CN117151543A - Engineering project quality management method and related device based on machine learning - Google Patents

Engineering project quality management method and related device based on machine learning Download PDF

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CN117151543A
CN117151543A CN202311243809.2A CN202311243809A CN117151543A CN 117151543 A CN117151543 A CN 117151543A CN 202311243809 A CN202311243809 A CN 202311243809A CN 117151543 A CN117151543 A CN 117151543A
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project
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孙志勇
寇丹
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Shaanxi Jiaojian Cloud Data Technology Co ltd
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Abstract

A project quality management method and a related device based on machine learning comprise the following steps: standard data of engineering quality management are obtained, and a database is established; preprocessing a database to obtain a corresponding relation model of data items and WBS decomposition of a work decomposition structure; according to the data of engineering projects, a quality management list and an engineering WBS decomposition tree are established based on a corresponding relation model, and the quality management flow is set according to quality management and acceptance criteria; and checking the accuracy and rationality of the engineering quality management flow through engineering parameters and a neural network model to form a final quality management result file. The quality management work is liberated from repeated and low-efficiency work, the speed, accuracy and rationality of engineering project quality management are greatly improved, and meanwhile, models for machine learning and training in the process are completely saved, so that automatic quality management is more intelligent, faster, more accurate and more reasonable.

Description

Engineering project quality management method and related device based on machine learning
Technical Field
The invention belongs to the technical field of engineering project quality management, and particularly relates to an engineering project quality management method based on machine learning and a related device.
Background
In recent years, the engineering industry has evolved, and the number of projects has increased, and the fluidity, instability and complexity characteristics of the construction industry have also increased, which has caused a number of problems and difficulties for engineering management and the development of the construction engineering industry itself. At present, the project management of the building engineering has the following four main problems:
firstly, it is difficult to perform globally optimal resource allocation. The traditional mode of assisting manpower by computer software is difficult to make globally optimal resource allocation in modern construction projects, and the phenomena of low efficiency, malallocation, surplus or insufficient resource utilization in the construction projects are very common, so that the project management level is seriously influenced.
Secondly, it is difficult to make a reasonable and timely management decision. At present, building engineering project managers can only perform passive response type management under many conditions, and active prediction type management is difficult to perform, so that low-quality even erroneous decisions are easily made, and the overall management level of engineering projects is affected.
Thirdly, real-time and accurate management control is difficult to achieve. The traditional construction project management is difficult to realize accurate management like manufacturing industry, most of the traditional construction project management adopts extensive management methods, waste of production data is easy to occur, and safety accidents and environmental pollution accidents are easy to occur.
Fourth, human resources meeting management requirements are lacking. The serious lack of human resources involved in project management of construction projects is a difficult problem for most project management teams. Many project management teams face the situation of one person with multiple posts and overload operation, and the improvement of project management level is seriously restricted.
Disclosure of Invention
The invention aims to provide an engineering project quality management method and a related device based on machine learning, which are used for solving the problems of low resource utilization efficiency, malposition configuration, surplus or insufficient resource utilization, unreasonable management decision and incapability of real-time management and control in construction projects.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
in a first aspect, the present invention provides a machine learning-based engineering project quality management method, including:
standard data of engineering quality management are obtained, and a database is established;
preprocessing a database to obtain a corresponding relation model of data items and WBS decomposition of a work decomposition structure;
according to the data of engineering projects, a quality management list and an engineering WBS decomposition tree are established based on a corresponding relation model, and the quality management flow is set according to quality management and acceptance criteria;
and checking the accuracy and rationality of the engineering quality management flow through engineering parameters and a neural network model to form a final quality management result file.
Further, the standard data comprises national quality acceptance standards and quality management standards, engineering project quality management schemes, historical project quality management result files and historical project quality management bases.
Further, the preprocessing is to perform semantic decomposition on the basic data table according to natural language analysis.
Further, a corresponding relation model of basic data items and standard WBS decomposition is established, namely, the specific content of each table of the quality inspection and acceptance table of the historical quality management file is identified and processed in natural language, global and context analysis is carried out on each data information of the quality inspection table and the quality acceptance table, and a correlation relation model of the quality inspection table and the quality acceptance table information under different method descriptions and the standard WBS decomposition information is established through machine learning.
Further, a quality management list and an engineering WBS decomposition tree are established, namely after machine learning is completed, a user firstly inputs an engineering quality inspection list and an acceptance list of engineering quality management and carries out recognition and natural language processing according to a unified method, and then the established association relation model is used for automatically forming the quality management list and the engineering WBS decomposition tree corresponding to the engineering project.
Further, automatically checking and verifying the accuracy and rationality of the engineering quality management level refers to that the parameter information of the engineering is put into an engineering quality management network model to be calculated, the data deviation of the engineering quality management file and the history file is calculated, and the accuracy and rationality of the engineering quality management level are checked and verified according to the data deviation.
Further, forming a final result file refers to automatically establishing a quality management file, decomposing a WBS standard, matching quality management with the WBS, automatically generating the quality management file, and automatically rechecking and checking through a quality data network model to form the final quality management result file.
In a second aspect, the present invention provides a machine learning-based engineering project quality management system, comprising:
the data acquisition module is used for acquiring standard data of engineering quality management and establishing a database;
the data processing module is used for preprocessing the database to obtain a corresponding relation model of the data item and the WBS decomposition of the work decomposition structure;
the decomposition module is used for establishing a quality management list and an engineering WBS decomposition tree based on the corresponding relation model according to the data of the engineering project, and completing the setting of a quality management flow according to quality management and acceptance criteria;
and the verification output module is used for rechecking the accuracy and rationality of the engineering quality management flow through engineering parameters and the neural network model to form a final quality management result file.
In a third aspect, the present invention provides a computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of a machine learning based engineering project quality management method when executing the computer program.
In a fourth aspect, the present invention provides a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of a machine learning based engineering project quality management method.
Compared with the prior art, the invention has the following technical effects:
the invention is based on national quality acceptance standard and quality management standard, project quality management scheme, history project quality management result file and history project quality management basis, which can not only build quality management list and project WBS decomposition tree, greatly improve the building efficiency of project quality management system, but also form project result file, recheck the rationality of quality management, release the quality management work from repeated and low-efficiency work, greatly improve the speed, accuracy and rationality of project quality management, and save the model of machine learning and training in the process, so that the automatic quality management is more intelligent, faster, more accurate and more reasonable.
Drawings
FIG. 1 is a flow chart of the present invention;
fig. 2 is a system configuration diagram of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings:
referring to fig. 1 to 2, a machine learning-based engineering project quality management method includes the following steps: establishing a basic data table, establishing a network model, automatically initiating a quality management flow and automatically rechecking;
step one: establishing a basic data table:
establishing a basic data table of engineering quality management, wherein the basic data table comprises national quality acceptance standards and quality management standards, engineering project quality management schemes, historical project quality management result files and historical project quality management bases;
step two: establishing a network model:
according to the first step, according to natural language analysis and artificial intelligence technology, carrying out semantic decomposition on a basic data table, and establishing a corresponding relation model of basic data table entries and standard WBS decomposition;
step three: automatic quality management flow
According to the second step, according to the information of the engineering project of the user, automatically establishing a quality management list and an engineering WBS decomposition tree, automatically applying an engineering project quality management scheme, and according to the related quality management and acceptance criteria, completing the setting of an automatic quality management flow;
step four: automatic rechecking
And thirdly, automatically checking and verifying the accuracy and rationality of the engineering quality management level through engineering parameters and a neural network model to form a final quality management result file.
The further improvement is that: in the first step, the basic data table of the engineering quality management refers to a basic data table conforming to national or regional industry standards and specifications, which is followed by compiling and examining engineering project quality management files.
The further improvement is that: in the second step, the building of the corresponding relation model of the basic data table entry and the standard WBS decomposition refers to the building of the association relation model of the quality check table and the quality check table information under different method descriptions and the standard WBS decomposition information by performing recognition and natural language processing on the specific contents of each table of the quality check and check table of the history quality management file, performing global and context analysis on each data information of the quality check table and the quality check table, and building the association relation model of the quality check table information and the standard WBS decomposition information under different method descriptions by a machine learning method.
The further improvement is that: in the third step, the quality management list and the engineering WBS decomposition tree are automatically established, namely, after the machine learning is completed, a user guides an engineering quality check list and an engineering quality acceptance list for engineering quality management, performs recognition and natural language processing according to a unified method, and then automatically forms the quality management list and the engineering WBS decomposition tree corresponding to the engineering project by using the established association relation model.
The further improvement is that: in the fourth step, the automatic checking and verifying of the accuracy and rationality of the engineering quality management level refers to that the parameter information of the engineering is put into the engineering quality management network model to calculate, and the data deviation between the engineering quality management file and the history file is calculated, so as to analyze the main reason of the deviation, and be used for automatically checking and verifying the accuracy and rationality of the engineering quality management level.
The engineering quality management network model is to put engineering information parameters, namely project parameters which are actually generated, into the network model for calculation, namely, the deviation of a model formed by data accumulated by a quality management file and a history file is detected by an engineering project quality management method based on machine learning.
The further improvement is that: in the fourth step, the final result file is formed by automatically establishing a quality management file, decomposing a WBS standard, matching quality management with the WBS, automatically generating the quality management file, and automatically rechecking and checking through a quality data network model.
Examples:
in one embodiment, the method and the device for managing the quality of the engineering project based on the machine learning are used for detecting the applicability of the method and the device.
The practical data generated in the construction process of a certain project is taken as a training set, the practical appearance is shown in a first table, the project is a reinforcing steel bar project, and the practical data is taken as an example of a quality inspection record table of an inspection batch of the reinforcing steel bar project.
From table one, we can extract key information: the sub-project is the reinforcement project, and the sub-project is the WBS. The quality acceptance standard to be followed is CJJ2-2008 "City bridge engineering construction and quality acceptance Specifications". The actual data in the project is a table like this, but the specific project and the specifications to be followed are different.
5000 construction quality description sentences generated in the collected engineering construction process are used as training corpus and divided into training sets and verification sets, wherein 3000 training sets and 2000 verification sets are used. And a Pytorch deep learning framework is adopted to build a multi-entry convolutional neural network model, the accuracy is gradually increased in the training process, the loss value is continuously reduced until convergence, and the final result shows that the accuracy of automatic rechecking and verification of the quality data network model is effectively improved.
Table I steel bar engineering inspection batch quality inspection and acceptance record table
To detect engineering text quality records and building codes based on deep neural network
The main engineering is concrete engineering in the process of construction of a certain engineering, and meanwhile, the main engineering relates to a plurality of sub-engineering such as excavation, grouting, metal structure installation and the like.
In still another embodiment of the present invention, a machine learning-based engineering project quality management system is provided, which can be used to implement the above-mentioned engineering project quality management method based on machine learning, and specifically, the machine learning-based engineering project quality management system includes:
the data acquisition module is used for acquiring standard data of engineering quality management and establishing a database;
the data processing module is used for preprocessing the database to obtain a corresponding relation model of the data item and the WBS decomposition of the work decomposition structure;
the decomposition module is used for establishing a quality management list and an engineering WBS decomposition tree based on the corresponding relation model according to the data of the engineering project, and completing the setting of a quality management flow according to quality management and acceptance criteria;
and the verification output module is used for rechecking the accuracy and rationality of the engineering quality management flow through engineering parameters and the neural network model to form a final quality management result file.
The division of the modules in the embodiments of the present invention is schematically only one logic function division, and there may be another division manner in actual implementation, and in addition, each functional module in each embodiment of the present invention may be integrated in one processor, or may exist separately and physically, or two or more modules may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules.
In yet another embodiment of the present invention, a computer device is provided that includes a processor and a memory for storing a computer program including program instructions, the processor for executing the program instructions stored by the computer storage medium. The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf Programmable gate arrays (FPGAs) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which are the computational core and control core of the terminal adapted to implement one or more instructions, in particular adapted to load and execute one or more instructions within a computer storage medium to implement the corresponding method flow or corresponding functions; the processor according to the embodiment of the invention can be used for the operation of an engineering project quality management method based on machine learning.
In yet another embodiment of the present invention, a storage medium, specifically a computer readable storage medium (Memory), is a Memory device in a computer device, for storing a program and data. It is understood that the computer readable storage medium herein may include both built-in storage media in a computer device and extended storage media supported by the computer device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also stored in the memory space are one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor. The computer readable storage medium herein may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps in the above-described embodiments with respect to a machine-learning-based engineering project quality management method.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.

Claims (10)

1. The engineering project quality management method based on machine learning is characterized by comprising the following steps:
standard data of engineering quality management are obtained, and a database is established;
preprocessing a database to obtain a corresponding relation model of data items and WBS decomposition of a work decomposition structure;
according to the data of engineering projects, a quality management list and an engineering WBS decomposition tree are established based on a corresponding relation model, and the quality management flow is set according to quality management and acceptance criteria;
and checking the accuracy and rationality of the engineering quality management flow through engineering parameters and a neural network model to form a final quality management result file.
2. The machine learning based project quality management method of claim 1, wherein the standard data includes national quality acceptance criteria and quality management criteria, project quality management schemes, historical project quality management result files, historical project quality management basis.
3. The machine learning based engineering project quality management method of claim 1, wherein the preprocessing is to semantically decompose a base data table according to natural language analysis.
4. A machine learning based engineering project quality management method according to claim 3, wherein the establishment of a corresponding relation model of basic data items and standard WBS decomposition refers to the establishment of a corresponding relation model of quality check list and quality check list information under different method descriptions and standard WBS decomposition information by performing recognition and natural language processing on specific contents of each list of quality check and check list of historical quality management files, performing global and context analysis on each data information of the quality check list and the quality check list, and performing machine learning.
5. The machine learning-based engineering project quality management method according to claim 1, wherein the quality management list and the engineering WBS decomposition tree are established, that is, after the machine learning is completed, a user first inputs an engineering quality inspection table and an acceptance table for engineering quality management and performs recognition and natural language processing according to a unified method, and then automatically forms the engineering project corresponding quality management list and the engineering WBS decomposition tree by using the established association relation model.
6. The machine learning-based engineering project quality management method according to claim 1, wherein automatically checking and verifying the accuracy and rationality of the engineering quality management level refers to placing parameter information of an engineering into an engineering quality management network model for calculation, calculating data deviation of the engineering quality management file and a history file, and checking and verifying the accuracy and rationality of the engineering quality management level according to the data deviation.
7. The machine learning based engineering project quality management method of claim 6, wherein the forming of the final result file is performed by automatically creating a quality management file, decomposing a WBS standard, matching quality management with a WBS, automatically generating the quality management file, and automatically checking and verifying the quality management file through a quality data network model.
8. An engineering project quality management system based on machine learning, comprising:
the data acquisition module is used for acquiring standard data of engineering quality management and establishing a database;
the data processing module is used for preprocessing the database to obtain a corresponding relation model of the data item and the WBS decomposition of the work decomposition structure;
the decomposition module is used for establishing a quality management list and an engineering WBS decomposition tree based on the corresponding relation model according to the data of the engineering project, and completing the setting of a quality management flow according to quality management and acceptance criteria;
and the verification output module is used for rechecking the accuracy and rationality of the engineering quality management flow through engineering parameters and the neural network model to form a final quality management result file.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of a machine learning based engineering project quality management method according to any one of claims 1 to 7 when the computer program is executed by the processor.
10. A computer-readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of a machine learning based engineering project quality management method according to any one of claims 1 to 7.
CN202311243809.2A 2023-09-25 2023-09-25 Engineering project quality management method and related device based on machine learning Pending CN117151543A (en)

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