Similarity calculation device and similarity calculation system

Abstract

Effective maintenance of equipment is performed. A similarity calculation device comprises: a synthesis unit that synthesizes first and second information groups to generate a synthesized information group; and a similarity calculation unit that calculates the similarity between a comparison source node and a comparison destination node in the synthesized information group. The first and second information groups each include a node and attribute information indicating an attribute of the node. The synthesis unit synthesizes the attribute information in the first and second information groups by associating identical nodes in the first and second information groups. The similarity calculation unit calculates the similarity by comparing the attribute information associated with the comparison source node and the attribute information associated with the comparison destination node.

Description

Similarity calculation device and similarity calculation system

[0001] The technology disclosed in this specification relates to a technology for calculating similarity between nodes.

[0002] In the development of equipment or facilities such as plants, control logic diagrams, which describe operation plans that define the operation specifications of the equipment using flowcharts and logical operators, are widely used.

[0003] International Publication No. 2017 / 158926

[0004] Maintenance (repair, inspection, servicing, etc.) of equipment or facilities in a plant or the like is carried out, for example, taking into consideration the deterioration tendency estimated based on an operation plan for the equipment.

[0005] However, the deterioration tendency of the above-mentioned devices or facilities varies not only depending on the attribute information of the devices or facilities themselves (e.g., years since they began use, model number, specifications, etc.), but also on the attribute information of the facility to which the devices or facilities belong, so effective maintenance may not be possible.

[0006] The technology disclosed in this specification has been made in consideration of the problems described above, and is a technology for performing effective maintenance of equipment.

[0007] A similarity calculation device, which is a first aspect of the technology disclosed in the present specification, comprises a synthesis unit for synthesizing a first information group and a second information group different from the first information group to generate at least one synthesized information group, and a similarity calculation unit for calculating the similarity between a comparison source node and a comparison target node in the synthesized information group, wherein the first information group and the second information group each include a node and attribute information indicating an attribute of the node, the synthesis unit synthesizes the attribute information in the first information group and the second information group by matching identical nodes in the first information group and the second information group, and the similarity calculation unit calculates the similarity by comparing the attribute information associated with the comparison source node with the attribute information associated with the comparison target node.

[0008] According to at least a first aspect of the technology disclosed herein, the accuracy of the calculation of the similarity between nodes can be improved by calculating the similarity between nodes using the combined attribute information, and thus information that can be used for maintenance, etc. can be obtained from the attribute information associated with the comparison node.

[0009] Furthermore, objects, features, aspects, and advantages associated with the technology disclosed herein will become more apparent from the detailed description and accompanying drawings set forth below.

[0010] FIG. 1 is a diagram conceptually illustrating an example of the configuration of a similarity calculation system related to an embodiment. FIG. 2 is a diagram illustrating an example of correspondence information between different systems. FIG. 3 is a diagram illustrating an example of merging of attribute information in combined graph data. FIG. 4 is a diagram illustrating an example of calculating similarity between nodes in two combined graph data. FIG. 5 is a diagram illustrating an example of calculating similarity between related nodes. FIG. 6 is a diagram illustrating an example of a similarity list of each comparison target node with respect to a comparison source node for which similarity has been calculated. FIG. 7 is a diagram illustrating a schematic example of a hardware configuration when the similarity calculation device shown in FIG. 1 is actually operated. FIG. 8 is a diagram illustrating a schematic example of a hardware configuration when the similarity calculation device shown in FIG. 1 is actually operated.

[0011] Hereinafter, embodiments will be described with reference to the accompanying drawings. In the following embodiments, detailed features are shown for the purpose of explaining the technology, but these are merely examples and are not necessarily essential features for enabling the embodiments to be implemented.

[0012] The drawings are schematic, and for the sake of convenience, components may be omitted or simplified as appropriate. The relative sizes and positions of components shown in different drawings are not necessarily accurately depicted and may be changed as appropriate. Hatching may also be used in drawings such as plan views that are not cross-sectional views to facilitate understanding of the embodiments.

[0013] In the following description, the same components are denoted by the same reference numerals, and their names and functions are also the same. Therefore, detailed descriptions of them may be omitted to avoid duplication.

[0014] Furthermore, in the description given in this specification, when a certain component is described as "comprising," "including," or "having," unless otherwise specified, this is not an exclusive expression that excludes the presence of other components.

[0015] Furthermore, in the description of this specification, even if ordinal numbers such as "first" or "second" are used, these terms are used for convenience to make it easier to understand the contents of the embodiments, and the contents of the embodiments are not limited to the order that may result from these ordinal numbers.

[0016] <Embodiment> A similarity calculation device and a similarity calculation system according to the present embodiment will be described below.

[0017] <Configuration of Similarity Calculation Apparatus> FIG. 1 is a diagram conceptually showing an example of the configuration of a similarity calculation system according to this embodiment.

[0018] 1, the similarity calculation system 1 includes a similarity calculation device 10 that calculates the similarity between nodes. The similarity calculation system 1 also includes an input device 20 that provides input to the similarity calculation device 10, and a display device 30 that displays output from the similarity calculation device 10. The similarity calculation system 1 can also include a storage device 40 and an attribute information storage device 50 that store data referenced by the similarity calculation device 10.

[0019] The similarity calculation device 10 includes an attribute information synthesis unit 12 , a similarity calculation unit 14 , a calculation parameter setting unit 17 , and a similarity list display unit 18 .

[0020] The attribute information synthesis unit 12 generates synthesized graph data by associating identical nodes in multiple graph data and synthesizes attribute information associated with the nodes. Here, attribute information is information belonging to a node (or edge) in the graph data. A node describes an object, event, person, etc., such as equipment. If the node is equipment, the information indicates the manufacturer, specifications, measurement data, output, output method, operating method, health status, power consumption, etc., and if the node is a location, the information indicates weather conditions including temperature, etc. The attribute information synthesis unit 12 includes an identical node determination unit 13, which determines the identity between nodes in multiple graph data based on heterogeneous system correspondence information 40A described below.

[0021] The similarity calculation unit 14 calculates the similarity between nodes based on their attribute information. The similarity calculation unit 14 includes an attribute information similarity calculation unit 15, which compares attribute information between target nodes. The similarity calculation unit 14 also includes an associated node extraction unit 16, which extracts associated nodes to be taken into consideration when comparing target nodes.

[0022] The calculation parameter setting unit 17 sets calculation parameters required for calculating the similarity between nodes in the similarity calculation unit 14. The calculation parameters may be set in advance or may be input by a user or the like via the input device 20.

[0023] The similarity list display unit 18 displays a similarity list, which is a list showing the similarities between nodes. The similarity list may be further displayed on the display device 30.

[0024] The storage device 40 stores heterogeneous system correspondence information 40A. The attribute information storage device 50 stores attribute information for a plurality of system types (system type A attribute information 50A, system type B attribute information 50B, system type C attribute information 50C, and system type C attribute information 50D). Note that the number of system type attribute information items stored is not limited to the number shown in FIG. 1.

[0025] In this embodiment, attribute information belongs to nodes (or edges) in graph data. Graph data is composed of nodes and edges that indicate relationships between the nodes, and attribute information (properties) belongs to the nodes and edges. Graph data is stored, for example, in a graph database. Note that the attribute information is not limited to attribute information belonging to nodes and edges in graph data, but may also be attribute information belonging to information groups (databases) in other formats.

[0026] 2 and 3 are diagrams showing an example of the heterogeneous system correspondence information 40 A. As shown in the examples of Fig. 2 and Fig. 3, the heterogeneous system correspondence information 40 A describes the definitions (such as names) of the same node in different systems in association with each other.

[0027] In the example of Figure 2, "facility A" in the inspection support system corresponds to the same object (node) as "facility 1" in the monitoring and control system. Similarly, "equipment A" in the inspection support system corresponds to the same object (node) as "equipment 1" in the monitoring and control system. Similarly, "equipment C" in the inspection support system corresponds to the same object (node) as "equipment 2" in the monitoring and control system.

[0028] In the example of Figure 3, "Hyogo" in the weather information system includes "A Treatment Plant" in the inspection support system (the association is "include"). Similarly, "Osaka" in the weather information system includes "B Building" in the inspection support system (the association is "include").

[0029] The same node determination unit 13 in Fig. 1 determines the identity between nodes in multiple graph data based on the heterogeneous system correspondence information 40A. Then, for nodes determined to be identical as shown in Fig. 2, the attribute information synthesis unit 12 overlaps the nodes on the graph data to create synthesized graph data and adds up the attribute information. Furthermore, for nodes determined to have a relationship as shown in Fig. 3, the nodes are connected by edges to create synthesized graph data.

[0030] Fig. 4 is a diagram showing an example of merging of attribute information in merged graph data. The merged graph data shown in Fig. 4 is generated by overlapping nodes determined to be identical by identical node determination unit 13 on the graph data, and is generated by merging system type A attribute information 50A, system type B attribute information 50B, system type C attribute information 50C, and system type C attribute information 50D stored in attribute information storage device 50. The attribute information belonging to the same node in each of the multiple graph data is added together as the attribute information to which the node belongs in the merged graph data.

[0031] <Regarding Calculation of Similarity> Fig. 5 is a diagram showing an example of calculating the similarity between nodes in two combined graph data. In Fig. 5, each of the nodes to be compared is a node in the combined graph data, but only one of the nodes to be compared may be a node in the combined graph data, and the other may be a node in graph data that has not been combined.

[0032] 5, a node 100 representing device A is compared with a node 102 representing device B to calculate the similarity between them. The nodes 100 and 102 represent different devices and are not the same node.

[0033] In order to calculate the similarity between the node 100 (comparison source node) and the node 102 (comparison destination node), the attribute information similarity calculation unit 15 compares the attribute information belonging to both nodes.

[0034] For example, if the node is a device, the similarity can be calculated as 1 if the character strings of the information shown as the manufacturer, output, and output method are the same, 0 if they are different or there is no corresponding information, 1 if the numerical values ​​of the information shown are the same, and 0 if they are different or there is no corresponding information, and the value obtained by adding these numerical values ​​together can be calculated as the similarity. Note that instead of 1 or 0, the similarity of the character strings or numerical values ​​can be calculated as a value between 0 and 1 and taken into account in the above similarity.

[0035] Furthermore, for example, the commonality of the types of nodes connected via edges between the comparison source node and the comparison target node may be considered as attribute information. The similarity may be calculated by adding up the values ​​of 1 if the types of directly connected nodes are the same, 0.5 if the types of nodes indirectly connected via one node are the same, and 0.25 if the types of nodes indirectly connected via two nodes are the same.

[0036] As described above, the attribute information similarity calculation unit 15 calculates the similarity between the node 100 and the node 102, thereby enabling the calculation of the similarity between the node 100 and the node 102. Here, at least one of the node 100 and the node 102 is a node in the combined graph data, and the attribute information to which it belongs is combined, thereby improving the accuracy of the similarity calculation. By calculating the similarity between nodes with high accuracy, it is possible to perform effective maintenance (repairs, inspections, servicing, etc.) that takes into account, for example, deterioration trends, based on the attribute information of similar nodes.

[0037] In order to accurately calculate the similarity between node 100 (source node) and node 102 (target node), the attribute information similarity calculation unit 15 can further compare attribute information belonging to related nodes associated with the source node (or target node).

[0038] The related node extraction unit extracts nodes connected via edges to node 100 (comparison source node) and node 102 (comparison target node) as related nodes. The attribute information similarity calculation unit 15 then compares the attribute information of the extracted related nodes to calculate the related similarity.

[0039] Here, the calculation parameter setting unit 17 can set weighting (parameters) when taking into account the relation similarity between node 100 (comparison source node) and node 102 (comparison target node). A specific weight may be set by a user or the like for a specific node (or a specific related node) and its attribute information, or the weight may be set so that it decreases according to the distance from the comparison source node or the comparison target node (the number of edges and nodes passed through). A specific related node may be specified, for example, by the path from the comparison source node or the comparison target node (the edges passed through and the attribute information of the nodes), or by the type of node. Furthermore, weighting may be set for each type of node and each purpose.

[0040] 6 is a diagram showing an example of calculating the similarity between related nodes. In this example, node 100 (comparison source node) and node 102 (comparison target node) are not the same node, but both include "Type N" in their attribute information.

[0041] Node 100 is connected to edge 100A, node 100B is connected to edge 100A, edge 100C is connected to node 100C, and edge 100C is connected to node 100D. The attribute information of edge 100A includes "type E1", the attribute information of node 100B includes "type N2", the attribute information of edge 100C includes "type E2", and the attribute information of node 100D includes "type N3".

[0042] Node 102 is connected to edge 102A, edge 102A is connected to node 102B, node 102B is connected to edge 102C, and edge 102C is connected to node 102D. The attribute information of edge 102A includes "type E1", the attribute information of node 102B includes "type N2", the attribute information of edge 102C includes "type E2", and the attribute information of node 102D includes "type N3".

[0043] In this case, nodes 100B and 100D are extracted as related nodes of node 100. Similarly, nodes 102B and 102D are extracted as related nodes of node 102.

[0044] Since the attribute information of node 100B is identical to the attribute information of node 102B, and the attribute information of edge 100A connecting node 100B and node 100 is identical to the attribute information of edge 102A connecting node 102B and node 102, the attribute information is added to the calculation of the similarity between node 100 (source node) and node 102 (destination node) as a related similarity having a specific weight (for example, 0.2).

[0045] Furthermore, the attribute information of node 100D is identical to the attribute information of node 102D, and the attribute information of edges 100C and 100A connecting nodes 100D, 100B, and 100 is identical to the attribute information of edges 102C and 102A connecting nodes 102D, 102B, and 102, and therefore is added to the calculation of the similarity between node 100 (source node) and node 102 (destination node) as a related similarity having a specific weight (for example, 0.5).

[0046] 7 is a diagram showing an example of a similarity list of each comparison target node for which the similarity has been calculated for the comparison source node. The similarity list is displayed on the similarity list display unit 18 and further on the display device 30.

[0047] As shown in the example in Fig. 7, for each comparison source node (comparison source object), each comparison target node (comparison target object) is displayed together with the corresponding similarity. As shown in Fig. 7, it is desirable to display the similarities in descending order.

[0048] According to this embodiment, the attribute information similarity calculation unit 15 calculates the similarity between nodes using the combined attribute information, thereby improving the accuracy of the calculation of the similarity between nodes. In other words, since the nodes to be compared are described by the combined graph data, more attribute information (including attribute information belonging to related nodes) is taken into consideration when calculating the similarity, and the accuracy of the similarity calculation improves.

[0049] In this way, information that can be used for maintenance and the like can be obtained not only from the attribute information associated with the comparison source node, but also from the attribute information associated with the comparison target node that has a high degree of similarity.

[0050] As a result, even if the specifications of the comparison source node and the comparison target node are different (even if node 100 and node 102 in Figure 5 are not directly similar), the similarity can be calculated with high accuracy based on the combined attribute information (including attribute information of related nodes such as those associated with dotted lines in Figure 5), and information that can be used for maintaining the comparison source node, etc. can be obtained from the attribute information associated with the comparison target node (including attribute information of related nodes).

[0051] The deterioration tendency or repair method of a device or facility depends not only on the attribute information of the device itself, but also on the attribute information of the facility or facility to which the device belongs, and the devices and parts owned by the device. Furthermore, it also depends on the attribute information of the same node managed by a different type of system (other information group). Therefore, as shown in this embodiment, obtaining information on the deterioration tendency or repair method of a device by taking into account the combined attribute information is effective from the perspective of performing effective device maintenance.

[0052] <Hardware Configuration of Similarity Calculation Apparatus> FIGS. 8 and 9 are diagrams illustrating a schematic example of a hardware configuration when the similarity calculation apparatus shown in FIG. 1 is actually operated.

[0053] Note that the hardware configurations illustrated in Figures 8 and 9 may not match the configuration illustrated in Figure 1 in terms of numbers, etc., but this is because the configuration illustrated in Figure 1 represents conceptual units.

[0054] Therefore, at least the following cases can be envisaged: a configuration illustrated in FIG. 1 is made up of multiple hardware configurations illustrated in FIGS. 8 and 9; a configuration illustrated in FIG. 1 corresponds to a part of the hardware configuration illustrated in FIGS. 8 and 9; and further, multiple configurations illustrated in FIG. 1 are provided in a single hardware configuration illustrated in FIGS. 8 and 9.

[0055] Figure 8 shows a hardware configuration for realizing the attribute information synthesis unit 12, similarity calculation unit 14, calculation parameter setting unit 17, similarity list display unit 18, etc. in Figure 1, including a processing circuit 1102A that performs calculations and a memory device 1103 that can store information.

[0056] 9 shows a processing circuit 1102B that performs calculations as a hardware configuration for realizing the attribute information synthesis unit 12, the similarity calculation unit 14, the calculation parameter setting unit 17, the similarity list display unit 18, etc. in FIG.

[0057] The storage device 1103 may be, for example, a memory (recording medium) including a volatile or non-volatile semiconductor memory such as a hard disk drive (HDD), random access memory (RAM), read only memory (ROM), flash memory, erasable programmable read only memory (EPROM) and electrically erasable programmable read-only memory (EEPROM), a magnetic disk, a flexible disk, an optical disk, a compact disk, a mini disk, or a DVD, or any recording medium that will be used in the future.

[0058] The processing circuit 1102A may execute a program stored in the storage device 1103, an external CD-ROM, an external DVD-ROM, an external flash memory, etc. That is, it may be, for example, a central processing unit (CPU), a microprocessor, a microcomputer, or a digital signal processor (DSP).

[0059] When the processing circuit 1102A executes a program stored in the storage device 1103, an external CD-ROM, an external DVD-ROM, or an external flash memory, the attribute information synthesis unit 12, the similarity calculation unit 14, the calculation parameter setting unit 17, and the similarity list display unit 18 are realized by software, firmware, or a combination of software and firmware, in which the processing circuit 1102A executes a program stored in the storage device 1103. Note that the functions of the attribute information synthesis unit 12, the similarity calculation unit 14, the calculation parameter setting unit 17, and the similarity list display unit 18 may be realized, for example, by a plurality of processing circuits working together.

[0060] The software and firmware may be written as a program and stored in the storage device 1103. In this case, the processing circuit 1102A realizes the above-described functions by reading and executing the program stored in the storage device 1103. In other words, the storage device 1103 may store a program that, when executed by the processing circuit 1102A, results in the above-described functions being realized.

[0061] The processing circuit 1102B may also be dedicated hardware, i.e., for example, a single circuit, multiple circuits, a programmed processor, a parallel programmed processor, an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination thereof.

[0062] When the processing circuit 1102B is dedicated hardware, the attribute information synthesis unit 12, the similarity calculation unit 14, the calculation parameter setting unit 17, and the similarity list display unit 18 are realized by the operation of the processing circuit 1102B. Note that the functions of the attribute information synthesis unit 12, the similarity calculation unit 14, the calculation parameter setting unit 17, and the similarity list display unit 18 may be realized by separate circuits or by a single circuit.

[0063] In addition, the functions of the attribute information synthesis unit 12, the similarity calculation unit 14, the calculation parameter setting unit 17, and the similarity list display unit 18 may be realized in part by a processing circuit 1102A that executes a program stored in a memory device 1103, and in part by a processing circuit 1102B that is dedicated hardware.

[0064] The similarity calculation system 1 is configured by connecting the input device 20, the display device 30, the storage device 40, and the attribute information storage device 50 to the similarity calculation device having the above hardware configuration via wired or wireless communication (see FIG. 1). However, the configuration of the similarity calculation system 1 may be entirely provided in the user's terminal device, or the user's terminal device may correspond only to the input device 20 and the display device 30, and the similarity calculation device 10, the storage device 40, and the attribute information storage device 50 may be provided in an external server (the server 200 including the similarity calculation device 10, the storage device 40, and the attribute information storage device 50 in FIG. 1).

[0065] <Regarding the Effects Produced by the Embodiments Described Above> Next, examples of the effects produced by the embodiments described above will be described. Note that in the following description, the effects will be described based on the specific configurations exemplified in the embodiments described above, but these may be replaced with other specific configurations exemplified in the present specification to the extent that similar effects are produced. In other words, for convenience, only one of the associated specific configurations may be described as a representative below, but the representatively described specific configuration may be replaced with another associated specific configuration.

[0066] According to the embodiment described above, the similarity calculation device includes a combining unit and a similarity calculation unit 14. Here, the combining unit corresponds to, for example, the attribute information combining unit 12. The attribute information combining unit 12 combines a first information group and a second information group different from the first information group to generate at least one combined information group. The similarity calculation unit 14 calculates the similarity between the comparison source node and the comparison target node in the combined information group. Here, the first information group and the second information group each include nodes and attribute information indicating the attributes of the nodes. The attribute information combining unit 12 then combines the attribute information in the first information group and the second information group by matching identical nodes in the first information group and the second information group. Furthermore, the similarity calculation unit 14 calculates the similarity by comparing the attribute information associated with the comparison source node and the attribute information associated with the comparison target node.

[0067] Furthermore, according to the embodiment described above, the similarity calculation device includes a processing circuit 1102A that executes a program and a storage device 1103 that stores the program to be executed. The processing circuit 1102A executes the program to realize the following operations.

[0068] That is, at least one combined information group is generated by combining a first information group and a second information group different from the first information group, and the similarity between the comparison source node and the comparison target node in the combined information group is calculated. Here, the first information group and the second information group each include a node and attribute information indicating the node's attribute. By matching identical nodes in the first information group and the second information group, the attribute information in the first information group and the second information group is combined, and the similarity is calculated by comparing the attribute information associated with the comparison source node and the attribute information associated with the comparison target node.

[0069] Furthermore, according to the embodiment described above, the similarity calculation device includes the processing circuit 1102B, which is dedicated hardware. The processing circuit 1102B, which is dedicated hardware, performs the following operations.

[0070] That is, the processing circuit 1102B, which is dedicated hardware, generates at least one combined information group by combining a first information group and a second information group different from the first information group, and calculates the similarity between the comparison source node and the comparison target node in the combined information group. Here, the first information group and the second information group each include nodes and attribute information indicating the node's attributes. The processing circuit 1102B, which is dedicated hardware, combines the attribute information in the first information group and the second information group by matching identical nodes in the first information group and the second information group. The processing circuit 1102B also calculates the similarity by comparing the attribute information associated with the comparison source node and the attribute information associated with the comparison target node.

[0071] With this configuration, the accuracy of the calculation of the similarity between nodes can be improved by calculating the similarity between nodes using the combined attribute information. As a result, information that can be used for maintenance and the like can be obtained from the attribute information associated with the comparison target node that has a high similarity.

[0072] Furthermore, even if other configurations shown as examples in this specification are appropriately added to the above configuration, that is, even if other configurations in this specification that were not mentioned as the above configuration are appropriately added, the same effect can be achieved.

[0073] Furthermore, according to the embodiment described above, the similarity calculation unit 14 calculates the similarity by comparing attribute information indicating the attributes of the comparison source node with attribute information indicating the attributes of the comparison target node. With this configuration, the attribute information is a combination of attribute information from at least one of the comparison source node and the comparison target node, improving the accuracy of the similarity calculation. By calculating the similarity between nodes with high accuracy, it is possible to perform effective maintenance (repairs, inspections, servicing, etc.) that takes into account, for example, deterioration trends, based on the attribute information of similar nodes.

[0074] Furthermore, according to the embodiment described above, the attribute information synthesis unit 12 generates a first synthesis information group and a second synthesis information group, which are synthesis information groups. The similarity calculation unit 14 then calculates the similarity by comparing the attribute information associated with the comparison source node in the first synthesis information group with the attribute information associated with the comparison target node in the second synthesis information group. With this configuration, the attribute information of both the comparison source node and the comparison target node is synthesized, improving the accuracy of the similarity calculation. By calculating the similarity between nodes with high accuracy, it is possible to perform effective maintenance (repairs, inspections, servicing, etc.) that takes into account, for example, deterioration trends, based on the attribute information of similar nodes.

[0075] Furthermore, according to the embodiment described above, the attribute information synthesis unit 12 includes an identical node determination unit 13 for determining identical nodes in the first information group and the second information group. The identical node determination unit 13 determines identical nodes based on correspondence information indicating the relationship between identical nodes in the first information group and the second information group. Here, the correspondence information corresponds to, for example, the heterogeneous system correspondence information 40A. The attribute information synthesis unit 12 then synthesizes the attribute information in the first information group and the second information group by matching nodes determined to be identical by the identical node determination unit 13. This configuration allows graph data to be synthesized starting from the same node, and synthetic graph data in which the attribute information in each graph data is synthesized can be generated. Therefore, the accuracy of similarity calculation can be improved based on the synthesized attribute information.

[0076] Furthermore, according to the embodiment described above, the similarity calculation unit 14 adds, to the similarity, the association similarity, which is the similarity between the attribute information of the first associated node associated with the comparison source node and the attribute information of the second associated node associated with the comparison target node. With this configuration, the accuracy of the similarity calculation is improved by taking into account the attribute information belonging to the associated nodes.

[0077] Furthermore, according to the embodiment described above, the first information group and the second information group each include edges connecting multiple nodes. The first associated node is connected to the comparison source node via a first edge. The second associated node is connected to the comparison target node via a second edge. The similarity calculation unit 14 adds an associated similarity, which is the similarity between the attribute information of the first edge and the attribute information of the second edge, to the similarity. This configuration improves the accuracy of the similarity calculation by also taking into account the attribute information belonging to the edge connecting the associated node and the comparison source node (or the comparison target node).

[0078] Furthermore, according to the embodiment described above, the similarity calculation device includes a similarity list display unit 18 for displaying a list of the similarities of the comparison target node with respect to the comparison source node. With this configuration, the similarity between nodes can be easily confirmed.

[0079] According to the embodiment described above, the similarity calculation system includes an attribute information synthesis unit 12 for generating at least one synthesized information group by synthesizing a first information group and a second information group different from the first information group, and a similarity calculation unit 14 for calculating the similarity between a comparison source node and a comparison target node in the synthesized information group. The first information group and the second information group each include nodes and attribute information indicating the attributes of the nodes. The attribute information synthesis unit 12 synthesizes the attribute information in the first information group and the second information group by matching identical nodes in the first information group and the second information group. The similarity calculation unit 14 calculates the similarity by comparing the attribute information associated with the comparison source node and the attribute information associated with the comparison target node.

[0080] With this configuration, the accuracy of the calculation of the similarity between nodes can be improved by calculating the similarity between nodes using the combined attribute information. As a result, information that can be used for maintenance and the like can be obtained from the attribute information associated with the comparison target node that has a high similarity.

[0081] Furthermore, even if other configurations shown as examples in this specification are appropriately added to the above configuration, that is, even if other configurations in this specification that were not mentioned as the above configuration are appropriately added, the same effect can be achieved.

[0082] Furthermore, according to the embodiment described above, the similarity calculation system includes a terminal device having an input device 20 and a display device 30, and a server 200 that communicates with the terminal device. The attribute information synthesis unit 12 and the similarity calculation unit 14 are provided in the server 200. With this configuration, it is possible to reduce the calculation load on the terminal device.

[0083] <Regarding Modifications of the Embodiments Described Above> In the embodiments described above, the dimensions, shapes, relative positional relationships, and implementation conditions of each component may be described, but these are merely examples in all aspects and are not limiting.

[0084] Thus, numerous variations and equivalents not shown are contemplated within the scope of the technology disclosed herein, including, for example, the modification, addition, or omission of at least one component.

[0085] Furthermore, unless a contradiction arises, when it is stated in the above-described embodiments that "one" component is provided, "one or more" of that component may be provided.

[0086] Furthermore, each component in the embodiments described above is a conceptual unit, and the scope of the technology disclosed in this specification includes cases where one component is made up of multiple structures, cases where one component corresponds to a part of a structure, and even cases where multiple components are provided in one structure.

[0087] Furthermore, each of the components in the embodiments described above includes structures having other structures or shapes as long as they perform the same function.

[0088] Furthermore, the descriptions in this specification are incorporated by reference for all purposes related to the present technology, and none of them are admitted to be prior art.

[0089] Furthermore, each component described in the above-described embodiments is envisioned as software or firmware, as well as corresponding hardware, and as software it is referred to as, for example, a "unit," and as hardware it is referred to as, for example, a "processing circuitry."

[0090] Furthermore, the technology disclosed in this specification may also be in a form in which each component is distributed across multiple devices, that is, in a form such as a system that is a combination of multiple devices.

[0091] 1 Similarity calculation system, 10 Similarity calculation device, 12 Attribute information synthesis unit, 13 Same node determination unit, 14 Similarity calculation unit, 15 Attribute information similarity calculation unit, 16 Related node extraction unit, 17 Calculation parameter setting unit, 18 Similarity list display unit, 20 Input device, 30 Display device, 40 Storage device, 40A Heterogeneous system correspondence information, 50 Attribute information storage device, 50A System type A attribute information, 50B System type B attribute information, 50C System type C attribute information, 50D System type C attribute information, 100 Node, 100A Edge, 100B Node, 100C Edge, 100D Node, 102 Node, 102A Edge, 102B Node, 102C Edge, 102D Node, 200 Server, 1102A Processing circuit, 1102B Processing circuit, 1103 Storage device.

Claims

1. A similarity calculation device comprising: a synthesis unit for synthesizing a first information group and a second information group different from the first information group to generate at least one synthesized information group; and a similarity calculation unit for calculating the similarity between a comparison source node and a comparison target node in the synthesized information group, wherein the first information group and the second information group each include nodes and attribute information indicating attributes of the nodes, the synthesis unit synthesizes the attribute information in the first information group and the second information group by matching identical nodes in the first information group and the second information group, and the similarity calculation unit calculates the similarity by comparing the attribute information associated with the comparison source node and the attribute information associated with the comparison target node.

2. A similarity calculation device according to claim 1, wherein the similarity calculation unit calculates the similarity by comparing the attribute information indicating the attributes of the comparison source node with the attribute information indicating the attributes of the comparison target node.

3. A similarity calculation device according to claim 1 or 2, wherein the synthesis unit generates a first synthesis information group and a second synthesis information group, which are the synthesis information groups, and the similarity calculation unit calculates the similarity by comparing the attribute information associated with the comparison source node in the first synthesis information group with the attribute information associated with the attribute of the comparison destination node in the second synthesis information group.

4. A similarity calculation device according to any one of claims 1 to 3, wherein the synthesis unit further comprises an identical node determination unit for determining whether the nodes in the first information group and the second information group are identical, the identical node determination unit determines whether the nodes are identical based on correspondence information indicating the relationship between the identical nodes in the first information group and the second information group, and the synthesis unit synthesizes the attribute information in the first information group and the second information group by matching the nodes determined to be identical by the identical node determination unit.

5. A similarity calculation device according to any one of claims 1 to 4, wherein the similarity calculation unit adds to the similarity a related similarity, which is the similarity between the attribute information of a first related node related to the comparison source node and the attribute information of a second related node related to the comparison target node.

6. A similarity calculation device according to claim 5, wherein the first information group and the second information group each include edges connecting a plurality of the nodes, the first associated node is connected to the comparison source node via a first edge which is the edge, and the second associated node is connected to the comparison destination node via a second edge which is the edge, and the similarity calculation unit adds the associated similarity, which is the similarity between the attribute information of the first edge and the attribute information of the second edge, to the similarity.

7. A similarity calculation device according to any one of claims 1 to 6, further comprising a similarity list display unit for displaying a list of similarities of the comparison target node with respect to the comparison source node.

8. A similarity calculation system comprising: a synthesis unit for synthesizing a first information group and a second information group different from the first information group to generate at least one synthesized information group; and a similarity calculation unit for calculating a similarity between a comparison source node and a comparison target node in the synthesized information group, wherein the first information group and the second information group each include a node and attribute information indicating an attribute of the node, the synthesis unit synthesizes the attribute information in the first information group and the second information group by matching identical nodes in the first information group and the second information group, and the similarity calculation unit calculates the similarity by comparing the attribute information associated with the comparison source node and the attribute information associated with the comparison target node.

9. A similarity calculation system according to claim 8, comprising a terminal device equipped with an input device and a display device, and a server that communicates with said terminal device, wherein said synthesis unit and said similarity calculation unit are provided in said server.

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