CN111651409A - Production project archive management system based on deep learning - Google Patents
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Abstract
The invention provides a production project file management system based on deep learning, which comprises a file acquisition module, an automatic assembly module, a search and classification module and a file value identification module. The archive acquisition module acquires archive information and performs compliance verification on the archive. The automatic assembly module realizes intelligent automatic assembly of the archives through artificial intelligence. The searching and classifying module realizes keyword retrieval and provides big data intelligent analysis. The file value identification module realizes intelligent identification of the file value, and the value score provides reference for management decision and daily work. Production project archive management system based on degree of depth study has improved the intelligent management level of utilization ratio and archives of archives resource, reduces administrative cost, guarantees the standardization, integrality and the suitability of production project archives.
Description
Technical Field
The invention relates to a file management system, in particular to a production project file management system based on deep learning.
Background
With the development situation of archive management work, the requirements of enterprises on archive management work, particularly production project archive management, are higher and higher, and how to better manage and utilize production project archive resources to serve various work becomes an important responsibility of archive management work. The management digitization of production project archives supports inadequately at present, and archives compliance inspection is mostly still gone on through the manual type, and archives quantity is numerous, and archives value can't be aassessment, need invest a large amount of manpowers and time, and its standardization, integrality and suitability can not obtain fine assurance. The unstructured data in the production project archive data accounts for about 90%, and the key information data and knowledge are usually hidden in the production project archive data and cannot exert the utilization value of the production project archive data.
In order to solve the problems, the invention develops the research and application of the production project archive management system based on advanced technologies such as artificial intelligence machine learning, improves the utilization rate of archive resources and the intelligent management level of archives, reduces the management cost, and ensures the normative, the integrity and the applicability of the production project archives.
Disclosure of Invention
The invention aims to provide a production project file management system based on deep learning, which utilizes advanced technologies such as artificial intelligence machine learning and the like to improve the utilization rate of file resources and the intelligent management level of files, reduce the management cost and ensure the normalization, integrity and applicability of production project files.
In order to achieve the purpose, the invention adopts the technical scheme that:
production project archive management system based on deep learning, including archives collection module, automatic assembly module, search and classification module, archives value appraisal module, its characterized in that:
the file acquisition module inputs file information and performs compliance verification on files, so that the verification cost is reduced;
the automatic assembly module realizes intelligent and automatic assembly of the files without manual paper assembly, improves the working efficiency of file assembly, and leads out and binds the files according to the needs;
the searching and classifying module realizes keyword retrieval and provides intelligent analysis of big data in real time;
the file value identification module realizes intelligent identification of the file value, and the file value score provides reference for management decision and daily work.
The file acquisition module is provided with a project preparation stage, a project design stage, a project construction (supervision) stage, a project debugging (test) stage and a project acceptance stage, project compliance rules are combed according to management specifications of all stages, a qualified production project file is input to serve as a normalized comparison document, the compliance of the production project file including whether stamping exists or not, whether the file is a red head file or not, time sequence verification and the like is automatically checked, similarity calculation is carried out on the input file content item and the normalized document item, further normalization evaluation is carried out, whether the file is qualified or not is judged, a qualified judgment conclusion is given to the qualified document, and problematic file data are listed to have problems and are alarmed.
The automatic assembly module utilizes the deep learning model to check the project data submitted for acceptance according to the integrality in the compliance rule, the system automatically identifies the archive information (including project type, project name and the like), and according to the identified archive information, the automatic sequencing and sorting are carried out on the assembly templates arranged in the project preparation stage, the project design stage, the project construction (supervision) stage, the project debugging (test) stage and the project acceptance stage, so that the intelligent automatic assembly of the archives is realized, the manual paper assembly is not needed, the working efficiency of the archive assembly is improved, the assembly and binding are exported as required, and the assembly and binding cost is reduced.
The searching and classifying module realizes keyword retrieval, intelligently analyzes content abstract, attribute extraction, label identification and content association, summarizes the association between the searched content and archive information, promotes a series of brand new search queries and provides data support for subsequent intelligent retrieval and analysis. Meanwhile, a big data intelligent analysis report is generated in real time, and a user can check information such as high-frequency word cloud and common problems.
The file value identification module comprises 4 submodules of technical identification, functional identification, typicality identification and time limit identification, carries out value identification on files based on the technical advancement, the functionality, the typicality and the time limit of production project files, sets a weight ratio for the scores of the submodules to obtain the total score of the file value, and provides reference for management decision and daily work according to the score of the file value. And setting a file value destruction standard score, and providing a destruction reminding and suggestion for files with scores lower than the destruction standard value.
The invention has the beneficial effects that:
the production project archive management system based on deep learning provided by the invention solves the difficulties and problems encountered in the current production project archive management, realizes the online verification of archive data compliance, automatic assembly, intelligent search classification and archive value identification, and improves the intelligent management level of production project archives.
Drawings
FIG. 1 is a schematic diagram of a production project archive management system according to the present invention.
FIG. 2 is a flow chart of the execution of the archive collection module of the production project archive management system of the present invention.
FIG. 3 is a flowchart illustrating the execution of the archive value determination module in the production project archive management system according to the present invention.
Detailed Description
In order to explain the implementation manner of the present invention in detail, the technical solutions in the embodiments of the present invention will be fully described below with reference to the accompanying drawings. The features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein.
As shown in figure 1, the production project archive management system based on deep learning comprises an archive collection module, an automatic assembly module, a search and classification module and an archive value identification module, and is characterized in that:
the file acquisition module inputs file information and performs compliance verification on files, so that the verification cost is reduced;
the automatic assembly module realizes intelligent and automatic assembly of the files without manual paper assembly, improves the working efficiency of file assembly, and leads out and binds the files as required to ensure the integrity of the files;
the searching and classifying module realizes keyword retrieval and provides intelligent analysis of big data in real time;
the file value identification module realizes intelligent identification of the file value, and the file value score provides reference for management decision and daily work.
The file acquisition module is provided with a project preparation stage, a project design stage, a project construction (supervision) stage, a project debugging (test) stage and a project acceptance stage, project compliance rules are combed according to management specifications of all stages, a qualified production project file is input to serve as a normalized comparison document, the compliance of the production project file including whether stamping exists or not, whether the file is a red head file or not, time sequence verification and the like is automatically checked, similarity calculation is carried out on the input file content item and the normalized document item, further normalization evaluation is carried out, whether the file is qualified or not is judged, a qualified judgment conclusion is given to the qualified document, and problematic file data are listed to have problems and are alarmed.
The automatic assembly module utilizes the deep learning model to check the project data submitted for acceptance according to the integrality in the compliance rule, the system automatically identifies the archive information (including project type, project name and the like), and according to the identified archive information, the automatic sequencing and sorting are carried out on the assembly templates arranged in the project preparation stage, the project design stage, the project construction (supervision) stage, the project debugging (test) stage and the project acceptance stage, so that the intelligent automatic assembly of the archives is realized, the manual paper assembly is not needed, the working efficiency of the archive assembly is improved, the assembly and binding are exported as required, and the assembly and binding cost is reduced.
The searching and classifying module realizes keyword retrieval, intelligently analyzes content abstract, attribute extraction, label identification and content association, summarizes the association between the searched content and archive information, promotes a series of brand new search queries and provides data support for subsequent intelligent retrieval and analysis. Meanwhile, a big data intelligent analysis report is generated in real time, and a user can check information such as high-frequency word cloud and common problems.
The file value identification module comprises 4 submodules of technical identification, functional identification, typicality identification and time limit identification, carries out value identification on files based on the technical advancement, the functionality, the typicality and the time limit of production project files, sets a weight ratio for the scores of the submodules to obtain the total score of the file value, and provides reference for management decision and daily work according to the score of the file value. And setting a file value destruction standard score, and providing a destruction reminding and suggestion for files with scores lower than the destruction standard value.
As shown in fig. 2, the process of executing the archive collection module in the production project archive management system is as follows:
the file acquisition module is divided into five stages, namely a project preparation stage, a project design stage, a project construction (supervision) stage, a project debugging (test) stage and a project acceptance stage, and the file materials in all the stages are different.
In the project preparation stage, inputting materials such as tree felling (house removal) compensation, project establishment, land pre-examination, permission, site selection, environmental protection, special argument, bidding documents, purchasing contracts, agreements and the like;
in the project design stage, materials such as geological survey, surveying and mapping, preliminary design, design change, engineering contact list, as well as after-completion design service and the like are input;
recording materials such as material acceptance of equipment, safety protocols, safety technology delivery (three-party delivery), construction schemes, main equipment unpacking inspection application tables, unpacking acceptance inspection record tables, supervision plans, supervision records, supervision audit and the like in a project construction (supervision) stage;
inputting materials such as a communication equipment debugging report and a primary equipment debugging report and a secondary equipment debugging report in a project debugging (testing) stage;
and special acceptance materials such as engineering, fire protection, environmental protection, test operation, quality evaluation, completion drawing and the like are input in the project acceptance stage.
The system firstly automatically checks the compliance of the production project files including whether the files are stamped, whether the files are red-headed files, time sequence verification and the like, secondly calculates the similarity of the content items of the input files and the normalized file items, further evaluates the normalization of the files, judges whether the files are qualified, gives qualified judgment conclusion to the qualified files, and lists the problematic file data and carries out alarm reminding.
As shown in fig. 3, the archive value determination module of the production project archive management system performs the following processes:
the file value identification module comprises 4 sub-modules of technical identification, functional identification, typicality identification and time limit identification.
The technical evaluation module is used for researching and analyzing the technical level of the archive content of the production project, comparing and analyzing the technical level of the archive content according to the prior art, judging the universality and the applicability of the technical requirement, and giving a value evaluation score according to the technical level of the archive content.
The function identification module is used for identifying the specific function of the production project archive and identifying the value of the production project archive according to the specific requirement of a storage unit on the object value of the production project archive. For example, the value of the production project is judged according to the relation between the production project and the building entity and the equipment entity and the specific function of the project and the objective requirement, and a function appraisal value score is given according to the value.
The representativeness module fully considers the representative meanings of the production project archive, particularly the representative meanings of the production project archive in the technical aspect and the historical aspect so as to ensure the scientificity and correctness of the identification of the production project archive. The production project archives with typical or representative significance in the science and technology and production development history of the society and enterprises belong to high-value archives, which reflect the historical appearance of the science and technology and production development of the society and enterprises, and the production project archives without typical values are sequentially judged to be scored according to the typicality and the representativeness.
The time limit identification module sets an age limit score standard according to the file management requirements of enterprises, and the longer the age limit is and the longer the time is, the lower the score is, the time limit value score of the production project files is given according to the time length.
The file value identification module carries out value identification on the files based on the technical advancement, functionality, typicality and time limit of the production project files, sets weight proportion on the scores of all the sub-modules to obtain the total score of the file value, and provides reference for management decision and daily work according to the score of the file value. And setting a file value destruction standard score, and providing a destruction reminding and suggestion for files with scores lower than the destruction standard value.
In summary, compared with the prior art, the production project archive management system based on deep learning provided by the invention has the following advantages and beneficial effects:
the invention solves the difficulties and problems encountered in the production project archive management at present, realizes the online checking of the archive data compliance, automatic assembly, intelligent search classification and archive value identification, and improves the intelligent management level of the production project archive.
While the present invention has been described in detail with reference to the preferred embodiments, it should be understood that the above description should not be taken as limiting the invention. Various modifications and alterations to this invention will become apparent to those skilled in the art upon reading the foregoing description. Accordingly, the scope of the invention should be determined from the following claims.
Claims (5)
1. The production project file management system based on deep learning comprises a file acquisition module, an automatic assembly module, a search and classification module and a file value identification module, and is characterized in that the file acquisition module acquires file information and performs compliance verification on files; the automatic assembly module realizes automatic assembly of the files and leads out and binds the files according to the needs; the searching and classifying module realizes keyword retrieval and provides intelligent analysis of big data; the file value identification module realizes intelligent identification of the file value, and the value score provides reference for management decision and daily work.
2. The file acquisition module of claim 1, which is configured to perform a project preparation phase, a project design phase, a project construction (supervision) phase, a project debugging (testing) phase and a project acceptance phase, wherein project compliance rules are combed according to management specifications of the phases, a qualified production project file is recorded as a normalized comparison document, the compliance of the production project file including whether or not there is a seal, whether the file is a red header file, time sequence verification and the like is automatically checked, similarity calculation is performed on the recorded file content items and the normalized document items, further normalization evaluation is performed, whether the file is qualified is determined, a qualified determination conclusion is given to the qualified document, and problematic file data is listed as having problems and is subjected to alarm reminding.
3. The automatic assembly module as claimed in claim 1 checks the integrity of the project data submitted for acceptance check according to the compliance rules, automatically identifies the archive information (such as project type, project name, etc.), and automatically sorts and arranges the assembly templates according to the identified archive information, and the assembly templates set in the project preparation stage, project design stage, project construction (supervision) stage, project debugging (experiment) stage and project acceptance check stage, so as to automatically assemble the archive, and export the binding as required.
4. The search and classification module of claim 1 implements keyword retrieval, performs content abstraction, attribute extraction, tag identification and content association, and generates a big data intelligent analysis report in real time for a user to view information such as high frequency word clouds, common problems, and the like.
5. The archive value identification module of claim 1, comprising 4 submodules of technical identification, functional identification, typicality identification and time limit identification, wherein the value identification is carried out on the archive based on the technical advancement, functionality, typicality and time limit of the production project archive, the scores of all the submodules are set with a weight ratio to obtain an overall score of the archive value, reference is provided for management decision and daily work, a score of an archive value destruction standard is set, and a destruction prompt and suggestion are provided for the archive with the score lower than the destruction standard.
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| CN116932463A (en) * | 2023-09-13 | 2023-10-24 | 浙江星汉信息技术股份有限公司 | Intelligent identification method and system for files |
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