Digital intelligent evaluation system for power grid operation based on digital standard

Abstract

The invention is suitable for the technical field, and provides a digital standard-based power grid operation digital intelligent review system, which comprises a platform home page, a digital standard middle table and an intelligent review platform, the platform home page is a general portal accessed by the whole platform and is used for realizing single sign-on and registration; the digital standard middle table is used for checking the data document, automatically searching for a quotation standard, searching for quotation literatures which do not accord with the latest standard in the data document, and pushing latest standard information; and the intelligent review platform is used for carrying out regional information extraction on the file and the picture, carrying out automatic analysis on the extracted regional information, obtaining review data after analysis, and carrying out intelligent review on the review data. According to the method, the submitted data can be automatically analyzed and intelligently reviewed, so that the automation and intelligence of the review process are realized, and the review efficiency and accuracy are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of review data processing, and in particular to a digital intelligent review system for power grid operations based on digital standards. Background Art

[0002] Digitalization of standards refers to the use of digital technology to empower the standards themselves and the entire life cycle of standardization work, create new machine-readable standard forms, innovate flexible, efficient and interactive standard development and implementation processes, and ultimately realize the digitalization and intelligence of standard use.

[0003] Digitalization of standards is a product of technological development and an inevitable choice. With the explosive growth of data and knowledge in the big data era, power grid operations are increasingly inseparable from digitalization. For example, how to acquire, manage, mine, and identify massive amounts of data and transform them into problem-solving knowledge and capabilities to support high-quality business development is a key issue in digital transformation. Big data and artificial intelligence technologies can mine and reveal the inherent relationships in data, unlocking the value inherent in it and supporting intelligent development.

[0004] However, the existing knowledge and capabilities for converting massive amounts of data are mostly achieved through manual retrieval, mining, and revealing the inherent connections of the data. Due to human errors and subjective factors, the results of the review are affected, and the business, data, technology, and standards of various professional fields in the digital production of the power grid are affected. Summary of the Invention

[0005] The present invention provides a digital intelligent review system for power grid operations based on digital standards, which can automatically analyze and intelligently review submitted data, realize the automation and intelligence of the review process, and improve the review efficiency and accuracy.

[0006] The present invention provides a digital standard-based intelligent review system for power grid operations, including a platform homepage, a digital standard middle platform, and an intelligent review platform;

[0007] The platform homepage is the main portal for accessing the entire platform and is used to implement single sign-on and registration;

[0008] The digital standard center is used to check data documents, automatically search for new references to standards, find references in data documents that do not meet the latest standards, and push the latest standard information;

[0009] The intelligent review platform is used to extract regional information from documents and images, and automatically parse the extracted regional information to obtain review data after parsing, and perform intelligent review on the review data.

[0010] Furthermore, the intelligent review of the submitted data includes the following steps:

[0011] Manually create pre-review materials, or obtain the submitted data from the project pre-review on the main network design platform to synchronize pre-review materials;

[0012] Obtain the submitted data, associate it with the document database according to the pre-examination export mechanism, and match the data in the document database with the parsed submitted data;

[0013] Establish a digital standard review model; use the digital standard review model to screen the list of materials that need to be blocked, and output the list of materials that need to be blocked as a blocking minutes document;

[0014] The intelligent review platform displays the records generated for each block, and by viewing the details of the block minutes, it can be determined whether the formal review can be entered;

[0015] If the review fails, the blocked minutes file will be sent back to the main network design platform through the data interface, and the project-related personnel will re-upload the review data on the main network design platform for another preliminary review.

[0016] Furthermore, the screening of the list of materials to be blocked by the digital standard review model includes the following steps:

[0017] Obtain the data in the document database and the parsed data submitted for review, and calculate the first cosine similarity based on the data in the document database and the parsed data submitted for review; take the first N cosine similarities whose values ​​are not 1 in the first cosine similarities to form a sequence VETA; record the maximum cosine similarity in VETA as VMax1; N is a preset value; obtain the data in the document database corresponding to VMax1 as DATA1;

[0018] Obtain the data in the database and calculate the second cosine similarity with DATA1, arrange the cosine similarities in descending order, and take the first N cosine similarities whose values ​​are not 1 and are not in VETA to form the sequence VETB;

[0019] Determine the relevance between DATA1 and the data submitted for review;

[0020] When DATA1 is weakly correlated with the data submitted for review, add the data submitted for review to the list of materials that need to be blocked.

[0021] Furthermore, when judging the correlation between DATA1 and the data submitted for review, set i to a sequence number between 1 and N, and traverse i for judgment. If all cosine similarities in VETA satisfy the conditions VETA(i)≤VETB(i)+VetMate(i), and VETA(i)>VETB(i)-VetMate(i), then it is judged that DATA1 and the data submitted for review are strongly correlated, otherwise they are weakly correlated.

[0022] Among them, VETA(i) is the i-th cosine similarity in VETA, VETB(i) is the i-th cosine similarity in VETB, and VetMate(i) is the correlation trend value between VETA(i) and VETB(i);

[0023] VetMate(i) is calculated using the following formula:

[0024]

[0025] Where j is a variable, TrUPAB(i,j) is the absolute value of the difference between the maximum value of the i-th to j-th cosine similarities in VETA and the maximum value of the i-th to j-th cosine similarities in VETB; TrDOAB(i,j) is the absolute value of the difference between the minimum value of the i-th to j-th cosine similarities in VETA and the minimum value of the i-th to j-th cosine similarities in VETB.

[0026] Furthermore, it also includes generating a digital standardized analysis report; the digital standardized analysis report is a digital standardized analysis report of monthly reports, annual reports, and design quality scores generated based on the block-out minutes file.

[0027] Furthermore, it also includes design quality scoring Ars.

[0028] Furthermore, the design quality score Ars is calculated using the following formula:

[0029] Ars = [the number of data submitted for review that are strongly correlated ÷ (the number of data submitted for review that are strongly correlated + the number of data submitted for review that are weakly correlated)] × 100.

[0030] Furthermore, the intelligent review platform is used to extract regional information from documents and pictures, including seal location extraction of tables, paragraphs, chapters, subsections, signatures and seal areas of documents and pictures.

[0031] Furthermore, the digital standard middle platform includes a standard knowledge base; the standard knowledge base includes a standard library, a term library, a chapter library, an indicator library, full-text data, XML data, a chart library and a map library.

[0032] Furthermore, the intelligent review platform includes standard novelty search, standard indicator retrieval and equipment parameter verification.

[0033] It can be seen from the above technical solutions that the present invention has the following advantages:

[0034] The present invention is composed of a platform homepage, a digital standard middle platform and an intelligent review platform. The platform homepage serves as a unified entrance, supports single sign-on and registration, and provides users with a convenient access method. The digital standard middle platform can verify data documents, automatically check new reference standards, identify and push the latest standard information, thereby ensuring the accuracy and timeliness of the review basis. The intelligent review platform is responsible for extracting and automatically parsing regional information in files and pictures, and conducting intelligent review after obtaining the submitted data, realizing the automation and intelligence of the review process, and improving the efficiency and accuracy of the review. In general, these three modules cooperate with each other to improve the digital level and intelligence of power grid operations, and provide a strong guarantee for the stable operation of the power grid.

[0035] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be learned from the practice of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 Schematic diagram of the power grid operation review system of the present invention.

[0037] Figure 2 Schematic diagram of the intelligent review structure of the present invention. DETAILED DESCRIPTION

[0038] 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 sequential order. 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 "corresponding to" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes 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.

[0039] Example 1

[0040] The implementation method in this embodiment can be implemented in the system, can be implemented in the server, and can also be implemented in the terminal, without specific limitation. The following will introduce the method in this application from the perspective of system implementation. As shown in the figure, a digital standard-based power grid operation digital intelligent review system includes a platform homepage, a digital standard middle platform and an intelligent review platform;

[0041] The platform homepage is the main portal for accessing the entire platform and is used to implement single sign-on and registration;

[0042] The digital standard center is used to check data documents, automatically search for new references to standards, find references in data documents that do not meet the latest standards, and push the latest standard information;

[0043] The intelligent review platform is used to extract regional information from documents and images, and automatically parse the extracted regional information to obtain review data after parsing, and perform intelligent review on the review data.

[0044] When in use, starting with standard PDF, applying OCR technology, and using standard processing tools, the standard PDF file is processed into structured XML data and imported into the backend to generate a knowledge base with the standard library as the core, forming a digital standard middle platform, including chapter library, image library, table library, formula library, term library, etc., to identify and extract the reference relationship and definition content in the standard content, and build a semantic association library;

[0045] Through the query interface on the platform homepage, retrieval, question and answer, calculation and other services are carried out. Based on knowledge association, difference analysis, indicator comparison, intelligent duplication detection, standard evaluation, customized push and other services are automatically carried out with the standard knowledge base within the digital standard center, thus providing reference for bidding and procurement, equipment maintenance, reliability management, indicator standard evaluation, operation risk monitoring, equipment operation management and standard review;

[0046] In addition, through the intelligent review platform, the platform automatically synchronizes the submitted data of the feasibility study, preliminary design, and construction drawing stages in the main network design platform - project pre-review. The platform automatically parses the obtained compressed package data, and the background is associated with the material database according to the pre-review export mechanism, and matches it with the parsed file to form a list of materials that need to be blocked, and automatically outputs the block-out minutes file; block-out record display and block-out minutes return: the platform displays the records generated for each block-out, and users can view the details of the blocked items to determine whether they can enter the formal review. If not passed, the block-out minutes will be directly returned to the main network design platform through the data interface, and the project-related personnel will upload the latest submitted data on the main network design platform. The system will be updated synchronously for a second pre-review.

[0047] Example 2

[0048] This embodiment differs from the first embodiment in that the intelligent review of the submitted data includes the following steps:

[0049] Manually create pre-review materials, or obtain the submitted data from the project pre-review on the main network design platform to synchronize pre-review materials;

[0050] Obtain the submitted data, associate it with the document database according to the pre-examination export mechanism, and match the data in the document database with the parsed submitted data;

[0051] Establish a digital standard review model; use the digital standard review model to screen the list of materials that need to be blocked, and output the list of materials that need to be blocked as a blocking minutes document;

[0052] The intelligent review platform displays the records generated for each block, and by viewing the details of the block minutes, it can be determined whether the formal review can be entered;

[0053] If the review fails, the blocked minutes file will be sent back to the main network design platform through the data interface, and the project-related personnel will re-upload the review data on the main network design platform for another preliminary review.

[0054] Manually create pre-review materials or synchronize the submission data of the main network design platform project pre-review; then obtain the submission data, associate it with the material database according to the pre-review export mechanism, and match the database data with the parsed submission data; then establish a digital standard review model, use this model to filter out the list of materials that need to be blocked, and output it as a block-out minutes file; the intelligent review platform displays the block-out records, and users view the minutes details to determine whether they can enter the formal review. If not, the minutes will be sent back and the submission data will be re-uploaded for pre-review again.

[0055] By linking and matching with the data database, we can better utilize existing data resources, conduct a comprehensive assessment of the submitted data, and reduce review omissions. The generation and return mechanism of the block minutes document allows project-related personnel to promptly understand the problems of the submitted data, make targeted modifications, and improve the quality of the submitted data.

[0056] Example 3

[0057] This embodiment differs from the second embodiment in that screening the list of materials to be blocked by using the digital standard review model includes the following steps:

[0058] Obtain the data in the document database and the parsed data submitted for review, and calculate the first cosine similarity based on the data in the document database and the parsed data submitted for review; take the first N cosine similarities whose values ​​are not 1 in the first cosine similarities to form a sequence VETA; record the maximum cosine similarity in VETA as VMax1; N is a preset value; obtain the data in the document database corresponding to VMax1 as DATA1;

[0059] Obtain the data in the database and calculate the second cosine similarity with DATA1, arrange the cosine similarities in descending order, and take the first N cosine similarities whose values ​​are not 1 and are not in VETA to form the sequence VETB;

[0060] Determine the relevance between DATA1 and the data submitted for review;

[0061] When DATA1 is weakly correlated with the data submitted for review, add the data submitted for review to the list of materials that need to be blocked.

[0062] By calculating cosine similarity and performing complex data analysis and judgment, we can more accurately identify submissions with weak correlations to standard data, thereby screening out materials that need to be rejected and improving the accuracy of the review. At the same time, using cosine similarity and pre-defined judgment rules to quantify data correlation judgments reduces the influence of subjective factors and makes the review results more objective and reliable.

[0063] Example 4

[0064] The difference between this embodiment and the third embodiment is that when judging the relevance between DATA1 and the submitted data, i is set to a sequence number between 1 and N, and i is traversed for judgment. If all cosine similarities in VETA meet the condition VETA(i)≤VETB(i)+VetMate(i), and VETA(i)>

[0065] If VETB(i)-VetMate(i), then DATA1 is judged to be strongly associated with the submitted data, otherwise it is weakly associated;

[0066] Among them, VETA(i) is the i-th cosine similarity in VETA, VETB(i) is the i-th cosine similarity in VETB, and VetMate(i) is the correlation trend value between VETA(i) and VETB(i);

[0067] VetMate(i) is calculated using the following formula:

[0068]

[0069] Where j is a variable, TrUPAB(i,j) is the absolute value of the difference between the maximum value of the i-th to j-th cosine similarities in VETA and the maximum value of the i-th to j-th cosine similarities in VETB; TrDOAB(i,j) is the absolute value of the difference between the minimum value of the i-th to j-th cosine similarities in VETA and the minimum value of the i-th to j-th cosine similarities in VETB.

[0070] Example 5

[0071] This embodiment differs from the fourth embodiment in that it further includes generating a digital standardized analysis report; the digital standardized analysis report is a digital standardized analysis report of monthly reports, annual reports, and design quality scores generated based on the block-out minutes file.

[0072] By generating digital standardized analysis reports, we can deeply explore and analyze the review results, providing strong data support for management decisions. Monthly and annual reports help to control and evaluate the quality of power grid operation design from a long-term perspective, promoting continuous improvement.

[0073] Example 6

[0074] This embodiment differs from the fifth embodiment in that it further includes performing a design quality score Ars.

[0075] The design quality score Ars is calculated using the following formula:

[0076] Ars = [the number of data submitted for review that are strongly correlated ÷ (the number of data submitted for review that are strongly correlated + the number of data submitted for review that are weakly correlated)] × 100.

[0077] The quality scoring formula provides a quantitative basis for evaluating the quality of submitted data, allowing reviewers to intuitively understand the design quality level. At the same time, a clear scoring mechanism is provided so that project-related personnel can improve the submitted data in a targeted manner and promote the improvement of the quality of power grid operation design.

[0078] Example 7

[0079] This embodiment differs from the sixth embodiment in that the intelligent review platform is used to extract regional information from documents and images, including locating and extracting tables, paragraphs, chapters, subsections, signatures, and seals. By locating and extracting various specific areas in documents and images, information extraction is more comprehensive and accurate, providing more accurate data support for subsequent intelligent reviews.

[0080] Example 8

[0081] The difference between this embodiment and embodiment seven is that the digital standard middle platform includes a standard knowledge base; the standard knowledge base includes a standard library, a term library, a chapter library, an indicator library, full-text data, XML data, a chart library and a map library.

[0082] The digital standards platform includes a standards knowledge base, encompassing a standards library, a terminology library, a clause library, an indicator library, full-text data, XML data, a chart library, and a diagram library, collectively forming a comprehensive standards knowledge system. The standards library stores various standard documents; the terminology library specifies the specialized terminology within the standards; the clause library subdivides and stores the clauses within the standards; the indicator library records the requirements for each indicator; the full-text data provides the complete text of the standard documents; the XML data stores the standard content in a structured manner; and the chart library and diagram library, respectively, store the charts and diagrams within the standards. This provides richer data support, providing more comprehensive and accurate data support for the review process, helping to improve the accuracy and reliability of the review.

[0083] Embodiment 9

[0084] This embodiment differs from the eighth embodiment in that the intelligent review platform includes standard novelty search, standard indicator retrieval and equipment parameter verification.

[0085] Standard Novelty Check: Check the standard documents cited in the standard specifications followed in the feasibility study report to be reviewed, intelligently determine whether the cited standard names and numbers are standardized, and whether the cited standards are the latest versions. If not, return the alternative standard names and numbers to the user; through standard novelty check, the standardization and timeliness of the review basis are guaranteed, avoiding review errors caused by the use of outdated or non-standard standards.

[0086] Standard indicator retrieval: Search by indicator name or indicator value. Users can enter keywords or select query conditions to search for relevant indicator data of the device based on different indicators of the device, such as temperature, humidity, voltage, etc. The search returns the corresponding standard catalog information and specific requirements of the indicator; it facilitates users to quickly obtain relevant indicator data of the device, improving the accuracy and convenience of indicator retrieval.

[0087] Equipment parameter verification: Check the compliance of equipment parameters with standards, trace the equipment indicators to standards and verify the compliance of parameters. If there are any non-compliance issues, users will be notified of non-compliance reminders and equipment parameters that meet the standards to confirm whether the performance and quality of the equipment meet the relevant standards and avoid unnecessary failures and damage. Through equipment parameter verification, it is ensured that the performance and quality of the equipment meet the relevant standards, avoiding failures and damage caused by equipment non-compliance, and reducing the risk of equipment operation.

[0088] In summary, the present invention is composed of a platform homepage, a digital standard middle platform, and an intelligent review platform. The platform homepage serves as a unified entrance, supports single sign-on and registration, and provides users with a convenient access method. The digital standard middle platform can verify data documents, automatically search for new reference standards, identify and push the latest standard information, thereby ensuring the accuracy and timeliness of the review basis. The intelligent review platform is responsible for extracting and automatically parsing regional information in files and pictures, and conducting intelligent review after obtaining the submitted data, thereby realizing the automation and intelligence of the review process, improving the efficiency and accuracy of the review, and enhancing the digitalization level and intelligence of power grid operations.

[0089] At the same time, the present invention recognizes and extracts files and pictures; realizes the positioning and extraction of "tables, paragraphs, chapters, sections, signatures, and stamp areas", and reads various review indicator information; establishes a digital standard review model; screens the list of materials that need to be blocked through the digital standard review model, and automatically outputs the list of materials that need to be blocked as a blocking minutes file, and generates digital standardized analysis reports such as monthly reports, annual reports, and design quality scores based on the blocking minutes file; realizes digital standardized intelligent review of feasibility studies, preliminary design and other stages, intelligently extracts review data, and deeply applies the data, and automatically outputs digital standardized analysis reports such as monthly reports, annual reports, and design quality scores to meet the needs of intelligent assisted review.

[0090] In addition, the present invention automatically mines and reveals the inherent correlations of data through big data, releasing the value of big data contained therein, and thus becoming more intelligent.

[0091] Finally, the present invention provides a reference through equipment operation management and standard review, reducing the time and cost of manual review.

[0092] It is understandable that those skilled in the art can, under the guidance of the above embodiments, combine various implementation methods in the above embodiments to obtain technical solutions of multiple implementation methods.

[0093] Those skilled in the art will appreciate that the units of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0094] In the embodiments provided by the present invention, it should be understood that the division of units is merely a logical function division, and there may be other division methods in actual implementation, for example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc.

[0095] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0096] 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 invention, 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. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nly Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc., various media that can store program code.

[0097] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A digital intelligent review system for power grid operations based on digital standards, characterized by: Including the platform homepage, digital standard platform and intelligent review platform; The platform homepage is the main portal for accessing the entire platform and is used to implement single sign-on and registration; The digital standard center is used to check data documents, automatically search for new references to standards, find references in data documents that do not meet the latest standards, and push the latest standard information; The intelligent review platform is used to extract regional information from documents and images, and automatically parse the extracted regional information to obtain review data after parsing, and perform intelligent review on the review data.

2. A digital standard-based intelligent review system for power grid operations according to claim 1, characterized in that: The intelligent review of the submitted data includes the following steps: Manually create pre-review materials, or obtain the submitted data from the project pre-review on the main network design platform to synchronize pre-review materials; Obtain the submitted data, associate it with the document database according to the pre-examination export mechanism, and match the data in the document database with the parsed submitted data; Establish a digital standard review model; use the digital standard review model to screen the list of materials that need to be blocked, and output the list of materials that need to be blocked as a blocking minutes document; The intelligent review platform displays the records generated for each block, and by viewing the details of the block minutes, it can be determined whether the formal review can be entered; If the review fails, the blocked minutes file will be sent back to the main network design platform through the data interface, and the project-related personnel will re-upload the review data on the main network design platform for another preliminary review.

3. The digital standard-based intelligent review system for power grid operations according to claim 1, characterized in that: The method of screening the list of materials to be blocked by the digital standard review model includes the following steps: Obtain the data in the document database and the parsed data submitted for review, and calculate the first cosine similarity based on the data in the document database and the parsed data submitted for review; take the first N cosine similarities whose values ​​are not 1 in the first cosine similarities to form a sequence VETA; record the maximum cosine similarity in VETA as VMax1; N is a preset value; obtain the data in the document database corresponding to VMax1 as DATA1; Obtain the data in the database and calculate the second cosine similarity with DATA1, arrange the cosine similarities in descending order, and take the first N cosine similarities whose values ​​are not 1 and are not in VETA to form the sequence VETB; Determine the relevance between DATA1 and the data submitted for review; When DATA1 is weakly correlated with the data submitted for review, add the data submitted for review to the list of materials that need to be blocked.

4. The digital standard-based intelligent review system for power grid operations according to claim 1, characterized in that: When judging the correlation between DATA1 and the data submitted for review, set i to a sequence number between 1 and N, traverse i for judgment, and if all cosine similarities in VETA meet the conditions VETA(i)≤VETB(i)+VetMate(i), and VETA(i)>VETB(i)-VetMate(i), then it is judged that DATA1 and the data submitted for review are strongly correlated, otherwise they are weakly correlated; Among them, VETA(i) is the i-th cosine similarity in VETA, VETB(i) is the i-th cosine similarity in VETB, and VetMate(i) is the correlation trend value between VETA(i) and VETB(i); VetMate(i) is calculated using the following formula: Where j is a variable, TrUPAB(i,j) is the absolute value of the difference between the maximum value of the i-th to j-th cosine similarities in VETA and the maximum value of the i-th to j-th cosine similarities in VETB; TrDOAB(i,j) is the absolute value of the difference between the minimum value of the i-th to j-th cosine similarities in VETA and the minimum value of the i-th to j-th cosine similarities in VETB.

5. The digital standard-based intelligent review system for power grid operations according to claim 1, characterized in that: It also includes generating digital standardized analysis reports; the digital standardized analysis reports are digital standardized analysis reports of monthly reports, annual reports, and design quality scores generated based on the block-out minutes files.

6. The digital standard-based intelligent review system for power grid operations according to claim 1, characterized in that: It also includes design quality scoring Ars.

7. A digital standard-based intelligent review system for power grid operations according to claim 6, characterized in that: The design quality score Ars is calculated using the following formula: Ars = [the number of data submitted for review that are strongly correlated ÷ (the number of data submitted for review that are strongly correlated + the number of data submitted for review that are weakly correlated)] × 100.

8. The digital standard-based intelligent review system for power grid operations according to claim 1, characterized in that: The intelligent review platform is used to extract regional information from documents and pictures, including seal location extraction of tables, paragraphs, chapters, subsections, signatures and seal areas of documents and pictures.

9. The digital standard-based intelligent review system for power grid operations according to claim 1, characterized in that: The digital standard middle platform includes a standard knowledge base; the standard knowledge base includes a standard library, a term library, a chapter library, an indicator library, full-text data, XML data, a chart library and a map library.

10. The digital standard-based intelligent review system for power grid operations according to claim 1, characterized in that: The intelligent review platform includes standard novelty search, standard indicator retrieval and equipment parameter verification.

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