Information processing device, information processing method, and program

Abstract

An information processing device, wherein a partial graph data generation means generates first partial graph data that corresponds to component data including data pertaining to a plurality of components used for manufacturing one product, and second partial graph data that corresponds to process data including data pertaining to a plurality of processes in the process of manufacturing the one product. An integration means integrates the first partial graph data and the second partial graph data to generate graph data. A training means trains a learning model using the graph data and relationship data that indicates a known relationship between nodes that are linked in the graph data. An analysis means, using the learning model that has been trained, performs an analysis that corresponds to a purpose of analysis related to the manufacturing of the one product. A display information generation means generates display information for showing the analysis results obtained by analysis, together with the grounds thereof.

Description

Information processing device, information processing method, and recording medium

[0001] The present disclosure relates to a technique for performing processing using graph data.

[0002] Graph data such as knowledge graphs have been known as data capable of expressing relationships between multiple entities. Graph data has also been utilized in recent years for purposes such as improving product productivity.

[0003] Specifically, for example, Patent Document 1 discloses a technology for identifying the part having the closest relationship to a part selected by a user, using a graph including nodes representing parts of a completed product and edges representing relationships between the parts.

[0004] JP 2019-153280 A

[0005] However, the technology disclosed in Patent Document 1 does not take into consideration the relationship between processes in the product manufacturing process, which poses a problem that the productivity of the product may decrease due to, for example, inconsistencies between processes in the product manufacturing process.

[0006] An object of the present disclosure is to provide an information processing device that can improve product productivity.

[0007] In one aspect of the present disclosure, an information processing device includes: a subgraph data generation means for generating first subgraph data corresponding to part data including data related to a plurality of parts used in manufacturing a single product; and second subgraph data corresponding to process data including data related to a plurality of processes in a manufacturing process of the single product; an integration means for generating graph data by integrating the first subgraph data and the second subgraph data; a learning means for training a learning model using the graph data and relationship data that is data indicating known relationships between linked nodes in the graph data; an analysis means for using the trained learning model to perform analysis according to an analysis purpose related to the manufacturing of the single product; and a display information generation means for generating display information to show the analysis results obtained by the analysis together with reasons.

[0008] In another aspect of the present disclosure, an information processing method generates first partial graph data corresponding to part data including data related to multiple parts used in manufacturing a single product, and second partial graph data corresponding to process data including data related to multiple processes in a manufacturing process of the single product, generates graph data by integrating the first partial graph data and the second partial graph data, trains a learning model using the graph data and relationship data that is data indicating known relationships between linked nodes in the graph data, uses the trained learning model to perform an analysis according to an analysis objective related to the manufacturing of the single product, and generates display information to show the analysis results obtained by the analysis together with reasons for the analysis.

[0009] In yet another aspect of the present disclosure, a recording medium records a program that causes a computer to execute the following processes: generate first partial graph data corresponding to part data including data related to multiple parts used in manufacturing a single product, and second partial graph data corresponding to process data including data related to multiple processes in a manufacturing process of the single product; generate graph data by integrating the first partial graph data and the second partial graph data; train a learning model using the graph data and relationship data that is data indicating known relationships between linked nodes in the graph data; use the trained learning model to perform an analysis according to an analysis purpose related to the manufacturing of the single product; and generate display information to show the analysis results obtained by the analysis together with reasons for the analysis.

[0010] According to the present disclosure, it is possible to improve the productivity of products.

[0011] 1 is a diagram showing a schematic configuration of an information processing system including a server device according to the first embodiment. FIG. 2 is a block diagram showing the hardware configuration of the server device according to the first embodiment. FIG. 3 is a block diagram showing the functional configuration of the server device according to the first embodiment. FIG. 4 is a diagram showing an example of a record obtained by converting input data using dictionary data. FIG. 5 is a diagram showing graph data generated based on each record in FIG. 4. FIG. 6 is a diagram showing an example of graph data obtained by processing of the server device according to the first embodiment. FIG. 7 is a diagram for explaining analysis results obtained by processing of the server device according to the first embodiment. FIG. 8 is a diagram showing example data obtained by processing of the server device according to the first embodiment. FIG. 9 is a diagram showing display information generated by processing of the server device according to the first embodiment. A flowchart showing an example of processing performed in the server device according to the first embodiment. A block diagram showing the functional configuration of an information processing device according to a second embodiment. A flowchart for explaining processing performed in the information processing device according to the second embodiment.

[0012] Hereinafter, preferred embodiments of the present disclosure will be described with reference to the drawings.

[0013] <First embodiment> [System configuration] Fig. 1 is a diagram showing a schematic configuration of an information processing system including a server device according to First embodiment. As shown in Fig. 1, the information processing system 1 includes a server device 100 and a terminal device 200.

[0014] The server device 100 is configured to be able to communicate with the terminal device 200. The server device 100 performs an analysis using a learning model described below to obtain, for example, an analysis result relating to defects that may occur due to a product design change and / or a process change. The server device 100 also generates display information for showing the analysis result together with the basis thereof, and outputs the generated display information to the terminal device 200.

[0015] The terminal device 200 has a function of communicating with the server device 100, a function of inputting information to be transmitted to the server device 100, and a function of displaying information received from the server device 100. The terminal device 200 also has a function of displaying information in response to a user operation. Specifically, the terminal device 200 may be configured by a device such as a personal computer, a smartphone, or a tablet computer, for example.

[0016] [Hardware Configuration] Fig. 2 is a block diagram showing the hardware configuration of the server device according to the first embodiment. As shown in Fig. 2, the server device 100 has an interface (IF) 111, a processor 112, a memory 113, a recording medium 114, and a database (DB) 115.

[0017] The IF 111 inputs and outputs data to and from external devices. For example, information transmitted from the terminal device 200 is input to the server device 100 via the IF 111.

[0018] The processor 112 is a computer such as a CPU (Central Processing Unit), and executes a prepared program to control the entire server device 100. Specifically, the processor 112 performs analysis using, for example, a learning model described below.

[0019] The memory 113 is configured by a ROM (Read Only Memory), a RAM (Random Access Memory), etc. The memory 113 is also used as a working memory while the processor 112 is executing various processes.

[0020] The recording medium 114 is a non-volatile, non-transitory recording medium such as a disk-shaped recording medium or a semiconductor memory, and is configured to be detachable from the server device 100. The recording medium 114 records various programs to be executed by the processor 112. When the server device 100 executes various processes, the programs recorded on the recording medium 114 are loaded into the memory 113 and executed by the processor 112.

[0021] The DB 115 stores, for example, information input via the IF 111 and processing results obtained by processing by the processor 112 .

[0022] [Functional Configuration] Fig. 3 is a block diagram showing the functional configuration of the server device according to the first embodiment. As shown in Fig. 3, the server device 100 has an analysis data storage unit 11, a graph data generation unit 12, a graph data storage unit 13, a learning processing unit 14, a processing result storage unit 15, a query generation unit 16, an analysis processing unit 17, and a display information generation unit 18.

[0023] The analytical data storage unit 11 stores part data ADP, process data ADK, and dictionary data JD.

[0024] The parts data ADP includes data related to multiple parts used in manufacturing one product. Specifically, the parts data ADP includes, for example, data indicating the attributes of each of multiple parts used in manufacturing product Y, which corresponds to a finished product, and the relationships between the multiple parts. The attributes of the parts may include, for example, the shape, material, and size of the parts. The relationships between the parts may include, for example, the hierarchical relationships between multiple parts when product Y is at the top. Note that the hierarchical relationships mentioned above may also be interpreted as parent-child relationships.

[0025] The process data ADK includes data related to multiple processes in a manufacturing process for one product. Specifically, the process data ADK includes, for example, data indicating the attributes of each of multiple processes in a manufacturing process for product Y, which is a finished product, and the relationships between the multiple processes. The process attributes may include, for example, the content of the work and the entity (person or device) performing the work. The relationships between the processes may include, for example, the order of the multiple processes in the manufacturing process for product Y.

[0026] The dictionary data JD includes data indicating rules to be referenced when the graph data generating unit 12 generates graph data and when the query generating unit 16 generates queries.

[0027] The graph data generation unit 12 generates graph data GD based on the part data ADP, process data ADK, and dictionary data JD read from the analysis data storage unit 11, and stores the generated graph data GD in the graph data storage unit 13. The graph data generation unit 12 also has a data conversion unit 12A and an integration processing unit 12B.

[0028] The data conversion unit 12A functions as a subgraph data generating means. The data conversion unit 12A converts the part data ADP into the subgraph data GDP based on the rules indicated by the dictionary data JD. The data conversion unit 12A also converts the process data ADK into the subgraph data GDK based on the rules indicated by the dictionary data JD.

[0029] The integration processing unit 12B functions as an integration unit. The integration processing unit 12B also generates graph data GD by integrating the partial graph data GDP and GDK, and stores the generated graph data GD in the graph data storage unit 13.

[0030] The graph data storage unit 13 stores the graph data GD generated by the graph data generation unit 12 .

[0031] The learning processing unit 14 functions as a learning means. The learning processing unit 14 acquires, as relationship data KD, data indicating known relationships between linked nodes in the graph data GD read from the graph data storage unit 13. The relationship data KD includes data indicating relationships between multiple nodes as universal rules by replacing each of multiple nodes having the same relationship with a variable node. The learning processing unit 14 also uses the graph data GD and the relationship data KD to train a learning model GMD so as to derive unknown relationships between unlinked nodes in the graph data GD. The learning processing unit 14 also stores the relationship data KD and the trained learning model GMD in the processing result storage unit 15.

[0032] The processing result storage unit 15 stores the relationship data KD and the learned learning model GMD as data obtained by the processing of the learning processing unit 14.

[0033] The query generation unit 16 functions as a query generation means. The query generation unit 16 generates a query according to the analysis purpose input from the terminal device 200 based on the rules indicated by the dictionary data JD. The analysis purpose may be set as something related to the manufacture of product Y, for example.

[0034] The analysis processing unit 17 functions as an analysis unit. The analysis processing unit 17 uses the trained learning model GMD read from the processing result storage unit 15 to analyze the query generated by the query generation unit 16. The analysis processing unit 17 outputs the analysis results obtained by the above-mentioned analysis to the display information generation unit 18.

[0035] The display information generation unit 18 functions as a display information generating means. The display information generation unit 18 extracts rules that serve as the basis for the analysis results obtained by the analysis processing unit 17 from among the rules included in the relationship data KD read from the processing result storage unit 15. The display information generation unit 18 identifies a portion of the graph data GD read from the graph data storage unit 13 where the extracted rules hold, and acquires example data ED corresponding to the data of the identified portion. The display information generation unit 18 generates display information HJ based on the analysis results obtained by the analysis processing unit 17 and the example data ED, and outputs the generated display information HJ to the terminal device 200.

[0036] [Specific Example] Next, a specific example of the processing performed in each unit of the server device 100 will be described.

[0037] The dictionary data JD contains rules for converting input data in various formats, such as a table format, into records in a predetermined format. According to these rules, for example, input data can be converted into records RA to RD as shown in Figure 4. Figure 4 shows examples of records obtained by converting input data using the dictionary data.

[0038] For each of records RA to RD, the leftmost element (the character strings "Jacket," "T-Shirt," and "Tom") represents the link source node in the graph, and the rightmost element (the character strings "clothes" and "Male") represents the link destination node in the graph. Furthermore, for each of records RA to RD, the central element (the character strings "is_category," "is_gender," and "interested_for") represents the relationship between the linked nodes in the graph. Furthermore, for each of records RA to RD, the order of each element (each character string) represents the link direction (the direction of the arrow in the graph) when linking nodes in the graph. Therefore, records RA to RD in FIG. 4 can be treated as the data that forms the basis of graph data GDX as shown in FIG. 5. Note that the arrows linking nodes in the graph may be interpreted as edges. FIG. 5 is a diagram showing graph data generated based on each record in FIG.

[0039] The data conversion unit 12A converts the part data ADP based on the rules described in the specific examples above while referring to the dictionary data JD, thereby generating subgraph data GDP including multiple records corresponding to the part data ADP. The data conversion unit 12A also converts the process data ADK based on the rules described in the specific examples above while referring to the dictionary data JD, thereby generating subgraph data GDK including multiple records corresponding to the process data ADK. That is, the data conversion unit 12A generates first subgraph data corresponding to the part data and second subgraph data corresponding to the process data. The data conversion unit 12A also generates the first subgraph data and the second subgraph data by converting each of the part data and the process data into data in a predetermined format that represents four elements: a link source node, a link destination node, a relationship between the nodes, and a link direction when linking the nodes.

[0040] The integration processing unit 12B generates graph data GD by integrating the subgraph data GDP and GDK, and stores the generated graph data GD in the graph data storage unit 13. Specifically, the integration processing unit 12B generates graph data GD as shown in Fig. 6 by integrating the subgraph data GDP and GDK based on common elements contained in the subgraph data GDP and GDK, and stores the generated graph data GD in the graph data storage unit 13. Fig. 6 is a diagram showing an example of graph data obtained by processing by the server device according to the first embodiment.

[0041] The graph data GD of FIG. 6 is generated when, for example, the parts data ADP that formed the basis of the subgraph data GDP contains data when a part P used in the manufacture of product Y is updated to a part Q, and the parts data ADK that formed the basis of the subgraph data GDK contains data when a process K in the manufacture of product Y is updated to a process L. The graph data GD of FIG. 6 includes a "part P" node, a "part Q" node, a "process K" node, and a "part L" node. The graph data GD of FIG. 6 also includes a "part type" node indicating that parts P and Q are the same type of parts. The graph data GD of FIG. 6 also includes a "process attribute" node indicating that processes K and L have the same attribute. The graph data GD of FIG. 6 also includes a "design change ID" node indicating a design change from part P to part Q and a "process change ID" node indicating a process change from process K to process L. The graph data GD of FIG. 6 also includes a "Defect DX" node representing a common defect that occurred when part R used in the manufacture of product Y was replaced with part Q, and when process M included in the manufacturing process of product Y was replaced with process K. Note that in this specific example, for simplicity, the illustration of nodes corresponding to the aforementioned part R and process M is omitted. The graph data GD of FIG. 6 also includes a "Superordinate Component" node representing a component that is superior to part P, and a "Subordinate Component" node representing a component that is subordinate to part P. The graph data GD of FIG. 6 also includes a "Previous Process" node representing a process immediately preceding process L, and a "Next Process" node representing a process immediately following process L. The arrow pointing from the "Part Q" node to the "Process K" node in the graph data GD of FIG. 6 indicates that process K is performed using part Q.

[0042] The learning processing unit 14 acquires relationship data KD indicating known relationships between linked nodes in the graph data GD read from the graph data storage unit 13 .

[0043] The relationship data KD includes data indicating relationships corresponding to each of the multiple arrows in the graph data GD of Fig. 6. Specifically, the relationship data KD includes, for example, data indicating a rule that a defect DX occurs when a part used in manufacturing a product Y is changed to a part Q, and data indicating a rule that the defect DX occurs when a manufacturing process for the product Y includes a process having the same attribute as a process K.

[0044] The learning processing unit 14 learns the learning model GMD constructed based on, for example, "KBLRN" by inputting feature values ​​corresponding to each node included in the graph data GD and feature values ​​corresponding to each relationship included in the relationship data KD to the learning model GMD. The learning processing unit 14 also stores the relationship data KD and the learned learning model GMD in the processing result storage unit 15.

[0045] The aforementioned "KBLRN" is disclosed, for example, in "KBLRN: End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features" by Alberto Garcia-Duran et al. Furthermore, the learning model GMD may be constructed based on a model other than "KBLRN" as long as it has a configuration capable of performing link prediction for graph data. Furthermore, when the learning processing unit 14 learns the learning model GMD using the graph data GD and the relationship data KD, it is desirable to perform zero-shot learning, such as that disclosed in Japanese Patent Application Laid-Open No. 2019-125364. Furthermore, according to this specific example, for example, each time at least one of the part data ADP and the process data ADK is updated, the graph data GD may be updated and the learning model GMD may be re-learned using the updated graph data GD.

[0046] Based on the rules indicated by the dictionary data JD, the query generation unit 16 generates a query corresponding to the analysis purpose input from the terminal device 200. For example, the query generation unit 16 generates a record in which a link source node, a relationship between the nodes, and a link direction are represented, but the link destination node is unknown. For example, when an analysis purpose such as "predicting defects that may occur when a design change from part P to part Q is made in the manufacture of product Y" is input, the query generation unit 16 generates a query QA corresponding to the analysis purpose. For example, the query QA is generated as a record in which a character string representing "design change from part P to part Q" corresponding to the link source node and a character string representing "potential defects" corresponding to the relationship between the nodes are present, and the word order of "design change from part P to part Q" and "potential defects" corresponding to the link direction is clear, but no character or character string representing the link destination node is present. Furthermore, when an analysis purpose such as "predicting defects that may occur when a process change from process K to process L is made in the manufacturing process of product Y" is input, the query generation unit 16 generates a query QB corresponding to the analysis purpose. The query QB is generated as a record in which, for example, there is a character string representing a "process change from process K to process L" corresponding to the link source node and a character string representing a "potential defect" corresponding to the relationship between the nodes, and the word order of "process change from process K to process L" and "potential defect" corresponding to the link direction is clear, but there is no character or character string representing the link destination node. Note that the query generation unit 16 may generate a record in which one of the four elements of the link source node, the link destination node, the relationship between the nodes, and the link direction is unknown, as a query according to the analysis purpose input from the terminal device 200.

[0047] The analysis processing unit 17 uses the trained learning model GMD read from the processing result storage unit 15 to analyze the query generated by the query generation unit 16. Furthermore, the analysis processing unit 17 uses the trained learning model GMD to analyze the query generated by the query generation unit 16, thereby obtaining an analysis result corresponding to one unknown element in the query. Furthermore, the analysis processing unit 17 outputs the analysis result obtained by the above-mentioned analysis to the display information generation unit 18.

[0048] For example, the analysis processing unit 17 uses the trained learning model GMD to perform an analysis to predict a link destination node in the record of the query QA. According to this analysis, the analysis processing unit 17 can obtain an analysis result corresponding to the dashed arrow pointing from the "design change ID" node to the "defect DX" node in the graph data GD, as shown in FIG. 7 . In other words, by using the trained learning model GMD to perform an analysis related to the query QA, the analysis processing unit 17 can obtain an analysis result indicating that defect DX may occur when part P used in the manufacture of product Y is changed to part Q. FIG. 7 is a diagram for explaining the analysis result obtained by processing by the server device according to the first embodiment.

[0049] Furthermore, for example, the analysis processing unit 17 uses the trained learning model GMD to perform an analysis to predict a linked node in the record of the query QB. According to this analysis, the analysis processing unit 17 can obtain an analysis result corresponding to the dashed arrow pointing from the "process change ID" node to the "defect DX" node in the graph data GD, as shown in FIG. 7 . In other words, by using the trained learning model GMD to perform an analysis related to the query QB, the analysis processing unit 17 can obtain an analysis result indicating that defect DX may occur when process K included in the manufacturing process of product Y is changed to process L.

[0050] For example, when graph data GD such as that shown in Fig. 6 is stored in the graph data storage unit 13 and an analysis result corresponding to a query QA is obtained by the analysis processing unit 17, the display information generation unit 18 acquires example data EDA such as that shown in Fig. 8. Fig. 8 is a diagram showing example data obtained by processing of the server device according to the first embodiment.

[0051] The example data EDA in Figure 8 corresponds to the part of the data in the graph data GD in Figure 6 that corresponds to the rule indicated by the relationship data KD read from the processing result storage unit 15 and is highly relevant to the analysis result corresponding to the query QA.

[0052] The display information generation unit 18 generates, for example, display information HJA as shown in Fig. 9 based on the example data EDA and the analysis result corresponding to the query QA, and outputs the generated display information HJA to the terminal device 200. Fig. 9 is a diagram showing display information generated by processing of the server device according to the first embodiment.

[0053] The display information HJA in FIG. 9 is configured as a graph including a "Part Q" node, a "Design Change P → Q" node, a "Design Change R → Q" node, and a "Defect DX" node. The nodes included in the display information HJA in FIG. 9 are linked by arrows indicating the relationships between the nodes. The "Design Change P → Q" node indicates the analysis objective that formed the basis of the query QA and indicates the design change from part P to part Q in product Y. The dashed arrow pointing from the "Design Change P → Q" node to the "Defect DX" node indicates the analysis result corresponding to the query QA and indicates that the design change from part P to part Q may result in defect DX. The "Design Change R → Q" node indicates the design change from part R to part Q in product Y that occurred before the design change from part P to part Q in product Y. The solid arrow pointing from the "Design Change R → Q" node to the "Defect DX" node indicates that defect DX occurred in the past due to the design change from part R to part Q in product Y. 9 , the display information HJA can present a prediction that defect DX may occur even when part P in product Y is changed to part Q, while showing, as evidence, a past case in which defect DX occurred when part R in product Y was changed to part Q. That is, the display information generation unit 18 can generate display information HJA for showing the analysis results together with the evidence, based on the actual case data EDA and the analysis results corresponding to the query QA. Furthermore, the display information generation unit 18 can generate display information for showing, as an analysis result, that defect DX may occur due to a second design change (changing part R to part Q) that was made before a first design change (changing part P to part Q) corresponding to the analysis objective, while showing, as evidence, that defect DX occurred due to the first design change.

[0054] According to this specific example, the display information generation unit 18 may generate display information including a comment such as, for example, "If part P is changed to part Q, there is a possibility that defect DX will occur."

[0055] Furthermore, the display information generation unit 18 can generate display information HJB for showing the analysis result corresponding to the query QB together with the evidence by performing a process similar to the process for generating the display information HJA described above. The display information HJB can show, as evidence, a past case in which defect DX occurred as a result of changing process M to process K in the manufacturing process of product Y, and can predict that defect DX may occur even if process K in the manufacturing process of product Y is changed to process L. In other words, the display information generation unit 18 can generate display information for showing, as an analysis result, that defect DX may occur due to a first process change corresponding to the analysis objective (changing process K to process L), while showing, as evidence, that defect DX occurred due to a second process change (changing process M to process K).

[0056] [Processing Flow] Next, a description will be given of the flow of processing performed in the server device according to the first embodiment. Fig. 10 is a flowchart showing an example of processing performed in the server device according to the first embodiment.

[0057] First, the server device 100 generates partial graph data corresponding to each of the part data and process data stored in the analysis data storage unit 11 (step S11).

[0058] Next, the server device 100 generates graph data by integrating the subgraph data generated in step S11 (step S12).

[0059] Next, the server device 100 performs learning of the learning model using the graph data generated in step S12 and relationship data indicating known relationships between linked nodes in the graph data (step S13).

[0060] Next, the server device 100 generates a query according to the analysis purpose input from the terminal device 200 (step S14).

[0061] Next, the server device 100 uses the trained learning model obtained in step S13 to analyze the query generated in step S14 (step S15).

[0062] Next, the server device 100 generates display information for showing the analysis results obtained in step S15 together with the reasons for the results (step S16).

[0063] Next, the server device 100 outputs the display information generated in step S16 to the terminal device 200 (step S17).

[0064] As described above, according to this embodiment, first subgraph data corresponding to component data and second subgraph data corresponding to process data are generated, and the learning model is trained using graph data obtained by integrating the first subgraph data and the second subgraph data. Furthermore, according to this embodiment, the learned learning model can be used to perform an analysis according to an analysis objective related to product manufacturing. Furthermore, according to this embodiment, the analysis results obtained by the analysis can be presented to a user along with the rationale for the analysis. Specifically, according to this embodiment, for example, analysis results predicting defects that may occur due to relationships between components and / or relationships between processes can be presented to a user along with the rationale for the analysis. Therefore, according to this embodiment, the occurrence of defects indicated in the analysis results can be prevented, thereby improving product productivity. Furthermore, according to this embodiment, the rationale for the defects indicated in the analysis results can be understood, thereby enabling appropriate countermeasures to be developed to prevent the occurrence of the defects. Furthermore, according to this embodiment, analysis using the learning model can be performed to obtain highly accurate analysis results according to the analysis objective.

[0065] Second Embodiment FIG. 11 is a block diagram showing the functional configuration of an information processing apparatus according to a second embodiment.

[0066] The information processing device 500 according to this embodiment has the same hardware configuration as the server device 100. The information processing device 500 also has a subgraph data generation unit 511, an integration unit 512, a learning unit 513, an analysis unit 514, and a display information generation unit 515.

[0067] FIG. 12 is a flowchart for explaining the processing performed in the information processing apparatus according to the second embodiment.

[0068] The subgraph data generation means 511 generates first subgraph data corresponding to part data including data relating to multiple parts used in manufacturing a single product, and second subgraph data corresponding to process data including data relating to multiple processes in the manufacturing process of the single product (step S51).

[0069] The integrating means 512 generates graph data by integrating the first subgraph data and the second subgraph data (step S52).

[0070] The learning means 513 learns the learning model 600 using the graph data and relationship data that indicates known relationships between linked nodes in the graph data (step S53).

[0071] The analysis means 514 uses the learned learning model 600 to perform an analysis according to an analysis purpose related to the manufacturing of one product (step S54).

[0072] The display information generating means 515 generates display information for showing the analysis results obtained by the analysis together with the reasons for the results (step S55).

[0073] According to this embodiment, it is possible to improve the productivity of products.

[0074] A part or all of the above-described embodiments can be described as, but not limited to, the following supplementary notes.

[0075] (Supplementary Note 1) An information processing device having: a subgraph data generation means for generating first subgraph data corresponding to part data including data relating to a plurality of parts used in the manufacture of a single product, and second subgraph data corresponding to process data including data relating to a plurality of processes in a manufacturing process of the single product; an integration means for generating graph data by integrating the first subgraph data and the second subgraph data; a learning means for training a learning model using the graph data and relationship data which is data indicating known relationships between linked nodes in the graph data; an analysis means for performing an analysis according to an analysis purpose related to the manufacture of the single product using the trained learning model; and a display information generation means for generating display information for showing analysis results obtained by the analysis together with reasons for the analysis.

[0076] (Supplementary Note 2) The information processing device of Supplementary Note 1, wherein the subgraph data generation means generates the first subgraph data and the second subgraph data by converting each of the part data and the process data into data in a predetermined format that represents four elements: a link source node, a link destination node, a relationship between the nodes, and a link direction when linking the nodes.

[0077] (Supplementary Note 3) The information processing device of Supplementary Note 2 further comprises a query generation means for generating data in which one of the four elements is unknown as a query according to the analysis purpose, wherein the analysis means uses the learning model that has undergone the learning to perform an analysis related to the query, thereby obtaining an analysis result corresponding to the one element.

[0078] (Supplementary Note 4) The information processing device of Supplementary Note 1, wherein the integration means generates the graph data by integrating the plurality of subgraph data based on a common element contained in the plurality of subgraph data.

[0079] (Supplementary Note 5) The information processing device of Supplementary Note 1, wherein the learning means learns the learning model so that unknown relationships between unlinked nodes in the graph data are derived.

[0080] (Supplementary Note 6) The information processing device of Supplementary Note 1, wherein the analysis means uses the learning model that has undergone the learning to perform an analysis to predict a defect that may occur when a first design change is made to the one product, and the display information generation means generates display information as the analysis result to show that the one defect may occur due to the first design change, while showing as the basis that the one defect occurred due to a second design change made before the first design change.

[0081] (Supplementary Note 7) The information processing device of Supplementary Note 1, wherein the analysis means uses the learning model that has undergone the learning to perform an analysis to predict a defect that may occur when a first process change is made to the one product, and the display information generation means generates display information as the analysis result that the one defect may occur due to the first process change, while indicating as the basis that the one defect occurred due to a second process change made before the first process change.

[0082] (Supplementary Note 8) An information processing method comprising: generating first partial graph data corresponding to part data including data relating to a plurality of parts used in the manufacture of a single product; and second partial graph data corresponding to process data including data relating to a plurality of processes in a manufacturing process of the single product; generating graph data by integrating the first partial graph data and the second partial graph data; training a learning model using the graph data and relationship data which is data indicating known relationships between linked nodes in the graph data; using the trained learning model to perform an analysis according to an analysis purpose related to the manufacture of the single product; and generating display information to show the analysis results obtained by the analysis together with reasons for the analysis.

[0083] (Supplementary Note 9) A recording medium having recorded thereon a program that causes a computer to execute the following processes: generate first partial graph data corresponding to part data including data related to multiple parts used in the manufacture of a single product, and second partial graph data corresponding to process data including data related to multiple processes in a manufacturing process of the single product; generate graph data by integrating the first partial graph data and the second partial graph data; train a learning model using the graph data and relationship data that is data indicating known relationships between linked nodes in the graph data; use the trained learning model to perform an analysis according to an analysis purpose related to the manufacture of the single product; and generate display information to show the analysis results obtained by the analysis together with reasons.

[0084] Although the present disclosure has been described above with reference to the embodiments, the present disclosure is not limited to the above-described embodiments and examples. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure.

[0085] 12 Graph data generation unit 12A Data conversion unit 12B Integration processing unit 14 Learning processing unit 16 Query generation unit 17 Analysis processing unit 18 Display information generation unit 100 Server device

Claims

1. a subgraph data generating means for generating first subgraph data corresponding to part data including data relating to a plurality of parts used in the manufacture of one product, and second subgraph data corresponding to process data including data relating to a plurality of processes in a manufacturing process of the one product; an integration means for generating graph data by integrating the first subgraph data and the second subgraph data; a learning means for learning a learning model using the graph data and relationship data that indicates known relationships between linked nodes in the graph data; an analysis means for performing an analysis according to an analysis purpose related to the manufacturing of the one product using the learning model that has undergone the learning; a display information generating means for generating display information for displaying the analysis results obtained by the analysis together with the reasons for the results; An information processing device having the above.

2. 2. The information processing device according to claim 1, wherein the subgraph data generation means generates the first subgraph data and the second subgraph data by converting each of the part data and the process data into data in a predetermined format that represents four elements: a link source node, a link destination node, a relationship between the nodes, and a link direction when linking the nodes.

3. further comprising a query generation means for generating data in which one of the four elements is unknown as a query according to the analysis purpose; The information processing device according to claim 2 , wherein the analysis means acquires an analysis result corresponding to the one element by performing an analysis related to the query using the learning model that has undergone the learning.

4. The information processing apparatus according to claim 1 , wherein the integrating means generates the graph data by integrating the plurality of subgraph data based on a common element contained in the plurality of subgraph data.

5. The information processing apparatus according to claim 1 , wherein the learning means performs learning of the learning model so that unknown relationships between nodes that are not linked in the graph data are derived.

6. the analysis means performs an analysis using the learning model that has undergone the learning to predict a defect that may occur when a first design change is made to the one product; 2. The information processing device according to claim 1, wherein the display information generating means generates display information for indicating, as the basis, that a certain defect occurred due to a second design change made before the first design change, and for indicating, as the analysis result, that the certain defect may occur due to the first design change.

7. the analysis means performs an analysis using the learning model that has undergone the learning to predict a defect that may occur when a first process change is made to the one product; 2. The information processing device according to claim 1, wherein the display information generating means generates display information for indicating, as the basis, that a defect occurred due to a second process change that was made before the first process change, and for indicating, as the analysis result, that the defect may occur due to the first process change.

8. A computer-implemented information processing method, comprising: generating first subgraph data corresponding to part data including data relating to a plurality of parts used in manufacturing one product, and second subgraph data corresponding to process data including data relating to a plurality of processes in a manufacturing process of the one product; generating graph data by integrating the first subgraph data and the second subgraph data; learning a learning model using the graph data and relationship data that is data indicating known relationships between linked nodes in the graph data; performing an analysis according to an analysis purpose related to the manufacturing of the one product using the learning model that has undergone the learning; An information processing method for generating display information for showing the analysis results obtained by the analysis together with the reasons for the results.

9. generating first subgraph data corresponding to part data including data relating to a plurality of parts used in manufacturing one product, and second subgraph data corresponding to process data including data relating to a plurality of processes in a manufacturing process of the one product; generating graph data by integrating the first subgraph data and the second subgraph data; learning a learning model using the graph data and relationship data that is data indicating known relationships between linked nodes in the graph data; performing an analysis according to an analysis purpose related to the manufacturing of the one product using the learning model that has undergone the learning; A program that causes a computer to execute a process of generating display information for showing the analysis results obtained by the analysis together with the reasons for the results.