Construction drawing quality management and control processing method and device, electronic equipment and storage medium

Abstract

This disclosure relates to the field of information management technology, and provides a method, device, electronic device, and storage medium for construction drawing quality control. The method includes: associating the design drawing model data to be processed with a preset standard database to obtain review task data; matching the review task data with review terminal identification data to obtain review issue record data; performing a check-out verification process on the review issue record data to obtain quality closed-loop data; and performing quantitative analysis on the quality closed-loop data to obtain quality control indicator data. This improves the standardization and consistency of construction drawing review task generation, achieves automated extraction of review standards through the association between the standard database and the drawing model data, enhances the completeness and accuracy of review issue tracking, improves the scientific nature and real-time performance of construction drawing quality evaluation, and provides data support for the continuous optimization of review standards through quantitative analysis.

Description

Technical Field

[0001] This disclosure relates to the field of information management technology, and in particular to a method, apparatus, electronic device and storage medium for construction drawing quality control. Background Technology

[0002] Product design quality control typically involves offline manual review, with drawings being passed between departments in paper or simple electronic form. However, this results in a time-consuming review process, potential omissions or errors in information transmission, low review efficiency, and a lack of unified and effective management methods for review standards. Different reviewers have different understandings, leading to inconsistent review quality. Furthermore, the quality control of drawings cannot detect potential problems in real time. In addition, the version management of electronic drawing transmission tools is chaotic and lacks collaborative review functions.

[0003] It is evident that existing technologies suffer from several problems: the offline manual transmission of audit processes leads to information omissions and deviations; audit standards lack unified management; the quality control of drawings and models lacks a systematic approach; audit evaluation data is delayed, resulting in low audit efficiency; poor quality consistency; difficulty in identifying potential problems; and low timeliness of design quality assessment. Summary of the Invention

[0004] In view of this, the present disclosure provides a construction drawing quality control processing method, apparatus, electronic device and readable storage medium to solve the problems in the prior art, such as manual offline transmission of review processes, easy omission and deviation of information, lack of unified management of review standards, lack of systematic quality control of drawings and models, lag in review evaluation data leading to low review efficiency, poor quality uniformity, difficulty in discovering potential problems, and low timeliness of design quality assessment.

[0005] A first aspect of this disclosure provides a construction drawing quality control processing method, comprising: associating design drawing model data to be processed with a preset standard database to obtain review task data; matching the review task data with review terminal identification data to obtain review issue record data; performing a check-out review on the review issue record data to obtain quality closed-loop data; and performing quantitative analysis on the quality closed-loop data to obtain quality control indicator data, wherein the quality control indicator data is used to update construction drawing review standards and review processes.

[0006] In some embodiments, the design drawing data to be processed is associated with a preset standard database to obtain audit task data, including: performing attribute parsing processing on the design drawing data to be processed to obtain drawing attribute data; performing rule matching processing on the drawing attribute data and the preset standard database to obtain list template data; and performing task encapsulation processing on the list template data to obtain audit task data.

[0007] In some embodiments, matching the review task data and the review terminal identification data to obtain review issue record data includes: performing grouping and parsing processing on the review terminal identification data to obtain functional grouping data; distributing the review task data based on the functional grouping data to obtain grouped review task data; performing map model parsing processing on the grouped review task data to obtain map model element data; and comparing the map model element data and the review task data to obtain review issue record data.

[0008] In some embodiments, the audit issue record data is subjected to a verification process to obtain quality closed-loop data, including: performing structured classification processing on the audit issue record data to obtain production issue list data; performing centralized disclosure processing on the production issue list data to obtain disclosure confirmation data; and performing verification marking processing on the disclosure confirmation data to obtain quality closed-loop data.

[0009] In some embodiments, quantitative analysis and processing of quality closed-loop data are performed to obtain quality control indicator data, including: performing completion statistics processing on quality closed-loop data to obtain node completion rate; performing problem distribution analysis processing on quality closed-loop data to obtain problem statistics; and performing indicator fusion processing on node completion rate and problem statistics to obtain quality control indicator data.

[0010] In some embodiments, comparing the map feature data and the audit task data to obtain audit issue record data includes: performing spatial detection processing on the map feature data to obtain spatial detection result data; performing checklist verification processing on the audit task data and the map feature data to obtain checklist verification result data; and summarizing the spatial detection result data and the checklist verification result data to obtain audit issue record data.

[0011] In some embodiments, the disclosure confirmation data is subjected to verification marking processing to obtain quality closed-loop data, including: extracting the modification content from the disclosure confirmation data to obtain modification description data; performing standard compliance comparison processing on the modification description data to obtain verification result data; and performing status marking processing on the verification result data to obtain quality closed-loop data.

[0012] A second aspect of this disclosure provides a construction drawing quality control processing device, comprising: a first processing module for associating design drawing model data to be processed with a preset standard database to obtain review task data; a second processing module for matching the review task data with review end identification data to obtain review issue record data; a third processing module for performing item verification processing on the review issue record data to obtain quality closed-loop data; and a fourth processing module for performing quantitative analysis processing on the quality closed-loop data to obtain quality control indicator data, wherein the quality control indicator data is used to update construction drawing review standards and review processes.

[0013] A third aspect of this disclosure provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0014] A fourth aspect of this disclosure provides a readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method.

[0015] The beneficial effects of this disclosed embodiment compared with the prior art are as follows: By associating the design drawing model data to be processed with a preset standard database, mandatory review list data is extracted to generate review task data; the review task data and review terminal identification data are matched to obtain review issue record data; the review issue record data is reviewed and verified to obtain quality closed-loop data; the quality closed-loop data is quantitatively analyzed to obtain quality control indicator data, thereby improving the standardization and consistency of construction drawing review task generation, and realizing the automated extraction of review standards through the association between the standard database and the drawing model data; enhancing the completeness and accuracy of review issue tracking, and forming quality closed-loop management through the review and verification mechanism; improving the scientificity and real-time nature of construction drawing quality evaluation, and providing data support for the continuous optimization of review standards through quantitative analysis. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram illustrating an application scenario of an embodiment of this disclosure; Figure 2This is a schematic flowchart of a construction drawing quality control processing method provided in an embodiment of this disclosure; Figure 3 This is a schematic flowchart of another construction drawing quality control method provided in this embodiment of the disclosure; Figure 4 This is a schematic diagram of the structure of a construction drawing quality control processing device provided in an embodiment of this disclosure; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0018] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, so as to provide a thorough understanding of the embodiments of this disclosure. However, those skilled in the art will understand that this disclosure may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this disclosure with unnecessary detail.

[0019] It should be noted that the user information (including but not limited to terminal device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties.

[0020] A method and apparatus for construction drawing quality control according to an embodiment of the present disclosure will now be described in detail with reference to the accompanying drawings.

[0021] Figure 1 This is a schematic diagram illustrating an application scenario of an embodiment of this disclosure. The application scenario may include terminal devices 1, 2, and 3, server 4, and network 5.

[0022] Terminal devices 1, 2, and 3 can be hardware or software. When terminal devices 1, 2, and 3 are hardware, they can be various electronic devices with displays and supporting communication with server 4, including but not limited to smartphones, tablets, laptops, and desktop computers. When terminal devices 1, 2, and 3 are software, they can be installed in the aforementioned electronic devices. Terminal devices 1, 2, and 3 can be implemented as multiple software programs or software modules, or as a single software program or software module; this disclosure does not limit this. Furthermore, various applications can be installed on terminal devices 1, 2, and 3, such as data processing applications, instant messaging tools, social platform software, search applications, shopping applications, etc.

[0023] Server 4 can be a server that provides various services, such as a backend server that receives requests sent by terminal devices with which it has established communication connections. This backend server can receive and analyze the requests sent by the terminal devices and generate processing results. Server 4 can be a single server, a server cluster consisting of several servers, or a cloud computing service center. This disclosure embodiment does not limit this.

[0024] It should be noted that server 4 can be either hardware or software. When server 4 is hardware, it can be various electronic devices that provide various services to terminal devices 1, 2, and 3. When server 4 is software, it can be multiple software programs or software modules that provide various services to terminal devices 1, 2, and 3, or it can be a single software program or software module that provides various services to terminal devices 1, 2, and 3. This disclosure does not limit the scope of the embodiments.

[0025] Network 5 can be a wired network using coaxial cable, twisted pair, and fiber optic connection, or it can be a wireless network that enables interconnection of various communication devices without wiring, such as Bluetooth, Near Field Communication (NFC), and Infrared. This disclosure does not limit the scope of the network.

[0026] Users can establish a communication connection with server 4 via network 5 through terminal devices 1, 2, and 3 to receive or send information. Specifically, server 4 can obtain design drawing data to be processed through terminal devices 1, 2, and 3, and extract mandatory review list data by associating the design drawing data with a preset standard database to generate review task data; match the review task data with the review end identification data to obtain review issue record data; perform item verification on the review issue record data to obtain quality closed-loop data; and perform quantitative analysis on the quality closed-loop data to obtain quality control indicator data.

[0027] It should be noted that the specific types, quantities, and combinations of terminal devices 1, 2, and 3, server 4, and network 5 can be adjusted according to the actual needs of the application scenario, and this disclosure embodiment does not impose any restrictions on this.

[0028] Figure 2 This is a schematic flowchart of a construction drawing quality control method provided in an embodiment of this disclosure. Figure 2 The construction drawing quality control and processing methods can be provided by Figure 1 The server executes the command. For example... Figure 2 As shown, the quality control methods for this construction drawing include: S201: The design drawing data to be processed is associated with the preset standard database to obtain the review task data.

[0029] Specifically, the design drawing data to be processed can be the original design documents to be reviewed for quality. The form of the design drawing data to be processed includes, but is not limited to, building information model files, computer-aided design drawings, etc. The design drawing data to be processed can be submitted to the online collaboration platform by the design party or the project party. The preset standard database can be a pre-built and stored set of structured rules and specifications. The content of the preset standard database can include building codes, enterprise design standards, safety clauses, energy-saving requirements and project-specific requirements, etc., without limitation here. The preset standard database can provide a unified and clear basis for the review work.

[0030] Association processing can be a data processing process that establishes a correspondence between the design model data to be reviewed and the relevant clauses in the preset standard database. It can be achieved by parsing the metadata and content structure of the model data and matching it with the keywords and category tags of the preset standard database.

[0031] Furthermore, in the online collaborative platform, design firms can upload model or drawing files containing information from multiple disciplines such as architecture, structure, and mechanical and electrical engineering, i.e., design drawing model data to be processed. Identifying the design drawing model data to be processed can obtain the file version, professional type, and design stage. It can also filter out all review standard items related to the file type, project, and design stage from the preset standard database, i.e., review task data.

[0032] Furthermore, during the association processing, semantic or rule matching can be performed between the layer information, component attributes, spatial relationships, and other elements contained in the design model data to be processed and the clauses in the preset standard database. The preset standard database can be used as a central knowledge base. The data in the preset standard database can be derived from the summarization and structured storage of the company's historical project experience, relevant regulations, and industry best practices. Through association processing, a list of mandatory review items that need to be checked for each design model data to be processed can be determined, and the corresponding standard clause content, inspection points, and problem classification templates can be bound to specific parts of the model data to form review task data with clear direction.

[0033] In addition, the association processing can also include the structured organization of audit task data. The generated audit task data can include not only the correspondence between the drawings to be audited and the standard clauses, but also the pre-allocation of context information required for task execution, such as suggested audit personnel roles, estimated audit time, and task priorities.

[0034] Furthermore, based on preset rules, conflict detection and deduplication optimization can be performed on the preliminary task data obtained from the association processing to ensure that the generated audit task data is logically clear and free of redundancy.

[0035] This application embodiment receives design template data to be processed and uploads it to an online collaborative platform. Subsequently, the design template data is parsed to identify key features such as metadata, content structure, and professional attributes. A preset standard database is accessed, and association processing is performed. The parsed template features are matched with keywords or category tags in the preset standard database to filter applicable standard clauses. These standard clauses are then bound to the corresponding parts of the design template data to generate structured review task data. This enhances the uniformity and authority of the design review basis; improves the automation and accuracy of review task generation; and increases the efficiency of matching multi-source design standards with template files, thereby improving the efficiency and startup speed of the review process.

[0036] For example, in the approval process of construction drawing designs for newly developed residential projects by real estate development companies, after the design institute completes the construction drawing design for a certain building in the project, it can upload the Building Information Model (BIM) file, which includes the building's floor plan, elevation, section, and integrated model of mechanical and electrical pipelines, as the design drawing model data to be processed to the company's online collaboration platform. Upon receiving this data, the platform can parse the model file, identify the project name, the design stage as "construction drawings," and the relevant disciplines including "architecture," "structure," "water supply and drainage," "heating, ventilation, and air conditioning," and "electrical." It can also access a pre-set standard database, which can store clauses of the "Residential Design Code," the company's internal "Residential Product Construction Standards," and "Green Building Evaluation Standards." The system can generate structured versions of documents such as the "Standards" and the project-specific "Special Requirements for Civil Defense Design". Based on the identified project attributes and professional information, it can match and extract all mandatory clauses, enterprise standard clauses, and project-specific requirements applicable to the construction drawing stage of this project and related to the above five professions from the preset standard database. For example, for the "Architecture" profession, it can associate standards such as "minimum bedroom area" and "kitchen ventilation opening area"; for the "HVAC" profession, it can associate standards such as "air conditioning condensate pipe slope" and "equipment room noise control". Then, it can logically associate the extracted standard clauses with the corresponding spaces or components in the uploaded model files to generate audit task data.

[0037] S202, match the audit task data and the drawing review terminal identification data to obtain audit issue record data.

[0038] Specifically, the review terminal identification data can be information used to uniquely identify and distinguish different review terminal devices, users, or review groups. The review terminal identification data can come from the review personnel group configuration information, or the account information used by the review personnel when logging into the online collaboration platform, or it can be a unique code assigned to a specific review expert group.

[0039] Matching processing is a data processing procedure that establishes the relationship between audit task data and review terminal identification data through data processing logic. Matching processing enables the tracing and attribution of audit issues, ensuring that each audit issue can be associated with its corresponding review terminal. Audit issue record data is a structured data record generated through matching processing, containing the specific content of the audit issue, the corresponding drawing model information, and the review terminal identification information associated with that drawing model information. Audit issue record data provides a unified and complete data foundation for subsequent audit issue summarization, centralized briefing, item verification, and quality evaluation.

[0040] In addition, the specific implementation of the matching process can be completed based on the backend data processing logic of the online collaboration platform. When the reviewer completes the review of a specific drawing model and submits the review findings at the review end, the identification information of the review end can be captured, and the review task data content submitted by it can be received. The review end identifier can be associated with the review task data record as a foreign key through database transactions, or the above two types of information can be written when generating new data records to form review issue record data.

[0041] Furthermore, the matching process can be designed to be performed in real time or in batches. In real-time matching mode, whenever an audit task is submitted, the matching process can be triggered immediately to generate corresponding audit issue record data. In batch matching mode, all audit task data generated within a certain period of time can be collected periodically and batch-associated with the corresponding audit end identifier data according to preset mapping rules.

[0042] Furthermore, the generated audit issue record data structure can be further standardized. For example, it can include a unique record number, an issue description field, an associated drawing model identifier field, an issue type field, an audit end identifier field, and a timestamp field, etc. There are no restrictions here.

[0043] This application embodiment matches the review task data with the review terminal identification data. Through database transactions or write logic, the review terminal identification information is associated and stored with the submitted review task data content to form structured data records, namely review issue record data. This enhances the traceability of review issues and the clarity of responsibility attribution; improves the automation efficiency of review record generation and association; enhances the uniformity and integrity of data collection from multiple review terminals; ensures the consistency of review quality; and supports real-time evaluation and continuous improvement of review quality and drawing quality.

[0044] For example, during the construction drawing process inspection, review experts and review expert groups can review a batch of construction process drawings submitted by the design institute through an online collaborative platform. Each expert can log in and start working on their assigned review terminal. The platform can assign a unique review terminal identifier to each expert or expert group. During the review process, based on the mandatory review list and review points provided by the platform, several issues related to fire safety spacing and structural detail drawings can be identified. These issues, along with information such as the drawing zoning number and the severity level of the issue, can be submitted as review task data. The platform can match the review terminal identifier of the expert with the submitted review task data to generate review issue record data containing fields such as "Issue content: Insufficient width of fire lane; Drawing area: Third floor plan of Area A; Review terminal: Group structural expert group - Zhang San; Submission time: XXXX".

[0045] S203 involves performing a final review of the audit issue record data to obtain closed-loop quality data.

[0046] Specifically, the item verification process can be a data processing procedure that tracks and verifies the rectification status of each recorded audit issue and confirms that the issues have been substantially resolved. This process can also check whether the modifications to the drawing review comments are in place, preventing issues from being left unresolved or falsely closed. It can use audit issue record data as the processing object. Specifically, the item verification process can include modifying construction drawings based on the audit issue record data received from the online collaboration platform, responding to each issue on the platform after modification, and submitting the modified drawings or models as evidence. It can also verify the issue modification status online and mark issues that have been confirmed to be rectified as "closed."

[0047] Furthermore, the sales verification process can also include a step of verifying the accuracy of potential problems discovered by the intelligent verification, thereby generating an intelligent verification problem list.

[0048] Furthermore, the process of reviewing and verifying the sales items can also include summarizing and verifying single-professional issues, as well as conducting cross-professional and cross-functional centralized briefings and reviews for complex issues involving multiple professions.

[0049] Furthermore, for each issue in the audit issue record data, the closing review process can update its status from "pending processing", "processing" to "reviewed and closed".

[0050] This application's embodiment uses audit issue record data as the processing object to trigger item completion review. Design institutes can modify drawings based on issue records and submit responses and evidence online. Review experts can review the modifications online through an online collaborative platform, checking each item against the original issues for verification. For issues confirmed to be rectified, their status can be updated to "completed." For issues not rectified, they are rejected and returned for modification until they pass review. The completion status of all audit issue record data can be summarized to generate quality closed-loop data, thereby enhancing the completeness and rigor of issue rectification tracking and verification; improving the efficiency of multi-disciplinary collaborative review and issue closed-loop processing; and enhancing the transparency and traceability of the construction drawing quality control process, thus increasing the transparency and credibility of quality control.

[0051] For example, in the quality control of construction drawings for real estate development projects, after reviewing the drawings, the review experts can generate a record of review issues from multiple disciplines; the design institute can log in to the platform to obtain the issue list and modify the drawings; after modification, the design institute can upload a modification description and a screenshot of the corresponding drawing area for each issue and submit a response on the platform; after receiving the response notification, the project review team leader can organize relevant professional review experts to conduct online review; the experts can retrieve the drawings before and after modification for comparison, and, in conjunction with the design institute's response description, verify whether each issue has been rectified according to standards; for complex issues such as spatial collisions, experts can use the model viewer integrated into the platform. The modification effect is verified from a three-dimensional perspective; after all issues are reviewed and confirmed to be correct, experts can change the issue status to "closed" on the platform; the closure status and review time nodes of all issues can be summarized and updated synchronously to the project's quality dashboard; for issues found to be inadequately rectified during the review, experts can reject the design institute's response on the platform and add new opinions, and the issue status can be rolled back to "processing" for the design institute to continue to modify until the closure review is passed; then, based on the completion status of the closure review of all review issue records, a data report is generated indicating that all issues in the project's drawing review have been closed and quality control has formed a closed loop, i.e., quality closed loop data.

[0052] In addition, quality closed-loop data can be a set of data that, after being confirmed through the item verification process, can be used to indicate that all audit issues have been effectively resolved and the entire construction drawing quality control process has formed a complete closed loop. Quality closed-loop data can serve as the basis for evaluating the quality of construction drawing delivery for a single project or stage, and can be used for subsequent quality traceability and analysis.

[0053] In addition, the output verification process can also be completed relying on the unified execution environment provided by the online collaboration platform; the online collaboration platform can be a software system that supports multi-party online collaboration, task management, and data flow, and can provide a centralized, transparent, and traceable operation space for the output verification process, ensuring that all processing activities are orderly, efficient, and auditable.

[0054] Furthermore, the online collaboration platform can drive the audit issue record data to flow to the corresponding design institute responsible person and subsequent review experts based on the set rules through a preset workflow engine.

[0055] Furthermore, the platform can ensure that all communications, document transfers, and status updates related to the output verification process are carried out in the same context.

[0056] S204, perform quantitative analysis processing on the quality closed-loop data to obtain quality control index data, where the quality control index data is used to update the construction drawing review standards and review processes.

[0057] Specifically, the quantitative analysis processing can be a data processing process that uses mathematical, statistical, or logical operation methods to calculate, compare, classify, summarize, or model the quality closed-loop data. The quantitative analysis processing can refine the scattered review process information into key indicators that can be used to characterize dimensions such as review efficiency, review quality, and problem trends. The quantitative analysis processing can include specific operations such as calculating the average duration of the review cycle, counting the occurrence frequency and distribution of different types of review problems, evaluating the completion rate and accuracy rate of the mandatory review list items, and analyzing the consistency and differences in problem discovery among different drawing review personnel or groups.

[0058] The quality control index data can be a data result obtained by performing quantitative analysis processing on the quality closed-loop data, which can specifically and quantitatively characterize the control levels and effects of each link in the construction drawing review. The quality control index data can provide a data basis for updating the construction drawing review standards and review processes. The content of the quality control index data can include review efficiency indicators, review quality indicators, and / or problem distribution indicators, etc., which are not limited here.

[0059] Furthermore, quality control indicator data can be used to update construction drawing review standards and processes. Construction drawing review standards are a set of normative documents that standardize the content and depth of construction drawing design and clarify review points, mandatory review items, and qualification criteria. The initial version of the construction drawing review standards can be imported through the review input module, and its updates can be based on the analysis and insights from quality control indicator data. For example, when quality control indicator data shows that a certain type of design problem occurs frequently in multiple projects, the review standards can be strengthened or refined to describe the review points for such problems, or they can be added as mandatory review items. The review process can be a sequence of steps and rules for construction drawings from submission, allocation to review, problem feedback, modification review, and completion. The initial design of the review process can be based on the working mode of an online collaboration platform, and its optimization and adjustment can be based on the process efficiency and collaboration issues revealed by the quality control indicator data. For example, if the indicator data shows that the time taken for cross-disciplinary collaborative review nodes is longer than other nodes, the review process can be adjusted to optimize the submission process and countersigning mechanism for cross-disciplinary issues, or online collaboration tools can be introduced to support it.

[0060] This application embodiment quantifies and analyzes quality closed-loop data, calculating specific numerical indicators through mathematical, statistical, or logical operations. The processing results are organized into a quality control indicator dataset. Based on this quality control indicator data, the construction drawing review standards and review processes are updated and optimized, thereby enhancing the data-driven capability of quality assessment and decision support; improving the scientific rigor and accuracy of continuous optimization of review standards and processes; and enhancing the systematization and forward-looking level of construction drawing design quality management.

[0061] For example, in the quality control of construction drawing design in residential development projects, the review of several batches of construction drawings can be completed through an online collaborative platform. This platform can aggregate and generate closed-loop quality data containing information such as a list of review issues for each batch, the status of item verification, review time records, and operation logs of participating personnel. Quantitative analysis can be performed on this closed-loop quality data, showing that the average review cycle for projects this quarter was shorter than the previous quarter, but the review cycle for mechanical and electrical drawings remained higher than the average. Statistics show that issues such as "pipeline collisions" and "deviations in reserved opening dimensions" have recurred frequently in the last three reviews, mainly concentrated in the basement area. Analysis can also reveal the relevant clauses of the "energy-saving design code." The consistency of judgments among different review experts fluctuates during the review process. A dataset of quality control indicators, including "average review cycle," "differences in professional review cycles," "high-frequency problem types and distribution," and "consistency rate of review standard implementation," can be generated based on quantitative analysis results. Based on this data, construction drawing review standards can be updated, strengthening specific inspection points for basement pipeline integration and opening reservations in the mandatory review list, and providing more detailed illustrated explanations of energy conservation code review points. The review process can be optimized by adding an automated pre-processing model space inspection step for electromechanical drawings, and setting clear collaborative timing and reminder mechanisms for review nodes involving multiple disciplines.

[0062] According to the technical solution provided in this disclosure, design drawing data to be processed is received, matched with a preset standard database through association processing, applicable standard clauses are filtered and bound, and review task data is generated; the review task data is matched with review terminal identification data, and stored through database transaction association to generate review issue record data; the review issue record data is reviewed for completion, and the issue rectification verification is completed through online review and status update, generating quality closed-loop data; the quality closed-loop data is quantitatively analyzed to calculate key indicators such as review efficiency and issue distribution, forming quality control indicator data, and then... Based on this, the construction drawing review standards and processes were updated and optimized, thereby enhancing the uniformity and authority of the design review basis; improving the automation and accuracy of review task generation; increasing the efficiency of matching multi-source design standards with drawing and model files; improving the standardization and consistency of construction drawing review task generation, and realizing the automated extraction of review standards through the association of standard database and drawing and model data; enhancing the completeness and accuracy of review issue tracking, and forming a closed-loop quality management through the item verification mechanism; improving the scientificity and real-time nature of construction drawing quality evaluation, and providing data support for the continuous optimization of review standards through quantitative analysis and processing.

[0063] In some embodiments, the design drawing data to be processed is associated with a preset standard database to obtain audit task data, including: performing attribute parsing processing on the design drawing data to be processed to obtain drawing attribute data; performing rule matching processing on the drawing attribute data and the preset standard database to obtain list template data; and performing task encapsulation processing on the list template data to obtain audit task data.

[0064] Specifically, attribute parsing processing can be a data processing process that reads and identifies the file structure, metadata, and content of the design model data to be processed by calling the built-in or integrated professional software parsing interface. Attribute parsing processing can convert unstructured original model files into a structured set of feature data that can be used for subsequent rule matching processing, namely model attribute data. Among them, model attribute data can be structured data obtained through attribute parsing processing, which can be used to characterize the basic features and element information of the original design model, such as drawing name, design discipline, drawing number, version number, model component type, and spatial coordinate information.

[0065] Furthermore, attribute parsing processing can employ corresponding parsing engines for different formats of drawing and model data. For example, for Building Information Modeling (BIM), the corresponding BIM software interface can be called to obtain the component attribute list, while for two-dimensional Computer-Aided Design (CAD) drawings, their layers, blocks, and annotation information can be read, thereby ensuring that the key attributes of various design results can be accurately and completely extracted to form standardized drawing and model attribute data.

[0066] For example, during the construction drawing design phase of a residential project, the completed baseline building information model (Autodesk Revit, REVIT) for architecture, structure, and MEP (Mechanical, Electrical, and Plumbing) and the accompanying CAD drawings can be uploaded as design model data to be processed. Attribute parsing processing can be initiated to read floor information, wall component attributes, piping system types from the REVIT model, as well as drawing label information and professional markings from the CAD drawings. This information can be integrated into structured model attribute data. For example, a record can be generated indicating "Profession: Water Supply and Drainage, Drawing Number: PL-01, Version: A1, Includes Components: DN100 Fire Water Pipe".

[0067] In addition, rule matching processing can be a data processing process that logically compares and associates various features in the drawing attribute data with the corresponding audit rules in the preset standard database. Rule matching processing can identify the specific audit items that the current drawing attribute data needs to follow and generate a preliminary task framework, namely the list template data. The list template data can be a list of items to be audited or a list of pre-judged issues generated based on the current drawing attribute association. For example, it can associate the "architectural profession - fire compartment" drawing with the mandatory audit points such as "fire resistance limit of firewall" and "evacuation width calculation". The list template data can be dynamically generated by matching the drawing attribute data with the preset standard database.

[0068] Furthermore, the rule matching process can index and query the key fields in the model attribute data in a preset standard database, extract and assemble all audit rule clauses that meet the conditions, and form a list template data related to the current design content.

[0069] For example, in the aforementioned application scenario, the parsed model attribute data, such as "profession: water supply and drainage" and "component: fire water pipe", can be matched with the "fire protection design code" stored in the preset standard database. The matching logic can identify that the design content includes relevant clauses such as "layout of indoor fire hydrant system" and "setting of fire pump room", and then generate a checklist template data containing specific inspection items such as "whether the diameter of fire water pipe meets the requirements" and "whether the location of water pump connection is reasonable".

[0070] In addition, task encapsulation processing can be a data processing process that supplements, organizes and formats the list template data. Task encapsulation processing can transform an abstract list of audit items into a complete task package containing information such as specific execution context, responsible person, and deadline, i.e., audit task data.

[0071] Furthermore, the task encapsulation process may include assigning a suitable audit expert group to each audit item in the checklist template data, attaching relevant links to access the original template data, setting task priorities and planned completion nodes, and integrating them into a unified user interface task card or message notification, thereby generating audit task data.

[0072] For example, in the aforementioned application scenario, the inspection items related to "fire water pipes" in the checklist template data can be packaged into an independent audit task data package. This audit task data package can specify the auditing person as the "Group Fire Protection Drawing Review Expert Group", can be associated with the corresponding REVIT model view and CAD drawing file, set the task deadline, and generate a task details page containing all items to be reviewed.

[0073] According to the technical solution provided in this disclosure, the design model data to be processed is analyzed for attributes. By calling a professional software interface to read and identify its file structure, metadata, and content, the model attribute data is extracted. The model attribute data is then matched against a preset standard database using rules. Based on key fields, the database is indexed and queried to extract and assemble all review rule clauses related to the current design content, generating a list template data. The list template data is then encapsulated into a complete task package containing a specific execution context by assigning review responsibilities, attaching relevant model links, and setting task attributes. This enhances the relevance and completeness of the review task generation, improves the automation level of model information analysis and standard rule matching, and increases the efficiency of structured encapsulation and distribution of review tasks.

[0074] In some embodiments, matching the review task data and the review terminal identification data to obtain review issue record data includes: performing grouping and parsing processing on the review terminal identification data to obtain functional grouping data; distributing the review task data based on the functional grouping data to obtain grouped review task data; performing map model parsing processing on the grouped review task data to obtain map model element data; and comparing the map model element data and the review task data to obtain review issue record data.

[0075] Specifically, grouping and parsing processing can be a data processing process that analyzes the review end identification data based on preset professional classification rules and classifies the review end identification data into different professional groups. Grouping and parsing processing can generate functional grouping data, which can be the result data after classifying review experts based on professional fields. Functional grouping data can provide a basis for the professional distribution of subsequent review tasks, ensuring that the design content of a specific profession is reviewed by experts in the corresponding field. Functional grouping data can be obtained by performing logical parsing and classification operations on the review end identification data.

[0076] In addition, the distribution processing can be a data processing process that breaks down the comprehensive review requirements contained in the review task data into sub-tasks related to each profession based on the information of each professional group defined by functional grouping data. For example, for a construction drawing review task that includes multiple professions such as architecture, structure, and mechanical and electrical engineering, the review points and standards related to the architecture profession can be distributed to the architecture review group and the structural part can be distributed to the structural review group based on the professional tags obtained from parsing the drawing template file. Grouped review task data can be review task data that has been professionally split and specifically allocated to a particular functional review group. Grouped review task data can be a concrete instance of review task data in the professional dimension. Grouped review task data can be obtained by performing logical distribution operations based on functional grouping data on the review task data.

[0077] Furthermore, the drawing model parsing process is a data processing procedure that reads and analyzes uploaded building information model files or two-dimensional computer-aided design drawings through a built-in parsing engine. The drawing model parsing process can identify the constituent elements in the design file, such as layers, blocks, entity objects, and attribute parameters. For building information models, the parsing engine can extract the geometric information, spatial location, and attribute information of components such as walls, slabs, columns, beams, pipes, and equipment. For computer-aided design drawings, it can extract lines, annotations, text descriptions, and element attributes, i.e., drawing model element data. Among them, drawing model element data can be a set of structured data units parsed from the original design file that can be used to represent the design content. Drawing model element data can be a digital and standardized expression of design information. Drawing model element data can transform unstructured graphics or model files into structured data that can be recognized and processed by computers.

[0078] Furthermore, the comparison processing can be a data processing procedure that matches and logically judges each element information in the drawing model element data with the corresponding audit rules in the audit task data one by one. The audit task data can include referenced audit standards, which can be converted into a machine-readable rule base, such as logical judgment conditions regarding minimum component dimensions, space clearance requirements, and mandatory provisions. For example, the parsed "parking space clearance" element value can be compared with the clause "parking space clearance not less than 2.2 meters" in the rule base; the "fire compartment area" element value can be compared with relevant fire protection code provisions; when there is a discrepancy between the drawing model element data and the audit rules, the specific location of the discrepancy, the violated rule item, and the problem description can be recorded to generate audit problem record data.

[0079] According to the technical solution provided in this disclosure, the identification data of the drawing review end is grouped and parsed, and classified according to professional classification rules to obtain functional grouping data; the review task data is distributed based on the functional grouping data, and it is broken down into sub-tasks related to each profession to generate grouped review task data; the grouped review task data is parsed, and the design documents are read and analyzed through the built-in engine to extract the drawing model element data; the drawing model element data is compared with the review task data, and the element information is matched with the review rules one by one. The discrepancies are recorded as review problem record data, thereby enhancing the accuracy and efficiency of professional distribution of drawing review tasks; improving the depth and automation level of structured parsing of drawing model information; and improving the accuracy and completeness of automated comparison between design elements and review standards.

[0080] In some embodiments, the audit issue record data is subjected to a verification process to obtain quality closed-loop data, including: performing structured classification processing on the audit issue record data to obtain production issue list data; performing centralized disclosure processing on the production issue list data to obtain disclosure confirmation data; and performing verification marking processing on the disclosure confirmation data to obtain quality closed-loop data.

[0081] Specifically, the production issue list data can be a structured list of issues formed by organizing and summarizing scattered and unorganized audit issue records according to preset classification rules. The production issue list data can be a collection of issue data organized according to multiple dimensions such as profession, issue type, severity level, and responsible party. It can be used to record and manage all pending issues found in the drawing review process. The production issue list data can serve as the core carrier for issue management and tracking, transforming scattered audit issues into clearly identifiable, assignable, and traceable task items.

[0082] Furthermore, structured classification processing can include classifying and tagging audit issue record data based on one or more dimensions such as the specialty to which the issue belongs, the type of issue, the related review standard items, or the severity of the issue, generating a list containing information such as issue description, location, responsible specialty, and related standards.

[0083] In addition, the disclosure confirmation data can be a record generated in the online collaborative platform environment after the production problem list, which has been categorized and organized, has been centrally communicated and explained to the relevant responsible parties and consensus has been reached. The disclosure confirmation data can record the problem disclosure process, the confirmation opinions of all parties, and the data status of subsequent modification responsibilities. The disclosure confirmation data can ensure that the design institute and other responsible parties have a comprehensive and accurate understanding of the problems found in the review and clarify the modification responsibilities and requirements.

[0084] Furthermore, centralized disclosure processing can be achieved by using online collaborative platforms for task allocation, online meetings, or annotation and comment functions to synchronously push the production problem list data to relevant design institute leaders or professional designers, requiring them to confirm or provide feedback on each problem item on the platform. The platform can record the confirmation status, responses, or promised modification plans of all parties, thereby generating disclosure confirmation data.

[0085] Furthermore, the verification marking process can be implemented by having the original reviewer or a designated reviewer verify the changes to the drawings or models before and after the changes are completed and submitted in accordance with the requirements of the disclosure confirmation data. On the online collaborative platform, the original reviewer or a designated reviewer can compare the drawings or models before and after the changes to verify the changes item by item. For issues that pass verification, the status of the issue item can be marked as "closed" or "closed" on the platform, and the records of all closed issues can be summarized to form quality closed-loop data.

[0086] For example, in the quality control of construction drawings for residential projects, review experts can complete the review of the benchmark construction drawings on an online collaborative platform, generating review issue record data containing dozens of questions. This review issue record data can involve multiple disciplines such as architecture, structure, and mechanical and electrical engineering. Structured classification processing can be performed, organizing scattered records into a production issue list based on the discipline to which the issue belongs and preset type tags such as "fire compartment" and "integrated pipelines." Each issue in the issue list can be associated with a specific drawing location and the referenced review standard clauses. The platform's online meeting function can be used to organize centralized briefings for heads of various disciplines within the design institute. The production issue list can be shared on the screen, with each item explained and discussed. All parties within the design institute can confirm issues they understand and agree on on the platform, and address any objections online. After soliciting opinions and negotiating, and reaching a consensus on all issues to be modified, the platform can generate disclosure confirmation data that records the confirmation status and final conclusions of all parties. The design institute can modify the drawings according to the requirements in the disclosure confirmation data and upload the modified drawing version to the platform, providing modification explanations for each issue. The review team can retrieve the modified drawings on the platform and compare and verify them with the issue list and the original drawings. For issues that have been confirmed to have been correctly modified, the reviewers can mark their status as "closed," such as the architectural issue of "insufficient evacuation width" and the mechanical and electrical issue of "duct collision with beam," which can be marked as closed after verification. When all issues in the list have been verified and marked, the quality closed-loop data for this batch of drawings can be generated, indicating that all issues found in this review have been effectively resolved and a management closed loop has been formed.

[0087] According to the technical solution provided in this disclosure, the audit problem record data is structured and categorized based on preset dimensions, and then classified and tagged to form a production problem list. This production problem list data is then centrally communicated to the responsible parties through an online collaboration platform to obtain consensus and generate confirmation data. After modifications are made based on the requirements of the confirmation data, the submitted modifications are verified and marked. By comparing the before-and-after images and verifying each one, the confirmed rectified problems are marked as closed. All closed problem records are then compiled to obtain quality closed-loop data. This enhances the systematicness and traceability of problem management, improves the collaborative efficiency of problem disclosure and responsibility confirmation, and enhances the accuracy and completeness of problem rectification verification and closed-loop status marking.

[0088] In some embodiments, quantitative analysis and processing of quality closed-loop data are performed to obtain quality control indicator data, including: performing completion statistics processing on quality closed-loop data to obtain node completion rate; performing problem distribution analysis processing on quality closed-loop data to obtain problem statistics; and performing indicator fusion processing on node completion rate and problem statistics to obtain quality control indicator data.

[0089] Specifically, the node completion rate can be used to measure the completion status of specific quality control node tasks. The node completion rate can be the ratio of the number of completed tasks to the total number of planned tasks. The node completion rate can be used to quantitatively evaluate the progress efficiency and plan execution of key nodes such as construction drawing review and problem elimination. The node completion rate can be obtained by statistically calculating the elimination status of mandatory review list items and the confirmation status of drawing modification in the quality closed loop data.

[0090] Completion statistics processing can be a data statistics process that counts and calculates the proportions of fields representing the task completion status in a dataset. Completion statistics processing can be applied to quality closed-loop data to extract quantitative indicators representing the overall progress of the task.

[0091] In addition, problem distribution analysis can be a data analysis process that classifies, aggregates and statistically analyzes problem items recorded in a dataset based on specific dimensions. Problem distribution analysis can be applied to quality closed-loop data to reveal the concentrated areas and type characteristics of problems found in the audit, and obtain problem statistics. Among them, problem statistics can be the result data obtained through problem distribution analysis, and problem statistics can be the number or distribution information of problems classified and statistically analyzed based on different dimensions. Problem statistics can be used to characterize the weak links and common problem types in the design quality of construction drawings.

[0092] In addition, indicator fusion processing can be a data integration process that combines multiple single-dimensional indicators into one or more composite indicators that can be used to represent the overall situation through logical association or weighted calculation. Indicator fusion processing can combine the node completion rate, which separately represents progress and quality, with the statistical data of problems.

[0093] Furthermore, the completion statistics processing can be specifically applied to project planned nodes, such as the benchmark construction drawing review node, to calculate the completion rate of all mandatory review items under the benchmark construction drawing review node and generate the completion rate of that node; it can also be applied to unplanned nodes, such as drawing process inspection, to calculate the proportion of initiated inspection tasks that have been reviewed and confirmed.

[0094] For example, after the joint review of construction drawings and subsequent modification and verification of completed items, a closed-loop quality data can be generated, containing the status of all tasks, issue records, and confirmation information for this joint review. This closed-loop quality data can be statistically processed to extract a list of all pending tasks for the "joint review of construction drawings," and the number of tasks marked "approved" and "closed" can be counted. The proportion of these tasks to the total number of tasks is calculated to obtain the node completion rate for this joint review. This node completion rate can be used to characterize the overall execution progress of this centralized review task. Furthermore, the same closed-loop quality data can be analyzed for issue distribution, based on the design specialty field to which the issues belong. All recorded audit issues are categorized, and the number of issues found in each of the following specialties—architecture, structure, water supply and drainage, HVAC, and electrical—is tallied. The severity of the issues can be analyzed to determine the number of issues at three levels: "Severe," "General," and "Recommended." These numbers together constitute the overall issue statistics. Indicator fusion processing can be performed, inputting the calculated node completion rate and the analyzed issue statistics into a pre-defined comprehensive evaluation model. This model assigns a weight to the node completion rate and converts the number of issues for each specialty into a deduction weight based on their severity, thus generating quality control indicator data.

[0095] According to the technical solution provided in this disclosure, by performing completion statistics on the quality closed-loop data, counting and calculating the proportion of task completion status fields, the node completion rate is extracted and calculated; the same quality closed-loop data is subjected to problem distribution analysis, and classified, collected, and statistically analyzed based on dimensions such as the professional field and severity of the problems to obtain problem statistics; the node completion rate and problem statistics are fused into indicators, and through preset logical associations or weighted calculation methods, they are comprehensively formed into quality control indicator data, thereby enhancing the systematicness of multi-dimensional quantitative evaluation of progress and quality; improving the automation and comprehensiveness of indicator data generation; and enhancing the accuracy of quality control insights and the effectiveness of decision support.

[0096] In some embodiments, comparing the map feature data and the audit task data to obtain audit issue record data includes: performing spatial detection processing on the map feature data to obtain spatial detection result data; performing checklist verification processing on the audit task data and the map feature data to obtain checklist verification result data; and summarizing the spatial detection result data and the checklist verification result data to obtain audit issue record data.

[0097] Specifically, spatial detection processing can be an algorithmic process that automatically analyzes and calculates the geometric entities and their spatial relationships represented in the model element data to discover problems that do not conform to preset spatial rules. Spatial detection processing can screen design defects from the perspective of physical space compliance. It can be executed by the spatial detection engine built into the online collaborative platform to calculate the geometric and positional information in the model element data.

[0098] Spatial detection result data can be a structured data list that records various spatial problems identified by spatial detection algorithms, providing objective and quantifiable spatial dimension problem basis for reviewing problem records. Spatial detection result data can be calculated from map element data through spatial detection processing algorithms, and can be used to characterize the violation or conflict information of the design in the spatial dimension.

[0099] Furthermore, spatial inspection can include the calculation of the net height of the building's interior space to verify whether the minimum usage requirements are met, such as checking the net height above parking spaces or corridor passages; it can also include collision detection between different professional models, such as checking for conflicts in the spatial location of structural beams and heating and ventilation ducts.

[0100] The checklist verification process is a rule-matching process that compares and verifies the semantic information extracted from the model element data with the mandatory review checklist items and review points specified in the review task data item by item. The checklist verification process can ensure that the design results meet the established standards in terms of standardization, completeness and design depth. It can be driven by the rule engine of the online collaboration platform and make logical judgments on the model element data according to the standards in the review task data.

[0101] The checklist verification results data can be a structured data list that records missing or incorrect issues in component attributes, design specification compliance, drawing depth, etc., discovered through rule matching. It provides a normative dimension basis for reviewing issue records based on standards and the checklist. The checklist verification results data can be generated after matching and verifying the drawing element data through the checklist verification processing rules. It can be used to characterize the missing or non-compliant items in the design in terms of normative dimensions.

[0102] Furthermore, the checklist can be reviewed based on the mandatory review list. For example, it can be checked whether the fire evacuation distance markings are complete, whether the energy-saving design specifications are complete, or whether the design parameters of specific components meet the company's construction standards.

[0103] In addition, the aggregation process can be used to merge, deduplicatize, classify, and assign a unified problem identifier and description format to problem data found from two independent processes: space inspection and inventory verification. The aggregation process can form a complete, structured, and traceable overview of audit issues.

[0104] For example, during the construction drawing review stage of residential development projects, reviewers can create review tasks on an online collaborative platform. The platform can then generate review task data that includes mandatory review items such as "net height of interior doorways," "maintenance space for pipe shafts," and "list of energy-saving materials," based on the project type, enterprise construction standards, and national energy-saving design specifications. Design institutes can upload the building information model of the project through the platform, which can then parse it and generate model element data containing the geometric dimensions and attributes of all walls, doors, windows, and equipment pipelines. The platform can also initiate space detection processing to calculate the height of interior doorways and compare it with relevant standards and regulations. By comparing the minimum clear height value, the location information of doorways that do not meet the requirements is recorded as spatial inspection result data; a checklist verification process can be performed to check whether the pipe well components in the model have the "maintainable" attribute label, and to verify whether the external wall insulation materials declared in the model are within the standard's allowed material list. Issues such as missing attribute labels and the use of non-standard materials are recorded as checklist verification result data; these two sets of result data can be summarized and processed to generate a unique number for each issue, associate it with the corresponding audit standard clause, and mark the issue's professional category as "architecture" or "HVAC", thus obtaining audit issue record data.

[0105] According to the technical solution provided in this disclosure, spatial detection processing is performed on the graphic model element data. A built-in spatial detection engine analyzes geometric entities and their spatial relationships, calculates and identifies spatial compliance issues, and obtains spatial detection result data. The review task data and graphic model element data undergo checklist verification processing. A rule engine compares and verifies the semantic information of the graphic model with the mandatory review checklist items and review points item by item, identifying any missing or incorrect normative issues, and obtaining checklist verification result data. The spatial detection result data and checklist verification result data are then aggregated, merged, deduplicated, categorized, and given a unified format to generate review issue record data. This enhances the multi-dimensional automatic detection capability of spatial compliance and normative conformity in design review; improves the comprehensiveness and objectivity of the issue discovery process; and enhances the integration efficiency and structuring level of the review issue record data.

[0106] In some embodiments, the disclosure confirmation data is subjected to verification marking processing to obtain quality closed-loop data, including: extracting the modification content from the disclosure confirmation data to obtain modification description data; performing standard compliance comparison processing on the modification description data to obtain verification result data; and performing status marking processing on the verification result data to obtain quality closed-loop data.

[0107] Specifically, the modification content extraction process involves identifying and extracting specific modification measures, modification locations, and modification content directly related to the review issues from the written responses, revision documents, and drawings marked with modification traces submitted by the design unit. This modification content extraction process can generate structured modification description data. The modification description data can be formatted data representing the specific design change actions and their content taken for each review issue. The modification description data can refine and summarize the modification information scattered in various response materials, providing a clear object for subsequent verification of whether the modifications meet the standards.

[0108] Furthermore, the extraction and processing of the modified content can be supported by the drawing review comment response unit in the online collaborative platform. When the design company responds to the drawing review comments one by one on the platform, the problem modification description filled in, the uploaded modification description document, and the updated version of the drawings can together constitute the original response materials.

[0109] Furthermore, the modification content extraction process can involve parsing and summarizing specific modification items from the original response materials to generate readable modification description data.

[0110] In addition, the standard compliance comparison processing can be a data processing process that compares and analyzes the extracted modification description data with the pre-set review standard library in the system to determine whether each modification meets the corresponding mandatory clauses, design specifications or project-specific requirements; the verification result data can be data that records the compliance conclusions drawn after comparing each modification with relevant standards, and the verification result data can determine the correctness and compliance of the modification behavior.

[0111] Furthermore, when performing standard compliance comparison processing, various standards can be associated, such as mandatory provisions, fire protection codes, or internal construction standards of the enterprise; the modification items mentioned in the modification instructions can be matched with these standard clauses, or the modification content can be manually reviewed by the review experts in the final review stage based on the standards to generate verification result data.

[0112] In addition, status labeling can be a data processing procedure that assigns a clear quality status identifier to each review issue and its corresponding modification item based on the verification result data.

[0113] Furthermore, when the verification results confirm that all modifications meet the standards, the issue can be marked as "completed" or "closed-loop completed"; if there are non-conformities, they can be marked as "to be modified again" and trigger a new round of modification and disclosure process; the target status of all issues, along with their process data, can be integrated into quality closed-loop data.

[0114] For example, during the review of the baseline version of construction drawings for residential projects, design institutes can complete the modification of the entire set of drawings based on the review issue list issued by the online collaboration platform; design institutes can fill in the modification instructions item by item in the drawing review opinion response unit of the platform and upload the updated drawing files, which can generate handover confirmation data; modification content extraction processing can be performed to automatically parse the specific modification instructions from the response, such as "the slope of the underground garage driveway has been modified from 10% to 8%", "the evacuation diagrams of all fire compartments have been supplemented", etc.; during the completion review stage, standard compliance comparison processing can be initiated to " The item "The lane slope has been modified to 8%" is compared with the "Garage Building Design Code" stored in the drawing review input module to confirm that it meets the code requirements. At the same time, it can be verified whether the "Supplementary Evacuation Diagram" meets the mandatory requirements of fire protection review. Based on the comparison results, verification result data can be generated, recording that both modifications are "compliant". All items in the issue list can be marked with status. Since all verification results are compliant, the entire issue list can be marked as "closed loop", and the data package containing complete modification, verification, and status information can be stored as the quality closed loop data for this drawing review node.

[0115] According to the technical solution provided in this disclosure, by extracting and processing the modified content from the disclosure confirmation data, specific modification measures, locations, and contents are identified and extracted from the text responses, revision instructions, and modification drawings submitted by the design unit, generating structured modification instruction data. The modification instruction data undergoes standard compliance comparison processing, comparing and analyzing it with a preset review standard library to determine whether each modification conforms to the corresponding mandatory clauses, design specifications, or project requirements, generating verification result data. The verification result data is then marked with a status, assigning a status identifier such as "completed" or "awaiting revision" to each review issue based on the compliance conclusion. The target status and related process data of all issues are integrated into quality closed-loop data, thereby enhancing the accuracy of modification content extraction and structured processing; improving the automation and standardization level of modification compliance verification; and enhancing the accuracy and efficiency of quality issue closed-loop status management.

[0116] All of the above-mentioned optional technical solutions can be combined in any way to form optional embodiments of this disclosure, and will not be described in detail here.

[0117] Figure 3 This is a flowchart illustrating another construction drawing quality control method provided in this embodiment. Figure 3 As shown, the quality control methods for this construction drawing include: Standards Import: First, through the drawing review input module, various drawing review standards (preset standard database) are imported into the online collaborative platform to provide a standard basis for subsequent drawing review work.

[0118] Preparation for drawing model review: Enter the drawing review task module to upload and confirm the drawing model (design drawing model data to be processed) to ensure the accuracy and completeness of the drawing model; at the same time, clarify the drawing model review standards so that the reviewers are clear about the review requirements.

[0119] Drawing Model Review: The grouped reviewers review the drawings on the platform according to the drawing model review standards and in conjunction with the spatial detection function, and conduct a comprehensive check on the quality of the drawings.

[0120] Problem handling and item closure: In the problem management and item closure review module, problems are summarized and reviewed, problem types are identified (quality closed-loop data), and centralized briefings are provided to the design institute; after the design institute modifies the drawings and responds to the drawing review comments, item closure review is conducted to ensure that the problems are effectively resolved.

[0121] Quality Evaluation and Presentation: Through the drawing review result evaluation module, quality evaluation (quality control indicator data) is conducted based on the completion status of the mandatory review list items, and relevant information on the review quality evaluation is presented in real time, providing a basis for the assessment and improvement of product design quality.

[0122] The drawing review input module's core function is to import various standards and guidelines for subsequent drawing review work. Specifically, importable standards include Longfor's construction standards, national and local mandatory provisions, fire safety, energy conservation, green building review and project optimization suggestions, and other comprehensive Longfor construction drawing review standards, as well as other quality-related standards for construction forms. The import of these standards provides clear and unified specifications for the drawing review work, ensuring that the review process is conducted in a structured manner.

[0123] The drawing review task module includes a drawing upload and confirmation unit that supports multiple drawing upload methods, such as BIM drawing upload, Revit model parsing, 2D drawing parsing, and CAD drawing parsing. After the drawing is uploaded, its version and content type are strictly confirmed to ensure its accuracy and completeness, laying a solid foundation for subsequent review work.

[0124] The drawing review standard unit clearly defines the mandatory review checklist, key review points, and a review issue list. The mandatory review checklist details the items that must be checked during the review process, the key review points explain the critical checkpoints for each review item, and the review issue list records any problems found during the review process. This provides reviewers with clear and specific review standards, making the review process more standardized and accurate.

[0125] Drawing review personnel are grouped and reviewed by designated units. Review personnel are divided into group-level functional drawing review experts and subsidiary-level drawing review expert groups. Utilizing an online collaborative platform, these personnel can manually review the drawings. Simultaneously, spatial detection functions, such as parking space clearance detection and space collision detection, are used to comprehensively check the quality of the drawings from multiple dimensions, ensuring that there are no issues with the spatial layout and other aspects of the drawings.

[0126] The Problem Management and Sales Review Module summarizes and categorizes audit issues, centrally managing and generating a production problem list. It also links problem types to drawing review standards, creating a structured problem classification. This approach facilitates subsequent problem tracking and resolution, and helps quickly pinpoint the root cause of issues.

[0127] The audit issue centralized briefing and closing review unit: On the one hand, it summarizes single-discipline issues and integrates cross-discipline and functional information, and provides centralized briefings to the design institute to ensure that the design institute can fully understand the existing issues; on the other hand, it verifies the accuracy of subjective issues for intelligent review issues, forms an intelligent review issue list, and ensures that issues are effectively resolved and no hidden dangers are left through a strict closing review process.

[0128] The drawing review feedback response unit: Based on the drawing review feedback transmitted through the online collaboration platform, the design company modifies the design drawings. After the modifications are completed, each drawing review feedback is responded to, and the modifications are checked at each stage. The drawing review team verifies the modifications to ensure that the issues are truly resolved.

[0129] Review Result Evaluation Module: Quality Evaluation Unit: Based on the completion status of the mandatory review list, quality evaluation is conducted. By assessing the completion status of the mandatory review items, it is determined whether the quality of the drawings meets the requirements.

[0130] The real-time quality evaluation unit presents key indicators such as node completion rate and completeness of delivered drawings in real time. It can also present information such as statistics on participating personnel, problem distribution, problem specialties, and review functions / suppliers. At the same time, it displays the project construction drawing problem and elimination list, project construction drawing delivery quality evaluation, and evaluation of drawing review participants, providing rich and real-time data support for a comprehensive assessment of product design quality.

[0131] According to the technical solution provided in this disclosure, work is carried out based on an online collaborative platform, realizing the online and collaborative nature of the drawing review process. Personnel at each stage can communicate and collaborate online in real time on the platform, eliminating the need for offline drawing transfers, reducing time costs, improving review efficiency, and enabling more efficient product design quality control. The online collaborative platform also ensures the strong implementation of unified review standards. During the review process, reviewers operate according to a clear review checklist and key points, avoiding inconsistencies in review quality caused by differences in individual understanding, ensuring the stability and consistency of review quality, and providing reliable assurance for product design quality. Through functions such as space detection, problems such as space collisions in the drawings can be detected in a timely manner, ensuring the quality of the drawings and improving product design quality from the source. The review quality evaluation is presented in real time, allowing for the rapid acquisition of key indicators such as node completion rate and the completeness of delivered drawings, as well as various problem statistics and personnel evaluations. This facilitates the timely identification of deficiencies in the product design quality control process, providing strong data support for subsequent improvements, thereby continuously optimizing the product design quality control process and continuously improving product design quality.

[0132] The following are embodiments of the apparatus disclosed herein, which can be used to execute embodiments of the method disclosed herein. For details not disclosed in the apparatus embodiments of this disclosure, please refer to the embodiments of the method disclosed herein.

[0133] Figure 4 This is a schematic diagram of a construction drawing quality control processing device provided in an embodiment of this disclosure. Figure 4 As shown, the construction drawing quality control processing device includes: The first processing module 401 is used to associate the design drawing data to be processed with the preset standard database to obtain the review task data; The second processing module 402 is used to match the audit task data and the drawing review terminal identification data to obtain audit issue record data; The third processing module 403 is used to perform a review of the audit issue record data to obtain quality closed-loop data. The fourth processing module 404 is used to perform quantitative analysis and processing on the quality closed-loop data to obtain quality control indicator data, which is used to update the construction drawing review standards and review process.

[0134] According to the technical solution provided in this disclosure, design drawing data to be processed is received, matched with a preset standard database through association processing, applicable standard clauses are filtered and bound, and review task data is generated; the review task data is matched with review terminal identification data, and stored through database transaction association to generate review issue record data; the review issue record data is reviewed for completion, and the issue rectification verification is completed through online review and status update, generating quality closed-loop data; the quality closed-loop data is quantitatively analyzed to calculate key indicators such as review efficiency and issue distribution, forming quality control indicator data, and then... Based on this, the construction drawing review standards and processes were updated and optimized, thereby enhancing the uniformity and authority of the design review basis; improving the automation and accuracy of review task generation; increasing the efficiency of matching multi-source design standards with drawing and model files; improving the standardization and consistency of construction drawing review task generation, and realizing the automated extraction of review standards through the association of standard database and drawing and model data; enhancing the completeness and accuracy of review issue tracking, and forming a closed-loop quality management through the item verification mechanism; improving the scientificity and real-time nature of construction drawing quality evaluation, and providing data support for the continuous optimization of review standards through quantitative analysis and processing.

[0135] In some embodiments, the first processing module 401 is specifically used to perform attribute parsing processing on the design drawing data to be processed to obtain drawing attribute data; perform rule matching processing on the drawing attribute data and a preset standard database to obtain list template data; and perform task encapsulation processing on the list template data to obtain audit task data.

[0136] In some embodiments, the second processing module 402 is specifically used to: perform grouping and parsing processing on the review end identification data to obtain functional grouping data; perform distribution processing on the review task data based on the functional grouping data to obtain grouped review task data; perform map model parsing processing on the grouped review task data to obtain map model element data; and perform comparison processing on the map model element data and the review task data to obtain review issue record data.

[0137] In some embodiments, the third processing module 403 is specifically used to: perform structured classification processing on the audit problem record data to obtain production problem list data; perform centralized disclosure processing on the production problem list data to obtain disclosure confirmation data; and perform verification marking processing on the disclosure confirmation data to obtain quality closed-loop data.

[0138] In some embodiments, the fourth processing module 404 is specifically used to perform completion statistics processing on the quality closed-loop data to obtain the node completion rate; perform problem distribution analysis processing on the quality closed-loop data to obtain problem statistics; and perform indicator fusion processing on the node completion rate and problem statistics to obtain quality control indicator data.

[0139] In some embodiments, comparing the map feature data and the audit task data to obtain audit issue record data specifically involves: performing spatial detection processing on the map feature data to obtain spatial detection result data; performing checklist verification processing on the audit task data and the map feature data to obtain checklist verification result data; and summarizing the spatial detection result data and the checklist verification result data to obtain audit issue record data.

[0140] In some embodiments, the verification marking process for the disclosure confirmation data to obtain quality closed-loop data is specifically used for: extracting modification content from the disclosure confirmation data to obtain modification description data; performing standard compliance comparison processing on the modification description data to obtain verification result data; and performing status marking processing on the verification result data to obtain quality closed-loop data.

[0141] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this disclosure.

[0142] Figure 5 This is a schematic diagram of the electronic device 5 provided in an embodiment of this disclosure. Figure 5 As shown, the electronic device 5 of this embodiment includes: a processor 501, a memory 502, and a computer program 503 stored in the memory 502 and executable on the processor 501. When the processor 501 executes the computer program 503, it implements the steps in the various method embodiments described above. Alternatively, when the processor 501 executes the computer program 503, it implements the functions of each module / unit in the various device embodiments described above.

[0143] Electronic device 5 can be a desktop computer, laptop, handheld computer, cloud server, or other electronic device. Electronic device 5 may include, but is not limited to, processor 501 and memory 502. Those skilled in the art will understand that... Figure 5 This is merely an example of electronic device 5 and does not constitute a limitation on electronic device 5. It may include more or fewer components than shown, or different components.

[0144] The processor 501 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0145] The memory 502 can be an internal storage unit of the electronic device 5, such as a hard disk or RAM of the electronic device 5. The memory 502 can also be an external storage device of the electronic device 5, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc., equipped on the electronic device 5. The memory 502 can also include both internal and external storage units of the electronic device 5. The memory 502 is used to store computer programs and other programs and data required by the electronic device.

[0146] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0147] If integrated modules / units are implemented as software functional units and sold or used as independent products, they can be stored in a readable storage medium (e.g., a computer-readable storage medium). Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program may include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. A computer-readable storage medium may include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0148] The above embodiments are only used to illustrate the technical solutions of this disclosure, and are not intended to limit it. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be included within the protection scope of this disclosure.

Claims

1. A method for quality control of construction drawings, characterized in that, include: The design drawing data to be processed is associated with a preset standard database to obtain the review task data; The audit task data and the drawing review terminal identification data are matched to obtain audit issue record data; The audit issue record data is processed for item verification to obtain quality closed-loop data; The quality closed-loop data is quantitatively analyzed to obtain quality control indicator data, which is used to update the construction drawing review standards and review process.

2. The construction drawing quality control method according to claim 1, characterized in that, The process of associating the design drawing data to be processed with a preset standard database to obtain the review task data includes: The design drawing data to be processed is subjected to attribute parsing processing to obtain drawing attribute data; The image template attribute data and the preset standard database are matched according to rules to obtain the list template data; The list template data is processed into task encapsulation to obtain the audit task data.

3. The construction drawing quality control method according to claim 1, characterized in that, The process of matching the review task data and the review terminal identifier data to obtain review issue record data includes: The identification data of the drawing review end is processed by grouping and parsing to obtain functional grouping data; The audit task data is distributed and processed based on the functional grouping data to obtain grouped audit task data; The grouped review task data is processed by graph model parsing to obtain graph model element data; The image element data and the audit task data are compared and processed to obtain the audit issue record data.

4. The construction drawing quality control method according to claim 1, characterized in that, The process of reviewing and verifying the audit issue records to obtain quality closed-loop data includes: The audit issue record data is structured and categorized to obtain a production issue list. The production problem list data is centrally processed for disclosure, resulting in disclosure confirmation data. The disclosure confirmation data is verified and marked to obtain the quality closed-loop data.

5. The construction drawing quality control method according to claim 1, characterized in that, The quantitative analysis and processing of the quality closed-loop data to obtain quality control indicator data includes: The completion rate of each node is obtained by performing completion statistics on the closed-loop quality data. The quality closed-loop data is subjected to problem distribution analysis to obtain problem statistics. The node completion rate and the problem statistics are fused together to obtain the quality control indicator data.

6. The construction drawing quality control method according to claim 3, characterized in that, The step of comparing the image element data and the review task data to obtain the review issue record data includes: Spatial detection processing is performed on the image feature data to obtain spatial detection result data; The audit task data and the graphic element data are subjected to list verification processing to obtain list verification result data; The spatial detection results data and the list verification results data are summarized and processed to obtain the audit issue record data.

7. The construction drawing quality control method according to claim 4, characterized in that, The process of verifying and marking the disclosed confirmation data to obtain the quality closed-loop data includes: The modified content is extracted from the disclosure confirmation data to obtain the modification description data; The modified description data is subjected to standard compliance comparison processing to obtain verification result data; The verification result data is processed by state labeling to obtain the quality closed-loop data.

8. A construction drawing quality control and processing device, characterized in that, include: The first processing module is used to associate the design drawing data to be processed with the preset standard database to obtain the review task data; The second processing module is used to match the audit task data and the drawing review terminal identification data to obtain audit issue record data; The third processing module is used to perform a review of the audit issue record data to obtain quality closed-loop data. The fourth processing module is used to perform quantitative analysis and processing on the quality closed-loop data to obtain quality control indicator data, wherein the quality control indicator data is used to update the construction drawing review standards and review process.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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