Engineering data management system and method based on artificial intelligence

Abstract

The invention discloses an artificial intelligence-based engineering data management system and method, and relates to the technical field of engineering data management, and the system comprises an engineering data collection module, a data content recognition module, a system database, a key label preset module, a correlation coefficient analysis module and an engineering data sorting module. The engineering data acquisition module is used for importing engineering data documents, identifying predicted keywords of the engineering data documents, marking keyword tags and key content tags on the engineering data documents, analyzing correlation coefficients of the keywords appearing in any engineering data document and the engineering data documents, screening the keywords by considering the correlation coefficients, and storing the screened keywords in the engineering data documents; according to the method, the common coefficient of any engineering data document is considered, the correlation coefficient of the keyword to any engineering data document is considered, the engineering data documents are sorted and pushed to the user according to the sequence, the influence of interference vocabularies on the engineering data documents is considered, the stability of keyword judgment is enhanced, and the workload of document retrieval is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering data management, and in particular to an engineering data management system and method based on artificial intelligence. Background Art

[0002] Engineering data is a general term for various documents and information involved in recording and describing the entire process of engineering projects from design, construction to completion. These materials usually include design drawings, construction plans, construction records, quality inspection reports and acceptance certificates, etc., which are important bases for engineering project management. Among the numerous engineering materials, it is particularly difficult to organize text materials. Considering that engineering involves a wide range of professional aspects and a large number of professional terms, engineering data management often requires a lot of manpower and time costs.

[0003] There is a lack of an engineering data management system based on artificial intelligence on the market. On the one hand, due to the large amount of engineering data and the wide range of professional aspects involved, it is difficult to organize the data by manpower, and there is also the possibility of human error. On the other hand, the existing system lacks analysis of interference statements, such as: excluding certain personnel, excluding certain equipment, or before a certain stage of the project. These interference statements will interfere with the system's analysis of engineering data documents and increase the workload of users to retrieve documents. On the other hand, even if the anti-interference requirements are met, when users search for keywords, it is difficult to take into account the number of times the engineering data documents are used, and it is difficult to recommend the most suitable engineering data documents for users to view.

[0004] Therefore, an engineering data management system and method based on artificial intelligence is needed to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide an engineering data management system and method based on artificial intelligence to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: an engineering data management system based on artificial intelligence, the system comprising: an engineering data collection module, a data content recognition module, a system database, a key tag preset module, a correlation coefficient analysis module and an engineering data sorting module; Importing engineering data documents into the system through the engineering data collection module; Identify the expected keywords of the engineering document by the document content identification module; Using the key tag preset module to add keyword tags and key content tags to the engineering data document; Analyze the correlation coefficient between the keywords appearing in any engineering data document and the engineering data document through the correlation coefficient analysis module, and select the keywords considering the correlation coefficient; The engineering data sorting module considers the common coefficient of any engineering data document, considers the correlation coefficient of the keyword to any engineering data document, sorts the engineering data documents and pushes them to the user according to the sorting.

[0007] Furthermore, the engineering data collection module imports engineering data documents into the system by scanning, taking photos or importing files, and saves them in the database; the data content recognition module identifies engineering data documents by text or image recognition, and specially marks the expected keywords in the engineering data documents by comparing them with preset focus words. The preset focus words are preset by the user in the system and saved in the system database. The database also stores related words and interference words of any preset focus words set by the user, and the number of times any engineering data document is used.

[0008] Furthermore, the key tag preset module calculates the number of occurrences of any expected keyword from the engineering data document through special mark identification. When the number of occurrences of any expected keyword is lower than the keyword judgment threshold, the key tag preset module determines that the expected keyword does not belong to the keyword of the engineering data document. When the number of occurrences of any expected keyword is higher than the keyword judgment threshold, the key tag preset module determines that the expected keyword belongs to the keyword of the engineering data document, converts the expected keyword into a keyword entry keyword tag, presets a key content tag for the entered keyword tag, and enters the number of occurrences of the keyword in the preset key content tag.

[0009] Furthermore, the correlation coefficient analysis module analyzes any keyword, considers the number of words between the keyword and related words appearing in the engineering data document, comprehensively considers the number of words between the keyword and interference words, calculates the correlation coefficient between the keyword and the engineering data document, and deletes the keyword tag of the keyword when the correlation coefficient of any keyword does not meet the system requirements; when the correlation coefficient of any keyword meets the system requirements, the keyword tag of the keyword is retained, and the correlation coefficient is recorded in the key content tag.

[0010] Furthermore, the engineering data sorting module monitors whether it is in a keyword search state. When a user searches for any keyword, it calls the engineering data document with the keyword tag, calls the correlation coefficient of the keyword in the key content tag to the engineering data document, calls the common coefficient of any engineering data document in the database, sorts the engineering data document and pushes it to the user, and updates the usage count of the engineering data document in the database.

[0011] An engineering data management method based on artificial intelligence comprises the following steps: S1: Import engineering data documents into the system by scanning, taking photos or importing files, and save them in the database; S2: storing preset focus words, related words of the preset focus words and interference words in a database, comparing the preset focus words, and identifying expected keywords in the engineering data document; S3: Considering the number of occurrences of any expected keyword, tag the engineering data document with a keyword tag and a key content tag; S4: analyzing the correlation coefficient between the keywords appearing in any engineering data document and the engineering data document, and screening the keywords considering the correlation coefficient; S5: When a user searches for any keyword, the common coefficient of any engineering document and the correlation coefficient of the keyword to any engineering document are considered, and the engineering document is sorted and pushed to the user according to the sort.

[0012] Furthermore, in step S2, the preset focus words, related words of the preset focus words and interference words are stored in the database, the engineering document is identified by text or image recognition, and the I expected keywords existing in the engineering document are found by comparing the preset focus words, and the I expected keywords existing in the engineering document are specially marked as expected keywords {A1, A2, ..., A I The preset focus words include: project stage, related equipment, responsible personnel and completion time. Project data is a general term for recording and describing various documents and information involved in the entire process of a project from design, construction to completion. Setting preset keywords is conducive to the preliminary analysis of project data documents and reduces the workload when screening keywords.

[0013] Further, in step S3, any engineering document is analyzed, and a judgment threshold i0 of any keyword is set in the system, and any predicted keyword A is i Perform analysis, where i=1,2,…,I, and count the expected keywords A in the engineering data document i The number of i1 is compared with i0. When i1<i0, delete the expected keyword A. i , when i1≥i0, the engineering document is tagged with the keyword B=A i , and for keyword B i Record the quantity in the key content tag and substitute i=1,2,…,I one by one to obtain the keywords of the engineering data document {B1,B2,…,B P}, enter the keyword tag, where keyword B p Corresponding key content label C p , C p Indicates B p In the engineering document, C ptimes, p=1,2,…,P, write keyword tags and key content tags in engineering data documents to reduce the database burden and increase the reliability of keyword data through classified storage.

[0014] Furthermore, for any keyword B in any engineering document p Perform analysis and call B in the database p The related words are {p1,p2,…,p D}, B p The interference words are {q1,q2,…,q E}, where the related words appear in engineering data documents F d The interference words appeared in the engineering data document G times. e Second, search with C p Keyword B p The related words with the least number of words between them are recorded, and the number of words between the related words and the keywords is calculated as H. p ; Search and C p Keyword B p The interference word with the least number of words between the interference word and the keyword is recorded, which is represented by {J1,J2,…,J Cp}, calculate keyword B p Correlation coefficient K with engineering data documents p : ; Where H is the number of words set by the system to determine whether the related words and keywords are adjacent. The minimum number of characters between interference words and keywords set by the system. p Correlation coefficient K with engineering data documents p ≤0, the system determines keyword B p The correlation coefficient with the engineering data document is low, so delete the keyword B in the keyword tag. p , when keyword B p Correlation coefficient K with engineering data documents p >0, the system determines keyword B p The correlation coefficient with the engineering data document is high, and the keyword B in the keyword tag is retained. p , and record keyword B in the key content tag p Correlation coefficient K for engineering data documents p , comprehensively consider the interference degree and relevance degree of all keywords appearing in the engineering materials, calculate the correlation coefficient between the keywords and the engineering materials, reduce the interference of interference words such as "except", "not including" and "before" on the engineering materials keyword analysis, and reduce the workload of users searching for engineering materials.

[0015] Further, in step S5, when the user searches for any keyword B p When searching for keyword tags containing keyword B p M engineering data documents, marked as {G1, G2, …, G M}, call the engineering data document G m Number of times used R m , keyword B p Engineering Data Document G m The correlation coefficient K m ,m=1,2,…,M,calculation engineering data file G m For keyword B p The ranking coefficient U m_p : ; Substitute m=1,2,…,M one by one, and calculate the M engineering data documents for keyword B p The ranking coefficients are {U 1_p ,U 2_p ,…,U M_p}, sort the M engineering data documents from high to low according to the sorting coefficients, and push them to the user one by one.

[0016] Compared with the prior art, the beneficial effects achieved by the present invention are: on the one hand, considering the large amount of engineering data and the wide range of professional aspects involved, it is difficult to organize the data by manpower, and an artificial intelligence engineering data management method is provided to reduce the cost of organizing data by manpower and reduce human errors. On the one hand, considering the impact of interference words on engineering data documents, interference words such as "except", "not including" and "before", etc., keywords are analyzed and screened, which enhances the stability of the system's judgment on keywords of engineering data documents and reduces the workload of users in retrieving documents. On the other hand, while meeting the anti-interference requirements, when users search for keywords, a recommended sorting method for engineering data documents is provided to facilitate users to find the required documents as quickly as possible. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 It is a structural diagram of an engineering data management system based on artificial intelligence of the present invention; Figure 2 The present invention is a flowchart of an engineering data management method based on artificial intelligence. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] See also Figure 1 and Figure 2 , the present invention provides a technical solution: an engineering data management system based on artificial intelligence, the system includes: an engineering data collection module, a data content recognition module, a system database, a key label preset module, a correlation coefficient analysis module and an engineering data sorting module; Importing engineering data documents into the system through the engineering data collection module; Identify the expected keywords of the engineering document by the document content identification module; Using the key tag preset module to add keyword tags and key content tags to the engineering data document; Analyze the correlation coefficient between the keywords appearing in any engineering data document and the engineering data document through the correlation coefficient analysis module, and select the keywords considering the correlation coefficient; The engineering data sorting module considers the common coefficient of any engineering data document, considers the correlation coefficient of the keyword to any engineering data document, sorts the engineering data documents and pushes them to the user according to the sorting.

[0020] The engineering data collection module imports engineering data documents into the system by scanning, taking photos or importing files, and saves them in the database; the data content recognition module identifies engineering data documents by text or image recognition, and specially marks the expected keywords in the engineering data documents by comparing them with preset focus words. The preset focus words are preset by the user in the system and saved in the system database. The database also stores related words and interference words of any preset focus word set by the user, and the number of times any engineering data document is used.

[0021] The key tag preset module calculates the number of occurrences of any expected keyword from the engineering data document through special mark identification. When the number of occurrences of any expected keyword is lower than the keyword judgment threshold, the key tag preset module determines that the expected keyword does not belong to the keyword of the engineering data document. When the number of occurrences of any expected keyword is higher than the keyword judgment threshold, the key tag preset module determines that the expected keyword belongs to the keyword of the engineering data document, converts the expected keyword into a keyword entry keyword tag, presets a key content tag for the entered keyword tag, and enters the number of occurrences of the keyword in the preset key content tag.

[0022] The correlation coefficient analysis module analyzes any keyword, considers the number of words between the keyword and related words appearing in the engineering data document, comprehensively considers the number of words between the keyword and interference words, calculates the correlation coefficient between the keyword and the engineering data document, and deletes the keyword tag of the keyword when the correlation coefficient of any keyword does not meet the system requirements; when the correlation coefficient of any keyword meets the system requirements, the keyword tag of the keyword is retained, and the correlation coefficient is recorded in the key content tag.

[0023] The engineering data sorting module monitors whether it is in the keyword search state. When the user searches for any keyword, it calls the engineering data document with the keyword tag, calls the correlation coefficient of the keyword in the key content tag to the engineering data document, calls the common coefficient of any engineering data document in the database, sorts the engineering data document and pushes it to the user. At this time, the number of times the engineering data document is used in the database is updated.

[0024] An engineering data management method based on artificial intelligence comprises the following steps: S1: Import engineering data documents into the system by scanning, taking photos or importing files, and save them in the database; S2: storing preset focus words, related words of the preset focus words and interference words in a database, comparing the preset focus words, and identifying expected keywords in the engineering data document; S3: Considering the number of occurrences of any expected keyword, tag the engineering data document with a keyword tag and a key content tag; S4: analyzing the correlation coefficient between the keywords appearing in any engineering data document and the engineering data document, and screening the keywords considering the correlation coefficient; S5: When a user searches for any keyword, the common coefficient of any engineering document and the correlation coefficient of the keyword to any engineering document are considered, and the engineering documents are sorted and pushed to the user according to the sorting. In step S2, the preset focus words, related words of the preset focus words and interference words are stored in the database, the engineering document is identified by text or image recognition, and the I expected keywords existing in the engineering document are found by comparing the preset focus words, and the I expected keywords existing in the engineering document are specially marked, and are marked as expected keywords {A1, A2, ..., A I The preset focus words include: project stage, related equipment, responsible personnel and completion time. Project data is a general term for recording and describing various documents and information involved in the entire process of a project from design, construction to completion. Setting preset keywords is conducive to the preliminary analysis of project data documents and reduces the workload when screening keywords.

[0025] In step S3, any engineering document is analyzed, and a judgment threshold i0 of any keyword is set in the system, and any predicted keyword A is i Perform analysis, where i=1,2,…,I, and count the expected keywords A in the engineering data document i The number of i1 is compared with i0. When i1<i0, delete the expected keyword A. i , when i1≥i0, the engineering document is tagged with the keyword B=A i , and for keyword B i Record the quantity in the key content tag and substitute i=1,2,…,I one by one to obtain the keywords of the engineering data document {B1,B2,…,B P}, enter the keyword tag, where keyword B p Corresponding key content label C p , C p Indicates B p In the engineering document, C p times, p=1,2,…,P, write keyword tags and key content tags in engineering data documents to reduce the database burden and increase the reliability of keyword data through classified storage.

[0026] In step S4, any keyword B in any engineering document p Perform analysis and call B in the database p The related words are {p1,p2,…,p D}, B p The interference words are {q1,q2,…,q E}, where the related words appear in engineering data documents F d The interference words appeared in the engineering data document G times. e Second, search with C p Keyword B p The related words with the least number of words between them are recorded, and the number of words between the related words and the keywords is calculated as H. p ; Search and C p Keyword B p The interference word with the least number of words between the interference word and the keyword is recorded, which is represented by {J1,J2,…,J Cp}, calculate keyword B p Correlation coefficient K with engineering data documents p : ; Where H is the number of words set by the system to determine whether the related words and keywords are adjacent. The minimum number of characters between interference words and keywords set by the system. p Correlation coefficient K with engineering data documents p ≤0, the system determines keyword B p The correlation coefficient with the engineering data document is low, so delete the keyword B in the keyword tag. p , when keyword B p Correlation coefficient K with engineering data documents p >0, the system determines keyword B p The correlation coefficient with the engineering data document is high, and the keyword B in the keyword tag is retained. p , and record keyword B in the key content tag p Correlation coefficient K for engineering data documents p , comprehensively consider the interference degree and relevance degree of all keywords appearing in the engineering materials, calculate the correlation coefficient between the keywords and the engineering materials, reduce the interference of interference words such as "except", "not including" and "before" on the engineering materials keyword analysis, and reduce the workload of users searching for engineering materials.

[0027] In step S5, when the user searches for any keyword B p When searching for keyword tags containing keyword B p M engineering data documents, marked as {G1, G2, …, G M}, call the engineering data document G m Number of times used R m , keyword B p Engineering Data Document G m The correlation coefficient K m ,m=1,2,…,M,calculation engineering data file G m For keyword B p The ranking coefficient U m_p : ; Substitute m=1,2,…,M one by one, and calculate the M engineering data documents for keyword B p The ranking coefficients are {U 1_p ,U 2_p ,…,U M_p}, sort the M engineering data documents from high to low according to the sorting coefficients, and push them to the user one by one.

[0028] Example 1: For any keyword B in any engineering document p Perform analysis and call B in the database p The related words are {p1,p2,…,p D}, B pThe interference words are {q1,q2,…,q E}, where the related words appear in engineering data documents F d = 30 times, the interference words appeared in the engineering data document G e = 20 times, search with C p =10 keywords B p The related words with the least number of words between them are recorded, and the number of words between the related words and the keywords is calculated as H. p =10; Search and C p =10 keywords B p The interference word with the least number of words between the interference word and the keyword is recorded, which is represented by {J1,J2,…,J Cp}, calculate keyword B p Correlation coefficient K with engineering data documents p : ; Where H is the number of words set by the system to determine whether the related words and keywords are adjacent. The minimum number of characters between interference words and keywords set by the system. p Correlation coefficient K with engineering data documents p >0, the system determines keyword B p The correlation coefficient with the engineering data document is high, and the keyword B in the keyword tag is retained. p , and record keyword B in the key content tag p Correlation coefficient K for engineering data documents p .

[0029] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An engineering data management system based on artificial intelligence, characterized by: The system includes: an engineering data collection module, a data content identification module, a system database, a key tag preset module, a correlation coefficient analysis module and an engineering data sorting module; Importing engineering data documents into the system through the engineering data collection module; Identify the expected keywords of the engineering document by the document content identification module; Using the key tag preset module to add keyword tags and key content tags to the engineering data document; Analyze the correlation coefficient between the keywords appearing in any engineering data document and the engineering data document through the correlation coefficient analysis module, and select the keywords considering the correlation coefficient; The engineering data sorting module considers the common coefficient of any engineering data document, considers the correlation coefficient of the keyword to any engineering data document, sorts the engineering data documents and pushes them to the user according to the sorting.

2. The engineering data management system based on artificial intelligence according to claim 1, characterized in that: The engineering data collection module imports engineering data documents into the system by scanning, taking photos or importing files, and saves them in the database; the data content recognition module identifies engineering data documents by text or image recognition, and specially marks the expected keywords in the engineering data documents by comparing them with preset focus words. The preset focus words are preset by the user in the system and saved in the system database. The database also stores related words and interference words of any preset focus word set by the user, and the number of times any engineering data document is used.

3. The engineering data management system based on artificial intelligence according to claim 1 is characterized by: The key tag preset module calculates the number of occurrences of any expected keyword from the engineering data document through special mark identification. When the number of occurrences of any expected keyword is lower than the keyword judgment threshold, the key tag preset module determines that the expected keyword does not belong to the keyword of the engineering data document. When the number of occurrences of any expected keyword is higher than the keyword judgment threshold, the key tag preset module determines that the expected keyword belongs to the keyword of the engineering data document, converts the expected keyword into a keyword entry keyword tag, presets a key content tag for the entered keyword tag, and enters the number of occurrences of the keyword in the preset key content tag.

4. The engineering data management system based on artificial intelligence according to claim 1 is characterized by: The correlation coefficient analysis module analyzes any keyword, considers the number of words between the keyword and related words appearing in the engineering data document, comprehensively considers the number of words between the keyword and interference words, calculates the correlation coefficient between the keyword and the engineering data document, and deletes the keyword tag of the keyword when the correlation coefficient of any keyword does not meet the system requirements; when the correlation coefficient of any keyword meets the system requirements, the keyword tag of the keyword is retained, and the correlation coefficient is recorded in the key content tag.

5. The engineering data management system based on artificial intelligence according to claim 1 is characterized by: The engineering data sorting module monitors whether it is in the keyword search state. When the user searches for any keyword, it calls the engineering data document with the keyword tag, calls the correlation coefficient of the keyword in the key content tag to the engineering data document, calls the common coefficient of any engineering data document in the database, sorts the engineering data document and pushes it to the user. At this time, the number of times the engineering data document is used in the database is updated.

6. An engineering data management method based on artificial intelligence, characterized by: The following steps are involved: S1: Import engineering data documents into the system by scanning, taking photos or importing files, and save them in the database; S2: storing preset focus words, related words of the preset focus words and interference words in a database, comparing the preset focus words, and identifying expected keywords in the engineering data document; S3: Considering the number of occurrences of any expected keyword, tag the engineering data document with a keyword tag and a key content tag; S4: analyzing the correlation coefficient between the keywords appearing in any engineering data document and the engineering data document, and screening the keywords considering the correlation coefficient; S5: When a user searches for any keyword, the common coefficient of any engineering document and the correlation coefficient of the keyword to any engineering document are considered, and the engineering document is sorted and pushed to the user according to the sort.

7. The method for managing engineering data based on artificial intelligence according to claim 6, characterized in that: In step S2, the preset focus words, related words and interference words of the preset focus words are stored in the database, the engineering document is identified by text or image recognition, and the I expected keywords existing in the engineering document are found by comparing the preset focus words, and the I expected keywords existing in the engineering document are specially marked as expected keywords {A1, A2, ..., A I }.

8. The method for managing engineering data based on artificial intelligence according to claim 7, characterized in that: In step S3, any engineering document is analyzed, and a judgment threshold i0 of any keyword is set in the system, and any predicted keyword A is i Perform analysis, where i=1,2,…,I, and count the expected keywords A in the engineering data document i The number of i1 is compared with i0. When i1<i0, delete the expected keyword A. i , when i1≥i0, the engineering document is tagged with the keyword B=A i , and for keyword B i Record the quantity in the key content tag and substitute i=1,2,…,I one by one to obtain the keywords of the engineering data document {B1,B2,…,B P }, enter the keyword tag, where keyword B p Corresponding key content label C p , C p Indicates B p In the engineering document, C p times, p=1,2,…,P.

9. The method for managing engineering data based on artificial intelligence according to claim 7, characterized in that: In step S4, any keyword B in any engineering document p Perform analysis and call B in the database p The related words are {p1,p2,…,p D }, B p The interference words are {q1,q2,…,q E }, where the related words appear in engineering data documents F d The interference words appeared in the engineering data document G times. e Second, search with C p Keyword B p The related words with the least number of words between them are recorded, and the number of words between the related words and the keywords is calculated as H. p ; Search and C p Keyword B p The interference word with the least number of words between the interference word and the keyword is recorded, which is represented by {J1,J2,…,J Cp }, calculate keyword B p Correlation coefficient K with engineering data documents p : ； Where H is the number of words set by the system to determine whether the related words and keywords are adjacent. The minimum number of characters between interference words and keywords set by the system. p Correlation coefficient K with engineering data documents p ≤0, the system determines keyword B p The correlation coefficient with the engineering data document is low, so delete the keyword B in the keyword tag. p , when keyword B p Correlation coefficient K with engineering data documents p >0, the system determines keyword B p The correlation coefficient with the engineering data document is high, and the keyword B in the keyword tag is retained. p , and record keyword B in the key content tag p Correlation coefficient K for engineering data documents p .

10. The method for managing engineering data based on artificial intelligence according to claim 8, characterized in that: In step S5, when the user searches for any keyword B p When searching for keyword tags containing keyword B p M engineering data documents, marked as {G1, G2, …, G M }, call the engineering data document G m Number of times used R m , keyword B p Engineering Data Document G m The correlation coefficient K m ,m=1,2,…,M,calculation engineering data file G m For keyword B p The ranking coefficient U m_p : ； Substitute m=1,2,…,M one by one, and calculate the M engineering data documents for keyword B p The ranking coefficients are {U 1_p ,U 2_p ,…,U M_p }, sort the M engineering data documents from high to low according to the sorting coefficients, and push them to the user one by one.

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