Drawing processing device, inference device, machine learning device, drawing processing method, inference method, and machine learning method

Abstract

To provide a drawing processor capable of appropriately selecting a request destination of processing or estimation of a workpiece described in a drawing without depending on knowledge or experience of a user.SOLUTION: This drawing processor 2A is provided with a drawing reception part 202 for receiving new drawing data D10 related with a processed article and a request destination selection part 203A for selecting the candidate of a request destination to request the processing or estimation of the processed article in the new drawing data D10 based on the new drawing data D10 received by the drawing reception part 202. The request destination selection unit 203A inputs the new drawing-data 203A to the first learning model D10 to acquire the processed-feature-data D11 for the new drawing-data D10, inputs the processed-feature-data D11 to the second learning model 220B to acquire the processed-request-data D10 for the new drawing-data, and selects the candidates for the request destination on the basis of the processed-request-data. In the example of FIG. D12 220A D12.SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] The present invention relates to a drawing processing device, an inference device, a machine learning device, a drawing processing method, an inference method, and a machine learning method. [Background technology]

[0002] Drawings used in various fields such as machinery, architecture, civil engineering, electricity, and apparel contain lines (external lines, dimension lines, etc.) that represent the shape and structure of processed products, as well as various types of information written in text. Because the various types of information written in drawings are important for understanding the details of processing, systems have been developed to utilize this information. For example, Patent Document 1 discloses a support system that acquires text that represents dimensional quantities written in association with the dimension lines of a figure and creates a list showing the dimensional quantities of the figure. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 10-293777 Summary of the Invention [Problem to be solved by the invention]

[0004] As described above, the support system disclosed in Patent Document 1 acquires information about processed products from drawings, but it only goes so far as acquiring information from drawings. Therefore, a user of the support system must select a supplier to request processing or a supplier to request an estimate prior to processing from the information described in the drawings, and the task of selecting an appropriate supplier requires advanced knowledge and experience in processing, making it a highly difficult task.

[0005] The present invention has been made in response to the above-mentioned problems, and aims to provide a drawing processing device, an inference device, a machine learning device, a drawing processing method, an inference method, and a machine learning method that can appropriately select a party to request processing or an estimate for a processed product described in a drawing, without relying on the user's knowledge or experience. [Means for solving the problem]

[0006] In order to achieve the above object, a drawing processing apparatus according to one aspect of the present invention comprises: a drawing receiving unit that receives new drawing data related to the processed product; and a request destination selection unit that selects, based on the new drawing data received by the drawing reception unit, candidates for a request destination to process or estimate the processed product in the new drawing data. [Effects of the Invention]

[0007] According to a drawing processing apparatus according to one aspect of the present invention, it is possible to appropriately select a party to request processing or quotation for a processed product shown in a drawing, without relying on the user's knowledge or experience.

[0008] Problems, configurations, and effects other than those described above will become apparent from the detailed description of the invention that follows. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is an overall configuration diagram showing an example of a drawing management system 1. FIG. [Figure 2A] FIG. 10 is a diagram showing an example of drawing data D10 relating to an assembly drawing. [Figure 2B] FIG. 10 is a diagram showing an example of drawing data D10 relating to a part drawing. [Figure 3] FIG. 2 is a diagram showing an example of a drawing database 210. [Figure 4] FIG. 1 is a block diagram showing an example of a drawing processing apparatus 2A according to a first embodiment. [Figure 5] FIG. 2 is a functional explanatory diagram illustrating an example of a learning model generation unit 201A according to the first embodiment. [Figure 6] 2 is a functional explanatory diagram showing an example of a drawing receiving unit 202 and a request destination selecting unit 203A according to the first embodiment. FIG. [Figure 7] FIG. 9 is a hardware configuration diagram showing an example of a computer 900. [Figure 8] 1 is a flowchart showing an example of the operation (drawing processing method) of the drawing processing apparatus 2A according to the first embodiment. [Figure 9] FIG. 10 is a block diagram showing an example of a drawing processing apparatus 2B according to a second embodiment. [Figure 10] FIG. 10 is a functional explanatory diagram showing an example of a learning model generation unit 201B according to the second embodiment. [Figure 11] 10 is a functional explanatory diagram showing an example of a drawing receiving unit 202 and a request destination selecting unit 203B according to the second embodiment. FIG. [Figure 12] 10 is a flowchart showing an example of the operation (drawing processing method) of a drawing processing apparatus 2B according to the second embodiment. [Figure 13] FIG. 10 is a block diagram showing an example of a drawing processing apparatus 2C according to a third embodiment. [Figure 14] FIG. 11 is a functional explanatory diagram illustrating an example of a drawing receiving unit 202 and a request destination selecting unit 203C according to the third embodiment. [Figure 15] 10 is a flowchart showing an example of the operation (drawing processing method) of a drawing processing apparatus 2C according to the third embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, an embodiment for carrying out the present invention will be described with reference to the drawings. The scope necessary for the explanation to achieve the object of the present invention will be schematically shown, and the scope necessary for explaining the relevant parts of the present invention will be mainly explained, and the parts that are omitted from the explanation will be based on publicly known techniques.

[0011] (First embodiment) 1 is a diagram showing an overall configuration of an example of a drawing management system 1. The drawing management system 1 functions as a system for managing drawing data D10 relating to processed products.

[0012] The drawing data D10 handled by the drawing management system 1 is, for example, any drawing recorded as digital data, such as mechanical drawings such as assembly drawings and parts drawings, architectural drawings, electrical circuit drawings, pneumatic circuit drawings, hydraulic circuit drawings, and apparel drawings. In this case, the drawing data D10 may be vector-format data or raster-format data. For example, the drawing data D10 may be CAD data (an example of vector format) output by various CAD software, or image data (an example of raster format) output by scanning a drawing printed on paper using a scanner or the like. The drawing data D10 may be drawn using any projection method or may be a three-dimensional drawing. The drawing data D10 may also include tables, and may be, for example, documents such as slips recorded as digital data.

[0013] As shown in Fig. 1, the drawing management system 1 includes a drawing processing device 2A and a user terminal device 3. The drawing processing device 2A and the user terminal device 3 are connected to a wired or wireless network 4 and are configured to be able to send and receive various data to and from each other. Note that the number of drawing processing devices 2A and user terminal devices 3 and the connection configuration of the network 4 are not limited to the example in Fig. 1 and may be changed as appropriate.

[0014] The drawing processing device 2A is a server-type computer or a cloud-type computer, and is a general-purpose or is composed of a dedicated computer (see FIG. 7 described later) or the like. The drawing processing device 2A accepts new drawing data D10 (hereinafter referred to as new drawing data D10) from the user terminal device 3 and performs various processes on the new drawing data D10. The drawing processing device 2A is provided with a drawing database 210 that can register the new drawing data D10 in association with processed product characteristic data D11 and processed product request data D12 obtained as a result of processing the new drawing data D10 in the drawing database 210. The drawing processing device 2A also provides the user terminal device 3 with display information for referencing and editing the existing drawing data D10 (hereinafter referred to as existing drawing data D10) that has been registered in the drawing database 210, and the processed product characteristic data D11 and processed product request data D12 associated with the existing drawing data D10.

[0015] The user terminal device 3 is a client-type computer, and is configured by a general-purpose or dedicated computer (see FIG. 7 described later), etc. The user terminal device 3 accepts various input operations via a display screen such as an application or a browser, and outputs various information via the display screen or voice, in order to register new drawing data D10, refer to and edit existing drawing data D10, etc.

[0016] Fig. 2A is a diagram showing an example of drawing data D10 relating to an assembly drawing, and Fig. 2B is a diagram showing an example of drawing data D10 relating to a part drawing.

[0017] The drawing area 10 of the drawing data D10 includes a shape area 11, a title block 12, and a parts list 13. The drawing area 10 corresponds to the entire area of ​​the paper when the drawing data D10 is printed on a paper medium.

[0018] The shape area 11 is an area where the shape and dimensions of an assembly or part are described using graphics and text. Lines defining the shape and structure of an assembly or part, such as outline lines, dimension lines, hidden lines, center lines, and imaginary lines, are described in the shape area 11. Text indicating dimensions, tolerances, part numbers, and the like is also described in the shape area 11. Note that the shape area 11 is not limited to six-sided drawings based on orthographic projection, and may represent other types of drawings, such as cross-sectional views, perspective views, and exploded views.

[0019] The title block 12 and the parts list 13 are types of tables included in the drawing data D10. Tables have vertical and horizontal lines and boxes separated by the lines. Characters are written in the boxes, and attributes of the characters written in the box are defined for each box. Boxes are also classified by properties that indicate the type of box. Properties include attribute header properties, serial number header properties, attributeless field properties, attributed field properties, etc.

[0020] As shown in FIGS. 2A and 2B , the attributes of the title block 12 include, for example, product name, drawing number, scale, creation date, designer, approver, etc. The boxes in the title block 12 are classified into attribute-containing field properties, attribute header properties, and attribute-free field properties. The boxes classified as attribute-containing field properties contain field characters (in the example of FIG. 2A , “1:2,” “2024 / 1 / 11,” “ASSY A,” and “F1-222-33-A”) as the contents of the title block 12, and attribute characters (in the example of FIG. 2A , “scale,” “creation date,” “product name,” and “drawing number”) that define the attributes of the field characters. The boxes classified as attribute header properties contain attribute characters (in the example of FIG. 2A , “designer,” and “approver”) that define the attributes of the field characters. The boxes classified as attribute-free field properties contain field characters (in the example of FIG. 2A , “AAA” and “BBB”) as the contents of the title block 12.

[0021] As shown in FIG. 2A, the attributes of the parts table 13 include, for example, number, item name, material, quantity, etc. The boxes in the first row on the top side of the parts table 13 are classified as attribute header properties, and attribute header characters (in the example of FIG. 2A, "number", "item name", "material", "quantity") that define the attributes of each column of the parts table 13 as headers are written in them. The boxes in the third column are classified as serial number header properties, and serial number header characters ("1", "2", "3" in the example of FIG. 2A) that define the serial numbers of each row of the parts table 13 as headers are entered. The other boxes in the parts table 13 are classified as attributeless field properties that do not have attribute characters entered in the boxes, and field characters ("COVER", "PPP", "1", "BODY", "QQQ", "1", "LEG", "RRR", "4" in the example of FIG. 2A) that represent the contents of the parts table 13 are entered.

[0022] The types of tables included in the drawing data D10 are not limited to the title block 12 and the parts list 13, but may be other types of tables. The arrangement and number of boxes constituting a table may be changed as appropriate depending on the type of table, and the attributes defined for each box are not limited to the above examples.

[0023] 3 is a diagram showing an example of the drawing database 210. In the drawing database 210, existing drawing data D10, processed product characteristic data D11 related to the processed product in the existing drawing data D10, and processed product request data D12 are registered in association with each existing drawing data D10.

[0024] The processed product characteristic data D11 is data indicating the characteristics of the processed product in the existing drawing data D10. The processed product characteristic data D11 is acquired from lines, characters, symbols, etc. included in the shape area 11, and characters (mainly field characters) included in the title block 12 and parts list 13. Attributes related to the characteristics of the processed product include, but are not limited to, the creation date, product name, drawing number, scale, shape, material, maximum dimension, quantity, and hole processing, as shown in FIG. 3 . Attributes related to hole processing include, but are not limited to, multiple attributes combining the hole diameter and the processing method (drilling, countersinking, countersinking, etc.).

[0025] For each attribute included in the processed product characteristic data D11, an attribute value in a data format corresponding to the attribute is registered. For attributes related to the product name and drawing number, a character string is registered as the attribute value. For attributes related to scale, shape, material, surface treatment, and welding, a value selected from multiple options prepared in advance is registered as the attribute value. In the example of FIG. 3, for example, "plate" is selected and registered for the shape from options such as plate, bar, and pipe. For attributes related to maximum dimension, quantity, and hole processing, a numerical value is registered as the attribute value. In the example of FIG. 3, for example, the numerical value "200" is registered for the maximum dimension.

[0026] The processed product request data D12 is data indicating a client who has been requested to process or estimate the processed product in the existing drawing data D10. The processed product request data D12 includes attributes related to the client, such as the name of the client who has been requested to process or estimate, the date and time when the processing or estimate was requested, the processing price or estimated price, etc.

[0027] For each attribute included in the processed product request data D12, an attribute value in a data format corresponding to the attribute is registered. For attributes relating to the destination to be requested for processing or estimation, a destination is selected from a destination list in which destinations are registered in advance, and for example, an identifier uniquely assigned to each destination is registered. Note that new destinations may be added to the destination list, and existing destinations may be deleted.

[0028] The drawing database 210 may be configured to allow data other than those described above to be registered, and the data formats of the attributes and attribute values ​​included in the processed product characteristic data D11 and the processed product request data D12 are not limited to the above examples.

[0029] Fig. 4 is a block diagram showing an example of a drawing processing device 2A according to the first embodiment. Fig. 5 is a functional explanatory diagram showing an example of a learning model generation unit 201A according to the first embodiment. Fig. 6 is a functional explanatory diagram showing an example of a drawing reception unit 202 and a request destination selection unit 203A according to the first embodiment.

[0030] The drawing processing device 2A includes a control unit 20, a data storage unit 21, a trained model storage unit 22, a communication unit 23, an input unit 24, and an output unit 25.

[0031] The communication unit 23 is connected to an external device (e.g., the user terminal device 3, etc.) via the network 4, and functions as a communication interface for transmitting and receiving various types of data. The input unit 24 accepts various input operations, and the output unit 25 functions as a user interface by outputting various types of information via a display screen or voice. Note that the input unit 24 and the output unit 25 may be omitted.

[0032] The data storage unit 21 stores a drawing database 210 and a drawing processing program 211A. As shown in Fig. 3, existing drawing data D10 and various information obtained from the existing drawing data D10 are registered in the drawing database 210 in association with each other. The specific configuration of the drawing database 210 is not limited to the example shown in Fig. 3 and may be designed as appropriate.

[0033] The trained model storage unit 22 stores a trained first training model 220A and a trained second training model 220B. The training models 220A and 220B stored in the trained model storage unit 22 may be provided to other devices via the network 4, a recording medium, or the like. Furthermore, the number of training models 220A and 220B stored in the trained model storage unit 22 is not limited to one each. For example, multiple training models 220A and 220B with different conditions, such as differences in machine learning methods or data, may be stored and made available selectively or in parallel.

[0034] 3, the data storage unit 21 and the trained model storage unit 22 are shown as two storage units, but they may be configured as a single storage unit or three or more storage units. Furthermore, at least one of the data storage unit 21 and the trained model storage unit 22 may be configured as a storage unit of an external computer (for example, a server-type computer or a cloud-type computer).

[0035] The control unit 20 functions as a transmission / reception control unit 200, a learning model generation unit 201A, a drawing reception unit 202, a request destination selection unit 203A, and a database management unit 204 by executing the drawing processing program 211A recorded in the data storage unit 21.

[0036] (Transmission and reception control unit 200) The transmission / reception control unit 200 transmits and receives various types of data to and from an external device (such as the user terminal device 3). For example, the transmission / reception control unit 200 transmits display information to the user terminal device 3 for outputting various display screens on the user terminal device 3, and receives operation information from the user terminal device 3 for accepting input operations performed on the display screen of the user terminal device 3. In this case, the transmission / reception control unit 200 cooperates with the respective units 201 to 204 to transmit display information to the user terminal device 3 and receive operation information from the user terminal device 3.

[0037] (Learning model generation unit 201A) As shown in FIGS. 4 and 5, the learning model generation unit 201A includes a learning data acquisition unit 2010A and a machine learning unit 2011A.

[0038] The learning data acquisition unit 2010A refers to the drawing database 210 and acquires first learning data D13A and second learning data D13B, each of which is composed of input data and output data. The learning data D13A and D13B are data used as teacher data (training data), verification data, and test data in supervised learning. The output data constituting the learning data D13A and D13B is data used as a correct answer label in supervised learning.

[0039] The input data constituting the first learning data D13A includes existing drawing data D10. The existing drawing data D10 included in the input data of the first learning data D13A may be data corresponding to the entire drawing area 10, or may be data corresponding to a partial area (e.g., shape area 11) cut out from the drawing area 10.

[0040] The output data constituting the first learning data D13A includes processed product characteristic data D11 that indicates the characteristics of the processed product in the existing drawing data D10. In this case, the processed product characteristic data D11 included in the output data of the first learning data D13A may relate to some of the multiple attributes related to the characteristics of the processed product shown in FIG.

[0041] When a selection from multiple options prepared in advance, such as shape, material, surface treatment, or welding, is specified, the processed product characteristic data D11 included in the output data of the first learning data D13A is defined as data in which "1" is assigned only to the flag corresponding to the selected option. For example, when "plate" is selected from options such as plate, bar, and tube for shape, the processed product characteristic data D11 included in the output data of the first learning data D13A is defined as data in which "1" is assigned only to the flag corresponding to "plate," such as "plate = 1, bar = 0, tube = 0."

[0042] Furthermore, when a maximum dimension, quantity, or drilling is specified as a numerical value, the processed product characteristic data D11 included in the output data of the first learning data D13A is defined as data into which the specified numerical value (which may be a normalized numerical value) is substituted. For example, when "200" is specified for the maximum dimension, the processed product characteristic data D11 included in the output data of the first learning data D13A is defined as data into which "200" is substituted for the maximum dimension parameter, such as "maximum dimension = 200." Note that when normalized with an upper limit of "1000," the processed product characteristic data D11 may be defined as data into which "0.2" is substituted for the maximum dimension parameter, such as "maximum dimension = 0.2."

[0043] The input data constituting the second training data D13B includes processed product characteristic data D11 that indicates the characteristics of the processed product in the existing drawing data D10, and is defined in the same manner as the processed product characteristic data D11 included in the output data of the first training data D13A. Note that, like the processed product characteristic data D11 included in the output data of the first training data D13A, the processed product characteristic data D11 included in the input data of the second training data D13B may relate to some of the multiple attributes related to the characteristics of the processed product shown in FIG. 3.

[0044] The output data constituting the second learning data D13B includes processed product request data D12 indicating a destination to request processing or an estimate for the processed product in the existing drawing data D10. The processed product characteristic data D11 included in the output data of the second learning data D13B relates to at least the destination to request processing or an estimate from among the multiple attributes related to the destination shown in FIG.

[0045] In this case, in a situation where multiple request destinations are registered in advance as a request destination list for the request destination, such as "S001, S002, ..., S011, S012, ..., S020", if "S011" is specified as the request destination, the output of the second learning data D13B The processed product characteristic data D11 included in the data is defined as data in which "1" is assigned to the flag for only "S011", such as "S001=0, S002=0, ..., S011=1, S012=0, ..., S020=0".

[0046] The learning data acquisition unit 2010A acquires first learning data D13A by referring to existing drawing data D10 registered in the drawing database 210 and processed product characteristic data D11 associated with the existing drawing data D10. The learning data acquisition unit 2010A also acquires second learning data D13B by referring to the processed product characteristic data D11 registered in the drawing database 210 and processed product request data D12 associated with the processed product characteristic data D11.

[0047] For example, the learning data acquisition unit 2010A acquires the first learning data D13A and the second learning data D13B by displaying on a display screen a list of existing drawing data D10 registered in the drawing database 210 in the drawing processing device 2A or the user terminal device 3, and reading out the existing drawing data D10 specified on the display screen and the processed product characteristic data D11 and processed product request data D12 associated with the existing drawing data D10 from the drawing database 210. The learning data acquisition unit 2010A may acquire the first learning data D13A and the second learning data D13B in cooperation with an external device connected via the network 4.

[0048] The machine learning unit 2011A performs machine learning to make the first learning model 220A learn the correlation between the input data (existing drawing data D10) and the output data (processed product characteristic data D11) using a plurality of sets of first learning data D13A acquired by the learning data acquisition unit 2010A. Also, the machine learning unit 2011A performs machine learning to make the second learning model 220B learn the correlation between the input data (processed product characteristic data D11) and the output data (processed product request data D12) using a plurality of sets of second learning data D13B acquired by the learning data acquisition unit 2010A.

[0049] The learning models 220A and 220B may be, for example, neural network types such as convolutional neural networks, recurrent neural networks, and vision transformers. (including deep learning), tree-based models such as decision trees and regression trees, bagging, and boosting Ensemble learning such as hierarchical clustering, non-hierarchical clustering, k-nearest neighbors, k-means, and other clustering methods, multivariate analysis such as principal component analysis, factor analysis, and logistic regression, and support vector machines can be used.

[0050] The timing at which the machine learning unit 2011A performs machine learning may be when the number of data items in the existing drawing data D10 registered in the drawing database 210 exceeds a predetermined number, or when instructions are received from the user, but is not limited to these.

[0051] (Drawing reception unit 202) The drawing receiving unit 202 receives the new drawing data D10, for example, by receiving the drawing data D10 from the user terminal device 3 via the transmission / reception control unit 200. At that time, the drawing receiving unit 202 may receive a designation as to whether to select a party to request processing or a party to request an estimate.

[0052] (Request destination selection unit 203A) The request destination selection unit 203A selects candidates for a destination to be requested to process or estimate the processed product in the new drawing data D10, based on the new drawing data D10 received by the drawing receiving unit 202. For example, when the drawing receiving unit 202 receives a designation of a destination to be requested to process or estimate the processed product, the request destination selection unit 203A selects candidates for a destination to be requested to process or estimate the processed product in the new drawing data D10, based on the designation. The requester may select either one or more candidates for the requester.

[0053] Specifically, the request destination selection unit 203A performs a feature acquisition process to acquire processed product feature data D11 indicating the features of the processed product in the new drawing data D10 from the new drawing data D10 accepted by the drawing accepting unit 202, and a candidate selection process to acquire processed product request data D12 indicating the destination to be requested for the processed product in the new drawing data D10 based on the processed product feature data D11 acquired in the feature acquisition process, and selects candidate destinations based on the processed product request data D12.

[0054] In this embodiment, the request destination selection unit 203A inputs new drawing data D10 accepted by the drawing acceptance unit 202 into the first learning model 220A as a feature acquisition process, thereby acquiring processed product feature data D11 for the processed product in the new drawing data D10.

[0055] At this time, for attributes defined by options, the first learning model 220A outputs a score for each attribute value, for example, for shape, such as "plate = 0.9, rod = 0.04, pipe = 0.06," and the "plate" with the highest score is acquired as the shape for the new drawing data D10. Also, for attributes defined by numerical values, for example, for maximum dimension, it outputs "maximum dimension = 250" (or "maximum dimension = 0.2" when normalized), and therefore "250" (or "0.2" when normalized) is acquired as the maximum dimension for the new drawing data D10.

[0056] In addition, as a candidate selection process, the request destination selection unit 203A inputs the processed product feature data D11 acquired in the feature acquisition process into the second learning model 220B to acquire processed product request data D12 for the new drawing data D10 accepted by the drawing acceptance unit 202, and selects candidate request destinations based on the processed product request data D12.

[0057] At this time, the second learning model 220B outputs a score for each of the multiple request recipients included in the request recipient list, such as "S001=0.12, S002=0.08, ..., S011=0, S012=0, ..., S020=0.8". Therefore, the request recipient assigned the identifier "S020" with the highest score is selected as the candidate request recipient. When multiple candidate request recipients are selected, the request recipients may be selected in descending order of score up to a predetermined rank, or a request recipient with a score equal to or greater than a predetermined reference value may be selected.

[0058] (Database Management Department 204) The database management unit 204 associates the processed product characteristic data D11 and processed product request data D12 acquired as a result of the characteristic acquisition process and the complementary selection process for the new drawing data D10 with the new drawing data D10 and registers them in the drawing database 210. Then, the database management unit 204 transmits display information for displaying the processing results of the request destination selection unit 203A to the user terminal device 3 via the transmission / reception control unit 200. Note that when candidate request destinations (which may be multiple) based on the processed product request data D12 are displayed on the user terminal device 3, an input operation may be accepted from the user to instruct the final decision on the request destination. In this case, the database management unit 204 may transmit request information related to the processing request or estimate request to the request destination corresponding to the input operation via the transmission / reception control unit 200.

[0059] Furthermore, the database management unit 204 reads out the existing drawing data D10 registered in the drawing database 210, and transmits display information for displaying the existing drawing data D10 to the user terminal device 3 via the transmission / reception control unit 200. Then, the database management unit 204 When operation information for editing the existing drawing data D10 is received from the user terminal device 3 via the transmission / reception control unit 200, the existing drawing data D10 registered in the drawing database 210 is corrected.

[0060] (Hardware configuration of each device) 7 is a hardware configuration diagram showing an example of a computer 900. The drawing processing device 2A and the user terminal device 3 in the drawing management system 1 are configured by a general-purpose or dedicated computer 900.

[0061] 7, the computer 900 includes, as its main components, a bus 910, a processor 912, a memory 914, an input device 916, an output device 917, a display device 918, a storage device 920, a communication I / F (interface) unit 922, an external device I / F unit 924, an I / O (input / output) device I / F unit 926, and a media input / output unit 928. Note that the above components may be omitted as appropriate depending on the application of the computer 900.

[0062] The processor 912 is composed of one or more arithmetic processing devices (such as a central processing unit (CPU), a micro-processing unit (MPU), a digital signal processor (DSP), or a graphics processing unit (GPU)), and operates as a control unit that controls the entire computer 900. The memory 914 stores various data and programs 930, and is composed of, for example, a volatile memory (such as a DRAM or SRAM) that functions as a main memory, a non-volatile memory (ROM), a flash memory, etc.

[0063] The input device 916 is composed of, for example, a keyboard, a mouse, a numeric keypad, an electronic pen, a microphone, etc., and functions as an input unit. The output device 917 is composed of, for example, a sound (audio) output device, a vibration device, etc., and functions as an output unit. The display device 918 is composed of, for example, a liquid crystal display, an organic EL display, electronic paper, a projector, etc., and functions as an output unit. The input device 916 and the display device 918 may be integrated into one device, such as a touch panel display. The storage device 920 is composed of, for example, an HDD, an SSD, etc., and functions as a storage unit. The storage device 920 stores various data necessary for executing the operating system and the program 930.

[0064] The communication I / F unit 922 is connected to a network 940 (which may be the same as network 4 in FIG. 1 ) such as the Internet or an intranet via a wired or wireless connection, and functions as a communication unit that transmits and receives data to and from other computers in accordance with a predetermined communication protocol. The external device I / F unit 924 is connected to an external device 950 (such as a camera, printer, scanner, or reader / writer) via a wired or wireless connection, and functions as a communication unit that transmits and receives data to and from the external device 950 in accordance with a predetermined communication protocol. The I / O device I / F unit 926 is connected to I / O devices 960 (such as various sensors and actuators), and functions as a communication unit that transmits and receives various signals and data, such as detection signals from sensors and control signals to actuators, to and from the I / O devices 960. The media input / output unit 928 is formed by a drive device such as a DVD drive or a CD drive, and reads and writes data from and to media (non-transitory storage media) 970 (such as DVDs and CDs).

[0065] In the computer 900 having the above configuration, the processor 912 loads the program 930 stored in the storage device 920 into the memory 914, executes the program, and controls each unit of the computer 900 via the bus 910. The program 930 may be stored in the memory 914 instead of the storage device 920. The program 930 is installed in the The program 930 may be recorded on the medium 970 in a downloadable file format or an executable file format and provided to the computer 900 via the media input / output unit 928. The program 930 may be provided to the computer 900 by being downloaded via the network 940 via the communication I / F unit 922. Furthermore, the computer 900 may implement the various functions that are realized by the processor 912 executing the program 930 using hardware such as an FPGA or an ASIC.

[0066] The computer 900 is an electronic device of any type, such as a desktop computer or a portable computer. The computer 900 may be a client computer, a server computer, a cloud computer, or an embedded computer such as a control panel or a controller (including a microcomputer, a programmable logic controller, or a sequencer).

[0067] (Operation of drawing processing device 2A) 8 is a flowchart showing an example of the operation (drawing processing method) of the drawing processing device 2A according to the first embodiment. Note that the series of drawing processing methods by the drawing processing device 2A shown in FIG. 8 will be described as being executed when the drawing processing device 2A receives new drawing data D10 from a user terminal device 3 operated by a user. Also, the description will be given assuming that the learned model storage unit 22 stores a learned first learning model 220A and a learned second learning model 220B by the learning model generation unit 201A performing a learning data acquisition step, a machine learning step, and a learned model storage step as shown in FIG. 5.

[0068] First, in step S100 (drawing reception step), the drawing reception unit 202 receives new drawing data D10 from the user terminal device 3, thereby receiving the new drawing data D10. At this time, the drawing reception unit 202 may receive a plurality of pieces of new drawing data D10.

[0069] Next, in step S110 (request destination selection process), the request destination selection unit 203A inputs the new drawing data D10 received in step S100 into the first learning model 220A as a feature acquisition process, thereby acquiring processed product feature data D11 for the processed product in the new drawing data D10. Note that when the drawing receiving unit 202 receives multiple pieces of new drawing data D10, it acquires processed product feature data D11 for each of the new drawing data D10.

[0070] Next, in step S111 (request destination selection process), the request destination selection unit 203A inputs the processed product characteristic data D11 acquired in step S110 into the second learning model 220B as a candidate selection process to acquire processed product request data D12 for the processed product in the new drawing data D10 accepted in step S100. Then, the request destination selection unit 203A selects the candidate request destinations indicated by the processed product request data D12 as candidate request destinations to process or estimate the processed product in the new drawing data D10 accepted in step S100. Note that when the drawing acceptance unit 202 accepts multiple pieces of new drawing data D10, it acquires processed product request data D12 for each piece of new drawing data D10 and selects candidate request destinations.

[0071] Next, in step S120 (database management process), the database management unit 204 associates the processed product characteristic data D11 and processed product request data D12 obtained as the processing results of steps S110 and S111 with the new drawing data D10 accepted in step S100 and registers them in the drawing database 210.

[0072] Then, in step S130, the database management unit 204 transmits display information for displaying the processing results of steps S110 and S111 to the user terminal device 3. As a result, the processed product characteristic data D11 and processed product request data D12 registered for the new drawing data D10 are displayed on the display screen of the user terminal device 3.

[0073] As described above, according to the drawing processing device 2A and drawing processing method of this embodiment, by using the first learning model 220A and the second learning model 220B that have been trained on past performance when selecting a contractor for existing drawing data D10, it is possible to appropriately select a contractor to process or estimate the processed product described in the drawing without relying on the user's knowledge or experience.

[0074] (Second embodiment) Fig. 9 is a block diagram showing an example of a drawing processing device 2B according to the second embodiment. Fig. 10 is a functional explanatory diagram showing an example of a learning model generation unit 201B according to the second embodiment. Fig. 11 is a functional explanatory diagram showing an example of a drawing reception unit 202 and a request destination selection unit 203B according to the second embodiment.

[0075] The second embodiment differs from the first embodiment in that the drawing processing device 2B uses one learning model 220 instead of two learning models 220A and 220B to select candidate contractors for processed products in new drawing data D10. The following describes the drawing processing device 2B according to the second embodiment, focusing on the differences from the first embodiment.

[0076] The trained model storage unit 22 stores trained learning models 220.

[0077] The control unit 20 functions as a transmission / reception control unit 200, a learning model generation unit 201B, a drawing reception unit 202, a request destination selection unit 203B, and a database management unit 204 by executing the drawing processing program 211B recorded in the data storage unit 21.

[0078] (Learning model generation unit 201B) As shown in FIGS. 9 and 10, the learning model generation unit 201B includes a learning data acquisition unit 2010B and a machine learning unit 2011B.

[0079] The learning data acquisition unit 2010B refers to the drawing database 210 and acquires the learning data D13.

[0080] The input data constituting the learning data D13 includes existing drawing data D10. The existing drawing data D10 included in the input data of the learning data D13 is the same data as the existing drawing data D10 included in the input data of the first learning data D13A in the first embodiment, and therefore a detailed description thereof will be omitted.

[0081] The output data constituting the learning data D13 includes processed product request data D12 indicating a request destination for processing or estimating the processed product in the existing drawing data D10. The processed product request data D12 included in the output data of the learning data D13 is the same data as the processed product request data D12 included in the output data of the second learning data D13B in the first embodiment, and therefore a detailed description thereof will be omitted.

[0082] The learning data acquisition unit 2010B acquires learning data D13 by referring to the existing drawing data D10 registered in the drawing database 210 and the processed product request data D12 associated with the existing drawing data D10. For example, similar to the first embodiment, the learning data acquisition unit 2010A displays a list of the existing drawing data D10 registered in the drawing database 210 on the display screen of the drawing processing device 2B or the user terminal device 3, and acquires learning data D13 by referring to the existing drawing data D10 specified on the display screen and the processed product request data D12 associated with the existing drawing data D10. The request data D12 is read from the drawing database 210 to obtain learning data D13.

[0083] The machine learning unit 2011B performs machine learning using multiple sets of learning data D13 acquired by the learning data acquisition unit 2010B to have the learning model 220 learn the correlation between the input data (existing drawing data D10) and the output data (processed product request data D12).

[0084] (Request destination selection unit 203B) The request destination selection unit 203B inputs the new drawing data D10 received by the drawing receiving unit 202 into the first learning model 220A, thereby acquiring processed product request data D12 for the processed product in the new drawing data D10.

[0085] (Operation of drawing processing device 2B) Fig. 12 is a flowchart showing an example of the operation (drawing processing method) of the drawing processing device 2B according to the second embodiment. Note that, since the steps having the same step numbers as those in the flowchart shown in Fig. 8 are the same as those in the first embodiment, the following description will mainly focus on the parts that are different from the first embodiment.

[0086] First, in step S100 (drawing reception step), the drawing reception unit 202 receives new drawing data D10.

[0087] Next, in step S112 (request destination selection process), the request destination selection unit 203B acquires processing request data D12 for the processed product in the new drawing data D10 by inputting the new drawing data D10 accepted in step S100 into the learning model 220. Then, the request destination selection unit 203A selects candidate request destinations indicated by the processing request data D12 as candidate request destinations to process or estimate the processed product in the new drawing data D10 accepted in step S100.

[0088] Next, in step S121 (database management step), the database management unit 204 associates the processed product request data D12 acquired as a processing result of step S112 with the new drawing data D10 accepted in step S100 and registers the data in the drawing database 210. Then, in step S131, the database management unit 204 transmits display information for displaying the processing result of step S112 to the user terminal device 3.

[0089] As described above, according to the drawing processing device 2B and drawing processing method of this embodiment, by using the learning model 220 that has learned past performance when selecting a contractor for existing drawing data D10, it is possible to appropriately select a contractor to process or estimate the processed product described in the drawing without relying on the user's knowledge or experience.

[0090] (Third embodiment) Fig. 13 is a block diagram showing an example of a drawing processing apparatus 2C according to the third embodiment. Fig. 14 is a functional explanatory diagram showing an example of a drawing receiving unit 202 and a request destination selecting unit 203C according to the third embodiment.

[0091] The third embodiment differs from the first embodiment in that the drawing processing device 2C selects candidate destinations for the processed product in the new drawing data D10 by performing feature acquisition processing and candidate selection processing without using the first learning model 220A and the second learning model 220B. The following describes the drawing processing device 2C according to the third embodiment, focusing on the differences from the first embodiment.

[0092] The control unit 20 executes the drawing processing program 211C recorded in the data storage unit 21, thereby functioning as a transmission / reception control unit 200, a drawing reception unit 202, a request destination selection unit 203C, and a database management unit 204.

[0093] (Request destination selection unit 203C) As a characteristic acquisition process, the request destination selection unit 203C performs character recognition processing on the new drawing data D10 accepted by the drawing acceptance unit 202, thereby acquiring processed product characteristic data D11 indicating the characteristics of the processed product in the new drawing data D10.

[0094] The destination selection unit 203C performs a known optical character recognition process (OCR) as the character recognition process, and reads the characters included in the drawing data D10 as text data to obtain the processed product characteristic data D11. Furthermore, if text data is embedded in the drawing data D10, the destination selection unit 203C reads the text data to obtain the characters included in the drawing data D10 as the processed product characteristic data D11. The results of the character recognition process include text data indicating the characters and position data indicating the position where the characters are written.

[0095] For example, if the character string "welding" is read, the processed product characteristic data D11 is defined as data in which only the flag for "welding" is set to "1," e.g., "yes" = "1, no" = "0." If the character string "4.5" is read in eight locations, the processed product characteristic data D11 is defined as data in which "8" is set to the parameter for 4.5 holes, e.g., "4.5" = "8." In this case, if the characters read by the character recognition process contain a mixture of characters that can be treated as synonyms, such as half-width and full-width characters or uppercase and lowercase characters, they may be treated as a single character. The characters read by the character recognition process may be displayed on the display screen of the user terminal device 3, where they may be edited by the user.

[0096] In addition, as a candidate selection process, the request destination selection unit 203C refers to the drawing database 210 and extracts, from the existing drawing data D10 registered in the drawing database 210, existing drawing data D10 that is identical to or similar to the characteristics indicated by the processed product characteristic data D11 for the new drawing data D10 acquired in the characteristic acquisition process, as similar drawing data D10.

[0097] For example, for attributes selected by options, existing drawing data D10 in which the options in the processed product characteristic data D11 for the new drawing data D10 are the same as or similar to the options in the processed product characteristic data D11 associated with the existing drawing data D10 is extracted from the drawing database 210. For attributes selected by numerical values, existing drawing data D10 in which the numerical values ​​in the processed product characteristic data D11 for the new drawing data D10 are the same as or fall within a predetermined numerical range are extracted from the drawing database 210. In this case, multiple attributes may be combined to extract existing drawing data D10 that is the same as or similar to the processed product characteristic data D11 for the new drawing data D10 from the drawing database 210.

[0098] Then, the request destination selection unit 203C acquires the processed product request data D12 indicating the destination to be requested to process or estimate the processed product in the new drawing data D10 accepted by the drawing accepting unit 202 based on the processed product request data D12 associated with the extracted similar drawing data D10, and selects candidate destinations based on the processed product request data D12.

[0099] For example, when the request destination selection unit 203C extracts one similar drawing data D10, The request destination indicated by the processed product request data D12 associated with the similar drawing data D10 is selected as a candidate request destination. Furthermore, when the request destination selection unit 203C extracts a plurality of similar drawing data D10 as shown in FIG. 14, the request destination selection unit 203C statistically processes the plurality of processed product request data D12 associated with the plurality of similar drawing data D10 to select the candidate request destinations. For example, the plurality of processed product request data D12 may be sorted in descending order of frequency as a request destination, and the most frequently requested destination may be selected as a candidate request destination, or the request destinations from the top of the order of frequency down to a predetermined rank may be selected as candidate request destinations.

[0100] (Operation of drawing processing device 2C) Fig. 15 is a flowchart showing an example of the operation (drawing processing method) of the drawing processing device 2C according to the third embodiment. Note that, since the steps having the same step numbers as those in the flowchart shown in Fig. 8 are the same as those in the first embodiment, the following description will mainly focus on the parts that are different from the first embodiment.

[0101] First, in step S100 (drawing reception step), the drawing reception unit 202 receives new drawing data D10.

[0102] Next, in step S113 (request destination selection process), the request destination selection unit 203C performs character recognition processing on the new drawing data D10 received in step S100 as a feature acquisition process, thereby acquiring processed product feature data D11 for the new drawing data D10.

[0103] Next, in step S114 (request destination selection process), the request destination selection unit 203C, as a candidate selection process, extracts, from the existing drawing data D10 registered in the drawing database 210, existing drawing data D10 that is identical to or similar to the characteristics indicated by the processed product characteristic data D11 acquired in step S113, as similar drawing data D10. Next, the request destination selection unit 203C acquires processing request data D12 for the processed product in the new drawing data D10 accepted in step S100, based on the processing request data D12 associated with the extracted similar drawing data D10. Then, the request destination selection unit 203C selects the candidate request destinations indicated by the processing request data D12 as candidate request destinations to process or estimate the processed product in the new drawing data D10 accepted in step S100.

[0104] Next, in step S122 (database management step), the database management unit 204 associates the processed product characteristic data D11 and the processed product request data D12 acquired as the processing results of steps S113 and S114 with the new drawing data D10 accepted in step S100 and registers them in the drawing database 210. Then, in step S132, the database management unit 204 transmits display information for displaying the processing results of steps S113 and S114 to the user terminal device 3.

[0105] As described above, according to the drawing processing device 2C and drawing processing method of this embodiment, by using the drawing database 210 in which past performance data when selecting a contractor for existing drawing data D10 is registered, it is possible to appropriately select a contractor to process or estimate the processed product described in the drawing without relying on the user's knowledge or experience.

[0106] (Other embodiments) The present invention is not limited to the above-described embodiment, and various modifications can be made without departing from the spirit and scope of the present invention, all of which are included in the technical concept of the present invention.

[0107] In the above embodiment, the drawing processing devices 2A to 2C are described as being configured as a single device, but they may be configured as multiple devices. For example, by distributing the units 200 to 204 of the drawing processing devices 2A to 2C across multiple devices, the drawing management system 1 may be configured as a machine learning device that includes learning model generation units 201A and 201B and performs the machine learning process, and a requestee selection device that includes requestee selection units 203A to 203C and performs the requestee selection process. In this case, each unit (each process) of each of the above devices may be realized by a program that can be executed by the computer 900.

[0108] In the above embodiment, the drawing processing devices 2A to 2C receive new drawing data D10 from the user terminal device 3 and select a request destination for the processed product in the drawing data D10. Alternatively, the user terminal device 3 may function as the drawing processing devices 2A to 2C.

[0109] The above-described embodiments can be combined as appropriate. For example, the request destination selection unit (request destination selection step) included in the drawing processing devices 2A to 2C may perform the feature acquisition process (step S110) in the first embodiment and the candidate selection process (step S114) in the third embodiment, or may perform the feature acquisition process (step S113) in the third embodiment and the candidate selection process (step S111) in the first embodiment.

[0110] In the above embodiment, the request destination selection units 203A to 203C of the drawing processing devices 2A to 2C acquire the processed product request data D12 for the new drawing data D10. However, useful information may also be acquired along with the processing request or estimate request. As useful information, the request destination selection units 203A to 203C may acquire detailed processing information, such as the type of processing equipment used in the processing step, the processing time required for the processing step, and whether manual labor is required. The detailed processing information may be included in the output data of either the first learning model 220A or the second learning model 220B, or may be included in the output data of the learning model 220. Furthermore, the detailed processing information may be registered in the drawing database 210 and acquired by referring to the drawing database 210.

[0111] (Inference device, inference method and inference program) The present invention can be provided not only in the form of the drawing processing devices 2A to 2C (drawing processing methods or drawing processing programs) according to the above-described embodiments, but also in the form of an inference device (inference method or inference program) used to infer candidate destinations for a processed product in new drawing data D10. In this case, the inference device (inference method or inference program) can include a memory and a processor, and the processor executes a series of processes. The series of processes includes a data acquisition process (data acquisition step) for acquiring new drawing data D10 related to the processed product, and an inference process (inference step) for inferring candidate destinations for processing or estimating the processed product in the new drawing data D10 once the new drawing data D10 has been acquired by the data acquisition process. [Explanation of symbols]

[0112] 1...Drawing management system, 2A to 2C...Drawing processing device, 3...User terminal device 10...Drawing area, 11...Shape area, 12...Title block, 13...Bill of materials, 20...control unit, 21...data storage unit, 22...trained model storage unit, 23...communication unit, 24...input unit, 25...output unit, 200...transmission and reception control unit, 201A, 201B...learning model generation unit, 202... Drawing reception unit, 203A to 203C... Request destination selection unit, 204...database management unit, 210...drawing database, 211A~211C...Drawing processing program, 220...Learning model, 220A...first learning model, 220B...second learning model, 2010A, 2010B...Learning data acquisition section, 2011A, 2011B…Machine Learning Department

Claims

1. a drawing receiving unit that receives new drawing data related to the processed product; and a request destination selection unit that selects, based on the new drawing data accepted by the drawing acceptance unit, candidates for a request destination to process or estimate the processed product in the new drawing data. Drawing processing equipment.

2. The request destination selection unit The new drawing data received by the drawing receiving unit is input into a learning model to obtain processed product request data indicating the request destination for the processed product in the new drawing data, and candidates for the request destination are selected based on the processed product request data; The learning model is The correlation between existing drawing data relating to a processed product and processed product request data indicating a requester to process or estimate the processed product in the existing drawing data is learned by machine learning. The drawing processing device according to claim 1 .

3. The request destination selection unit a characteristic acquisition process for acquiring, from the new drawing data accepted by the drawing accepting unit, processed product characteristic data indicating characteristics of the processed product in the new drawing data; a candidate selection process for selecting candidates for the request destination based on the processed product request data, the candidate selection process for selecting candidate candidates for the request destination based on the processed product characteristic data acquired in the characteristic acquisition process; The drawing processing device according to claim 1 .

4. The request destination selection unit As the feature acquisition process, the new drawing data accepted by the drawing acceptance unit is input into a first learning model to acquire the processed product feature data for the processed product in the new drawing data; The first learning model is The correlation between existing drawing data relating to a processed product and the processed product characteristic data for the processed product in the existing drawing data is learned by machine learning. The drawing processing device according to claim 3 .

5. The request destination selection unit As the candidate selection process, the processed product feature data acquired in the feature acquisition process is input into a second learning model to acquire the processed product request data for the processed product in the new drawing data accepted by the drawing acceptance unit, and the candidate request recipient is selected based on the processed product request data; The second learning model is A correlation between the processed product characteristic data and the processed product request data for the processed product having the characteristics indicated by the processed product characteristic data is learned by machine learning.

5. The drawing processing apparatus according to claim 3 or 4.

6. The request destination selection unit The feature acquisition process includes: performing character recognition processing on the new drawing data accepted by the drawing accepting unit to acquire the processed product characteristic data for the processed product in the new drawing data; The drawing processing device according to claim 3 .

7. The request destination selection unit As the candidate selection process, Refer to a drawing database in which existing drawing data relating to a processed product, the processed product characteristic data for the processed product in the existing drawing data, and the processed product request data for the processed product in the existing drawing data are registered in association with each other; extracting, from the existing drawing data registered in the drawing database, the existing drawing data that is identical to or similar to the features indicated by the processed product feature data based on the processed product feature data acquired in the feature acquisition process, as similar drawing data; selecting candidates for the request destination for the processed product in the new drawing data accepted by the drawing accepting unit based on the processed product request data associated with the similar drawing data; 7. The drawing processing apparatus according to claim 3 or 6.

8. An inference device comprising a memory and a processor, The processor: A data acquisition process for acquiring new drawing data related to the processed product; When the new drawing data is acquired by the data acquisition process, an inference process is executed to infer candidates for a client to be requested to process or estimate the processed product in the new drawing data. Reasoning device.

9. a learning data acquisition unit that acquires multiple sets of learning data each consisting of input data and output data; a machine learning unit that uses the plurality of sets of learning data acquired by the learning data acquisition unit to train a learning model by machine learning to learn a correlation between the input data and the output data; a learned model storage unit that stores the learned model in which the correlation has been learned by the machine learning unit, The input data is Including existing drawing data for processed products, The output data is The processing request data indicates a requesting party to process or estimate the processed product in the existing drawing data. Machine learning device.

10. 1. A computer-implemented method for processing drawings, comprising: a drawing receiving step of receiving new drawing data relating to the processed product; and a requester selection step of selecting candidates for requesting processing or an estimate for the processed product in the new drawing data based on the new drawing data received in the drawing receiving step. Drawing processing methods.

11. An inference method executed by an inference device having a memory and a processor, The processor: A data acquisition process for acquiring new drawing data related to the processed product; When the new drawing data is acquired by the data acquisition process, an inference process is executed to infer candidates for a client to be requested to process or estimate the processed product in the new drawing data. Reasoning method.

12. 1. A computer-implemented machine learning method comprising: a learning data acquisition step of acquiring a plurality of sets of learning data each composed of input data and output data; a machine learning step of causing a learning model to learn a correlation between the input data and the output data by machine learning using the plurality of sets of learning data acquired by the learning data acquisition step; a trained model storage step of storing the trained model, which has learned the correlation through the machine learning step, in a trained model storage unit; The input data is Including existing drawing data for processed products, The output data is The processing request data indicates a requesting party to process or estimate the processed product in the existing drawing data. Machine learning methods.

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