Building engineering quality intelligent pre-control system based on BIM and AI fusion

Abstract

The invention relates to the technical field of constructional engineering, and discloses a BIM and AI fusion-based constructional engineering quality intelligent pre-control system, which comprises a multi-dimensional data integration module, a BIM model establishment module, a data analysis module, a prediction module and a feedback module, the multi-dimensional data integration module is used for collecting an engineering type data set, a building material data set, a building environment data set and a building equipment data set, and the BIM model establishment module is used for training a BIM model and determining final model parameter data according to a final training result. The data analysis module is used for calculating a material quality index, an environment quality index, an equipment quality index and a comprehensive influence index, and the prediction module is used for predicting the quality of the constructional engineering and sending a prediction result to the feedback module, so that the engineering quality of the constructional engineering is evaluated in a targeted and multi-dimensional manner, and the quality state of the constructional engineering is comprehensively reflected; errors are reduced, and the accuracy of a pre-control result is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of construction engineering, and particularly to an intelligent pre-control system for construction engineering quality based on the integration of BIM and AI. Background Art

[0002] Construction engineering is an engineering entity formed by the construction of various housing buildings and their ancillary facilities and the installation of pipelines and equipment supporting them. It is the art and science of building and constructing spaces for human habitation, work, entertainment, and worship. It is not just about piling up bricks and stones, but a comprehensive process involving multiple links such as planning, design, construction, management, and maintenance, aiming to create architectural works that are both practical and beautiful, safe and environmentally friendly. In this process, multiple parties such as architects, engineers, craftsmen, technicians, and project managers work together to transform abstract concepts into specific architectural entities to meet various social needs.

[0003] With the rapid growth of the global population and the acceleration of the urbanization process, the demand for housing, office space, and public facilities has surged, driving the continuous development of the construction engineering industry. At the same time, urbanization has also brought problems such as land resource tension and environmental pollution, posing higher requirements for architectural design. With the increasing emphasis on environmental protection, construction engineering has begun to focus on the design principles of energy conservation, emission reduction, and green ecology. Concepts such as green buildings and zero-energy buildings have emerged, aiming to reduce the impact of buildings on the environment and achieve harmonious coexistence between humans and nature. In the context of globalization, cultural exchanges and integrations are becoming increasingly frequent, and construction engineering also pays more attention to the expression of regional characteristics and cultural connotations. Designers strive to incorporate traditional cultural elements into modern designs to create architectural works with unique charm.

[0004] In order to ensure the project quality during the construction process of construction engineering, it is necessary to conduct quality pre-control on construction engineering. Existing quality pre-control systems often focus on the monitoring and early warning of a few key indicators, such as concrete strength, steel structure stress, etc. However, construction engineering quality is a multi-dimensional concept affected by various factors, and relying solely on individual key indicators cannot comprehensively reflect the project quality status. Summary of the Invention

[0005] (I) Technical Problems to be Solved

[0006] In view of the deficiencies of the prior art, the present invention provides an intelligent pre-control system for building engineering quality based on the integration of BIM and AI. It can consider the different quality requirements of different construction types for building engineering, conduct in-depth research on their quality influencing factors, combine the integration of BIM and AI algorithms, comprehensively reflect the situation of building engineering, reduce data errors, improve the accuracy of calculation results, evaluate the engineering quality of building engineering from multiple dimensions, discover potential quality problems in advance, and be able to take targeted measures to solve them according to different warning signals, effectively avoiding the occurrence of engineering quality accidents, controlling quality risks in the bud, and ensuring construction quality and other advantages.

[0007] (II) Technical Solution

[0008] To achieve the above object, the present invention provides the following technical solution: An intelligent pre-control system for building engineering quality based on the integration of BIM and AI, including a multi-dimensional data integration module, a BIM model establishment module, a data analysis module, a prediction module, and a feedback module;

[0009] The multi-dimensional data integration module includes an engineering type data integration unit and an engineering construction data integration unit. The engineering type data integration unit is used to form an engineering type data set according to the construction type of the building project. The engineering construction data integration unit is used to form a building material data set, a building environment data set, and a building equipment data set according to the construction-related parameters of the building project type. The multi-dimensional data integration module sends the integrated engineering type data set, building material data set, building environment data set, and building equipment data set to the BIM model establishment module;

[0010] The BIM model establishment module trains the BIM model according to the received engineering type data set, building material data set, building environment data set, and building equipment data set, and determines the final model parameter data according to the final training result. The BIM model establishment module is connected to the data analysis module through the network;

[0011] The data analysis module calculates the material quality index, environmental quality index, equipment quality index, and comprehensive influence index according to the building material data set, building environment data set, and building equipment data set and sends them to the prediction module;

[0012] The prediction module predicts the building engineering quality according to the material quality index, environmental quality index, equipment quality index, and comprehensive influence index, and sends the prediction result to the feedback module;

[0013] The feedback module includes a display unit and a warning unit. The display unit displays the prediction result, and the warning unit issues a warning according to the prediction result.

[0014] Preferably, the engineering type dataset includes multiple construction type data of construction projects. The expression of the engineering type dataset is: {Z1, Z2, Z3 ···, Z8}, where Z1 to Z8 represent 8 engineering types in the engineering type dataset in sequence. The multiple construction types of the construction project are specifically: masonry structure, concrete structure, steel structure, wooden structure, composite structure, composite material structure, aluminum alloy structure, and other structures.

[0015] Preferably, the building material dataset includes multiple building materials required for different construction types. The expression of the building material dataset is: In the expression, to represent the material types required during the construction process of the corresponding construction types in sequence. 1 to n represent the number of material types, Z represents the specific construction type, and l represents the performance parameters of the corresponding material type;

[0016] The building environment dataset includes multiple environmental factors that affect the construction quality of construction projects. The expression of the building environment dataset is: {Hq x 、Hd x 、Hs x}, where Hq x represents the environmental meteorological conditions, and the superscript x represents the specific parameters in the environmental meteorological conditions; Hd x represents the environmental geological conditions, and the superscript x represents the specific parameters in the environmental geological conditions; Hs x represents the environmental hydrological conditions, and the superscript x represents the specific parameters in the environmental hydrological conditions;

[0017] The building equipment dataset includes the building equipment parameters during the construction process of construction projects. The expression of the building equipment dataset is: to represent the equipment required during the construction process of the corresponding construction types in sequence. 1 to n represent the number of equipment types, Z represents the specific construction type, and p represents the performance parameters of the corresponding equipment.

[0018] Preferably, the specific steps for training the BIM model are:

[0019] (1). Extract multiple building material datasets, building environment datasets, and building equipment datasets corresponding to multiple engineering types with qualified project acceptance in the engineering type dataset as the model experimental dataset. The expression of the model experimental dataset is: Among them, M represents the model experimental dataset, to represent the model data in the model experimental dataset. 1 to y represent the number of model data, Zi It represents that the corresponding model data belongs to the \(i\)th construction type in the engineering type dataset. \(cl\), \(hj\), and \(sb\) represent the parameters of the corresponding building material dataset, building environment dataset, and building equipment dataset respectively. The model experiment dataset is brought into the BIM model for training to obtain \(y\) BIM training model parameters and \(y\) model data included in the model experiment dataset;

[0020] (2) Calculate the final model parameter data according to the model experiment dataset. The final model parameter data includes material parameters environment parameters equipment parameters The specific calculation formula is:

[0021]

[0022] In the calculation formula, represents \(Z\) i The material parameters of the construction type, represents calculating the sum of all building material data in the model experiment dataset starting from \(j = 1\), \(cl\) j represents the material parameters of the \(j\)th building material dataset, and \(w\) represents the total number of building material data in the calculated model experiment dataset. represents the average value of all building material data of the \(i\)th construction type in the model experiment dataset, which is the material parameter

[0023]

[0024] In the calculation formula, represents \(Z\) i The environment parameters of the construction type, represents calculating the sum of all building environment data in the model experiment dataset starting from \(j = 1\), \(cl\) j represents the environment parameters of the \(j\)th building environment dataset, and \(v\) represents the total number of building environment data in the calculated model experiment dataset. represents the average value of all building environment data of the \(i\)th construction type in the model experiment dataset, which is the environment parameter

[0025]

[0026] In the calculation formula, represents \(Z\) i The equipment parameters of the construction type, represents calculating the sum of all building equipment data in the model experiment dataset starting from \(j = 1\), \(cl\) j represents the equipment parameters of the \(j\)th building equipment dataset, represents the total number of construction equipment data in the model experiment dataset for calculation, represents the average value of all construction equipment data of the i-th construction type in the model experiment dataset, which is the equipment parameter

[0027] Preferably, the calculation formula for the material quality index is:

[0028]

[0029] In the calculation formula, represents the material quality index, represents the current actual building material data, represents the difference between the current actual building material data and the material parameter, represents the ratio of the difference to the material parameter, that is, the material quality index.

[0030] Preferably, the calculation formula for the environmental quality index is:

[0031]

[0032] In the calculation formula, represents the environmental quality index, represents the current actual building environment data, represents the difference between the current actual building environment data and the environmental parameter, represents the ratio of the difference to the material parameter, that is, the material quality index.

[0033] Preferably, the calculation formula for the equipment quality index is:

[0034]

[0035] In the calculation formula, represents the equipment quality index, represents the current actual building equipment data, represents the difference between the current actual building equipment data and the equipment parameter, represents the ratio of the difference to the equipment parameter, that is, the equipment quality index.

[0036] Preferably, the calculation formula for the comprehensive influence index is:

[0037]

[0038] In the calculation formula, represents the comprehensive influence index, α, β, and γ respectively represent the weights in terms of materials, environment, and equipment, and α + β + γ = 1.

[0039] Preferably, the prediction of construction project quality includes prediction of material quality, prediction of environmental quality, prediction of equipment quality, and prediction of comprehensive influence.

[0040] Preferably, material prediction threshold, environmental prediction threshold, and equipment prediction threshold are set inside the prediction module. When the material quality index is less than the material prediction threshold, the environmental quality index is less than the environmental prediction threshold, and the equipment quality index is less than the equipment prediction threshold, it means that the corresponding factors will affect the construction project quality, and material warning, environmental warning, and equipment warning signals are sent to the feedback module;

[0041] A comprehensive influence threshold is also set inside the prediction module. When the comprehensive influence index is less than the comprehensive influence threshold, it means that the combination of current multiple factors will affect the construction project quality, and a comprehensive influence warning signal is sent to the feedback module.

[0042] Compared with the prior art, the present invention provides an intelligent pre-control system for construction project quality based on the integration of BIM and AI, which has the following beneficial effects:

[0043] 1. The present invention differentiates construction projects according to construction types, considering that different construction types have different quality requirements for construction projects. Then, combined with the building material data set, building environment data set, and building equipment data set, it can deeply study the quality influencing factors for each construction type, adopt more accurate prediction methods and models, thereby improving the accuracy of prediction. The refined prediction indicators can more comprehensively and accurately reflect the project quality status.

[0044] 2. The present invention combines the integration of BIM and AI algorithms, comprehensively reflects the situation of construction projects, reduces data errors, and improves the accuracy of calculation results. It evaluates the project quality of construction projects from multiple dimensions of material quality index, environmental quality index, equipment quality index, and comprehensive influence index, and then issues corresponding warning signals according to the evaluation results, discovers potential quality problems in advance, and can take targeted measures to solve them according to different warning signals, effectively avoiding the occurrence of project quality accidents, controlling the quality risk in the budding state, and ensuring the construction quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic diagram of the system of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0047] Please refer to Figure 1 , a building engineering quality intelligent pre-control system based on the integration of BIM and AI, including a multi-dimensional data integration module, a BIM model establishment module, a data analysis module, a prediction module, and a feedback module;

[0048] The multi-dimensional data integration module includes an engineering type data integration unit and an engineering building data integration unit. The engineering type data integration unit is used to form an engineering type data set according to the construction types of the building project;

[0049] The engineering type data set includes multiple construction type data of the building project. The expression of the engineering type data set is: {Z1, Z2, Z3 ···, Z8}, where Z1 to Z8 represent 8 engineering types in the engineering type data set in sequence. The multiple construction types of the building project are specifically: masonry structure, concrete structure, steel structure, wood structure, composite structure, composite material structure, aluminum alloy structure, other structures;

[0050] Different construction types have different quality requirements for building projects. By distinguishing building projects according to construction types, it is possible to deeply study the quality influencing factors for each construction type, adopt more accurate prediction methods and models, thereby improving the accuracy of prediction. The refined prediction indicators can more comprehensively and accurately reflect the engineering quality status;

[0051] The engineering building data integration unit is used to form a building material data set, a building environment data set, and a building equipment data set according to the construction-related parameters of the building project type;

[0052] The building material data set includes multiple building materials required for different construction types. The expression of the building material data set is: In the expression, to represent the material types required during the construction process of the corresponding construction type in sequence. 1 to n represent the number of material types, Z represents the specific construction type, and l represents the performance parameters of the corresponding material type;

[0053] The building environment data set includes multiple environmental factors that affect the construction quality of the building project. The expression of the building environment data set is: {Hq x , Hd x , Hs x}, where Hq x represents environmental meteorological conditions, and the superscript x represents specific parameters in the environmental meteorological conditions. The specific parameters in the environmental meteorological conditions are temperature, humidity, precipitation, wind force, and sunshine intensity; Hd x represents environmental geological conditions, and the superscript x represents specific parameters in the environmental geological conditions. The specific parameters in the environmental geological conditions are: foundation bearing capacity, groundwater level, soil type, and terrain slope; Hs x represents environmental hydrological conditions, and the superscript x represents specific parameters in the environmental hydrological conditions. The specific parameters in the environmental hydrological conditions are: surface water volume and groundwater flow velocity;

[0054] The building equipment dataset includes the building equipment parameters during the construction process of the building project. The expression of the building equipment dataset is: to successively correspond to the equipment required during the construction process of the corresponding construction type. 1 to n represent the number of equipment types, Z represents the specific construction type, and p represents the performance parameters of the corresponding equipment;

[0055] Combining the building material dataset, building environment dataset, and building equipment dataset, the quality of the building project is evaluated multi-dimensionally, fully considering the impacts of material, environment, and equipment factors on the building project, comprehensively and specifically predicting the quality of the building project, and improving the pre-control results of the project quality;

[0056] The multi-dimensional data integration module sends the integrated engineering type dataset, building material dataset, building environment dataset, and building equipment dataset to the BIM model establishment module;

[0057] The BIM model establishment module trains the BIM model based on the received engineering type dataset, building material dataset, building environment dataset, and building equipment dataset, and determines the final model parameter data according to the final training result;

[0058] The specific steps for training the BIM model are:

[0059] (1). Extract the corresponding multiple building material datasets, building environment datasets, and building equipment datasets with multiple project acceptances in the engineering type dataset as the model experiment dataset. The expression of the model experiment dataset is: where M represents the model experiment dataset, to represent the model data in the model experiment dataset. 1 to y represent the number of model data, Z iIt is represented that the corresponding model data belongs to the \(i\)-th construction type in the engineering type dataset. \(cl\), \(hj\), and \(sb\) represent the parameters of the corresponding building material dataset, building environment dataset, and building equipment dataset respectively. The model experiment dataset is brought into the BIM model for training to obtain \(y\) BIM training model parameters and \(y\) model data included in the model experiment dataset;

[0060] (2) Calculate the final model parameter data according to the model experiment dataset. The final model parameter data includes material parameters environmental parameters equipment parameters The specific calculation formula is as follows:

[0061]

[0062] In the calculation formula, represents \(Z\) i the material parameters of the construction type, represents the sum of all building material data in the model experiment dataset starting from \(j = 1\), \(cl\) j represents the material parameters of the \(j\)-th building material dataset, and \(w\) represents the total number of building material data in the calculated model experiment dataset. represents the average value of all building material data of the \(i\)-th construction type in the model experiment dataset, which is the material parameter

[0063]

[0064] In the calculation formula, represents \(Z\) i the environmental parameters of the construction type, represents the sum of all building environment data in the model experiment dataset starting from \(j = 1\), \(cl\) j represents the environmental parameters of the \(j\)-th building environment dataset, and \(v\) represents the total number of building environment data in the calculated model experiment dataset. represents the average value of all building environment data of the \(i\)-th construction type in the model experiment dataset, which is the environmental parameter

[0065]

[0066] In the calculation formula, represents \(Z\) i the equipment parameters of the construction type, represents the sum of all building equipment data in the model experiment dataset starting from \(j = 1\), \(cl\) j represents the equipment parameters of the \(j\)-th building equipment dataset, represents the total number of construction equipment data in the model experiment dataset for calculation, represents the average value of all construction equipment data of the i-th construction type in the model experiment dataset, which is the equipment parameter

[0067] The BIM model establishment module calculates the final model parameter data according to the model experiment dataset, realizes the improvement of the model accuracy by combining multiple groups of data, provides a stable data basis for subsequent project quality pre-control, comprehensively reflects the situation of the construction project, reduces data errors, and improves the accuracy of calculation results;

[0068] The BIM model establishment module is connected to the data analysis module through the network;

[0069] The data analysis module calculates the material quality index, environmental quality index, equipment quality index and comprehensive influence index according to the building material dataset, building environment dataset and building equipment dataset and sends them to the prediction module;

[0070] The calculation formula for the material quality index is:

[0071]

[0072] In the calculation formula, represents the material quality index, represents the current actual building material data, represents the difference between the current actual building material data and the material parameter, represents the ratio of the difference to the material parameter, that is, the material quality index;

[0073] The calculation formula for the environmental quality index is:

[0074]

[0075] In the calculation formula, represents the environmental quality index, represents the current actual building environment data, represents the difference between the current actual building environment data and the environmental parameter, represents the ratio of the difference to the material parameter, that is, the material quality index;

[0076] The calculation formula for the equipment quality index is:

[0077]

[0078] In the calculation formula, represents the equipment quality index, represents the current actual building equipment data, represents the difference between the current actual construction equipment data and the equipment parameters, represents the ratio of the difference to the equipment parameters, i.e., the equipment quality index;

[0079] The calculation formula for the comprehensive influence index is:

[0080]

[0081] In the calculation formula, represents the comprehensive influence index, α, β, and γ respectively represent the weights in terms of materials, environment, and equipment, and α + β + γ = 1;

[0082] The prediction module predicts the construction project quality based on the material quality index, environmental quality index, equipment quality index, and comprehensive influence index, and sends the prediction result to the feedback module;

[0083] The construction project quality prediction includes material quality prediction, environmental quality prediction, equipment quality prediction, and comprehensive influence prediction;

[0084] The prediction module is internally set with a material prediction threshold, an environmental prediction threshold, and an equipment prediction threshold. When the material quality index is less than the material prediction threshold, the environmental quality index is less than the environmental prediction threshold, and the equipment quality index is less than the equipment prediction threshold, it means that the corresponding factors will affect the construction project quality, and a material warning, an environmental warning, and an equipment warning signal are sent to the feedback module;

[0085] The prediction module is also internally set with a comprehensive influence threshold. When the comprehensive influence index is less than the comprehensive influence threshold, it means that the combination of current multiple factors will affect the construction project quality, and a comprehensive influence warning signal is sent to the feedback module;

[0086] The feedback module includes a display unit and a warning unit. The display unit displays the prediction result, and the warning unit issues a warning according to the prediction result;

[0087] The engineering quality of the construction project is evaluated from multiple dimensions such as the material quality index, environmental quality index, equipment quality index, and comprehensive influence index, and corresponding warning signals are sent according to the evaluation result to discover potential quality problems in advance. According to different warning signals, targeted measures can be taken to solve them, effectively avoiding the occurrence of engineering quality accidents, controlling the quality risk in the bud, and ensuring the construction quality.

[0088] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent pre-control system for building engineering quality based on the integration of BIM and AI, characterized in that: It includes a multi-dimensional data integration module, a BIM model establishment module, a data analysis module, a prediction module, and a feedback module; The multi-dimensional data integration module includes an engineering type data integration unit and an engineering construction data integration unit. The engineering type data integration unit is used to form an engineering type data set according to the construction types of construction projects. The engineering construction data integration unit is used to form a building material data set, a building environment data set, and a building equipment data set according to the construction-related parameters of the construction project types. The multi-dimensional data integration module sends the integrated engineering type data set, building material data set, building environment data set, and building equipment data set to the BIM model establishment module; The BIM model establishment module trains the BIM model according to the received engineering type data set, building material data set, building environment data set, and building equipment data set, and determines the final model parameter data according to the final training result. The BIM model establishment module is connected to the data analysis module through the network; The data analysis module calculates the material quality index, environmental quality index, equipment quality index, and comprehensive influence index according to the building material data set, building environment data set, and building equipment data set, and sends them to the prediction module; The prediction module predicts the construction project quality according to the material quality index, environmental quality index, equipment quality index, and comprehensive influence index, and sends the prediction result to the feedback module; The feedback module includes a display unit and a warning unit. The display unit displays the prediction result, and the warning unit issues a warning according to the prediction result.

2. The intelligent pre-control system for building engineering quality based on the integration of BIM and AI according to claim 1, characterized in that: The engineering type data set includes multiple construction type data of the construction project. The expression of the engineering type data set is: {Z1, Z2, Z3 ···, Z8}, where Z1 to Z8 represent 8 engineering types in the engineering type data set in sequence. The multiple construction types of the construction project are specifically: masonry structure, concrete structure, steel structure, wood structure, composite structure, composite material structure, aluminum alloy structure, and other structures.

3. The intelligent pre-control system for building engineering quality based on the integration of BIM and AI according to claim 2, characterized in that: The building material dataset includes multiple building materials required for different construction types, and the expression of the building material dataset is: In the expression, to successively represent the types of materials required during the construction process of the corresponding construction types. 1 to n represent the number of material types, Z represents the specific construction type, and l represents the performance parameters of the corresponding material type; The building environment dataset includes multiple environmental factors that affect the construction quality of building projects. The expression of the building environment dataset is: {Hq x , Hd x , Hs x}, where Hq x represents environmental meteorological conditions, and the superscript x represents specific parameters in the environmental meteorological conditions; Hd x represents environmental geological conditions, and the superscript x represents specific parameters in the environmental geological conditions; Hs x represents environmental hydrological conditions, and the superscript x represents specific parameters in the environmental hydrological conditions. The construction equipment dataset includes construction equipment parameters during the construction process of a construction project. The expression of the construction equipment dataset is as follows: to successively points to the equipment required during the construction process of the corresponding construction type. 1 to n represent the number of equipment types, Z represents the specific construction type, and o represents the performance parameters of the corresponding equipment.

4. The intelligent pre-control system for building engineering quality based on the integration of BIM and AI according to claim 3, characterized in that: The specific steps for training the BIM model are: (1) Extract the corresponding multiple building material datasets, building environment datasets, and building equipment datasets that are qualified for acceptance in multiple engineering types datasets as the model experiment dataset. The expression of the model experiment dataset is: Among them, M represents the model experiment dataset, to represent the model data in the model experiment dataset, 1 to y represent the number of model data, and Z i represents that the corresponding model data belongs to the i-th construction type in the engineering types dataset. cl, hj, sb represent the parameters of the corresponding building material dataset, building environment dataset, and building equipment dataset. Substitute the model experiment dataset into the BIM model for training to obtain y BIM training model parameters and the y model data included in the model experiment dataset; (2) Calculate the final model parameter data based on the model experiment data set, and the final model parameter data includes material parameters environmental parameters equipment parameters The specific calculation formula is as follows: In the calculation formula, represents Z i the material parameter of the construction type, represents the sum of all building material data in the model experiment dataset starting from j = 1, cl j represents the material parameter of the j-th building material dataset, w represents the total number of building material data in the calculated model experiment dataset, represents the average value of all building material data of the i-th construction type in the model experiment dataset, which is the material parameter In the calculation formula, represents Z i the environmental parameters of the construction type, represents the sum of all building environmental data in the model experiment dataset starting from j = 1, cl j represents the environmental parameters of the j-th building environmental dataset, v represents the total number of building environmental data in the calculated model experiment dataset, represents the average value of all building environmental data of the i-th construction type in the model experiment dataset, which is the environmental parameter In the calculation formula, represents Z i the equipment parameters of the construction type, represents the sum of all building equipment data in the model experiment dataset starting from j = 1, cl j represents the equipment parameters of the j-th building equipment dataset, represents the total number of building equipment data in the calculated model experiment dataset, represents the average value of all building equipment data of the l-th construction type in the model experiment dataset, which is the equipment parameter 5. The intelligent pre-control system for building engineering quality based on the integration of BIM and AI according to claim 4, characterized in that: The calculation formula for the material quality index is: In the calculation formula, represents the material quality index, represents the current actual building material data, represents the difference between the current actual building material data and the material parameters, represents the ratio of the difference to the material parameters, that is, the material quality index.

6. The intelligent pre-control system for construction project quality based on the integration of BIM and AI according to claim 5, characterized in that: The calculation formula for the environmental quality index is: In the calculation formula, represents the environmental quality index, represents the current actual building environment data, represents the difference between the current actual building environment data and the environmental parameters, represents the ratio of the difference to the material parameters, that is, the material quality index.

7. The intelligent pre-control system for building engineering quality based on the integration of BIM and AI according to claim 6, characterized in that: The calculation formula for the equipment quality index is: In the calculation formula, represents the equipment quality index, represents the current actual building equipment data, represents the difference between the current actual building equipment data and the equipment parameters, represents the ratio of the difference to the equipment parameters, that is, the equipment quality index.

8. The intelligent pre-control system for building engineering quality based on the integration of BIM and AI according to claim 7, characterized in that: The calculation formula for the comprehensive influence index is: In the calculation formula, represents the comprehensive influence index, where α, β, and γ represent the weights in terms of materials, environment, and equipment respectively, and α + β + γ = 1.

9. The intelligent pre-control system for construction project quality based on the integration of BIM and AI according to claim 8, characterized in that: The construction project quality prediction includes material quality prediction, environmental quality prediction, equipment quality prediction, and comprehensive influence prediction.

10. The intelligent pre-control system for construction project quality based on the integration of BIM and AI according to claim 9, characterized in that: The prediction module internally sets a material prediction threshold, an environmental prediction threshold, and an equipment prediction threshold. When the material quality index is less than the material prediction threshold, the environmental quality index is less than the environmental prediction threshold, and the equipment quality index is less than the equipment prediction threshold, it means that the corresponding factors will affect the construction project quality, and sends material warning, environmental warning, and equipment warning signals to the feedback module; The prediction module also internally sets a comprehensive influence threshold. When the comprehensive influence index is less than the comprehensive influence threshold, it means that the combination of current multiple factors will affect the construction project quality, and sends a comprehensive influence warning signal to the feedback module.

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