Method and system for checking electronic graph paper and storage medium

Abstract

The application provides a verification method and system of electronic graph paper and a storage medium. The verification method comprises: importing a set of electronic graph paper; wherein the set of electronic graph paper comprises a directory page graph paper and at least one other electronic graph paper; identifying directory page data of the directory page graph paper by using a first model obtained by training, verifying the archiving condition of the set of electronic graph paper based on the directory page data, and outputting a first verification result of the archiving condition of the set of electronic graph paper; and outputting a second verification result of the archiving condition of the set of electronic graph paper according to feedback input of a user on the first verification result. The verification method can ensure the data integrity and accuracy of the electronic graph paper in the conversion and transmission process, reduce the dependence on manual checking, improve the verification efficiency of the electronic graph paper, and enhance the reliability and interactivity of the work flow.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic drawing processing, and in particular to a method, system and storage medium for verifying electronic drawings. Background Art

[0002] Currently, pilot projects in many locations are implementing a "one set of drawings" closed-loop management system for the entire design, construction, completion, and archiving process. This emphasizes the importance of digital drawings as the basis for oversight and inspection by relevant departments, acceptance inspections, and quality and safety incident investigations. When processing electronic drawings, basic drawing information (such as design parameters, annotations, drawing titles, drawing numbers, drawing sizes, and dates) must be verified to ensure their integrity, usability, and consistency. Traditionally, manual proofreading is time-consuming, inefficient, and lacks accuracy. Furthermore, the cataloging process can be prone to errors such as incorrect data entry, missing information, and formatting errors, especially when dealing with large amounts of complex drawing data. These errors can directly impact project quality review and final acceptance, increasing the cost and time of rework and revisions.

[0003] In this context, the traditional manual verification of drawing integrity, consistency, and documentation is no longer suitable for the efficient and high-quality management of digital drawings. Therefore, developing an efficient and accurate electronic drawing verification method is crucial to ensure the accuracy and reliability of design drawings and the archiving results of electronic drawings. Summary of the Invention

[0004] Based on this, the present invention aims to provide an improvement to solve at least one of the above problems.

[0005] In a first aspect, the present application provides a method for verifying an electronic drawing, comprising:

[0006] Importing a set of electronic drawings; wherein the set of electronic drawings includes a catalog page drawing and at least one other electronic drawing;

[0007] Using the trained first model to identify the catalog page data of the catalog page drawing, and verifying the filing status of the group of electronic drawings based on the catalog page data, and outputting a first verification result of the filing status of the group of electronic drawings;

[0008] According to the user's feedback input on the first verification result, a second verification result of the filing status of the group of electronic drawings is output.

[0009] The above method uses the trained first model to complete the recognition of the catalog page data of the catalog page drawing, and uses it to verify the archiving status of the group of electronic drawings, outputting a first verification result, and then forming feedback input through the user's confirmation and editing of the first verification result to obtain a more accurate second verification result. This verification method uses automated verification technology to ensure the data integrity and accuracy of electronic drawings during the conversion and transmission process, reduce reliance on manual verification, and improve the verification efficiency of electronic drawings; on the other hand, it can significantly reduce errors caused by manual entry and enhance the reliability of the workflow; in addition, it can also enhance interactivity, making it convenient for users to view verification results in real time and edit and modify them directly on the interface, improving the user experience and enabling non-professionals to effectively verify electronic drawings.

[0010] In one embodiment, the method further includes: training and optimizing the first model based on the second verification result to obtain a second model, wherein the second model is configured to verify the next set of electronic drawings.

[0011] In one embodiment, the verifying of the archiving status of the group of electronic drawings based on the catalog page data includes: verifying based on the catalog page data whether the group of electronic drawings is missing electronic drawings recorded by the catalog page data and / or has extra electronic drawings not recorded by the catalog page data; and verifying based on the catalog page data whether the cataloging data of other electronic drawings is consistent with that recorded in the catalog page data.

[0012] In one embodiment, the first model is determined according to a first training process, which includes: obtaining a first training sample; wherein the first training sample includes a plurality of verified electronic drawings and historical verification results, and the historical verification results include error examples and correct examples; training an initial model based on the first training sample to obtain the first model; wherein the initial model is an OCR model that applies a deep learning model.

[0013] In one embodiment, the training of the initial model based on the first training sample to obtain the first model includes: training the initial model to recognize the catalog page data of the electronic drawing and the frame information data of other electronic drawings in the first training sample; and training the initial model to distinguish between erroneous examples and correct examples of historical verification results in the first training sample.

[0014] In one embodiment, the training of the initial model to identify the table of contents page data of the electronic drawing and the frame information data of other electronic drawings in the first training sample includes: training the initial model to extract one or more of the text feature data, border feature data, table line feature data, symbol feature data, drawing index feature data, serial number data, drawing number data, format feature data, and spatial relationship feature data of the electronic drawing in the first training sample; and training the initial model to identify the table of contents page data and the frame information data of other electronic drawings in the first training sample based on the extracted feature data.

[0015] In one embodiment, the training of the initial model based on the first training sample to obtain the first model further includes: training the initial model to post-process the identified data; wherein the post-processing step includes one or more steps of semantic correction, format standardization, noise removal, checking the continuity of drawing numbers, and checking the progressive relationship of drawing versions.

[0016] In one embodiment, the training of the initial model based on the first training sample to obtain the first model also includes: training the initial model to provide corrections and / or correction suggestions based on the type of error examples after identifying abnormal situations; wherein, when the error example is a regular error, the initial model is trained to correct the error example according to predefined correction rules; when the error example is an irregular error, the initial model is trained to provide one or more solution options for correcting the error example.

[0017] In one embodiment, the method further includes: preprocessing the group of electronic drawings before verifying the group of electronic drawings using the first model; wherein the preprocessing step includes one or more steps of format conversion of the electronic drawing files, identification of directory files, output of a list of electronic drawing files, and acquisition of the structure of subfolders.

[0018] In a second aspect, the present application provides an electronic drawing verification system, comprising: at least one processor and at least one memory; wherein, at least one of the memories is used to store computer instructions, and at least one of the processors is used to execute at least part of the computer instructions to implement the steps of the method described in any of the above embodiments; and, an interactive component, coupled to the processor, configured to display verification results and interact with a user.

[0019] The above system can ensure the data integrity and accuracy of electronic drawings during the conversion and transmission process through automated verification, reduce reliance on manual verification, and improve the verification efficiency of electronic drawings; and can significantly reduce errors caused by manual entry and enhance the reliability of the workflow; in addition, interactive components can be used to facilitate users to view verification results in real time and edit and modify them directly on the interface, improving the user experience so that non-professionals can also effectively verify electronic drawings.

[0020] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method described in any of the above embodiments when the computer program is executed. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the implementation methods of this specification or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the implementation methods or the description of the prior art. Obviously, the drawings described below are only some implementation methods recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0022] Figure 1 This is a flowchart of the steps of a verification method according to an embodiment of the present application;

[0023] Figure 2 A schematic diagram of an interactive interface of a verification system according to an embodiment of the present application;

[0024] Figure 3 This is a schematic diagram showing the contents of a directory page drawing of an embodiment of the present application;

[0025] Figure 4 This is a schematic diagram showing the contents of another electronic drawing according to an embodiment of the present application;

[0026] Figure 5 This is a schematic diagram showing a first verification result according to an embodiment of the present application;

[0027] Figure 6 This is a flowchart of the steps of a verification method according to an embodiment of the present application;

[0028] Figure 7 This is a flowchart of the steps of a verification method according to an embodiment of the present application;

[0029] Figure 8 This is a flowchart of the steps of the first training process according to an embodiment of the present application;

[0030] Figure 9 This is a flowchart of the steps of the first training process according to an embodiment of the present application;

[0031] Figure 10 A step flow chart of the first training process of an embodiment of the present application;

[0032] Figure 11 A step flow chart of the first training process of an embodiment of the present application;

[0033] Figure 12 A step flow chart of the verification method of an embodiment of the present application;

[0034] Figure 13 A component schematic diagram of the verification system of an embodiment of the present application. DETAILED DESCRIPTION

[0035] In order to make the above objectives, characteristics and advantages of the present application more apparent, specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, the present application can be practiced in a number of different ways beyond the specific embodiments described and it is therefore contemplated to cover all such modifications as fall within the scope of the application. It should be noted that the specific embodiments of the present application are presented for the purpose of illustration and description and are not intended to limit the scope of the present application.

[0036] In the description of the present application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the purpose of facilitating the description of the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application.

[0037] In addition, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second", etc. can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "a plurality of" is at least two, such as two, three, etc., unless otherwise specifically limited.

[0038] It should be noted that when an element is referred to as being "fixed to" or "disposed on" another element, it may be directly on the other element or there may be an intermediate element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be an intermediate element. The terms "vertical," "horizontal," "upper," "lower," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only implementation methods.

[0039] The traditional archiving method of urban construction drawings has the following main problems:

[0040] 1. Poor data accuracy: In traditional methods, when converting or not converting the original CAD drawings, manual verification of the drawing catalog is still required, which will inevitably lead to problems such as data errors and omissions.

[0041] 2. Low execution efficiency and high labor costs: Traditional methods inevitably require a lot of manual entry and verification, cannot be automated, and have high labor costs. In some solutions, directory input can only be performed through batch files or small text processing programs, and automatic extraction and proofreading cannot be achieved, resulting in low execution efficiency.

[0042] Error detection and correction: Traditional methods require manual verification, so for such problems, the automated processing mode of the present invention is needed to perform error detection and correction.

[0043] To address these challenges, this application provides a method for verifying electronic drawings. This method uses deep learning and optical character recognition (OCR) technology to perform in-depth verification on converted drawing files, ensuring precise matching of various data types and advanced error detection. This system can significantly improve user efficiency and accuracy across various stages of approval, supervision, construction, and archiving. It also provides strong technical support for the digital management of urban construction archives, filling a technological gap in the market.

[0044] The embodiment of the present application provides a method for verifying electronic drawings. The first model obtained through training completes the recognition of the catalog page data of the catalog page drawing, and uses it to verify the archiving status of the electronic drawing, outputs a first verification result, and then forms feedback input through the user's confirmation and editing of the first verification result to obtain a more accurate second verification result. In this way, through automated verification technology, on the one hand, the data integrity and accuracy of the electronic drawings during the conversion and transmission process can be ensured, reducing the dependence on manual verification and improving the verification efficiency of the electronic drawings; on the other hand, it can significantly reduce the errors caused by manual input and enhance the reliability of the workflow; in addition, it can also enhance interactivity, making it convenient for users to view the verification results in real time and edit and modify them directly on the interface, improving the user experience, and enabling non-professionals to effectively verify electronic drawings.

[0045] In some embodiments, as Figure 2 As shown, the above verification method includes the following steps:

[0046] S100: Import a group of electronic drawings; wherein the group of electronic drawings includes a catalog page drawing and at least one other electronic drawing.

[0047] For example, Figure 2 As shown, the imported electronic drawings include a catalog page drawing 100 and at least one other drawing 200 (such as a floor plan drawing, a staircase drawing, a workshop drawing, etc.). As can be seen, the verification method of this embodiment can verify multiple electronic drawings in batches, greatly improving the efficiency of the drawing management process.

[0048] S300: Using the trained first model to identify the catalog page data of the group of electronic drawings, verifying the filing status of the group of electronic drawings based on the catalog page data, and outputting a first verification result of the filing status of the group of electronic drawings.

[0049] For example, the catalog page data may include the name data, serial number data, date data, drawing number data, drawing name data, symbol data, designer data, supervisor data, drawing size data, etc. of each electronic drawing. Figure 2 For example, "0100&&A-01A-0000K&&Drawing Catalog" is the name data, "0020", "0101" and other data are the sequence data, "A-01A-0000K" and other data are the drawing number data, "Drawing Catalog", "First Floor Plan" and other data are the drawing name data. Figure 3As shown in the example, data such as "2024-04-17" in the directory file is date data. In addition, there are also design unit data, plot number data, project number data, and the archiving data of the drawings under the drawing directory column (such as number data, name data, drawing size data, design time data, etc.). After the directory page data is identified by the first model, the archiving data of other electronic drawings in the group of electronic drawings can be verified with the identified directory page data as a reference to determine whether errors occurred in the previous archiving process of other electronic drawings, and the first verification result is output after the verification is completed. Among them, the archiving data of other electronic drawings includes the name data of other electronic drawings (such as Figure 2 As shown) and other frame information data in electronic drawings (such as Figure 4 (data selected by the black dashed box in the figure).

[0050] S400: Output a second verification result of the filing status of the group of electronic drawings according to the user's feedback input on the first verification result.

[0051] For example, since the verification algorithm of the first model may still be imperfect, the verification result can be improved by means of interaction with the user. Figure 5 As shown, an interactive component can be set up to display the first verification result (including detailed information on errors and warnings), allowing users to view the verification results in real time. Simultaneously, users can edit or confirm the first verification result on the interface to generate a second verification result with higher accuracy. This design not only improves the user experience but also enables non-professionals to effectively use the system for electronic drawing verification. It is understandable that after the user confirms that the first verification result is correct, they can click "Confirm" or "Export Verification Results (e.g., Excel)." This can also be understood as the user's feedback input on the first verification result, and the second verification result is output accordingly (in this case, the second verification result is consistent with the content of the first verification result).

[0052] For example, the user can also make modifications directly on the interface according to the second verification result, thereby completing the error revision of the electronic drawing in a timely manner and saving modification time.

[0053] In some embodiments, as Figure 6 As shown, the above verification method also includes:

[0054] S500: Based on the second verification result, the first model is trained and optimized to obtain a second model, and the second model is configured to verify the next set of electronic drawings.

[0055] For example, the first model can learn based on the second verification result, adjust and optimize the data recognition and verification algorithm, improve the overall accuracy and response speed of the model, make the model more intelligent, and also help adapt to changing verification needs.

[0056] In some embodiments, as Figure 7 As shown, step S300 may include:

[0057] S310: Check based on the catalog page data whether the group of electronic drawings is missing electronic drawings recorded in the catalog page data and / or contains extra electronic drawings not recorded in the catalog page data.

[0058] For example, the sequence number data of the electronic drawings in the catalog page data can be used to verify whether the set of electronic drawings contains any missing or extra electronic drawings. For example, if the first model identifies that the set of electronic drawings does not contain any electronic drawings with sequence numbers recorded in the catalog page data, it can be considered that the set of electronic drawings contains any missing electronic drawings; and / or if the first model identifies that the set of electronic drawings contains any electronic drawings with sequence numbers not recorded in the catalog page data, it can be considered that the set of electronic drawings contains any extra electronic drawings.

[0059] S320. Verify based on the catalog page data whether the description data of other electronic drawings is consistent with the data recorded on the catalog page.

[0060] For example, the descriptive data of other electronic drawings may include symbol data, drawing number data, drawing title data, and other data contained in the other electronic drawings, as well as date data, designer data, supervisor data, and drawing dimensions. Part of the descriptive data of other electronic drawings may be recorded in the drawing frame information area. By identifying and comparing the catalog page data with the descriptive data of other electronic drawings, the descriptive data of the group of electronic drawings can be effectively verified.

[0061] The existence of the electronic drawing can be verified through step S310, and the electronic drawing catalog data can be verified through step S320. The above two-fold verification can effectively ensure the integrity and accuracy of the electronic drawing data during the archiving process.

[0062] In some embodiments, as Figure 8 As shown, the first model can be determined according to a first training process, which may include:

[0063] SA, obtaining a first training sample; wherein the first training sample includes a plurality of verified electronic drawings and historical verification results, the historical verification results including error examples and correct examples;

[0064] SB, training the initial model based on the first training sample to obtain a first model; wherein the initial model is an OCR model applying a deep learning model.

[0065] Exemplarily, the model training for the catalog page table recognition based on the basic OCR model can be performed by applying a convolutional neural network (CNN) and a recurrent neural network (RNN) or a Transformer model. The first training sample can include a plurality of verified electronic drawings, and the initial model is trained to extract text features and graphic features from the verified electronic drawings. The extraction of the text features mainly includes extraction of complete text lines in the electronic drawings, including continuous character recognition and spatial relationship recognition, to correctly parse the data in the table; and the extraction of the graphic features mainly includes border detection, symbol recognition (such as symbols), and recognition of graphics related to the text (such as table lines, dividing lines, etc.).

[0066] Exemplarily, in order to more accurately recognize a specific type of drawing (such as a city construction drawing), a training data set containing specific feature data can be prepared in advance, and the OCR model using deep learning technology is trained through the training data set, so that it can more intelligently recognize the catalog page data in the city construction drawing. Optionally, the specific feature data can include drawing index feature data, serial number data, symbol data, figure number data, format feature data, and spatial relationship feature data of graphics and text.

[0067] Optionally, as shown in Figure 9 SB can include:

[0068] SB1, training the initial model to recognize the catalog page data of the electronic drawings in the first training sample and the frame information data of other electronic drawings;

[0069] SB3, training the initial model to distinguish between error examples and correct examples of the historical verification results in the first training sample.

[0070] Exemplarily, the text information of each electronic drawing in the first training sample can appear in different fonts, sizes, and formats, thereby ensuring that the initial model can adapt to various inputs.

[0071] Exemplarily, the initial model can be trained to recognize correct examples to learn correct text forms, and at the same time, to recognize error examples to improve error recognition capability. Optionally, the error examples can include misspellings, mismatch between figure numbers and descriptions, and errors in drawing size data.

[0072] Optionally, step SB1 can include:

[0073] SB11, training the initial model to extract one or more of text feature data, border feature data, table line feature data, symbol feature data, drawing index feature data, sequence number data, drawing number data, format feature data, and spatial relationship feature data between graphics and text from the electronic drawing in the first training sample;

[0074] SB12: Train the initial model to identify the catalog page data of the electronic drawing and the frame information data of other electronic drawings in the first training sample based on the extracted feature data.

[0075] For example, by training the initial model to extract the above-mentioned feature data and identifying the catalog page data and the frame information data of other electronic drawings based on the extracted feature data, the initial model can gradually master the ability to recognize the text information of specific drawings (such as urban construction drawings).

[0076] In some embodiments, as Figure 10 As shown, step SB may further include:

[0077] SB2. Train the initial model to post-process the identified data; wherein the post-processing steps include one or more steps of semantic correction, format standardization, noise removal, checking the continuity of drawing numbers, and checking the progressive relationship of drawing versions.

[0078] For example, the initial model can be trained to perform semantic correction on the recognized data, such as correcting spelling errors in OCR recognition; the initial model can be trained to standardize the format of the recognized data, such as unifying the data format (including font and size); the initial model can be trained to remove noise from the recognized data, such as removing non-data text (including footer and header information); the initial model can be trained to check the continuity of drawing numbers and the progressive relationship of drawing versions on the recognized data, for example, by introducing natural language processing (NLP) technology for training.

[0079] In some embodiments, as Figure 11 As shown, step SB may further include:

[0080] SB4. After identifying an abnormal situation, the training initial model provides corrections and / or correction suggestions based on the type of error example; when the error example is a regular error, the training initial model corrects the error example according to predefined correction rules; when the error example is an irregular error, the training initial model provides one or more options for correcting the error example.

[0081] For example, when some common and rule-based errors are identified, such as incorrect date format, omitted units, etc., an automatic correction process can be triggered, and the initial model can be trained to apply a predefined automatic correction algorithm for direct correction; when irregular errors are identified, such as date errors, personnel information errors, etc., a semi-automatic correction process can be triggered. This process will show the user where the problem lies and provide several possible correction options. The user can choose the most appropriate correction solution from the options provided, and the system can learn from the user's choice to optimize future recommendation strategies.

[0082] In some embodiments, as Figure 12 As shown, the above verification method also includes:

[0083] S200. Before verifying the set of electronic drawings using the first model, preprocess the set of electronic drawings; wherein the preprocessing step includes one or more steps of converting the format of the electronic drawing files, identifying the directory file, outputting a list of the electronic drawing files, and obtaining the structure of the subfolders.

[0084] For example, it is difficult for existing OCR models to directly recognize text information from CAD drawing files. Therefore, the uploaded file type can be identified first and necessary format conversion can be performed. For example, DWG format and DXF format drawing files can be converted into PDF files. Then, directory files can be identified (such as files with "directory" in the name), other drawing files can be displayed in a list (to facilitate user click to view and edit), and the subfolder structure can be obtained.

[0085] This application also provides a verification system for electronic drawings that can receive user-uploaded CAD files or directly connect to a drawing database through a user-friendly interface. Once a drawing is uploaded or selected, the system will automatically start the verification process. This process can include three main steps: file format identification and conversion, content extraction, and verification. During the file format identification and conversion stage, the system can automatically determine the file type and apply the corresponding parsing rules to determine whether to convert the native CAD drawing file format to a PDF file without user intervention; during the content extraction stage, the system can use OCR technology to extract necessary information from the drawing, such as drawing size data, layer information, and annotations; then, the system can compare the extracted information with the uploaded drawing, automatically identify any non-standard differences, and check the accuracy of the archived information; finally, all verification results will be automatically recorded by the system and a detailed verification report will be generated. The report lists all the problems found and their locations. Users can directly access the corresponding drawing part from the report for review or modification. In addition, to further improve the efficiency of the process, the system also integrates machine learning models. These models can continuously optimize parameters based on historical verification data, making the system more accurate and efficient as it is used over time. In addition, the system also provides an intuitive and highly interactive user interface that allows users to easily manage drawing files, view verification results in real time, and edit and modify them directly on the interface. The system will also adjust itself based on user feedback to continuously improve verification quality.

[0086] In some embodiments, as Figure 13 As shown, the verification system 100 may include at least one processor and at least one memory; wherein, at least one of the memories is used to store computer instructions, and at least one of the processors is used to execute at least part of the computer instructions to implement the steps of the method described in any of the above embodiments; and, an interactive component, coupled to the processor, is configured to display the verification results and interact with the user.

[0087] The above system can ensure the data integrity and accuracy of electronic drawings during the conversion and transmission process through automated verification, reduce reliance on manual verification, and improve the verification efficiency of electronic drawings; and can significantly reduce errors caused by manual entry and enhance the reliability of the workflow; in addition, interactive components can be used to facilitate users to view verification results in real time and edit and modify them directly on the interface, improving the user experience so that non-professionals can also effectively verify electronic drawings.

[0088] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed, the steps of the method described in any of the above embodiments are implemented.

[0089] It should be noted that the numbers representing quantities or properties used to describe and claim certain embodiments of the present application should be understood as being modified by the terms "roughly", "about", "approximately" or "substantially" in some cases. For example, unless otherwise stated, "roughly", "about", "approximately" or "substantially" can indicate a ±20% variation of the value described. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required characteristics of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical fields and parameters used to confirm the breadth of their range in some embodiments of the present application are approximate values, in specific embodiments, the settings of such numerical values ​​are as accurate as possible within the feasible range.

[0090] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0091] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A method for verifying electronic drawings, characterized in that: include: Importing a set of electronic drawings; wherein the set of electronic drawings includes a catalog page drawing and at least one other electronic drawing; The first model obtained through training is used to identify the catalog page data of the catalog page drawing, and based on the catalog page data, the filing status of the group of electronic drawings is verified, and a first verification result of the filing status of the group of electronic drawings is output; wherein the verification of the filing status of the group of electronic drawings based on the catalog page data includes: verifying, based on the catalog page data, whether the group of electronic drawings is missing electronic drawings recorded in the catalog page data and / or whether there are additional electronic drawings not recorded in the catalog page data; and verifying, based on the catalog page data, whether the bibliographic data of other electronic drawings is consistent with the bibliographic data. Outputting a second verification result of the filing status of the set of electronic drawings according to the user's feedback input on the first verification result; The first model is trained and optimized based on the second verification result to obtain a second model, and the second model is configured to verify the next set of electronic drawings.

2. The method according to claim 1, characterized in that The first model is determined according to a first training process, and the first training process includes: Obtaining a first training sample; wherein the first training sample includes a plurality of verified electronic drawings and historical verification results, wherein the historical verification results include error examples and correct examples; The initial model is trained based on the first training sample to obtain the first model; wherein the initial model is an OCR model that applies a deep learning model.

3. The method according to claim 2, characterized in that The step of training the initial model based on the first training sample to obtain the first model includes: Training the initial model to recognize the catalog page data of the electronic drawing and the frame information data of other electronic drawings in the first training sample; The initial model is trained to distinguish between erroneous examples and correct examples of historical verification results in the first training sample.

4. The method according to claim 3, characterized in that The training of the initial model to recognize the catalog page data of the electronic drawing and the frame information data of other electronic drawings in the first training sample includes: Training the initial model to extract one or more of text feature data, border feature data, table line feature data, symbol feature data, drawing index feature data, sequence number data, drawing number data, format feature data, and spatial relationship feature data between graphics and text of the electronic drawing in the first training sample; The initial model is trained to identify the catalog page data of the electronic drawing and the frame information data of other electronic drawings in the first training sample according to the extracted feature data.

5. The method according to claim 3, characterized in that The training of the initial model based on the first training sample to obtain the first model further includes: The initial model is trained to perform post-processing on the identified data; wherein the post-processing steps include one or more steps of semantic correction, format standardization, noise removal, checking the continuity of drawing numbers, and checking the progressive relationship of drawing versions.

6. The method according to claim 3, characterized in that The training of the initial model based on the first training sample to obtain the first model also includes: training the initial model to provide corrections and / or correction suggestions based on the type of error examples after identifying abnormal situations; wherein, when the error example is a regular error, the initial model is trained to correct the error example according to predefined correction rules; when the error example is an irregular error, the initial model is trained to provide one or more solution options for correcting the error example.

7. The method according to claim 1, characterized in that The method further comprises: Before verifying the set of electronic drawings using the first model, the set of electronic drawings is preprocessed; wherein the preprocessing step includes format conversion of the electronic drawing files, and one or more steps of identifying the directory file, outputting a list of the electronic drawing files, and obtaining the structure of the subfolders.

8. An electronic drawing verification system, characterized in that: include: At least one processor and at least one memory; wherein at least one of the memories is used to store computer instructions, and at least one of the processors is used to execute at least part of the computer instructions to implement the steps of any one of claims 1 to 7; as well as, The interactive component is coupled to the processor and is configured to display the verification result and interact with the user.

9. A computer-readable storage medium, characterized in that The storage medium stores a computer program, which, when executed, implements the steps of the method according to any one of claims 1 to 7.

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