CN117910982A - Intelligent management method and system for 5D full life cycle project - Google Patents
Intelligent management method and system for 5D full life cycle project Download PDFInfo
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Abstract
The invention provides a 5D full life cycle project intelligent management method and a system thereof, belonging to the project management technical field, wherein the method comprises the following steps: step S1, acquiring a 5D full life cycle data set of an item; s2, carrying out data mining on the 5D full life cycle data set through an AI large model to acquire project characteristics; s3, inputting project features into a decision model created based on a neural network to acquire project decisions; step S4, pushing the project decision to a pre-associated intelligent terminal in real time; step S5, acquiring decision feedback from each intelligent terminal, and constructing a digital delivery model based on the decision feedback and the 5D full life cycle data set; and S6, controlling the intelligent terminal to work by an operation and maintenance platform running on the server based on the digital delivery model so as to operate and maintain the project. The invention has the advantages that: the integration, reliability, intellectualization and project work efficiency of project management are greatly improved, and the construction quality is ensured.
Description
Technical Field
The invention relates to the technical field of project management, in particular to a 5D full life cycle project intelligent management method and system.
Background
Along with the continuous development of technology, particularly, artificial intelligence makes an important breakthrough in the fields of computer vision, natural language processing, machine learning and the like, and the traditional project management method cannot meet the information management requirement of the modern building industry.
For example, the application date is 2023.07.18, the chinese patent application number is cn202310880472.X discloses a high-efficient convenient wisdom building site management system, and this management system mainly surrounds work progress management, realizes the visual management of engineering construction through data acquisition feedback, mainly includes safety management unit, quality management unit, personnel management unit, progress plan management unit and green construction unit, but this management system has following problem:
The method has the advantages that the influence of city basic data, cost, operation and maintenance and the like on projects is not considered, the consideration of each stage of the life cycle is imperfect, a large model processing link is lacked, artificial intelligent guiding construction cannot be realized, project development trend cannot be predicted, project standing, design, construction and operation and maintenance are not integrated, and the data analysis result of the management system causes misjudgment on project decision, so that construction quality is affected.
Therefore, how to provide a method and a system for intelligent management of a 5D full life cycle project, which achieve improvement of the integration, reliability, intellectualization and project work efficiency of project management, and guarantee of construction quality, become a technical problem to be solved urgently.
Disclosure of Invention
The invention aims to solve the technical problem of providing a 5D full life cycle project intelligent management method and system, which can improve the integration, reliability, intellectualization and project work efficiency of project management and ensure construction quality.
In a first aspect, the present invention provides a method for intelligently managing a 5D full life cycle project, including the steps of:
step S1, acquiring a 5D full life cycle data set of projects including city data, safety data, quality data, progress data and cost data;
S2, carrying out data mining on the 5D full life cycle data set through a pre-trained AI large model to acquire project characteristics;
s3, inputting the project characteristics into a decision model created and trained based on a neural network, and obtaining a project decision;
Step S4, pushing the project decision to a pre-associated intelligent terminal in real time;
s5, acquiring decision feedback from each intelligent terminal, and constructing a digital delivery model based on the decision feedback and a 5D full life cycle data set;
and S6, controlling the intelligent terminal to work by an operation and maintenance platform running on the server based on the digital delivery model so as to operate and maintain the project.
Further, in the step S1, the city data at least includes surrounding environment data, traffic data, population data and policy data;
The safety data at least comprises mechanical terminal management data, personnel management data, safety inspection data, safety monitoring data, risk prediction data and green construction data;
the quality data at least comprises material management data and data management data;
The progress data at least comprise progress plan data, progress display data, progress management data, progress prediction data and progress optimization data;
the cost data includes at least contract management data, cost plan data, funds management data, cost comparison data, cost prediction data, and cost optimization data.
Further, the step S3 specifically includes:
Creating a decision model based on a neural network, marking a project decision on a historical data set constructed based on the characteristics of a historical project, and dividing the historical data set into a training set, a verification set and a test set according to a preset proportion;
training the decision model through the training set, verifying the trained decision model through the verification set, testing the verified decision model through the testing set, and continuously optimizing the loss function, the optimizer function and the super parameters of the decision model;
and inputting the project characteristics into the tested decision model to obtain project decisions.
Further, the step S4 specifically includes:
And pushing the project decision to each pre-associated intelligent terminal in real time through a wireless communication module based on preset permission.
Further, the step S5 specifically includes:
the server acquires decision feedback from each intelligent terminal through the wireless communication module, constructs a digital delivery model based on the decision feedback and the 5D full life cycle data set, stores the digital delivery model into a database, and encrypts the database through a national encryption algorithm.
In a second aspect, the present invention provides a 5D full lifecycle project intelligent management system, including the following modules:
The system comprises a 5D full life cycle data set acquisition module, a data processing module and a data processing module, wherein the 5D full life cycle data set acquisition module is used for acquiring a 5D full life cycle data set of projects including city data, safety data, quality data, progress data and cost data;
the project characteristic acquisition module is used for carrying out data mining on the 5D full life cycle data set through a pre-trained AI large model to acquire project characteristics;
The project decision acquisition module is used for inputting the project characteristics into a decision model created and trained based on a neural network to acquire a project decision;
the project decision pushing module is used for pushing the project decision to the pre-associated intelligent terminal in real time;
The digital delivery model building module is used for obtaining decision feedback from each intelligent terminal and building a digital delivery model based on the decision feedback and the 5D full life cycle data set;
And the operation and maintenance module is used for controlling the work of the intelligent terminal by the operation and maintenance platform running on the server based on the digital delivery model so as to operate and maintain the project.
Further, in the 5D full life cycle data set acquisition module, the city data at least includes surrounding environment data, traffic data, population data, and policy data;
The safety data at least comprises mechanical terminal management data, personnel management data, safety inspection data, safety monitoring data, risk prediction data and green construction data;
the quality data at least comprises material management data and data management data;
The progress data at least comprise progress plan data, progress display data, progress management data, progress prediction data and progress optimization data;
the cost data includes at least contract management data, cost plan data, funds management data, cost comparison data, cost prediction data, and cost optimization data.
Further, the project decision obtaining module is specifically configured to:
Creating a decision model based on a neural network, marking a project decision on a historical data set constructed based on the characteristics of a historical project, and dividing the historical data set into a training set, a verification set and a test set according to a preset proportion;
training the decision model through the training set, verifying the trained decision model through the verification set, testing the verified decision model through the testing set, and continuously optimizing the loss function, the optimizer function and the super parameters of the decision model;
and inputting the project characteristics into the tested decision model to obtain project decisions.
Further, the project decision pushing module is specifically configured to:
And pushing the project decision to each pre-associated intelligent terminal in real time through a wireless communication module based on preset permission.
Further, the digital delivery model construction module is specifically configured to:
the server acquires decision feedback from each intelligent terminal through the wireless communication module, constructs a digital delivery model based on the decision feedback and the 5D full life cycle data set, stores the digital delivery model into a database, and encrypts the database through a national encryption algorithm.
The invention has the advantages that:
The method comprises the steps of obtaining a 5D full life cycle data set of projects comprising city data, safety data, quality data, progress data and cost data, carrying out data mining on the 5D full life cycle data set through an AI large model to obtain project features, inputting the project features into a decision model created and trained based on a neural network to obtain project decisions, pushing the project decisions to pre-associated intelligent terminals in real time, obtaining decision feedback from the intelligent terminals, constructing a digital delivery model based on the decision feedback and the 5D full life cycle data set, and finally controlling the work of the intelligent terminals by an operation and maintenance platform running on a server based on the digital delivery model to operate and maintain the projects; in the process of project management, 5D full life cycle data of city data, safety data, quality data, progress data and cost data are combined, influences of all stages of the life cycle on the project are fully considered, data mining is carried out through an AI large model to obtain project characteristics, the project characteristics are input into a decision model to obtain project decisions, an artificial intelligence technology is fully utilized, the project decisions can be more attached to actual scenes of the project, project development trend can be predicted, namely project integration, reliability, intelligence and project work efficiency of project management are improved greatly, and construction quality is guaranteed.
Drawings
The invention will be further described with reference to examples of embodiments with reference to the accompanying drawings.
FIG. 1 is a flow chart of a method for intelligent management of 5D full life cycle items according to the present invention.
Fig. 2 is a schematic structural diagram of a 5D full life cycle project intelligent management system according to the present invention.
Fig. 3 is a hardware architecture diagram of the present invention.
Detailed Description
The technical scheme in the embodiment of the application has the following overall thought: in the process of project management, 5D full life cycle data comprising city data, safety data, quality data, progress data and cost data are combined, influences of all stages of the life cycle on the project are fully considered, data mining is carried out through an AI large model to obtain project characteristics, the project characteristics are input into a decision model to obtain project decisions, an artificial intelligence technology is fully utilized, the project decisions can be more attached to actual scenes of the project, project development trends can be predicted, the project is integrated from standings, design, construction and operation and maintenance, integration, reliability, intellectualization and project work efficiency of project management are further improved, and construction quality is guaranteed.
Referring to fig. 1 to 3, a preferred embodiment of a 5D full life cycle project intelligent management method of the present invention includes the following steps:
Step S1, acquiring a 5D full life cycle data set of projects including city data, safety data, quality data, progress data and cost data; the 5D full life cycle data set can be obtained through a sensor group or a database;
S2, carrying out data mining on the 5D full life cycle data set through a pre-trained AI large model to acquire project characteristics;
s3, inputting the project characteristics into a decision model created and trained based on a neural network, and obtaining a project decision;
Step S4, pushing the project decision to a pre-associated intelligent terminal in real time;
s5, acquiring decision feedback from each intelligent terminal, and constructing a digital delivery model based on the decision feedback and a 5D full life cycle data set;
And S6, controlling the intelligent terminal to work by an operation and maintenance platform running on the server based on the digital delivery model so as to operate and maintain the project. The operation and maintenance platform can display population dynamic distribution, control intelligent terminal groups, emergency alarm, building health monitoring and building energy consumption management. The intelligent terminal can execute the instruction sent by the operation and maintenance platform to make corresponding reaction so as to realize intelligent building management, meanwhile, real-time dynamic data of the building can be fed back to different demand parties according to different authorities, users can pre-judge lines according to the mobile terminal data, life efficiency and use sense of the building are improved, and the operation and maintenance personnel can contact manufacturers through digital delivery model basic data and health monitoring data to maintain or timely replace equipment.
In the step S1, the city data at least includes surrounding environment data, traffic data, population data and policy data;
The city data is used for capturing Internet and government data according to authority on the periphery of the project to be built in the project establishment stage, and the sources can be related databases or manually filled; the AI large model can conduct project feasibility research analysis according to city data and generate feasibility research reports to assist project investment decisions;
The safety data at least comprises mechanical terminal management data, personnel management data, safety inspection data, safety monitoring data, risk prediction data and green construction data;
The security data is used for security supervision of project construction processes, and can be called by the AI large model to form report text material, chart material, PPT, or the like. The mechanical terminal management data are used for monitoring potential safety hazards in the construction process of the mechanical terminal and mainly comprise safety production information, inspection maintenance information and safety use information; the safety production information and the inspection and maintenance information are recorded by the form, so that regular reminding of maintenance can be realized; the safety use information comprises the input of special operators, and the identification operation of the mechanical terminal by the specific operators can be realized after the input. The personnel management data are used for labor service real-name management, attendance management, personnel certificate management, personnel dynamic distribution management and the like, and can be used for real-time dynamic management, inquiring of personnel movement tracks and reasonable planning of working faces. The safety inspection data is filled with inspection results and correction feedback conditions by inspection personnel. The safety monitoring data are used for foundation pit safety monitoring, main structure monitoring and surrounding environment monitoring, and the data are automatically captured through the sensor group and uploaded to the server. And the risk prediction data is based on the filling data and video acquired by the camera, and the risk source analysis is carried out by an AI visual recognition technology so as to realize risk prediction. The green construction data is used for civilized construction and green construction, can reasonably plan field cloth, dust collection and humiture monitoring, can realize automatic control spraying system through dust collection data, realize water conservation, through temperature humidity monitoring, realize high temperature early warning, ensure operating personnel occupation health.
The quality data at least comprises material management data and data management data;
The quality management data are used for quality management of the whole life cycle of the project, and aim at three processes of pre-prevention, in-process control and operation maintenance repair. The material management data can be automatically acquired by a sensor group or manually input to form a form, is convenient to inquire and call, and is used as basic data of an operation and maintenance stage. The data management data comprise project stand data, drawing management, construction scheme management, technical acceptance data, quality defect information, correction conditions and the like, and are manually input and uploaded to a server for calling at any time and any place. The quality management data can be captured by an AI large model, quality comparison is realized, quality defects are found, a solution is provided through internet retrieval or a company intelligent library, and reporting text materials, chart materials, PPT and the like can be called by the AI large model.
The progress data at least comprise progress plan data, progress display data, progress management data, progress prediction data and progress optimization data;
The progress management data are used for progress management in the project construction process, progress planning, adjustment, key path and key work determination are realized through a form, the construction progress is synchronized through automatic grabbing calculation and manual modification authority of material management information, data information and the like, the actual progress is compared with the planned progress, the plan deviation is analyzed in a comparison manner, the project progress is predicted, early warning is carried out on work needing to be overtaken, an optimization direction is formed on the condition of progress lag, and an optimization thought is provided; the progress management data may be invoked by the AI big model to form report text material, chart material, PPT, or the like.
The cost data includes at least contract management data, cost plan data, funds management data, cost comparison data, cost prediction data, and cost optimization data.
The cost data is used for cost management in project construction processes. The contract management data comprises contract information, contract examination and approval conditions, contract dynamic performance conditions and risk point identification early warning conditions, a preliminary cost plan can be formed based on a BIM model and a design file, dynamic adjustment is carried out according to actual conditions in the execution process, and basis is provided for project fund management; comprehensively analyzing according to contract execution conditions to form cost comparison and prediction, and finally providing a cost optimization direction and thought; the cost data may be invoked by the AI large model to form report text material, chart material, PPT, or the like.
The step S3 specifically comprises the following steps:
Creating a decision model based on a neural network, marking a project decision on a historical data set constructed based on the characteristics of a historical project, and dividing the historical data set into a training set, a verification set and a test set according to a preset proportion;
training the decision model through the training set, verifying the trained decision model through the verification set, testing the verified decision model through the testing set, and continuously optimizing the loss function, the optimizer function and the super parameters of the decision model;
and inputting the project characteristics into the tested decision model to obtain project decisions.
The step S4 specifically includes:
And pushing the project decision to each pre-associated intelligent terminal in real time through a wireless communication module based on preset permission.
The step S5 specifically comprises the following steps:
The server acquires decision feedback from each intelligent terminal through the wireless communication module, constructs a digital delivery model based on the decision feedback and the 5D full life cycle data set, stores the digital delivery model into a database, and encrypts the database through a national encryption algorithm. The digital delivery model is a delivery digital twin model which meets operation and maintenance requirements and is formed according to contract standards aiming at project construction data, the digital delivery model is used as basic data of operation and maintenance stages, and an operation unit can monitor the project according to the basic data after the project is put into use.
The invention relates to a preferred embodiment of a 5D full life cycle project intelligent management system, which comprises the following modules:
The system comprises a 5D full life cycle data set acquisition module, a data processing module and a data processing module, wherein the 5D full life cycle data set acquisition module is used for acquiring a 5D full life cycle data set of projects including city data, safety data, quality data, progress data and cost data; the 5D full life cycle data set can be obtained through a sensor group or a database;
the project characteristic acquisition module is used for carrying out data mining on the 5D full life cycle data set through a pre-trained AI large model to acquire project characteristics;
The project decision acquisition module is used for inputting the project characteristics into a decision model created and trained based on a neural network to acquire a project decision;
the project decision pushing module is used for pushing the project decision to the pre-associated intelligent terminal in real time;
The digital delivery model building module is used for obtaining decision feedback from each intelligent terminal and building a digital delivery model based on the decision feedback and the 5D full life cycle data set;
And the operation and maintenance module is used for controlling the work of the intelligent terminal by the operation and maintenance platform running on the server based on the digital delivery model so as to operate and maintain the project. The operation and maintenance platform can display population dynamic distribution, control intelligent terminal groups, emergency alarm, building health monitoring and building energy consumption management. The intelligent terminal can execute the instruction sent by the operation and maintenance platform to make corresponding reaction so as to realize intelligent building management, meanwhile, real-time dynamic data of the building can be fed back to different demand parties according to different authorities, users can pre-judge lines according to the mobile terminal data, life efficiency and use sense of the building are improved, and the operation and maintenance personnel can contact manufacturers through digital delivery model basic data and health monitoring data to maintain or timely replace equipment.
In the 5D full life cycle data set acquisition module, the city data at least includes surrounding environment data, traffic data, population data, and policy data;
The city data is used for capturing Internet and government data according to authority on the periphery of the project to be built in the project establishment stage, and the sources can be related databases or manually filled; the AI large model can conduct project feasibility research analysis according to city data and generate feasibility research reports to assist project investment decisions;
The safety data at least comprises mechanical terminal management data, personnel management data, safety inspection data, safety monitoring data, risk prediction data and green construction data;
The security data is used for security supervision of project construction processes, and can be called by the AI large model to form report text material, chart material, PPT, or the like. The mechanical terminal management data are used for monitoring potential safety hazards in the construction process of the mechanical terminal and mainly comprise safety production information, inspection maintenance information and safety use information; the safety production information and the inspection and maintenance information are recorded by the form, so that regular reminding of maintenance can be realized; the safety use information comprises the input of special operators, and the identification operation of the mechanical terminal by the specific operators can be realized after the input. The personnel management data are used for labor service real-name management, attendance management, personnel certificate management, personnel dynamic distribution management and the like, and can be used for real-time dynamic management, inquiring of personnel movement tracks and reasonable planning of working faces. The safety inspection data is filled with inspection results and correction feedback conditions by inspection personnel. The safety monitoring data are used for foundation pit safety monitoring, main structure monitoring and surrounding environment monitoring, and the data are automatically captured through the sensor group and uploaded to the server. And the risk prediction data is based on the filling data and video acquired by the camera, and the risk source analysis is carried out by an AI visual recognition technology so as to realize risk prediction. The green construction data is used for civilized construction and green construction, can reasonably plan field cloth, dust collection and humiture monitoring, can realize automatic control spraying system through dust collection data, realize water conservation, through temperature humidity monitoring, realize high temperature early warning, ensure operating personnel occupation health.
The quality data at least comprises material management data and data management data;
The quality management data are used for quality management of the whole life cycle of the project, and aim at three processes of pre-prevention, in-process control and operation maintenance repair. The material management data can be automatically acquired by a sensor group or manually input to form a form, is convenient to inquire and call, and is used as basic data of an operation and maintenance stage. The data management data comprise project stand data, drawing management, construction scheme management, technical acceptance data, quality defect information, correction conditions and the like, and are manually input and uploaded to a server for calling at any time and any place. The quality management data can be captured by an AI large model, quality comparison is realized, quality defects are found, a solution is provided through internet retrieval or a company intelligent library, and reporting text materials, chart materials, PPT and the like can be called by the AI large model.
The progress data at least comprise progress plan data, progress display data, progress management data, progress prediction data and progress optimization data;
The progress management data are used for progress management in the project construction process, progress planning, adjustment, key path and key work determination are realized through a form, the construction progress is synchronized through automatic grabbing calculation and manual modification authority of material management information, data information and the like, the actual progress is compared with the planned progress, the plan deviation is analyzed in a comparison manner, the project progress is predicted, early warning is carried out on work needing to be overtaken, an optimization direction is formed on the condition of progress lag, and an optimization thought is provided; the progress management data may be invoked by the AI big model to form report text material, chart material, PPT, or the like.
The cost data includes at least contract management data, cost plan data, funds management data, cost comparison data, cost prediction data, and cost optimization data.
The cost data is used for cost management in project construction processes. The contract management data comprises contract information, contract examination and approval conditions, contract dynamic performance conditions and risk point identification early warning conditions, a preliminary cost plan can be formed based on a BIM model and a design file, dynamic adjustment is carried out according to actual conditions in the execution process, and basis is provided for project fund management; comprehensively analyzing according to contract execution conditions to form cost comparison and prediction, and finally providing a cost optimization direction and thought; the cost data may be invoked by the AI large model to form report text material, chart material, PPT, or the like.
The project decision acquisition module is specifically configured to:
Creating a decision model based on a neural network, marking a project decision on a historical data set constructed based on the characteristics of a historical project, and dividing the historical data set into a training set, a verification set and a test set according to a preset proportion;
training the decision model through the training set, verifying the trained decision model through the verification set, testing the verified decision model through the testing set, and continuously optimizing the loss function, the optimizer function and the super parameters of the decision model;
and inputting the project characteristics into the tested decision model to obtain project decisions.
The project decision pushing module is specifically configured to:
And pushing the project decision to each pre-associated intelligent terminal in real time through a wireless communication module based on preset permission.
The digital delivery model construction module is specifically used for:
The server acquires decision feedback from each intelligent terminal through the wireless communication module, constructs a digital delivery model based on the decision feedback and the 5D full life cycle data set, stores the digital delivery model into a database, and encrypts the database through a national encryption algorithm. The digital delivery model is a delivery digital twin model which meets operation and maintenance requirements and is formed according to contract standards aiming at project construction data, the digital delivery model is used as basic data of operation and maintenance stages, and an operation unit can monitor the project according to the basic data after the project is put into use.
In summary, the invention has the advantages that:
The method comprises the steps of obtaining a 5D full life cycle data set of projects comprising city data, safety data, quality data, progress data and cost data, carrying out data mining on the 5D full life cycle data set through an AI large model to obtain project features, inputting the project features into a decision model created and trained based on a neural network to obtain project decisions, pushing the project decisions to pre-associated intelligent terminals in real time, obtaining decision feedback from the intelligent terminals, constructing a digital delivery model based on the decision feedback and the 5D full life cycle data set, and finally controlling the work of the intelligent terminals by an operation and maintenance platform running on a server based on the digital delivery model to operate and maintain the projects; in the process of project management, 5D full life cycle data of city data, safety data, quality data, progress data and cost data are combined, influences of all stages of the life cycle on the project are fully considered, data mining is carried out through an AI large model to obtain project characteristics, the project characteristics are input into a decision model to obtain project decisions, an artificial intelligence technology is fully utilized, the project decisions can be more attached to actual scenes of the project, project development trend can be predicted, namely project integration, reliability, intelligence and project work efficiency of project management are improved greatly, and construction quality is guaranteed.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that the specific embodiments described are illustrative only and not intended to limit the scope of the invention, and that equivalent modifications and variations of the invention in light of the spirit of the invention will be covered by the claims of the present invention.
Claims (10)
1. A5D full life cycle project intelligent management method is characterized in that: the method comprises the following steps:
step S1, acquiring a 5D full life cycle data set of projects including city data, safety data, quality data, progress data and cost data;
S2, carrying out data mining on the 5D full life cycle data set through a pre-trained AI large model to acquire project characteristics;
s3, inputting the project characteristics into a decision model created and trained based on a neural network, and obtaining a project decision;
Step S4, pushing the project decision to a pre-associated intelligent terminal in real time;
s5, acquiring decision feedback from each intelligent terminal, and constructing a digital delivery model based on the decision feedback and a 5D full life cycle data set;
and S6, controlling the intelligent terminal to work by an operation and maintenance platform running on the server based on the digital delivery model so as to operate and maintain the project.
2. The intelligent management method for the 5D full life cycle project according to claim 1, wherein: in the step S1, the city data at least includes surrounding environment data, traffic data, population data and policy data;
The safety data at least comprises mechanical terminal management data, personnel management data, safety inspection data, safety monitoring data, risk prediction data and green construction data;
the quality data at least comprises material management data and data management data;
The progress data at least comprise progress plan data, progress display data, progress management data, progress prediction data and progress optimization data;
the cost data includes at least contract management data, cost plan data, funds management data, cost comparison data, cost prediction data, and cost optimization data.
3. The intelligent management method for the 5D full life cycle project according to claim 1, wherein: the step S3 specifically comprises the following steps:
Creating a decision model based on a neural network, marking a project decision on a historical data set constructed based on the characteristics of a historical project, and dividing the historical data set into a training set, a verification set and a test set according to a preset proportion;
training the decision model through the training set, verifying the trained decision model through the verification set, testing the verified decision model through the testing set, and continuously optimizing the loss function, the optimizer function and the super parameters of the decision model;
and inputting the project characteristics into the tested decision model to obtain project decisions.
4. The intelligent management method for the 5D full life cycle project according to claim 1, wherein: the step S4 specifically includes:
And pushing the project decision to each pre-associated intelligent terminal in real time through a wireless communication module based on preset permission.
5. The intelligent management method for the 5D full life cycle project according to claim 1, wherein: the step S5 specifically comprises the following steps:
the server acquires decision feedback from each intelligent terminal through the wireless communication module, constructs a digital delivery model based on the decision feedback and the 5D full life cycle data set, stores the digital delivery model into a database, and encrypts the database through a national encryption algorithm.
6. A5D full life cycle project intelligent management system is characterized in that: the device comprises the following modules:
The system comprises a 5D full life cycle data set acquisition module, a data processing module and a data processing module, wherein the 5D full life cycle data set acquisition module is used for acquiring a 5D full life cycle data set of projects including city data, safety data, quality data, progress data and cost data;
the project characteristic acquisition module is used for carrying out data mining on the 5D full life cycle data set through a pre-trained AI large model to acquire project characteristics;
The project decision acquisition module is used for inputting the project characteristics into a decision model created and trained based on a neural network to acquire a project decision;
the project decision pushing module is used for pushing the project decision to the pre-associated intelligent terminal in real time;
The digital delivery model building module is used for obtaining decision feedback from each intelligent terminal and building a digital delivery model based on the decision feedback and the 5D full life cycle data set;
And the operation and maintenance module is used for controlling the work of the intelligent terminal by the operation and maintenance platform running on the server based on the digital delivery model so as to operate and maintain the project.
7. The intelligent 5D full lifecycle project management system of claim 6, wherein: in the 5D full life cycle data set acquisition module, the city data at least includes surrounding environment data, traffic data, population data, and policy data;
The safety data at least comprises mechanical terminal management data, personnel management data, safety inspection data, safety monitoring data, risk prediction data and green construction data;
the quality data at least comprises material management data and data management data;
The progress data at least comprise progress plan data, progress display data, progress management data, progress prediction data and progress optimization data;
the cost data includes at least contract management data, cost plan data, funds management data, cost comparison data, cost prediction data, and cost optimization data.
8. The intelligent 5D full lifecycle project management system of claim 6, wherein: the project decision acquisition module is specifically configured to:
Creating a decision model based on a neural network, marking a project decision on a historical data set constructed based on the characteristics of a historical project, and dividing the historical data set into a training set, a verification set and a test set according to a preset proportion;
training the decision model through the training set, verifying the trained decision model through the verification set, testing the verified decision model through the testing set, and continuously optimizing the loss function, the optimizer function and the super parameters of the decision model;
and inputting the project characteristics into the tested decision model to obtain project decisions.
9. The intelligent 5D full lifecycle project management system of claim 6, wherein: the project decision pushing module is specifically configured to:
And pushing the project decision to each pre-associated intelligent terminal in real time through a wireless communication module based on preset permission.
10. The intelligent 5D full lifecycle project management system of claim 6, wherein: the digital delivery model construction module is specifically used for:
the server acquires decision feedback from each intelligent terminal through the wireless communication module, constructs a digital delivery model based on the decision feedback and the 5D full life cycle data set, stores the digital delivery model into a database, and encrypts the database through a national encryption algorithm.
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