JP7806530B2 - Information processing device, information processing method, and information processing program - Google Patents

Description

The present invention relates to an information processing device, an information processing method, and an information processing program.

Various manufacturing equipment and production lines are controlled by control devices such as PLCs (programmable logic controllers). When a malfunction occurs, an approach is taken to identify the cause of the malfunction by understanding the causal relationships within the machinery involved.

As an example of prior art for understanding such causal relationships, Japanese Patent Application Laid-Open No. 2011-150496 (Patent Document 1) discloses a factor analysis device that can easily understand the relationships between measurement items for products that are manufactured through the measurement of multiple items during the manufacturing process, even if the user has no knowledge of the product.

JP 2011-150496 A

To understand the causal relationships within a machine, a cause-and-effect diagram (or a different representation of the relationships shown in a cause-and-effect diagram) is typically used, which systematically connects the relationships between effects and factors with lines.

In order to create a cause-and-effect diagram, information such as the target machine's specifications, usage, and parameters is required, but generally the designer who determines this information is not the same person who creates the cause-and-effect diagram. Furthermore, the amount of information about the target machine is often vast and difficult to grasp. As a result, it may not be possible to create a cause-and-effect diagram properly.

Therefore, one object of the present invention is to provide technology for creating cause-and-effect diagrams more efficiently.

An information processing device according to one example of the present invention includes an acquisition unit that acquires data written in accordance with AutomationML, a search unit that searches the data for elements corresponding to input keywords, a first extraction unit that extracts blocks that include the searched elements and elements present below the searched elements, and a creation unit that creates a cause-and-effect diagram based on the elements included in the extracted blocks.

With this configuration, the information processing device creates a cause-and-effect diagram from data written in accordance with AutomationML, allowing even users with limited specialized knowledge to create cause-and-effect diagrams more efficiently.

The creation unit may identify the attributes of the elements contained in the block and determine which of the 5Ms (Man, Method, Machine, Material, Measure) they are associated with. This configuration allows for the creation of a cause-and-effect diagram from the perspective of 5M analysis.

The cause-and-effect diagram may include edges corresponding to each of the 5M. The creation unit may map elements included in a block to an edge associated with the block in accordance with the hierarchical structure of the block. This configuration makes it possible to create a cause-and-effect diagram that reflects the hierarchical structure of the block.

The creation unit may map the block containing the searched element to a position closer to the characteristic in the cause-and-effect diagram the closer the match between the searched element and the keyword. With this configuration, the element that matches the keyword more closely is more likely to be the cause, making it possible to more efficiently identify the cause of the defect.

The creation unit may determine the position of each edge in the cause-and-effect diagram based on at least one of the number of elements mapped to each edge and the degree of match with the keyword. With this configuration, it is easier to identify, among multiple edges, which edge is most likely to be the cause in the cause-and-effect diagram.

The information processing device may further include a second extraction unit that extracts keywords contained in the data, and a presentation unit that presents the extracted keywords to the user. The search unit may be configured to accept input for the keywords presented to the user. With this configuration, by presenting the keywords contained in the data, the user can more easily select the keyword corresponding to the target defect.

The search unit may search for elements that correspond to the entered keyword, including synonyms. This configuration allows the user to freely enter keywords, rather than keywords contained in the data.

According to another example of the present invention, a computer-executed information processing method is provided. The information processing method includes the steps of acquiring data written in accordance with AutomationML, searching the data for elements corresponding to input keywords, extracting blocks that include the searched elements and elements present below the searched elements, and creating a cause-and-effect diagram based on the elements included in the extracted blocks.

An information processing program according to yet another example of the present invention causes a computer to execute the steps of acquiring data written in accordance with AutomationML, searching the data for elements corresponding to input keywords, extracting blocks that include the searched elements and elements present below the searched elements, and creating a cause-and-effect diagram based on the elements included in the extracted blocks.

This invention allows for more efficient creation of cause-and-effect diagrams.

FIG. 2 is a diagram for explaining an outline of processing performed by the information processing device according to the present embodiment. 1 is a block diagram illustrating an example of a hardware configuration of an information processing device according to an embodiment of the present invention. 10 is a flowchart showing a processing procedure for creating a cause-and-effect diagram by the information processing device according to the present embodiment. 2 is a schematic diagram illustrating an example of data described in accordance with AutomationML that is processed by the information processing device according to the present embodiment. FIG. FIG. 10 is a schematic diagram illustrating an example of an extraction process of an element corresponding to an input keyword according to the present embodiment. FIG. 1 is a schematic diagram illustrating an example of a process for creating a cause-and-effect diagram according to an embodiment of the present invention. 4 is a flowchart showing a detailed processing procedure of step S7 shown in FIG. 3.

Embodiments of the present invention will be described in detail with reference to the drawings. Note that identical or equivalent parts in the drawings will be designated by the same reference numerals and their description will not be repeated.

<A. Application example＞
First, an example of a situation in which the present invention is applied will be described.

In this specification, "AutomationML (Automation Markup Language)" refers to a data format based on XML (Extensible Markup Language) for plant engineering information. For more details, please refer to IEC 62714-1:2018, etc.

FIG. 1 is a diagram illustrating an overview of the processing performed by information processing device 100 according to this embodiment. Referring to FIG. 1, information processing device 100 includes, as its functional configuration, an acquisition unit 150, a keyword extraction unit 152, a presentation unit 154, a search unit 156, a block extraction unit 158, and a creation unit 160.

The acquisition unit 150 acquires data 200 written in accordance with AutomationML. The keyword extraction unit 152 extracts keywords contained in the data 200. The presentation unit 154 presents a keyword list containing the extracted keywords to the user.

The search unit 156 searches the data 200 for elements corresponding to the input keywords. Note that the search unit 156 may also accept input for keywords presented to the user.

The block extraction unit 158 extracts blocks that contain the searched elements and elements that exist below the searched elements. The creation unit 160 creates a cause-and-effect diagram 250 based on the elements contained in the extracted blocks.

The creation unit 160 may identify the attributes of the elements included in the block and determine which of the 5M (Man, Method, Machine, Material, Measure) they are associated with. In this case, the cause-and-effect diagram 250 may include edges corresponding to each of the 5M.

The creation unit 160 may also map elements contained in a block to edges associated with the block according to the hierarchical structure of the block.

Furthermore, the creation unit 160 may map the block containing the searched element to a position closer to the characteristic in the cause-and-effect diagram 250 the higher the degree of match between the searched element and the keyword.

The creation unit 160 may also determine the position of each edge in the cause-and-effect diagram 250 based on at least one of the number of elements mapped to each edge and the degree of match with the keyword.

In this way, the information processing device 100 according to this embodiment creates a cause-and-effect diagram 250 from data 200 described in accordance with AutomationML, so even a user with little experience can easily create the cause-and-effect diagram 250 required to identify the cause of a malfunction.

<B. Hardware Configuration Example>
Next, a description will be given of an example of a hardware configuration of the information processing device 100 according to the present embodiment. As an example, the information processing device 100 according to the present embodiment is realized by executing a program using hardware (for example, a general-purpose personal computer) that conforms to a general-purpose architecture.

FIG. 2 is a block diagram showing an example of the hardware configuration of an information processing device 100 according to this embodiment. Referring to FIG. 2, the information processing device 100 includes a processor 102 such as a CPU (Central Processing Unit) or MPU (Micro-Processing Unit), an optical drive 104, a main memory device 106, a secondary memory device 108, a USB (Universal Serial Bus) controller 112, a network controller 114, an input unit 116, and a display unit 118. These components are connected via a bus 120.

The processor 102 reads various programs stored in the secondary storage device 108, deploys them in the main storage device 106, and executes them to realize various processes described below.

The secondary storage device 108 is composed of, for example, an HDD (Hard Disk Drive) or SSD (Flash Solid State Drive). The secondary storage device 108 typically stores a PLC interface program 124 for exchanging necessary data between the OS 122 and a PLC (Programmable Logic Controller), a development program 126 for creating user programs (source code) to be executed on the information processing device 100, debugging the created user programs, defining system configurations, setting various parameters, and the like, and a cause-and-effect diagram creation program 128 for creating cause-and-effect diagrams as described below. All or part of these programs correspond to information processing programs. The secondary storage device 108 may also store necessary programs other than the programs shown in FIG. 2.

The information processing device 100 has an optical drive 104, and the program stored on a recording medium 105 (for example, an optical recording medium such as a DVD (Digital Versatile Disc)) that non-transiently stores a computer-readable program is read and installed on a secondary storage device 108, etc.

The various programs executed by the information processing device 100 may be installed via a computer-readable recording medium 105, or may be installed by downloading them from a server device on a network. Furthermore, the functions provided by the information processing device 100 according to this embodiment may also be realized by utilizing some of the modules provided by the OS 122.

The USB controller 112 controls data exchange with the PLC via a USB connection. The network controller 114 controls data exchange with other devices via any network.

The input unit 116 is composed of a keyboard, mouse, etc., and accepts user operations. The display unit 118 is composed of a display, various indicators, a printer, etc., and outputs processing results from the processor 102.

Figure 2 shows an example configuration in which the necessary functions are provided by the processor 102 executing a program, but some or all of these provided functions may be implemented using dedicated hardware circuits (e.g., an ASIC (Application Specific Integrated Circuit) or an FPGA (Field-Programmable Gate Array)).

<C. Creation of Fishbone Diagram>
The information processing apparatus 100 according to this embodiment creates a cause-and-effect diagram from data described in accordance with AutomationML. The process of creating the cause-and-effect diagram will be described in detail below.

(c1: Overall processing procedure)
Data described in accordance with AutomationML includes the hierarchically arranged geometry and control logic of a machine.

Figure 3 is a flowchart showing the processing steps for creating a cause-and-effect diagram by the information processing device 100 according to this embodiment. Each step shown in Figure 3 is typically realized by the processor 102 of the information processing device 100 executing the cause-and-effect diagram creation program 128. In other words, the cause-and-effect diagram creation program 128 is an example of an information processing program.

With reference to FIG. 3, the information processing device 100 acquires data written in accordance with the target AutomationML (step S1), extracts keywords contained in the acquired data (step S2), and presents the extracted keywords to the user (step S3). The information processing device 100 then accepts keyword input from the user (step S4). That is, the information processing device 100 accepts input in response to the keywords presented to the user.

The user enters a keyword related to the problem from the provided keywords. For example, if the data is related to a grinding machine and there is a problem with the workpiece receiving section of the grinding machine, the user would select a keyword such as "Entrance" from the provided keywords.

The information processing device 100 searches the data for elements corresponding to the input keywords (step S5), extracts blocks containing the searched elements (step S6), and creates a cause-and-effect diagram based on the elements contained in the extracted blocks (step S7). Finally, the information processing device 100 presents the created cause-and-effect diagram to the user (step S8).

The user can identify the cause of the problem by sequentially investigating the factors and parts associated with the problem in question while referring to the presented cause-and-effect diagram.

(c2: Data written according to AutomationML)
4 is a schematic diagram showing an example of data written in accordance with AutomationML and processed by information processing device 100 according to this embodiment. Referring to FIG. 4, data 200 has a hierarchical description. Typically, data 200 written in accordance with AutomationML has a tree structure.

The data 200 illustrated in Figure 4 is directed to a polishing apparatus and includes a description 202 corresponding to the receiving unit, a description 204 corresponding to the polishing unit, a description 206 corresponding to the discharge unit, a description 208 corresponding to the control of the polishing apparatus, a description 210 corresponding to the robot that transports the workpiece to the receiving unit, and a description 212 corresponding to the robot that transports the workpiece from the discharge unit.

(c3: Keyword extraction (step S2))
3, the information processing device 100 extracts, as keywords, words used in the data 200. That is, the information processing device 100 breaks down the data 200 to the word level and creates a list (index) of the words used.

(c4: Search for elements corresponding to keywords and extract blocks (steps S6 and S7))
The information processing device 100 searches for an element (node) containing the input keyword and extracts the child elements and subsequent parts (lower layers) of the searched element (node). In other words, the information processing device 100 extracts a block that includes the searched element and elements present below the searched element.

Data 200 written in accordance with AutomationML includes machine structure and control logic, so one or more elements corresponding to the input keyword are extracted.

Figure 5 is a schematic diagram showing an example of the process of extracting elements corresponding to input keywords according to this embodiment. Figure 5 shows an example of elements corresponding to keywords being extracted from the data 200 shown in Figure 4.

Referring to FIG. 5, for example, assume that "Entrance" is input as a keyword. The information processing device 100 searches for elements that include the input keyword "Entrance." In the example shown in FIG. 5, the description 202 corresponding to the receiving unit contains an element 220 that (completely) matches "Entrance," and the description 210 contains an element 230 called "EntranceRobotController" that includes "Entrance."

In addition to elements 220 and 230 containing the input keyword "Entrance," the information processing device 100 extracts the child elements and subsequent parts (lower layers) of each element. As a result, block 222 containing element 220 and block 232 containing element 230 are extracted.

(c5: Creation of a cause-and-effect diagram (step S7))
The information processing device 100 uses elements extracted from data 200 described in accordance with AutomationML to create a cause-and-effect diagram based on the attributes of each element, the degree of match between each element and keywords, and the position of each element in the hierarchical structure.

Figure 6 is a schematic diagram showing an example of the process for creating a cause-and-effect diagram according to this embodiment. As an example, Figure 6 shows a cause-and-effect diagram 250 corresponding to the 5Ms (Man, Method, Machine, Material, Measure) of production. The cause-and-effect diagram 250 includes edges corresponding to each of the 5Ms.

More specifically, the cause-and-effect diagram 250 shown in Figure 6 includes, for the main stream 256 connected to the input keyword (characteristic 258), an edge 251 corresponding to "Man," an edge 252 corresponding to "Method," an edge 253 corresponding to "Machine," an edge 254 corresponding to "Material," and an edge 255 corresponding to "Measure." The distance from the input keyword to each of edges 251-255 (the ranking among edges 251-255) is determined depending on the number of elements mapped to each edge, the degree of similarity, etc.

Referring to Figure 6, the elements included in blocks 222 and 232 extracted in Figure 5 are assigned to a cause-and-effect diagram 250. More specifically, the information processing device 100 determines which of the 5Ms of production each element corresponds to, and maps it to the corresponding position on the cause-and-effect diagram 250.

First, regarding the attributes of each element, for example, an element that has the attribute of machine structure (geometry) can be determined to be associated with "Machine." Also, an element that has the attribute of control logic (logic) can be determined to be associated with "Method."

Note that control logic attributes may not be explicitly stated. Even in such cases, elements containing terms such as "PLCopen" or "Control" can be presumed to have control logic attributes, and as a result, can be determined to be associated with "Method."

The attributes of an element may be determined by referencing a technical terminology dictionary. The technical terminology dictionary contains a list of terms and their corresponding meanings. For example, if a term included in an element has a meaning related to the geometry of a machine, the meaning of the term can be identified as a term related to the geometry of a machine, and based on this identification, the element can be mapped to an edge 253 corresponding to Machine.

Next, regarding the degree of match between each element and the keyword, for example, an element that completely matches the keyword has a high degree of match and is therefore mapped to a position closer to the characteristic 258. On the other hand, an element that partially matches the keyword has a relatively low degree of match and is therefore mapped to a position farther away from the main stream 256.

In the example shown in Figure 6, element 220 is an exact match for the input keyword "Entrance," and elements 223 to 226, which are child elements of element 220, are mapped to edge 251 corresponding to "Man," and edge 251 to which these elements are mapped is mapped to a position close to characteristic 258.

In this way, the information processing device 100 maps the block containing the searched element to a position closer to characteristic 258 in the cause-and-effect diagram 250 the higher the degree of match between the searched element and the input keyword. Note that the degree of match may be evaluated not only based on character-based judgment such as exact match/partial match, but also based on the meaning of the keywords.

Next, with regard to the position of each element in the hierarchical structure, elements in the extracted block that exist in higher layers are mapped to positions closer to characteristic 258. In other words, the element corresponding to the input keyword is mapped to the position closest to characteristic 258, and elements are further mapped in order of distance from the element corresponding to the input keyword.

Referring to FIG. 6, in block 222, elements 223 and 226 are associated with element 220 at the next level. Therefore, elements 223 and 226 are mapped to positions closer to characteristic 258. Furthermore, elements 224 and 225 are associated with element 223 at the next level. Therefore, elements 224 and 225 are mapped to positions next closest to characteristic 258 after element 223.

Furthermore, element 220 in block 222 is an exact match for the input keyword "Entrance," while element 230 in block 232 is only a partial match for the input keyword "Entrance," so the element contained in block 222 is mapped closer to characteristic 258.

The attributes of the elements contained in block 222 and block 232 can both be determined to be associated with "Machine." Therefore, element 230 contained in block 232 is mapped in association with edge 252.

Furthermore, it can be determined that the element contained in block 232 also has the "Method" attribute. Therefore, element 230 contained in block 232 is also associated with and mapped to edge 253.

The positions of edges 251 to 255 are determined depending on the number of elements mapped to each edge, the degree of similarity, and other factors. In the example shown in Figure 6, the most elements are mapped to edge 252, followed by edge 253, with the second most elements mapped to edge 253. As a result, edge 252 is mapped to the position closest to characteristic 258, followed by edge 253, which is mapped to the position second closest to characteristic 258. Note that edges 251, 254, and 255 have no elements mapped to them, so they are mapped to positions farthest from characteristic 258.

Through the above-described processing procedure, a cause-and-effect diagram is created.
Fig. 7 is a flowchart showing the detailed processing procedure of step S7 shown in Fig. 3. Referring to Fig. 7, information processing device 100 calculates the degree of match with the keyword for each extracted block (step S71), and determines the priority order among the blocks (step S72).

Next, the information processing device 100 selects one of the extracted blocks that has the highest priority among the unselected blocks (step S73). The information processing device 100 identifies the attributes of the elements included in the selected block (step S74) and determines the associated edge based on the identified attributes (step S75). In this way, the information processing device 100 identifies the attributes of the elements included in the block and determines which of the 5M elements the block will be associated with.

Note that multiple attributes may be extracted, in which case one block will be associated with multiple edges.

Then, the information processing device 100 maps the elements included in the selected block to the determined edge (step S76). When mapping elements to edges, the information processing device 100 determines the mapping position of each element in accordance with the hierarchical structure of each block. In other words, the information processing device 100 maps the elements included in a block to the edge associated with that block in accordance with the hierarchical structure of that block.

The information processing device 100 then determines whether all of the extracted blocks have been selected (step S77). If any extracted blocks remain unselected (NO in step S77), the processing from step S73 onwards is repeated.

If all of the extracted blocks have been selected (YES in step S77), the information processing device 100 determines the priority between edges based on the number of elements mapped to the edges, the degree of similarity, etc. (step S78). Then, the information processing device 100 maps each edge to the main stream based on the determined priority between edges (step S79). In other words, the information processing device 100 determines the position of each edge in the cause-and-effect diagram based on at least one of the number of elements mapped to each edge and the degree of similarity with the keyword.

Through the above process, a cause-and-effect diagram is created.
D. Modifications
Next, some modified examples of the process for creating the cause-and-effect diagram according to this embodiment will be described.

(1) Synonyms In the above explanation, an example was given in which words contained in data written in accordance with AutomationML were extracted and presented as keywords. However, it is also possible to present and/or input any words as keywords, not just words contained in the data.

More specifically, words contained in the data may be extracted, and synonyms of the extracted words may be included in the keyword group to be presented to the user. In this case, synonyms corresponding to the extracted words can be determined by referring to a dictionary of synonyms prepared in advance.

Furthermore, when accepting input of any keyword from the user, a search may be performed between the input keyword and words contained in the data, including synonyms. In other words, the information processing device 100 may search for corresponding elements within a range that includes synonyms of the input keyword.

In this way, by being able to perform keyword searches not only for words contained in data written according to AutomationML, but also for synonyms, users can more efficiently create the fishbone diagrams they want.

(2) Presenting the degree of agreement (priority) in the fishbone diagram
The priority of each factor making up the cause-and-effect diagram may be presented to the user based on the degree of match between the elements (factors making up the cause-and-effect diagram) included in the data written in accordance with AutomationML and the input keywords (characteristics of the cause-and-effect diagram).

The priority of each factor may be displayed as a numerical value indicating the priority for each factor, or the higher the priority, the closer the factor is to being mapped to the characteristic.

Typically, after creating a cause-and-effect diagram, the creator of the cause-and-effect diagram will investigate the cause of the defect based on the diagram they created, but by referring to the priorities presented in the cause-and-effect diagram, they can determine the target more efficiently and shorten the time it takes to complete the investigation.

(3) Representation of the Cause and Effect Diagram In the above explanation, a cause and effect diagram in the form of a graph is used as an example, but any representation format may be used. For example, instead of the graph format, a cause and effect diagram in the form of a table or a tree diagram may be used.

When a cause-and-effect diagram contains a large number of elements, it can become very large and difficult to see the overall picture. Therefore, by changing the presentation format appropriately, you can understand the cause-and-effect diagram more efficiently.

(4) Data Structure In the above explanation, the standardized AutomationML was used as an example, but this technology can also be applied to data formats that will be developed from AutomationML in the future. In addition, this technology can basically be applied to any data format that describes plant engineering information in a hierarchical structure (tree structure).

<E. Notes>
The present embodiment as described above includes the following technical idea.

[Configuration 1]
An acquisition unit (150) that acquires data (200) described in accordance with AutomationML;
a search unit (156) for searching the data for elements corresponding to the input keyword;
a first extraction unit (158) that extracts a block including the searched element and an element present below the searched element;
and a creating unit (160) that creates a cause-and-effect diagram (250) based on elements included in the extracted block.

[Configuration 2]
2. The information processing device according to configuration 1, wherein the creation unit identifies attributes of elements included in the block and determines which of 5Ms (Man, Method, Machine, Material, Measure) the block is associated with.

[Configuration 3]
The cause-and-effect diagram includes edges (251 to 255) corresponding to 5M, respectively;
3. The information processing device according to configuration 2, wherein the creation unit maps elements included in the block to the edge associated with the block in accordance with the hierarchical structure of the block.

[Configuration 4]
The information processing device according to any one of configurations 1 to 3, wherein the creation unit maps a block including the searched element to a position closer to the characteristic in the cause-and-effect diagram the higher the degree of match between the searched element and the keyword.

[Configuration 5]
5. The information processing device according to configuration 3 or 4, wherein the creation unit determines a position of each edge in the cause-and-effect diagram based on at least one of the number of elements mapped to each edge and the degree of match with the keyword.

[Configuration 6]
a second extraction unit (152) that extracts keywords included in the data;
a presentation unit (154) that presents the extracted keywords to a user,
5. The information processing device according to any one of configurations 1 to 4, wherein the search unit accepts an input for a keyword presented to the user.

[Configuration 7]
7. The information processing device according to any one of configurations 1 to 6, wherein the search unit searches for corresponding elements within a range including synonyms of the input keyword.

[Configuration 8]
An information processing method executed by a computer (100),
A step (S1) of acquiring data (200) described in accordance with AutomationML;
A step (S5) of searching the data for elements corresponding to the input keyword;
A step (S6) of extracting a block including the searched element and an element present in a layer below the searched element;
and creating (S7) a cause-and-effect diagram (250) based on the elements included in the extracted block.

[Configuration 9]
On the computer,
A step (S1) of acquiring data (200) described in accordance with AutomationML;
A step (S5) of searching the data for elements corresponding to the input keyword;
A step (S6) of extracting a block including the searched element and an element present in a layer below the searched element;
and a step (S7) of creating a cause-and-effect diagram based on the elements included in the extracted block.

F. Advantages
The information processing device according to this embodiment automatically creates a cause-and-effect diagram required to identify the cause of a malfunction without the need for user analysis. Such automatic creation can shorten the time required to identify the cause of a malfunction and increase the accuracy of cause identification. It can also reduce the time required to create a cause-and-effect diagram to almost zero. Furthermore, a cause-and-effect diagram can be created even if the user does not know the history or detailed specifications of the target machine.

The embodiments disclosed herein should be considered in all respects to be illustrative and not restrictive. The scope of the present invention is indicated by the claims, not the above description, and is intended to include all modifications that are equivalent in meaning to and within the scope of the claims.

100 Information processing device, 102 Processor, 104 Optical drive, 105 Recording medium, 106 Main memory device, 108 Secondary memory device, 112 USB controller, 114 Network controller, 116 Input unit, 118 Display unit, 120 Bus, 122 OS, 124 Interface program, 126 Development program, 128 Cause and effect diagram creation program, 150 Acquisition unit, 152 Keyword extraction unit, 154 Presentation unit, 156 Search unit, 158 Block extraction unit, 160 Creation unit, 200 Data, 202, 204, 206, 208, 210, 212 Description, 220, 223, 224, 225, 226, 230 Elements, 222, 232 Blocks, 250 Cause and effect diagrams, 251, 252, 253, 254, 255 Edge, 256 Mainstream, 258 Characteristics.

Claims (8)

an acquisition unit that acquires data described in accordance with AutomationML;
a search unit that searches the data for elements corresponding to the input keyword;
a first extraction unit that extracts a block including the searched element and an element present below the searched element;
and a creation unit that creates a cause-and-effect diagram based on elements included in the extracted block. The information processing device of claim 1, wherein the creation unit identifies attributes of elements included in the block and determines which of the 5Ms (Man, Method, Machine, Material, Measure) the block is associated with. The fishbone diagram includes edges corresponding to 5M,
The information processing device according to claim 2 , wherein the creating unit maps elements included in the block to the edge associated with the block in accordance with a hierarchical structure of the block. The information processing device of any one of claims 1 to 3, wherein the creation unit maps a block containing the searched element to a position closer to the characteristic in the cause-and-effect diagram the higher the degree of match between the searched element and the keyword. The information processing device of claim 3 or 4, wherein the creation unit determines the position of each edge in the cause-and-effect diagram based on at least one of the number of elements mapped to each edge and the degree of match with the keyword. a second extraction unit that extracts keywords included in the data;
a presentation unit that presents the extracted keywords to a user,
5. The information processing device according to claim 1, wherein the search unit accepts an input for a keyword presented to the user. 1. A computer-implemented information processing method, comprising:
obtaining data described according to AutomationML;
A step of searching the data for elements corresponding to the input keyword;
extracting a block including the searched element and an element present below the searched element;
and creating a cause-and-effect diagram based on the elements included in the extracted block. On the computer,
obtaining data described according to AutomationML;
A step of searching the data for elements corresponding to the input keyword;
extracting a block including the searched element and an element present below the searched element;
and creating a cause-and-effect diagram based on the elements included in the extracted block.