Data model generating device, data model generating system, and data model generating method

Abstract

The object of the present invention is to provide a technology that can generate an appropriate data model even without domain information such as detailed classification conditions. The data model generation device includes an attribute candidate generation unit that generates attribute candidates from a plurality of selected attribute information based on evaluation values ​​of the plurality of selected attribute information obtained by repeating selection for the selected attribute information and calculation of evaluation values ​​of the selected attribute information, and a data model editing unit that updates the data model based on label information and the attribute candidates or based on the attribute candidates.

Description

[Technical field]

[0001] The present disclosure relates to a data model generating device, a data model generating system, and a data model generating method. [Background technology]

[0002] A data model (hereinafter sometimes abbreviated as "model") that expresses the relationships between data using a graph structure has been proposed for the purpose of improving information searchability and visualizing the relationships between data. This data model is used, for example, in social graphs, recommendations, geospatial information, and master data management.

[0003] In large-scale facilities such as water treatment and power plants, the number of plant components to be monitored is enormous, and the scale of the data model for managing the data and alarms collected from the plant components becomes large. There is a problem that it takes a lot of effort to manually create, manage, and maintain such a data model. In addition, as the scale of the model increases, there is a problem that the relationships of data that are important to users who refer to the model are buried, and the visibility of the model is impaired.

[0004] Regarding the labor cost, methods have been proposed to generate models based on existing domain information such as engineering diagrams and knowledge graphs, and to extend existing small-scale models. Regarding the visibility of models, methods have been proposed to improve the visibility of large-scale models by classifying model components into predetermined categories or by linking and aggregating related elements.

[0005] For example, Patent Document 1 discloses a technology for generating a data model by applying image recognition to engineering diagrams and other engineering sources to extract plant components and associating them based on predetermined classification conditions. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Special Publication No. 2022-524642 Summary of the Invention [Problem to be solved by the invention]

[0007] However, in the conventional technology, since it is necessary to design the classification conditions, i.e., the domain information, in detail, there is a problem that, for example, when structuring hierarchically according to multiple types of classification, the number of steps required for designing the domain information increases. On the other hand, when the domain information is not designed in detail, there is a problem that it becomes difficult to classify the information in a way that reflects the user's preferences.

[0008] Therefore, the present disclosure has been made in consideration of the above-mentioned problems, and aims to provide a technology that can generate an appropriate data model even without domain information such as detailed classification conditions. [Means for solving the problem]

[0009] A data model generation device according to the present disclosure includes a setting reception unit that receives, from an external device, label information including at least one of a classification label and classification label configuration information associated with the classification label; an attribute information extraction management unit that extracts a set of attribute information from a data model to be monitored; a selection unit that selects, from the set of attribute information, a subset of the attribute information as selected attribute information based on the received label information; an evaluation unit that calculates an evaluation value of the selected attribute information; and an evaluation unit that calculates an evaluation value of the selected attribute information based on the evaluation values ​​of a plurality of the selected attribute information obtained by repeating selection for the selected attribute information and calculation of the evaluation value of the selected attribute information. The above The data model editing unit includes an attribute candidate generation unit that generates attribute candidates from selected attribute information, and a data model editing unit that updates the data model based on the attribute candidates and the label information related to the classification label that represents the attribute candidates, or based on the attribute candidates. Effect of the Invention

[0010] According to the present disclosure, attribute candidates are generated from a plurality of selected attribute information based on evaluation values ​​of the plurality of selected attribute information obtained by repeating selection for the selected attribute information and calculation of evaluation values ​​of the selected attribute information, and a data model is updated based on label information and the attribute candidates or based on the attribute candidates. With such a configuration, an appropriate data model can be generated even without domain information such as detailed classification conditions.

[0011] The objects, features, aspects and advantages of the present disclosure will become more apparent from the following detailed description and the accompanying drawings. [Brief description of the drawings]

[0012] [Figure 1] 1 is a diagram illustrating a configuration of a data model generating device according to a first embodiment. [Diagram 2] 4 is a flowchart showing an outline of a processing procedure of the data model generating device according to the first embodiment. [Diagram 3] 4A to 4C are diagrams showing examples of attribute information extraction information and attribute information selection evaluation information according to the first embodiment. [Figure 4] 4 is a diagram showing an example of data held by a data model information accumulation unit according to the first embodiment; FIG. [Diagram 5] 10 is a flowchart showing a processing procedure of an attribute information extraction management unit according to the first embodiment. [Figure 6] 4 is a diagram for explaining the operation of an attribute information extraction management unit according to the first embodiment. FIG. [Figure 7] 4 is a diagram for explaining the operation of an attribute information extraction management unit according to the first embodiment. FIG. [Figure 8] 4 is a diagram for explaining the operation of an attribute information extraction management unit according to the first embodiment. FIG. [Figure 9] 4 is a diagram showing an example of information managed in an attribute information extraction management unit according to the first embodiment. FIG. [Figure 10] 10 is a flowchart showing a processing procedure of an attribute candidate generating unit according to the first embodiment. [Figure 11] 4 is a diagram for explaining the operation of an attribute candidate generating unit according to the first embodiment. FIG. [Figure 12] 4 is a diagram for explaining the operation of an attribute candidate generating unit according to the first embodiment. FIG. [Figure 13] 10 is a flowchart showing a processing procedure of a selection unit according to the first embodiment. [Figure 14] 4 is a diagram for explaining the operation of a selection unit according to the first embodiment. FIG. [Figure 15] 10 is a flowchart showing a processing procedure of an evaluation unit according to the first embodiment. [Figure 16] 4 is a diagram for explaining the operation of an evaluation unit according to the first embodiment. FIG. [Figure 17] 11 is a flowchart showing a processing procedure of a data model editing unit according to the first embodiment. [Figure 18] 4 is a diagram for explaining the operation of a data model editing unit according to the first embodiment; FIG. [Figure 19] 4 is a diagram for explaining the operation of a data model editing unit according to the first embodiment; FIG. [Figure 20] FIG. 11 is a diagram illustrating a configuration of a data model generating device according to a second embodiment. [Figure 21] 13 is a flowchart showing a processing procedure of an attribute candidate generating unit according to the second embodiment. [Figure 22] 13 is a diagram for explaining the operation of an attribute candidate generating unit according to the second embodiment. FIG. [Diagram 23] 13 is a flowchart showing a processing procedure of a selection unit according to the second embodiment. [Figure 24] FIG. 11 is a diagram illustrating a configuration of a data model generating device according to a third embodiment. [Diagram 25] 13 is a flowchart showing a processing procedure of an attribute candidate generating unit according to the third embodiment. [Figure 26] 13 is a flowchart showing a processing procedure of an evaluation learning unit according to the third embodiment. [Figure 27] FIG. 13 is a diagram illustrating a configuration of a data model generating device according to a fourth embodiment. [Figure 28] FIG. 13 is a diagram for explaining the operation of an attribute candidate generating unit according to the fourth embodiment. [Figure 29] 13 is a flowchart showing a processing procedure of an evaluation learning unit according to the fourth embodiment. [Diagram 30] 13 is a flowchart showing a processing procedure of a selection learning unit according to the fourth embodiment. [Diagram 31] FIG. 13 is a diagram illustrating a configuration of a data model generating device according to a fifth embodiment. [Diagram 32] 13 is a flowchart showing a processing procedure of a selection learning unit according to the fifth embodiment. [Diagram 33] FIG. 13 is a block diagram showing a hardware configuration of a data model generating device according to another modified example. [Diagram 34] FIG. 13 is a block diagram showing a hardware configuration of a data model generating device according to another modified example. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] <Embodiment 1> Fig. 1 is a diagram showing the configuration of a data model generation device according to the embodiment 1. The data model generation device in Fig. 1 includes a data model information storage unit 101, an attribute information extraction management unit 102, an extraction setting storage unit 103, an attribute candidate generation unit 104, a setting reception unit 107, and a data model editing unit 108. The attribute candidate generation unit 104 includes a selection unit 105 and an evaluation unit 106.

[0014] As described below, the data model generation device extracts a set of attribute information from the data model of the monitored object, aggregates the set of attribute information to generate attribute candidates, and updates the data model by associating the attribute candidates with classification labels that represent the attribute candidates. This makes it possible to newly generate a data model that has visibility and searchability even without domain information such as detailed classification conditions.

[0015] FIG. 2 is a flowchart showing an outline of a processing procedure of the data model generating device according to the first embodiment.

[0016] In step S201, the setting receiving unit 107 receives information including attribute information extraction information and attribute information selection evaluation information from an external user, and records the information in the extraction setting storage unit 103. Although details will be described later, the attribute information extraction information is information for acquiring a data model and extracting a set of attribute information from the data model, and the attribute information selection evaluation information is information for selecting attribute information and evaluating the selected attribute information.

[0017] In step S202, the attribute information extraction management unit 102 acquires attribute information extraction information from the extraction setting storage unit 103. The attribute information extraction management unit 102 acquires a corresponding data model from the data model information storage unit 101 based on the designation of a data model name included in the attribute information extraction information.

[0018] In step S203, the attribute information extraction management unit 102 extracts and manages a set of attribute information from the data model acquired in step S202 based on the attribute information extraction information acquired in step S202.

[0019] In step S 204 , the attribute candidate generating unit 104 acquires a set of attribute information managed by the attribute information extraction managing unit 102 , and acquires attribute information selection evaluation information from the extraction setting accumulating unit 103 .

[0020] In step S205, the attribute candidate generation unit 104 selects a subset of attribute information to be selected attribute information from the set of attribute information based on the attribute selection evaluation information acquired in step S204. That is, the attribute candidate generation unit 104 selects a subset of attribute information from the set of attribute information as selected attribute information based on the attribute selection evaluation information. Then, the attribute candidate generation unit 104 evaluates the selected attribute information based on the attribute selection evaluation information acquired in step S204, and generates attribute candidates based on the evaluation result.

[0021] In step S206, the data model editing unit 108 acquires the attribute candidates generated by the attribute candidate generation unit 104, and the attribute information extraction information and attribute information selection evaluation information held by the extraction setting storage unit 103. In addition, the data model editing unit 108 acquires a corresponding data model from the data model information storage unit 101 based on the designation of a data model name included in the acquired attribute information extraction information.

[0022] In step S207, the data model editing unit 108 generates new classification labels representing the attribute candidates based on the attribute information selection evaluation information or the attribute candidates acquired in step S206. Then, the data model editing unit 108 associates the generated classification labels with each piece of attribute information included in the attribute candidates acquired in step S206, and updates the data model acquired in step S206, thereby generating a new data model.

[0023] In step S208, the data model editing unit 108 saves the data model generated in step S207 in the data model information storage unit 101, or overwrites the original data model held in the data model information storage unit 101 with the data model.

[0024] The outline of the data model generating device has been described above. Next, each component of the data model generating device will be described in detail.

[0025] <Settings reception section> The setting receiving unit 107 receives information on attribute information for which searchability and visibility are desired to be improved from the vast amount of data held by the data model from the user as extraction settings, formats the extraction settings, and stores them in the extraction setting storage unit 103. As shown in Fig. 3, the extraction settings are broadly divided into two pieces of information: attribute information extraction information and attribute information selection evaluation information.

[0026] The attribute information extraction information in FIG. 3 is information for acquiring a data model and extracting a set of attribute information from the data model.

[0027] The "data model name" is the name of the data model that is to be the target of attribute information extraction. This specifies the data model that is to be the target of attribute information extraction among the data models existing in the data model information storage unit 101.

[0028] The "attribute information target" is information indicating the criteria for data to be treated as attribute information among the data of the data model.

[0029] An "entity target" is information indicating the criteria for treating data as an entity associated with attribute information among data in a data model. An entity is used to update the data model for selected attribute information.

[0030] "Classification label target" is information indicating the criteria for data of a data model that is treated as a classification label that can be used as a viewpoint or aspect when searching a data model. A classification label is data that can be used as a viewpoint or aspect when searching a data model, and several pieces of attribute information are grouped together by one classification label.

[0031] The attribute information selection evaluation information in FIG. 3 is information for selecting a subset of attribute information from a set of attribute information as selected attribute information and for evaluating the selected attribute information.

[0032] The "selection method" is an index for determining attribute information to be preferentially selected. The attribute candidate generation unit 104 acquires the selection method and passes it to the selection unit 105, whereby the selection unit 105 uses the selection method as a policy for selecting a subset of attribute information from a set of attribute information.

[0033] An "evaluation method" is an index for evaluating a subset of selected attribute information, that is, selected attribute information. The attribute candidate generation unit 104 acquires the evaluation method and passes it to the evaluation unit 106, and the evaluation unit 106 uses the evaluation method as a guideline for calculating the evaluation value of the selected attribute information.

[0034] The "reference value" is a standard for the evaluation value used to determine whether the evaluation value calculated based on the "evaluation method" is high or low. When the attribute candidate generation unit 104 acquires the reference value, the attribute candidate generation unit 104 determines whether the evaluation value calculated by the evaluation unit 106 is high or low using the reference value.

[0035] A "classification label" is a classification label to be newly generated for an existing data model. The classification label is used when the data model editing unit 108 generates a new classification label for an existing data model. The classification label may also be used when the selection unit 105 selects attribute information or when the evaluation unit 106 evaluates the selected attribute information. For example, as described in the second embodiment, when "power optimization" is specified as the classification label, the selection unit 105 may actively select attribute information including "power" or "optimization" included in "power optimization."

[0036] "Classification label configuration information" is information associated with a "classification label" and is several pieces of information that explain the classification label in detail. The classification label configuration information may be used when the selection unit 105 selects a subset of attribute information as selected attribute information, or when the evaluation unit 106 evaluates the selected attribute information. For example, since "EV", "Optimized", "Smart Grid", etc. are specified as classification label configuration information in FIG. 3, the selection unit 105 may actively select attribute information related to these.

[0037] The "aggregation upper limit number" is the upper limit value of attribute information that can be selected as selected attribute information by the selection unit 105. When evaluating the selected attribute information, the aggregation upper limit number is used by the evaluation unit 106 when evaluating the selected attribute information based on the amount of violation of the restriction of the selection upper limit number, etc.

[0038] The setting reception unit 107 may be, for example, an input device through which the user inputs text or the like, or a communication device that accepts forms, applications, etc. input from a web browser. Furthermore, the setting reception unit 107 may exchange information with an external device or the like through a file such as text.

[0039] In the above-described first embodiment, the attribute information selection evaluation information received by the setting receiving unit 107 includes the classification label and the classification label configuration information, and therefore corresponds to the label information related to the classification label. In the first and second embodiments, the attribute information selection evaluation information only needs to include at least one of the classification label and the classification label configuration information. In this specification, for example, at least one of A, B, C, ..., and Z means any one of all combinations of one or more items extracted from the group of A, B, C, ..., and Z.

[0040] <Extraction setting storage section> The extraction setting storage unit 103 is an information storage unit that holds information received by the setting reception unit 107, and holds information such as that shown in Fig. 3. The extraction setting storage unit 103 outputs the information it holds when it receives an output instruction from the attribute information extraction management unit 102, the attribute candidate generation unit 104, or the data model editing unit 108.

[0041] <Data Model Information Storage Unit> The data model information accumulation unit 101 holds a plurality of data models to be monitored, and manages each data model by a data model name. Fig. 4 is a diagram showing an example of data held by the data model information accumulation unit 101. In the example shown in Fig. 4, the data model information accumulation unit 101 holds data models with data model names such as "academic paper", "magazine", and "web article". The data format of each data model is not limited, and may be, for example, text, a relational database, a graph database, or the like.

[0042] <Attribute information extraction management section> The attribute information extraction management unit 102 receives attribute information extraction information held in the extraction setting storage unit 103 and a data model to be extracted held in the data model information storage unit 101. The attribute information extraction management unit 102 extracts and manages a set of attribute information from the data model to be extracted based on the attribute information extraction information.

[0043] FIG. 5 is a flowchart showing a processing procedure of the attribute information extraction management unit 102 according to the first embodiment.

[0044] In step S501, the attribute information extraction management unit 102 reads attribute information extraction information such as that shown in FIG. 3 from the extraction setting storage unit 103, and obtains the name of the data model to be extracted from the attribute information extraction information.

[0045] In step S502, the attribute information extraction management unit 102 acquires a data model specified by the data model name acquired in step S501 from the data model information accumulation unit 101. In the attribute information extraction information in Fig. 3, "academic paper" is specified as the data model name. Therefore, when the information in Fig. 4 is held in the data model information accumulation unit 101, the attribute information extraction management unit 102 acquires a data model whose data model name is "academic paper" from the data model information accumulation unit 101 as shown in Fig. 6. The data model shown in the example in Fig. 6 is expressed in a graph database format, and has a hierarchical structure composed of nodes and edges as shown in the legend.

[0046] In step S503, the attribute information extraction management unit 102 extracts a set of attribute information from the data model acquired in step S502. For example, the attribute information extraction management unit 102 refers to the attribute information target, entity target, and classification label target included in the attribute information extraction information acquired in step S501, and assigns each element of the data model to an attribute, entity, and classification label as shown in FIG. 7. In the example shown in FIG. 7, the attribute is an end node of the graph, the entity is the parent node of the attribute, and the classification label is the parent node of the entity. Then, the attribute information extraction management unit 102 extracts information held by the node classified into the attribute as attribute information. FIG. 8 shows an example of a set of attribute information extracted from the name of the end node of the data model in FIG. 6.

[0047] In step S504, the attribute information extraction management unit 102 manages the set of attribute information as shown in Fig. 8 extracted in step S503 together with the entities related to each piece of attribute information as shown in Fig. 9. According to the above operation, the attribute information extraction management unit 102 can manage the set of attribute information and the entities related to each piece of attribute information, regardless of the data format of the data model to be extracted. A memory or the like may be used for the management, or a method of persisting in a database or the like may be used.

[0048] <Attribute candidate generation section> 1, the attribute candidate generation unit 104 includes a selection unit 105 and an evaluation unit 106. However, this is not limited thereto, and the selection unit 105 and the evaluation unit 106 may be provided separately from the attribute candidate generation unit 104, rather than being provided in the attribute candidate generation unit 104.

[0049] The attribute candidate generation unit 104 receives as input a set of attribute information managed by the attribute information extraction management unit 102 and attribute information selection evaluation information acquired from the extraction setting accumulation unit 103. The attribute information selection evaluation information is used as a guideline for selecting selected attribute information and a guideline for evaluating the selected attribute information. The attribute candidate generation unit 104 can generate attribute candidates from multiple selected attribute information based on evaluation values ​​of multiple selected attribute information obtained by repeating selection for the selected attribute information and calculation of evaluation values ​​of the selected attribute information, and outputs the attribute candidates to the data model editing unit 108.

[0050] FIG. 10 is a flowchart showing the processing procedure of the attribute candidate generating unit 104 according to the first embodiment.

[0051] In step S1001, the attribute candidate generation unit 104 acquires a set of attribute information as shown in Fig. 9 managed by the attribute information extraction management unit 102. In addition, the attribute candidate generation unit 104 acquires attribute information selection evaluation information as shown in Fig. 3 from the extraction setting accumulation unit 103.

[0052] In step S1002, the attribute candidate generation unit 104 creates and holds parameters as shown in Fig. 11 using the set of attribute information acquired in step S1001 and the attribute information selection evaluation information acquired in step S1001. The parameters include a "list of attribute information" in which a set of attribute information excluding entities from the set of attribute information as shown in Fig. 9 is set. The parameters also include initialized "selected attribute information" and "evaluation value of attribute candidate" out of the attribute information selection evaluation information, and "attribute information selection evaluation information" as shown in Fig. 3.

[0053] In step S1003, the attribute candidate generating unit 104 passes the parameters created in step S1002 to the selecting unit 105, and causes the selecting unit 105 to select one piece of attribute information from the set of attribute information. To this end, the selecting unit 105 selects one piece of attribute information according to the "selection method" included in the parameters. Details of the selection process of the selecting unit 105 will be described later.

[0054] In step S1004, the attribute candidate generation unit 104 acquires one piece of attribute information selected by the selection unit 105, and adds it to "selected attribute information" included in the parameters. If the one piece of attribute information selected by the selection unit 105 already exists in the "selected attribute information", the attribute candidate generation unit 104 deletes the one piece of attribute information selected by the selection unit 105 from the "selected attribute information".

[0055] In step S1005, the attribute candidate generation unit 104 passes the parameters to the evaluation unit 106, and causes the evaluation unit 106 to calculate an evaluation value for a subset of attribute information included in the "selected attribute information", i.e., an evaluation value for the selected attribute information. For this reason, the evaluation unit 106 calculates the evaluation value of the selected attribute information according to the "evaluation method" included in the parameters.

[0056] In step S1006, the attribute candidate generating unit 104 acquires the evaluation value calculated by the evaluating unit 106, and updates the "evaluation value of selected attribute information" included in the parameters with the evaluation value.

[0057] In step S1007, the attribute candidate generation unit 104 appropriately calculates a reference value based on the "reference value" included in the parameter. Then, the attribute candidate generation unit 104 determines whether the evaluation value acquired in step S1006 is equal to or greater than the reference value. If the evaluation value is equal to or greater than the reference value, the process proceeds to step S1008, and if the evaluation value is not equal to or greater than the reference value, the process returns to step S1003.

[0058] In step S1008, the attribute candidate generation unit 104 generates attribute candidates by associating a subset of attribute information included in the "selected attribute information" of the parameter with the corresponding entity, and outputs the generated attribute candidates to the data model editing unit 108. For example, when the set of attribute information and the entity have a correspondence relationship as shown in Fig. 9 and the selected attribute information that is equal to or greater than a reference value is "EV", "optimization", and "Smart Grid", the attribute candidate generation unit 104 outputs attribute candidates as shown in Fig. 12.

[0059] The attribute candidate generating unit 104 repeats the processes of steps S1003 to S1007 to appropriately change the combination of attribute information included in the selected attribute information, and calculates evaluation values ​​of a plurality of selected attribute information over a certain span. After that, the attribute candidate generating unit 104 performs the process of step S1008 to generate selected attribute information that is equal to or greater than a threshold value among the plurality of selected attribute information over a certain span as attribute candidates.

[0060] <Selection section> The selection unit 105 receives parameters provided by the attribute candidate generation unit 104. The selection unit 105 selects one piece of attribute information from the set of attribute information in the "list of attribute information" included in the parameters, and outputs the selected piece of attribute information to the attribute candidate generation unit 104. The attribute candidate generation unit 104 performs the processing procedure in Fig. 10 described above, so that the selection unit 105 selects a subset of attribute information from the set of attribute information as selected attribute information.

[0061] FIG. 13 is a flowchart showing the processing procedure of the selection unit 105 according to the first embodiment.

[0062] In step S1301, the selection unit 105 acquires parameters such as those shown in FIG.

[0063] In step S1302, the selection unit 105 selects one piece of attribute information from the set of attribute information set in the "list of attribute information" in accordance with the "selection method" included in the parameters acquired in step S1301. In the selection method, at least one piece of information, a classification label and classification label configuration information, is specified, and attribute information related to the specified information is preferentially selected. For example, in the parameters shown in Fig. 11, the degree of association with the classification label configuration information is specified in the "selection method", so attribute information related to the classification label configuration information is preferentially selected.

[0064] In the example shown in Fig. 14, since "EV" exists in both the set of attribute information set in the "list of attribute information" and the classification label configuration information, when the "selection method" is specified as in Fig. 11, the selection unit 105 preferentially selects "EV." Although not shown, when the degree of association with a classification label is specified in the "selection method," the selection unit 105 preferentially selects attribute information associated with a classification label.

[0065] In step S1303, the selection unit 105 outputs the one piece of attribute information selected in step S1302 to the attribute candidate generation unit 104. In the example shown in Fig. 14, "EV", "Optimized", and "Smart Grid" exist in both the set of attribute information set in the "List of attribute information" and the classification label configuration information. Therefore, when the processes in steps S1003 to S1007 in Fig. 10 are repeated, the subsets of attribute information, "EV", "Optimized", and "Smart Grid", are preferentially selected as selected attribute information.

[0066] <Evaluation Department> The evaluation unit 106 receives parameters provided by the attribute candidate generation unit 104. The evaluation unit 106 calculates an evaluation value of the “selected attribute information” included in the parameters and outputs the evaluation value to the attribute candidate generation unit 104.

[0067] FIG. 15 is a flowchart showing the processing procedure of the evaluation unit 106 according to the first embodiment.

[0068] In step S1501, the evaluation unit 106 acquires, from the attribute candidate generation unit 104, parameters reflecting the selection made by the selection unit 105.

[0069] In step S1502, the evaluation unit 106 calculates an evaluation value of the "selected attribute information" according to the "evaluation method" included in the parameters acquired in step S1501. The evaluation method may be a method of calculating an evaluation value using an index such as the degree of association between the attribute information included in the selected attribute information and the attribute information selection evaluation information included in the parameters, or a method of calculating an evaluation value using a weighted sum of several indexes.

[0070] For example, in the parameters shown in Fig. 11, "degree of relevance with classification label configuration information + amount of violation of constraint on aggregation upper limit number" is specified as an index in the "evaluation method", and the evaluation unit 106 calculates the evaluation value of the selected attribute information according to this index. In the example of calculating the evaluation value shown in Fig. 16, the evaluation unit 106 calculates "degree of relevance with classification label configuration information" and "amount of violation of constraint on aggregation upper limit number" specified as indexes in the "evaluation method", and calculates the sum of them as the evaluation value.

[0071] In the former "degree of association with classification label configuration information", since "EV", "optimized", and "Smart Grid" specified in the classification label configuration information are included in the "selected attribute information", the evaluation unit 106 calculates "3" as the degree of association. In the latter "amount of violation of constraint of aggregation upper limit number", since the number of pieces of attribute information included in the "selected attribute information" is "3", which does not violate the aggregation upper limit number of "3", the evaluation unit 106 calculates "1" as the amount of violation of constraint. Although not shown, if the number of pieces of attribute information included in the "selected attribute information" violates the aggregation upper limit number, the evaluation unit 106 calculates "0" or a negative value as the amount of violation of constraint. Then, the evaluation unit 106 calculates the sum ("4" in the example of FIG. 16) of the value calculated in the former "degree of association with classification label configuration information" and the value calculated in the latter "amount of violation of constraint of aggregation upper limit number" as the evaluation value.

[0072] In the above description, the evaluation unit 106 calculates the evaluation value based on the degree of association corresponding to the degree of coincidence between the selected attribute information and the classification label configuration information, but the present invention is not limited to this. For example, the evaluation unit 106 may calculate the evaluation value based on a difference that is in an inverse relationship to the degree of association between the selected attribute information and the classification label configuration information. Here, the evaluation unit 106 calculates the evaluation value based on the degree of association between the selected attribute information and the classification label configuration information, but the evaluation value may be calculated based on the degree of association between the selected attribute information and the classification label, or the evaluation value may be calculated based on the difference between the selected attribute information and the classification label. In other words, the evaluation unit 106 may calculate the evaluation value based on the degree of association or difference between the selected attribute information and the attribute information selection evaluation information.

[0073] In step S1503, the evaluation unit 106 outputs the evaluation value calculated in step S1502 to the attribute candidate generation unit 104.

[0074] <Data Model Editorial Department> The data model editing unit 108 receives information from the data model information storage unit 101, the attribute candidate generating unit 104, and the extraction setting storage unit 103. The data model editing unit 108 updates the data structure of the data model based on the input information. In the first embodiment, the data model editing unit 108 generates a new classification label based on the attribute information selection evaluation information or the attribute candidate. Then, the data model editing unit 108 updates the data model by associating the generated classification label with the attribute information included in the attribute candidate. That is, the data model editing unit 108 updates the data model based on the attribute information selection evaluation information and the attribute candidate, or based on the attribute candidate. The data model editing unit 108 outputs the updated data model to the data model information storage unit 101.

[0075] FIG. 17 is a flowchart showing the processing procedure of the data model editing unit 108 according to the first embodiment.

[0076] In step S 1701 , the data model editing unit 108 acquires the attribute information extraction information and attribute information selection evaluation information that are accepted by the setting accepting unit 107 from the user and are held in the extraction setting accumulation unit 103 .

[0077] In step S 1702 , the data model editing unit 108 obtains the data model name included in the attribute information extraction information obtained in step S 1701 , and obtains the data model specified by the data model name from the data model information accumulation unit 101 .

[0078] In step S 1703 , the data model editing unit 108 acquires attribute candidates from the attribute candidate generating unit 104 .

[0079] In step S1704, the data model editing unit 108 generates a new classification label based on the attribute information selection evaluation information acquired in step S1701. For example, if the attribute information selection evaluation information includes a "classification label," the data model editing unit 108 generates a new classification label using the "classification label." For example, if the attribute information selection evaluation information does not include a "classification label," the data model editing unit 108 generates a new classification label using attribute information selection evaluation information other than the "classification label," or attribute information included in an attribute candidate. Attribute information selection evaluation information other than the "classification label" is, for example, classification label configuration information.

[0080] In step S1705, the data model editing unit 108 updates the data model by associating each attribute information included in the attribute candidate with the classification label generated in step S1704 via the entities. FIG. 18 is a diagram showing an example of updating the data model. The data model shown in FIG. 18 is a data model in the graph database format shown in FIG. 6. The data model editing unit 108 connects the attribute information "EV", "Optimization", and "Smart Grid" included in the attribute candidate with the newly generated classification label by an edge via the entities "Document A" and "Document X" of the attribute information. As a result, as shown in FIG. 19, the data model editing unit 108 generates a new data model in which the new classification label is associated with the attribute information included in the attribute candidate. Note that the entity and the newly generated classification label may be in different hierarchies such as a parent-child relationship, or may be in the same hierarchical level.

[0081] In step S 1706 , the data model editing unit 108 stores the data model generated in step S 1705 in the data model information accumulation unit 101 .

[0082] <Summary of the first embodiment> According to the data model generating device of the first embodiment, a set of attribute information is extracted from a data model, and related attribute candidates are generated by repeating the selection for selected attribute information and the calculation of an evaluation value of the selected attribute information, and the data model is updated based on information such as the attribute candidates. With such a configuration, even if there is no domain information such as detailed classification conditions, a data model with a hierarchical structure having visibility and searchability can be generated. Note that, when the data model editing unit 108 generates new classification labels based on the attribute candidates generated by the attribute candidate generating unit 104 without using attribute information selection evaluation information from the outside such as a user, a data model having unknown classification labels that are not expected by the user can be generated.

[0083] In the first embodiment, the selection unit 105 selects the selected attribute information based on the attribute information selection evaluation information including at least one of the classification label and the classification label configuration information. With this configuration, the selection of the selected attribute information can be performed according to the user's preferences.

[0084] In the first embodiment, the evaluation unit 106 calculates an evaluation value of the selected attribute information based on the relevance or difference between the selected attribute information and the classification label configuration information. With this configuration, the selected attribute information can be evaluated and attribute candidates can be generated according to the user's preferences.

[0085] <Embodiment 2> 20 is a diagram showing the configuration of a data model generating device according to the present embodiment 2. In the following, among the components according to the present embodiment 2, components that are the same as or similar to the components described above are given the same or similar reference symbols, and different components will be mainly described.

[0086] The configuration in FIG. 20 is the same as that in FIG. 1, except that the attribute candidate generating unit 104 and the selecting unit 105 are changed to an attribute candidate generating unit 104A and a selecting unit 105A, respectively.

[0087] As a first example, assume that the attribute information selection evaluation information received by the setting receiving unit 107 and held in the extraction setting storage unit 103 has a "classification label" but is missing "classification label configuration information." In such a case, the attribute candidate generation unit 104A generates "classification label configuration information" based on the "classification label" and provides parameters including the "classification label configuration information" to the selection unit 105A. The selection unit 105A selects a subset of attribute information from the set of attribute information as selected attribute information, based on the "classification label configuration information" included in the parameters. Note that the selection unit 105A, rather than the attribute candidate generation unit 104A, may generate the "classification label configuration information" based on the "classification label."

[0088] As a second example, assume that the attribute information selection evaluation information received by the setting receiving unit 107 and held in the extraction setting storage unit 103 is missing a "classification label" and a "classification label configuration information." In such a case, the selection unit 105A selects a subset of attribute information from the set of attribute information as selected attribute information according to a predetermined procedure. For example, the selection unit 105A may select the selected attribute information randomly as the predetermined procedure, or may select the selected attribute information using heuristics.

[0089] Fig. 21 is a flowchart showing the processing procedure of the attribute candidate generating unit 104A according to the embodiment 2. Since the processing of steps S2101 and S2103 to S2108 in Fig. 21 is similar to the processing of steps S1001 and S1003 to S1008 in Fig. 10, respectively, the processing of step S2102 will be mainly described below.

[0090] In step S2102, the attribute candidate generation unit 104A creates and holds parameters such as those shown in Fig. 11 using the set of attribute information acquired in step S2101 and the attribute information selection evaluation information acquired in step S2101. If a "classification label" exists in the attribute information selection evaluation information but the "classification label configuration information" is missing, the attribute candidate generation unit 104A generates "classification label configuration information" using the "classification label" or other attribute information selection evaluation information. In this way, the attribute candidate generation unit 104A complements the "classification label configuration information".

[0091] FIG. 22 is a diagram showing an example of a complementation process of the "classification label configuration information." For example, when "processing classification" is set in the classification label, the attribute candidate generation unit 104A generates the word "processing" or "classification" from "processing classification." Then, the attribute candidate generation unit 104A generates several words including "processing" or "classification" as the parameter "classification label configuration information." The format of the generated classification label configuration information may be a word format or a format such as a regular expression.

[0092] In step S2103, the attribute candidate generation unit 104A passes the parameters created in step S2102 to the selection unit 105A, and causes the selection unit 105A to select one piece of attribute information from the set of attribute information. For this reason, the selection unit 105A makes the selection according to the "selection method" included in the parameters.

[0093] Fig. 23 is a flowchart showing the processing procedure of the selection unit 105A according to the embodiment 2. Since the processing of steps S2301 and S2303 in Fig. 23 is similar to the processing of steps S1301 and S1303 in Fig. 13, respectively, the processing of step S2302 will be mainly described below.

[0094] In step S2302, the selection unit 105A selects one piece of attribute information from the set of attribute information set in the "list of attribute information" according to the "selection method" included in the parameters acquired in step S2102. In the selection method, at least one piece of information, a classification label and classification label configuration information, is specified, and attribute information related to the specified information is preferentially selected. Depending on the specification of the "selection method", the selection unit 105A may randomly select one piece of attribute information, or may select one piece of attribute information using heuristics or the like.

[0095] <Summary of the second embodiment> According to the data model generating device of the second embodiment as described above, the selection unit 105A makes a selection for the selected attribute information based on the classification label configuration information generated from the classification label. With such a configuration, even if the "classification label configuration information" is missing, the selection unit 105A can make a selection for the selected attribute information. In addition, it is possible to obtain an unknown classification that the user has not anticipated, that is, a combination of unknown attribute information.

[0096] In the second embodiment, the selection unit 105A performs selection for selected attribute information according to a predetermined procedure, such as random or heuristic. With such a configuration, even if the "classification label" and the "classification label configuration information" are missing, the selection unit 105A can perform selection for selected attribute information. Also, unknown classifications not anticipated by the user, that is, combinations of unknown attribute information, can be obtained. Note that, when heuristics are used, it is expected that the number of trials required to generate a data model that matches the user's preferences can be reduced compared to when random is used.

[0097] <Embodiment 3> 24 is a diagram showing the configuration of a data model generating device according to the present embodiment 3. In the following, among the components according to the present embodiment 3, components that are the same as or similar to the components described above are given the same or similar reference symbols, and different components will be mainly described.

[0098] The configuration of Figure 24 is the same as that of Figure 20, except that the attribute candidate generation unit 104A, evaluation unit 106, and setting reception unit 107 are changed to an attribute candidate generation unit 104B, an evaluation learning unit 106B, and a setting reception unit 107B, respectively.

[0099] The setting reception unit 107B has the functions of the setting reception unit 107 described in the first embodiment, a function of presenting at least a part of the parameters including the selected attribute information to the user, and a function of receiving feedback on the selected attribute information from the user. The feedback on the selected attribute information includes, for example, designation of the selected attribute information to be used as an attribute candidate.

[0100] The evaluation learning unit 106B learns an evaluation value using the feedback on the selected attribute information presented to the user and accepted by the setting acceptance unit 107B and the selected attribute information as learning data, and calculates an evaluation value based on the selected attribute information and the learning result. The learning is, for example, machine learning (training) such as deep learning, the learning result is, for example, a learned model, and the learning data is, for example, teacher data.

[0101] Fig. 25 is a flowchart showing the processing procedure of the attribute candidate generating unit 104B according to the embodiment 3. Since the processing of steps S2501 to S2504 and S2506 to S2508 in Fig. 25 is similar to the processing of steps S2101 to S2104 and S2106 to S2108 in Fig. 21, respectively, the processing of step S2505 will be mainly described below.

[0102] In step S2505, the attribute candidate generation unit 104B passes the parameters to the evaluation learning unit 106B, and causes the evaluation learning unit 106B to calculate an evaluation value for a subset of attribute information included in the “selected attribute information.” For this reason, the evaluation learning unit 106B calculates the evaluation value as follows.

[0103] First, evaluation learning unit 106B passes at least a portion of the parameters including selected attribute information to setting receiving unit 107B, and setting receiving unit 107B presents the at least a portion to the user and receives feedback on the selected attribute information from the user.

[0104] The evaluation learning unit 106B learns an evaluation value corresponding to the relationship between the feedback and the selected attribute information, using the feedback for the selected attribute information received by the setting receiving unit 107B and the selected attribute information as learning data. Then, the evaluation learning unit 106B calculates an evaluation value for the selected attribute information based on the selected attribute information and the learning result.

[0105] FIG. 26 is a flowchart showing the processing procedure of evaluation learning unit 106B according to the third embodiment.

[0106] In step S2601, evaluation learning unit 106B acquires parameters including "selected attribute information" from attribute candidate generating unit 104B.

[0107] In step S2602, evaluation learning unit 106B passes at least a portion of the parameters acquired in step S2601 to setting receiving unit 107B. At least a portion of the parameters includes "selected attribute information" and at least a portion of "attribute information selection evaluation information." At least a portion of the "attribute information selection evaluation information" is information related to the index specified in the "evaluation method" of the parameter, and includes, for example, "classification label" and "classification label configuration information."

[0108] In step S2603, evaluation learning unit 106B acquires, from setting reception unit 107B, feedback on the "selected attribute information" received from the user by setting reception unit 107B.

[0109] In step S2604, the evaluation learning unit 106B performs learning on the evaluation value of the selected attribute information using the "selected attribute information" and the feedback acquired in step S2603 as learning data. The learning in the evaluation learning unit 106B may be learning that performs weighting that reflects the time at which the feedback is acquired from the setting acceptance unit 107B in the evaluation value. The time at which the feedback is acquired may be the time it takes for the user to input the feedback to the setting acceptance unit 107B. In this case, for example, the weighting can be changed depending on whether the user immediately decides on the feedback or takes a long time to think about it. The time at which the feedback is acquired may be the time from when the data model generation device is operated to when the user inputs the feedback to the setting acceptance unit 107B. In this case, for example, the weighting can be made heavier for feedback inputted after a long time has passed since the data model generation device is operated than for feedback inputted after a short time has passed since the data model generation device is operated.

[0110] Alternatively, in step S2604, the learning in the evaluation learning unit 106B may be learning in which the setting receiving unit 107B performs weighting to reflect the attributes of the user whose feedback has been received in the evaluation value. The user attributes include, for example, at least one of the user's job title, experience in the assigned job, suitability for the assigned job, and whether or not the user is an expert. This makes it possible to weight the feedback of an experienced user more heavily than that of a new user, for example.

[0111] Furthermore, the evaluation learning unit 106B may perform pre-learning using history information that pairs the "selected attribute information" with feedback for that information. This allows the evaluation learning unit 106B to properly calculate the evaluation value for the "selected attribute information" after learning, without feedback from the user.

[0112] In step S2605, evaluation learning unit 106B calculates an evaluation value for the "selected attribute information" included in at least a portion of the parameters, based on at least a portion of the parameters including the "selected attribute information" and the learning results obtained in step S2604.

[0113] In step S2606, evaluation learning unit 106B outputs the evaluation value calculated in step S2605 to attribute candidate generating unit 104B.

[0114] <Summary of the Third Embodiment> According to the data model generating device of the third embodiment as described above, an evaluation value is learned using feedback on selected attribute information presented externally to a user or the like and the selected attribute information as learning data, and an evaluation value is calculated based on the selected attribute information and the learning result. According to such a configuration, after learning, it is possible to evaluate the selected attribute information that matches the user's preferences without feedback from the user.

[0115] <Modification> The learning and calculation of the evaluation value by the evaluation learning unit 106B is not limited to the learning and calculation described in the third embodiment. For example, the evaluation learning unit 106B may learn the evaluation value using the classification labels and attribute candidates associated in the past as learning data. Then, the evaluation learning unit 106B may calculate the degree of correspondence between the classification labels and the selected attribute information as an evaluation value based on the classification labels included in the attribute information selection evaluation information accepted by the setting acceptance unit 107B, the selected attribute information, and the learning result. Even with such a configuration, after learning, it is possible to evaluate the selected attribute information that matches the user's preferences without feedback from the user.

[0116] Furthermore, the evaluation learning unit 106B may learn the evaluation value using the previously associated classification labels and selected attribute candidates as learning data, instead of the previously associated classification labels and attribute candidates. For example, the evaluation learning unit 106B may learn the evaluation value by converting each attribute information of the selected attribute information into a feature vector, and learning a nonlinear approximation function that associates the relationship between the distance between the vectors, etc., and the evaluation value.

[0117] In the third embodiment, the evaluation learning unit 106B calculates the evaluation value by learning based on the feedback on the selected attribute information presented externally, but this is not limited to this. For example, if the feedback on the selected attribute information presented externally is an evaluation value from a user, the evaluation unit 106 described in the first embodiment may use the evaluation value from the user as it is.

[0118] <Fourth embodiment> 27 is a diagram showing the configuration of a data model generating device according to the present embodiment 4. In the following, among the components according to the present embodiment 4, components that are the same as or similar to the components described above are given the same or similar reference symbols, and different components will be mainly described.

[0119] The configuration of Figure 27 is the same as that of Figure 24, except that the attribute candidate generation unit 104B, selection unit 105A, evaluation learning unit 106B, and setting reception unit 107B of the configuration of Figure 24 are changed to an attribute candidate generation unit 104C, selection learning unit 105C, evaluation learning unit 106C, and setting reception unit 107C, respectively.

[0120] Evaluation learning unit 106C has the function of evaluation learning unit 106B according to the third embodiment, and a loop function of feeding back the evaluation value of selected attribute information to selection learning unit 105C.

[0121] The setting reception unit 107C has the functions of the setting reception unit 107B described in embodiment 3, a function of presenting a monitoring and control screen represented by attribute information to a user, a function of accepting user operations on the monitoring and control screen, and a function of accepting plant-specific information including the context of the processing steps of the plant represented by the attribute information.

[0122] The selective learning unit 105C performs at least one of the following first learning, second learning, and third learning. In the following, an example in which the selective learning unit 105C performs all of the first learning, second learning, and third learning will be described.

[0123] <First lesson> The selection learning unit 105C learns selections for the selected attribute information using past selected attribute information and the evaluation value of the selected attribute information fed back from the evaluation learning unit 106C as learning data, and makes selections for the selected attribute information based on the set of attribute information and the learning results.

[0124] <Second Study> The selection learning unit 105C learns a selection for the selected attribute information using the operation history of the user on the monitoring control screen as learning data, and makes a selection for the selected attribute information based on the set of attribute information and the learning result.

[0125] <Third Study> The selection learning unit 105C learns a selection for the selected attribute information using the plant unique information as learning data, and makes a selection for the selected attribute information based on the set of attribute information and the learning result.

[0126] After sufficient learning, the selection learning unit 105C and the evaluation learning unit 106C automatically select and evaluate the selected attribute information. As a result, after learning, it is possible to select and evaluate the selected attribute information that matches the user's preferences, and ultimately generate attribute candidates that match the user's preferences, without feedback from the user.

[0127] Fig. 28 is a flowchart showing the processing procedure of the attribute candidate generating unit 104C according to the embodiment 4. Since the processing of steps S2801, S2802, and S2804 to S2808 in Fig. 28 is similar to the processing of steps S2501, S2502, and S2504 to S2508 in Fig. 25, the processing of step S2803 will be mainly described below.

[0128] In step S2803, attribute candidate generation unit 104C passes the parameters created in step S2802 to selection learning unit 105C, and causes selection learning unit 105C to select one piece of attribute information from the collection of attribute information. By causing attribute candidate generation unit 104C to include information specifying whether or not to learn in the parameters, in steps S2803 and S2805, selection learning unit 105C and evaluation learning unit 106C can selectively perform learning.

[0129] Fig. 29 is a flowchart showing the processing procedure of evaluation learning unit 106C according to Embodiment 4. Since the processing of steps S2901 and S2903 to S2907 in Fig. 29 is similar to the processing of steps S2601 and S2602 to S2606 in Fig. 26, respectively, the processing of step S2902 will be mainly described below.

[0130] In step S2902, evaluation learning unit 106C determines whether to execute learning based on information specifying whether to execute learning or not, included in the parameters acquired in step S2901. If it is determined that learning is to be executed, the process proceeds to step S2903, and if it is determined that learning is not to be executed, the process proceeds to step S2906.

[0131] After step S2906, in step S2907, evaluation learning unit 106C outputs the evaluation value calculated in step S2906. The evaluation value output from evaluation learning unit 106C is used to update the parameter "evaluation value of selected attribute information" in attribute candidate generation unit 104C, and is also output to selection learning unit 105C.

[0132] Fig. 30 is a flowchart showing the processing procedure of selective learning unit 105C according to embodiment 4. Since the processing of steps S3001 and S3006 in Fig. 30 is similar to the processing of steps S2301 and S2303 in Fig. 23, respectively, the processing of steps S3002 to S3005 will be mainly described below.

[0133] In step S3002, selection learning unit 105C determines whether to execute learning based on information specifying whether to execute learning or not included in the parameters acquired in step S3001. If it is determined that learning is to be executed, the process proceeds to step S3003, and if it is determined that learning is not to be executed, the process proceeds to step S3005.

[0134] In step S3003, the selection learning unit 105C acquires the evaluation value of the “evaluation value of selected attribute information” included in the parameter as learning data as feedback from the evaluation learning unit 106C. In addition, when the setting receiving unit 107C has accepted the user's operation history on the monitoring and control screen and the plant-specific information, the selection learning unit 105C acquires the operation history and the plant-specific information from the setting receiving unit 107C as learning data.

[0135] In step S3004, the selection learning unit 105C learns about the selection for the selected attribute information by using the learning data acquired in step S3003. Through this learning, the tendency of a subset of attribute information that is considered to be likely to increase the evaluation value of the "selected attribute information" is learned.

[0136] In step S3005, the selection learning unit 105C selects one piece of attribute information that is thought to be likely to increase the evaluation value from the set of attribute information based on the set of attribute information set in the parameter "List of attribute information" and the learning result obtained in step S3004.

[0137] <Summary of the fourth embodiment> According to the data model generating device of the fourth embodiment as described above, selection for the selected attribute information is learned using the previously selected attribute information and the evaluation value of the selected attribute information as learning data, and selection for the selected attribute information is performed based on the set of attribute information and the learning result. According to such a configuration, after learning, it is possible to preferentially select attribute information that is thought to be likely to increase the evaluation value without feedback from the user.

[0138] In the fourth embodiment, the operation history of the user on the monitoring and control screen represented by the attribute information is used as learning data to learn the selection for the selected attribute information, and the selection for the selected attribute information is made based on the set of attribute information and the learning result. With this configuration, learning can be performed using the operation history, which is a type of domain information, so that know-how in the operation of the monitoring and control system can be reflected in the selection for the selected attribute information.

[0139] In the fourth embodiment, the plant-specific information represented by the attribute information is used as learning data to learn the selection for the selected attribute information, and the selection for the selected attribute information is made based on the set of attribute information and the learning result. With this configuration, learning can be performed using the plant-specific information, which is a type of domain information, so that know-how in the operation of the monitoring and control system can be reflected in the selection for the selected attribute information.

[0140] <Modification> In the fourth embodiment, the selection learning unit 105C automatically performs the selection for the selected attribute information, but the present invention is not limited to this. For example, similar to the above-mentioned <first learning unit>, the selection learning unit 105C first learns the selection for the selected attribute information using past selected attribute information and the evaluation value of the selected attribute information as learning data, and selects candidates for the selected attribute information based on the set of attribute information and the learning result.

[0141] Then, the setting reception unit 107C may present the candidates of selected attribute information selected by the selection learning unit 105C to the user and receive feedback on the candidates of selected attribute information from the user. Then, the selection learning unit 105C may select selected attribute information based on the feedback on the candidates of selected attribute information received by the setting reception unit 107C. With this configuration, the selection for selected attribute information that matches the user's preferences can be performed semi-automatically.

[0142] <Fifth Preferred Embodiment> 31 is a diagram showing the configuration of a data model generating device according to the present embodiment 5. In the following, among the components according to the present embodiment 5, components that are the same as or similar to the components described above are given the same or similar reference symbols, and different components will be mainly described.

[0143] The configuration in FIG. 31 is the same as the configuration in FIG. 27 except that selection learning unit 105C in the configuration in FIG. 27 is changed to selection learning unit 105D.

[0144] Selection learning unit 105D has the functions of selection units 105 and 105A according to embodiments 1 and 2, and the functions of selection learning unit 105C according to embodiment 3. Selection learning unit 105D also has a function of making selections for selected attribute information, taking into consideration an increase or decrease in the evaluation value of the newly generated "selected attribute information." In other words, selection learning unit 105D is capable of preferentially selecting attribute information so as not to decrease the evaluation value of the "selected attribute information."

[0145] When selection learning unit 105D selects attribute information taking into consideration an increase or decrease in the evaluation value of the "selected attribute information," evaluation learning unit 106C does not execute learning, that is, proceeds from the process of step S2902 to the process of S2906 in Fig. 29, and calculates an evaluation value using the learning result. Then, in step S2907, evaluation learning unit 106C outputs the evaluation value calculated in step S2906 to selection learning unit 105D, not to attribute candidate generation unit 104C.

[0146] The selection learning unit 105D receives as input the parameters provided by the attribute candidate generating unit 104C and the evaluation value fed back from the evaluation learning unit 106C. Based on the parameters and the evaluation value, the selection learning unit 105D selects one piece of attribute information from the "list of attribute information" included in the parameters. As in the fourth embodiment, when the setting receiving unit 107C acquires domain information such as a user's operation history or plant-specific information, the domain information may be input to the selection learning unit 105D.

[0147] Fig. 32 is a flowchart showing the processing procedure of selective learning unit 105D according to embodiment 5. The processing of steps S3201 to S3204, S3207, and S3208 in Fig. 32 is similar to the processing of steps S3001 to S3004, S3005, and S3006 in Fig. 30, respectively, and therefore the processing of steps S3205 and S3206 will be mainly described below.

[0148] In step S3205, selection learning unit 105D refers to the parameters acquired in step S3201 and determines whether or not to perform collaborative processing for selecting one piece of attribute information in collaboration with evaluation learning unit 106C. Note that collaborative processing may be determined to be performed with a certain probability. If it is determined that collaborative processing is to be performed, processing proceeds to step S3206, and if it is determined that collaborative processing is not to be performed, processing proceeds to step S3207.

[0149] In step S3206, the selection learning unit 105D selects some attribute information from the set of attribute information included in the "list of attribute information" parameter, and inputs it to the evaluation learning unit 106C. The selection learning unit 105D may select some attribute information using heuristics or the like. The evaluation learning unit 106C calculates an evaluation value of the selected attribute information by using some attribute information as selected attribute information without executing learning in the evaluation learning unit 106C. Then, the selection learning unit 105D selects one attribute information based on the calculated evaluation value. For example, the selection learning unit 105D checks whether the evaluation value of the selected attribute information increases or decreases, and selects one attribute information that maximizes the evaluation value of the selected attribute information.

[0150] <Summary of the fifth embodiment> In the learning as in the fourth embodiment, one of a plurality of pieces of selected attribute information that are similar to each other within a certain range tends to be selected, so that when the evaluation value of selected attribute information outside the certain range is the maximum, appropriate selected attribute information may not be obtained. In contrast, in the fifth embodiment, the selection learning unit 105D appropriately uses a function similar to that of the selection units 105 and 105A, and therefore, when selected attribute information outside the certain range, that is, selected attribute information that cannot be obtained by learning, is appropriate, the selected attribute information can be used. In other words, the selection learning unit 105D can use selected attribute information such as mutations obtained by a genetic algorithm, and therefore, the possibility of using truly optimal selected attribute information can be increased.

[0151] <Other Modifications> The attribute information extraction management unit 102, the attribute candidate generation unit 104, the selection unit 105, the evaluation unit 106, and the data model editing unit 108 in Fig. 1 described above are hereinafter referred to as "the attribute information extraction management unit 102, etc." The attribute information extraction management unit 102, etc. are realized by a processing circuit 81 shown in Fig. 33. That is, the processing circuit 81 includes the attribute information extraction management unit 102 that extracts a set of attribute information from a data model of a monitoring target, the selection unit 105 that selects a subset of attribute information from the set of attribute information as selected attribute information, the evaluation unit 106 that calculates an evaluation value of the selected attribute information, the attribute candidate generation unit 104 that generates attribute candidates from a plurality of selected attribute information based on evaluation values ​​of the plurality of selected attribute information obtained by repeating selection for the selected attribute information and calculation of the evaluation value of the selected attribute information, and the data model editing unit 108 that updates the data model based on label information related to classification labels representative of the attribute candidates and attribute candidates, or based on the attribute candidates. Dedicated hardware or a processor that executes a program stored in a memory may be applied to the processing circuit 81. Examples of the processor include a central processing unit, a processing unit, an arithmetic unit, a microprocessor, a microcomputer, and a DSP (Digital Signal Processor).

[0152] When the processing circuit 81 is a dedicated hardware, the processing circuit 81 corresponds to, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or a combination of these. Each function of the attribute information extraction management unit 102 and the like may be realized by a circuit in which the processing circuits are distributed, or the functions of each unit may be realized by a single processing circuit.

[0153] When the processing circuit 81 is a processor, the functions of the attribute information extraction management unit 102 and the like are realized by a combination with software and the like. The software and the like includes, for example, software, firmware, or software and firmware. The software and the like are written as a program and stored in a memory. As shown in FIG. 34, the processor 82 applied to the processing circuit 81 realizes the functions of each unit by reading and executing a program stored in the memory 83. That is, the data model generation device includes a memory 83 for storing a program that, when executed by the processing circuit 81, results in the execution of the following steps: extracting a set of attribute information from a data model to be monitored; selecting a subset of attribute information from the set of attribute information as selected attribute information; calculating an evaluation value of the selected attribute information; generating attribute candidates from a plurality of selected attribute information based on evaluation values ​​of a plurality of selected attribute information obtained by repeating the selection for the selected attribute information and the calculation of the evaluation value of the selected attribute information; and updating the data model based on label information and attribute candidates related to classification labels representing the attribute candidates, or based on the attribute candidates. In other words, this program can be said to cause a computer to execute the procedures and methods of the attribute information extraction management unit 102, etc. Here, the memory 83 may be, for example, a non-volatile or volatile semiconductor memory such as a random access memory (RAM), a read only memory (ROM), a flash memory, an erasable programmable read only memory (EPROM), or an electrically erasable programmable read only memory (EEPROM), a hard disk drive (HDD), a magnetic disk, a flexible disk, an optical disk, a compact disk, a mini disk, a digital versatile disk (DVD), a drive device for these, or any storage medium to be used in the future.

[0154] The above describes a configuration in which each function of the attribute information extraction management unit 102, etc. is realized either by hardware or software, etc. However, the present invention is not limited to this, and a configuration in which part of the attribute information extraction management unit 102, etc. is realized by dedicated hardware and another part is realized by software, etc. For example, the function of the attribute information extraction management unit 102 can be realized by the processing circuit 81 as dedicated hardware, and the other functions can be realized by the processing circuit 81 as the processor 82 reading and executing a program stored in the memory 83.

[0155] As described above, the processing circuitry 81 can realize each of the above-mentioned functions by hardware, software, or a combination of these.

[0156] The data model generating device may be configured by a single device or by a combination of multiple devices. The elements of the data model generating device may be configured by causing a computer to execute a program, or may be configured by hardware that does not execute a program. In other words, processing may be performed by software, or signal processing may be performed by hardware.

[0157] In addition, the data model generation device described above can also be applied to a data model generation system including a data model generation unit, which is a data model generation device other than setting receiving units 107, 107B, 107C, and a setting receiving unit having the same functions as setting receiving units 107, 107B, 107C and capable of communicating with the data model generation unit via the Internet or the like.

[0158] It should be noted that the embodiments and modifications may be freely combined, and the embodiments and modifications may be modified or omitted as appropriate.

[0159] The above description is illustrative in all respects and is not restrictive. It is understood that countless variations not illustrated can be envisioned. [Explanation of symbols]

[0160] 102 attribute information extraction management unit, 104, 104A, 104B, 104C attribute candidate generation unit, 105, 105A selection unit, 105C, 105D selection learning unit, 106 evaluation unit, 106B, 106C evaluation learning unit, 107, 107B, 107C setting reception unit, 108 data model editing unit.

Claims

[

1. ] A setting reception unit that receives from the outside label information including at least one of a classification label and classification label configuration information associated with the classification label; An attribute information extraction management unit that extracts a set of attribute information from a data model to be monitored; A selection unit that selects, as selected attribute information, a subset of the attribute information based on the received label information from the set of the attribute information; An evaluation unit that calculates an evaluation value of the selected attribute information; An attribute candidate generation unit that generates an attribute candidate from the plurality of selected attribute information based on the evaluation values of the plurality of selected attribute information obtained by repeating the selection for the selected attribute information and the calculation of the evaluation value of the selected attribute information; A data model editing unit that updates the data model based on the label information related to the classification label representing the attribute candidate and the attribute candidate, or based on the attribute candidate A data model generation device comprising: [

2. ] The data model generation device according to claim 1, wherein the setting reception unit receives the label information including the classification label from the outside, and classification label configuration information associated with the classification label is generated based on the classification label included in the received label information; wherein the selection unit makes a selection for the selected attribute information based on the classification label configuration information. A data model generation device. [

3. ] The data model generation device according to claim 1, wherein the selection unit makes a selection for the selected attribute information according to a predetermined procedure. A data model generation device. [

4. ] The data model generation device according to claim 1, wherein the selection unit learns the selection for the selected attribute information using the past selected attribute information and the evaluation value of the selected attribute information as learning data, selects candidates for the selected attribute information based on the set of the attribute information and the learning result, and makes a selection for the selected attribute information based on the feedback received by the setting reception unit for the candidates for the selected attribute information presented to the outside. A selection learning unit that performs the selection. A data model generation device. [

5. ] The data model generation device according to claim 1, wherein the selection unit A data model generation device that uses the past selection attribute information and the evaluation value of the selection attribute information as learning data to learn the selection for the selection attribute information, and makes a selection for the selection attribute information based on the set of the attribute information and the learning result.

6. The data model generation device according to claim 1, wherein the setting reception unit receives a user operation history for a monitoring control screen represented by the attribute information, and the selection unit is a selection learning unit that uses the operation history as learning data to learn the selection for the selection attribute information, and makes a selection for the selection attribute information based on the set of the attribute information and the learning result.

7. The data model generation device according to claim 1, wherein the setting reception unit receives plant-specific information including the context of the processing steps of the plant represented by the attribute information, and the selection unit is a selection learning unit that uses the plant-specific information as learning data to learn the selection for the selection attribute information, and makes a selection for the selection attribute information based on the set of the attribute information and the learning result.

8. The data model generation device according to claim 1, wherein the evaluation unit is a data model generation device that calculates the evaluation value based on the degree of association or difference between the selection attribute information and the label information.

9. The data model generation device according to claim 1, wherein the setting reception unit receives feedback from the outside for the selection attribute information presented to the outside, and the evaluation unit is a data model generation device that calculates the evaluation value based on the feedback.

10. The data model generation device according to claim 1, wherein the setting reception unit receives the label information including the classification label from the outside, and the evaluation unit is an evaluation learning unit that learns the evaluation value using the classification label and the attribute candidate associated in the past, or the classification label and the selection attribute information associated in the past as learning data, and calculates the evaluation value based on the classification label included in the received label information, the selection attribute information, and the learning result.

11. The data model generation device according to claim 9, wherein the evaluation unit is An evaluation learning unit that learns the evaluation value using the feedback and the selected attribute information as learning data, and calculates the evaluation value based on the selected attribute information and the learning result, and performs weighting that reflects the time when the feedback is acquired from the setting reception unit on the evaluation value, a data model generation device.

12. The data model generation device according to claim 9, wherein the evaluation unit is an evaluation learning unit that learns the evaluation value using the feedback and the selected attribute information as learning data, and calculates the evaluation value based on the selected attribute information and the learning result, and performs weighting that reflects the attributes of the user outside, who is the one who the setting reception unit has received the feedback from, on the evaluation value, a data model generation device.

13. The data model generation device according to any one of claims 1 to 12, wherein the data model editing unit generates the classification label newly based on the label information or the attribute candidates, and updates the data model by associating the generated classification label with the attribute information included in the attribute candidates, a data model generation device.

14. The data model generation device according to any one of claims 1 to 12, a data model information storage unit that stores the data model used in the attribute information extraction management unit and the data model updated in the data model editing unit, and an extraction setting storage unit that stores the label information used in the data model editing unit further comprising a data model generation device.

15. a data model generation unit, and a setting reception unit that can communicate with the data model generation unit via the Internet comprising wherein the setting reception unit receives from the outside label information including at least one of a classification label and classification label configuration information associated with the classification label, wherein the data model generation unit an attribute information extraction management unit that extracts a set of attribute information from the data model to be monitored, a selection unit that selects a subset of the attribute information as selected attribute information based on the received label information from the set of the attribute information, and an evaluation unit that calculates an evaluation value of the selected attribute information An attribute candidate generation unit that generates attribute candidates from the plurality of pieces of selection attribute information based on the evaluation values of the plurality of pieces of selection attribute information obtained by repeating the selection for the selection attribute information and the calculation of the evaluation value of the selection attribute information; A data model editing unit that updates the data model based on the label information related to the classification label representing the attribute candidate and the attribute candidate, or based on the attribute candidate comprising The data model generation system, wherein the data model generation unit receives the label information.

16. A data model generation method, comprising receiving, from the outside, label information including at least one of a classification label and classification label configuration information associated with the classification label, extracting a set of attribute information from a data model to be monitored, selecting, as selection attribute information, a subset of the attribute information from the set of the attribute information based on the received label information, calculating an evaluation value of the selection attribute information, generating attribute candidates from the plurality of pieces of selection attribute information based on the evaluation values of the plurality of pieces of selection attribute information obtained by repeating the selection for the selection attribute information and the calculation of the evaluation value of the selection attribute information, and updating the data model based on the label information related to the classification label representing the attribute candidate and the attribute candidate, or based on the attribute candidate.

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