AI-assisted BIM construction progress intelligent simulation and management system

Abstract

The present invention relates to the field of intelligent construction, and specifically discloses an AI-assisted BIM construction progress intelligent simulation and management system. The system trains a neural network by acquiring historical construction information of engineering projects of the same or similar type, fits a construction plan and a progress forecast plan based on the collected information related to the current engineering project, detects and evaluates the construction plan, and uses BIM technology to generate a demonstration animation. The present invention can achieve the purpose of efficiently allocating project resources, effectively predicting factors affecting the construction progress and taking countermeasures therefor, and intuitively demonstrates the results of engineering project management.

Description

Technical Field

[0001] The present invention relates to the field of intelligent construction, and in particular to an AI-assisted BIM construction progress intelligent simulation and management system. Background Art

[0002] Traditional BIM construction progress intelligent simulation and management systems usually rely on real-time input and modification from all parties involved in the project. This method is difficult to meet the real-time follow-up of engineering projects due to its large workload. With the development of AI, the BIM construction progress intelligent simulation and management system has made it possible to predict and manage the construction progress in real time.

[0003] At present, AI-assisted BIM construction progress intelligent simulation and management has made great progress in the field of intelligent construction. Through the input, editing and modification of information from all parties in the project, it is possible to grasp the entire project life from design, construction to acceptance, maintenance and demolition. However, the current application of BIM technology still relies more on the skills and experience of the staff, and has higher technical and experience requirements for the staff, which also increases the workload for the staff.

[0004] Therefore, there is an urgent need for a BIM construction progress intelligent simulation and management system that enhances AI accuracy and practicality. Summary of the invention

[0005] In order to address the deficiencies in the prior art, the present invention aims to provide an AI-assisted BIM construction progress intelligent simulation and management system, which fits construction plans and progress forecasts through a trained neural network, uses BIM technology to generate demonstration animations, and manages the project progress accurately and efficiently.

[0006] In order to achieve the above objectives, this application provides the following technical solutions:

[0007] This application provides an AI-assisted BIM construction progress intelligent simulation and management system, the system comprising: an information collection module, an information processing module, an operation simulation module, and a result display module;

[0008] The information collection module, information processing module, operation simulation module, and result display module are connected in sequence;

[0009] The information collection module is used to collect project-related information at the initial stage of the project and to regularly collect construction-related information during the construction stage;

[0010] The information processing module is used to process the project-related information collected by the information collection module and generate an initial version of the construction data package; process the construction-related information and generate an updated version of the construction data package in combination with the latest version of the construction data package, which is recorded as the latest version of the construction data package;

[0011] The operation simulation module is used to use BIM technology to fit the construction plan and perform feasibility testing based on the relevant information of the engineering project and the initial version of the construction data package, give optimization reference opinions, and generate the initial version of the construction plan and progress forecast through optimization; generate an updated version of the construction plan and progress forecast based on the latest version of the construction data package and in combination with the latest version of the construction plan and progress forecast;

[0012] The result display module is used to generate an interactive construction demonstration animation based on the construction plan and progress forecast using BIM technology.

[0013] Furthermore, the system also includes: a project information collection submodule, a project type identification submodule, and a historical information collection submodule;

[0014] The project information collection submodule, the project type identification submodule, and the historical information collection submodule are connected in sequence;

[0015] The project information collection submodule is used to collect project information, including project type, project design, project requirements, financial information, material information, construction personnel information, construction tool information, and construction environment information;

[0016] The project type identification submodule is used to sub-classify the project according to the construction environment, building characteristics, and project requirements, and assign type labels;

[0017] The historical information collection submodule is used to collect the engineering project information and construction logs of the same or similar construction projects in history, and to collect the engineering project information and construction logs of the historical construction projects around the construction address.

[0018] Furthermore, the system further comprises:

[0019] The construction information collection submodule is used to collect information uploaded in real time by all parties involved in the project and to collect information transmitted in real time by monitoring tools.

[0020] Furthermore, the system further comprises: a network information monitoring unit, a monitoring sensor unit, and a manual input unit;

[0021] The network information monitoring unit is used to monitor the key words of the project influencing factors updated or released by the database or network high-credibility information source;

[0022] The monitoring sensor unit is used to monitor resource flows and construction environment changes within the construction environment;

[0023] The manual input unit is used to collect information actively uploaded, edited or modified by the operator.

[0024] Furthermore, the system also includes: an analysis submodule, a data packet generation submodule;

[0025] The analysis submodule is connected to the data packet generation submodule;

[0026] The analysis submodule is used to grasp the basic construction process of the same or similar types of engineering projects through deep learning using the information collected by the historical information collection submodule; establish the relationship between the engineering project information and the construction progress and analyze the factors affecting the construction progress and the expected degree of influence of the factors, learn the response or solution to the factors; establish the scoring criteria for specific indicators of the engineering project;

[0027] The data packet generation submodule is used to generate the construction data packet using the analysis submodule based on the information collected by the information collection module. The construction data packet includes financial parameters, personnel parameters, material parameters, tool parameters, outsourcing company parameters, influencing factor parameters and a basic construction process template.

[0028] Furthermore, the system also includes a construction process learning unit, a construction progress analysis unit, an influencing factor analysis unit, and a project indicator scoring unit;

[0029] The construction process learning unit is connected to the construction progress analysis unit and the influencing factor analysis unit respectively, and the construction progress analysis unit and the influencing factor analysis unit are connected to the project index scoring unit respectively;

[0030] The construction process learning unit determines multiple conventional construction methods for the same or similar types of engineering projects according to the information collected by the historical information collection submodule based on keywords, keyword frequency curves combined with engineering models or drawings and divides them into multiple construction stages and multiple construction units;

[0031] The construction progress analysis unit is used to obtain a construction rate change curve of the construction unit according to the daily engineering volume of the construction unit, intercept the construction content corresponding to the time period when the derivative function value of the construction rate change curve exceeds a preset threshold as an influencing factor analysis sample, and retain the construction method and basic parameters corresponding to the time period when the derivative function value does not exceed the preset threshold as a construction progress analysis sample; train a neural network that can analyze the relationship between the construction rate and the construction method and basic parameters according to the construction progress analysis sample;

[0032] The influencing factor analysis unit is used to extract the keywords, intensity and construction rate change degree of the influencing factors in the influencing factor analysis sample and train a neural network that can analyze the relationship between the construction rate change degree and the type and intensity of the influencing factors based on the influencing factor analysis sample; extract the frequency of occurrence of influencing factors with the same keywords and intensity or the difference not exceeding a preset threshold in the information collected by the historical information collection submodule and train a neural network that can analyze the relationship between the possibility of occurrence of a certain intensity influencing factor and the engineering project information;

[0033] The project index scoring unit is used to extract relevant index information of the same or similar type of engineering projects including total construction time, total cost, and engineering quality assessment based on the information collected by the historical information collection submodule, and summarize the index variation range that the same or similar type of engineering projects should have without being affected by the influencing factors, and use the index variation range as the scoring reference S m , according to the mathematical expectation of the influencing factors E(x n ) and the degree of change in the construction rate corresponding to the intensity of the influencing factor f(x) are used to assign points to the relevant indicators at each stage of the construction plan and perform weighted average according to the requirements of the project, with the weight being W m , and the total score of the construction plan is calculated by summing up. The total score S z The calculation method is;

[0034]

[0035] Furthermore, the system also includes: a construction plan fitting submodule and a feasibility detection submodule;

[0036] The construction scheme fitting submodule is used to use the Monte Carlo algorithm to arrange and combine the construction units on the time axis according to the construction stages and extract the arrangement and combination schemes that can meet the requirements of the engineering project, and then extract the arrangement and combination scheme with the highest score through the project indicator scoring unit and record it as the ideal construction scheme; after optimization, the construction scheme and progress forecast are generated;

[0037] The feasibility detection submodule is used to perform conflict detection on the ideal construction plan, substitute the parameters in the construction data package to perform construction condition detection, and use the analysis submodule to score the construction plan and provide optimization reference opinions.

[0038] Furthermore, the system also includes: an animation display submodule and an explanation submodule;

[0039] The animation display submodule is used to display the arrangement and related parameters of different construction units in different construction stages in the form of interactive animation;

[0040] The explanation submodule is used to explain the arrangement and influencing factors of each construction stage in text or voice form.

[0041] The beneficial effects of the present invention are:

[0042] 1. Efficient allocation of project resources is a major advantage of the system. By collecting historical information on the same or similar types of projects and refining the construction process, the system can fit the construction plan and schedule forecast according to the project requirements and project resources, which can play a guiding role for conventional projects and a reference role for unconventional projects.

[0043] 2. The system can also effectively predict the factors affecting the construction progress. By collecting historical information on the same or similar types of projects and construction environment information, extracting the key words, intensity and corresponding impact information of the influencing factors, the influencing factors can be added to the construction progress simulation, which helps to reduce the impact on the construction process.

[0044] 3. The system can quickly follow up the construction progress, reduce the frequent and large-scale information input and modification of all parties involved in the project during the construction process, and reduce the workload of staff.

[0045] In summary, the AI-assisted BI M construction progress intelligent simulation and management system can efficiently coordinate and plan the entire construction process, with significant beneficial effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0047] Figure 1 This is a flowchart of the AI-assisted BIM construction progress intelligent simulation and management system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described in the drawings here can be arranged and designed in different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but only represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0049] Before giving examples, it is necessary to explain the application scenarios of the present invention. The present invention is an AI-assisted BIM construction progress intelligent simulation and management system. By collecting historical project information and training AI that can analyze the construction progress and the mathematical expectation of influencing factors based on the project information, the construction progress can be intelligently simulated and managed. For example, in the initial stage of the project, by inputting the project information, the construction plan, progress forecast and demonstration animation can be quickly obtained, and a clear grasp of the entire construction process can be obtained. For example, when the key words of influencing factors are monitored in a high-credibility information source, the influencing factor elements can be automatically added to the BIM model, including the type of influencing factors, the intensity prediction of influencing factors, the degree of influence prediction of influencing factors, and the reference plan for coping or solving, so as to enhance the project's resistance to uncontrollable factors.

[0050] Example

[0051] This embodiment provides an AI-assisted BIM construction progress intelligent simulation and management system, the system comprising:

[0052] AI-assisted BIM construction progress intelligent simulation and management system, the system comprising: an information collection module, an information processing module, an operation simulation module, and a result display module;

[0053] The information collection module, information processing module, operation simulation module, and result display module are connected in sequence;

[0054] S1. The information collection module is used to collect project-related information in the initial stage of the project and regularly collect construction-related information during the construction stage; information collection is divided into passive collection and active collection, wherein active collection includes searching and identifying by keywords from reliable information sources in a database or on the Internet, and also includes obtaining real-time dynamic information from monitoring equipment deployed in various locations in the construction environment; the project design includes annotated design drawings or design modeling, etc., the financial information includes the balance of a designated electronic account and the balance of a designated cash, etc., the material information includes the type, model, and storage status of the material, etc., the construction personnel information includes the health status, work experience, and salary requirements of the construction personnel, etc., and the construction tool information includes the type, model, quantity, and usage fee, etc.

[0055] S2. The information processing module is used to process the project-related information collected by the information collection module and generate an initial version of the construction data package; process the construction-related information and generate an updated version of the construction data package in combination with the latest version of the construction data package, which is recorded as the latest version of the construction data package; this module is used to process the collected information and convert it into a readable or executable construction data package, and data mining tools such as Tableau or Power BI can be used to help visualize and understand data patterns.

[0056] S3. The operation simulation module is used to use BIM technology to fit the construction plan and perform feasibility testing based on the relevant information of the engineering project and the initial version of the construction data package, provide optimization reference opinions and generate the initial version of the construction plan and progress forecast through optimization; generate an updated version of the construction plan and progress forecast based on the latest version of the construction data package and in combination with the latest version of the construction plan and progress forecast; this module reads and executes the construction data package, tests and optimizes the construction plan and progress forecast, and generates a new version of the construction plan and progress forecast, which can be achieved by using Autodesk Revit, Tekla Structures, etc.

[0057] S4. The result display module is used to generate an interactive construction demonstration animation based on the construction plan and progress forecast using BIM technology; as the output end of this system, BIM technology is used to convert the entire construction process into an intuitive and detailed demonstration animation, and the automatic advancement of progress can effectively reduce the workload of staff. Autodesk Revit and Autodesk Navisworks can provide powerful BIM model visualization and animation functions.

[0058] It should be noted that

[0059] The information collection module includes: a project information collection submodule, a project type identification submodule, and a historical information collection submodule;

[0060] The project information collection submodule, the project type identification submodule, and the historical information collection submodule are connected in sequence;

[0061] The project information collection submodule is used to collect project information, including project type, project design, project requirements, financial information, material information, construction personnel information, construction tool information, and construction environment information. This module is used to collect information related to this project as the initial independent variable of the data processing neural network of this system, among which financial information, material information, construction personnel information, and construction tool information are recorded as basic parameters of the project. Professional-level project management software such as PrimaveraP6 can be used to support multi-dimensional information entry.

[0062] The project type identification submodule is used to sub-classify the engineering projects according to the construction environment, building characteristics, and engineering project requirements, and assign type labels; the construction environment includes topography, population, transportation, policies, etc., the building characteristics include structure, function, etc., and the engineering project requirements include acceptance standards, construction period, budget level, etc.

[0063] The historical information collection submodule is used to collect the project information and construction logs of the same or similar types of construction projects in history, and collect the project information and construction logs of the historical construction projects around the construction address; this module is used to collect training samples for the data processing neural network of this system, and the similar types of construction projects refer to type tags with the same key tags and the same proportion of non-key tags accounting for 60% or more; Apache Hive, Apache Pig or Apache Spark and other tools can be used to analyze large-scale data and extract historical construction project information and construction logs.

[0064] The construction information collection submodule is used to collect information uploaded by all parties of the project in real time and to collect information transmitted by monitoring tools in real time. This module mainly collects text information. The transmission of other forms of information such as images and sounds requires the mounting of recognition tools. Tools such as Microsoft Azure Computer Vision and Microsoft Azure Speech Service can be used for recognition and format conversion.

[0065] The construction information collection submodule includes: a network information monitoring unit, a monitoring sensor unit, and a manual input unit;

[0066] The network information monitoring unit is used to monitor the key words of project influencing factors updated or released by the database or network high-credibility information sources; the high-credibility information sources include news released by official media such as meteorological departments and transportation departments; Web crawler tools such as Scrapy and BeautifulSoup can be used to crawl data from information sources on the Internet, and natural language processing tools such as NLTK and spaCy can be used to process text data, including extracting, analyzing and identifying key words.

[0067] The monitoring sensor unit is used to monitor the resource flow and construction environment changes within the construction environment; the resource flow includes the movement and consumption of personnel, tools, and materials in different functional areas of the construction site, and the construction environment changes include changes in ambient temperature, humidity, air quality and other factors that may affect normal construction.

[0068] The manual input unit is used to collect information actively uploaded, edited or modified by the operator; this unit is mainly used for manual modification by the staff when the error between the information collected by the network information monitoring unit or the monitoring sensor unit and the actual information exceeds the tolerance value.

[0069] The information processing module includes: an analysis submodule and a data packet generation submodule;

[0070] The analysis submodule is connected to the data packet generation submodule;

[0071] The analysis submodule is used to grasp the basic construction process of the same or similar types of engineering projects through deep learning using the information collected by the historical information collection submodule; establish the relationship between the engineering project information and the construction progress and analyze the influencing factors of the construction progress and the expected degree of influence of the influencing factors, and learn the response or solution to the influencing factors; establish scoring standards for specific indicators of engineering projects; use commonly used deep learning frameworks such as TensorFlow, PyTorch or Keras to process historical information data, and use neural network models to learn construction processes, progress influencing factors and scoring standards. Data preprocessing libraries such as NumPy and Pandas can be used for data cleaning, integration and formatting.

[0072] The data packet generation submodule is used to generate the construction data packet using the analysis submodule according to the information collected by the information collection module. The construction data packet includes financial parameters, personnel parameters, material parameters, tool parameters, outsourcing company parameters, influencing factor parameters and a basic construction process template. Tools such as Trifacta or Talend can be used to extract and integrate data and fill in to form a construction data packet. The construction data packet can be output in CSV, JSON, XML and other formats. The outsourcing company parameters include company address, construction experience for a certain construction unit, construction costs, etc.

[0073] The analysis submodule includes: a construction process learning unit, a construction progress analysis unit, an influencing factor analysis unit, and a project indicator scoring unit;

[0074] The construction process learning unit is connected to the construction progress analysis unit and the influencing factor analysis unit respectively, and the construction progress analysis unit and the influencing factor analysis unit are connected to the project index scoring unit respectively;

[0075] The construction process learning unit, based on the information collected by the historical information collection submodule, determines multiple conventional construction methods for the same or similar types of engineering projects based on keywords, keyword frequency curves, and engineering models or drawings, and splits them into multiple construction stages and multiple construction units; when the keyword frequency exceeds a preset threshold, it can be regarded as relevant, first determine the construction stage and then subdivide the construction units from the construction stage based on the keywords and their frequency of occurrence; NLP libraries in Python, such as spaCy, NLTK, etc., can be used to process the text data in the collected information and extract keywords and keyword frequency curves.

[0076] The construction progress analysis unit is used to obtain the construction rate change curve of the construction unit according to the daily engineering volume of the construction unit, intercept the construction content corresponding to the time period when the derivative function value of the construction rate change curve exceeds a preset threshold as an influencing factor analysis sample, and retain the construction method and basic parameters corresponding to the time period when the derivative function value does not exceed the preset threshold as a construction progress analysis sample; train a neural network that can analyze the relationship between the construction rate, the construction method and the basic parameters according to the construction progress analysis samples; TensorFlow, Keras or PyTorch can be used to train the neural network model to analyze the relationship between the construction rate, the construction method and the basic parameters, and use indicators such as accuracy, recall rate, F1-score and ROC to evaluate the performance of the neural network model.

[0077] The influencing factor analysis unit is used to extract the keywords, intensity and construction rate change degree of the influencing factors in the influencing factor analysis sample and train a neural network that can analyze the relationship between the construction rate change degree and the type and intensity of the influencing factors based on the influencing factor analysis sample; extract the frequency of occurrence of influencing factors with the same keywords and intensity or the difference not exceeding a preset threshold in the information collected by the historical information collection submodule and train a neural network that can analyze the relationship between the possibility of occurrence of a certain intensity influencing factor and the engineering project information; illustratively, when the influencing factor is an earthquake, the intensity of the influencing factor can be obtained by fitting according to the energy released by the earthquake; when training the neural network, the data of a certain intensity influencing factor whose possibility of occurrence is lower than the preset threshold should be discarded; the NLP library can be used to vectorize the influencing factors and the engineering project information.

[0078] The project index scoring unit is used to extract relevant index information of the same or similar type of engineering projects including total construction time, total cost, and engineering quality assessment based on the information collected by the historical information collection submodule, and summarize the index variation range that the same or similar type of engineering projects should have without being affected by the influencing factors, and use the index variation range as the scoring reference S m , according to the mathematical expectation of the influencing factors E(x n ) and the degree of change in the construction rate corresponding to the intensity of the influencing factor f(x) are used to assign points to the relevant indicators at each stage of the construction plan and perform weighted average according to the requirements of the project, with the weight being W m , and the total score of the construction plan is calculated by summing up. The total score S z The calculation method is;

[0079]

[0080] The mathematical expectation E(x n ) can be based on the intensity of the influencing factors x n and its probability of occurrence q(x n ) to obtain the mathematical expectation E(x n ) is calculated as follows;

[0081]

[0082] The construction scheme fitting submodule is used to use the Monte Carlo algorithm to arrange and combine the construction units on the timeline according to the construction stages and extract the arrangement and combination schemes that can meet the requirements of the engineering project, and then extract the highest-scoring arrangement and combination scheme through the project indicator scoring unit and record it as the ideal construction scheme; generate the construction plan and progress forecast after optimization; and use the random number generation function in Numpy to generate random construction unit arrangements and combinations.

[0083] The feasibility detection submodule is used to perform conflict detection on the ideal construction plan, substitute the parameters in the construction data package to perform construction condition detection, and use the analysis submodule to score the construction plan and provide optimization reference opinions; the BIM tool can be used to perform three-dimensional conflict detection on the ideal construction plan, and the rule engine can be used to detect the parameters in the construction data package to ensure that the construction conditions meet the requirements.

[0084] The animation display submodule is used to express the arrangement and related parameters of different construction units in different construction stages in the form of interactive animation; 3D modeling and animation software such as 3ds Max can be used to import the BIM model into the selected animation software, design the arrangement and related parameters of different construction units in different construction stages, create corresponding animation effects, add common interactive functions such as switching construction stages, viewing specific parameters, etc., render and optimize the animation, test the animation effect, and adjust and optimize according to feedback.

[0085] The explanation submodule is used to explain the arrangement and influencing factors of each construction stage in text or voice form; the neural network can be trained to generate relevant text content of the arrangement and influencing factors of each construction stage based on keywords, and the text content is converted into voice using TTS technology.

[0086] Finally, it should be noted that: Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. AI-assisted BIM construction progress intelligent simulation and management system, characterized by: The system includes: an information collection module, an information processing module, an operation simulation module, and a result display module; The information collection module, information processing module, operation simulation module, and result display module are connected in sequence; The information collection module is used to collect project-related information at the initial stage of the project and to regularly collect construction-related information during the construction stage; The information collection module includes: a project information collection submodule, a project type identification submodule, and a historical information collection submodule; The project information collection submodule, the project type identification submodule, and the historical information collection submodule are connected in sequence; The project information collection submodule is used to collect project information, including project type, project design, project requirements, financial information, material information, construction personnel information, construction tool information, and construction environment information; The project type identification submodule is used to sub-classify the project according to the construction environment, building characteristics, and project requirements, and assign type labels; The historical information collection submodule is used to collect the engineering project information and construction logs of the same or similar construction projects in history, and to collect the engineering project information and construction logs of the historical construction projects around the construction address; The information collection module also includes: a construction information collection submodule, which is used to collect information uploaded by all parties of the project in real time and collect information transmitted by monitoring tools in real time; the construction information collection submodule includes: a network information monitoring unit, a monitoring sensor unit, and a manual input unit; The network information monitoring unit is used to monitor the key words of the project influencing factors updated or released by the database or network high-credibility information source; The monitoring sensor unit is used to monitor resource flows and changes in the construction environment; The manual input unit is used to collect information actively uploaded, edited or modified by the operator; The information processing module is used to process the project-related information collected by the information collection module and generate an initial version of the construction data package; process the construction-related information and generate an updated version of the construction data package in combination with the latest version of the construction data package, which is recorded as the latest version of the construction data package; The information processing module includes: an analysis submodule and a data packet generation submodule; The analysis submodule is connected to the data packet generation submodule; The analysis submodule is used to grasp the basic construction process of the same or similar types of engineering projects through deep learning using the information collected by the historical information collection submodule; establish the relationship between the engineering project information and the construction progress and analyze the factors affecting the construction progress and the expected degree of influence of the factors, learn the response or solution to the factors; establish the scoring criteria for specific indicators of the engineering project; The analysis submodule includes: a construction process learning unit, a construction progress analysis unit, an influencing factor analysis unit, and a project indicator scoring unit; The construction process learning unit is connected to the construction progress analysis unit and the influencing factor analysis unit respectively, and the construction progress analysis unit and the influencing factor analysis unit are connected to the project index scoring unit respectively; The construction process learning unit determines multiple conventional construction methods for the same or similar types of engineering projects according to the information collected by the historical information collection submodule based on keywords, keyword frequency curves combined with engineering models or drawings and divides them into multiple construction stages and multiple construction units; The construction progress analysis unit is used to obtain a construction rate change curve of the construction unit according to the daily engineering volume of the construction unit, intercept the construction content corresponding to the time period when the derivative function value of the construction rate change curve exceeds a preset threshold as an influencing factor analysis sample, and retain the construction method and basic parameters corresponding to the time period when the derivative function value does not exceed the preset threshold as a construction progress analysis sample; train a neural network that can analyze the relationship between the construction rate and the construction method and basic parameters according to the construction progress analysis sample; The influencing factor analysis unit is used to extract the keywords, intensity and construction rate change degree of the influencing factors in the influencing factor analysis sample and train a neural network that can analyze the relationship between the construction rate change degree and the type and intensity of the influencing factors based on the influencing factor analysis sample; extract the frequency of occurrence of influencing factors with the same keywords and intensity or the difference not exceeding a preset threshold in the information collected by the historical information collection submodule and train a neural network that can analyze the relationship between the possibility of occurrence of a certain intensity influencing factor and the engineering project information; The project index scoring unit is used to extract relevant index information of the same or similar type of engineering projects including total construction time, total cost, and engineering quality assessment based on the information collected by the historical information collection submodule, and summarize the index variation range that the same or similar type of engineering projects should have without being affected by the influencing factors, and use the index variation range as the scoring reference S m , according to the mathematical expectation of the influencing factors E(x n ) and the degree of change in the construction rate corresponding to the intensity of the influencing factor f(x) are used to assign points to the relevant indicators at each stage of the construction plan and perform weighted average according to the requirements of the project, with the weight being W m , and the total score of the construction plan is calculated by summing up. The total score S z The calculation method is: The mathematical expectation E(x n ) can be based on the intensity of the influencing factors x n and its probability of occurrence q(x n ) to obtain the mathematical expectation E(x n ) is calculated as: The data packet generation submodule is used to generate the construction data packet using the analysis submodule according to the information collected by the information collection module, wherein the construction data packet includes financial parameters, personnel parameters, material parameters, tool parameters, outsourcing company parameters, influencing factor parameters and a basic construction process template; The operation simulation module is used to use BIM technology to fit the construction plan and perform feasibility testing based on the relevant information of the engineering project and the initial version of the construction data package, give optimization reference opinions, and generate the initial version of the construction plan and progress forecast through optimization; generate an updated version of the construction plan and progress forecast based on the latest version of the construction data package and in combination with the latest version of the construction plan and progress forecast; The operation simulation module includes: a construction plan fitting submodule and a feasibility detection submodule; The construction scheme fitting submodule is used to use the Monte Carlo algorithm to arrange and combine the construction units on the time axis according to the construction stages and extract the arrangement and combination schemes that can meet the requirements of the engineering project, and then extract the arrangement and combination scheme with the highest score through the project indicator scoring unit and record it as the ideal construction scheme; after optimization, the construction scheme and progress forecast are generated; The feasibility detection submodule is used to perform conflict detection on the ideal construction plan, substitute the parameters in the construction data package to perform construction condition detection, and use the analysis submodule to score the construction plan and provide optimization reference opinions; The result display module is used to generate an interactive construction demonstration animation based on the construction plan and progress forecast using BIM technology; The result display module includes: an animation display submodule and an explanation submodule; The animation display submodule is used to display the arrangement and related parameters of different construction units in different construction stages in the form of interactive animation; The explanation submodule is used to explain the arrangement and influencing factors of each construction stage in text or voice form.

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