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

Abstract

To provide a drawing processing device capable of appropriately selecting a requesting party for processing or estimating a processed product described in a drawing, without depending on a user's knowledge or experience. [Solution] The drawing processing device 2A includes a drawing receiving unit 202 that receives new drawing data D10 related to a processed product, and a requestee selection unit 203A that selects candidate requestees 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. The requestee selection unit 203A inputs the new drawing data D10 to a first learning model 220A to obtain processed product characteristic data D11 for the new drawing data D10, and inputs the processed product characteristic data D11 to a second learning model 220B to obtain processed product request data D12 for the new drawing data D10, and selects the candidate requestees based on the processed product request data D12.

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, for example, lines (external lines, dimension lines, etc.) for expressing the shape and structure of processed products, as well as various types of information written in characters. Since the various types of information written in drawings are important for understanding the content of processing, systems have been developed for utilizing this information. For example, Patent Document 1 discloses a support system that obtains characters representing 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] The support system disclosed in Patent Document 1, as described above, acquires information about processed products from drawings, but it stops at acquiring information from drawings. Therefore, a user who uses the support system must select a contractor to request processing or a contractor to request an estimate prior to processing from the information described in the drawings, and the task of selecting an appropriate contractor requires advanced knowledge and experience about 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 are capable of appropriately selecting a party to request processing or an estimate for a processed product depicted 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 relating to a processed product; The system further includes a request destination selection unit that selects, based on the new drawing data accepted by the drawing acceptance unit, candidates for request destinations to process or estimate the processed product in the new drawing data. Effect of the Invention

[0007] According to the drawing processing apparatus according to one aspect of the present invention, it is possible to appropriately select a supplier to process or estimate a processed product depicted in a drawing, without relying on the knowledge or experience of the user.

[0008] Other objects, configurations and effects will become apparent from the detailed description of the invention described below. [Brief description 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. 13 is a diagram showing an example of drawing data D10 relating to an assembly drawing. [Figure 2B] FIG. 13 is a diagram showing an example of drawing data D10 relating to a part drawing. [Diagram 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. [Diagram 5] FIG. 2 is a functional explanatory diagram showing 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 a drawing processing apparatus 2A according to the first embodiment. [Figure 9] FIG. 11 is a block diagram showing an example of a drawing processing apparatus 2B according to a second embodiment. [Figure 10] FIG. 11 is a functional explanatory diagram showing an example of a learning model generation unit 201B according to the second embodiment. [Figure 11] 13 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] 13 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. 13 is a block diagram showing an example of a drawing processing apparatus 2C according to a third embodiment. [Figure 14] FIG. 13 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] 13 is a flowchart showing an example of the operation (drawing processing method) of a drawing processing device 2C according to the third embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, an embodiment for carrying out the present invention will be described with reference to the drawings. In the following, the scope necessary for the explanation to achieve the object of the present invention will be shown in a schematic manner, and the scope necessary for the explanation of the relevant part of the present invention will be mainly explained, and the parts that are omitted will be explained as being related to the publicly known technology.

[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 a processed product.

[0012] The drawing data D10 handled by the drawing management system 1 is, for example, any drawing such as mechanical drawings such as assembly drawings and parts drawings, architectural drawings, electric circuit drawings, pneumatic circuit drawings, hydraulic circuit drawings, apparel drawings, etc., recorded as digital data. 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 reading a drawing printed on a paper medium with a scanner or the like. The drawing data D10 may be drawn by any projection method, or may be a three-dimensional drawing. In addition, the drawing data D10 may be any data including a table, and may be, for example, a document such as a slip 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 transmit and receive various data to and from each other. Note that the numbers of the drawing processing devices 2A and the 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, the processed product characteristic data D11 and the processed product request data D12 obtained as a result of processing the new drawing data D10, in association with each other in the drawing database 210. The drawing processing device 2A also provides the user terminal device 3 with display information for referring to and editing the existing drawing data D10 (hereinafter referred to as existing drawing data D10) already registered in the drawing database 210, and the processed product characteristic data D11 and the 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 composed of 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] A 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 in which the shape and dimensions of an assembly or part are described using figures and characters. In the shape area 11, lines that define the shape and structure of an assembly or part, such as outline lines, dimension lines, hidden lines, center lines, imaginary lines, etc., are described. Furthermore, in the shape area 11, characters indicating dimensions, tolerances, part numbers, etc. are described. Note that the shape area 11 is not limited to six-sided views based on an orthographic projection method, 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 a type of table included in the drawing data D10. A table has a combination of 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. Examples of properties include an attribute heading property, a serial number heading property, a non-attribute field property, and an attributed field property.

[0020] As shown in FIG. 2A and FIG. 2B, the attributes of the title block 12 include, for example, the item name, drawing number, scale, creation date, designer, approver, etc. Each box in the title block 12 is classified into an attributed field property, an attribute header property, and an attributeless field property. In the box classified as the attributed field property, field characters (in the example of FIG. 2A, "1:2", "2024 / 1 / 11", "ASSY A", "F1-222-33-A") as the contents of the title block 12 and attribute characters (in the example of FIG. 2A, "scale", "creation date", "item name", "drawing number") that define the attributes of the field characters are described. In the box classified as the attribute header property, attribute characters (in the example of FIG. 2A, "designer", "approver") that define the attributes of the field characters are described. In the box classified as the attributeless field property, field characters (in the example of FIG. 2A, "AAA", "BBB") as the contents of the title block 12 are described.

[0021] 2A, the attributes of the parts table 13 include, for example, number, item name, material, and quantity. The boxes in the first row from the top 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", and "quantity") that define the attributes of each column of the parts table 13 as headers are written in the boxes. The boxes in the columns 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 box, 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 the parts list 13. Attributes relating 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, hole processing, etc., as shown in Fig. 3. Attributes relating to hole processing include, but are not limited to, a plurality of 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 a plurality of 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 tube. 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 requester to process or estimate the processed product in the existing drawing data D10. In the processed product request data D12, attributes related to the requester include, for example, the name of the requester to process or estimate, the date and time of the request for processing or estimate, the processing price or the 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 related to a request destination for processing or quotation, a request destination is selected from a request destination list in which the request destinations are registered in advance, and for example, an identifier uniquely assigned to each request destination is registered. Note that a new request destination may be added to the request destination list, and an existing request destination 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 generating unit 201A according to the first embodiment. Fig. 6 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.

[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 data. The input unit 24 accepts various input operations, and the output unit 25 functions as a user interface by outputting various information via a display screen or sound. 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 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 trained 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. The number of trained models 220A and 220B stored in the trained model storage unit 22 is not limited to one each, and multiple trained models 220A and 220B with different conditions, such as differences in machine learning methods and data, may be stored and used 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. 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 executes the drawing processing program 211A recorded in the data memory unit 21, thereby functioning 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.

[0036] (Transmission / 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 for outputting various display screens to the user terminal device 3 to the user terminal device 3, and receives operation information for accepting an input operation performed on the display screen of the user terminal device 3 from the user terminal device 3. In this case, the transmission / reception control unit 200 cooperates with each unit 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 are 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, etc.) cut out from the drawing area 10.

[0040] The output data constituting the first learning data D13A includes processed product characteristic data D11 indicating 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 relating to the characteristics of the processed product shown in FIG.

[0041] When a selection from a plurality of 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 only the flag for the selected option is assigned with "1." 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 only the flag for "plate" is assigned with "1," such as "plate=1, bar=0, tube=0."

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

[0043] The input data constituting the second learning data D13B includes processed product characteristic data D11 indicating the characteristics of the processed product in the existing drawing data D10, and is defined similarly to the processed product characteristic data D11 included in the output data of the first learning data D13A. Note that the processed product characteristic data D11 included in the input data of the second learning data D13B may relate to some of the multiple attributes relating to the characteristics of the processed product shown in FIG. 3, similar to the processed product characteristic data D11 included in the output data of the first learning data D13A.

[0044] The output data constituting the second learning data D13B includes processed product request data D12 indicating a requestee to process or estimate 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 requestee to process or estimate the processed product among a plurality of attributes related to the requestee 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", when "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 substituted for the flag for "S011" only, 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 displays a list of existing drawing data D10 registered in the drawing database 210 on a display screen in the drawing processing device 2A or the user terminal device 3, and acquires the first learning data D13A and the second learning data D13B by reading the existing drawing data D10 specified on the display screen and the processed product characteristic data D11 and the 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. The machine learning unit 2011A also 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. Deep learning, decision trees, regression trees, bagging, boosting Ensemble learning such as those mentioned above, clustering types such as hierarchical clustering, non-hierarchical clustering, k-nearest neighbors and k-means, multivariate analyses 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 of 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 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 request destination to process or estimate the processed product in the new drawing data D10 based on the new drawing data D10 received by the drawing reception unit 202. For example, when the drawing reception unit 202 receives a designation of a request destination for processing or an estimate, the request destination selection unit 203A selects candidates for a request destination to process or estimate the processed product in the new drawing data D10 based on the designation. The requester may select either one of the candidates for the request for the construction or the requester for the estimate.

[0053] Specifically, the destination selection unit 203A performs a characteristic acquisition process to acquire processed product characteristic data D11 indicating the characteristics 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 characteristic data D11 acquired in the characteristic acquisition process, and selects candidate destinations based on the processed product request data D12.

[0054] In this embodiment, the destination selection unit 203A inputs new drawing data D10 accepted by the drawing accepting 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, the first learning model 220A outputs scores for each attribute value for attributes defined by options, for example, for shape, such as "board=0.9, rod=0.04, pipe=0.06", so that the "board" 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, such as "maximum dimension=250" (when normalized, "maximum dimension=0.2") is output, so that "250" (when normalized, "0.2") is acquired as the maximum dimension for the new drawing data D10.

[0056] In addition, as a candidate selection process, the destination selection unit 203A inputs the processed product characteristic data D11 acquired in the characteristic 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 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 destinations included in the request destination list, such as "S001=0.12, S002=0.08, ..., S011=0, S012=0, ..., S020=0.8". Therefore, the request destination to which the identifier "S020" with the highest score is assigned is selected as the candidate request destination. When multiple request destination candidates are selected, the request destinations from the top to a predetermined rank in descending order of score may be selected, or a request destination 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 registers the processed product characteristic data D11 and the 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 in the drawing database 210 in association with the new drawing data D10. Then, the database management unit 204 transmits display information for displaying the processing result of the request destination selection unit 203A to the user terminal device 3 via the transmission / reception control unit 200. Note that when a candidate (or multiple candidates) of the request destination based on the processed product request data D12 are displayed on the user terminal device 3, an input operation for instructing the user to make a final decision on the request destination may be accepted. At that time, the database management unit 204 may transmit request information related to the processing request or the estimate request to the request destination corresponding to the input operation via the transmission / reception control unit 200.

[0059] In addition, 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 modified.

[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 (DRAM, SRAM, etc.) 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 integrally configured, such as a touch panel display. The storage device 920 is composed of, for example, a HDD, an SSD, etc., and functions as a storage unit. The storage device 920 stores various data necessary for the execution of 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 the network 4 in FIG. 1) such as the Internet or an intranet by wire or wirelessly, and functions as a communication unit that transmits and receives data to and from other computers according to a predetermined communication standard. The external device I / F unit 924 is connected to an external device 950 such as a camera, a printer, a scanner, a reader / writer by wire or wirelessly, and functions as a communication unit that transmits and receives data to and from the external device 950 according to a predetermined communication standard. The I / O device I / F unit 926 is connected to an I / O device 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, between the I / O device 960. The media input / output unit 928 is composed of a drive device such as a DVD drive, a CD drive, and reads and writes data from and to a medium (non-temporary storage medium) 970 such as a DVD or a CD.

[0065] In the computer 900 having the above configuration, the processor 912 loads a 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 through the communication I / F unit 922. Furthermore, the computer 900 may realize various functions realized by the processor 912 executing the program 930 using hardware such as an FPGA or an ASIC.

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

[0067] (Operation of drawing processing device 2A) Fig. 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 learned model storage unit 22 will be described as having a learned first learning model 220A and a second learning model 220B stored therein 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 accepting the new drawing data D10. At that time, the drawing reception unit 202 may accept 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 accepted in step S100 into the first learning model 220A as a feature acquisition process, thereby acquiring processed product characteristic data D11 for the processed product in the new drawing data D10. Note that, when the drawing acceptance unit 202 accepts a plurality of new drawing data D10, it acquires processed product characteristic 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. The request destination selection unit 203A then selects the candidate request destinations indicated by the processed product request data D12 as candidates for request destinations to process or estimate the processed product in the new drawing data D10 accepted in step S100. When the drawing acceptance unit 202 accepts multiple new drawing data D10, it acquires processed product request data D12 for each of the new drawing data D10 and selects candidate request destinations.

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

[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 the 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 which 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 generating unit 201B according to the second embodiment. Fig. 11 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.

[0075] The second embodiment differs from the first embodiment in that the drawing processing device 2B selects candidates for a request destination for a processed product in new drawing data D10 using one learning model 220 instead of two learning models 220A and 220B. 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 a trained learning model 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 memory 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 similar to 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 to process or estimate 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 similar to 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, the learning data acquisition unit 2010A, like the first embodiment, displays a list of the existing drawing data D10 registered in the drawing database 210 on a display screen in 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 out from the drawing database 210 to obtain learning data D13.

[0083] The machine learning unit 2011B performs machine learning 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) using multiple sets of learning data D13 acquired by the learning data acquisition unit 2010B.

[0084] (Request destination selection unit 203B) The request destination selection unit 203B inputs the new drawing data D10 accepted by the drawing acceptance 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 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, so the following description will 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 product 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 the candidate request destinations indicated by the processing 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.

[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 device 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 a drawing processing program 211C recorded in the data storage unit 21 to function 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) The destination selection unit 203C performs character recognition processing on the new drawing data D10 accepted by the drawing acceptance unit 202 as a characteristic acquisition processing, 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. 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. As a result of the character recognition process, text data indicating the characters and position data indicating the position where the characters are written are obtained.

[0095] For example, when the character "welding" is read, the processed product characteristic data D11 is defined as data in which only the flag for "welding" is substituted with "1" for the attribute of welding, such as "Yes=1, No=0". When the character "4.5 drill" is read in eight places, the processed product characteristic data D11 is defined as data in which "8" is substituted for the parameter of 4.5 drill, such as "4.5 drill=8", for the attribute of hole drilling. In this case, when 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, uppercase and lowercase characters, they may be integrated and treated. The characters read by the character recognition process may be displayed on the display screen of the user terminal device 3, and the user may perform an editing operation.

[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 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 options in the processed product characteristic data D11 for the new drawing data D10 are the same as or similar to options in the processed product characteristic data D11 associated with the existing drawing data D10 are extracted from the drawing database 210. For attributes selected by numerical values, existing drawing data D10 in which 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 destination selection unit 203C acquires the processed product request data D12 indicating a 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 request destination candidate. 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 them as request destination candidates. For example, the plurality of processed product request data D12 may be sorted in order of frequency as a request destination, and the request destination with the highest frequency may be selected as a request destination candidate, or the request destinations from the top to a predetermined rank in order of frequency may be selected as request destination candidates.

[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 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, so the following description will 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 step), the request destination selection unit 203C performs character recognition processing on the new drawing data D10 accepted in step S100 as a characteristic acquisition processing, thereby acquiring processed product characteristic data D11 for the new drawing data D10.

[0103] Next, in step S114 (request destination selection process), the request destination selection unit 203C extracts, as a candidate selection process, existing drawing data D10 that is identical or similar to the characteristics indicated by the processed product characteristic data D11 obtained in step S113 from the existing drawing data D10 registered in the drawing database 210 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 candidates for 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 such modifications are included in the technical concept of the present invention.

[0107] In the above embodiment, the drawing processing apparatuses 2A to 2C are described as being configured as a single device, but may be configured as a plurality of devices. For example, the units 200 to 204 of the drawing processing apparatuses 2A to 2C may be distributed to a plurality of devices, so that the drawing management system 1 may be configured as a machine learning device that includes learning model generating units 201A and 201B and performs a machine learning process, and a request destination selecting device that includes request destination selecting units 203A to 203C and performs a request destination selecting process. In this case, each unit (each process) of each of the above devices may be realized by a program executable by the computer 900.

[0108] In the above embodiment, the drawing processing devices 2A to 2C have been described as receiving new drawing data D10 from the user terminal device 3 and selecting a request destination for a 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-mentioned embodiments can be appropriately combined. 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-203C of the drawing processing devices 2A-2C have been described as acquiring the processed product request data D12 for the new drawing data D10. However, information useful for the processing request or the quotation request may also be acquired. As the useful information, the request destination selection units 203A-203C may acquire detailed processing information, such as the type of processing device used in the processing process, the processing time required for the processing process, and the presence or absence of manual labor. 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. 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 apparatuses 2A to 2C (drawing processing methods or drawing processing programs) according to the above-mentioned embodiments, but also in the form of an inference apparatus (inference method or inference program) used to infer candidates for a request destination of a processed product in new drawing data D10. In this case, the inference apparatus (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 candidates for a request destination to process or estimate the processed product in the new drawing data D10 when the new drawing data D10 is 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: Requester 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 relating to a processed product; a request destination selection unit that selects candidates for a request destination to which processing or an estimate of the processed product in the new drawing data is to be requested based on the new drawing data accepted by the drawing acceptance unit, 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 a candidate for the request destination is selected based on the processed product request data; The learning model is A 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. Drawing processing equipment.

2. a drawing receiving unit that receives new drawing data relating to a processed product; a request destination selection unit that selects candidates for a request destination to which processing or an estimate of the processed product in the new drawing data is to be requested based on the new drawing data accepted by the drawing acceptance unit, The request destination selection unit, a characteristic acquisition process for acquiring processed product characteristic data indicating characteristics of the processed product in the new drawing data from the new drawing data accepted by the drawing acceptance unit; a candidate selection process for selecting candidates for the request destination based on the processed product request data, the candidate selection process being a candidate for the processed product request data, the candidate selection process being a candidate for the request destination based on the processed product characteristic data obtained in the characteristic acquisition process; The request destination selection unit, As the characteristic acquisition process, the new drawing data accepted by the drawing acceptance unit is input into a first learning model to acquire the processed product characteristic data for the processed product in the new drawing data; The first learning model is A 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. Drawing processing equipment.

3. a drawing receiving unit that receives new drawing data relating to a processed product; a request destination selection unit that selects candidates for a request destination to which processing or an estimate of the processed product in the new drawing data is to be requested based on the new drawing data accepted by the drawing acceptance unit, The request destination selection unit, a characteristic acquisition process for acquiring processed product characteristic data indicating characteristics of the processed product in the new drawing data from the new drawing data accepted by the drawing acceptance unit; a candidate selection process for selecting candidates for the request destination based on the processed product request data, the candidate selection process being a candidate for the processed product request data, the candidate selection process being a candidate for the request destination based on the processed product characteristic data obtained in the characteristic acquisition process; The request destination selection unit, As the candidate selection process, the processed product characteristic data acquired in the characteristic 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 candidates for the request destination are 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. Drawing processing equipment.

4. a drawing receiving unit that receives new drawing data relating to a processed product; a request destination selection unit that selects candidates for a request destination to which processing or an estimate of the processed product in the new drawing data is to be requested based on the new drawing data accepted by the drawing acceptance unit, The request destination selection unit, a characteristic acquisition process for acquiring processed product characteristic data indicating characteristics of the processed product in the new drawing data from the new drawing data accepted by the drawing acceptance unit; a candidate selection process for selecting candidates for the request destination based on the processed product request data, the candidate selection process being a candidate for the processed product request data, the candidate selection process being a candidate for the request destination based on the processed product characteristic data obtained in the characteristic acquisition process; 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 or similar to the characteristics indicated by the processed product characteristic data based on the processed product characteristic data acquired in the characteristic 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; Drawing processing equipment.

5. a learning data acquisition unit that acquires a plurality of sets of learning data each composed of input data and output data; The input is subjected to the training using a plurality of sets of the training data acquired by the training data acquisition unit. a machine learning unit that causes a learning model to learn a correlation between force data and the output data by machine learning; a learned model storage unit that stores the learned model in which the correlation is 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 request destination to process or estimate the processed product in the existing drawing data. Machine learning device.

6. 1. A computer-implemented method for processing drawings, comprising the steps of: a drawing receiving process for receiving new drawing data relating to the processed product; and a request destination selection step of selecting candidates for request destinations to process or estimate the processed product in the new drawing data based on the new drawing data received by the drawing receiving step, The request destination selection step includes: The new drawing data received in the drawing receiving step 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 A 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. How to process drawings.

7. 1. A computer-implemented method for processing drawings, comprising the steps of: a drawing receiving process for receiving new drawing data relating to the processed product; and a request destination selection step of selecting candidates for request destinations to process or estimate the processed product in the new drawing data based on the new drawing data received by the drawing receiving step, The request destination selection step includes: a characteristic acquisition process for acquiring processed product characteristic data indicating characteristics of the processed product in the new drawing data from the new drawing data accepted by the drawing acceptance process; a candidate selection process for selecting candidates for the request destination based on the processed product request data, the candidate selection process being a candidate for the processed product request data, the candidate selection process being a candidate for the request destination based on the processed product characteristic data obtained in the characteristic acquisition process; The request destination selection step includes: As the characteristic acquisition process, the new drawing data accepted in the drawing acceptance step is input into a first learning model to acquire the processed product characteristic data for the new drawing data; The first learning model is A 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. How to process drawings.

8. 1. A computer-implemented method for processing drawings, comprising the steps of: a drawing receiving process for receiving new drawing data relating to the processed product; A request destination selection step for selecting a candidate of a request destination to which the processing or estimate of the processed product in the new drawing data is requested based on the new drawing data received in the drawing receiving step. A determination step, The request destination selection step includes: a characteristic acquisition process for acquiring processed product characteristic data indicating characteristics of the processed product in the new drawing data from the new drawing data accepted by the drawing acceptance process; a candidate selection process for selecting candidates for the request destination based on the processed product request data, the candidate selection process being a candidate for the processed product request data, the candidate selection process being a candidate for the request destination based on the processed product characteristic data obtained in the characteristic acquisition process; The request destination selection step includes: As the candidate selection process, the processed product characteristic data acquired in the characteristic 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 in the drawing acceptance process, and candidates for the request destination are 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. How to process drawings.

9. 1. A computer-implemented method for processing drawings, comprising the steps of: a drawing receiving process for receiving new drawing data relating to the processed product; and a request destination selection step of selecting candidates for request destinations to process or estimate the processed product in the new drawing data based on the new drawing data received by the drawing receiving step, The request destination selection step includes: a characteristic acquisition process for acquiring processed product characteristic data indicating characteristics of the processed product in the new drawing data from the new drawing data accepted by the drawing acceptance process; a candidate selection process for selecting candidates for the request destination based on the processed product request data, the candidate selection process being a candidate for the processed product request data, the candidate selection process being a candidate for the request destination based on the processed product characteristic data obtained in the characteristic acquisition process; The request destination selection step includes: 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 or similar to the characteristics indicated by the processed product characteristic data based on the processed product characteristic data acquired in the characteristic 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 step, based on the processed product request data associated with the similar drawing data; How to process drawings.

10. 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 process of causing a learning model to learn a correlation between the input data and the output data by machine learning using a plurality of sets of the learning data acquired by the learning data acquisition process; a learned model storage step of storing the learned model in a learned model storage unit, the learned model having learned the correlation by the machine learning step; The input data is Including existing drawing data for processed products, The output data is The processing request data indicates a request destination to process or estimate the processed product in the existing drawing data. Machine learning methods.

Images