Information processing device, information processing method, and information processing program

Abstract

To provide a technique for more efficiently creating a characteristic factor diagram.SOLUTION: An information processing device includes: an acquisition unit for acquiring data described according to Automation ML; a retrieval unit for retrieving an element corresponding to an input keyword from data; an extraction unit for extracting a block including the retrieved element and an element existing on the lower layer of the retrieved element; and a creation unit for creating a characteristic factor diagram on the basis of the element included in the extracted block.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to an information processing device, an information processing method, and an information processing program. [Background technology]

[0002] 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.

[0003] As a prior art for understanding such causal relationships, for example, 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. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2011-150496 Summary of the Invention [Problem to be solved by the invention]

[0005] To understand the causal relationships within a machine, a fishbone diagram (or a different representation of the relationships shown in a fishbone diagram) is typically used, which systematically connects the relationships between effects and factors.

[0006] To create a cause-and-effect diagram, information such as the use, usage, and parameters of the target machine is required, but generally, the designer who determines this information is not the same person who creates the cause-and-effect diagram. In addition, 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.

[0007] Therefore, an object of the present invention is to provide a technique for more efficiently creating a cause-and-effect diagram. [Means for solving the problem]

[0008] 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.

[0009] According to this configuration, the information processing device creates a cause-and-effect diagram from data described in accordance with AutomationML, so even a user with little specialized knowledge can create a cause-and-effect diagram more efficiently.

[0010] The creation unit may identify the attributes of the elements included in the block and determine which of the 5Ms (Man, Method, Machine, Material, Measure) the block is associated with. With this configuration, it is possible to create a cause-and-effect diagram from the perspective of 5M analysis.

[0011] The cause-and-effect diagram may include edges corresponding to the 5M elements. The creation unit may map elements included in a block to an edge associated with the block according to the hierarchical structure of the block. This configuration allows creation of a cause-and-effect diagram that reflects the hierarchical structure of the block.

[0012] The creation unit may map a 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 is. With this configuration, the element that matches the keyword more is more likely to be the cause, so the cause of the defect can be identified more efficiently.

[0013] 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 possible to more easily identify, from among multiple edges, an edge that is likely to be the cause in the cause-and-effect diagram.

[0014] The information processing device may further include a second extraction unit that extracts keywords included 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 presented keywords to the user. According to this configuration, by presenting the keywords included in the data, the user can more easily select a keyword corresponding to the target defect.

[0015] The search unit may search for elements that correspond to the input keyword, including synonyms of the input keyword. This configuration allows the user to freely input keywords, rather than keywords contained in the data.

[0016] According to another embodiment of the present invention, there is provided a computer-executed information processing method, which includes the steps of acquiring data described in accordance with AutomationML, searching the data for elements corresponding to input keywords, extracting blocks including 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.

[0017] 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. [Effects of the Invention]

[0018] According to the present invention, a cause-and-effect diagram can be created more efficiently. [Brief explanation of the drawings]

[0019] [Figure 1] FIG. 2 is a diagram for explaining an outline of processing performed by the information processing device according to the present embodiment. [Figure 2] 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. [Figure 3] 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. [Figure 4] 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. [Figure 5] 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. [Figure 6] 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. [Figure 7] 4 is a flowchart showing the detailed processing procedure of step S7 shown in FIG. 3. DETAILED DESCRIPTION OF THE INVENTION

[0020] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described in detail with reference to the accompanying drawings, in which the same or corresponding parts are designated by the same reference numerals and will not be described repeatedly.

[0021] <A. Application Example> First, an example of the scenario to which the present invention is applied will be described.

[0022] "AutomationML (Automation Markup Language)" described in this specification means a data format based on XML (Extensible Markup Language) for plant engineering information. For details, refer to IEC 62714-1:2018 etc.

[0023] FIG. 1 is a diagram for explaining the processing outline of the information processing apparatus 100 according to the present embodiment. Referring to FIG. 1, the information processing apparatus 100 includes, as functional components, 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.

[0024] The acquisition unit 150 acquires data 200 described according to AutomationML. The keyword extraction unit 152 extracts keywords included in the data 200. The presentation unit 154 presents a keyword list including the extracted keywords to the user.

[0025] The search unit ********** searches for an element corresponding to the input keyword from the data 200. Note that the search unit 156 may be configured to receive an input for the keyword presented to the user.

[0026] The block extraction unit 158 extracts a block including the searched element and elements existing in the lower layer of the searched element. The creation unit 160 creates a characteristic factor diagram 250 based on the elements included in the extracted block.

[0027] It should be noted that there is an unclear part in the original text for item where "**********" is presented. You may want to check and correct it for a more accurate translation.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) it is associated with. In this case, the characteristic cause-and-effect diagram 250 may include edges corresponding to the 5M respectively.

[0028] Further, the creation unit 160 may map the elements included in the block to the edges associated with the block according to the hierarchical structure of the block.

[0029] Also, the creation unit 160 may map the block including the searched element to a position closer to the characteristic in the characteristic cause-and-effect diagram 250 as the degree of coincidence between the searched element and the keyword is higher.

[0030] <� Also, the creation unit 160 may determine the position of each edge in the characteristic cause-and-effect diagram 250 based on at least one of the number of elements mapped to each edge and the degree of coincidence with the keyword.

[0031] As described above, since the information processing apparatus 100 according to the present embodiment creates the characteristic cause-and-effect diagram 250 from the data 200 described according to AutomationML, even a user with little experience or the like can easily create the characteristic cause-and-effect diagram 250 necessary for identifying the cause of a defect.

[0032] <B. Hardware Configuration Example> Next, a hardware configuration example of the information processing apparatus 100 according to the present embodiment will be described. As an example, the information processing apparatus 100 according to the present embodiment is realized by executing a program using hardware (for example, a general-purpose personal computer) according to a general-purpose architecture.

[0033] Fig. 2 is a block diagram showing an example of a 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 an MPU (Micro-Processing Unit), an optical drive 104, a main storage device 106, a secondary storage 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.

[0034] The processor 102 reads out various programs stored in the secondary storage device 108, loads them into the main storage device 106, and executes them to realize various processes as described below.

[0035] The secondary storage device 108 is configured, for example, by an HDD (Hard Disk Drive) or an 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 in 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 a cause-and-effect diagram as described below. All or part of these programs correspond to the information processing program. The secondary storage device 108 may store necessary programs other than the programs shown in FIG. 2.

[0036] The information processing device 100 has an optical drive 104, and a program stored in a recording medium 105 (e.g., an optical recording medium such as a DVD (Digital Versatile Disc)) that non-transiently stores a computer-readable program is read and installed in a secondary storage device 108, etc.

[0037] The various programs executed by the information processing apparatus 100 may be installed via a computer-readable recording medium 105, or may be installed by downloading from a server device or the like on a network. Further, the functions provided by the information processing apparatus 100 according to the present embodiment may be realized by using a part of the modules provided by the OS 122.

[0038] The USB controller 112 controls the data exchange with the PLC via the USB connection. The network controller 114 controls the data exchange with other devices via an arbitrary network.

[0039] The input unit 116 is composed of a keyboard, a mouse, etc., and receives user operations. The display unit 118 is composed of a display, various indicators, a printer, etc., and outputs processing results from the processor 102.

[0040] FIG. 2 shows a configuration example in which functions necessary for the processor 102 to execute a program are provided. However, some or all of these provided functions may be implemented using a dedicated hardware circuit (for example, an ASIC (Application Specific Integrated Circuit) or an FPGA (Field-Programmable Gate Array)).

[0041] <C. Creation Process of Characteristic Cause Diagram> The information processing apparatus 100 according to the present embodiment creates a characteristic cause diagram from data described according to AutomationML. The details of the process of creating the characteristic cause diagram will be described.

[0042] (c1: Overall Processing Procedure) The data described according to AutomationML includes the structure (geometry) and control logic of the machine arranged hierarchically.

[0043] Fig. 3 is a flowchart showing a processing procedure for creating a cause-and-effect diagram by the information processing device 100 according to this embodiment. Each step shown in Fig. 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.

[0044] 3, the information processing device 100 acquires data written in accordance with the target AutomationML (step S1), extracts keywords included in the acquired data (step S2), and presents the extracted keywords to the user (step S3). Then, the information processing device 100 accepts input of keywords from the user (step S4). That is, the information processing device 100 accepts input for the keywords presented to the user.

[0045] The user inputs a keyword related to the problem from the presented keywords. For example, if the target data is a grinding machine and a problem has occurred in the workpiece receiving section of the grinding machine, the user would select "Entrance" from the presented keywords.

[0046] The information processing device 100 searches the data for elements corresponding to the input keyword (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).

[0047] The user refers to the presented cause-and-effect diagram and sequentially investigates the factors and parts associated with the target defect, thereby identifying the cause of the defect.

[0048] (c2: Data written according to AutomationML) 4 is a schematic diagram showing an example of data described in accordance with AutomationML that is processed by information processing device 100 according to the present embodiment. Referring to FIG. 4, data 200 has a hierarchical description. Typically, data 200 described in accordance with AutomationML has a tree structure.

[0049] The data 200 illustrated in FIG. 4 is directed to a polishing apparatus and includes a description 202 corresponding to the receiving section, a description 204 corresponding to the polishing section, a description 206 corresponding to the discharge section, a description 208 corresponding to the control of the polishing apparatus, a description 210 corresponding to a robot that transports the workpiece to the receiving section, and a description 212 corresponding to a robot that transports the workpiece from the discharge section.

[0050] (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.

[0051] (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). That is, the information processing device 100 extracts a block including the searched element and elements present in the lower layers of the searched element.

[0052] Since the data 200 written in accordance with AutomationML includes the structure and control logic of the machine, one or more elements corresponding to the input keyword are extracted.

[0053] 5 is a schematic diagram showing an example of a process for extracting elements corresponding to input keywords according to the present embodiment. Fig. 5 shows an example in which elements corresponding to keywords are extracted from the data 200 shown in Fig. 4.

[0054] With reference 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, an element 220 that (completely) matches "Entrance" exists in the description 202 corresponding to the receiving unit, and an element 230 called "EntranceRobotController" that includes "Entrance" exists in the description 210.

[0055] Information processing device 100 extracts elements 220 and 230 that include the input keyword "Entrance," as well as the child elements and subsequent parts (lower layers) of each element. As a result, block 222 that includes element 220 and block 232 that includes element 230 are extracted.

[0056] (c5: Creating a fishbone diagram (step S7)) The information processing device 100 uses elements extracted from data 200 written 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 a keyword, the position of each element in a hierarchical structure, and the like.

[0057] Fig. 6 is a schematic diagram showing an example of a process for creating a cause-and-effect diagram according to this embodiment. Fig. 6 shows, as an example, 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.

[0058] More specifically, the cause-and-effect diagram 250 shown in FIG. 6 includes, for a main stream 256 connected to an 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 the edges 251 to 255 (the order among the edges 251 to 255) is determined depending on the number of elements mapped to each edge, the degree of match, etc.

[0059] 6, the elements included in the block 222 and the block 232 extracted in FIG. 5 are assigned to a cause-and-effect diagram 250. More specifically, the information processing device 100 determines, for each element, which of the 5Ms of production it corresponds to, and maps it to the corresponding position on the cause-and-effect diagram 250.

[0060] First, regarding the attributes of each element, for example, an element having an attribute of machine geometry can be determined to be associated with "Machine," and an element having an attribute of control logic can be determined to be associated with "Method."

[0061] Note that the control logic attribute may not be explicitly stated. Even in such cases, elements containing terms such as "PLCopen" or "Control" can be assumed to have the control logic attribute and, as a result, can be determined to be associated with "Method."

[0062] The attributes of an element may be determined by referring to a technical dictionary. The technical dictionary includes 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 the element can be mapped to an edge 253 corresponding to Machine based on the identified content.

[0063] 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 mainstream 256.

[0064] In the example shown in FIG. 6, element 220 perfectly matches 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.

[0065] In this way, the higher the degree of match between the searched element and the input keyword, the more the information processing device 100 maps the block including the searched element to a position closer to the characteristic 258 in the cause-and-effect diagram 250. Note that the degree of match may be evaluated not only based on character-based judgment such as perfect match / partial match but also based on the meaning of the keyword.

[0066] Next, with regard to the position of each element in the hierarchical structure, the elements in the extracted block that exist in a higher layer are mapped to positions closer to the characteristic 258. That is, the element corresponding to the input keyword is mapped to the position closest to the characteristic 258, and further, the elements are mapped in descending order of distance from the element corresponding to the input keyword.

[0067] 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.

[0068] Also, 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 in block 222 is mapped closer to characteristic 258.

[0069] It can be determined that the attributes of the elements contained in block 222 and block 232 are both associated with “Machine.” Therefore, element 230 contained in block 232 is associated with edge 252 and mapped.

[0070] Furthermore, it can be determined that the element included in the block 232 also has the attribute "Method." Therefore, the element 230 included in the block 232 is also associated with and mapped to the edge 253.

[0071] The positions of edges 251 to 255 are determined depending on the number of elements mapped to each edge, the degree of match, etc. In the example shown in Fig. 6, the largest number of elements are mapped to edge 252, followed by edge 253, with the second largest number of elements mapped. 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 thereto, and are therefore mapped to positions farthest from characteristic 258.

[0072] 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).

[0073] Next, the information processing device 100 selects one block with the highest priority among the extracted blocks that have not been selected (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.

[0074] It should be noted that multiple attributes may be extracted, in which case one block will be associated with multiple edges.

[0075] Then, the information processing device 100 maps the elements included in the selected block to the determined edges (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. That is, the information processing device 100 maps the elements included in a block to the edges associated with the block in accordance with the hierarchical structure of the block.

[0076] Subsequently, the information processing device 100 determines whether or not 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 onward is repeated.

[0077] 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 match, 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). That is, 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 match with the keyword.

[0078] By the above processing, a characteristic factor diagram is created. <D. Modified Example> Next, several modified examples will be described regarding the processing for creating the characteristic factor diagram according to this embodiment.

[0079] (1) Synonyms In the above description, an example of extracting words included in the data described according to AutomationML and presenting them as keywords has been described. Not only the words included in the data, but also any words may be presented and / or input as keywords.

[0080] More specifically, words included in the data may be extracted, and a group of keywords to be presented to the user may be configured including synonyms of the extracted words. At this time, by referring to a previously prepared thesaurus or the like, synonyms corresponding to the extracted words can be determined.

[0081] Also, when accepting input of any keyword from the user, a search may be performed including synonyms between the input keyword and the words included in the data. That is, the information processing apparatus 100 may search for corresponding elements within a range including synonyms of the input keyword. <l

[0082] In this way, by enabling keyword search processing and the like not only for words included in the data described according to AutomationML but also within the range of synonyms, the characteristic factor diagram desired by the user can be created more efficiently.

[0083] (2) Presentation of Degree of Match (Priority) in Characteristic Factor Diagram Based on the degree of match between the elements included in the data described according to AutomationML (factors constituting the characteristic factor diagram) and the input keyword (characteristics of the characteristic factor diagram), the priority for each factor constituting the characteristic factor diagram may be presented to the user.

[0084] For each cause, the priority may be indicated by a numerical value for each cause, or the higher the priority, the closer the cause may be mapped to a position closer to the characteristic.

[0085] Normally, after creating a characteristic cause-and-effect diagram, the creator of the characteristic cause-and-effect diagram will investigate the cause of the defect based on the created characteristic cause-and-effect diagram. However, by referring to the priority presented in the characteristic cause-and-effect diagram, the target can be determined more efficiently and the time until the investigation is completed can be shortened.

[0086] (3) Expression of the characteristic cause-and-effect diagram In the above description, a characteristic cause-and-effect diagram in graph form was exemplified, but any expression form may be adopted. For example, instead of the graph form, a characteristic cause-and-effect diagram in table form or tree diagram form may be adopted.

[0087] When the number of elements included in the characteristic cause-and-effect diagram increases, the characteristic cause-and-effect diagram may become huge and the overall visibility may deteriorate. Therefore, by appropriately changing the expression form, the characteristic cause-and-effect diagram can be understood more efficiently.

[0088] (4) Data structure In the above description, standardized AutomationML was cited as an example, but this technology is also applicable to a data format that is a future development of AutomationML. Also, basically, as long as it is a data format in which plant engineering information is described in a hierarchical structure (tree structure), this technology is applicable.

[0089] <E. Supplementary Note> The present embodiment as described above includes the following technical ideas.

[0090] [Configuration 1] An acquisition unit (150) that acquires data (200) described in accordance with AutomationML, A search unit (156) that searches for elements corresponding to the input keyword from the data, a first extraction unit (158) that extracts a block including the searched element and an element present in a layer below the searched element; and a creation unit (160) that creates a cause-and-effect diagram (250) based on elements included in the extracted block.

[0091] [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.

[0092] [Configuration 3] The fishbone diagram includes edges (251 to 255) corresponding to 5M, 3. The information processing device according to configuration 2, wherein the creation unit maps elements included in the block to an edge associated with the block in accordance with a hierarchical structure of the block.

[0093] [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.

[0094] [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.

[0095] [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.

[0096] [Configuration 7] The information processing apparatus 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.

[0097] [Configuration 8] An information processing method executed by a computer (100), comprising: a step (S1) of acquiring data (200) described according to AutomationML; a step (S5) of searching for an element corresponding to an input keyword from the data; a step (S6) of extracting a block including the searched element and elements existing under the searched element; and a step (S7) of creating a characteristic factor diagram (250) based on the elements included in the extracted block. The information processing method.

[0098] [Configuration 9] Causing a computer to execute a step (S1) of acquiring data (200) described according to AutomationML; execute a step (S5) of searching for an element corresponding to an input keyword from the data; execute a step (S6) of extracting a block including the searched element and elements existing under the searched element; and execute a step (S7) of creating a characteristic factor diagram based on the elements included in the extracted block. An information processing program (128).

[0099] [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.

[0100] The embodiments disclosed herein should be considered to be illustrative in all respects and not restrictive. The scope of the present invention is defined by the claims, not by the above description, and is intended to include all modifications within the meaning and scope of the claims. [Explanation of symbols]

[0101] 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 Element, 222, 232 Block, 250 Cause and effect diagram, 251, 252, 253, 254, 255 Edge, 256 Mainstream, 258 characteristics.

Claims

1. 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.

2. The information processing apparatus according to claim 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.

3. 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.

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

5. The information processing device according to claim 3 , 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.

6. 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.

7. 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.

8. 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.

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