A method and system for tracing quality of prefabricated building construction

Abstract

The present invention relates to the field of construction engineering technology, and in particular to a method and system for tracing the quality of prefabricated building construction. The method comprises the following steps: obtaining construction site sensor data and constructing a construction site sensor network; obtaining prefabricated building design data and constructing a prefabricated building structure model; constructing a site building structure model according to the construction site sensor network, and performing building parameter error calculation to obtain construction site error data; performing geological error correction on the construction site error data to obtain construction site error correction data; performing construction quality assessment on the construction site error correction data according to the construction site sensor network, thereby obtaining construction quality data, and performing construction structure quality grade classification on the site building structure model to obtain a site building structure quality model; performing strategy analysis on the site building structure quality model to obtain a construction optimization strategy. The present invention can improve the level of construction quality management.

Description

Technical Field

[0001] The present invention relates to the technical field of construction engineering, and in particular to a method and system for tracing the construction quality of assembled buildings. Background Art

[0002] With the acceleration of urbanization and the development of the construction industry, prefabricated buildings, as a new type of construction method, have gradually attracted widespread attention and application. Compared with traditional buildings, prefabricated buildings have the advantages of short construction period, controllable quality, and environmental friendliness, so they are favored by governments, enterprises and consumers. However, due to the particularity of prefabricated buildings, their construction quality management faces a series of challenges, including the difficulty of quality control in material selection, processing and manufacturing, transportation and installation, and traditional construction quality management methods have been difficult to meet actual needs. Traditional construction quality management mainly relies on manual records and management, which is prone to problems such as inaccurate information records and data omissions, resulting in incomplete and inaccurate quality traceability. Traditional construction quality management is usually sorted and counted afterwards, and it is impossible to monitor and promptly feedback quality problems in the construction process in real time, resulting in quality problems being discovered late and difficult to handle and correct in time. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a method for tracing the quality of prefabricated building construction to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above purpose, a method for tracing the quality of prefabricated building construction is provided, comprising the following steps:

[0005] Step S1: Acquire construction site sensor data, and construct a sensor network based on the construction site sensor data, thereby obtaining a construction site sensor network;

[0006] Step S2: obtaining prefabricated building design data, and extracting building structure features from the prefabricated building design data, thereby obtaining prefabricated building structure data; performing digital modeling of the prefabricated building topology structure according to the prefabricated building structure data, thereby obtaining a prefabricated building structure model;

[0007] Step S3: digitally modeling the construction progress of the construction site according to the construction site sensor network, thereby obtaining the on-site building structure model, and calculating the building parameter error of the on-site building structure model according to the assembled building structure model, thereby obtaining the construction site error data;

[0008] Step S4: performing building geological difference analysis on the construction site sensor data and the prefabricated building design data to obtain geological difference data, and performing geological error correction on the construction site error data according to the geological difference data to obtain construction site error correction data;

[0009] Step S5: performing construction quality assessment on the construction site error correction data according to the construction site sensor network, thereby obtaining construction quality data, and classifying the construction structure quality grade of the on-site building structure model according to the construction quality data, thereby obtaining the on-site building structure quality model;

[0010] Step S6: Perform subsequent construction optimization strategy analysis on the on-site building structure quality model according to the prefabricated building design data, so as to obtain a building construction optimization strategy.

[0011] The present invention can realize real-time monitoring and data collection of the construction process by acquiring sensor data of the construction site, thereby improving the accuracy and integrity of the construction site data. The construction of the sensor network can help establish a detailed data network and provide a data basis for subsequent quality management. The acquisition of prefabricated building design data and the extraction of structural features are helpful to understand the design and construction characteristics of the building, and provide a basis for subsequent digital modeling. In this way, the accuracy and integrity of the building structure can be ensured. Through digital modeling and parameter error calculation, real-time monitoring and quality evaluation of the construction site can be realized. This helps to timely discover problems in the construction process and take corresponding measures to adjust and correct them, thereby improving the timeliness and accuracy of the construction quality. Geological difference analysis and error correction can help understand the characteristics and influence of the construction site environment, thereby more accurately evaluating the true situation of the construction quality. Through the correction of geological errors, the influence of geological factors on the construction quality evaluation can be reduced, and the accuracy of the evaluation results can be improved. Through construction quality evaluation and quality model division, the quality problems in the construction process can be comprehensively evaluated and analyzed, providing a reference basis for subsequent construction optimization. At the same time, the establishment of an on-site building structure quality model can help supervise and manage quality changes in the construction process, and ensure the stability and controllability of the construction quality. Through construction optimization strategy analysis, problems existing in the construction process can be identified and solved, and corresponding improvement measures and optimization plans can be proposed. This helps to improve construction efficiency and quality, reduce construction costs, and achieve optimization and improvement of the construction process.

[0012] Optionally, step S1 specifically includes:

[0013] Step S11: Acquire construction site sensor data, and perform construction site camera image extraction on the construction site sensor data, thereby obtaining a construction site camera image;

[0014] Step S12: extracting construction site spatial features based on the construction site camera images, thereby obtaining construction site spatial feature data;

[0015] Step S13: constructing a construction site spatial coordinate system based on the construction site spatial feature data;

[0016] Step S14: Perform three-dimensional spatial sensing fusion on the construction site spatial coordinate system according to the construction site sensor data, thereby obtaining a construction site sensor network.

[0017] The present invention can realize visual monitoring of the construction process by acquiring camera images of the construction site. Camera images can provide more intuitive and comprehensive construction site information, which is helpful to discover and identify problems and obstacles in the construction process. By extracting spatial features from camera images of the construction site, key information such as the structure and layout of the construction site can be obtained. This helps to understand the actual situation of the construction site and provides a basis for subsequent spatial coordinate system construction and sensor fusion. Establishing a spatial coordinate system for the construction site can provide a unified reference framework for integrating and processing spatial data from different sources. This helps to associate and analyze camera images and sensor data of the construction site, and realize effective management and utilization of data. By performing three-dimensional spatial sensor fusion on sensor data of the construction site, data from different sensors can be integrated into the same spatial coordinate system. This helps to improve the consistency and accuracy of the data, and provides more reliable data support for subsequent construction quality assessment and problem identification.

[0018] Optionally, step S2 specifically includes:

[0019] Step S21: Acquire prefabricated building design data, and extract building structure features and building material features from the prefabricated building design data, thereby obtaining prefabricated building structure data and prefabricated building material data;

[0020] Step S22: performing prefabricated building topological structure analysis based on the prefabricated building structure data, thereby obtaining a prefabricated building topological structure model;

[0021] Step S23: Analyze the building material characteristics according to the prefabricated building material data, so as to obtain the building material characteristic data;

[0022] Step S24: filling the material property space of the prefabricated building topological structure model based on the building material property data, thereby obtaining the prefabricated building structure property model;

[0023] Step S25: Performing building load analysis on the prefabricated building structure characteristic model to obtain building load data, and filling building load attributes on the prefabricated building structure characteristic model based on the building load data to obtain the prefabricated building structure model.

[0024] The present invention can provide a basis for subsequent building analysis and model building by acquiring and extracting structural features and material features in prefabricated building design data. These features include the structural form, material type, size, etc. of the building, which are crucial for subsequent topological structure analysis and characteristic model building. By analyzing the topological structure of the prefabricated building, the relationship and connection mode between its components can be understood. This helps to determine the overall structural form of the building and provides a basis for subsequent model building and analysis. By analyzing the material properties used in the prefabricated building, its strength, durability, thermal insulation and other properties can be understood. This helps to select suitable materials, optimize building design, and ensure the safety and reliability of the building structure. Combining the building material property data with the building topological structure model, a structural property model of the prefabricated building can be generated. This model can reflect the distribution and performance characteristics of building materials in different parts, and provide a basis for subsequent load analysis and attribute filling. By performing load analysis on the prefabricated building structural property model, the bearing capacity and deformation of the building under different load conditions can be evaluated. Filling the structural model with attributes based on load data can further improve the building structure model and provide guidance for design optimization and engineering implementation.

[0025] Optionally, step S22 is specifically:

[0026] Step S221: constructing an initial prefabricated building structure model based on the prefabricated building structure data;

[0027] Step S222: classifying the initial prefabricated building structure model according to its structural functions, thereby obtaining a supporting structure model, a connecting structure model, and a main structure model;

[0028] Step S223: Associating the connection structure model and the support structure model with respect to the structural connection relationship according to the initial prefabricated building structure model, thereby obtaining support structure connection relationship data;

[0029] Step S224: Associating the connection structure model and the main structure model with respect to the structural connection relationship according to the initial prefabricated building structure model, thereby obtaining main structure connection relationship data;

[0030] Step S225: fusing the supporting structure connection relationship data and the main structure connection relationship data with building structure connection characteristics, thereby obtaining prefabricated building structure connection relationship data;

[0031] Step S226: Performing building main body spatial distribution statistics according to the main structure data, thereby obtaining building main body distribution data;

[0032] Step S227: constructing a prefabricated building topological structure model according to the prefabricated building structure connection relationship data and the building main body distribution data.

[0033] The present invention can integrate the basic form and components of the building structure by constructing an initial assembled building structure model, providing a basis for subsequent analysis and design. By classifying the initial model into structural functions, the building structure can be decomposed into different parts such as a supporting structure, a connecting structure and a main structure, which helps to clarify the function and composition relationship of the building structure. By associating the connecting structure and the supporting structure, the connection mode and function between them can be understood, which helps to ensure the stability and safety of the building structure. By associating the connecting structure and the main structure, the connection mode and position can be determined, which helps to optimize the layout and design of the building structure. By integrating the connection features of the supporting structure and the main structure, the overall connection mode of the building structure can be fully understood, providing a basis for subsequent topological structure modeling. By statistically analyzing the spatial distribution of the building main body, the overall form and layout of the building can be understood, which helps to determine the optimization plan of the building structure. By constructing an assembled building topological structure model, the connection relationship and spatial distribution of the building structure can be comprehensively considered, providing guidance for the overall design, and improving the efficiency and performance of the building structure.

[0034] Optionally, step S3 specifically includes:

[0035] Step S31: extracting the construction site camera image from the construction site sensor network to obtain the construction site camera image;

[0036] Step S32: classifying the construction site camera images by on-site building structure, thereby obtaining an above-ground building structure image and an underground building structure image;

[0037] Step S33: extracting underground building structure features from the underground building structure image to obtain underground building structure data, and constructing an underground building structure model based on the underground building structure data;

[0038] Step S34: extracting the above-ground building structure features from the above-ground building structure image, thereby obtaining above-ground building structure data, and constructing an above-ground building structure model according to the above-ground building structure data;

[0039] Step S35: Combining the underground building structure model and the above-ground building structure model to obtain a field structure model;

[0040] Step S36: acquiring on-site building material data, and filling the on-site structural model with material properties according to the on-site building material data, thereby obtaining the on-site building structural model;

[0041] Step S37: Calculate the building parameter errors of the on-site building structure model according to the prefabricated building structure model, so as to obtain the building site error data.

[0042] The present invention can obtain real-time construction site information by extracting camera images of the construction site, and provide data support for subsequent analysis and monitoring. By classifying the building structure of the camera images, the above-ground building structure and the underground building structure can be identified, which is helpful for subsequent processing of different types of building structures. By extracting the features and constructing the data of the underground building structure images, the morphology and characteristics of the underground building structure can be understood, and a reference can be provided for the design and construction of the underground structure. By extracting the features and constructing the data of the above-ground building structure images, the morphology and characteristics of the above-ground building structure can be understood, and a reference can be provided for the design and construction of the building structure. By combining the underground building structure model and the above-ground building structure model, a complete on-site structure model can be obtained, which provides a basis for subsequent analysis and design. By obtaining the on-site building material data and filling it into the structure model, the material properties of the on-site building structure can be more realistically reflected, which is helpful for accurately evaluating the performance and behavior of the structure. By calculating the parameter error between the assembled building structure model and the on-site building structure model, the accuracy and consistency of the on-site building structure can be evaluated, which provides a reference for optimizing the construction process and improving the building quality.

[0043] Optionally, step S37 is specifically:

[0044] S371: Calculating building structure errors on the prefabricated building structure model and the on-site building structure model, thereby obtaining building structure error data;

[0045] S372: Calculating material property errors on the prefabricated building structure model and the on-site building structure model, thereby obtaining building material error data;

[0046] S373: extracting foundation settlement sensing features from the construction site sensor network, thereby obtaining on-site foundation settlement sensing data;

[0047] S374: dividing the prefabricated building structure model into a synchronous building structure model according to the on-site building structure model, thereby obtaining an on-site building structure design model;

[0048] S375: extracting soil bearing characteristics of the building area from the prefabricated building design data, thereby obtaining ideal soil bearing data, and simulating foundation settlement of the on-site building structure design model based on the ideal soil bearing data, thereby obtaining ideal foundation settlement data;

[0049] Step S376: performing foundation settlement difference calculation on the ideal foundation settlement data and the on-site foundation settlement sensing data, thereby obtaining foundation settlement difference data;

[0050] Step S377: Merge the building structure error data, building material error data and foundation settlement difference data to obtain building site error data.

[0051] The present invention can evaluate the structural errors occurring during the construction process by comparing the differences between the assembled building structure model and the on-site building structure model, which helps to timely discover and solve potential structural problems and ensure the safety and stability of the building structure. By comparing the material property errors between the assembled building structure model and the on-site building structure model, the material differences existing in the construction process can be evaluated, which helps to adjust the material selection and use in the construction process to ensure the quality and performance of the building structure. By extracting the foundation settlement sensing characteristics, the foundation settlement of the construction site can be monitored in real time, and the foundation settlement anomaly can be discovered in time, providing data support for ensuring the stability of the building structure. By dividing the assembled building structure model into building structure models of the same progress, it can be better compared with the on-site building structure model, and the difference between the assembled building structure and the actual site can be found, providing a basis for adjustment and optimization during the construction process. By extracting the soil bearing characteristics of the building area and performing foundation settlement simulation, the foundation bearing capacity and foundation settlement can be evaluated, providing a reference for the design and construction of the building structure, and ensuring the stability and safety of the building structure. By calculating the difference between the ideal foundation settlement and the on-site foundation settlement, the deviation of the foundation settlement in the actual construction can be evaluated, providing a basis for subsequent adjustments and corrections to ensure the stability and safety of the building structure. By combining the building structure error, building material error and foundation settlement difference data, the error situation of the construction site can be comprehensively evaluated, providing comprehensive data support for optimizing the construction process and improving the building quality, ensuring the safety and stability of the building structure.

[0052] Optionally, step S4 is specifically:

[0053] Step S41: extracting geological sensor features from the construction site sensor data to obtain on-site geological sensor data;

[0054] Step S42: extracting geological features of the building area from the prefabricated building design data, thereby obtaining ideal geological data for the designed building;

[0055] Step S43: performing geological difference evaluation on the on-site geological sensor data and the ideal geological data of the designed building, thereby obtaining geological difference data;

[0056] Step S44: performing foundation settlement difference correction on the construction site error data according to the geological difference data, thereby obtaining construction site error correction data.

[0057] The present invention can monitor geological conditions in real time, such as soil density, groundwater level, etc., by extracting geological sensing features of the construction site, which helps to timely discover potential geological problems and provide data support for the stability of the building structure. By extracting geological features in the prefabricated building design data, such as geological structure, soil type, etc., the degree of consideration of geological conditions in the design stage can be evaluated, and a reference standard for ideal geological conditions can be provided for the subsequent construction process. By comparing and evaluating the on-site geological sensing data and the ideal geological data of the designed building, the difference between the geological conditions at the construction site and the ideal geological conditions of the design can be found, providing a basis for early warning and handling of geological problems that arise in the subsequent construction process. By correcting the foundation settlement difference of the construction site error data according to the geological difference data, the errors caused by the geological conditions can be corrected, the stability and safety of the building structure can be improved, and the quality of the construction project can be ensured.

[0058] Optionally, step S5 specifically includes:

[0059] Step S51: extracting stress and strain sensing features from the construction site sensing network to obtain on-site stress and strain sensing data, wherein the on-site stress and strain sensing data includes material stress and strain sensing data and structural stress and strain sensing data;

[0060] Step S52: extracting material error features and structural error features from the construction site error correction data, thereby obtaining building material error data and building structure error data;

[0061] Step S53: performing material strength quality assessment on the building material error data according to the material stress and strain sensing data, thereby obtaining material strength quality data;

[0062] Step S54: performing structural strength quality assessment on the building structure error data according to the structural stress and strain sensing data, thereby obtaining structural strength quality data;

[0063] Step S55: performing foundation strength quality assessment on the construction site error correction data, thereby obtaining foundation strength quality data;

[0064] Step S56: Perform a comprehensive quality assessment on the material strength quality data, the structural strength quality data and the foundation strength quality data to obtain construction quality data, and classify the on-site building structure model into construction structure quality grades according to the construction quality data to obtain the on-site building structure quality model.

[0065] The present invention can monitor the changes of materials and structures in real time by extracting on-site stress and strain sensing data, including the stress and strain conditions of materials and structures, and is helpful to timely discover potential structural problems and ensure the safety and stability of construction. By extracting building materials and structural error data, the problems of materials and structures in the construction process can be analyzed, providing a basis for subsequent quality evaluation and improvement. By evaluating the material stress and strain sensing data, the strength quality of building materials can be analyzed, including the bearing capacity and stability of materials, providing a reference for material selection and use. By evaluating the structural stress and strain sensing data, the strength and stability of the building structure can be analyzed, potential structural problems can be identified, and guidance and improvement directions can be provided for structural design and construction. By evaluating the strength and quality of the foundation, the bearing capacity and stability of the foundation can be analyzed, foundation problems can be discovered in time, and a basis can be provided for foundation treatment and reinforcement. By comprehensively evaluating various quality data, the quality status in the construction process can be fully understood, and the on-site building structure can be divided according to the quality grade, providing a reference standard for construction quality management and supervision, and ensuring the quality and safety of the construction project.

[0066] Optionally, step S56 is specifically:

[0067] Step S561: constructing a construction quality assessment system according to the material strength quality data, the structural strength quality data and the foundation strength quality data;

[0068] Step S562: Evaluate the construction quality of the building structure on the construction site sensor network through the construction quality evaluation system, thereby obtaining construction quality data;

[0069] Step S563: classify and calculate the construction quality data, thereby obtaining high-quality construction structure data and low-quality construction structure data;

[0070] Step S564: marking the construction structure quality grade of the on-site building structure model according to the high-quality construction structure data and the low-quality construction structure data, thereby obtaining the on-site building structure quality model.

[0071] The present invention can establish a comprehensive evaluation framework to ensure the comprehensiveness and reliability of construction quality by integrating the strength quality data of materials, structures and foundations and constructing a construction quality evaluation system. This system can help identify various problems that may occur in construction and provide benchmarks for evaluation and improvement. Through the construction quality evaluation system, the construction quality of the building structure is evaluated on the construction site sensor network, and each link in the construction process can be monitored and evaluated in real time, and quality problems that may exist in the construction can be discovered in time, so that corrective measures can be taken in time to ensure the quality and stability of the building structure. By classifying and calculating the construction quality data, high-quality construction structure data and low-quality construction structure data can be clearly distinguished, providing a specific basis and direction for subsequent quality analysis and traceability. By marking the quality level of the on-site building structure model according to the high-quality construction structure data and the low-quality construction structure data, the quality level of each part in the construction can be clearly identified, providing a reference basis for quality control and supervision. This grade marking helps decision makers and relevant personnel to understand the quality status of the building structure in a timely manner and take necessary actions to improve the construction quality.

[0072] Optionally, the present specification further provides a system for tracing the quality of prefabricated building construction, which is used to execute the method for tracing the quality of prefabricated building construction as described above. The system for tracing the quality of prefabricated building construction includes:

[0073] A sensor network construction module is used to obtain sensor data of the construction site and construct a sensor network according to the sensor data of the construction site, thereby obtaining a sensor network of the construction site;

[0074] The structural model building module is used to obtain the prefabricated building design data and extract the building structure features of the prefabricated building design data, thereby obtaining the prefabricated building structure data; digitally model the prefabricated building topological structure according to the prefabricated building structure data, thereby obtaining the prefabricated building structure model;

[0075] A building error calculation module is used to perform digital modeling of the construction progress of the construction site according to the construction site sensor network, thereby obtaining the on-site building structure model, and to perform building parameter error calculation on the on-site building structure model according to the assembled building structure model, thereby obtaining the building site error data;

[0076] A geological error correction module is used to perform building geological difference analysis on the construction site sensor data and the prefabricated building design data, thereby obtaining geological difference data, and to perform geological error correction on the building site error data according to the geological difference data, thereby obtaining building site error correction data;

[0077] A construction quality assessment module is used to perform construction quality assessment on the construction site error correction data according to the construction site sensor network, thereby obtaining construction quality data, and to classify the construction structure quality grade of the on-site building structure model according to the construction quality data, thereby obtaining the on-site building structure quality model;

[0078] The construction strategy optimization module is used to conduct subsequent construction optimization strategy analysis on the on-site building structure quality model based on the prefabricated building design data, so as to obtain the building construction optimization strategy.

[0079] The system for tracing the quality of prefabricated building construction of the present invention can implement any one of the methods for tracing the quality of prefabricated building construction of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the method for tracing the quality of prefabricated building construction. The internal modules of the system cooperate with each other, thereby improving the management level of construction quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0081] Figure 1 It is a schematic diagram of the steps of the method for tracing the quality of prefabricated building construction according to the present invention;

[0082] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0083] Figure 3 Detailed step flow diagram of step S2 in the present invention;

[0084] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0085] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0086] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0087] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0088] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for tracing the quality of prefabricated building construction, the method comprising the following steps:

[0089] Step S1: Acquire construction site sensor data, and construct a sensor network based on the construction site sensor data, thereby obtaining a construction site sensor network;

[0090] In this embodiment, sensors such as temperature, humidity and pressure sensors are installed at the construction site to obtain real-time data. The data collected by these sensors is sent to the data center for processing via wireless transmission technology (such as LoRaWAN). In the data center, a sensor network is built using an IoT platform (such as AWS IoT Core), and real-time monitoring and alarming of data are achieved by configuring device shadows and rule engines. For example, when the temperature exceeds the set threshold, the system automatically sends an alarm to notify the on-site staff to make adjustments.

[0091] Step S2: obtaining prefabricated building design data, and extracting building structure features from the prefabricated building design data, thereby obtaining prefabricated building structure data; performing digital modeling of the prefabricated building topology structure according to the prefabricated building structure data, thereby obtaining a prefabricated building structure model;

[0092] In this embodiment, the BIM model and CAD drawings of the prefabricated building are collected, and the building structure is modeled and analyzed using BIM software (such as Revit). Through the parametric design function in BIM, the key features of the building structure, such as floor thickness and column spacing, are extracted. Then, the data export function in BIM is used to convert the model into a digital topological structure model (such as IFC format) for subsequent data analysis and optimization.

[0093] Step S3: digitally modeling the construction progress of the construction site according to the construction site sensor network, thereby obtaining the on-site building structure model, and calculating the building parameter error of the on-site building structure model according to the assembled building structure model, thereby obtaining the construction site error data;

[0094] In this embodiment, based on the construction site sensor network, the data of the construction site can be obtained in real time, and the digital modeling of the construction progress can be performed using BIM software and on-site measured data. At the same time, the design model of the prefabricated building is compared with the on-site model to calculate the building parameter error. For example, by comparing the actual progress of the construction site with the design progress, the delay or advance in the construction process can be calculated, thereby obtaining the construction site error data. At the same time, the collision detection function in the BIM software can also be used to identify problems that occur during construction, and the construction plan can be adjusted in time to reduce errors.

[0095] Step S4: performing building geological difference analysis on the construction site sensor data and the prefabricated building design data to obtain geological difference data, and performing geological error correction on the construction site error data according to the geological difference data to obtain construction site error correction data;

[0096] In this embodiment, by performing geological difference analysis on the construction site sensor data and the prefabricated building design data, the differences under different geological conditions can be identified. Then, based on these differences, the construction site error data is corrected for geological errors. For example, if the construction site is located in a soft soil area, it may cause settlement problems of the building structure. The error data can be corrected based on the geological difference data to take into account the impact of soil conditions on the building.

[0097] Step S5: performing construction quality assessment on the construction site error correction data according to the construction site sensor network, thereby obtaining construction quality data, and classifying the construction structure quality grade of the on-site building structure model according to the construction quality data, thereby obtaining the on-site building structure quality model;

[0098] In this embodiment, the construction site error correction data is monitored and evaluated in real time through the construction site sensor network to obtain construction quality data. The construction quality data is statistically and visually analyzed using data analysis tools (such as pandas and matplotlib libraries in Python). According to the quality indicators, the quality level of the on-site building structure is divided, and improvement measures and management strategies are formulated. For example, based on the concrete strength test data and the structural construction progress, the quality of the building structure can be evaluated and divided into different levels for further improvement and management.

[0099] Step S6: Perform subsequent construction optimization strategy analysis on the on-site building structure quality model according to the prefabricated building design data, so as to obtain a building construction optimization strategy.

[0100] In this embodiment, based on the BIM model and on-site measured data, the optimization strategy analysis of the on-site building structure is performed using building information model analysis software (such as Autodesk Navisworks). By simulating the effects of different optimization schemes, their impact on construction progress and quality is evaluated, and the best solution is proposed. For example, the size of components and material selection are optimized to improve assembly efficiency and structural stability.

[0101] The present invention can realize real-time monitoring and data collection of the construction process by acquiring sensor data of the construction site, thereby improving the accuracy and integrity of the construction site data. The construction of the sensor network can help establish a detailed data network and provide a data basis for subsequent quality management. The acquisition of prefabricated building design data and the extraction of structural features are helpful to understand the design and construction characteristics of the building, and provide a basis for subsequent digital modeling. In this way, the accuracy and integrity of the building structure can be ensured. Through digital modeling and parameter error calculation, real-time monitoring and quality evaluation of the construction site can be realized. This helps to timely discover problems in the construction process and take corresponding measures to adjust and correct them, thereby improving the timeliness and accuracy of the construction quality. Geological difference analysis and error correction can help understand the characteristics and influence of the construction site environment, thereby more accurately evaluating the true situation of the construction quality. Through the correction of geological errors, the influence of geological factors on the construction quality evaluation can be reduced, and the accuracy of the evaluation results can be improved. Through construction quality evaluation and quality model division, the quality problems in the construction process can be comprehensively evaluated and analyzed, providing a reference basis for subsequent construction optimization. At the same time, the establishment of an on-site building structure quality model can help supervise and manage quality changes in the construction process, and ensure the stability and controllability of the construction quality. Through construction optimization strategy analysis, problems existing in the construction process can be identified and solved, and corresponding improvement measures and optimization plans can be proposed. This helps to improve construction efficiency and quality, reduce construction costs, and achieve optimization and improvement of the construction process.

[0102] Optionally, step S1 specifically includes:

[0103] Step S11: Acquire construction site sensor data, and perform construction site camera image extraction on the construction site sensor data, thereby obtaining a construction site camera image;

[0104] In this embodiment, in order to obtain sensor data of the construction site, various types of sensors, such as temperature, humidity, vibration, etc., are first deployed to cover the entire construction site. These sensors can be based on Internet of Things technology and can transmit data to the data processing center in real time. Through image processing technology, the camera video of the construction site is analyzed and extracted, for example, using the target detection and tracking algorithm in computer vision technology to identify key features in the construction process and convert them into digital image data.

[0105] Step S12: extracting construction site spatial features based on the construction site camera images, thereby obtaining construction site spatial feature data;

[0106] In this embodiment, image processing and computer vision technology are used to extract spatial features from the construction site camera images. This can be achieved through image segmentation and feature extraction algorithms, such as using a convolutional neural network (CNN) to perform semantic segmentation on the image, identify targets such as buildings and mechanical equipment, and then extract their spatial position and feature information, such as coordinates, size, shape, etc., to construct spatial feature data of the construction site.

[0107] Step S13: constructing a construction site spatial coordinate system based on the construction site spatial feature data;

[0108] In this embodiment, constructing a spatial coordinate system of the construction site based on the spatial feature data of the construction site is a key step. This requires the use of modern geographic information system (GIS) technology to perform geographic spatial reference and coordinate conversion on the extracted spatial feature data to ensure its accurate spatial position information. By performing three-dimensional modeling on the topography, landforms and other features of the construction site, a spatial reference framework of the construction site is established.

[0109] Step S14: Perform three-dimensional spatial sensing fusion on the construction site spatial coordinate system according to the construction site sensor data, thereby obtaining a construction site sensor network.

[0110] In this embodiment, the construction site sensor data and the established spatial coordinate system are used to perform three-dimensional spatial sensor fusion. This involves data fusion and spatial association algorithms, which integrate and associate data from different sensors to obtain more comprehensive and accurate construction site information. For example, using multi-sensor fusion technology, camera image data is fused with other sensor data (such as temperature and humidity) to achieve all-round monitoring and analysis of the construction site.

[0111] The present invention can realize visual monitoring of the construction process by acquiring camera images of the construction site. Camera images can provide more intuitive and comprehensive construction site information, which is helpful to discover and identify problems and obstacles in the construction process. By extracting spatial features from camera images of the construction site, key information such as the structure and layout of the construction site can be obtained. This helps to understand the actual situation of the construction site and provides a basis for subsequent spatial coordinate system construction and sensor fusion. Establishing a spatial coordinate system for the construction site can provide a unified reference framework for integrating and processing spatial data from different sources. This helps to associate and analyze camera images and sensor data of the construction site, and realize effective management and utilization of data. By performing three-dimensional spatial sensor fusion on sensor data of the construction site, data from different sensors can be integrated into the same spatial coordinate system. This helps to improve the consistency and accuracy of the data, and provides more reliable data support for subsequent construction quality assessment and problem identification.

[0112] Optionally, step S2 specifically includes:

[0113] Step S21: Acquire prefabricated building design data, and extract building structure features and building material features from the prefabricated building design data, thereby obtaining prefabricated building structure data and prefabricated building material data;

[0114] In this embodiment, through cooperation with the architectural designer and the engineering team, the prefabricated building design data, including CAD drawings, BIM models, etc., are obtained. Computer-aided design software is used to process and analyze these data, and image processing and pattern recognition technology are used to extract building structure features, such as the position, shape and size of columns, beams, walls, etc., and building material features, such as the material, density, strength and other information of the concrete, steel, glass, etc. used, so as to obtain prefabricated building structure data and prefabricated building material data.

[0115] Step S22: performing prefabricated building topological structure analysis based on the prefabricated building structure data, thereby obtaining a prefabricated building topological structure model;

[0116] In this embodiment, the prefabricated building structure data is used to analyze the building structure using a topological analysis method. The topological structure of the prefabricated building is modeled and analyzed through computer simulation and emulation technology to determine the connection relationship and interaction between the components, thereby obtaining a prefabricated building topological structure model. This can be done using engineering software such as finite element analysis to ensure the stability and safety of the structure.

[0117] Step S23: Analyzing the characteristics of building materials according to the prefabricated building material data, thereby obtaining the characteristic data of building materials;

[0118] In this embodiment, the building material characteristics analysis is performed based on the prefabricated building material data. The theoretical knowledge of material science and engineering mechanics is used to analyze the various building materials used, including their mechanical properties, durability, thermal insulation properties, etc. This can be done through experimental testing and numerical simulation to obtain detailed building material characteristic data.

[0119] Step S24: filling the material property space of the prefabricated building topological structure model based on the building material property data, thereby obtaining the prefabricated building structure property model;

[0120] In this embodiment, the material property space filling of the prefabricated building topological structure model based on the building material property data can be implemented by using computer-aided design software and engineering simulation software. The acquired building material property data is applied to the prefabricated building topological structure model, and the material space filling of each component is performed by setting the material properties. This may involve adjusting the size, shape and material of the component to meet the design requirements and safety standards.

[0121] Step S25: Performing building load analysis on the prefabricated building structure characteristic model to obtain building load data, and filling building load attributes on the prefabricated building structure characteristic model based on the building load data to obtain the prefabricated building structure model.

[0122] In this embodiment, the building load analysis is performed using the building structure characteristic model. According to the purpose and environmental conditions of the building design, the theory and method of engineering mechanics are used to analyze and calculate the stress conditions of the prefabricated building structure to obtain the building load data. Then, based on these building load data, the building load attributes of the prefabricated building structure characteristic model are filled using computer-aided design software and engineering simulation software to determine the stress state and load bearing capacity of each component, and finally obtain a complete prefabricated building structure model.

[0123] The present invention can provide a basis for subsequent building analysis and model building by acquiring and extracting structural features and material features in prefabricated building design data. These features include the structural form, material type, size, etc. of the building, which are crucial for subsequent topological structure analysis and characteristic model building. By analyzing the topological structure of the prefabricated building, the relationship and connection mode between its components can be understood. This helps to determine the overall structural form of the building and provides a basis for subsequent model building and analysis. By analyzing the material properties used in the prefabricated building, its strength, durability, thermal insulation and other properties can be understood. This helps to select suitable materials, optimize building design, and ensure the safety and reliability of the building structure. Combining the building material property data with the building topological structure model, a structural property model of the prefabricated building can be generated. This model can reflect the distribution and performance characteristics of building materials in different parts, and provide a basis for subsequent load analysis and attribute filling. By performing load analysis on the prefabricated building structural property model, the bearing capacity and deformation of the building under different load conditions can be evaluated. Filling the structural model with attributes based on load data can further improve the building structure model and provide guidance for design optimization and engineering implementation.

[0124] Optionally, step S22 is specifically:

[0125] Step S221: constructing an initial prefabricated building structure model based on the prefabricated building structure data;

[0126] In this embodiment, when constructing the initial prefabricated building structure model based on the prefabricated building structure data, computer-aided design (CAD) software or building information modeling (BIM) platform is used to model the basic components of the building structure such as walls, floor slabs, columns, etc. This may involve digital conversion of architectural design drawings, or directly modeling in the software according to design specifications and parameters.

[0127] Step S222: classifying the initial prefabricated building structure model according to its structural functions, thereby obtaining a supporting structure model, a connecting structure model, and a main structure model;

[0128] In this embodiment, when the initial prefabricated building structure model is classified into structural functions, it is necessary to classify it according to the function and role of the building structure. For example, the supporting structure model is defined as the structural element responsible for bearing weight and providing stability, the connecting structure model is defined as the structural element connecting various components, and the main structure model is defined as the main structural component of the building, such as floor slabs and walls.

[0129] Step S223: Associating the connection structure model and the support structure model with respect to the structural connection relationship according to the initial prefabricated building structure model, thereby obtaining support structure connection relationship data;

[0130] In this embodiment, by associating the structural connection relationship of the initial prefabricated building structure model, the supporting structure connection relationship data can be obtained. This can be achieved by analyzing the connection mode and relationship between each component in the structural model, such as bolt connection, welding connection, etc.

[0131] Step S224: Associating the connection structure model and the main structure model with respect to the structural connection relationship according to the initial prefabricated building structure model, thereby obtaining main structure connection relationship data;

[0132] In this embodiment, the structural connection relationship data of the main structure can be obtained by associating the connection structure model and the main structure model according to the initial prefabricated building structure model. This involves analyzing the connection mode and relationship between various components in the main structure model, such as the connection mode between the floor slab and the wall.

[0133] Step S225: fusing the supporting structure connection relationship data and the main structure connection relationship data with building structure connection characteristics, thereby obtaining prefabricated building structure connection relationship data;

[0134] In this embodiment, when the building structure connection feature fusion is performed on the supporting structure connection relationship data and the main structure connection relationship data, the connection features of the two need to be integrated to obtain the complete connection relationship data of the assembled building structure. This can be achieved by matching the connection mode and relationship of the two, while taking into account the stability and integrity of the structure.

[0135] Step S226: Performing building main body spatial distribution statistics according to the main structure data, thereby obtaining building main body distribution data;

[0136] In this embodiment, by performing spatial distribution statistics of the building main body according to the main structure data, the building main body distribution data can be obtained. This can include analyzing the position, shape and quantity of each main component in the building structure, as well as their layout and distribution in space.

[0137] Step S227: constructing a prefabricated building topological structure model according to the prefabricated building structure connection relationship data and the building main body distribution data.

[0138] In this embodiment, a prefabricated building topological structure model is constructed based on the prefabricated building structure connection relationship data and the building main body distribution data. This includes combining the connection relationship data and the main body distribution data, and using graph theory or network analysis methods to construct a prefabricated building topological structure model to describe the connection relationship and spatial layout between various components in the building structure.

[0139] The present invention can integrate the basic form and components of the building structure by constructing an initial assembled building structure model, providing a basis for subsequent analysis and design. By classifying the initial model into structural functions, the building structure can be decomposed into different parts such as a supporting structure, a connecting structure and a main structure, which helps to clarify the function and composition relationship of the building structure. By associating the connecting structure and the supporting structure, the connection mode and function between them can be understood, which helps to ensure the stability and safety of the building structure. By associating the connecting structure and the main structure, the connection mode and position can be determined, which helps to optimize the layout and design of the building structure. By integrating the connection features of the supporting structure and the main structure, the overall connection mode of the building structure can be fully understood, providing a basis for subsequent topological structure modeling. By statistically analyzing the spatial distribution of the building main body, the overall form and layout of the building can be understood, which helps to determine the optimization plan of the building structure. By constructing an assembled building topological structure model, the connection relationship and spatial distribution of the building structure can be comprehensively considered, providing guidance for the overall design, and improving the efficiency and performance of the building structure.

[0140] Optionally, step S3 specifically includes:

[0141] Step S31: extracting the construction site camera image from the construction site sensor network to obtain the construction site camera image;

[0142] In this embodiment, sensors such as cameras and lidar deployed at the construction site are used to monitor the construction site in real time and collect data. Through image processing algorithms, the construction site camera images are extracted from the construction site sensor network composed of sensors to ensure that the images are clear and complete, including the building structure and the surrounding environment, to support subsequent analysis and modeling work.

[0143] Step S32: classifying the construction site camera images by on-site building structure, thereby obtaining an above-ground building structure image and an underground building structure image;

[0144] In this embodiment, the construction site camera images are classified into building structures through image recognition and deep learning technology, and the above-ground building structures and underground building structures are effectively distinguished. The object detection algorithm is used to identify the outline and features of the building, so as to obtain clear images of the above-ground and underground building structures.

[0145] Step S33: extracting underground building structure features from the underground building structure image to obtain underground building structure data, and constructing an underground building structure model based on the underground building structure data;

[0146] In this embodiment, image feature extraction algorithms such as HOG (Histogram of Oriented Gradients) and LBP (Local Binary Pattern) are used to extract features from underground building structure images. By analyzing features such as texture, shape, and color of the image, the location and form of the underground building structure are identified, and the corresponding feature vectors are extracted. Using these feature vectors, combined with machine learning algorithms (such as support vector machines, random forests, etc.), underground building structure models are constructed, including three-dimensional models of structures such as pipelines and basements.

[0147] Step S34: extracting the above-ground building structure features from the above-ground building structure image, thereby obtaining above-ground building structure data, and constructing an above-ground building structure model according to the above-ground building structure data;

[0148] In this embodiment, edge detection, contour extraction and feature description are performed on the image of the above-ground building structure to obtain information such as the shape, size and location of the building. Using deep learning technology, a building detection and recognition model is trained to identify building objects in the image and extract their features. For example, a convolutional neural network is used to detect and recognize buildings to obtain clear images of above-ground building structures and extract relevant features.

[0149] Step S35: Combining the underground building structure model and the above-ground building structure model to obtain a field structure model;

[0150] In this embodiment, the above-ground and underground building structure models are effectively combined by using geometric matching algorithms and 3D reconstruction technology. By matching the spatial position relationship between the underground and above-ground building structures, the two are aligned and fused to obtain a complete on-site structure model. For example, the ICP (Iterative Closest Point) algorithm is used to align the underground and above-ground building structures to obtain a consistent spatial coordinate system, and the two are fused into a unified on-site structure model.

[0151] Step S36: acquiring on-site building material data, and filling the on-site structural model with material properties according to the on-site building material data, thereby obtaining the on-site building structural model;

[0152] In this embodiment, the material database and building material parameters are used to fill the material properties of the on-site structural model. According to the architectural design drawings and the material list, specific material parameters are applied to the structural model, including the physical properties of materials such as concrete, steel, and glass. For example, according to the material's density, elastic modulus, tensile strength and other parameters, the material properties of the on-site structural model are assigned to establish a real building structure model.

[0153] Step S37: Calculate the building parameter errors of the on-site building structure model according to the prefabricated building structure model, so as to obtain the building site error data.

[0154] In this embodiment, by establishing a building parameter error model, the error calculation and analysis of the on-site building structure model is performed. The assembled building structure model is used as a standard and compared with the on-site building structure model to calculate the difference and deviation between the two. For example, the similarity and difference between the on-site building structure model and the standard model are evaluated using indicators such as Euclidean distance or root mean square error to obtain the building site error data and guide subsequent construction and adjustment work.

[0155] The present invention can obtain real-time construction site information by extracting camera images of the construction site, and provide data support for subsequent analysis and monitoring. By classifying the building structure of the camera images, the above-ground building structure and the underground building structure can be identified, which is helpful for subsequent processing of different types of building structures. By extracting the features and constructing the data of the underground building structure images, the morphology and characteristics of the underground building structure can be understood, and a reference can be provided for the design and construction of the underground structure. By extracting the features and constructing the data of the above-ground building structure images, the morphology and characteristics of the above-ground building structure can be understood, and a reference can be provided for the design and construction of the building structure. By combining the underground building structure model and the above-ground building structure model, a complete on-site structure model can be obtained, which provides a basis for subsequent analysis and design. By obtaining the on-site building material data and filling it into the structure model, the material properties of the on-site building structure can be more realistically reflected, which is helpful for accurately evaluating the performance and behavior of the structure. By calculating the parameter error between the assembled building structure model and the on-site building structure model, the accuracy and consistency of the on-site building structure can be evaluated, which provides a reference for optimizing the construction process and improving the building quality.

[0156] Optionally, step S37 is specifically:

[0157] S371: Calculating building structure errors on the prefabricated building structure model and the on-site building structure model, thereby obtaining building structure error data;

[0158] In this embodiment, for the prefabricated building structure model and the on-site building structure model, a unified coordinate system needs to be established first to ensure the accuracy of the comparison. Through the three-dimensional modeling software, the two models are imported and aligned, and then the structural error calculation is performed. This includes comparing the differences in the position, size, inclination, etc. of key structural elements (such as columns, beams, walls, etc.). This step can be easily achieved using Building Information Modeling (BIM) software, whose built-in collision detection and structural comparison functions can intuitively display the differences between the models, thereby obtaining accurate building structure error data.

[0159] S372: Calculating material property errors on the prefabricated building structure model and the on-site building structure model, thereby obtaining building material error data;

[0160] In this embodiment, when calculating the error of building material properties, we must first understand the specific material parameters used by the prefabricated building structure model and the on-site building structure model. This includes but is not limited to the density, elastic modulus, tensile strength and other properties of materials such as concrete, steel, and glass. By comparing the material parameters used by the two models and calculating the difference between them, we can obtain the building material error data. Using professional structural analysis software, we can quickly and accurately extract material properties and compare them to find any potential differences.

[0161] S373: extracting foundation settlement sensing features from the construction site sensor network, thereby obtaining on-site foundation settlement sensing data;

[0162] In this embodiment, the acquisition of foundation settlement sensor data requires the deployment of sensors at the construction site and real-time monitoring. These sensors generally include inclinometers, depth sounders and other equipment for measuring foundation settlement. By connecting these sensors to a data acquisition system, changes in foundation settlement can be recorded in real time. Data processing software is used to extract features from the data collected by the sensors, such as identifying trends and periodic changes in foundation settlement, so as to obtain accurate on-site foundation settlement sensor data.

[0163] S374: dividing the prefabricated building structure model into a synchronous building structure model according to the on-site building structure model, thereby obtaining an on-site building structure design model;

[0164] In this embodiment, when the on-site building structure model is divided into the prefabricated building structure model, the similarities and differences between the two need to be taken into account. According to the on-site construction progress and the actual building structure situation, the prefabricated building structure model is divided into multiple stages and compared with the on-site building structure model. This can be achieved through the timeline function in the BIM software, dividing the building structure model according to different construction stages and comparing it with the actual situation on site, so as to obtain an accurate on-site building structure design model.

[0165] S375: extracting soil bearing characteristics of the building area from the prefabricated building design data, thereby obtaining ideal soil bearing data, and simulating foundation settlement of the on-site building structure design model based on the ideal soil bearing data, thereby obtaining ideal foundation settlement data;

[0166] In this embodiment, when extracting soil bearing characteristics from prefabricated building design data, it is necessary to consider the ideal geological conditions and soil characteristics of the area where the building is located in the design plan. Through on-site surveys and laboratory tests, parameters such as the bearing capacity of the soil in the ideal area are obtained. These parameters are then applied to the prefabricated building design data, and based on soil compressibility analysis, load analysis, and foundation design, calculation methods such as elastic foundation response method and finite element method are used to calculate the ideal settlement of the foundation.

[0167] Step S376: performing foundation settlement difference calculation on the ideal foundation settlement data and the on-site foundation settlement sensing data, thereby obtaining foundation settlement difference data;

[0168] In this embodiment, the calculation of the foundation settlement difference data requires comparing the ideal foundation settlement data with the on-site foundation settlement sensor data. By subtracting the two, the difference data of the foundation settlement is obtained, that is, the deviation between the ideal foundation settlement and the actual foundation settlement. This can be performed through professional data processing software, such as a geographic information system (GIS) or statistical analysis software, so as to accurately calculate the difference in foundation settlement.

[0169] Step S377: Merge the building structure error data, building material error data and foundation settlement difference data to obtain building site error data.

[0170] In this embodiment, the merging of the construction site error data requires the integration of the building structure error data, the building material error data, and the foundation settlement difference data. This can be done through statistical analysis software, and the data are weighted averaged or comprehensively scored according to certain weights to obtain the final construction site error data. For example, according to the importance and influencing factors of each data, a corresponding weight coefficient can be set, and then each data is weighted to obtain a comprehensive construction site error assessment result.

[0171] The present invention can evaluate the structural errors occurring during the construction process by comparing the differences between the assembled building structure model and the on-site building structure model, which helps to timely discover and solve potential structural problems and ensure the safety and stability of the building structure. By comparing the material property errors between the assembled building structure model and the on-site building structure model, the material differences existing in the construction process can be evaluated, which helps to adjust the material selection and use in the construction process to ensure the quality and performance of the building structure. By extracting the foundation settlement sensing characteristics, the foundation settlement of the construction site can be monitored in real time, and the foundation settlement anomaly can be discovered in time, providing data support for ensuring the stability of the building structure. By dividing the assembled building structure model into building structure models of the same progress, it can be better compared with the on-site building structure model, and the difference between the assembled building structure and the actual site can be found, providing a basis for adjustment and optimization during the construction process. By extracting the soil bearing characteristics of the building area and performing foundation settlement simulation, the foundation bearing capacity and foundation settlement can be evaluated, providing a reference for the design and construction of the building structure, and ensuring the stability and safety of the building structure. By calculating the difference between the ideal foundation settlement and the on-site foundation settlement, the deviation of the foundation settlement in the actual construction can be evaluated, providing a basis for subsequent adjustments and corrections to ensure the stability and safety of the building structure. By combining the building structure error, building material error and foundation settlement difference data, the error situation of the construction site can be comprehensively evaluated, providing comprehensive data support for optimizing the construction process and improving the building quality, ensuring the safety and stability of the building structure.

[0172] Optionally, step S4 is specifically:

[0173] Step S41: extracting geological sensor features from the construction site sensor data to obtain on-site geological sensor data;

[0174] In this embodiment, various geological sensing data, such as groundwater level, soil density, soil moisture, etc., are collected at the construction site by sensors arranged at the construction site. Then, the data measured by the sensors are converted into geological sensing features by using data processing techniques, such as signal filtering, feature extraction algorithms, etc. For example, the frequency characteristics of vibration sensor data can be extracted using a frequency domain analysis method, or the waveform characteristics of soil moisture sensor data can be extracted using a time domain feature extraction algorithm. Finally, field geological sensing data with geological characteristics are obtained for subsequent analysis and processing.

[0175] Step S42: extracting geological features of the building area from the prefabricated building design data, thereby obtaining ideal geological data for the designed building;

[0176] In this embodiment, the geological feature extraction of the design data of the prefabricated building is to obtain the ideal geological data for the design building. The geological features of the building area, such as soil type, groundwater conditions, topography, etc., can be extracted by using technologies such as the Geological Information System (GIS) in combination with the building design drawings and geological survey reports. For example, the surface features can be obtained by remote sensing technology, and combined with the geological survey data, the ideal geological data for the design building can be established.

[0177] Step S43: performing geological difference evaluation on the on-site geological sensor data and the ideal geological data of the designed building, thereby obtaining geological difference data;

[0178] In this embodiment, the on-site geological sensor data and the ideal geological data of the designed building are compared and analyzed to evaluate the geological differences between them. Methods such as data matching algorithms and spatial analysis techniques can be used to compare the differences between the two and quantify the geological difference data. For example, the difference index between the on-site geological sensor data and the ideal geological data of the designed building, such as the root mean square error (RMSE) or the correlation coefficient, can be calculated to evaluate the consistency and accuracy of the geological data. Through the geological difference assessment, the difference between the on-site geological conditions and the design expectations can be understood, providing a basis for subsequent error correction.

[0179] Step S44: performing foundation settlement difference correction on the construction site error data according to the geological difference data, thereby obtaining construction site error correction data.

[0180] In this embodiment, the construction site error data is corrected for foundation settlement differences based on the geological difference data. Specifically, the errors related to the geology of the construction site error data are adjusted based on the geological difference data to obtain more accurate construction site error correction data. For example, if the geological difference data indicates that the actual soil density is higher than the design expectation, the foundation settlement error in the construction site error data can be reduced by correcting the measured value of the soil density sensor. In this way, the corrected construction site error data will be more consistent with the actual situation, improving the reliability and accuracy of the data.

[0181] The present invention can monitor geological conditions in real time, such as soil density, groundwater level, etc., by extracting geological sensing features of the construction site, which helps to timely discover potential geological problems and provide data support for the stability of the building structure. By extracting geological features in the prefabricated building design data, such as geological structure, soil type, etc., the degree of consideration of geological conditions in the design stage can be evaluated, and a reference standard for ideal geological conditions can be provided for the subsequent construction process. By comparing and evaluating the on-site geological sensing data and the ideal geological data of the designed building, the difference between the geological conditions at the construction site and the ideal geological conditions of the design can be found, providing a basis for early warning and handling of geological problems that arise in the subsequent construction process. By correcting the foundation settlement difference of the construction site error data according to the geological difference data, the errors caused by the geological conditions can be corrected, the stability and safety of the building structure can be improved, and the quality of the construction project can be ensured.

[0182] Optionally, step S5 specifically includes:

[0183] Step S51: extracting stress and strain sensing features from the construction site sensing network to obtain on-site stress and strain sensing data, wherein the on-site stress and strain sensing data includes material stress and strain sensing data and structural stress and strain sensing data;

[0184] In this embodiment, a stress-strain sensing network including various sensors, such as strain gauges, pressure sensors, etc., can be deployed at the construction site to monitor the stress and strain conditions of materials and structures in real time. Then, the data collected by the sensor is collected and sorted by the data acquisition system to extract stress-strain sensing features. Material stress-strain sensing data and structural stress-strain sensing data are extracted through signal processing techniques, such as filtering, Fourier transform, etc. For example, for concrete structures, strain gauges can be used to monitor the deformation in the concrete to obtain structural stress-strain data. For steel materials, strain gauges can be used to monitor their strain changes to obtain material stress-strain data. Finally, field sensing data containing material and structural stress-strain information is obtained.

[0185] Step S52: extracting material error features and structural error features from the construction site error correction data, thereby obtaining building material error data and building structure error data;

[0186] In this embodiment, for the construction site error correction data, material error features and structural error features are first extracted. For example, in the material error feature extraction, parameters such as the strength, density, and elastic modulus of the material can be analyzed, and the quality of the material can be evaluated based on the deviation in the error correction data. For the structural error feature extraction, the deformation and crack conditions of the structure can be analyzed to evaluate the stability and safety of the structure. Through these feature extractions, building material error data and building structure error data are obtained, providing a basis for subsequent quality evaluation.

[0187] Step S53: performing material strength quality assessment on the building material error data according to the material stress and strain sensing data, thereby obtaining material strength quality data;

[0188] In this embodiment, material stress and strain sensing data are used to evaluate the material strength quality of building material error data. For example, for concrete materials, the compressive strength, tensile strength and other parameters of the concrete can be calculated based on the stress and strain data, and compared and analyzed with the design requirements to evaluate the strength quality of the material. Through such an evaluation, the difference between the actual performance of the material and the design requirements can be understood, providing a reference basis for the subsequent construction quality.

[0189] Step S54: performing structural strength quality assessment on the building structure error data according to the structural stress and strain sensing data, thereby obtaining structural strength quality data;

[0190] In this embodiment, the structural strength quality assessment is performed on the building structure error data based on the structural stress and strain sensing data. For example, for a steel structure, the structural load-bearing capacity, stiffness and other parameters can be analyzed based on the stress and strain data, and compared and evaluated with the design standards. Through such an assessment, the difference between the actual load-bearing capacity of the structure and the design requirements can be understood, providing a basis for subsequent structural safety assessment.

[0191] Step S55: performing foundation strength quality assessment on the construction site error correction data, thereby obtaining foundation strength quality data;

[0192] In this embodiment, the foundation strength quality assessment is performed on the construction site error correction data. The foundation bearing capacity, stability and other indicators of the foundation can be assessed using the foundation bearing conditions contained in the construction site error correction data, so as to understand the strength quality of the foundation. Such an assessment helps to timely discover foundation problems and ensure the safety of the building structure.

[0193] Step S56: Perform a comprehensive quality assessment on the material strength quality data, the structural strength quality data and the foundation strength quality data to obtain construction quality data, and classify the on-site building structure model into construction structure quality grades according to the construction quality data to obtain the on-site building structure quality model.

[0194] In this embodiment, the material strength quality data, the structural strength quality data and the foundation strength quality data are comprehensively considered to conduct a comprehensive evaluation of the construction quality. By establishing a comprehensive evaluation model and combining the weights of various evaluation indicators, the construction quality is comprehensively evaluated. According to the evaluation results, the quality level of the on-site building structure can be divided, thereby establishing an on-site building structure quality model. Such a model can provide guidance for subsequent construction management and quality control to ensure the safety and stability of the building structure.

[0195] The present invention can monitor the changes of materials and structures in real time by extracting on-site stress and strain sensing data, including the stress and strain conditions of materials and structures, and is helpful to timely discover potential structural problems and ensure the safety and stability of construction. By extracting building materials and structural error data, the problems of materials and structures in the construction process can be analyzed, providing a basis for subsequent quality evaluation and improvement. By evaluating the material stress and strain sensing data, the strength quality of building materials can be analyzed, including the bearing capacity and stability of materials, providing a reference for material selection and use. By evaluating the structural stress and strain sensing data, the strength and stability of the building structure can be analyzed, potential structural problems can be identified, and guidance and improvement directions can be provided for structural design and construction. By evaluating the strength and quality of the foundation, the bearing capacity and stability of the foundation can be analyzed, foundation problems can be discovered in time, and a basis can be provided for foundation treatment and reinforcement. By comprehensively evaluating various quality data, the quality status in the construction process can be fully understood, and the on-site building structure can be divided according to the quality grade, providing a reference standard for construction quality management and supervision, and ensuring the quality and safety of the construction project.

[0196] Optionally, step S56 is specifically:

[0197] Step S561: constructing a construction quality assessment system according to the material strength quality data, the structural strength quality data and the foundation strength quality data;

[0198] In this embodiment, a construction quality evaluation system is established based on material strength quality data, structural strength quality data, and foundation strength quality data. In this system, the weights and interrelationships of various indicators are considered to ensure a comprehensive evaluation of all aspects involved in the construction process. For example, indicators such as material strength, structural stability, and foundation bearing capacity are quantified, and weights are determined according to their importance, thereby constructing a comprehensive evaluation system.

[0199] Step S562: Evaluate the construction quality of the building structure on the construction site sensor network through the construction quality evaluation system, thereby obtaining construction quality data;

[0200] In this embodiment, the constructed construction quality assessment system is used to evaluate the construction quality of the building structure on the construction site sensor network. This involves real-time monitoring of various data at the construction site, including material stress and strain sensor data, structural stress and strain sensor data, and foundation stress and strain sensor data. By combining these data with the assessment system, quality problems that may exist in the construction process can be discovered in a timely manner, so that corresponding measures can be taken to adjust and improve, ensuring effective control of construction quality.

[0201] Step S563: classify and calculate the construction quality data, thereby obtaining high-quality construction structure data and low-quality construction structure data;

[0202] In this embodiment, after obtaining the construction quality data, the construction quality data is classified and calculated to distinguish high-quality construction structure data from low-quality construction structure data. This involves setting a series of standards or thresholds, and screening and analyzing the construction quality data according to these standards. For example, certain strength standards and deformation limits can be set, and the construction quality data can be classified as high quality or low quality according to various indicators in the construction quality data. Such classification calculation can clearly understand the actual situation of construction quality and provide a basis for subsequent quality improvement.

[0203] Step S564: marking the construction structure quality grade of the on-site building structure model according to the high-quality construction structure data and the low-quality construction structure data, thereby obtaining the on-site building structure quality model.

[0204] In this embodiment, the construction structure quality level of the on-site building structure model is marked based on the high-quality construction structure data and the low-quality construction structure data. This includes evaluating the structural quality of each part and marking it with a level according to the evaluation results, such as excellent, good, qualified, unqualified, etc. Through such marking, the quality level of the construction structure can be clearly displayed, providing an important reference for project management and decision-making, and also helping to timely discover and solve potential quality problems to ensure the ultimate achievement of construction quality.

[0205] The present invention can establish a comprehensive evaluation framework to ensure the comprehensiveness and reliability of construction quality by integrating the strength quality data of materials, structures and foundations and constructing a construction quality evaluation system. This system can help identify various problems that may occur in construction and provide benchmarks for evaluation and improvement. Through the construction quality evaluation system, the construction quality of the building structure is evaluated on the construction site sensor network, and each link in the construction process can be monitored and evaluated in real time, and quality problems that may exist in the construction can be discovered in time, so that corrective measures can be taken in time to ensure the quality and stability of the building structure. By classifying and calculating the construction quality data, high-quality construction structure data and low-quality construction structure data can be clearly distinguished, providing a specific basis and direction for subsequent quality analysis and traceability. By marking the quality level of the on-site building structure model according to the high-quality construction structure data and the low-quality construction structure data, the quality level of each part in the construction can be clearly identified, providing a reference basis for quality control and supervision. This grade marking helps decision makers and relevant personnel to understand the quality status of the building structure in a timely manner and take necessary actions to improve the construction quality.

[0206] Optionally, the present specification further provides a system for tracing the quality of prefabricated building construction, which is used to execute the method for tracing the quality of prefabricated building construction as described above. The system for tracing the quality of prefabricated building construction includes:

[0207] A sensor network construction module is used to obtain sensor data of the construction site and construct a sensor network according to the sensor data of the construction site, thereby obtaining a sensor network of the construction site;

[0208] The structural model building module is used to obtain the prefabricated building design data and extract the building structure features of the prefabricated building design data, thereby obtaining the prefabricated building structure data; digitally model the prefabricated building topological structure according to the prefabricated building structure data, thereby obtaining the prefabricated building structure model;

[0209] A building error calculation module is used to perform digital modeling of the construction progress of the construction site according to the construction site sensor network, thereby obtaining the on-site building structure model, and to perform building parameter error calculation on the on-site building structure model according to the assembled building structure model, thereby obtaining the building site error data;

[0210] A geological error correction module is used to perform building geological difference analysis on the construction site sensor data and the prefabricated building design data, thereby obtaining geological difference data, and to perform geological error correction on the building site error data according to the geological difference data, thereby obtaining building site error correction data;

[0211] A construction quality assessment module is used to perform construction quality assessment on the construction site error correction data according to the construction site sensor network, thereby obtaining construction quality data, and to classify the construction structure quality grade of the on-site building structure model according to the construction quality data, thereby obtaining the on-site building structure quality model;

[0212] The construction strategy optimization module is used to conduct subsequent construction optimization strategy analysis on the on-site building structure quality model based on the prefabricated building design data, so as to obtain the building construction optimization strategy.

[0213] The system for tracing the quality of prefabricated building construction of the present invention can implement any one of the methods for tracing the quality of prefabricated building construction of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the method for tracing the quality of prefabricated building construction. The internal modules of the system cooperate with each other, thereby improving the management level of construction quality.

[0214] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0215] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for tracing the quality of prefabricated building construction, characterized in that: The following steps are involved: Step S1: Acquire construction site sensor data, and construct a sensor network based on the construction site sensor data, thereby obtaining a construction site sensor network; Step S2: obtaining prefabricated building design data, and extracting building structure features from the prefabricated building design data, thereby obtaining prefabricated building structure data; Digital modeling of the prefabricated building topology structure is performed based on the prefabricated building structure data, thereby obtaining the prefabricated building structure model; Step S3: digitally modeling the construction progress of the construction site according to the construction site sensor network, thereby obtaining the on-site building structure model, and calculating the building parameter error of the on-site building structure model according to the assembled building structure model, thereby obtaining the construction site error data; Step S4: performing building geological difference analysis on the construction site sensor data and the prefabricated building design data to obtain geological difference data, and performing geological error correction on the construction site error data according to the geological difference data to obtain construction site error correction data; Step S5: Perform construction quality assessment on the construction site error correction data according to the construction site sensor network, thereby obtaining construction quality data, and classify the construction structure quality grade of the on-site building structure model according to the construction quality data, thereby obtaining the on-site building structure quality model. Step S5 is specifically as follows: Step S51: extracting stress and strain sensing features from the construction site sensing network to obtain on-site stress and strain sensing data, wherein the on-site stress and strain sensing data includes material stress and strain sensing data and structural stress and strain sensing data; Step S52: extracting material error features and structural error features from the construction site error correction data, thereby obtaining building material error data and building structure error data; Step S53: performing material strength quality assessment on the building material error data according to the material stress and strain sensing data, thereby obtaining material strength quality data; Step S54: performing structural strength quality assessment on the building structure error data according to the structural stress and strain sensing data, thereby obtaining structural strength quality data; Step S55: performing foundation strength quality assessment on the construction site error correction data, thereby obtaining foundation strength quality data; Step S56: Performing a comprehensive quality assessment on the material strength quality data, the structural strength quality data and the foundation strength quality data, thereby obtaining construction quality data, and classifying the on-site building structure model into construction structure quality grades according to the construction quality data, thereby obtaining an on-site building structure quality model; Step S6: Perform subsequent construction optimization strategy analysis on the on-site building structure quality model according to the prefabricated building design data, so as to obtain a building construction optimization strategy.

2. The method for tracing the quality of prefabricated building construction according to claim 1 is characterized in that: Step S1 is specifically as follows: Step S11: Acquire construction site sensor data, and perform construction site camera image extraction on the construction site sensor data, thereby obtaining a construction site camera image; Step S12: extracting construction site spatial features based on the construction site camera images, thereby obtaining construction site spatial feature data; Step S13: constructing a construction site spatial coordinate system based on the construction site spatial feature data; Step S14: Perform three-dimensional spatial sensing fusion on the construction site spatial coordinate system according to the construction site sensor data, thereby obtaining a construction site sensor network.

3. The method for tracing the quality of prefabricated building construction according to claim 1 is characterized in that: Step S2 is specifically as follows: Step S21: Acquire prefabricated building design data, and extract building structure features and building material features from the prefabricated building design data, thereby obtaining prefabricated building structure data and prefabricated building material data; Step S22: performing prefabricated building topological structure analysis based on the prefabricated building structure data, thereby obtaining a prefabricated building topological structure model; Step S23: Analyzing the characteristics of building materials according to the prefabricated building material data, thereby obtaining the characteristic data of building materials; Step S24: filling the material property space of the prefabricated building topological structure model based on the building material property data, thereby obtaining the prefabricated building structure property model; Step S25: Performing building load analysis on the prefabricated building structure characteristic model to obtain building load data, and filling building load attributes on the prefabricated building structure characteristic model based on the building load data to obtain the prefabricated building structure model.

4. The method for tracing the quality of prefabricated building construction according to claim 3 is characterized in that: Step S22 is specifically as follows: Step S221: constructing an initial prefabricated building structure model based on the prefabricated building structure data; Step S222: classifying the initial prefabricated building structure model according to its structural functions, thereby obtaining a supporting structure model, a connecting structure model, and a main structure model; Step S223: Associating the connection structure model and the support structure model with respect to the structural connection relationship according to the initial prefabricated building structure model, thereby obtaining support structure connection relationship data; Step S224: Associating the connection structure model and the main structure model with respect to the structural connection relationship according to the initial prefabricated building structure model, thereby obtaining main structure connection relationship data; Step S225: fusing the supporting structure connection relationship data and the main structure connection relationship data with building structure connection characteristics, thereby obtaining prefabricated building structure connection relationship data; Step S226: Performing building main body spatial distribution statistics according to the main structure data, thereby obtaining building main body distribution data; Step S227: constructing a prefabricated building topological structure model according to the prefabricated building structure connection relationship data and the building main body distribution data.

5. The method for tracing the quality of prefabricated building construction according to claim 1 is characterized in that: Step S3 is specifically as follows: Step S31: extracting the construction site camera image from the construction site sensor network to obtain the construction site camera image; Step S32: classifying the construction site camera images by on-site building structure, thereby obtaining an above-ground building structure image and an underground building structure image; Step S33: extracting underground building structure features from the underground building structure image to obtain underground building structure data, and constructing an underground building structure model based on the underground building structure data; Step S34: extracting the above-ground building structure features from the above-ground building structure image, thereby obtaining above-ground building structure data, and constructing an above-ground building structure model according to the above-ground building structure data; Step S35: Combining the underground building structure model and the above-ground building structure model to obtain a field structure model; Step S36: acquiring on-site building material data, and filling the on-site structural model with material properties according to the on-site building material data, thereby obtaining the on-site building structural model; Step S37: Calculate the building parameter errors of the on-site building structure model according to the prefabricated building structure model, so as to obtain the building site error data.

6. The method for tracing the quality of prefabricated building construction according to claim 5 is characterized in that: Step S37 is specifically as follows: S371: Calculating building structure errors on the prefabricated building structure model and the on-site building structure model, thereby obtaining building structure error data; S372: Calculating material property errors on the prefabricated building structure model and the on-site building structure model, thereby obtaining building material error data; S373: extracting foundation settlement sensing features from the construction site sensor network, thereby obtaining on-site foundation settlement sensing data; S374: dividing the prefabricated building structure model into a synchronous building structure model according to the on-site building structure model, thereby obtaining an on-site building structure design model; S375: extracting soil bearing characteristics of the building area from the prefabricated building design data, thereby obtaining ideal soil bearing data, and simulating foundation settlement of the on-site building structure design model based on the ideal soil bearing data, thereby obtaining ideal foundation settlement data; Step S376: performing foundation settlement difference calculation on the ideal foundation settlement data and the on-site foundation settlement sensing data, thereby obtaining foundation settlement difference data; Step S377: Merge the building structure error data, building material error data and foundation settlement difference data to obtain building site error data.

7. The method for tracing the quality of prefabricated building construction according to claim 1 is characterized in that: Step S4 is specifically as follows: Step S41: extracting geological sensor features from the construction site sensor data to obtain on-site geological sensor data; Step S42: extracting geological features of the building area from the prefabricated building design data, thereby obtaining ideal geological data for the designed building; Step S43: Performing geological difference evaluation on the on-site geological sensor data and the ideal geological data of the designed building, thereby obtaining geological difference data; Step S44: performing foundation settlement difference correction on the construction site error data according to the geological difference data, thereby obtaining construction site error correction data.

8. The method for tracing the quality of prefabricated building construction according to claim 1 is characterized in that: Step S56 is specifically as follows: Step S561: constructing a construction quality assessment system according to the material strength quality data, the structural strength quality data and the foundation strength quality data; Step S562: Evaluate the construction quality of the building structure on the construction site sensor network through the construction quality evaluation system, thereby obtaining construction quality data; Step S563: performing classification calculation on the construction quality data, thereby obtaining high-quality construction structure data and low-quality construction structure data; Step S564: marking the construction structure quality grade of the on-site building structure model according to the high-quality construction structure data and the low-quality construction structure data, thereby obtaining the on-site building structure quality model.

9. A quality tracing system based on prefabricated building construction, characterized in that: Used to execute the method for tracing the quality of prefabricated building construction according to claim 1, the system for tracing the quality of prefabricated building construction comprises: A sensor network construction module is used to obtain sensor data of the construction site and construct a sensor network according to the sensor data of the construction site, thereby obtaining a sensor network of the construction site; The structural model building module is used to obtain the prefabricated building design data and extract the building structure features of the prefabricated building design data, thereby obtaining the prefabricated building structure data; digitally model the prefabricated building topological structure according to the prefabricated building structure data, thereby obtaining the prefabricated building structure model; A building error calculation module is used to perform digital modeling of the construction progress of the construction site according to the construction site sensor network, thereby obtaining the on-site building structure model, and to perform building parameter error calculation on the on-site building structure model according to the assembled building structure model, thereby obtaining the building site error data; A geological error correction module is used to perform building geological difference analysis on the construction site sensor data and the prefabricated building design data, thereby obtaining geological difference data, and to perform geological error correction on the building site error data according to the geological difference data, thereby obtaining building site error correction data; A construction quality assessment module is used to perform construction quality assessment on the construction site error correction data according to the construction site sensor network, thereby obtaining construction quality data, and to classify the construction structure quality grade of the on-site building structure model according to the construction quality data, thereby obtaining the on-site building structure quality model; The construction strategy optimization module is used to conduct subsequent construction optimization strategy analysis on the on-site building structure quality model based on the prefabricated building design data, so as to obtain the building construction optimization strategy.

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