LU600407B1

LU600407B1 - System for construction engineering archive data management

Info

Publication number: LU600407B1
Application number: LU600407A
Authority: LU
Inventors: Manli Ou
Original assignee: Univ Hunan Technology
Priority date: 2025-02-27
Filing date: 2025-02-27
Publication date: 2025-08-27

Abstract

The system for construction engineering archive data management, including: a sequentially connected data collection module, a data governance module, a data catalog management module, a data analysis module, a data management and a service module; the data collection module is used to extract electronic data from paper archives of construction engineering projects and to aggregate the data extracted from paper archives and data from electronic archives to generate archive data; the data governance module is used to validate the archive data and, based on the validation results, generate engineering data for the construction engineering project; the data catalog management module is used to organize and catalog the engineering data of the construction engineering project to generate a data resource library catalog; the data analysis module is used to analyze the engineering data to generate corresponding project warning results and an associated data map of the project.

Description

DESCRIPTION LU600407

SYSTEM FOR CONSTRUCTION ENGINEERING ARCHIVE DATA MANAGEMENT

TECHNICAL FIELD

The invention belongs to the field of information management technology, and particularly relates to a system for construction engineering archive data management.

BACKGROUND

Digitalization is an emerging technology with significant development potential. The core of digital transformation in the construction industry lies in integrating advanced digital technologies into various fields of the industry, reshaping its operational methods, management models, and innovation paradigms. The significance of digital development in the construction industry is mainly reflected in the following aspects: improving the operational efficiency of related enterprises, reducing costs, enhancing market competitiveness, and optimizing partner relationships.

With the continuous development and application of technologies such as big data and artificial intelligence, as well as the digital development of the construction industry, the role of engineering data elements in the construction industry has become increasingly prominent, and research in this area continues to deepen. Currently, the application of engineering data elements in the construction industry mainly focuses on the following aspects: (1) Data collection and integration: by collecting and integrating data from various stages of construction projects, a comprehensive and accurate engineering database is established, providing a foundation for subsequent data analysis and mining. (2) Data analysis and mining: using big data technology to analyze and mine engineering data, revealing correlations and patterns in the data, and providing data support for project management and decision-making. (3) Data application and practice: applying the results of data analysis to actual engineering projects to optimize project management processes and improve project quality and efficiency.

However, there are still issues with the application of engineering data elements in the construction industry, such as inconsistent data standards, uneven data quality, and ineffective data utilization, which hinder the effective collection, integration, analysis, and further application of engineering data elements in the construction industry.

These issues constrain the further management and application of digital data in tn 600407 construction industry, necessitating an information management system to address these problems and further enhance the management and application of digital data in the construction industry.

SUMMARY

To solve the above technical problems, the present invention proposes an information management system for construction engineering to address the issues in the existing technology.

To achieve the above objective, the present invention provides an information management system for construction engineering, including: sequentially connected data collection module, a data governance module, a data catalog management module, a data analysis module, a data management and a service module; the data collection module is used to extract electronic data from paper archives of construction engineering projects and to aggregate the data extracted from paper archives and data from electronic archives to generate archive data; the data governance module is used to validate the archive data and, based on the validation results, generate engineering data for the construction engineering project; the data catalog management module is used to organize and catalog the engineering data of the construction engineering project to generate a data resource library catalog; the data analysis module is used to analyze the engineering data to generate corresponding project warning results and an associated data map of the project; and the data management module is used to classify and integrate the data resource library catalog to generate a data category system for information management of the construction engineering project.

Optionally, in the data collection module, the process of extracting data from papef 500407 archives includes: digitizing paper archives through a scanner to generate digital scan data; converting the digital scan data into text data using optical character recognition; extracting key information from the text data using natural language processing and text mining methods; and cleaning and standardizing the extracted data to generate electronic archive data.

Optionally, in the data collection module, before generating archive data, the process further includes: acquiring new archives, where if the new archive is a paper archive, data extraction is performed on the new archive data according to the data extraction process for paper archives; if the new archive is an electronic archive, data is extracted from the electronic archive to generate new archive data; descriptions are added to the new archive data; and the new archive data is aggregated into the archive data.

Optionally, in the data governance module, the process of generating engineering data includes: based on the data format of the data standard, detecting the format of the numbering format in the archive data, and using data validation methods to detect the spatial coordinate system of the housing spatial information in the archive data, if the format and coordinate detection are met, the archive data is retained as engineering data.

Optionally, in the data governance module, before generating engineering data, before the data generation the process further includes: unifying the archive data: by constructing a cloud data system using metadata, data dictionaries, and data models, the metadata system unifies the data standards of the archive data to generate unified archive data, and based on the unified archive data, engineering data is generated.

Optionally, in the data catalog management module, the process of organizing and cataloging the engineering data includes: integrating the engineering data according to the stages of the construction engineering project, and within the same stage, integrating the engineering data again according to data categories, based on the re-integrated data and the corresponding stages and data categories, the engineering data is classified and cataloged hierarchically to generate an engineering data catalog.

Optionally, in the data analysis module, the process of analyzing the engineering 500407 data includes: constructing a query comparison analysis model using data mining methods, and using the query comparison analysis model to analyze the engineering data to generate application warning results for the engineering project; using image recognition and machine learning to extract feature data from the engineering data, and performing completeness and consistency checks based on the feature data to generate material warning results for the engineering project; and extracting map feature data from the engineering data, and constructing an associated data map of the engineering project based on the map feature data, where the map feature data includes associated data of enterprises, personnel, and projects.

Optionally, the data management module is also connected to a data sharing module, the data sharing module includes desensitizing the stored engineering data to generate publishable data, publicly releasing the publishable data, and sharing the stored engineering data across different organizational levels.

Compared to existing technologies, the present invention has the following advantages and technical effects: the present invention aggregates archive data of different types through the data collection module and unifies the standards and validates the data through the data governance module to achieve data standardization and improve data quality, subsequently, the data is organized and cataloged through the data catalog management module and the data management and service module, facilitating data traceability and application for relevant personnel, additionally, the data analysis module provides data guidance based on the data, enhancing its practicality.

BRIEF DESCRIPTION OF THE FIGURES

The drawings, which form a part of this application, are intended to provide a further understanding of the application. The schematic embodiments and descriptions of the application are used to explain the application and do not constitute an improper limitation of the application. In the figures:

Fig. 1 shows a schematic diagram of the system according to embodiments of the present invention.

DESCRIPTION OF THE INVENTION LU600407

It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The application will be described in detail below with reference to the figures and embodiments.

It should be noted that the steps shown in the flowchart of the figures can be executed in a computer system, such as a set of computer-executable instructions, although the flowchart shows a logical order, in some cases, the steps shown or described can be executed in a different order.

As shown in Fig. 1, this embodiment provides an information management system for construction engineering, including: a sequentially connected data collection module, a data governance module, a data catalog management module, a data analysis module, a data management and service module; the data collection module is used to extract electronic data from paper archives of construction engineering projects and to aggregate the data extracted from paper archives and data from electronic archives to generate archive data; the data governance module is used to validate the archive data and, based on the validation results, generate engineering data for the construction engineering project; the data catalog management module is used to organize and catalog the engineering data of the construction engineering project to generate a data resource library catalog; the data analysis module is used to analyze the engineering data to generate corresponding project warning results and an associated data map of the project; the data management module is used to classify and integrate the data resource library catalog to generate a data category system for information management of the construction engineering project.

The following provides a detailed description of the above modules: data collection module: the relevant data of construction engineering exists in the form of paper or electronic archive data files. To aggregate construction industry data resources, it is necessary to centrally aggregate archive data files from various stages of project construction.

However, structured and digital resources are relatively scarce. This means that archive data files need to be managed in two aspects: the aggregation of existing archive data files and the aggregation of new archive data files.

For existing archive data files, that is, retained archive files, the aggregation process 500407 includes: first, extracting electronic data from paper archives of construction engineering projects, converting paper archives into electronic format archives, and collecting electronic archives, the data extracted from paper archives and electronic archives are aggregated, that is, stored in the same database or cloud platform, to generate overall archive data. This represents a shift from file-level to data-level aggregation.

The process of digitizing existing paper archive files includes: data extraction technology involves a series of steps and tools for extracting electronic format archives from paper archives. The process is as follows: (1) digital scanning: first, paper archives are digitized using a high-resolution scanner to convert them into digital scan files; this step ensures that the content of the paper documents can be recognized and processed by a computer. (2) Optical character recognition (OCR):

OCR technology is used to convert scanned images into text. Through OCR technology, text in images can be extracted and converted into editable and searchable text format. (3) Data extraction: after converting to text, data extraction technology can be used to extract key information. This typically involves natural language processing (NLP) and text mining techniques to identify and extract specific data fields, such as dates, names, addresses, etc. (4) Data cleaning and standardization: the extracted data needs to be cleaned and standardized to eliminate errors, inconsistencies, and redundancies. This includes verifying the accuracy of the data, removing duplicate information, and converting the data into a unified format and standard to generate corresponding electronic format archives.

For new archive files, i.e., new archive data aggregation: the structured technology for new archive data files in the construction industry involves electronic and structured processing of new archive data. If the new archive is a paper archive, data extraction is performed on the new archive data according to the data extraction process for paper archives; if the new archive is an electronic archive, data is extracted from the electronic archive to generate new archive data; descriptions are added to the new archive data; and the new archive data is aggregated into the archive data, i.e., the new archive data is added to the archive data storage space, and subsequent operations of different modules are executed.

Data governance module: LU600407 construction engineering data includes structured, semi-structured, and unstructured data, to enable data application, it is necessary to increase the generation of structured data. The data governance module mainly involves data standardization and basic error correction. This is primarily achieved by constructing a metadata system using metadata, data dictionaries, and data models. The metadata system unifies the data standards of archive data in different table forms to generate standardized data, and check under the unified standard data to see if there are obvious format errors, among which, the data format based on data standard identifies the format error of enterprise qualification certificate number, and the space coordinate system of the 2000 National Geodetic

Coordinate System (CGCS2000), the national standard of housing space information, is checked by data checking method. For identifying integer numbers based on data standard formats, the process first determines the number of digits in the number, looks up the corresponding data format for the number of digits, and if it cannot be found, the number of digits is incorrect, otherwise, the number is divided according to the pre-divided positions of the digits or letters in the data format, and different intervals or fixed numerical contents are set for the data in different positions. If the digits or letters in certain positions are not within the intervals or cannot be found in the fixed numerical contents, the number format is incorrect. Similarly, other fixed number formats can be validated using the same data format validation method. For validating errors in the spatial coordinate system of housing spatial information using data validation methods, the process first checks if there are acceptance materials that have passed the quality inspection and acceptance of the

CGCS2000 conversion results. Subsequently, the point positions in the coordinate system in the data samples are statistically analyzed, and their actual positions are simulated and regressed to generate auxiliary positions. The auxiliary positions are compared with the point positions in the data samples to check if they are within a certain range, which is set based on empirical experience. If they are within the range, the validation is passed. After completing the format and coordinate validation, the standardized data that passes the validation is retained as engineering data. For data that does not pass the validation, relevant personnel are notified to modify or adjust it until it passes the validation and is retained.

Data catalog management module: LU600407 according to the full lifecycle of construction industry data resources, engineering data is extracted and integrated, and data information is sorted and coded according to a certain classification method: first, the engineering data is organized: the full lifecycle business orientation of construction projects refers to the entire process from project initiation to operation, including project initiation, project land planning, project construction permit, project completion acceptance, and project operation management. The catalog mainly summarizes and organizes the form files, drawing files, and metadata files corresponding to each stage of the engineering project, extracts the full data bytes, and classifies and catalogs them according to data standards to form a data element catalog. Among them, according to different stages, the above engineering data is divided and integrated, and integrated into engineering data under different stages, which is placed in the same storage file or storage space, and according to the above forms, drawings or metadata, it is integrated again according to the data standard format, and the data under the same data standard format is stored in the corresponding stage storage location. The above content forms a multi-directory storage method according to the storage of engineering data under different stages, and under each root directory, subdirectories are formed according to the storage location of data formats, that is, data categories, and under the subdirectories, directories related to personnel, equipment, materials, etc. are formed.

According to the above formed root directory and corresponding subdirectories and lower directories as the engineering data directory, at the same time, in order to facilitate the replacement of relevant data, mapping relationships are established between data formats with certain relationships to facilitate viewing.

Data analysis module: LU600407 for the stored engineering data, a data analysis algorithm model is established, mainly including: professional analysis model: based on data mining technology, design the application scenarios of construction industry project data elements, such as unapproved construction warning, unaccepted delivery warning, construction risk point control and other analysis models, to achieve digital application management of the construction industry. Among them, for the above professional analysis model, through the engineering data involved, such as the time nodes corresponding to different operations, in the huge data, through the preset keywords or key character search methods to search, after the search is completed, according to the preset rules to judge, first for unapproved construction, first search for the corresponding time of building a house in the construction project, and search for the approval time, calculate the difference, when the difference is negative, then warn, unaccepted delivery first search for the delivery time and then search for the corresponding acceptance time, no acceptance then warn, otherwise calculate the difference, the difference is negative then warn, construction risk points need to find and extract the equipment, personnel, materials and other different standard format data involved in the construction process, through the above data through the preset interval or relevant qualification query and other methods to find and compare whether they all meet the data involved in the safety process, otherwise warn.

Intelligent recognition model: exploring the use of image recognition, machine learning and other methods to intelligently verify the key data in the application forms of engineering construction projects, enterprise qualification applications, personnel registration applications, etc., and the completeness and consistency of the application material attachments, improve the approval efficiency, and achieve intelligent processing.

Among them, image recognition adopts a deep recognition model for images, such as convolutional neural network or artificial intelligence network, etc., to extract text from the image data in the above materials, and through machine learning algorithms to extract key data from the text or text data extracted from the images, after the extraction is completed, first check whether the extracted content is complete, whether there is missing data in the key data, and combine the key data with other engineering data to find the corresponding data, and judge whether they are consistent.

Knowledge graph: researching and building the construction industry knowledge 500407 graph, open up the "blood" relationship of business data, build the associated data graph of enterprises, personnel, projects, etc., and achieve precise supervision and proactive services of the competent department. For a single engineering data, first extract the associated data of enterprises, personnel, and projects in the engineering data according to the corresponding deep learning model, including the types of enterprises, personnel, and projects and their associations. The deep learning module adopts a fully connected neural network. For image data, first convert it into text data through the above image recognition method, and then extract the above types and associations through the fully connected neural network. The fully connected neural network adopts the structure of input layer-hidden layer-fully connected layer-output layer, among which the hidden layer is 3 layers, each layer has 500 neurons, and is trained through relevant historical samples.

The fully connected network can extract the relationship between input data and input data and output data through the fully connected structure to ensure the accuracy of extraction, and input the types of enterprises, personnel, and projects as nodes and the associated data as edges into the graph neural network, and update the initial graph data according to the above nodes and edges through the graph neural network to generate a knowledge graph.

Data management module: the data resource library catalog of engineering data shows the hierarchical classification structure of different data under stages. In order to better apply it, you can choose to adjust according to a certain object form, such as personnel, materials, equipment, acceptance or finished products under different stages, according to the root directory or subdirectory in the data resource library catalog, the form is stage - aspect - data format, form a new data directory, retain the data directories corresponding to the above different categories, to form a more practical data category system. And extract the data according to it, and display the whole process of construction in a graphical and digital way.

The data management module is also connected to a data sharing module: LU600407 data sharing is related to the issue of data openness, and data openness is the premise of the shared use of construction engineering data. Mainly including: data desensitization: by training relevant deep learning models, find sensitive data in engineering data, and desensitize sensitive data before sharing and opening through rule-based or encryption-based desensitization methods. Multi-level sharing: according to the different levels of enterprises or different organizations, data sharing is carried out, and the data is encrypted and decrypted by adopting the cascade connection of national standard protocols and the data encryption method of national secret algorithms, to achieve data sharing at different levels, including upward cascade, downward cascade and other sharing types.

The present invention aggregates archive data of different types through the data collection module and unifies the standards and validates the data through the data governance module to achieve data standardization and improve data quality, subsequently, the data is organized and cataloged through the data catalog management module and the data management and service module, facilitating data traceability and application for relevant personnel, additionally, the data analysis module provides data guidance based on the data, enhancing its practicality.

The above is only the preferred embodiment of this application, but the protection scope of this application is not limited to this. Any change or replacement that can be easily thought of by a person familiar with this technical field within the technical scope disclosed in this application should be included in the protection scope of this application.

Therefore, the protection scope of this application should be based on the protection scope of the claims.

Claims

1. À system for construction engineering archive data management, comprising: sequentially connected data collection module, a data governance module, a data catalog management module, a data analysis module, a data management and service module; the data collection module is used to extract electronic data from paper archives of construction engineering projects and to aggregate the data extracted from paper archives and data from electronic archives to generate archive data; the data governance module is used to validate the archive data and, based on the validation results, generate engineering data for the construction engineering project; the data catalog management module is used to organize and catalog the engineering data of the construction engineering project to generate a data resource library catalog; the data analysis module is used to analyze the engineering data to generate corresponding project warning results and an associated data map of the project; and the data management module is used to classify and integrate the data resource library catalog to generate a data category system for information management of the construction engineering project.

2. The system according to claim 1, characterized in that in the data collection module, the process of extracting data from paper archives comprises: digitizing paper archives through a scanner to generate digital scan data; converting the digital scan data into text data using optical character recognition; extracting key information from the text data using natural language processing and text mining methods; and cleaning and standardizing the extracted data to generate electronic archive data.

3. The system according to claim 1, characterized in that in the data collection 500407 module, before generating archive data, the process further comprises: acquiring new archives, wherein if the new archive is a paper archive, data extraction is performed on the new archive data according to the data extraction process for paper archives; if the new archive is an electronic archive, data is extracted from the electronic archive to generate new archive data; descriptions are added to the new archive data; and the new archive data is aggregated into the archive data.

4. The system according to claim 1, characterized in that in the data governance module, the process of generating engineering data comprises: based on the data format of the data standard, detecting the format of the numbering format in the archive data, and using data validation methods to detect the spatial coordinate system of the housing spatial information in the archive data, if the format and coordinate detection are met, the archive data is retained as engineering data.

5. The system according to claim 1, characterized in that in the data governance module, before generating engineering data, before the data generation the process further comprises: unifying the archive data: by constructing a cloud data system using metadata, data dictionaries, and data models, the metadata system unifies the data standards of the archive data to generate unified archive data, and based on the unified archive data, engineering data is generated.

6. The system according to claim 1, characterized in that in the data catalog management module, the process of organizing and cataloging the engineering data comprises: integrating the engineering data according to the stages of the construction engineering project, and within the same stage, integrating the engineering data again according to data categories, based on the re-integrated data and the corresponding stages and data categories, the engineering data is classified and cataloged hierarchically to generate an engineering data catalog.

7. The system according to claim 1, characterized in that in the data analysis module 500407 the process of analyzing the engineering data comprises: constructing a query comparison analysis model using data mining methods, and using the query comparison analysis model to analyze the engineering data to generate application warning results for the engineering project; using image recognition and machine learning to extract feature data from the engineering data, and performing completeness and consistency checks based on the feature data to generate material warning results for the engineering project; and extracting map feature data from the engineering data, and constructing an associated data map of the engineering project based on the map feature data, wherein the map feature data includes associated data of enterprises, personnel, and projects.

8. The system according to claim 1, characterized in that the data management module is also connected to a data sharing module, the data sharing module comprises desensitizing the stored engineering data to generate publishable data, publicly releasing the publishable data, and sharing the stored engineering data across different organizational levels.