File quality detection method and system based on design process, equipment and medium

Abstract

The invention provides a file quality detection method and system based on a design process, equipment and a medium, and belongs to the technical field of file management. The method comprises the steps of obtaining a target file group, wherein the target file group comprises a detection file, and file error data and file attribute data of the detection file in a file design process; determining a target reference auditing error rate based on the second file error sub-data and the target file group; performing file error rate detection on the detection file based on the second file error sub-data to obtain a file auditing error rate; based on the target reference auditing error rate, the file auditing error rate and the file attribute data, editing and correcting quality detection is carried out, and an editing and correcting quantitative score is obtained; checking effective rate detection is carried out based on the first file error subdata and the target file group to obtain a file checking effective rate; and carrying out file quality detection based on the compiling and checking quantitative score and the file checking efficiency. According to the embodiment of the invention, the accuracy of file quality detection in each node link in the design process can be improved.

Description

Technical Field

[0001] The present application relates to the field of file management technology, and in particular to a file quality detection method, system, device, and medium based on a design process. Background Art

[0002] Design documents are the texts, drawings and technical data gradually formed in the process of product development and production. They are the main carrier of design work and the main manifestation of design results. Their quality is directly related to the quality of engineering construction and physical results. They are the direct and important basis for regulators, suppliers, owners, etc. to evaluate technical work. In practical applications, the design process of design documents can include multiple design nodes, such as preparation, verification, review, approval, etc.

[0003] The document quality detection method based on the design process adopted by related technologies usually evaluates the problems existing in the design process after quality problems occur in the design documents. In this way, the quality management of design documents at each link mainly relies on the sense of responsibility of node managers. Due to the lack of necessary quality detection methods for design documents at different nodes in the design process, there are still many quality problems in the design documents used for execution after review by nodes such as editing and proofreading, which affects the progress and quality of engineering construction. Therefore, how to propose a method that can quantitatively evaluate the quality of documents at different nodes in the design process, improve the accuracy of document quality detection at each node in the design process, and avoid a large number of quality problems in subsequent implementation has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to propose a file quality detection method and system, equipment, and medium based on the design process, which can improve the accuracy of file quality detection at each node in the design process, thereby effectively avoiding a large number of quality problems in subsequent implementation.

[0005] To achieve the above objectives, a first aspect of an embodiment of the present application proposes a file quality detection method based on a design process, the method comprising:

[0006] Acquire a target file group, the target file group includes a test file, file error data of the test file in a file design process, and file attribute data of the test file; wherein the file design process includes an editing node and an auditing node, the node level of the auditing node is higher than the editing node, and the file error data includes first file error sub-data of the editing node and second file error sub-data of the auditing node;

[0007] Performing a file error benchmark detection based on the second file error sub-data and the target file group, and determining a target benchmark audit error rate of the target file group at the audit node;

[0008] Performing file error rate detection on the detection file based on the second file error sub-data to obtain a file audit error rate of the detection file at the audit node;

[0009] Performing editing quality detection based on the target benchmark review error rate, the file review error rate and the file attribute data to obtain the editing quantitative score of the detected file at the editing node;

[0010] Performing a verification efficiency test based on the first file error sub-data and the target file group to obtain a file verification efficiency;

[0011] The document quality of the inspection document is inspected based on the editing quantitative score and the document verification efficiency.

[0012] In some embodiments, the editing node includes an editing sub-node and a checking sub-node, and the file quality detection of the detection file based on the editing quantitative score and the file checking efficiency includes:

[0013] Performing a file verification test based on the editing and proofreading quantitative score and the file verification efficiency, and obtaining a file verification score of the detected file at the verification sub-node;

[0014] Calculating the difference between the editing and proofreading quantitative score and the document verification score to obtain a document verification difference;

[0015] Obtaining the job level of the preparation object of the detection file and the target file weight of the detection file;

[0016] Performing object compilation detection based on the compilation object rank and the target file weight to obtain an object compilation parameter, wherein the object compilation parameter is used to indicate the difference between the compilation object rank and the target file weight;

[0017] A file compilation test is performed based on the object compilation parameter and the file verification difference to obtain a file compilation score of the detected file at the compilation sub-node.

[0018] In some embodiments, the file design process further includes an approval node, the node level of the approval node is higher than the review node, the file error data further includes third file error sub-data, the third file error sub-data is used to indicate the file error detected by the approval node for the test file, and the method further includes:

[0019] Performing a file error benchmark detection based on the third file error sub-data and the target file group, and determining a target benchmark annotation error rate of the target file group at the approval node;

[0020] Performing file error rate detection on the detection file based on the third file error sub-data to obtain a file annotation error rate of the detection file at the approval node;

[0021] An audit quality test is performed based on the target benchmark annotation error rate, the file annotation error rate and the file attribute data to obtain a file audit score of the test file at the audit node.

[0022] In some embodiments, the target file group includes a detection object, and after performing the audit quality detection based on the target benchmark annotation error rate, the file annotation error rate and the file attribute data to obtain the file audit score of the detection file at the audit node, the method further includes:

[0023] determining an object compilation score for the detection object based on the document compilation score and the target document group;

[0024] determining an object verification score for the inspection object based on the file verification score and the target file group;

[0025] determining an object review score for the detection object based on the file review score and the target file group;

[0026] Acquire average workload data of the target file group and object workload data of the detection object;

[0027] Performing workload detection on the detection object based on the object workload data and the average workload data to obtain a workload coefficient;

[0028] The object file quality is detected based on the object compilation score, the object verification score, the object audit score and the workload coefficient to obtain an object file quality score.

[0029] In some embodiments, the performing of editing quality detection based on the target benchmark review error rate, the file review error rate and the file attribute data to obtain the editing quantitative score of the detected file at the editing node includes:

[0030] Performing a numerical comparison on the target benchmark audit error rate and the document audit error rate to obtain a numerical comparison result;

[0031] Determining a quantitative scoring direction of the detection file based on the numerical comparison result;

[0032] Calculating the error rate difference between the target benchmark audit error rate and the document audit error rate to obtain the document audit error rate difference;

[0033] The editing and proofreading quality detection is performed based on the quantitative scoring direction, the file attribute data and the file review error rate difference to obtain the editing and proofreading quantitative score.

[0034] In some embodiments, the editing quality detection based on the quantitative scoring direction, the file attribute data and the file review error rate difference to obtain the editing quantitative score includes:

[0035] Acquire the target file weight, target file type and target file version of the detected file from the file attribute data;

[0036] Matching weight coefficients based on the quantitative scoring direction and the target file weight to obtain a file weight coefficient;

[0037] Perform type coefficient matching based on the quantitative scoring direction and the target file type to obtain a file type coefficient;

[0038] Matching version coefficients based on the quantitative scoring direction and the target file version to obtain a file version coefficient;

[0039] The file weight coefficient, the file type coefficient and the file version coefficient are multiplied to obtain a target file coefficient;

[0040] The editing and proofreading quality detection is performed based on the target file coefficient, the quantitative scoring direction and the file review error rate difference to obtain the editing and proofreading quantitative score.

[0041] In some embodiments, performing file error benchmark detection based on the second file error sub-data and the target file group to determine a target benchmark audit error rate of the target file group at the audit node includes:

[0042] Counting the number of pages of all the detected files in the target file group to obtain the number of target file pages;

[0043] Counting the number of errors in the second file error sub-data of all the detected files in the target file group to obtain a total number of audited errors;

[0044] The target baseline audit error rate is determined based on the total number of audit errors and the target document page number.

[0045] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application proposes a file quality detection system based on a design process, the system comprising:

[0046] an acquisition module, for acquiring a target file group, wherein the target file group includes a test file, file error data of the test file in a file design process, and file attribute data of the test file; wherein the file design process includes an editing node and an auditing node, the node level of the auditing node is higher than the editing node, and the file error data includes first file error sub-data of the editing node and second file error sub-data of the auditing node;

[0047] A benchmark detection module, configured to perform a file error benchmark detection based on the second file error sub-data and the target file group, and determine a target benchmark audit error rate of the target file group at the audit node;

[0048] An error rate detection module, configured to perform a file error rate detection on the detection file based on the second file error sub-data, and obtain a file audit error rate of the detection file at the audit node;

[0049] An editing quality detection module, used to perform editing quality detection based on the target benchmark review error rate, the file review error rate and the file attribute data, and obtain the editing quantitative score of the detected file at the editing node;

[0050] An efficiency detection module, configured to perform a verification efficiency detection based on the first file error sub-data and the target file group to obtain a file verification efficiency;

[0051] The file quality detection module is used to perform file quality detection on the detection file based on the editing quantitative score and the file verification efficiency.

[0052] To achieve the above objectives, a third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in the first aspect is implemented.

[0053] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0054] The embodiment of the present application proposes a file quality detection method, system, device, and medium based on the design process. By acquiring a target file group, the target file group includes a detection file, file error data of the detection file in the file design process, and file attribute data of the detection file; wherein the file design process includes an editing node and an audit node, the node level of the audit node is higher than the editing node, and the file error data includes first file error sub-data of the editing node and second file error sub-data of the audit node; further, based on the second file error sub-data and the target file group, a file error benchmark detection is performed to determine the target file group. The target benchmark audit error rate of the target file group at the audit node; based on the second file error sub-data, the file error rate of the test file is detected to obtain the file audit error rate of the test file at the audit node; further, based on the target benchmark audit error rate, the file audit error rate and the file attribute data, the editing quality detection is performed to obtain the editing quantitative score of the test file at the editing node; further, based on the first file error sub-data and the target file group, the verification efficiency is detected to obtain the file verification efficiency; further, the file quality detection of the test file is performed based on the editing quantitative score and the file verification efficiency. Compared with the related art, it is usually to evaluate the problems existing in the design process after the quality problems of the design file occur, so that the design file used for execution still has many quality problems. This application deeply understands the relationship between adjacent nodes, that is, firstly performs editing quality detection through the target benchmark audit error rate of the determined target file group at the audit node, the file audit error rate of the test file at the audit node and the file attribute data, and obtains the editing quantitative score of the test file at the editing node, so as to quantitatively evaluate the detection accuracy of the editing node for file errors. Furthermore, conducting file quality inspection on test files based on editing and proofreading quantitative scores and file verification efficiency can improve the accuracy of file quality inspection at each node in the design process, thereby effectively avoiding a large number of quality problems in subsequent implementation. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is the first flow chart of the file quality detection method based on the design process provided by the embodiment of the present application;

[0056] Figure 2 It is a node setting schematic diagram of the file design process provided by the embodiment of the present application;

[0057] Figure 3 It is a system structure diagram of a document quality detection method based on the design process;

[0058] Figure 4 yes Figure 1 A flowchart of step S120 in FIG.

[0059] Figure 5 yes Figure 1 A flowchart of step S140 in FIG.

[0060] Figure 6 It is a schematic diagram of the quality evaluation of editing nodes based on baseline thinking;

[0061] Figure 7 yes Figure 5 A flowchart of step S540 in FIG.

[0062] Figure 8 It is a structural diagram of the structural classification of design documents based on the overall project in the field of nuclear power plants;

[0063] Fig. 9 yes Figure 1 A flowchart of step S160 in FIG.

[0064] Fig.10 It is an optional flow chart for efficient redaction score allocation and correction of document verification provided by the embodiment of the present application;

[0065] Fig.11 This is a second optional flow chart of the design process-based file quality detection method provided in an embodiment of the present application;

[0066] Fig.12 This is a third optional flow chart of the file quality detection method based on the design process provided in the embodiment of the present application;

[0067] Fig.13 It is a specific framework schematic diagram of the file quality detection method based on the design process provided in the embodiment of the present application;

[0068] Fig.14 It is a system architecture diagram of a design document quality evaluation application provided by an embodiment of the present application;

[0069] Fig.15 It is a structural schematic diagram of a file quality detection system based on a design process provided in an embodiment of the present application;

[0070] Fig.16 It is a hardware structure diagram of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0071] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0072] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0073] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0074] Design documents (also called finished design products) are the texts, drawings and technical data gradually formed in the process of product development and production. They are the main carrier of design work and the main manifestation of design results. Their quality is directly related to the quality of engineering construction and physical results. They are the direct and important basis for the inspection management, suppliers, owners and other parties to evaluate technical work. In order to ensure that the design documents meet the quality standards, avoid a large number of quality problems in subsequent implementation, and effectively grasp the quality of the department's finished design products, it is necessary to conduct document quality inspection on the design documents, so as to scientifically and reasonably reward and punish the relevant designers according to the results of the document quality inspection, and improve the stability and accuracy of document quality writing. In practical applications, the design process of design documents can include multiple design nodes, such as preparation, verification, review, approval, etc., to control the quality of the documents.

[0075] The document quality detection method based on the design process adopted by the relevant technology usually evaluates the problems existing in the design process after the quality problems appear in the design documents. In this way, the quality management of design documents in each link mainly depends on the sense of responsibility of the node management personnel. Due to the lack of necessary quality detection methods for design documents at different nodes in the design process, there are still many quality problems in the design documents used for execution after the review of nodes such as editing and proofreading, which affects the progress and quality of engineering construction. For example, from the analysis of the review data and design changes of nuclear power plants in the past, the problems found by professional review are far less than the number of changes caused by professional design errors. The design quality events (including problems reported by the competent authorities) and internal and external supervision and inspection of nuclear power plants over the years have also exposed outstanding problems in the effectiveness of design document review and the implementation of review responsibilities. Therefore, how to propose a method that can quantitatively evaluate the quality of documents at different nodes in the design process, improve the accuracy of document quality detection at each node in the design process, and avoid a large number of quality problems in subsequent implementation has become a technical problem that needs to be solved urgently.

[0076] Based on this, the embodiments of the present application provide a file quality detection method, system, device, and medium based on the design process, which can improve the accuracy of file quality detection at each node in the design process, thereby effectively avoiding a large number of quality problems in subsequent implementation.

[0077] The file quality detection method based on the design process provided in the embodiment of the present application can be applied to the terminal, can also be applied to the server side, and can also be software running in the terminal or the server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or a server cluster or distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN) and big data and artificial intelligence platforms; the software can be an application that implements the file quality detection method based on the design process, etc., but is not limited to the above forms.

[0078] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network personal computers (Personal Computer, PC), minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0079] It should be noted that in each specific implementation of the present application, when the detection file involved needs to be processed according to the object information, object attribute information, object rank and other data related to the identity or characteristics of the object, the permission or consent of the object will be obtained first, and the collection, use and processing of these data will comply with relevant laws, regulations and standards. In addition, when the embodiment of the present application needs to obtain the sensitive personal information of the object, the separate permission or separate consent of the object will be obtained through a pop-up window or jump to a confirmation page. After clearly obtaining the separate permission or separate consent of the object, the necessary object-related data for enabling the normal operation of the embodiment of the present application will be obtained.

[0080] See also Figure 1 , Figure 1 is an optional flow chart of a file quality detection method based on a design process provided in an embodiment of the present application. In some embodiments of the present application, Figure 1 The method may specifically include but is not limited to steps S110 to S160. Figure 1 These six steps are introduced in detail.

[0081] Step S110, obtaining a target file group;

[0082] Step S120, performing a file error benchmark detection based on the second file error sub-data and the target file group, and determining a target benchmark audit error rate of the target file group at the audit node;

[0083] Step S130, performing file error rate detection on the detection file based on the second file error sub-data to obtain the file audit error rate of the detection file at the audit node;

[0084] Step S140, performing editing quality detection based on the target benchmark review error rate, the file review error rate and the file attribute data, and obtaining the editing quantitative score of the detected file at the editing node;

[0085] Step S150, performing verification efficiency detection based on the first file error sub-data and the target file group to obtain the file verification efficiency;

[0086] Step S160 , performing a file quality check on the check file based on the editing quantitative score and the file verification efficiency.

[0087] In step S110 of some embodiments, the target file group refers to a combination of a batch of test files for file quality detection. For example, the target file group can be a combination of all design files designed by a design team within a preset time interval (such as 12 months), and each design file is equivalent to a test file. The target file group includes test files, file error data of test files in the file design process, and file attribute data of test files. Test files refer to any design file in the target file group that needs to be tested. File attribute data is used to characterize attribute data corresponding to the test file, and the file attribute data may include file weight (i.e., weight determined based on the technical difficulty of file preparation, the importance of the project corresponding to the file, the cost impact of the project corresponding to the file, etc.), file type (i.e., reflecting the impact of design file type differences on file quality assessment), file version (i.e., reflecting the impact of file version differences on file quality assessment), etc. File error data refers to data that does not meet the requirements for the test file based on the file quality assessment standard and the nature of the error.

[0088] It should be noted that, considering the differences between design professions, technical fields, and design activity characteristics, the target file group can be used as an evaluation unit, which is a design team composed of editors and reviewers of all the test files to be evaluated. It is also because of the same profession, the same nature and content of work that the test files are comparable and reasonable. In this way, the average level of the target file group can be used as an evaluation benchmark, and the target benchmark review error rate, target benchmark approval error rate, etc. can be determined in subsequent embodiments, which can more objectively, reasonably and accurately reflect the quality level of each file.

[0089] It should be noted that for a company that needs to conduct document quality inspection, it can include multiple organizations divided step by step, and each organization is equivalent to a work team divided according to certain rules, and personnel are the components of the organization. For example, a company can include companies, design institutes, professional institutes, professional rooms, professional groups, design field teams and other organizations from large to small, but in order to make the design files in the same target file group comparable, the inspection files contained in the target file group of this application can come from the same organization and have the same or similar professional background. Furthermore, this application can consider the differences in the characteristics of professional design activities and divide the entire institute into 12 professional groups, i.e., 12 evaluation units, according to administrative offices and administrative groups. Taking professional groups as evaluation units, the influence of professional differences, field differences, file differences, design activity differences, etc. on the evaluation results can be eliminated from the methodological level, so that the files and personnel within the statistical period have strong comparability, which is not specifically limited here.

[0090] Please note that Figure 2 , Figure 2It is a node setting schematic diagram of the file design process provided by the embodiment of the present application. The file design process of the present application may include editing nodes, review nodes, approval nodes, etc. (such as there is an on-site execution stage after the approval node, to evaluate the execution quality of the approval link through quality problems found through on-site execution), and the node levels are successively increased, that is, the node level of the review node is higher than the editing node, and the node level of the approval node is higher than the review node. In this way, objects at different node levels can be set according to design qualifications and technical capabilities, such as the audit object can be a high-level technical leader, the verification object can be a low-level technical leader, and the authorization must pass the assessment and certification.

[0091] In addition, this application detects the quality of documents based on the document design process, taking into account the convergence principle, that is, effective quality process control must be a control trend that converges along the control target. Based on this basic principle of quality control, in the document review process, the execution quality of the previous links can be evaluated by the error rate of the subsequent links in the quality barriers at all levels, so as to intuitively judge the effectiveness of the barriers. For example, the lower the error rate found by the audit node, the higher the quality of the document detection performed by the editing node; the lower the error rate found by the approval node, the higher the quality of the document detection performed by the audit node.

[0092] It should be noted that the present application uses the example that the file design process only includes the editing node, the review node, and the approval node for explanation. The file error data of the present application may include the first file error sub-data of the editing node, the second file error sub-data of the review node, and the third file error sub-data of the approval node. Among them, the first file error sub-data is used to indicate the file error detected by the editing node for the test file, the second file error sub-data is used to indicate the file error detected by the review node for the test file, and the third file error sub-data is used to indicate the file error detected by the approval node for the test file. In this way, the quality control of the previous link at different node links can be reflected.

[0093] Please note that Figure 3 , Figure 3A system structure diagram of the file quality detection method based on the design process provided by the present application is shown. In practical applications, a design process quality evaluation model can be constructed based on the file quality detection method based on the design process of the present application. The design process quality evaluation model may include a data input part, a unified data acquisition part, a data calculation part and a data service part. Among them, the data of the data input part may come from the design and production system (the system may obtain file error data, such as proofreading error information), the training authorization system (the system may obtain file attribute data), the supervision and management system (the system may obtain object workload information, finished product spot check information, etc.), the human resources system (the system may obtain object rank information, etc.), etc. The data acquisition part may be used to collect and obtain the relevant data of the target file group, and input the relevant data into the data calculation part. The data calculation part may execute the file quality detection method based on the design process of the present application, which is the core module of the design process quality evaluation, and its purpose is to establish a design process quality evaluation model based on the file data input by the system, and to perform quantitative quality evaluation on files, personnel and organizations. In other words, the design process quality evaluation model constructed by this application can quantitatively reflect and evaluate the quality level of the finished product of each document, each employee, and each organization in the document design process, and effectively promote the improvement of design document quality control.

[0094] In step S120 of some embodiments, when performing quality assessment on the editing node, the present application can use the relevant audit data of the target file group at the audit node as the benchmark data to evaluate the quality audit of the file by the object in the editing node. Specifically, the present application can first perform a file error benchmark detection based on the second file error sub-data and the target file group, and determine the target benchmark audit error rate of the target file group at the audit node. Among them, the target benchmark audit error rate is equivalent to a benchmark error rate baseline, which can be used to characterize the zero-point benchmark for quality detection of the detection file in the audit stage, and the target benchmark audit error rate can be flexibly changed as the quality level of the target file group changes. The second file error sub-data is used to characterize the number of defects proposed by the audit object according to the file quality assessment standard. In this way, compared with the related art that evaluates the problems existing in the design process after the quality problems occur in the design file, the present application can deeply understand the relationship between adjacent nodes, that is, combine the convergence principle to evaluate the execution quality of the previous link through the error rate of the subsequent link in the quality barrier at each level, which can improve the accuracy of the file quality detection of each node link in the design process.

[0095] In some embodiments, see Figure 4 , Figure 4 is an optional flowchart of step S120 provided in the embodiment of the present application. The step S120 may specifically include but is not limited to steps S410 to S430. Figure 4 These three steps are introduced in detail.

[0096] Step S410, counting the number of pages of all detected files in the target file group to obtain the number of target file pages;

[0097] Step S420, counting the number of errors in the second file error sub-data of all the detected files in the target file group to obtain the total number of audit errors;

[0098] Step S430, determining a target baseline audit error rate based on the total number of audit errors and the number of target file pages.

[0099] In steps S410 to S430 of some embodiments, when calculating the target baseline audit error rate of the target file group at the audit node, the number of file pages of all the detection files of the target file group can be first counted to obtain the target file page number, which is used to characterize the total number of pages of all the detection files contained in the target file group. Further, the number of errors in the second file error sub-data of all the detection files of the target file group is counted to obtain the total number of audit errors. Afterwards, the total number of audit errors is divided by the number of target file pages to obtain the mean audit error rate, and the mean audit error rate is used as the target baseline audit error rate. In this way, the influence of the number of file pages in each detection file on the error rate can be avoided from the overall perspective of the target file group. Therefore, the process of calculating the target baseline audit error rate can be specifically shown in the following formula 1:

[0100]

[0101] In other embodiments, when calculating the target baseline audit error rate of the target file group at the audit node, the file audit error rate of each detection file in the target file group can also be calculated separately (the specific calculation method is described in the subsequent embodiments), and then the file audit error rates of all detection files in the target file group are added and calculated, and the result of the addition calculation is divided by the number of detection files to obtain the average file audit error rate, which can be used as the target baseline audit error rate. In this way, the influence of the number of file pages in each detection file on the error rate can be avoided separately. Therefore, the process of calculating the target baseline audit error rate can also be specifically shown in the following formula 2:

[0102]

[0103] In the above embodiment, the present application uses the target file group as the evaluation unit and evaluates the work quality of designers relative to the average level of the target file group, which can improve the accuracy of file quality detection at each node in the design process.

[0104] It should be noted that, in practical applications, this application can take all design documents published by the target file group in the past year (ie, the past 12 months) as the statistical sample size, ie, as the test files.

[0105] In step S130 of some embodiments, the present application can also perform file error rate detection on the detection file based on the second file error sub-data of each detection file to obtain the file review error rate of each detection file at the review node. Specifically, first obtain the number of file review errors proposed by the review object according to the file quality assessment standard and the nature of the error, and then divide the number of file review errors by the number of file pages of the corresponding detection file to obtain the file review error rate of the detection file.

[0106] It should be noted that the second file error sub-data and the number of file pages of the test file are both objective data and have no necessary relationship. De-dimensionalization can be achieved by calculating the ratio of the second file error sub-data and the number of file pages of the test file. At this time, the file review error rate obtained can characterize the number of defects on a single page, thereby eliminating the influence of the number of file pages. Otherwise, files with more pages at the same compilation level will definitely be found to have more problems, which effectively improves the accuracy of file quality detection.

[0107] In step S140 of some embodiments, the present application may further combine the target benchmark review error rate, the file review error rate and the file attribute data to evaluate a quantitative score of editing quality, namely, an editing quantitative score, to reflect the editing quality of the detected file at the editing node.

[0108] In some embodiments, see Figure 5 , Figure 5 is an optional flowchart of step S140 provided in the embodiment of the present application. The step S140 may specifically include but is not limited to steps S510 to S540. Figure 5 These four steps are introduced in detail.

[0109] Step S510, numerically comparing the target benchmark audit error rate and the document audit error rate to obtain a numerical comparison result;

[0110] Step S520, determining a quantitative scoring direction of the detection file based on the numerical comparison result;

[0111] Step S530, calculating the error rate difference between the target benchmark audit error rate and the document audit error rate to obtain the document audit error rate difference;

[0112] Step S540, performing editing and proofreading quality detection based on the quantitative scoring direction, file attribute data and file review error rate difference to obtain an editing and proofreading quantitative score.

[0113] In step S510 and step S520 of some embodiments, since the convergence rule needs to be followed in the file design process, the scoring idea for the editing node link of the present application can be to compare the target benchmark audit error rate and the file audit error rate numerically, and obtain the numerical comparison result, so as to judge the quantitative scoring direction of the detection file according to the numerical comparison result and the rule of quality control convergence. This is because the errors detected in the later links should be fewer. In this way, when the file audit error rate of the detection file is greater than the target benchmark audit error rate, it means that the editing quality of the previous sequence is lower than the average level of the target file group, and the quantitative scoring direction is negative; when the file audit error rate is less than the target benchmark audit error rate, it means that the editing quality of the previous sequence is higher than the average level of the target file group, and the quantitative scoring direction is positive; when the file audit error rate is equal to the target benchmark audit error rate, it means that the editing quality of the previous sequence meets the average level of the target file group, and the quantitative scoring direction is 0.

[0114] It should be noted that if Figure 6 As shown, Figure 6 This is a schematic diagram of the quality evaluation of editing nodes based on baseline thinking provided by the embodiment of the present application. Among them, the horizontal axis is the baseline for the quality evaluation of editing nodes, which is equivalent to the target baseline audit error rate, and the vertical axis is the initial editing quantitative score. When the file audit error rate of the detected file is greater than the target baseline audit error rate, it means that the editing quantitative score is a negative value; when the file audit error rate is less than the target baseline audit error rate, it means that the editing quantitative score is a positive value; when the file audit error rate is equal to the target baseline audit error rate, it means that the editing quantitative score is 0.

[0115] In step S530 and step S540 of some embodiments, at the same time, the present application can calculate the error rate difference between the target baseline audit error rate and the file audit error rate, and the obtained file audit error rate difference is the absolute value of the difference between the two. Further, the initial editing and proofreading quantitative score of the detected file can be determined based on the quantitative scoring direction and the file audit error rate difference. Further, the initial editing and proofreading quantitative score can be updated in combination with the file attribute data to obtain the final editing and proofreading quantitative score. Figure 6For example, if the file review error rate of file A is less than the target benchmark review error rate, and the difference in the file review error rate obtained after the difference calculation between the two is 0.35, then the editing and proofreading quantitative score is a positive value, so the initial editing and proofreading quantitative score can be +0.35. If the file review error rate of file B is equal to the target benchmark review error rate, then the editing and proofreading quantitative score is 0. If the file review error rate of file C is greater than the target benchmark review error rate, and the difference in the file review error rate obtained after the difference calculation between the two is 0.25, then the editing and proofreading quantitative score is a negative value, so the initial editing and proofreading quantitative score can be -0.25. In other words, in order to maximize the motivation of editors and proofreaders to work together to improve the quality of documents (avoiding the impact of internal harmony on the evaluation of work quality by the number of errors), this application can adopt the idea of ​​"editing and proofreading bundling" to evaluate the quality of document editing and proofreading as a whole (that is, taking the editing and proofreading process as a whole node) to form an editing and proofreading quantitative score.

[0116] In some embodiments, see Figure 7 , Figure 7 This is an optional flowchart of step S540 provided in the embodiment of the present application. The step S540 may specifically include but is not limited to steps S710 to S760. Figure 7 These six steps are introduced in detail.

[0117] Step S710, obtaining the target file weight, target file type and target file version of the detected file from the file attribute data;

[0118] Step S720, matching weight coefficients based on the quantitative scoring direction and the target file weight to obtain a file weight coefficient;

[0119] Step S730, performing type coefficient matching based on the quantitative scoring direction and the target file type to obtain a file type coefficient;

[0120] Step S740, performing version coefficient matching based on the quantitative scoring direction and the target file version to obtain the file version coefficient;

[0121] Step S750, multiplying the file weight coefficient, the file type coefficient and the file version coefficient to obtain the target file coefficient;

[0122] Step S760, performing editing and proofreading quality detection based on the target file coefficient, the quantitative scoring direction and the file review error rate difference to obtain the editing and proofreading quantitative score.

[0123] In step S710 of some embodiments, when scoring the editing and proofreading quality of each detected file, the present application will also consider the file attribute data of each detected file. Specifically, the target file weight, target file type and target file version corresponding to the detected file can be first obtained from the file attribute data to fully consider the impact of different attribute characteristics of the detected file on the editing and proofreading quantitative score. In addition, the file attribute data can also include other attributes, such as file maturity, personalization, etc., which will not be elaborated here.

[0124] In step S720 of some embodiments, in order to reflect the impact of the difference in difficulty of design files in different fields (including the resulting offline analysis, calculation and other work input) on file quality assessment, the present application can determine the file weight (i.e., technical difficulty) according to the design activity weight corresponding to the preset structured classification of design files. In this way, for detection files with high target file weights, greater positive incentives (i.e., positive scores) or reduced negative incentives (i.e., negative scores) can be given, and for detection files with low target file weights, greater negative incentives can be given or positive incentives can be reduced. For example, an example matching relationship between the target file weight and the quantitative scoring direction can be shown in Table 1 below, and the value corresponding to the intersection between the target file weight and the quantitative scoring direction is the file weight coefficient (W). Among them, the design activity weight is set to any value in [1,10].

[0125]

[0126] Table 1

[0127] Please note that Figure 8 , Figure 8 A schematic diagram of the structured classification of design documents corresponding to the overall engineering in the field of nuclear power plants is shown. The overall engineering is divided into five layers in the structured classification of design documents (such as Figure 8 The previous level contains the relevant types of files in the next level. In addition, the figure assigns a corresponding design activity weight to each type of design file. For example, this application first sets the total weight of the overall project evaluation unit to 100 (this total weight can be set flexibly). According to Figure 8 After structured decomposition, the score of 100 can be divided into each file type, so as to determine the weight corresponding to each file type, so as to reflect the difference of evaluation types within the evaluation unit. Fig. 9The corresponding design activity weight is attached in the brackets corresponding to each file type. For example, the design activity weight of the "overall design requirements" type under the design module is 78, and the design activity weight of the "safety design" type under the design field is 55. Since the design activity weight at this time is different from the range corresponding to the file weight, the design activity weight can be processed and adjusted to any value in [1,10] to match the value in Table 1. It should be noted that Figure 8 The weights divided in the structured classification of design documents shown can be constructed based on at least one factor such as the technical difficulty, project importance, cost impact, etc. of each type of document, without limitation.

[0128] In step S730 of some embodiments, in order to reflect the impact of differences in design file types on file quality assessment, the present application can set different file type coefficients for different file types, such as Class A (Identical), Class B (Modified), Class C (New), and other files with increasing difficulty in compilation. In this way, the more modified content relative to the reference file, that is, the higher the difficulty corresponding to the file type, the greater the positive incentive can be given and the negative incentive can be reduced. For example, Class C files can increase positive scores and reduce negative scores; Class A files can reduce positive scores and increase negative scores; Class B files can maintain the original scores. In this way, an example matching relationship between the target file type and the quantitative scoring direction can be shown in Table 2 below, and the value corresponding to the intersection between the target file type and the quantitative scoring direction is the file type coefficient (T).

[0129]

[0130] Table 2

[0131] In step S740 of some embodiments, in order to reflect the impact of file version differences on file quality assessment, the present application can set different file version coefficients for different file versions, such as version A (first version), version B (first upgraded version), version C (second upgraded version) and other files, and set different file type coefficients for them. For example, the first version maintains the original score, and the positive scores of subsequent versions decrease, while the negative scores increase; or, the first version of the file is given a greater positive incentive, and the negative incentives are reduced, etc., without specific limitation. In this way, an example matching relationship between the target file version and the quantitative scoring direction can be shown in Table 3 below, and the value corresponding to the intersection between the target file version and the quantitative scoring direction is the file version coefficient (V).

[0132]

[0133] Table 3

[0134] In step S750 and step S760 of some embodiments, after the file weight coefficient, file type coefficient and file version coefficient are determined, these coefficients may be multiplied to obtain the target file coefficient, that is, the target file coefficient = file weight coefficient (W i )× file type coefficient (T i )×File Version Coefficient(V ′ ), where i represents the number of the detection file, then W i Represents the file weight coefficient of the i-th detected file. Further, the editing quality detection can be performed based on the target file coefficient, the quantitative scoring direction and the file review error rate difference to obtain the editing quantitative score. In this way, the process of calculating the editing quantitative score can be specifically shown in the following formula 3:

[0135] Editing and proofreading quantitative score = ±|Document review error rate difference|×target document coefficient (Formula 3)

[0136] The choice of ± depends on the direction of the quantitative scoring.

[0137] It should be noted that the table corresponding to the matching relationship mentioned in this application can be flexibly adjusted according to actual needs and will not be described in detail.

[0138] In step S150 of some embodiments, after determining the editing and proofreading quantitative score, the present application can further perform specific detection on the editing and proofreading node. Among them, the editing and proofreading node of the present application includes a compilation sub-node and a verification sub-node, that is, firstly adopt the idea of ​​"editing and proofreading bundling" to evaluate the quality of file editing and proofreading as a whole, form an editing and proofreading quantitative score, and then further perform separate detection on the compilation sub-node and the verification sub-node. Specifically, firstly perform a verification efficiency detection based on the first file error sub-data and the target file group to obtain the file verification efficiency, that is, determine the quality contribution of the verification object in the detection file to the file detection.

[0139] It should be noted that, considering that the files in the same target file group and the same period are comparable, the present application can first divide the number of the first file error sub-data (i.e., the number of file defects detected by the verification object in the test file) by the number of file pages of the test file to obtain the file verification error rate. Furthermore, the ratio of the file verification error rate to the maximum verification error rate of all the test files of the target file group in the statistical period is defined as the verification efficiency. The specific calculation of the maximum verification error rate of all the test files of the target file group in the statistical period is: numerically compare the file verification error rates of all the test files in the target file group, and take the file verification error rate with the largest value as the maximum verification error rate.

[0140] It should be noted that proofreading and review are two different stages of document quality control and can be reviewed by different personnel. The maximum proofreading error rate is intended to determine the effectiveness of proofreading, thereby determining the proportion of the editor's allocation to the total editing score. The target benchmark review error rate of this application is intended to determine the quantitative editing score.

[0141] In step S160 of some embodiments, after determining the editing quantitative score and the file verification efficiency, the present application can perform a specific file quality assessment on the detection file in the editing sub-node and the verification sub-node based on these data.

[0142] In some embodiments, see Fig. 9 , Fig. 9 This is an optional flowchart of step S160 provided in the embodiment of the present application. The step S160 may specifically include but is not limited to step S910 to step S950. Fig. 9 These five steps are introduced in detail.

[0143] Step S910, performing file verification detection based on the editing and proofreading quantitative score and the file verification efficiency, and obtaining the file verification score of the detected file in the verification sub-node;

[0144] Step S920, calculating the difference between the editing and proofreading quantitative score and the document verification score to obtain the document verification difference;

[0145] Step S930, obtaining the job level of the preparation object of the inspection file and the target file weight of the inspection file;

[0146] Step S940, performing object establishment detection based on the establishment object rank and target file weight to obtain object establishment parameters;

[0147] Step S950, performing file compilation detection based on the object compilation parameter and the file verification difference, and obtaining the file compilation score of the detected file in the compilation sub-node.

[0148] In step S910 of some embodiments, after the editing and proofreading quantitative score is determined, the present application can assign scores based on the file verification effectiveness, that is, the score of the verification stage can be extracted from the editing and proofreading quantitative score based on the file verification effectiveness (that is, the file verification score, which also represents the score that can be assigned to the verification object), and the remaining score can be assigned to the compilation stage (that is, the file compilation score, which also represents the score that can be assigned to the compilation object).

[0149] It should be noted that, when the file verification efficiency is at two extremes of 100% and 0%, there will be an unreasonable situation where the verification object allocates the score alone without considering the contribution of the compilation object. For this reason, the present application can also optimize the value range of the file verification efficiency, that is, to correct the file verification efficiency. Specifically, there are two situations: (1) When the editing and proofreading quantitative score is a positive score, the present application can proportionally reduce the file verification efficiency by a factor of 0.8, that is, the file verification efficiency at this time is up to 80% (indicating sufficient verification), so as to avoid the situation where the verification object allocates all positive scores when the file verification efficiency is 100%. In this way, when the file verification efficiency is 100%, it can also ensure that 80% of the positive editing and proofreading quantitative scores belong to the verification object, and 20% of the positive editing and proofreading quantitative scores belong to the compilation object. At this time, if the file verification efficiency is 0%, 100% of the positive editing and proofreading quantitative scores belong to the compilation object (indicating that the compilation object has completed the work conscientiously). (2) When the editing and proofreading quantitative score is negative, the present application can proportionally reduce the file verification efficiency by a coefficient of 0.8 and increase it by 20%, that is, the minimum file verification efficiency at this time is 20% (indicating ineffective verification). In this way, 80% of the negative editing and proofreading quantitative scores can be attributed to the verification object, and 20% of the negative editing and proofreading quantitative scores can be attributed to the compilation object. At this time, if the verification efficiency is 100% (indicating sufficient verification), then 100% of the negative editing and proofreading quantitative scores are all attributed to the compilation object (indicating that the verification object has completed the work conscientiously).

[0150] It should be noted that 0.8 mentioned in the embodiment of this application is the limit proportion of the score distribution of the editing object. This value can be preset artificially, or it can be based on the distribution of the proportion of design hours invested by the editing objects during the statistical period, and the value corresponding to the maximum value or the maximum probability (that is, most files) is taken without specific limitation.

[0151] It should be noted that the specific process of calculating the file verification score of the detection file in the verification sub-node can be shown in the following formula 4:

[0152] Document verification score = Document verification efficiency (corrected value) × editing quantitative score (Formula 4)

[0153] In steps S920 to S950 of some embodiments, the object compilation parameter is used to indicate the difference between the file weight matched by the compilation object's rank and the target file weight. When calculating the file compilation score, this application will consider the impact and contribution of employees of different ranks (i.e., the detection object of this application) to the quality activities, that is, when the compilation object's rank is low, if the file with a higher file weight is processed, the difficulty for the compilation object will be higher.

[0154] Specifically, the object compilation parameters can be determined based on the difficulty deviation. The compilation object grade is equivalent to the grade factor (that is, in an organization or enterprise, a level division of positions is performed based on factors such as the size of employees' work responsibilities, the complexity and difficulty of the work, and the ability requirements of the incumbent). The difficulty deviation is the deviation between the target file weight and the grade factor, which indicates whether the corresponding compilation object has undertaken work that exceeds or is lower than the technical difficulty corresponding to the job grade. At this time, if the difficulty deviation (that is, the object compilation parameter) is greater than a certain threshold, the positive score of the compilation link can be increased or the negative score of the compilation link can be reduced; if the difficulty deviation is less than a certain threshold, the negative score of the compilation link can be increased or the positive score of the compilation link can be reduced. Therefore, the specific process of calculating the file compilation score of the detection file in the compilation sub-node can be shown in the following formula 5:

[0155] Document preparation score = (editing quantitative score - document verification score) × object preparation parameter (Formula 5)

[0156] For example, in order to reflect the influence and contribution of objects of different levels of preparation objects on the quality of documents, this application may define the matching relationship between the levels of preparation objects and the corresponding document weights as shown in Table 4 below:

[0157] Rank three Four five six seven eight Rank Factor 0 2 4 6 8 10 File weight 1 [2,3) [3,5) [5,7) [7,9] 10

[0158] Table 4

[0159] Among them, the job grade factor includes six levels from low to high, and each level corresponds to a different file weight.

[0160] In addition, the difficulty deviation of this application = file weight - job level, so the corresponding object establishment parameters can be determined. Therefore, the matching relationship between the quantitative scoring direction and the difficulty deviation of this application is shown in Table 5 below, where the value corresponding to the intersection between the quantitative scoring direction and the difficulty deviation is the object establishment parameter (equivalent to the job level coefficient).

[0161]

[0162] Table 5

[0163] It should be noted that in order to consider the reasonable fluctuation range, this application can set the target benchmark review error rate ±1.5% (parameters can be adjusted) as the benchmark range. If the file review error rate is within the benchmark range, no points will be added or deducted for the editing object. In addition, the editing quantitative score of this application is equivalent to the file review score, that is, the total score of the editing node.

[0164] Please note that Fig.10 , Fig.10It is an optional flow chart for the allocation and correction of editing and proofreading scores for the efficiency of file verification provided by an embodiment of the present application. Among them, the present application can determine the efficiency of file verification based on the ratio of the file verification error rate of each detected file to the target file group statistics and the maximum verification error rate within the cycle. Furthermore, the verification object can allocate the verification score from the editing and proofreading quantitative score according to the proportion, and at the same time, consider the contribution of the writing object and correct the value range of the file verification efficiency. Based on the quantitative allocation of editing and proofreading of file verification efficiency, while avoiding the conflict between editing and proofreading (the conflict between editing and proofreading refers to the distinction in functions and responsibilities between the two work links of editing and proofreading, as well as their different roles and importance in the publishing process), it effectively motivates the verification objects to improve their own skills and fulfill their proofreading responsibilities through the benchmark effect.

[0165] This application determines the evaluation benchmark based on the files of the target file group, that is, the target benchmark audit error rate is determined based on the file audit error rate of all the test files of the target file group, which can intuitively determine the quality performance of the file or object in the team, and is more conducive to obtaining real and effective data, avoiding bad guidance on design quality behavior. In addition, the editing and proofreading quantitative score corresponding to each test file is determined based on the file audit error rate of each test file actually obtained, that is, the total score of the editing and proofreading node is determined. In addition, the evaluation benchmark of this application is updated synchronously with the statistical cycle to achieve dynamic association and timely adjustment of the baseline level and the business scope. Among them, the dynamic association of the baseline level with the business can be achieved by updating the scope of the calculation sample data of the benchmark error rate on a monthly basis, that is, each month, the file samples of the previous 12 months will be used to calculate the latest benchmark error rate (including the target benchmark audit error rate and the target benchmark approval error rate), and the editing and proofreading quantitative score and verification efficiency are used to evaluate the editing and proofreading quality.

[0166] In some embodiments, see Fig.11 , Fig.11 This is another optional flow chart of the file quality detection method based on the design process provided by the embodiment of the present application. The file quality detection method based on the design process provided by the present application may also specifically include but is not limited to steps S1110 to S1130.

[0167] Step S1110, performing a file error benchmark detection based on the third file error sub-data and the target file group, and determining a target benchmark annotation error rate of the target file group at the approval node;

[0168] Step S1120, performing file error rate detection on the detection file based on the third file error sub-data to obtain the file annotation error rate of the detection file at the approval node;

[0169] Step S1130, performing audit quality detection based on the target benchmark annotation error rate, the file annotation error rate and the file attribute data, and obtaining the file audit score of the detected file at the audit node.

[0170] In some embodiments, in steps S1110 to S1130, when it is necessary to perform a quality assessment on the review node through the approval node, the process of steps S110 to S160 can be combined with the convergence evaluation principle to perform a file review score detection. Among them, step S1110 can refer to step S120, step S1120 can refer to step S130, and step S1130 can refer to step S140. In order to save space, it will not be repeated here.

[0171] It should be noted that, for step S1130, when determining the file review score, the present application may also use the target benchmark annotation error rate as a baseline for review node quality evaluation. Figure 6 At this time, the horizontal axis is the baseline for evaluating the quality of the audit nodes, which is equivalent to the target benchmark approval error rate. The absolute value of the numerical value corresponding to the vertical axis represents the absolute value of the difference between the target benchmark annotation error rate and the file annotation error rate. When the target benchmark annotation error rate is greater than the file annotation error rate, it indicates that the corresponding file audit score is positive; when the target benchmark annotation error rate is less than the file annotation error rate, it indicates that the corresponding file audit score is negative; when the target benchmark annotation error rate is equal to the file annotation error rate, it indicates that the corresponding file audit score is 0.

[0172] In some embodiments, the target file group includes multiple detection objects, and the detection object can be an object that performs at least one work operation such as compiling, checking and reviewing the detection file, without limitation. In this way, the present application can also perform object scoring on each detection object in the target file group to reflect the detection object's work attitude towards the detection file it is responsible for, that is, the higher the object file quality score obtained, the more serious the detection object is about the detection file it is responsible for; the lower the object file quality score, the less serious the detection object is about the detection file it is responsible for, and the higher the probability of problems with the detection file.

[0173] Based on this, see Fig.12 , Fig.12 It is another optional flow chart of the file quality detection method based on the design process provided by the embodiment of the present application. After step S1130, the file quality detection method based on the design process provided by the present application may also specifically include but not limited to steps S1210 to S1260.

[0174] Step S1210, determining the object compilation score of the detection object based on the file compilation score and the target file group;

[0175] Step S1220, determining the object verification score of the detection object based on the file verification score and the target file group;

[0176] Step S1230, determining the object review score of the detection object based on the file review score and the target file group;

[0177] Step S1240, obtaining average workload data of the target file group and object workload data of the detection object;

[0178] Step S1250, performing workload detection on the detection object based on the object workload data and the average workload data to obtain a workload coefficient;

[0179] Step S1260, performing object file quality detection based on the object compilation score, object verification score, object review score and workload coefficient to obtain an object file quality score.

[0180] In step S1210 of some embodiments, since each detection object may be involved in at least one of the tasks of preparing, checking, and reviewing different detection files at the same time, the object file quality score can be calculated by determining the object preparation score, object verification score, and object verification score from all the detection files that the detection object is involved in. Therefore, the specific process of calculating the object preparation score of each detection object can be shown in the following formula 6:

[0181]

[0182] Among them, ∑ 编 The file preparation score represents the total score of the file preparation scores of all the test files that the test object participated in compiling. 编 Indicates the total number of files in all test files compiled by the test object.

[0183] In step S1220 of some embodiments, the specific process of calculating the object verification score of each detection object can be shown in the following formula 7:

[0184]

[0185] Among them, ∑ 校 The file verification score represents the total score of all the test files that the test object participates in the verification. 校 Indicates the total number of files of all inspection files involved in the verification of the inspection object.

[0186] It should be noted that in the case of multiple people checking, the object checking score can be allocated according to the proportion of the number of errors raised by each checking object to the total number of errors. If no errors are raised by all checking objects, the object checking score is divided equally according to the number of checkers.

[0187] In step S1230 of some embodiments, the specific process of calculating the object review score of each detection object can be shown in the following formula 8:

[0188]

[0189] Among them, ∑ 审 The file verification score represents the total score of the file review scores of all the test files that the test object participates in the review. 校 Indicates the total number of files in all the inspection files that the inspection object participates in the review.

[0190] In step S1240 and step S1250 of some embodiments, in order to reflect the impact of differences in document publishing workload on object quality assessment, the present application can formulate different weight coefficients for the volume of documents of different work types, thereby determining the object workload data of the detection object and comparing it with the average workload data of the target file group. Since the workload required for different types of work (such as compilation, verification and auditing) is different, and the standard workload required for compiling documents (i.e., the length of time invested in a document) is relatively large, a higher weight coefficient can be assigned to it, such as a weight coefficient of 0.7 for compilation, a weight coefficient of 0.2 for verification, and a weight coefficient of 0.1 for auditing. In this way, the specific calculation process of the object workload data of the detection object is: object workload data = workload of compiling documents × 0.7 + workload of verifying documents × 0.2 + workload of auditing documents × 0.1. In addition, the specific calculation process of the average workload data is shown in the following formula 9:

[0191]

[0192] Wherein, Li represents the object workload data of the i-th detection object, and the present application can determine the workload status of the detection object based on the average workload data La and the object workload data Li. In addition, the workload status includes a standard status (indicating that a sufficient amount of file publishing work has been completed), an excess status (indicating that a sufficient amount of file publishing work has been completed), and an insufficient status (indicating that a sufficient amount of file publishing work has not been completed).

[0193] For example, the standard state can be set as Li ∈ [La - La * 2 / 3, La + La * 2 / 3], the excess state as Li > La + La * 2 / 3, and the shortage state as Li < La - La * 2 / 3. Additionally, the present application can set different intervals for different states without specific limitations. At this time, if the object workload data indicates that the detection object is in the excess state, a greater positive incentive can be given to the detection object or the negative incentive can be reduced; if the object workload data indicates that the detection object is in the shortage state, a greater negative incentive can be given to the detection object or the positive incentive can be reduced; if the object workload data indicates that the detection object is in the standard state, the original score can be maintained.

[0194] Moreover, the matching relationship table for determining the workload coefficient based on the quantization scoring direction and the workload state in the present application can be as shown in Table 6 below, where the value corresponding to the intersection point between the quantization scoring direction and the workload state is the workload coefficient.

[0195]

[0196] Table 6

[0197] In step S1260 of some embodiments, the calculation of the object file quality score in the present application is specifically: object file quality score = (α * object compilation score + β * object checking score + γ * object review score) * workload coefficient, where α represents the scoring weight corresponding to the object compilation score, β represents the scoring weight corresponding to the object checking score, γ represents the scoring weight corresponding to the object review score, and these weights can be determined according to the object rank without limitation.

[0198] It should be noted that for the comprehensive report led by the overall entity (published through the sub-process + finalization process), the professional department sorts out the self-compiled sub-processes of this professional department (incorporated into the OED file structured classification and given weights), and calculates the score according to the file publication process. For the multi-professional joint compilation file (published through a single proofreading process by multiple professionals), the compilation score does not consider the joint compilation personnel, and for the proofreading score, the number of errors proposed by the OED proofreading personnel is extracted as the scoring basis (including determining the scoring benchmark, file checking efficiency, compilation, checking, and review scores, etc.).

[0199] In a specific embodiment, please refer to Fig.13 , Fig.13It is a specific framework diagram of the file quality detection method based on the design process provided by the embodiment of the present application. Among them, the present application can determine whether the detection file meets the verification convergence based on the difference between the file review error rate of the audit node and the file verification error rate of the verification node. And the convergence evaluation of the present application is a kind of idea or principle, that is, the lower the error rate found later, the lower it should be to meet the quality control law, that is, the error rate found at the audit node should be less than the error rate found at the verification node, and the error rate found at the approval node should be less than the error rate found at the audit node. Based on this idea, in the specific implementation, the convergence is determined by comparing the error rate of the subsequent links of the detection file with the average level (equivalent to the benchmark) during the statistical period of the organization. If it is higher than the benchmark, it is considered to be inconsistent with convergence, and a negative score is assigned to the previous link; if it is lower than the benchmark, it is considered to be consistent with convergence, and a positive score is assigned to the previous link, and the absolute value is the difference between the two (the degree of non-compliance or compliance with convergence). In this way, judging convergence based on the statistical data of the target file group is more convenient for members of the target file group to use big data to determine the relative relationship between personal and organizational quality levels, rather than focusing on the comparison and judgment of the number of errors in adjacent links of a single file, and promoting the file review process to be more in line with the natural law of quality control convergence. For example, although the error rate of the verification of the test file is greater than the error rate of the audit, the error rate of the audit is still higher than the average level of the organization, indicating that the verification object has not fulfilled its responsibilities completely, and more errors can still be found to further improve the convergence of the verification. In this case, it is reasonable to give the editor an overall negative score, which can improve the accuracy of the file quality detection at each node in the design process, thereby effectively avoiding a large number of quality problems in the subsequent implementation.

[0200] It should be noted that the present application can also conduct an organizational quality assessment on the target file group, that is, it is mainly aimed at the design verification quality. Since effective quality process control must be a control trend that converges along the control target, based on this basic principle of quality control, by detecting the convergence of the number of defects found in two adjacent control barriers, the effectiveness of the barrier can be intuitively judged. In the file review process, when the error rate of the verification link is greater than that of the audit link, it is considered to be an effective verification. Therefore, by defining the ratio of the number of effectively verified files of the target file group to the number of all files during the statistical period, the organizational verification convergence rate can be determined. Then the organizational verification convergence rate = Neffective / N, where Neffective represents the number of files that meet the effective verification, and N represents the number of files contained in the target file group.

[0201] In some embodiments, see Fig.14 , Fig.14A system architecture diagram of the design document quality evaluation application is shown. Combined with the design process-based document quality detection method of this application, it can be applied to quality inspection, quality evaluation and performance appraisal. Among them, quality inspection is a stage in which inspectors raise design errors (i.e., document errors) based on the design finished document; quality evaluation (i.e., quality inspection of this application) is a stage in which quality scores are given to designers and organizations based on the quality scores of finished document; performance appraisal is a stage in which data analysis is performed on designers and organizations.

[0202] In some embodiments, after obtaining the detection scores of each detection file at different nodes and the object file quality scores of each detection object, the present application can also adopt a visual method, that is, it can be divided into three parts: personal quality performance (i.e., employee level), office-level quality performance (i.e., office-level level), and institute-level quality performance (i.e., institute-level level) for scoring display. In this way, the quality performance data results and trend curves can be displayed from different levels and dimensions, dynamically updated every month, and the latest evaluation results can be displayed in real time. In addition, a file scoring query function can be provided for individuals and organizations, which is convenient for employees and organizations to trace the source of the score, find problems and make rectifications in time. In addition, the displayed evaluation results can be in the form of lists, curves, change bar charts, etc., without specific limitation.

[0203] The document quality detection method based on the design process provided by the embodiment of the present application proposes a quality evaluation method based on the whole process of writing-checking-reviewing-finished product of the design document, which can accurately locate each document, each design link, and each editor and reviewer, forming a quality performance quantitative evaluation system for the whole process, all elements, and all data of the design document. Based on the design document quality score (i.e., the document preparation score, document check score, and document review score of this application, etc.), the quality level of each link (writing, checking, and review) of each designer involved in the document editing and review can be accurately evaluated, and the editor and reviewer's performance of duties can be monitored, and the editor and reviewer are urged to perform their duties conscientiously. In addition, this application can evaluate the effectiveness of the review of professional organizations of any granularity by checking the convergence rate. And this system can dynamically and accurately display the quality performance of each document, each design link, each editor and reviewer, and each design organization (professional group, room, institute), forming a quality performance quantitative evaluation system for the whole process, all elements, and all data of the design document. Compared with the related technology, it is usually to evaluate the problems in the design process after the quality problems of the design document appear, so that the design documents used for execution still have many quality problems. This application deeply understands the relationship between adjacent nodes, that is, firstly, by determining the target file group at the audit node's target benchmark audit error rate, the test file at the audit node's file audit error rate and file attribute data to perform editing and proofreading quality detection, and obtains the editing and proofreading quantitative score of the test file at the editing and proofreading node, so as to quantitatively evaluate the accuracy of the editing and proofreading node in detecting file errors. Furthermore, based on the editing and proofreading quantitative score and the file verification efficiency, the test file is tested for file quality, and the correlation between the baseline level and the business scope is achieved, which can improve the accuracy of file quality detection at each node in the design process, thereby effectively avoiding a large number of quality problems in subsequent implementation.

[0204] See also Fig.15 , Fig.15 : is a schematic diagram of the structure of a file quality detection system based on the design process provided by an embodiment of the present application. The system can implement the file quality detection method based on the design process of the above embodiment. The system includes:

[0205] The acquisition module 1510 is used to acquire a target file group, the target file group includes a detection file, file error data of the detection file in the file design process, and file attribute data of the detection file; wherein the file design process includes an editing node and an auditing node, the node level of the auditing node is higher than the editing node, and the file error data includes first file error sub-data of the editing node and second file error sub-data of the auditing node;

[0206] A benchmark detection module 1520, configured to perform a file error benchmark detection based on the second file error sub-data and the target file group, and determine a target benchmark audit error rate of the target file group at the audit node;

[0207] The error rate detection module 1530 is used to perform file error rate detection on the detection file based on the second file error sub-data to obtain the file review error rate of the detection file at the review node;

[0208] The editing quality detection module 1540 is used to perform editing quality detection based on the target benchmark review error rate, the file review error rate and the file attribute data, and obtain the editing quantitative score of the detected file at the editing node;

[0209] An efficiency detection module 1550 is used to perform a verification efficiency detection based on the first file error sub-data and the target file group to obtain a file verification efficiency;

[0210] The file quality detection module 1560 is used to perform file quality detection on the detected file based on the editing quantitative score and the file verification efficiency.

[0211] It should be noted that the file quality detection system based on the design process of the embodiment of the present application is used to implement the file quality detection method based on the design process of the above embodiment, and corresponds to the aforementioned file quality detection method based on the design process. Please refer to the aforementioned file quality detection method based on the design process for the specific processing process, which will not be repeated here.

[0212] The embodiment of the present application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the file quality detection method based on the design process in the above embodiment when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0213] See also Fig.16 , Fig.16 The hardware structure of an electronic device according to another embodiment is illustrated. The computer device includes:

[0214] The processor 1610 may be implemented by a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0215] The memory 1620 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1620 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 1620, and the processor 1610 calls and executes the file quality detection method based on the design process of the embodiment of this application;

[0216] Input / output interface 1630, used to implement information input and output;

[0217] Communication interface 1640, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0218] Bus 1650 , which transmits information between various components of the device (e.g., processor 1610 , memory 1620 , input / output interface 1630 , and communication interface 1640 );

[0219] The processor 1610 , the memory 1620 , the input / output interface 1630 , and the communication interface 1640 are connected to each other in communication within the device via the bus 1650 .

[0220] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program, and the computer program is used to enable a computer to execute the file quality detection method based on the design process in the above embodiment.

[0221] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0222] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0223] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0224] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0225] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0226] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0227] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0228] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0229] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0230] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0231] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0232] The preferred embodiments of the present invention are described with reference to the accompanying drawings, but the scope of the present invention is not limited by the above. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A document quality detection method based on the design process, characterized in that: The method comprises: Acquire a target file group, the target file group includes a test file, file error data of the test file in a file design process, and file attribute data of the test file; wherein the file design process includes an editing node and an auditing node, the node level of the auditing node is higher than the editing node, and the file error data includes first file error sub-data of the editing node and second file error sub-data of the auditing node; Performing a file error benchmark detection based on the second file error sub-data and the target file group, and determining a target benchmark audit error rate of the target file group at the audit node; Performing file error rate detection on the detection file based on the second file error sub-data to obtain a file audit error rate of the detection file at the audit node; Performing editing quality detection based on the target benchmark review error rate, the file review error rate and the file attribute data to obtain the editing quantitative score of the detected file at the editing node; Performing verification efficiency detection based on the first file error sub-data and the target file group to obtain a file verification efficiency; The document quality of the inspection document is inspected based on the editing quantitative score and the document verification efficiency.

2. The method according to claim 1, characterized in that: The editing node includes an editing sub-node and a checking sub-node, and the file quality detection of the detection file based on the editing quantitative score and the file checking efficiency includes: Performing a file verification test based on the editing and proofreading quantitative score and the file verification efficiency, and obtaining a file verification score of the detected file at the verification sub-node; Calculating the difference between the editing and proofreading quantitative score and the document verification score to obtain a document verification difference; Obtaining the job level of the preparation object of the detection file and the target file weight of the detection file; Performing object compilation detection based on the compilation object rank and the target file weight to obtain an object compilation parameter, wherein the object compilation parameter is used to indicate the difference between the compilation object rank and the target file weight; A file compilation test is performed based on the object compilation parameter and the file verification difference, and a file compilation score of the detected file at the compilation sub-node is obtained.

3. The method according to claim 2, characterized in that The file design process further includes an approval node, the node level of the approval node is higher than the review node, the file error data further includes third file error sub-data, the third file error sub-data is used to indicate the file error detected by the approval node for the test file, and the method further includes: Performing a file error benchmark detection based on the third file error sub-data and the target file group, and determining a target benchmark annotation error rate of the target file group at the approval node; Performing file error rate detection on the detection file based on the third file error sub-data to obtain a file annotation error rate of the detection file at the approval node; An audit quality test is performed based on the target benchmark annotation error rate, the file annotation error rate and the file attribute data to obtain a file audit score of the tested file at the audit node.

4. The method according to claim 3, characterized in that The target file group includes a detection object, and after performing the audit quality detection based on the target benchmark annotation error rate, the file annotation error rate and the file attribute data to obtain the file audit score of the detection file at the audit node, the method further includes: determining an object compilation score for the detection object based on the document compilation score and the target document group; determining an object verification score for the inspection object based on the file verification score and the target file group; determining an object review score for the detection object based on the file review score and the target file group; Acquire average workload data of the target file group and object workload data of the detection object; Performing workload detection on the detection object based on the object workload data and the average workload data to obtain a workload coefficient; The object file quality is detected based on the object compilation score, the object verification score, the object audit score and the workload coefficient to obtain an object file quality score.

5. The method according to any one of claims 1 to 4, characterized in that: The editing quality detection based on the target benchmark audit error rate, the file audit error rate and the file attribute data to obtain the editing quantitative score of the detected file at the editing node includes: Performing a numerical comparison between the target benchmark audit error rate and the document audit error rate to obtain a numerical comparison result; Determining a quantitative scoring direction of the detection file based on the numerical comparison result; Calculating the error rate difference between the target benchmark audit error rate and the document audit error rate to obtain the document audit error rate difference; The editing and proofreading quality detection is performed based on the quantitative scoring direction, the file attribute data and the file review error rate difference to obtain the editing and proofreading quantitative score.

6. The method according to claim 5, characterized in that The editing and proofreading quality detection based on the quantitative scoring direction, the file attribute data and the file review error rate difference to obtain the editing and proofreading quantitative score includes: Acquire the target file weight, target file type and target file version of the detected file from the file attribute data; Matching weight coefficients based on the quantitative scoring direction and the target file weight to obtain a file weight coefficient; Perform type coefficient matching based on the quantitative scoring direction and the target file type to obtain a file type coefficient; Matching version coefficients based on the quantitative scoring direction and the target file version to obtain a file version coefficient; The file weight coefficient, the file type coefficient and the file version coefficient are multiplied to obtain a target file coefficient; The editing and proofreading quality detection is performed based on the target file coefficient, the quantitative scoring direction and the file review error rate difference to obtain the editing and proofreading quantitative score.

7. The method according to any one of claims 1 to 4, characterized in that: The performing file error benchmark detection based on the second file error sub-data and the target file group to determine the target benchmark audit error rate of the target file group at the audit node includes: Counting the number of pages of all the detected files in the target file group to obtain the number of target file pages; Counting the number of errors in the second file error sub-data of all the detected files in the target file group to obtain a total number of audited errors; The target baseline audit error rate is determined based on the total number of audit errors and the target document page number.

8. A document quality detection system based on the design process, characterized in that: The system comprises: an acquisition module, for acquiring a target file group, wherein the target file group includes a test file, file error data of the test file in a file design process, and file attribute data of the test file; wherein the file design process includes an editing node and an auditing node, the node level of the auditing node is higher than the editing node, and the file error data includes first file error sub-data of the editing node and second file error sub-data of the auditing node; A benchmark detection module, configured to perform a file error benchmark detection based on the second file error sub-data and the target file group, and determine a target benchmark audit error rate of the target file group at the audit node; An error rate detection module, configured to perform a file error rate detection on the detection file based on the second file error sub-data, and obtain a file audit error rate of the detection file at the audit node; An editing quality detection module, used to perform editing quality detection based on the target benchmark review error rate, the file review error rate and the file attribute data, and obtain the editing quantitative score of the detected file at the editing node; An efficiency detection module, configured to perform a verification efficiency detection based on the first file error sub-data and the target file group to obtain a file verification efficiency; The file quality detection module is used to perform file quality detection on the detection file based on the editing quantitative score and the file verification efficiency.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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