CN112352230A - Search device, search method, and machine learning device - Google Patents

Abstract

A search device (1) for searching for specification data similar to search conditions from a specification data group including 1 or more specification item values each showing details of an item indicating a product specification, the search device (1) comprising: a similarity learning unit (14) which learns the degree to which 2 specification item values of the same item are similar to each other for all combinations of specification item values based on a specification data group stored in a product design database (13); an input unit (12) that receives an input of a specification item value used as a search condition; and a similar specification search unit (16) that searches for specification data similar to the input specification data composed of the specification item values received by the input unit (12) on the basis of the learning result of the similarity learning unit (14).

Description

Search device, search method, and machine learning device

Technical Field

The present invention relates to a search device, a search method, and a machine learning device for searching for data relating to a specification similar to a specification indicated by input data from data relating to a past product specification.

Background

In a design site of a product with various specifications requested by a customer, when a new product is designed, a part of data of a design case of a past product with similar specifications is reused, thereby realizing high efficiency of design work and simplification of inspection of design adequacy. However, in order to efficiently search data of design cases of past products having similar specifications, profound knowledge about the corresponding products is required, and a part depending on the experience of a design person in charge is large.

Therefore, the invention described in patent document 1 has been proposed as a technique for efficiently searching data of a design case of a past product without depending on experience of a designer. The invention described in patent document 1 includes: a design case library storing data of past design cases; a knowledge base storing knowledge for judging according to a design specification used in accordance with a required specification; and an inference engine for making a decision by using a knowledge base in accordance with a design specification to be used, wherein similar design cases are searched by taking design restrictions and interference relationships between specification items constituting a required specification into consideration.

Patent document 1: japanese laid-open patent publication No. 8-221437

Disclosure of Invention

However, in the invention described in patent document 1, knowledge for determining a design specification used in accordance with a required specification needs to be described as a rule in a knowledge base by manual work, which is a problem of a great deal of time and labor.

The present invention has been made in view of the above problems, and an object of the present invention is to obtain a search device capable of reducing a workload of a user.

In order to solve the above-described problems, the present invention is a search device that searches for specification data similar to search conditions from a specification data group including 1 or more specification item values each showing details of an item indicating a product specification. The search device has: a similarity learning unit that learns, for all combinations of specification item values, how similar two specification item values of the same item are to each other, based on a specification data group stored in a product design database; and an input unit that receives an input of a specification item value used as a search condition. The search device further includes a similar specification search unit that searches for specification data similar to input specification data composed of the specification item values received by the input unit, based on the learning result of the similarity learning unit.

ADVANTAGEOUS EFFECTS OF INVENTION

The search device according to the present invention achieves the effect of reducing the workload of the user.

Drawings

Fig. 1 is a diagram showing a configuration example of a search device according to embodiment 1.

Fig. 2 is a diagram showing an example of a hardware configuration for realizing the search device according to embodiment 1.

Fig. 3 is a diagram showing another example of a hardware configuration for realizing the search device according to embodiment 1.

Fig. 4 is a diagram showing an example of the detailed configuration of the similarity learning unit included in the search device according to embodiment 1.

Fig. 5 is a diagram showing an example of the configuration of a product design database included in the search device according to embodiment 1.

Fig. 6 is a diagram showing an example of a specification list stored in the product design database according to embodiment 1.

Fig. 7 is a diagram showing an example of a component configuration table stored in the product design database according to embodiment 1.

Fig. 8 is a flowchart showing an example of the operation of the similarity learning unit included in the search device according to embodiment 1.

Fig. 9 is a diagram showing the concept of a method of calculating the similarity between item values in the similarity learning unit included in the search device according to embodiment 1.

Fig. 10 is a diagram showing an example of similarity data generated by the similarity learning unit included in the search device according to embodiment 1.

Fig. 11 is a diagram showing another example of the similarity data generated by the similarity learning unit included in the search device according to embodiment 1.

Fig. 12 is a flowchart showing an example of the search operation of the search device according to embodiment 1.

Fig. 13 is a diagram showing an example of a specification input screen displayed by the search device according to embodiment 1.

Fig. 14 is a diagram showing an example of the detailed configuration of the similarity specification search unit included in the search device according to embodiment 1.

Fig. 15 is a diagram showing an example of similarity weight data used by the similarity specification search unit of the search device according to embodiment 1.

Fig. 16 is a flowchart showing an example of the operation of the similarity specification search unit included in the search device according to embodiment 1.

Fig. 17 is a diagram showing an example of the calculation result of the specification similarity calculation unit of the search device according to embodiment 1.

Fig. 18 is a diagram showing an example of the similar specification search result displayed on the search result display unit of the search device according to embodiment 1.

Fig. 19 is a diagram showing a modification 1 of the search device according to embodiment 1.

Fig. 20 is a flowchart showing an example of the search operation of the search device according to variation 1 of embodiment 1.

Fig. 21 is a diagram showing an example of the similar specification search result displayed on the search result detail display unit of the search device according to variation 1 of embodiment 1.

Fig. 22 is a diagram showing a modification example 2 of the search device according to embodiment 1.

Fig. 23 is a flowchart showing an example of the search operation of the search device according to variation 2 of embodiment 1.

Fig. 24 is a diagram showing an example of a similarity weight data change screen displayed by the search device according to variation 2 of embodiment 1.

Fig. 25 is a diagram showing an example of the similar specification search result displayed on the search result detail display unit of the search device according to variation 2 of embodiment 1.

Fig. 26 is a diagram showing a configuration example of the search device according to embodiment 2.

Fig. 27 is a flowchart showing an example of the specification similarity learning process in the search device according to embodiment 2.

Fig. 28 is a diagram showing an example of an inter-item-value similarity adjustment screen displayed by the search device according to embodiment 2.

Fig. 29 is a diagram showing an example of adjusted similarity data output by the similarity adjustment unit of the search device according to embodiment 2.

Fig. 30 is a diagram showing a configuration example of a similar specification search unit included in the search device according to embodiment 2.

Fig. 31 is a diagram showing a configuration example of a similarity learning unit included in the search device according to embodiment 3.

Fig. 32 is a flowchart showing an example of the operation of the similarity learning unit included in the search device according to embodiment 3.

Fig. 33 is a diagram showing an example of a use component selection definition used by the similarity learning unit of the search device according to embodiment 3.

Fig. 34 is a diagram showing the concept of the specification similarity learning process executed by the similarity learning unit of the search device according to embodiment 3.

Fig. 35 is a diagram showing a configuration example of the search device according to embodiment 4.

Fig. 36 is a flowchart showing an example of the search operation of the search device according to embodiment 4.

Fig. 37 is a diagram showing an example of a specification input screen displayed by the search device according to embodiment 4.

Fig. 38 is a diagram showing a configuration example of a similar specification search unit included in the search device according to embodiment 4.

Fig. 39 is a diagram showing an example of extended similarity weight data used by the similarity specification search unit of the search device according to embodiment 4.

Fig. 40 is a flowchart showing an example of the operation of the similarity specification search unit included in the search device according to embodiment 4.

Fig. 41 is a diagram showing the concept of the special specification similarity calculation performed by the special specification similarity calculation unit of the similar specification search unit included in the search device according to embodiment 4.

Fig. 42 is a diagram showing an example of the similar specification search result displayed on the search result detail display unit of the search device according to embodiment 4.

Fig. 43 is a diagram showing a configuration example of a similar specification search unit included in the search device according to embodiment 5.

Fig. 44 is a flowchart showing an example of the operation of the similarity specification search unit included in the search device according to embodiment 5.

Fig. 45 is a diagram showing an example of keywords classified by items used by the similarity specification search unit of the search device according to embodiment 5.

Fig. 46 is a diagram showing an example of a search result of the search device according to embodiment 5.

Detailed Description

Hereinafter, a search device, a search method, and a machine learning device according to embodiments of the present invention will be described in detail with reference to the drawings. The present invention is not limited to the embodiments.

Embodiment 1.

Fig. 1 is a diagram showing a configuration example of a search device according to embodiment 1 of the present invention. The search device 1 according to embodiment 1 includes a control unit 11, an input unit 12, a product design database 13, a similarity learning unit 14, a similarity specification search unit 16, and a search result display unit 17.

The control unit 11 controls all processes executed by the search device 1, and in the present embodiment, controls the input unit 12, the similarity learning unit 14, the similarity specification search unit 16, and the search result display unit 17.

The input unit 12 is provided for the user to input information of product specifications as a condition for searching for necessary data from the data stored in the product design database 13. The input unit 12 acquires information on product specifications input by the user. The information on the product specification acquired by the input unit 12 is assumed to be information indicating a newly designed product specification.

The product design database 13 holds data relating to product specifications of past designs. Details of the data held by the product design database 13 will be described later.

The similarity learning unit 14 analyzes the data stored in the product design database 13, learns the similarity between the item values of the product specifications, and generates similarity data 15 used in the search processing of the similarity specification search unit 16. The item value of the product specification is a value representing each item of the product specification. The similarity learning section 14 constitutes a machine learning device.

The similar specification searching unit 16 searches for specification data indicating a specification similar to the specification indicated by the information acquired by the input unit 12 from among the specification data groups stored in the product design database 13, based on the similarity data 15 which is the learning result of the similarity learning unit 14.

The search result display unit 17 displays the search result of the similarity specification search unit 16.

In the present embodiment, the description is made on the premise that the product design database 13 is present inside the search device 1, but the product design database 13 may be present outside the search device 1. For example, the search device 1 and the product design database 13 may be connected via a communication network, and the search device 1 may acquire necessary data from the product design database 13 via the communication network.

Fig. 2 is a diagram showing an example of a hardware configuration for realizing the search device according to embodiment 1. The search device 1 is implemented by a processing circuit 101, a storage device 102, an input device 103, and a display device 104 shown in fig. 2.

The processing circuit 101 is a circuit that executes various processes in the search device 1, and is, 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 thereof. The storage device 102 is a hard disk drive, flash memory, or the like. The input device 103 is a keyboard, a mouse, a touch panel, or the like, and may be any device as long as it can input information necessary for the operation of the search device 1. The display device 104 is a display, a liquid crystal monitor device, or the like. The display device 104 may be any device as long as the user can refer to information input by using the input device 103, a search result of the similarity specification search unit 16, and the like, and may be a projector or the like.

The control unit 11, the similarity learning unit 14, and the similarity specification searching unit 16 of the search device 1 are realized by a processing circuit 101. The product design database 13 is implemented by the storage means 102. The storage device 102 also stores the similarity data 15, information input by the user via the input unit 12, search results of the similarity specification search unit 16, and the like. The input unit 12 is realized by an input device 103, and the search result display unit 17 is realized by a display device 104.

In addition, all or a part of the control unit 11, the similarity learning unit 14, and the similarity specification searching unit 16 of the searching apparatus 1 may be realized by a common processor and memory. In this case, the hardware configuration of the search device 1 is as shown in fig. 3. Fig. 3 is a diagram showing another example of a hardware configuration for realizing the search device according to embodiment 1.

The processor 201 shown in fig. 3 is a CPU (Central Processing Unit, also referred to as a Central Processing Unit, arithmetic Unit, microprocessor, microcomputer, dsp (digital Signal processor), system lsi (large Scale integration), or the like. The Memory 202 is a ram (random Access Memory), a rom (Read Only Memory), an eprom (Erasable Programmable Read Only Memory), an EEPROM (registered trademark), or the like. The storage device 102, the input device 103, and the display device 104 shown in fig. 3 are the same as the storage device 102, the input device 103, and the display device 104 shown in fig. 2.

The hardware of the configuration shown in fig. 3 may be a tablet PC (personal computer), a desktop PC, or a server-client server and a client PC. In the case where the control unit 11, the similarity learning unit 14, and the similar specification searching unit 16 are realized by the processor 201 and the memory 202 shown in fig. 3, a program for causing the processor 201 to function as the control unit 11, the similarity learning unit 14, and the similar specification searching unit 16 is stored in the memory 202 or the like. The processor 201 reads and executes the program stored in the memory 202, thereby realizing the control unit 11, the similarity learning unit 14, and the similarity specification searching unit 16.

The search device according to embodiment 2 and the following embodiments is also realized by hardware having a configuration shown in fig. 2 or 3.

Next, the operation of the search device 1 according to embodiment 1 will be described. First, the specification similarity learning process by the similarity learning unit 14 will be described, followed by a description of the search operation of the search device 1. Next, an example of search operation in the case of designing a motor will be described, but the present invention can be applied to any product having data equivalent to data stored in a product design database 13 described later.

(processing for specification similarity learning by the similarity learning unit 14)

Fig. 4 is a diagram showing an example of the detailed configuration of the similarity learning unit 14 included in the search device 1 according to embodiment 1. The similarity learning unit 14 constituting the machine learning device includes a use component extracting unit 41 and a similarity calculating unit 42. The use component extracting section 41 extracts a list of use components for each specification item value from the product design database 13. The similarity calculation unit 42 calculates the similarity between the item values based on the list of the use parts for each specification item value extracted by the use part extraction unit 41.

Fig. 5 is a diagram showing a configuration example of the product design database 13 included in the search device 1 according to embodiment 1. The product design database 13 includes a specification list 131 listing product specifications of past designs and a component configuration table 132 recording component configurations of respective products. The specification list 131 corresponds to the specification data. Fig. 6 shows an example of the specification list 131, and fig. 7 shows an example of the component configuration table 132.

Fig. 6 is a diagram showing an example of a specification list stored in the product design database 13 according to embodiment 1. The specification list 131 includes at least: a model id (identification)601 for identifying each product, a series name 602 which is a name of a group of specific products having similar specifications, a rated power 603 of each series product, a rated rotational speed 604 of each series product, a brake 605 which indicates whether each series product has an electromagnetic brake, and a special specification 606 which indicates a special specification of each series product. The special specification 606 stores text data, which is natural text. The series of special specification 606 of '-' indicates that no text data representing the special specification is stored. In an actual motor, there are many specifications such as the maximum rotation speed, the presence or absence of an oil seal, and the shape of the shaft end, but for simplification of the description, only 5 out of 6 shown in fig. 6 except for the special specification 606 will be used. Note that the number of items used for the search may be any number as long as it is 1 or more. Hereinafter, products belonging to the same series, that is, product groups to which the same model ID is given may be collectively referred to as models.

Fig. 7 is a diagram showing an example of a component configuration table stored in the product design database 13 according to embodiment 1. The component configuration table 132 contains at least a model ID 601 for identifying each product, a component hierarchy 701, a component ID 702 for identifying each component, a component name 703, and the number 704 indicating the number of components used in one product. The component configuration table 132 is information of components constituting each product corresponding to the model ID 601. The model ID 601 included in the component configuration table 132 is the same as the model ID 601 included in the specification list 131. A common code is used as the model ID 601 in the specification list 131 and the component configuration table 132. The component hierarchy 701 indicates a hierarchy to which a component belongs when the component is managed by being hierarchical. For example, in the case where a product is constituted by 1 st parts of a plurality of types and a certain 1 st part is constituted by a plurality of 2 nd parts, the part hierarchy 701 of the 1 st part is '1', and the part hierarchy 701 of the 2 nd part is '2'. Also, in the case where the 2 nd part is composed of the 3 rd part, the part hierarchy 701 of the 3 rd part is '3'. The actual motor includes other items such as material and size, but only 5 items shown in fig. 7 are used for the sake of simplicity of description.

Fig. 8 is a flowchart showing an example of the operation of the similarity learning unit 14 included in the search device 1 according to embodiment 1, and shows a flow of the specification similarity learning process. For example, when the search device 1 receives an operation from the user to instruct the start of the specification similarity learning, the similarity learning unit 14 starts the specification similarity learning process shown in fig. 8.

When the similarity learning unit 14 starts the specification similarity learning process, it first executes a specification list acquisition process (step S11). Specifically, the specification list 131 of the contents shown in fig. 6 is acquired from the product design database 13 using the component extracting unit 41.

Next, the similarity learning unit 14 repeats the processing of steps S12 and S13 for each specification item included in the specification list 131. Specifically, the similarity learning unit 14 repeats the processing of steps S12 and S13 for each specification item of the series name 602, the rated power 603, and the rated rotation speed 604. At this time, the similarity learning unit 14 repeats the processing of step S12 for each specification item value belonging to the specification item. For example, when the specification item values a1, a2, and a3 belong to the specification item a, the similarity learning unit 14 repeatedly executes the processing of step S12 for each of the specification item values a1, a2, and a3 when the processing of steps S12 and S13 is executed for the specification item a. In step S12, the similarity learning unit 14 extracts a part corresponding to the specification item value from the part configuration table 132. In step S13, the similarity learning unit 14 calculates the similarity between the item values by using a method described later. Step S12 is a process performed using the component extraction section 41, and step S13 is a process performed by the similarity calculation section 42.

For example, the series name 602 in the specification list 131 shown in fig. 6 includes a series a, B, C, D, and …. Therefore, in the processing of steps S12 and S13 performed for the series name 602, the similarity learning unit 14 repeatedly executes the processing of step S12 for the a series, B series, C series, D series, and … to extract corresponding components, and then calculates the similarity between the item values using the extracted components.

Here, in the case of the process of step S12 targeting the a series, there are a product with a model ID of '1' and a product with a model ID of '2' in the a series. Therefore, the similarity learning section 14 acquires all the components used in the product with the model ID of '1' and all the components used in the product with the model ID of '2' from the component configuration table 132 shown in fig. 7. For example, parts used in a product with a model ID of '1' are parts with part IDs PA001, PA002, PA003, PA004, ….

The rated power 603 in the specification list 131 shown in fig. 6 includes 3.5kW, 2.0kW, 1.5kW, 400W, and …. Therefore, the similarity learning unit 14 executes the processing of step S12 for 3.5kW, 2.0kW, 1.5kW, 400W, and … in the processing of steps S12 and S13 for the rated power 603 to extract corresponding components, and then calculates the similarity between the item values using the extracted components.

Here, in the case of the process of step S12 directed to 2.0kW, there are a product with a model ID of '2', a product with a model ID of '3', and a product with a model ID of '4' among products with a rated power of 2.0 kW. Therefore, the similarity learning section 14 acquires all the parts used in the product with the model ID '2', all the parts used in the product with the model ID '3', and all the parts used in the product with the model ID '4' from the part configuration table 132 shown in fig. 7.

In step S13, the similarity calculation unit 42 calculates a value indicating the similarity between the item values, that is, how similar the specification item values are to each other, based on the data of the use component obtained by the use component extraction unit 41 executing step S12 on each specification item value included in the specification item to be processed. Hereinafter, the value calculated by the similarity calculation unit 42 is referred to as a similarity. The similarity calculation unit 42 calculates a similarity between the specification item value Va and the specification item value Vb, that is, a value of the inter-item-value similarity SimSpecValue (Va, Vb) according to the following expression (1).

[ formula 1]

Here, part (V) is a set of components used in all models having V as a specification item value. In addition, count (P) is the number of types of parts in the part set P. Fig. 9 is a diagram showing the concept of a method of calculating the similarity between item values in the similarity learning unit 14 included in the search device 1 according to embodiment 1. For example, 90 types of parts used in the a-series model are PA001, PA002, …, and PA090, and 80 types of parts used in the B-series model are PB001, PB002, …, and PB 080. Wherein, in the case of 70 components in common, simspvalue ('a series', 'B series') is 70/(90+80-70) is 0.7.

Although the example in which the similarity between the item values is calculated for the a series and the B series has been described, when there are other series than the a series and the B series, the similarity between the item values is calculated for the combination of all the series in step S13. For example, when there is a C-series in addition to the a-series and the B-series, the similarity calculation unit 42 calculates the similarity between the item values of the a-series and the B-series, the similarity between the item values of the a-series and the C-series, and the similarity between the item values of the B-series and the C-series.

In step S13, if the similarity calculation unit 42 completes the calculation of the inter-item-value similarity for all the combinations of specification item values, it generates and outputs the similarity data 15 including the calculated inter-item-value similarity.

In this way, the similarity learning unit 14 analyzes the specification list 131 and the component configuration table 132, which are the specification data sets stored in the product design database 13, and learns the degree to which two specification item values of the same specification item are similar to each other for all combinations of specification item values for each specification item indicating the specification of the product. That is, the similarity learning unit 14 learns, based on the specification data set stored in the product design database 13, how similar two specification item values of the same use item are to each other for all combinations of the specification item values.

Fig. 10 is a diagram showing an example of the similarity data 15 generated by the similarity learning unit 14 included in the search device 1 according to embodiment 1. The similarity data 15 at least includes specification items, a specification item value #1, a specification item value #2, and a similarity degree. In the following example, the similarity between the specification item values is set to a relationship of 1 to 1, that is, the similarity between 1 specification item, but the similarity calculation unit 42 may calculate the same similarity for combinations of a plurality of specification items as shown in fig. 11 to generate the similarity data 15. Fig. 11 shows an example of the similarity data 15 including the similarity regarding the combination of the series name and the rated power, and the similarity regarding the combination of the rated power and the rated rotational speed.

(search operation of search device 1)

Next, a search operation of the search device 1 will be described. Fig. 12 is a flowchart showing an example of the search operation of the search device 1 according to embodiment 1. For example, when the search device 1 receives an operation from the user to instruct the start of the search process, the search device 1 starts the operation following the flowchart shown in fig. 12.

When the search device 1 starts the search process, first, the input unit 12 receives specification input, that is, a specification item value input by a user, that is, a designer, as a search condition (step S21). In step S21, the search device 1 displays, for example, the specification input screen shown in fig. 13 on a display unit not shown in fig. 1, and waits for the user to input a specification item value. The display unit is realized by the display device 104 shown in fig. 2 and 3. The specification input screen shown in fig. 13 includes a series name input field 1301, a rated power input field 1302, a rated rotational speed input field 1303, and a brake/brake presence input field 1304. In the example of the specification input screen shown in fig. 13, the specification item value is input by selecting the pull-down menu, but may be input by any method. The specification item value may also be automatically set based on order data through cooperation with other systems such as a sales system.

When the input of the specification item value by the input unit 12 is completed, the similar specification searching unit 16 then searches for a similar specification (step S22). That is, the similar specification searching unit 16 searches the product design database 13 based on the similarity data 15 and the specification item values input in step S21, and specifies a model number of a specification similar to the specification indicated by the specification item values input in step S21. Next, the details of the search processing by the similarity specification search unit 16 will be described.

Fig. 14 is a diagram showing an example of the detailed configuration of the similarity specification searching unit 16 included in the searching apparatus 1 according to embodiment 1. The similarity specification searching section 16 includes a specification similarity calculating section 61 and a data arranging section 62.

The specification similarity calculation section 61 calculates similarities with specifications shown by the input specification data 63 output from the input section 12 for each of the specifications of past models, which are models stored in the product design database 13, based on the similarity weight data 65 and the similarity data 15 generated by the similarity learning section 14. The input specification data 63 is data including each specification item value that the input unit 12 received in step S21. Details of the similarity weight data 65 will be described otherwise.

The data arranging unit 62 rearranges the similarity of the specifications of the past models, which is the calculation result of the specification similarity calculating unit 61, in descending order of similarity, and outputs the similarity of the specifications of the past models after rearrangement as a similar specification search result 64.

Fig. 15 is a diagram showing an example of similarity weight data used by the similarity specification search unit 16 of the search device 1 according to embodiment 1. The similarity weight data 65 includes specification items 1501 and similarity weights 1502. The similarity weight data 65 of the example shown in fig. 15 includes the series name, the rated power, the rated rotational speed, and the similarity weight, which is the weight coefficient of each brake, included in the specification list 131 of the product design database 13 as the specification items. These similarity weights are used when calculating the similarity of the specifications described later. The weighting of each specification item is defined manually. For example, the search device 1 has a function of creating similarity weight data 65, and the designer uses this function to create the similarity weight data 65. The designer may also use an external device such as a personal computer to create the similarity weight data 65. When the operation of the search device 1 is started, the search result may be evaluated by trial operation using the similarity weights that are temporarily set, and the values of the similarity weights may be adjusted based on the evaluation result.

Fig. 16 is a flowchart showing an example of the operation of the similar specification search unit 16 included in the search device 1 according to embodiment 1, and shows a flow of the similar specification search process. For example, when the search device 1 receives an operation from the user to instruct the start of the search for similar specifications, the similar specification search unit 16 starts the similar specification search process shown in fig. 16.

When the similar specification search unit 16 starts the similar specification search process, first, the specification list acquisition process is executed (step S31). Specifically, the specification similarity calculation unit 61 acquires the specification list 131 from the product design database 13.

Next, the similar specification searching section 16 repeats the processing of step S32 for each past model included in the specification list 131. For example, when models having model IDs of 1 to 5 are included in the specification list 131, the similar specification searching unit 16 executes the processing of step S32 for each of 5 past models having model IDs of 1 to 5, and calculates the specification similarity for each item, which will be described later.

In step S32, the specification similarity calculation section 61 of the similar specification search section 16 calculates the similarity between the specification of the past model stored in the product design database 13 and the input specification data 63 based on the similarity data 15 and the similarity weight data 65. Specifically, the specification similarity calculation unit 61 calculates a similarity SimSpec (Sinp, Smx) between the input specification Sinp and the specification Smx of the model mx according to the following expression (2).

[ formula 2]

Here, Sim (sinp(s), smx (s)) is a value determined by whether or not the s-th specification item sinp(s) and smx(s) out of the 4 specification items included in the specification list 131 match each other. Sim (sinp(s), smx (s)) is "1" in the case where sinp(s) and smx(s) match, and is a value of the similarity degree included in the similarity data 15 shown in fig. 10 in the case where they do not match. Wdef(s) is a value of the similarity weight corresponding to the s-th specification item defined by the similarity weight data 65.

Fig. 17 is a diagram showing an example of the calculation result of the specification similarity calculation unit 61 of the similar specification search unit 16 included in the search device 1 according to embodiment 1. The example shown in fig. 17 shows the result of calculating the similarity SimSpec between the model IDs of "1", "2", and "3" in the specification list 131 shown in fig. 6 by the specification similarity calculation unit 61 when the specification of the content shown in fig. 13 is input in step S21.

In the specification shown in fig. 13, the series name is "a series", the rated power is "2.0 kW", the rated rotational speed is "2000 r/min", and the brake is "none". Therefore, for example, according to the contents shown in fig. 6, the series name, the rated rotational speed, and the specification of the brake of the past model having the model ID of "1" are matched, and the similarity of these specification items is "1". On the other hand, the specifications of the rated powers are not in agreement, and the similarity of the rated powers is "0.05" in the similarity data 15 shown in fig. 10, where the 3.5kW rated power and 2.0kW rated power are included.

In the example of the calculation result shown in fig. 17, the similarity SimSpec (Sinp, Sm1) of the past model with the model ID "1" is 0.83, the similarity SimSpec (Sinp, Sm2) of the past model with the model ID "2" is 0.75, and the similarity SimSpec (Sinp, Sm3) of the past model with the model ID "3" is 0.89. The series name of the past model whose model ID is "3" is different from the series name input in the above-described step S21, but the similarity is highest among the 3 models due to the effect of the above-described similarity data 15.

Returning to the explanation of fig. 16, after the specification similarity calculation section 61 has performed the processing of step S32 for all the past models, the data arrangement section 62 rearranges the similarity calculation results SimSpec (Sinp, Smx) of the specification similarity calculation section 61 (step S33). That is, the data arranging section 62 rearranges the similarity calculation results simspc (Sinp, Smx) in such a manner that the similarities are arranged in order from high to low.

Returning to the description of fig. 12, if the similar specification search processing of step S22 by the similar specification search unit 16 is completed, the search result display unit 17 displays the search result of the similar specification and presents the search result to the designer (step S23). Fig. 18 is a diagram showing an example of the similar specification search result displayed on the search result display unit 17 of the search device 1 according to embodiment 1. Fig. 18 shows an example in which similar specification search results are displayed together with the specification input in step S21. In the example shown in fig. 18, model IDs of past models and the above-described similarity SimSpec (Sinp, Smx), i.e., similarity scores, are displayed in order of similarity score from high to low. In addition, when the number of past models is large, the search result display unit 17 may extract and display past models having similarity scores larger than a predetermined value, or may extract and display a predetermined number of past models in order from the past model having the highest similarity score. The designer appropriately selects a model similar to the required specification based on the search result displayed by the search result display unit 17, and designs a new model satisfying the required specification.

In addition, the search device 1 may be configured to display the search result of the similar specification search unit 16 in detail with a search score that is a basis of the search result in order to clearly indicate why the model is searched. Fig. 19 is a diagram showing a modification 1 of the search device according to embodiment 1. The search device 1a shown in fig. 19 is obtained by replacing the search result display unit 17 of the search device 1 shown in fig. 1 with the search result detail display unit 18. The configuration of the search device 1a other than the search result detail display unit 18 is the same as that of the search device 1.

Fig. 20 is a flowchart showing an example of the search operation of the search device 1a according to variation 1 of embodiment 1. The flowchart shown in fig. 20 is obtained by replacing step S23 of the flowchart shown in fig. 12 with step S24. Step S23 is a process executed by the search result detail display unit 18. That is, in step S23, the search result detail display unit 18 displays a search result based on the search result output from the similar specification search unit 16, and presents the search result of the similar specification to the designer. The search result detail display unit 18 displays a search result of a similar specification having a configuration shown in fig. 21, for example. In the example shown in fig. 21, in addition to the content of the similarity specification search result shown in fig. 18, the details of the similarity score corresponding to the search score are displayed. The search result detail display unit 18 corresponds to a search result display unit that displays the search result of the similarity specification search unit 16 together with the similarity corresponding to the search result.

Further, by configuring the search device 1 to include search weight changing means for changing the weight of the specification similarity, it is possible to easily change the search condition for making the search result desired by the designer. Fig. 22 is a diagram showing a modification example 2 of the search device according to embodiment 1. The search device 1b shown in fig. 22 is obtained by adding a weight data changing unit 21 as a search weight changing means to the search device 1a shown in fig. 19. The configuration of the search device 1b other than the weight data changing unit 21 is the same as that of the search device 1 a.

Fig. 23 is a flowchart showing an example of the search operation of the search device 1b according to variation 2 of embodiment 1. The flowchart shown in fig. 23 is obtained by adding step S25 to step S24 of the flowchart shown in fig. 20.

In step S25, the weight data changing unit 21 receives a change operation of the similarity weight from the user. At this time, the weight data changing unit 21 displays, for example, a similarity weight data changing screen shown in fig. 24 on a display unit, not shown in fig. 22, and waits for the user to perform an operation for correcting the similarity weight data. Fig. 24 shows an example in which the similarity weight with the series name being emphasized is changed to be uniform. Specifically, fig. 24 shows an example in which the similarity weight of the series name is changed from 2.0 to 5.0, and the similarity weight of the rated rotational speed is changed from 1.5 to 1.0.

When receiving the operation of changing the similarity weight data in step S25, the search device 1b performs a re-search in step S22 using the changed similarity weight data, and displays the re-search result in step S23. Fig. 25 is a diagram showing an example of a result of a re-search performed by changing the similarity weight data. By changing the similarity weight in step S25, the past model numbers having the same series name become candidates for the top rank as the designer wants.

As described above, the search device according to the present embodiment includes: a similarity learning unit that learns similarities between item values of specifications of past models that are past designed products based on data relating to the past designed products; and a similar specification searching unit that searches for specification data indicating a specification similar to the specification specified by the designer, based on the similarity data that is the learning result of the similarity learning unit. This eliminates the need for the designer to create a complicated rule such as the knowledge base described in patent document 1 in advance, and reduces the workload of the designer involved in searching for the data to be reused. In addition, efficient search independent of the experience of the designer can be realized.

In addition, the search device according to the present embodiment can notify the designer of the basis of the search result when the details of the similarity score are displayed in addition to the similarity specification search result as the configuration shown in fig. 19.

In addition, the search device according to the present embodiment is configured as shown in fig. 22, and is configured to display the details of the similarity score in addition to the similarity specification search result, and to change the search condition so that the search result desired by the designer is obtained when a change in the weighting of the similarity for each specification item is received.

Embodiment 2.

Fig. 26 is a diagram showing a configuration example of the search device according to embodiment 2. The search device 1c according to embodiment 2 is obtained by replacing the similar specification search unit 16 of the search device 1 (see fig. 1) according to embodiment 1 with a similar specification search unit 16c and adding a similarity adjustment unit 31. In the search device 1c, the similarity specification search unit 16c performs search processing using the adjusted similarity data generated by the similarity adjustment unit 31. The configuration of the search device 1c other than the similarity adjustment unit 31 and the similarity specification search unit 16c is the same as that of the search device 1, and therefore the same reference numerals as those in fig. 1 are given thereto, and the description thereof is omitted.

The basic operation of the search device 1c when searching for a past model having a similar specification to that specified by the designer is the same as that of the search device 1 (see fig. 12). The specification similarity learning processing of the search device 1c is partially different from that of the search device 1.

Fig. 27 is a flowchart showing an example of the specification similarity learning process in the search device 1c according to embodiment 2. The flowchart shown in fig. 27 is obtained by adding step S41 to the flowchart shown in fig. 8. Except for step S41, since the processing is the same as the specification similarity learning processing described in embodiment 1, the same step numbers as those of the corresponding processing in fig. 8 are assigned and the description thereof is omitted.

In step S41, the similarity between the item values is adjusted. This adjustment is an adjustment of the similarity data 15 which is a result of learning by the similarity learning unit 14, and the designer uses the similarity adjustment unit 31 to perform this adjustment. When executing step S41, the similarity adjustment unit 31 causes the inter-item-value similarity adjustment screen shown in fig. 28 to be displayed on a display unit, which is not shown in fig. 26, for example. The similarity adjustment unit 31 waits for an operation by the designer while causing the display unit to display the inter-item-value similarity adjustment screen. When the similarity needs to be adjusted, the designer inputs the adjusted similarity for the similarity desired to be adjusted, and presses the save button to end the operation if the input is ended. When the similarity does not need to be adjusted, the designer presses the save button or presses the cancel button without inputting the adjusted similarity, and ends the operation. Fig. 28 shows an example in which the user adjusts the similarity between the a-series and the B-series and the similarity between the a-series and the C-series.

If the operation of adjusting the similarity by the designer is finished, the similarity adjusting section 31 outputs all the inter-item-value similarities including the adjusted similarity as the adjusted similarity data 32. Fig. 29 is a diagram showing an example of the adjusted similarity data 32. The adjusted similarity data 32 is configured by adding the adjusted similarity to the similarity data 15 (see fig. 10) described in embodiment 1.

Fig. 30 is a diagram showing a configuration example of the similar specification searching unit 16c included in the searching apparatus 1c according to embodiment 2. The similar specification searching unit 16c is obtained by using the specification similarity calculating unit 61 of the similar specification searching unit 16 (see fig. 14) described in embodiment 1 as the specification similarity calculating unit 61 c. The specification similarity calculation section 61c calculates the similarity between the specification of the past model stored in the product design database 13 and the input specification data 63 output from the input section 12, based on the similarity weight data 65 and the adjusted similarity data 32 generated by the similarity adjustment section 31. At this time, the specification similarity calculation section 61c performs calculation using the adjusted similarity included in the adjusted similarity data 32. The method of calculating the similarity of the specification similarity calculating section 61c is the same as the method of calculating the similarity of the specification similarity calculating section 61, and therefore, the description thereof is omitted.

As described above, the search device 1c according to the present embodiment includes the similarity adjusting unit 31 that adjusts the similarity data 15 that is the learning result of the similarity learning unit 14. Thus, the designer can specify the similarity desired to be enhanced and the similarity desired to be suppressed, and appropriately adjust the value of the similarity. Further, according to the search device 1c, even when the similarity data is not adjusted, the designer can confirm whether or not the appropriate learning is performed, and can confirm the value of the similarity data 15.

Embodiment 3.

Fig. 31 is a diagram showing a configuration example of the similarity learning unit 14d included in the search device according to embodiment 3. The configuration of the search device according to embodiment 3 except for the similarity learning unit 14d is the same as that of the search device 1 according to embodiment 1. Therefore, the description of the constituent elements other than the similarity learning section 14d is omitted.

The similarity learning unit 14d is obtained by adding the component selection unit 43 to the similarity learning unit 14 described in embodiment 1.

Fig. 32 is a flowchart showing an example of the operation of the similarity learning unit 14d included in the search device according to embodiment 3. The flowchart shown in fig. 32 is obtained by adding step S51 to the flowchart shown in fig. 8. Since the processing other than step S51 is the same as the processing included in the flowchart shown in fig. 8, the same step numbers as those of the corresponding processing in fig. 8 are assigned and the description thereof is omitted.

Step S51 is a used component selection process classified by item value. In step S51, the use component selection unit 43 selects, from the extraction results of the use component extraction unit 41, the extraction result used when the similarity calculation unit 42 calculates the similarity between the item values, based on the use component selection definition 44. Fig. 33 is a diagram showing an example of using the component selection definition 44. As shown in fig. 33, the use part selection definition 44 includes at least the specification item 3301 and a list 3302 of related part numbers corresponding to the specification item 3301. The usage component selection definition 44 is created in advance, for example, by a designer. The creation of the use component selection definition 44 may be performed using a device external to the search device, for example, a personal computer, or the search device may have a function of creating the use component selection definition 44 and the designer may create it using this function.

In step S51, using parts selector 43 selects parts of the part numbers described in associated part number list 3302 associated with the respective specification items using parts selection definition 44, from among the parts corresponding to the specification item values extracted in step S12. The use component selection section 43 outputs the selection result to the similarity calculation section 42.

In step S13, the similarity learning unit 14d calculates the similarity between the item values for the item values selected by the use item selection unit 43, instead of calculating the similarity between the item values for all the item values extracted by the use item extraction unit 41.

Fig. 34 is a diagram showing the concept of the specification similarity learning process executed by the similarity learning unit 14d of the search device according to embodiment 3. In the example shown in fig. 34, when the similarity between the model X and the model Y is calculated, the similarity changes depending on whether the specification item "rated power" or the specification item "rated rotation speed" is focused. That is, if attention is paid to "rated power", the component PA010 and the component PA011 are common, and therefore the similarity can be said to be high. On the other hand, if the "rated rotation speed" is focused, since the model X is composed of the component PB020 and the component PB021 and the model Y is composed of the component PC020 and the component PC021, it can be said that both have no commonality and the similarity is low. The similarity learning unit 14d can improve the calculation accuracy of the similarity data 15 by calculating the similarity after selecting the characteristic component group for each specification item using the use component selection definition 44.

As described above, in the search device according to the present embodiment, the similarity learning unit 14d includes the use component selection unit 43, and the use component selection unit 43 calculates the similarity between the item values after selecting the characteristic component group for each specification item. This can improve the calculation accuracy of the similarity data.

Embodiment 4.

Fig. 35 is a diagram showing a configuration example of the search device according to embodiment 4. The search device 1e according to embodiment 4 is obtained by replacing the similar specification search unit 16 of the search device 1a shown in fig. 19 with the similar specification search unit 16e and adding the special specification input unit 51. The configuration of the search device 1e other than the special specification input unit 51 and the similar specification search unit 16e is the same as that of the search device 1a, and therefore the same reference numerals as those in fig. 19 are given thereto, and the description thereof is omitted.

Fig. 36 is a flowchart showing an example of the search operation of the search device 1e according to embodiment 4. The flowchart shown in fig. 36 is obtained by adding step S61 to the flowchart shown in fig. 20. Since the processing other than step S61 is the same as the processing included in the flowchart shown in fig. 12, the same step numbers as those of the corresponding processing in fig. 20 are assigned, and the description thereof is omitted.

In step S61, the special specification input unit 51 receives an input of a special specification from the designer. At this time, the search device 1e displays the specification input screen shown in fig. 37 on a display unit not shown in fig. 35. The special specification input unit 51 waits for an operation by a designer while the specification input screen is displayed on the display unit. The specification input screen shown in fig. 37 is configured by adding a special specification input field 3701 to the specification input screen shown in fig. 13. The specification input screen of fig. 37 is displayed when step S21 starts.

The designer inputs a series name, a rated power, and the like to the search device 1e, and also inputs a special specification. When the input operation of the special specification by the designer is finished, the special specification input unit 51 generates data indicating the contents input to the input field 3701 of the special specification and outputs the data to the similar specification search unit 16 e.

Fig. 38 is a diagram showing a configuration example of the similar specification search unit 16e included in the search device 1e according to embodiment 4. The similar specification search unit 16e is obtained by replacing the specification similarity calculation unit 61 of the similar specification search unit 16 (see fig. 14) described in embodiment 1 with the special specification similarity calculation unit 81 and the extended specification similarity calculation unit 82.

The special specification similarity calculation unit 81 calculates the similarity between the input special specification data 83, which is the data output from the special specification input unit 51, and the special specification of the past model stored in the product design database 13. The extended specification similarity calculation section 82 calculates the similarity between the specification of the past model stored in the product design database 13 and the input specification data 63 output from the input section 12, based on the similarity of the extended similarity weight data 84 and the special specification calculated by the special specification similarity calculation section 81.

Fig. 39 is a diagram showing an example of the extended similarity weight data 84. The extended similarity weight data 84 includes specification items 3901 and similarity weights 3902. The extended similarity weight data in the example shown in fig. 39 includes similarity weights, which are weight coefficients of the specification items included in the specification list 131 of the product design database 13, that is, the series name, the rated power, the rated rotational speed, the brake, and the special specification. The weighting of each specification item is defined manually. When the operation of the search device 1e is started, the search result may be evaluated by trial operation using the similarity weights that are temporarily set, and the values of the similarity weights may be adjusted based on the evaluation result.

Fig. 40 is a flowchart showing an example of the operation of the similarity specification search unit 16e included in the search device according to embodiment 4. The flowchart shown in fig. 40 is obtained by replacing step S32 in the flowchart shown in fig. 16 with steps S71 and S72. Since the processes other than steps S71 and S72 are the same as those included in the flowchart shown in fig. 16, the same step numbers as those of the corresponding processes in fig. 16 are assigned, and the description thereof is omitted.

Step S71 is a special specification similarity calculation process. In step S71, the special specification similarity calculation section 81 calculates the similarity between the special specification of the past model stored in the product design database 13 and the input special specification data 83. Specifically, the special specification similarity calculation unit 81 calculates a similarity SpecialSimSpec (SSinp, SSmx) between the input special specification SSinp and the special specification SSmx of the model mx according to the following expression (3).

[ formula 3]

Here, the SSinp (t1) and SSmx (t2) are t1 th and t2 th texts when each special specification is separated by comma (,), and the SSim (SSinp (t1), SSmx (t2)) is the edit distance between these texts. In addition, MAX (1-SSim (SSinp (t1), SSmx (t2))/L) represents a value at t2 at which the value of the left expression is maximized with respect to SSinp (t 1). L is the total number of characters of the texts ssinp (t) and ssmx (t), and n represents the number of texts in the case where the input special specification is divided by comma (,). The edit distance between texts is calculated by a known method, for example, a method shown in patent document "international publication No. 2015/040793".

Fig. 41 is a diagram showing the concept of the special specification similarity calculation performed by the special specification similarity calculation unit 81 of the similar specification search unit 16e included in the search device according to embodiment 4. As shown in fig. 41, the special specification similarity calculation unit 81 divides the text indicated by the input special specification data 83 and the text corresponding to the special specification of the past model mx by commas, and calculates the similarity for the divided texts. In the example shown in fig. 41, the special specification similarity calculation unit 81 divides each text into 2 pieces and calculates the similarity for the divided texts. The special specification similarity calculation unit 81 outputs the calculated average value of the similarities as the similarity of the special specification.

Step S72 is specification similarity calculation processing for each extension item category. In step S72, the extended specification similarity calculation section 82 calculates the similarity between the specification of the past model stored in the product design database 13 and the input specification data 63 based on the extended similarity weight data 84 and the similarity of the special specification calculated by the special specification similarity calculation section 81 in step S71. Specifically, the extended specification similarity calculation unit 82 calculates the similarity ESimSpec (Sinp, SSinp, Smx, SSmx) between the input specification Sinp and the input special specification SSinp and the specification Smx and the special specification SSmx of the model mx according to the following expression (4).

[ formula 4]

Here, Sim (sinp(s), smx (s)) is a value determined according to whether or not the s-th specification item sinp(s) and smx(s) out of the 4 specification items included in the specification list 131 match each other, as described in embodiment 1. That is, Sim (sinp(s), smx (s)) is "1" when sinp(s) and smx(s) match, and is a value of the similarity included in the similarity data 15 shown in fig. 10 when they do not match. Also, ewdef(s) is a value of the similarity weight corresponding to the s-th specification item defined by the extended similarity weight data 84.

In step S33 of fig. 40, the data arrangement section 62 rearranges the similarity calculation result ESimSpec (Sinp, SSinp, Smx, SSmx) obtained by the extended specification similarity calculation section 82. That is, the data arranging section 62 rearranges the similarity calculation results ESimSpec (Sinp, SSinp, Smx, SSmx) in order from the similarity calculation result having a high degree of similarity.

The search result detail display unit 18 causes the display unit to display a search result of a similar specification to the configuration shown in fig. 42, for example, and presents the search result to the designer (fig. 36, step S24).

As described above, the similar specification searching unit 16e of the searching device according to the present embodiment includes the specific specification similarity calculating unit 81, the specific specification similarity calculating unit 81 analyzes the specific specification described in the text, i.e., the natural language, and calculates the similarity of the specific specification, and the similar specification searching unit 16e searches for the past model of the specification similar to the specification designated by the designer, taking into consideration the similarity of the specific specification. This makes it possible to search for a past model having high similarity, taking into account the special specification, and to improve the search accuracy.

Embodiment 5.

Fig. 43 is a diagram showing a configuration example of the similarity specification searching unit 16f included in the searching device according to embodiment 5. The configuration of the search device according to embodiment 5 is the same as that of the search device 1e according to embodiment 4, except for the similarity specification search unit 16 f. Therefore, the description of the components other than the similar specification search unit 16f is omitted.

The similar specification searching unit 16f is obtained by adding a weight adjusting unit 85 to the similar specification searching unit 16e according to embodiment 4. The weight adjustment unit 85 adjusts the extended similarity weight data 84 based on the input special specification data 83 and the keyword 86 classified by item.

Fig. 44 is a flowchart showing an example of the operation of the similarity specification search unit 16f included in the search device according to embodiment 5. The flowchart shown in fig. 44 is obtained by adding step S81 to the flowchart shown in fig. 40. Since the processing other than step S81 is the same as the processing included in the flowchart shown in fig. 40, the same step numbers as those of the corresponding processing in fig. 40 are assigned and the description thereof is omitted.

Step S81 is a similarity weight adjustment process. In step S81, the weight adjustment unit 85 checks whether or not the input special specification data 83 includes a keyword registered in the keyword 86 classified by item, and adjusts the value of the extended similarity weight data 84 based on the result of the check. The extended similarity weight data 84 adjusted by the weight adjusting unit 85 is input to the extended specification similarity calculating unit 82. The weight adjustment unit 85 adjusts the extended similarity weight data 84 by a flow defined in advance. For example, when the input special specification data 83 includes a keyword registered in the keyword 86 classified by item, the weight adjustment unit 85 changes the similarity weight of the specification item corresponding to the keyword included in the input special specification data 83 among the similarity weights included in the expanded similarity weight data 84 to a large value. An example of the processing in which the weight adjustment unit 85 changes the similarity weight to a large value is processing in which the similarity weight is multiplied by a coefficient having a value larger than 1.

Fig. 45 is a diagram showing an example of a keyword 86 classified by item used by the similarity specification search unit 16f of the search device according to embodiment 5. The keywords 86 classified by item include specification items 4501 and a keyword list 4502.

Fig. 46 is a diagram showing an example of a search result of the search device according to embodiment 5. The example shown in fig. 46 shows the search result in the case where the series name included in the information input using the input unit 12 and the special specification input unit 51 is "B series", the rated power is "2.0 kW", the rated rotational speed is "1000 r/min", the brake is "present", the special specification is "brake torque special", and the keyword list 4502 is the keyword list shown in fig. 45.

In the above example, the specification is "brake torque special", and "brake torque special" is present in the keyword list of the specification item "brake" of the keyword 86 classified by items shown in fig. 45. Therefore, the search result shown in fig. 46 is a result of rearranging the similarity calculated by multiplying the similarity weight 3902 corresponding to the stopper of the specification item 3901 in the extended similarity weight data 84 shown in fig. 39 by 2. The similarity weight corresponding to the brake is adjusted to 2 times for simplifying the explanation. Other values may be adjusted.

The keywords 86 classified by item are created in advance by a designer, for example. The creation of the keyword 86 classified by item may be performed using an external device of the search device, for example, a personal computer, or the search device may have a function of creating the keyword 86 classified by item and the designer may use this function to create the keyword.

As described above, the similarity specification search unit 16f of the search device according to the present embodiment includes the weight adjustment unit 85, and the weight adjustment unit 85 adjusts the similarity weight classified into the specification items based on the keywords classified into the items and the text included in the special specification. Thus, the similarity weight is adjusted according to the description content of the special specification, and the similarity is calculated, so that the past model with high similarity can be searched.

The configuration described in the above embodiment is an example of the contents of the present invention, and may be combined with other known techniques, and a part of the configuration may be omitted or modified without departing from the scope of the present invention.

Description of the reference numerals

1. 1a, 1b, 1c, 1e search device, 11 control section, 12 input section, 13 product design database, 14d similarity learning section, 15 similarity data, 16c, 16e, 16f similarity specification search section, 17 search result display section, 18 search result detail display section, 21 weight data change section, 31 similarity adjustment section, 32 adjusted similarity data, 41 used component extraction section, 42 similarity calculation section, 43 used component selection section, 44 used component selection definition, 51 special specification input section, 61c specification similarity calculation section, 62 data arrangement section, 63 input specification data, 64 similar specification search result, 65 similarity weight data, 81 special specification similarity calculation section, 82 extended specification similarity calculation section, 83 input special specification data, 84 extended similarity weight data, and 85 a weight adjustment unit 86 for adjusting the weight of the keyword classified by the item.

Claims (12)

1. A search device searches for specification data similar to search conditions from a specification data group including 1 or more specification item values each showing details of an item indicating a product specification,

the search device is characterized by comprising:

a similarity learning unit that learns, for all combinations of specification item values, how similar 2 specification item values of the same item are to each other, based on the specification data group stored in the product design database;

an input unit that receives an input of a specification item value used as the search condition; and

and a similar specification searching unit that searches for specification data similar to the input specification data composed of the specification item values that the input unit has received the input, based on the learning result of the similarity learning unit.

2. The retrieval device of claim 1,

the search result display unit displays the search result of the similar specification search unit.

3. The retrieval device of claim 2,

the similar specification search unit calculates a similarity degree indicating how similar each specification item value received by the input unit is to a corresponding specification item value of the specification data stored in the product design database, for all the specification data stored in the product design database based on the learning result, and performs the search based on the calculated similarity degree,

the search result display unit displays the search result of the similarity specification search unit together with the similarity corresponding to the search result.

4. The retrieval device according to any one of claims 1 to 3,

the similarity learning unit performs the learning by calculating a similarity between item values indicating how similar 2 specification item values are to each other based on the specification data and data of components constituting each product corresponding to each of the specification data,

the similarity specification search unit performs the search based on the similarity between the item values.

5. The retrieval device of claim 4,

the similarity specification search unit weights the inter-item-value similarity and performs the search based on the weighted inter-item-value similarity.

6. The retrieval device of claim 5,

the similarity specification search unit searches for similarity between the item values, and the similarity specification search unit searches for similarity between the item values.

7. The retrieval device according to any one of claims 1 to 6,

a similarity adjusting section for adjusting the learning result of the similarity learning section,

the similarity specification search unit performs the search based on the learning result adjusted by the similarity adjustment unit.

8. The retrieval device according to any one of claims 1 to 7,

the similarity learning unit performs the learning with a specified specification item value among specification item values included in the specification data stored in the product design database as a target.

9. The retrieval device according to any one of claims 1 to 8,

a special specification data acquiring unit for acquiring text data indicating a special specification of the product,

the similar specification searching unit searches specification data similar to the input specification data and the special specification.

10. The retrieval device of claim 9,

the similarity specification search unit adjusts the learning result based on the text data, and performs the search based on the adjusted learning result.

11. A search method executed by a search device that searches specification data similar to search conditions from specification data groups respectively constituted by containing 1 or more specification item values showing details of items representing product specifications,

the search method is characterized by comprising the following steps:

a similarity learning step of learning, based on the specification data group held in the product design database, how similar 2 specification item values of the same item are to each other with respect to all combinations of the specification item values;

an input step of receiving an input of a specification item value used as the search condition; and

a similar specification searching step of searching for specification data similar to the input specification data composed of the specification item values received as input in the inputting step, based on a learning result in the similarity learning step.

12. A machine learning device is characterized in that,

a similarity learning unit having a search means for searching for specification data similar to a search condition from a specification data group including 1 or more specification item values each showing details of an item indicating a product specification, the similarity learning unit learning, for all combinations of the specification item values, to what extent 2 specification item values of the same item in the specification data group are similar to each other,

the similarity learning unit performs the learning by calculating a similarity between item values indicating how similar 2 specification item values are to each other, based on the specification data and data of components constituting each product corresponding to each of the specification data.

Images