage unit 12. Each of the other functions is mainly implemented by the CPU 11.

[0060] [Data Storage Unit]

[0061] The data storage unit 100 stores the data required for acquiring acquisition data. For example, the data storage unit 100 stores the database DB. The data stored in the data storage unit 100 is not limited to the database DB, and the data storage unit 100 may also store, for example, each of the following: at least one parser, an IoT module, a setting tool, a user-created control program, a user-created process program, definition information about the industrial machine 30 to be controlled by the upper layer control device 20, and definition information about the process executed by the industrial machine 30.

[0062] Figure 5 is a table for showing an example of data storage in the database DB. As Figure 5 shown, the database DB is a database that stores each individual acquisition setting specified by the user and the acquisition data acquired based on these acquisition settings. For example, the database DB stores the machine ID, the subject ID, the setting name, the feedback variable name, the acquisition settings, the acquisition data before parsing processing, and the acquisition data after parsing processing. The database DB stores the setting name, the feedback variable name, the acquisition settings, the acquisition data before parsing processing, and the acquisition data after parsing processing for each set of the machine ID and the subject ID.

[0063] The feedback variable name is the name of the variable for collecting the feedback of the setting. The collected data before parsing processing is the data for which parsing processing has not been performed. The collected data received from the upper control device 20 is directly stored as the collected data before parsing processing. The collected data before parsing processing is sometimes referred to as "raw data". The collected data after parsing processing is the data converted by the parsing processing. After the collected data after parsing processing is stored, the collected data before parsing processing can be deleted.

[0064] For example, when collecting the collected data for each of a plurality of objects based on a certain collection setting, the collected data is stored in the database DB for each object. By storing the collected data for each object, the traceability of the object is ensured. The data stored in the database DB is not limited to Figure 5 the examples, but any data related to data collection can be stored therein. For example, the collection date and time of the collected data can be stored, and the information capable of identifying the unit to which the industrial machine 30 belongs can be stored.

[0065] In the first embodiment, the case where the data is collectively managed in one database DB is described, but the data can be distributed to multiple databases and can be managed in a distributed manner. For example, the database for storing the collection setting and the database for storing the collected data can exist independently of each other. In addition, for example, there can be an independent database for each industrial machine 30, or there can be an independent database for each collection setting.

[0066] [Setting module]

[0067] The setting module 101 sets the machine ID based on the definition information related to the control target of the upper control device 20. The definition information is the information related to the industrial machine 30 that can be controlled by the upper control device 20. For example, the user uses a setting tool to create the definition information. The definition information includes the machine ID of the industrial machine 30 that can be controlled by the upper control device 20. The machine ID may not be included in the definition information, but can be generated from the information included in the definition information, or can be specified by the user.

[0068] The machine ID is an example of the first identification information. Therefore, in the first embodiment, the part described as "machine ID" can be replaced by "first identification information". The first identification information only needs to be the information that can identify the industrial machine 30, and can be other information, such as an ID other than the machine ID, the machine name of the industrial machine 30, or the IP address of the industrial machine 30.

[0069] In the first embodiment, the setting module 101 obtains the machine ID included in the definition information stored in the data storage unit 100 of the data acquisition device 10 or the data storage unit 200 of the upper control device 20, and sets the obtained machine ID as the machine ID associated with the setting information. The setting of the machine ID is to identify or determine the machine ID to be associated with the acquisition settings. The machine ID may not be set from the definition information, but may be manually input by the user, or may be automatically set based on a predetermined ID issuance rule.

[0070] For example, the setting module 101 sets the machine ID of the industrial machine 30 specified by the user to each of the input forms F1 and F2 of the setting screen G. The setting module 101 displays the industrial machine 30 indicated by the machine ID included in the definition information on the input form F1 so that the industrial machine 30 can be selected. When the user selects the button B1 to indicate registration of the acquisition settings, the setting module 101 sets the machine ID specified in each of the input form F1 and the input form F2 as the machine ID associated with the acquisition settings to be registered.

[0071] [Receiving module]

[0072] The receiving module 102 receives the user's specification of the identification information and the acquisition settings. The identification information is information capable of identifying the acquisition settings. In the first embodiment, a set of the machine ID and the subject ID corresponds to the identification information. Therefore, the part describing the set of the machine ID and the subject ID in the first embodiment can be replaced with "identification information". The identification information is not limited to the set of the machine ID and the subject ID, but only requires to be information capable of identifying the acquisition settings. For example, only one of the machine ID and the subject ID may correspond to the identification information. In addition, for example, the identification information may be another ID, or may be information other than an ID, such as a setting name.

[0073] In the first embodiment, the user specifies various settings related to data acquisition on the setting screen G, so that the receiving module 102 receives an operation on the setting screen G, and thus receives the specification of the machine ID, the subject ID, and the acquisition settings. For example, the receiving module 102 receives the selection of the industrial machine 30 in the input form F1, and thus receives the specification of the machine ID. In addition, for example, the receiving module 102 receives the specification of the set of the machine ID and the subject ID in the input form F2.

[0074] In addition, for example, the receiving module 102 receives the specification of the acquisition settings in the input form F2. In Figure 3In the example, the receiving module 102 receives the specification of a trigger, the variable name of the variable to be the acquisition target, the time schedule, and the sampling period as the acquisition settings. The trigger can be any condition, for example, it can be at least one of the following: the value of a variable of the industrial machine 30, the value calculated from the variable, the time information managed by the industrial machine 30, the input signal of the industrial machine 30, or the detection result obtained by a sensor connected to the industrial machine 30. The receiving module 102 receives the specification of these conditions.

[0075] The variable name included in the acquisition settings is the name of the variable to be acquired among the variables stored in the industrial machine 30. The variable name can be directly input by the user, or can be obtained from the variable definition stored in the data storage unit 100. The receiving module 102 receives the user's specification of the variable name. The time schedule only needs to be information capable of identifying the timing of performing data acquisition, and for example, it is the day of the week, the date, or the time period for performing data acquisition. The receiving module 102 receives the specification of the day of the week or the like. The sampling period only needs to be a value capable of identifying the time interval of data acquisition. The receiving module 102 receives the specification of the value indicating the sampling period.

[0076] [Storage module]

[0077] The storage module 103 stores the set of machine ID and subject ID and the acquisition settings in the database DB in an associated manner. In the first embodiment, the case where the storage module 103 stores the set of machine ID and subject ID specified by the user and the acquisition settings specified by the user in the database DB in an associated manner is described, but at least one of the set or the acquisition settings can be automatically generated by a computer (for example, the data acquisition device 10). The storage module 103 can store the automatically generated set of machine ID and subject ID and the automatically generated acquisition settings in the database DB in an associated manner. The storage module 103 stores the set of machine ID and subject ID and the acquisition settings in the database DB so that the acquisition settings can be searched by using the set of machine ID and subject ID as a query. The acquisition settings correspond to the search index. The storage module 103 stores the set of machine ID and subject ID and the acquisition settings in the same record of the database DB.

[0078] [Acquisition module]

[0079] The acquisition module 104 acquires acquisition data related to the industrial machine 30 based on a predetermined acquisition setting. The acquisition module 104 acquires the acquisition data generated by the industrial machine 30 based on the acquisition setting, or the processed acquisition data, which is obtained by performing certain processing on the acquisition data generated by the industrial machine 30 based on the acquisition setting. It is assumed that such processing is performed by the upper control device 20 or the industrial machine 30. In the first embodiment, a set of a machine ID and a subject ID is associated with the acquisition data, and the acquisition module 104 thus acquires the acquisition data associated with this set.

[0080] As described above, the identification information in the first embodiment includes a machine ID that can identify the industrial machine 30 and a subject ID different from the machine ID, and the acquisition module 104 thus obtains the acquisition data associated with the machine ID and the subject ID. The subject ID is an example of the second identification information. Thus, the part described as "subject ID" in the first embodiment can be replaced by "second identification information". The second identification information is information that can identify the acquisition setting and is not limited to the subject ID. For example, the second identification information can be other information, such as an ID other than the subject ID or the setting name of the acquisition setting.

[0081] The subject ID in the first embodiment is not only used to identify the acquisition setting but also used as part of each variable name of the variables for feedback of the acquisition setting. For example, the subject ID is all or part of the variable name that can identify the variables related to the acquisition data used by the acquisition application. This variable is not limited to the variable for feedback but can also be a variable for another purpose of data acquisition. The variable name is an example of variable identification information. Thus, the part described as "variable name" in the first embodiment can be replaced by "variable identification information". The variable identification information is not limited to the variable name and can also be other information, such as an ID.

[0082] Figure 6 is a diagram for illustrating an example of mutually distinguishing variable names for feedback based on the subject ID. In Figure 6 the example, a case where two acquisition settings are specified is illustrated. These two acquisition settings are "acquisition setting X", where the machine ID is "1" and the subject ID is "1", and "acquisition setting Y", where the machine ID is "1" and the subject ID is "2". It is assumed that three variables, "variable A", "variable B", and "variable C", are prepared as variables for feedback of these two acquisition settings to the upper control device 20. For example, "variable A" corresponds to the machine ID, "variable B" corresponds to the subject ID, and "variable C" corresponds to the acquisition setting. These variable names are default and the same. As a result, the upper control device 20 cannot identify which of the two acquisition settings is to be fed back.

[0083] Thus, in the first embodiment, variable names for feedback can be distinguished by adding a subject ID to the end of the variable name. These variable names are the "feedback variable names" stored in the Figure 5 database DB. As Figure 6 shown in, for example, the acquisition module 104 sets the variable names "Variable A_1", "Variable B_1", and "Variable C_1" obtained by adding "1" as the subject ID to the end as variables for feedback of "acquisition setting X". Additionally, for example, the acquisition module 104 sets the variable names "Variable A_2", "Variable B_2", and "Variable C_2" obtained by adding "2" as the subject ID to the end as variables for feedback of "acquisition setting Y".

[0084] The acquisition module 104 performs feedback to the upper layer control device 20 so that the variables with the variable names set as described above indicate the values of individual items of the acquisition settings. The acquisition application of the upper layer control device 20 monitors the variables with these variable names, and detects the execution of the feedback of the acquisition settings when the feedback of the acquisition module 104 is performed (when the value of the variable changes), and acquires the acquisition settings. The control program of the upper layer control device 20 determines whether feedback is detected by the acquisition application while controlling the industrial machine 30. When the acquisition application detects feedback, the control program adjusts the task so that the acquisition application can acquire acquisition data in a series of processes of controlling the industrial machine 30.

[0085] As described above, the acquisition application acquires the acquisition settings fed back from the data acquisition device 10 based on the variables with variable names including the subject ID as a part. The subject ID can be included in any position of the variable for feedback, and this position is not limited to the Figure 6 end as shown in. For example, the subject ID can be included at the beginning or in the middle of the variable name. Additionally, for example, the subject ID may not be included in the variable name of the variable for feedback, but may be included in the variable name of the variable corresponding to the acquisition data. In the first embodiment, the acquisition application performs processing related to the acquisition of acquisition data corresponding to the acquisition settings based on the variables for feedback. The acquisition module 104 acquires the acquisition data based on the processing performed by the acquisition application. The acquisition module 104 acquires the acquisition data transmitted by the acquisition application.

[0086] When there are multiple industrial machines 30, the acquisition data acquired from the industrial machines 30 can be stored in each database DB prepared for each industrial machine 30. In this case, it is possible to identify from which industrial machine 30 the acquisition data is acquired based on the machine ID associated with the acquisition data, and the acquisition module 104 only needs to identify in which database DB the acquisition data will be stored based on this machine ID.

[0087] Figure 7 This is a diagram for illustrating an example of preparing a database DB for each industrial machine 30. In Figure 7 the example, a case where there are two industrial machines 30, namely industrial machine 30A and industrial machine 30B, is illustrated. The machine ID of industrial machine 30A is "1", and the machine name is "Eq_1". The machine ID of industrial machine 30B is "2", and the machine name is "Eq_2". In Figure 7 it, reference signs "30A" and "30B" are illustrated in the definition information, but there is no physical industrial machine 30 in the upper layer control device 20.

[0088] In the first embodiment, the machine ID is associated with the acquired data, and the acquisition module 104 thus identifies which industrial machine 30 generated the acquired data based on the machine ID associated with the acquired data. For example, the acquisition application of the upper layer control device 20 associates the acquired data collected from industrial machine 30A with the machine ID "1" indicating this industrial machine 30A. The subject ID is also associated with the acquired data, but is omitted in Figure 7 it. The acquisition application of the upper layer control device 20 associates the acquired data collected from industrial machine 30B with the machine ID "2" indicating this industrial machine 30B.

[0089] The acquisition module 104 refers to the machine ID associated with the acquired data collected from the upper layer control device 20, and thus identifies the database DB storing this acquired data. For example, when the machine ID associated with the acquired data collected from the upper layer control device 20 is "1", the acquisition module 104 stores the acquired data before parsing in the database DB1 for industrial machine 30A. In addition, for example, when the machine ID associated with the acquired data collected from the upper layer control device 20 is "2", the acquisition module 104 stores the acquired data before parsing in the database DB2 for industrial machine 30B.

[0090] In Figure 7 it, the processes performed by the acquisition module 104, the parser identification module 105, and the acquisition setting identification module 106 are illustrated as processes performed by the IoT module. These processes may not be performed by one module (such as the IoT module), but may be distributed to the modules based on the individual functions of the modules. As Figure 7As shown in the figure, in the parser recognition module 105 described below, when the machine ID associated with the acquisition data collected from the upper-layer control device 20 is "1", the acquisition module 104 recognizes the parser P1 for the industrial machine 30A. In addition, for example, when the machine ID associated with the acquisition data collected from the upper-layer control device 20 is "2", the acquisition module 104 recognizes the parser P2 for the industrial machine 30B. Each of these acquisition data after the parsing process is stored by the acquisition module 104 in the database DB1 or the database DB2.

[0091] In addition, as described above, in the first embodiment, the control cycles of the upper-layer control device 20 and the industrial machine 30 are different from each other. In addition, in the first embodiment, the case where the control cycle of the upper-layer control device 20 is longer than the control cycle of the industrial machine 30 is described, but the control cycle of the upper-layer control device 20 may be shorter than the control cycle of the industrial machine 30. In addition, for example, it is not particularly required to perform loop control in each of the upper-layer control device 20 and the industrial machine 30. The acquisition module 104 acquires the acquisition data generated by the upper-layer control device 20 based on the control cycle of the industrial machine 30 that is different from the control cycle of the upper-layer control device 20.

[0092] When the control cycles are different from each other, it is considered that the acquisition time point of the acquisition data cannot be recognized, but it is assumed that the upper-layer control device 20 and the industrial machine 30 are synchronized with each other in time. For example, each of the upper-layer control device 20 and the industrial machine 30 manages its own time information (e.g., timer). The upper-layer control device 20 sends a synchronization instruction including the time information of the upper-layer control device 20 to the industrial machine 30. When the industrial machine 30 receives the synchronization instruction, the industrial machine 30 sets the time information of the industrial machine 30 to the value included in the synchronization instruction. As a result, synchronization in time is achieved.

[0093] For example, the acquisition module 104 acquires the acquisition data including the time information of each industrial machine 30. When multiple industrial machines 30 are connected under the upper-layer control device 20, each industrial machine 30 manages unique time information. The acquisition data generated by a certain industrial machine 30 includes the time information managed by this industrial machine 30. For example, when acquiring the time-series change of a certain physical quantity in a certain industrial machine 30, the acquisition module 104 acquires such acquisition data: in this acquisition data, the physical quantity detected at each of the multiple time points indicated by the time information managed by this industrial machine 30 is associated with this time information.

[0094] In the first embodiment, the industrial machine 30 collects acquisition data based on a predetermined trigger. The acquisition module 104 collects acquisition data including time information corresponding to the trigger. The trigger is a condition for starting the acquisition of data. Any trigger can be set for the industrial machine 30, and the trigger is, for example, a condition related to a physical quantity detected by a sensor connected to the industrial machine 30, a condition related to an input signal of the industrial machine 30, a condition related to the state of an object detected by using a vision sensor, or a condition related to a variable stored in the industrial machine 30.

[0095] The time information corresponding to the trigger is the time information when the trigger is satisfied. This time information can be the time information managed by the industrial machine 30, or other time information after the time information managed by the industrial machine 30 is converted into other time information. This time information can be the time information for determining the time point when the trigger is satisfied, or the time information of a later time point. The later time point is within the range from the time point when the trigger is determined to be satisfied to the time point when the generation of the acquisition data is completed. In addition, for example, the time information can be the time information of the time point when the upper control device 20 sends the trigger to the industrial machine 30.

[0096] In the first embodiment, the acquisition module 104 collects acquisition data and stores the collected acquisition data in the database DB. The acquisition module 104 can store the acquisition data in a database other than the database DB that stores the acquisition settings, or can store the acquisition data in a database of a computer other than the data acquisition device 10 or an external information storage medium.

[0097] [Parser Identification Module]

[0098] The parser identification module 105 identifies at least one parser from a plurality of parsers based on a set of a machine ID and a subject ID associated with the acquisition data. It is assumed that the correspondence between the set of the machine ID and the subject ID and the parser is pre-stored in the data storage unit 100. In the first embodiment, the case where the correspondence is stored in the database DB is described, but this correspondence can be defined as data other than the database DB.

[0099] The set of the machine ID and the subject ID and the parser can be associated with each other on a one-to-one basis, a one-to-many basis, or a many-to-one basis. When a data conversion rule is common to a plurality of acquisition settings, a common parser can be used for the plurality of acquisition settings. The parser identification module 105 identifies at least one parser corresponding to the set of the machine ID and the subject ID. In the first embodiment, the parser identification module 105 refers to the set of the machine ID and the subject ID associated with the acquisition data, and thus identifies the parser associated with this set in the database DB.

[0100] [Collection setting recognition module]

[0101] The collection setting recognition module 106 recognizes the collection settings based on a set of machine ID and subject ID (recognition information, i.e., the first recognition information and the second recognition information) associated with the collected data. In the first embodiment, the set of machine ID and subject ID is specified by the user. Thus, the collection setting recognition module 106 recognizes the collection settings specified by the user based on the set of machine ID and subject ID associated with the collected data and specified by the user. For example, the collection setting recognition module 106 recognizes the collection settings based on the database DB and the set of machine ID and subject ID associated with the collected data. The collection setting recognition module 106 refers to the database DB to recognize the collection settings associated with the set of machine ID and subject ID.

[0102] [Execution module]

[0103] The execution module 107 performs parsing processing related to the collected data based on the collection settings recognized by the collection setting recognition module 106. The execution module 107 inputs the collection settings and the collected data into a parser and obtains the parsed collected data output from the parser. The parser recognizes the data structure of the collected data based on the input collection settings and performs parsing processing on the collected data based on the recognized data structure.

[0104] For example, the parser recognizes at least one of the data type or data size of a variable based on the variable name that is the collection target included in the collection settings. Assume that the data type and data size of the variable are included in the definition information about the variable. The parser performs parsing processing on the collected data based on at least one of the recognized data type or data size. In the first embodiment, the collected data is collected to ensure the traceability of an object, and the collected data is thus converted into a data structure that can be used for program processing to trace the object.

[0105] For example, when information of the "string" type is included in the collected data collected based on certain collection settings, and the information to be input into the program for tracing the object is of the "char" type, the parser corresponding to this collection setting converts the information of the "string" type into information of the "char" type. In addition, for example, when Unicode characters are included in the collected data collected based on certain collection settings, and the information to be input into the program for tracing the object is represented by characters of another code, the parser corresponding to this collection setting converts the Unicode characters into information represented by characters of another code. The parser can perform various other types of conversions, and the conversions performed by the parser itself can be various well-known conversions.

[0106] Parsing processing can be performed for various purposes. For example, compression of data size, syntax analysis of data, dilution of data, or change of file extension can be performed by parsing processing. The acquired data is often input into a program for performing a certain analysis, so that the parser only needs to convert the acquired data into a format that can be processed by this program. When the acquired data is analyzed by the user, the parser only needs to convert the acquired data into a format suitable for display.

[0107] For example, the execution module 107 performs parsing processing based on the acquisition settings identified by the acquisition setting identification module 106 and at least one parser identified by the parser identification module. The execution module 107 inputs the acquisition settings and the acquired data into at least one parser identified by the parser identification module, and obtains the acquired data after parsing processing output from the at least one parser.

[0108] In the first embodiment, the acquired data before parsing processing is temporarily stored in the database DB. Thus, the execution module 107 performs parsing processing on the acquired data stored in the database DB, and stores the acquired data that has undergone parsing processing in the database DB. The execution module 107 can store the acquired data that has undergone parsing processing in another database. The acquired data before parsing processing may not be stored in the database DB, and the execution module 107 can perform parsing processing on the acquired data that is not stored in the database DB.

[0109] [Function Implemented in the Upper-Level Control Device]

[0110] As Figure 4 shown, in the upper-level control device 20, a data storage unit 200, a machine control module 201, an acquisition module 202, and a transmission module 203 are implemented. Each function is mainly implemented by at least one of the CPU 21 or the IoT unit 24.

[0111] [Data Storage Unit]

[0112] The data storage unit 200 stores the data required for acquiring the acquired data. For example, the data storage unit 200 stores acquisition applications, machine IDs, subject IDs, acquisition settings, control programs, definition information about the industrial machine 30, and definition information about variables. The data storage unit 200 can store multiple acquisition applications. In this case, acquisition applications can be prepared for each acquisition setting. Suppose that in the control program, the execution conditions for each process and the instruction content for the industrial machine 30 are defined. The control program can be created in any language, such as ladder diagram language or robot language.

[0113] [Machine Control Module]

[0114] The machine control module 201 controls the industrial machine 30 based on a control program. For example, the machine control module 201 determines whether the execution conditions for each process are met based on the control program. When the execution conditions for a certain process are met, the machine control module 201 instructs the industrial machine 30 that is to execute the process to start the execution of the process. When the machine control module 201 receives a response from this industrial machine 30 indicating that the execution of the process is completed, the machine control module 201 determines that the execution conditions for the next process are met, and instructs the industrial machine 30 that is to execute the next process to start the execution of the next process. After that, the execution of each process is similarly instructed.

[0115] [Acquisition module]

[0116] The acquisition module 202 acquires acquisition data based on a predetermined acquisition setting. For example, the acquisition module 202 obtains the acquisition setting fed back from the data acquisition device 10 based on an acquisition application, and records the obtained acquisition setting in the data storage unit 200. The acquisition module 202 identifies the industrial machine 30 for which the acquisition setting is to be set based on the machine ID, and sends the acquisition setting to this industrial machine 30. The acquisition module 202 sets a trigger for this industrial machine 30. The trigger can be set when the acquisition setting is sent. The acquisition module 202 can send time information to the acquisition application when the trigger is set. In this case, this time information can be associated with the acquisition data. When the acquisition module 202 receives a notification from the industrial machine 30 indicating the completion of the acquisition of the acquisition data, the acquisition module 202 requests the acquisition data, thereby acquiring the acquisition data.

[0117] [Transfer module]

[0118] The transfer module 203 transfers the acquisition data acquired by the acquisition module 202 to the data acquisition device 10. The transfer module 203 associates the acquisition data generated based on certain acquisition settings with a set of machine ID and subject ID that can identify the acquisition settings, and transfers the associated acquisition data to the data acquisition device 10. In the first embodiment, the case where this set is stored in the header of the packet is described, but this set can be stored in the body part of the packet. The acquisition data can be transferred as a packet of asynchronous communication.

[0119] [1-3-3. Functions implemented in the industrial machine]

[0120] As Figure 4 shown, in the industrial machine 30, a data storage 300, a process execution module 301, and a generation module 302 are implemented. The data storage unit 300 is mainly implemented by the storage unit 32. The process execution module 301 is mainly implemented by the CPU 31.

[0121] [Data storage unit]

[0122] The data storage unit 300 stores the data required for collecting the collected data. For example, the data storage unit 300 stores the process program that defines the operations of each process, and variables that at least one of each process program refers to or changes. Suppose that in the process program, the execution conditions of each process and the detailed operations of individual processes are defined. The process program can be created in any language, such as ladder diagram language or robot language. The data storage module 300 can store time information, the collected data being generated, and so on.

[0123] [Process execution module]

[0124] The process execution module 301 executes the process based on the process program. The process execution module 301 executes the process program to determine whether the execution conditions of the process are satisfied. The execution conditions can be any conditions, and for example, are conditions where a predetermined variable reaches a predetermined value, conditions where a predetermined signal is received from a sensor, conditions where an object moves to a predetermined position, conditions where a predetermined time is reached, conditions where predetermined information is received from another industrial machine 30, or conditions where a predetermined instruction is received from the upper layer control device 20. When the execution conditions of the process are satisfied, the process execution module 301 executes this process.

[0125] [Generation module]

[0126] The generation module 302 generates the collected data based on the collection settings. For example, the generation module 302 records the collection settings received from the upper layer control device 20 in the data storage unit 300. The generation module 302 records the trigger received from the upper layer control device 20 in the data storage unit 300. The generation module 302 determines whether the trigger is satisfied, and when the trigger is satisfied, starts generating the collected data based on the collection settings. When the generation of the collected data is completed, the generation module 302 notifies the upper layer control device 20 of the completion status. When the upper layer control device 20 requests the collected data, the generation module 302 sends the collected data to the upper layer control device 20. The generation module 302 only needs to generate the collected data based on the execution result of the process program, the date and time obtained by using a real-time clock, a timer, etc., the signal from the sensor, or a combination of these.

[0127] [1-4. Processes performed by the data acquisition system]

[0128] Figure 8 and Figure 9 are flowcharts for illustrating examples of the processes performed in the data acquisition system 1 according to the first embodiment. Figure 8 and Figure 9The processes shown are implemented by the CPUs 11, 21, and 31 and the IoT unit 24 respectively executing the programs stored in the storage units 12, 22, and 32 and the IoT unit 24. Figure 8 and Figure 9 The process shown in Figure 4 is an example of a process executed by the functional block of Figure 2 and is the detail of the process flow of

[0129] As Figure 8 shown, the data acquisition device 10 starts the setting tool stored in the storage unit 12 based on an operation from the operation interface 14 and displays a setting screen G on the display 15 (step S1). The data acquisition device 10 receives the designation of the acquisition settings by the user on the setting screen G (step S2). In step S2, the data acquisition device 10 receives each of the designation of the industrial machine 30 in the input form F1 and the designation of the acquisition settings in the input form F2. The processing steps of step S1 and step S2 are Figure 2 the details of the process 1 shown in

[0130] The data acquisition device 10 receives the selection of the button B1 on the setting screen G, thereby receiving a registration request for the acquisition settings from the user (step S3). The data acquisition device 10 passes the acquisition settings designated on the setting screen G to the IoT module (step S4). The IoT module registers the acquisition settings in the database DB (step S5). The processing steps of step S3 and step S4 are Figure 2 the details of the process 2 shown in Figure 2 The processing steps of step S5 are

[0131] The data acquisition device 10 uses the IoT module to receive the selection of the button B2 on the setting screen G, thereby receiving a start request for data acquisition from the user (step S6). In step S6, based on the acquisition settings identified by the set of the machine ID and the subject ID designated on the setting screen G, a request to start data acquisition is made. The processing steps of step S6 are Figure 2 the details of the process 4 shown in

[0132] The data acquisition device 10 uses the IoT module to read out the acquisition settings corresponding to the request to start data acquisition from the database DB (step S7). In step S7, the acquisition settings associated with the set of the machine ID and the subject ID are read out from the database DB. The processing steps of step S7 are Figure 2 the details of the process 5 shown in

[0133] The data acquisition device 10 uses the IoT module to send the acquisition settings read in step S7 to the IoT unit 24 of the upper layer control device 20 (step S8). The IoT unit 24 of the upper layer control device 20 transfers the acquisition settings received from the data acquisition device 10 to the CPU 21 (step S9). The CPU 21 of the upper layer control device 20 records the acquisition settings in the memory or the storage unit 22 inside the CPU 21 (step S10). The processing steps from step S8 to step S10 are Figure 2 the details of process 6 shown in

[0134] The CPU 21 of the upper layer control device 20 uses the acquisition application to obtain the acquisition settings transferred in step S9 (step S10), and performs the acquisition of acquisition data based on the obtained acquisition settings (step S11). In step S10, the acquisition settings stored in the register of the CPU 21 are obtained by the acquisition application. In step S11, the acquisition application reflects the acquisition settings in the industrial machine 30, starts data acquisition in the industrial machine 30, reflects the trigger in the industrial machine 30, and reads the acquisition data from the industrial machine 30. The processing steps of step S10 and step S11 are Figure 2 the details of process 7 shown in

[0135] The CPU 21 of the upper layer control device 20 uses the acquisition application to collect acquisition data from the industrial machine 30 based on the acquisition settings (step S12). In step S12, when the industrial machine 30 finishes generating the acquisition data, a predetermined variable of the industrial machine 30 becomes a predetermined value. When the upper layer control device 20 detects that this variable changes to this value, the upper layer control device 20 requests the industrial machine 30 to send the acquisition data. When the industrial machine 30 receives this request, the industrial machine 30 sends the acquisition data to the upper layer control device 20. It is assumed that the acquisition data is sent through asynchronous communication, but it can also be sent through synchronous communication. The processing steps of step S12 are Figure 2 the details of process 8 shown in

[0136] Referring to Figure 9 , the CPU 21 of the upper layer control device 20 assigns a machine ID and a topic ID to the acquisition data obtained from the industrial machine 30 (step S13), and transfers the acquisition data to the IoT unit 24 (step S14). The IoT unit 24 of the upper layer control device 20 stores the machine ID and the topic ID assigned in step S13 in the header of the packet, and sends this packet including the acquisition data to the data acquisition device 10 (step S15). The data acquisition device 10 uses the IoT module to receive the packet including the acquisition data from the IoT unit 24 of the upper layer control device 20 (step S16). The processing steps from step S13 to step S16 are Figure 2Details of process 9 shown in

[0137] The data acquisition device 10 uses the IoT module to store the acquisition data included in the packet received from the upper layer control device 20 in the database DB as the acquisition data before parsing processing (step S17). The data acquisition device 10 uses the IoT module to identify the parser that requests parsing processing of the received acquisition data (step S18). The data acquisition device 10 uses the IoT module to request the parser identified in step S18 to perform the parsing processing of the acquisition data (step S19). The processing steps of step S17 to step S19 are Figure 2 Details of process 10 shown in

[0138] The data acquisition device 10 uses the parser to obtain the acquisition settings corresponding to the acquisition data based on the database DB (step S20). In step S20, the data acquisition device 10 obtains the acquisition settings associated with the set of the machine ID and the topic ID stored in the header of the packet including the acquisition data. The processing step of step S20 is Figure 2 Details of process 11 illustrated in. The data acquisition device 10 uses the parser to perform the parsing processing of the acquisition data based on the acquisition settings obtained in step S20 (step S21). The processing step of step S21 is Figure 2 Details of process 12 shown in

[0139] The data acquisition device 10 requests to transfer the acquisition data after parsing processing from the parser to the IoT module, and requests to register the acquisition data after parsing processing in the database DB (step S22). The IoT module registers the acquisition data after parsing processing in the database DB (step S23). The processing steps of step S22 and step S23 are Figure 2 Details of process 13 and process 14 shown in

[0140] According to the data acquisition system 1 of the first embodiment, the acquisition settings are identified based on a set of machine IDs and topic IDs associated with the acquired data, and the parsing process of the acquired data is performed based on the identified acquisition settings, so that the parsing process corresponding to the acquisition settings of the acquired data can be realized. For example, the acquired data collected from the industrial machine 30 has a data structure corresponding to the acquisition settings, so there may be a situation where the acquired data cannot be directly used for a predetermined purpose, such as operation analysis. It can be imagined that a parsing process is performed on the acquired data to obtain a data structure suitable for the predetermined purpose, but the process, such as data conversion through the parsing process, needs to correspond to the acquisition settings. In this regard, a specific parsing process can be identified based on a set of machine IDs and topic IDs associated with the acquired data, so that the parsing process corresponding to the acquisition settings of the acquired data can be realized. As a result, the acquired data can be converted into acquired data with an optimal data structure corresponding to a predetermined purpose (such as operation analysis), so that the processing accuracy can be improved and the processing speed for the predetermined purpose can be increased.

[0141] In addition, the data acquisition system 1 identifies the acquisition settings based on a set of machine IDs and topic IDs associated with the acquired data, so that a more flexible parsing process can be performed. For example, even when there are multiple industrial machines 30 from which data is to be acquired, the individual industrial machines 30 can be distinguished based on the machine ID, so that a parsing process corresponding to the acquisition settings of the individual industrial machines 30 can be performed. In addition, for example, even when there are multiple acquisition settings for a specific industrial machine 30, the individual acquisition settings can be distinguished from each other based on the topic ID, so that a parsing process corresponding to the individual acquisition settings can be performed.

[0142] In addition, the data acquisition system 1 sets the machine ID based on the definition information about the control target of the upper control device 20, so that the machine ID that can accurately identify the industrial machine 30 designated as the control target of the upper control device 20 can be set. As a result, for example, the situation where the industrial machine 30 corresponding to the acquired data cannot be identified can be prevented.

[0143] In addition, the topic ID is all or part of the variable name that can identify the variables used by the application for acquiring the acquired data, so that the data acquisition system 1 can use the topic ID to distinguish the variables used by the application. Thus, for example, the state where the variables used by a certain application cannot be distinguished from the variables used by another application and appropriate acquisition settings cannot be performed can be prevented.

[0144] In addition, the data acquisition system 1 identifies at least one of a plurality of parsers based on a set of machine IDs and topic IDs associated with the acquired data, and performs a parsing process based on the identified parser, thereby enabling identification of an optimal parser for the parsing process to achieve accurate parsing. For example, when a general parser is used for a plurality of industrial machines 30 and a plurality of acquisition settings, it is necessary to unify the data structure to some extent among the plurality of industrial machines 30 and the plurality of acquisition settings, and the flexibility of the acquisition settings cannot be guaranteed. However, by preparing a plurality of parsers, flexible acquisition settings can be achieved.

[0145] In addition, the data acquisition system 1 receives a user's specification of a set of machine IDs and topic IDs and acquisition settings, thereby enabling flexible data acquisition corresponding to the user.

[0146] In addition, the data acquisition system 1 stores the set of machine IDs and topic IDs and the acquisition settings in the database DB in an associated manner, and identifies the acquisition settings by using the database DB when acquiring the acquisition data. Therefore, the management of the set of machine IDs and topic IDs and the acquisition settings is facilitated.

[0147] In addition, the data acquisition system 1 acquires acquisition data generated based on a control cycle of the industrial machine 30 different from the control cycle of the upper control device 20, thereby enabling a parsing process corresponding to the acquisition settings of the industrial machine 30 even when the control cycles of the upper control device 20 and the industrial machine 30 are different from each other.

[0148] In addition, the data acquisition system 1 acquires acquisition data including time information of each industrial machine 30, thereby enabling the parsing process to be performed along the correct time axis.

[0149] In addition, the data acquisition system 1 acquires acquisition data including time information corresponding to a trigger, thereby enabling acquisition data to be obtained along a time axis with the trigger as a reference.

[0150] In addition, the data acquisition system 1 performs a parsing process on the acquisition data temporarily stored in the database DB, and stores the acquisition data on which the parsing process has been performed in the database DB or another database DB, thereby enabling efficient management of the acquisition data on which the parsing process has been performed.

[0151] [2. Second Embodiment]

[0152] In the first embodiment, the following situation is described: the acquisition settings are fed back from the data acquisition device 10 to the upper-layer control device 20, and the parsing process is executed in the data acquisition device 10. In the second embodiment of the present disclosure, a description is given of the following situation: it is not necessary to feed back the acquisition settings from the data acquisition device 10 to the upper-layer control device 20, and data acquisition is executed in the upper-layer control device 20. In the second embodiment, the description of the configuration equivalent to that in the first embodiment is omitted.

[0153] In the second embodiment, the main functions related to data acquisition are implemented in the upper-layer control device 20. Therefore, among the Figure 4 functions, at least the acquisition setting identification module 106 and the execution module 107 are implemented by the upper-layer control device 20. Each of the acquisition setting identification module 106 and the execution module 107 in the second embodiment is mainly implemented by at least one of the CPU 21 or the IoT unit 24. In addition, the acquisition module 202 may have the same functions as the Figure 4 acquisition module 104. These points are the same in the third to fifth embodiments of the present disclosure.

[0154] Figure 10 is a diagram for illustrating an example of the process of data acquisition in the data acquisition system 1 according to the second embodiment. As Figure 10 shown, in the second embodiment, the execution process of data acquisition is different from the Figure 2 process of processes 1 to 14 shown in the first embodiment. In the second embodiment, it is assumed that the pre-specified acquisition settings and various instructions for data acquisition are included in a control program created in ladder diagram language or a similar language. That is, the acquisition settings are described in ladder diagram language or a similar language as part of the control program. However, the acquisition settings may be external data to be referred to by the control program.

[0155] The instructions for data acquisition corresponding to the functions of the acquisition application described in the first embodiment are expressed in ladder diagram language or a similar language. For example, the control program includes an acquisition instruction for obtaining the acquisition settings in the control program, an acquisition setting instruction for setting the acquisition settings to the industrial machine 30, a start instruction for instructing the industrial machine 30 to start acquisition, a trigger setting instruction for setting a trigger to the industrial machine 30, and a read instruction for reading the acquisition data from the industrial machine 30. The machine ID and the subject ID may be included in the control program or may be stored in the upper-layer control device 20 as data different from the control program.

[0156] As Figure 10As shown in the figure, the upper control device 20 executes a control program to start the control of the industrial machine 30 (Process 1). In the second embodiment, as part of the control of the industrial machine 30, acquisition data is collected from the industrial machine 30. For example, when the acquisition data is collected for the purpose of object traceability, an instruction for data collection is described at a timing near the end of each process of the object. The upper control device 20 executes an acquisition instruction included in the control program to acquire an acquisition setting included in the control program (Process 2).

[0157] The upper control device 20 executes each of an acquisition setting instruction, a start instruction, a trigger setting instruction, and a read instruction included in the control program to perform data collection based on the acquired acquisition setting (Process 3). Figure 10 Process 3 of is different from Process 7 executed by the acquisition application in the first embodiment in that Process 3 is executed by the control program. Other points are the same. In addition, Figure 2 Processes 4 and 5 of are different from Processes 8 and 9 executed by the acquisition application in the first embodiment in that Processes 4 and 5 are executed by the control program. Other points are the same. It is also the same that a set of machine ID and subject ID is associated with the acquisition data when the acquisition data is transmitted in Process 5. Figure 10 in that Processes 4 and 5 are executed by the control program. Other points are the same. It is also the same that a set of machine ID and subject ID is associated with the acquisition data when the acquisition data is transmitted in Process 5. Figure 2 Processes 4 and 5 of are different from Processes 8 and 9 executed by the acquisition application in the first embodiment

[0158] In the second embodiment, a parser exists in the upper control device 20. It is assumed that the parser is stored in the IoT unit 24, but it may also be stored in the storage unit 22. When the IoT unit 24 receives the acquisition data transmitted from the CPU 21, the IoT unit 24 requests a module that internally holds the acquisition data to execute a parsing process (Process 6). This module is a program having the same function as the IoT module described in the first embodiment and is a program for performing a parsing process and the like on the acquisition data.

[0159] Processes 7 to 9 executed after Process 6 Figure 10 are the same as Processes executed by the data acquisition device 10 in the first embodiment Figure 2The difference between Process 10 and Process 13 is that Processes 7 to 9 are executed by the IoT unit 24. Other points are the same. For example, in Process 7, the IoT unit 24 requests the control program executed by the CPU 21 to obtain the acquisition settings. Assume that this request includes a set of machine IDs and topic IDs associated with the acquisition data that is the target of the parsing process. The IoT unit 24 only needs to obtain the acquisition settings associated with this set from the control program and execute the parsing process in Process 8. In Process 9, the acquisition data after the parsing process is sent from the IoT unit 24 to the data acquisition device 10. In Process 10, the data acquisition device 10 stores the acquisition data after the parsing process in the database DB.

[0160] In Figure 10 it is described that the parsing process is executed by the IoT unit 24, but the parsing process can be executed by the CPU 21. In this case, a parser can be embedded in the control program, or an external parser can be called from the control program to execute the parsing process. It is only required that the CPU 21 send the acquisition data after the parsing process to the IoT unit 24, and the IoT unit 24 transmit the acquisition data after the parsing process to the data acquisition device 10.

[0161] According to the data acquisition system 1 of the second embodiment, the acquisition settings are included in the control program executed by the upper layer control device 20, and the parsing process is also executed on the upper layer control device 20 side, so that the main processes related to data acquisition can be executed on the upper layer control device 20 side. Thus, even when the user does not specifically operate the data acquisition device 10, the upper layer control device 20 can acquire the acquisition data to execute the parsing process and obtain the acquisition data after the parsing process. In this case, the user only needs to manage the upper layer control device 20 and the industrial machine 30, thereby reducing the user's labor.

[0162] [3. Third Embodiment]

[0163] In the second embodiment, it is described that the acquisition data after the parsing process is stored in the database DB of the data acquisition device 10. However, in the third embodiment, it is described that the acquisition data after the parsing process is stored in the upper layer control device 20. In the third embodiment, the data acquisition device 10 can be omitted, and the data acquisition device 10 may not be included in the data acquisition system 1. In the third embodiment, the description of the configurations equivalent to those in the first and second embodiments is omitted.

[0164] Figure 11 is a diagram for illustrating an example of the data acquisition process in the data acquisition system 1 according to the third embodiment. As Figure 11As shown, the general process of data collection in the third embodiment is equivalent to that in the second embodiment, except that process 10 is executed by IoT unit 24. In Figure 11 In process 10 of Figure 11 , IoT unit 24 stores the parsed and processed collected data in database DB included in upper layer control device 20.

[0165] It is assumed that database DB is stored in storage unit 22, but database DB can be stored in IoT unit 24, or when a non-volatile memory is included in CPU 21, database DB can be stored in CPU 21. In addition, database DB can be stored in an external information storage medium connected to upper layer control device 20, or can be stored in another computer (e.g., a cloud server) different from data collection device 10.

[0166] In addition, similarly, in the third embodiment, similar to the second embodiment, the parsing process can be executed by CPU 21. In this case, CPU 21 registers the parsed and processed collected data in database DB. In this configuration, upper layer control device 20 does not send data to an external computer, and thus IoT unit 24 can be omitted. That is, upper layer control device 20 may not include IoT unit 24, and data collection can be completed inside upper layer control device 20 itself.

[0167] According to data collection system 1 of the third embodiment, the parsed and processed collected data can be stored in database DB included in upper layer control device 20. In addition, data collection device 10 can be omitted from data collection system 1, and thus the user does not need to manage data collection device 10. Therefore, the labor of the user can be reduced.

[0168] [4. Fourth Embodiment]

[0169] In the first embodiment, the case of specifying collection settings from data collection device 10 and instructing the start of collection is described, but the specification of collection settings and the instruction of the start of collection can be executed from HMI device 25. In the fourth embodiment, the case where the user specifies collection settings from HMI device 25 and instructs the start of collection is described. In the fourth embodiment, it is not necessary to include collection settings in the control program as in the second and third embodiments. The control program in the fourth embodiment can be equivalent to the control program in the first embodiment, and it is assumed in the fourth embodiment that the collection application described in the first embodiment is stored in upper layer control device 20. In the fourth embodiment, the description of configurations equivalent to those in the first to third embodiments is omitted.

[0170] Figure 12This is a diagram for illustrating an example of the data acquisition process in the data acquisition system 1 according to the fourth embodiment. As Figure 12 shown, in the fourth embodiment, the process 1 equivalent to the process 1 described with reference to Figure 2 in the first embodiment is executed by the HMI device 25. For example, a setting screen G equivalent to Figure 3 the setting screen G is displayed on the HMI device 25, and the user's specification of the acquisition settings is received. The HMI device 25 registers the acquisition settings specified by the user together with the set of machine ID and subject ID in the upper layer control device 20 (process 2).

[0171] When the upper layer control device 20 receives the acquisition settings specified by the user from the HMI device 25, the upper layer control device 20 stores the acquisition settings together with the set of machine ID and subject ID in the database DB2 stored by the upper layer control device 20 itself (process 3). The database DB2 is equivalent to the part of the database DB in the first embodiment that stores the set of machine ID and subject ID and the acquisition settings. It is assumed that the database DB2 is stored in the storage unit 22, but the database DB2 can be stored in the non-volatile memory included in the CPU 21, the IoT unit 24, an external storage medium, or another computer. The HMI device 25 receives an instruction to start acquisition from the user (process 4). It is assumed that the instruction to start acquisition includes the set of machine ID and subject ID of the acquisition settings that are the target of the acquisition.

[0172] When the upper layer control device 20 receives a notification from the HMI device 25 indicating that the user has instructed the start of acquisition, the upper layer control device 20 acquires the acquisition settings associated with the set of machine ID and subject ID included in the instruction to start acquisition from the database DB2 (process 5). Figure 12 The subsequent processes 6 to 13 are equivalent to Figure 10 the processes 3 to 10. However, Figure 10 the processes 3 to 5 are mainly executed by the control program, but Figure 12 the processes 6 to 8 are mainly executed by the acquisition application. In addition, in Figure 10 process 7, the acquisition settings are obtained from the control program, but in Figure 12 process 10, the acquisition settings are obtained from the database DB2. The database DB1 is equivalent to the part of the database DB in the first embodiment that stores the acquired data after the analysis process. In addition, similarly, in the fourth embodiment, similar to the second embodiment, the analysis process can be executed by the CPU 21.

[0173] According to the data acquisition system 1 of the fourth embodiment, even when the user does not operate the data acquisition device 10, but the user designates the acquisition settings from the HMI device 25, the upper control device 20 also acquires the acquisition data and performs the analysis process, so that the acquisition data after the analysis process can be obtained. In this case, the user only needs to operate the HMI device 25 with which the user is familiar, thereby reducing the labor of the user.

[0174] [5. Fifth Embodiment]

[0175] In the fourth embodiment, the case where the acquisition data after the analysis process is stored in the database DB1 of the data acquisition device 10 is described. However, in the fifth embodiment, the case where the acquisition data after the analysis process is stored in the upper control device 20 is described. In the fifth embodiment, the data acquisition device 10 can be omitted, and the data acquisition device 10 may not be included in the data acquisition system 1. In the fifth embodiment, the description of the configuration equivalent to the configuration in the first to fourth embodiments is omitted.

[0176] Figure 13 is a diagram for illustrating an example of the data acquisition process in the data acquisition system 1 according to the fifth embodiment. As Figure 13 shown, the general process of data acquisition in the fifth embodiment is equivalent to the process in the fourth embodiment, but the difference is that the process 13 is performed by the IoT unit 24. In Figure 13 process 10, the IoT unit 24 stores the acquisition data after the analysis process in the database DB included in the upper control device 20. Figure 13 The database DB of Figure 12 is formed by combining the database DB1 and the database DB2 of

[0177] and also stores the acquisition settings.

[0178] In addition, similarly, in the fifth embodiment, similar to the fourth embodiment, the analysis process can be performed by the CPU 21. In this case, the CPU 21 registers the acquisition data after the analysis process in the database DB. In this configuration, the upper control device 20 does not send data to an external computer, and thus the IoT unit 24 can be omitted. That is, the upper control device 20 may not include the IoT unit 24, and the data acquisition can be completed inside the upper control device 20 itself.

[0178] According to the data acquisition system 1 of the fifth embodiment, the acquisition data after the analysis process can be stored in the database DB included in the upper control device 20. In addition, the data acquisition device 10 can be omitted from the data acquisition system 1, so that the user does not need to manage the data acquisition device 10. Therefore, the labor of the user can be reduced.

[0179] [6. Modification Examples]

[0180] The present disclosure is not limited to the above embodiments. Without departing from the purpose of the present disclosure, the present disclosure can be appropriately modified.

[0181] Figure 14 is a functional block diagram of a modification example of the present disclosure. As Figure 14 shown, in the modification example described below, the analysis application identification module 108 and the analysis execution module 109 are implemented. These modules are mainly implemented by the CPU 11. In Figure 14 , a description is given based on the first embodiment, but the modification example described below can also be applied to the second to fifth embodiments.

[0182] The analysis application identification module 108 identifies an analysis application related to the analysis of the collected data from among a plurality of analysis applications. The analysis application is an application for analyzing the collected data after parsing processing. The parsing processing is performed to convert the collected data into data having a data structure that can be input into the analysis application. The analysis application is created by the user. When the collected data is input into the analysis application, the analysis application analyzes its content and outputs an analysis result. The analysis result can be the occurrence of an abnormality or the quality of an object.

[0183] The data storage unit 100 in this modification example stores a plurality of analysis applications. For example, in the database DB, analysis applications corresponding to individual collection settings can be defined. That is, in the database DB, an analysis application into which the collected data collected based on certain collection settings will be input can be defined. When performing the parsing processing on a certain collected data, the analysis application identification module 108 identifies the analysis application associated with the collection settings of this collected data as the analysis application related to this collected data. This identification can be performed by the parser. That is, the processing performed by the analysis application identification module 108 can be a part of the processing performed by the parser.

[0184] The analysis execution module 109 causes the identified analysis application to analyze the collected data on which the parsing processing has been performed. The analysis execution module 109 inputs the collected data on which the parsing processing has been performed into the analysis application and obtains the analysis result output from the analysis application. This analysis result can be stored in the database DB or can be displayed on the display 15. In addition, for example, this analysis result can be output to another computer or an external information storage medium other than the data collection device 10. The analysis execution module 109 can cause the analysis application to analyze the entire collected data, or can cause the analysis application to analyze only a part of the collected data. When the analysis application only analyzes a part of the collected data, the analysis application can only analyze the collected data collected when an alarm occurs, the collected data collected near the time when the alarm occurs, or the collected data including a predetermined value.

[0185] Based on the above modification example, identify the analysis application corresponding to the analysis of the collected data from multiple analysis applications, and the identified analysis application analyzes the collected data for which parsing processing has been performed, thereby enabling fast analysis.

[0186] In addition, for example, the subject ID may not be the variable name of a variable specifically used for feedback. In addition, for example, there may be only one general parser, and in this case, the identification of the parser may not be performed. In addition, for example, the correspondence between the set of the machine ID and the subject ID and the acquisition settings may not be stored in the database DB, but may be stored in another storage area. In addition, for example, the collected data may not be associated with the time information of each industrial machine 30, but may be associated with the time information of the upper control device 20 or the data acquisition device 10. In addition, for example, the parser and the IoT module may be registered in the data acquisition device 10 by installing an additional program. In addition, for example, the status of the ongoing data acquisition may be displayed on the setting screen G.

[0187] In addition, for example, each of the functions described above only needs to be implemented in any device included in the data acquisition system 1. For example, the function described as being implemented in the data acquisition device 10 may be implemented in the upper control device 20 or the industrial machine 30. In addition, for example, the function described as being implemented in the upper control device 20 may be implemented in the data acquisition device 10 or the industrial machine 30. In addition, for example, each function may not be distributed to multiple devices, but may be implemented by one device.

Claims

1. A data acquisition system, comprising: A data acquisition device; And An upper control device configured to control an industrial machine, Wherein, the upper control device is configured to: Collect acquisition data related to the industrial machine based on a predetermined acquisition setting; and Transmit the acquisition data and identification information to the data acquisition device, the identification information including first identification information for identifying the industrial machine and second identification information different from the first identification information, wherein a set of the first identification information and the second identification information identifies the predetermined acquisition setting and a parser; Wherein, the data acquisition device is configured to: Store the first identification information, the second identification information, the predetermined acquisition setting, and the parser; Identify the predetermined acquisition setting based on the transmitted first identification information and the transmitted second identification information; Identify the parser based on the transmitted first identification information and the transmitted second identification information; and Perform parsing processing related to the acquisition data based on the identified predetermined acquisition setting and the identified parser.

2. The data acquisition system according to claim 1, Among them, The industrial machine is configured to be controlled by the upper control device, and Wherein, the data acquisition system further includes a setting module configured to set the first identification information based on definition information related to a control target of the upper control device.

3. The data acquisition system according to claim 1 or 2, Among them, The second identification information is part or all of variable identification information for identifying variables used by an acquisition application related to the acquisition data, Wherein, the acquisition application is configured to perform processing related to acquisition of acquisition data corresponding to the predetermined acquisition setting based on the variables, and Wherein, the data acquisition system is configured to acquire the acquisition data based on processing performed by the acquisition application.

4. The data acquisition system according to claim 1 or 2, wherein, The data acquisition system is configured to: Receive a user's specification of the identification information and the predetermined acquisition setting, Wherein, the acquisition setting identification module is configured to identify the predetermined acquisition setting specified by the user based on the identification information associated with the acquisition data and specified by the user.

5. The data acquisition system according to claim 1 or 2, wherein, The data acquisition system is configured to: Store the identification information and the predetermined acquisition setting in a database in an associated manner, Wherein, the acquisition setting identification module is configured to identify the predetermined acquisition setting based on the database and the identification information associated with the acquisition data.

6. The data acquisition system according to claim 1 or 2, Among them, The industrial machine is configured to be controlled by the upper control device, Wherein, a control period of the upper control device and a control period of the industrial machine are different from each other, and Wherein, the data acquisition system is configured to acquire acquisition data generated based on the control period of the industrial machine different from the control period of the upper control device through the upper control device.

7. The data acquisition system according to claim 1 or 2, wherein, The data acquisition system includes a plurality of industrial machines, the plurality of industrial machines including the industrial machine, and the data acquisition system is configured to acquire acquisition data including time information for each industrial machine.

8. The data acquisition system according to claim 1 or 2, Among them, the industrial machine is configured to acquire the acquisition data based on a predetermined trigger, and wherein the data acquisition system is configured to acquire acquisition data including time information corresponding to the predetermined trigger.

9. The data acquisition system according to claim 1 or 2, wherein, The data acquisition system is configured to: acquire the acquisition data to store the acquisition data in a database, and perform the parsing process on the acquisition data stored in the database, and store the acquisition data on which the parsing process has been performed in one of the database or another database.

10. The data acquisition system according to claim 1 or 2, wherein The data acquisition system is configured to: identify an analysis application related to the analysis of the acquisition data from a plurality of analysis applications; and cause the identified analysis application to analyze the acquisition data on which the parsing process has been performed.

11. A data acquisition method, comprising: acquiring, by an upper control device configured to control an industrial machine, acquisition data related to the industrial machine based on a predetermined acquisition setting; transmitting, by the upper control device, the acquisition data and identification information to a data acquisition device, the identification information including first identification information for identifying the industrial machine and second identification information different from the first identification information, wherein a set of the first identification information and the second identification information identifies the predetermined acquisition setting and a parser; storing, by the data acquisition device, the first identification information, the second identification information, the predetermined acquisition setting, and the parser; identifying, by the data acquisition device, the predetermined acquisition setting based on the transmitted first identification information and the transmitted second identification information; identifying, by the data acquisition device, the parser based on the transmitted first identification information and the transmitted second identification information; and performing, by the data acquisition device, a parsing process related to the acquisition data based on the identified predetermined acquisition setting and the identified parser.

12. An information storage medium storing a program, the program causing a data acquisition device to communicate with an upper control device configured to control an industrial machine, Among them, the upper control device being configured to: acquire acquisition data related to the industrial machine based on a predetermined acquisition setting; and transmit the acquisition data and identification information to the data acquisition device, the identification information including first identification information for identifying the industrial machine and second identification information different from the first identification information, wherein a set of the first identification information and the second identification information identifies the predetermined acquisition setting and a parser; wherein the data acquisition device stores the first identification information, the second identification information, the predetermined acquisition setting, and the parser; and wherein the program causes the data acquisition device to: identify a predetermined acquisition setting based on the transmitted first identification information and the transmitted second identification information; Identify the parser based on the transmitted first identification information and the transmitted second identification information; and Perform parsing processing related to the collected data based on the identified predetermined acquisition settings and the identified parser.

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