An intelligent management system for building automation construction sites based on big data

Abstract

The present invention belongs to the field of intelligent management and relates to an intelligent management system for building automation construction sites based on big data. The system includes modules such as qualified analysis of building component dimensions, qualified determination of steel bar quality, qualified judgment of building component quality, building component model splicing, and qualified determination of prefabricated houses. First, collect the dimension data of building components, create an actual model in BIM software, and analyze the qualified degree of the area. Then, based on the model, analyze the steel bar distribution and defects to determine the quality of the steel bars. Next, comprehensively consider the area and the quality of the steel bars to judge the quality of the building components and give early warnings. If all components are qualified, simulate splicing to obtain the overall model and judge its qualifiedness. Finally, assemble on-site and fill with concrete, collect images to analyze wall pollution and defects, comprehensively evaluate the quality of the prefabricated house and give early warnings. This system ensures comprehensive quality inspection and qualified determination of prefabricated houses from components to the whole.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent management and relates to an intelligent management system for an automated construction site of a building based on big data. Background Art

[0002] With the rapid development of information technology, the construction industry is undergoing a transformation from a traditional construction mode to an intelligent and automated construction mode. This transformation aims to improve construction efficiency, reduce safety risks, achieve refined management, and promote the development of green buildings. Especially in the construction management of building components, the traditional manual management method has been difficult to meet the high requirements of the current construction industry for efficiency, quality, and safety.

[0003] Although the existing intelligent management system for an automated construction site of a building based on big data meets certain requirements, there are still limitations, which are specifically reflected in: 1. There are obvious limitations in the existing technology for analyzing the overall quality of prefabricated residential building components. It neither organically combines key factors such as residential size and steel bar quality for in-depth analysis to comprehensively evaluate the overall quality, nor has a step of simulating and splicing the building models of each building component to verify the splicing effect in advance. As a result, it is difficult to grasp the mutual relationship between various factors and their synergistic influence on the final quality, restricting the in-depth understanding of building quality, and it is impossible to comprehensively and intuitively present the complete state of the components after splicing before actual construction, making it difficult to timely discover and correct potential problems such as component mismatch and unreasonable design before construction.

[0004] 2. Most of the existing technologies lack an effective mechanism to timely judge whether the quality is qualified and give early warnings after the construction of prefabricated residences is completed. Usually, it is only discovered when obvious quality problems occur during subsequent use, and it is impossible to detect potential quality hazards in advance before delivery and use and take corresponding measures. This makes it possible for residences with quality problems to be directly delivered, bringing many subsequent inconveniences and safety risks to users. Summary of the Invention

[0005] In view of this, to solve the problems raised in the above background art, an intelligent management system for an automated construction site of a building based on big data is now proposed.

[0006] The object of the present invention can be achieved through the following technical solutions: The present invention provides an intelligent management system for an automated construction site of a building based on big data, including: a building component size qualification analysis module: used to collect the size data of each building component of a prefabricated residence, create the actual building models of each building component of the prefabricated residence in building information modeling software based on the size data of each building component of the prefabricated residence, and then analyze the area qualification degree index of each building component of the prefabricated residence.

[0007] Steel bar quality qualification determination module: Used to analyze the distribution qualification index and defect degree index of steel bars in each building component of the prefabricated house based on the actual building models of each building component of the prefabricated house, and then analyze the quality qualification index of steel bars in each building component of the prefabricated house.

[0008] Building component quality qualification judgment module: Used to analyze the quality qualification index of each building component of the prefabricated house based on the area qualification index of each building component of the prefabricated house and the quality qualification index of steel bars, judge whether the quality of each building component of the prefabricated house is qualified, and then issue a warning.

[0009] Building component model splicing module: Used to simulate and splice the actual building models of each building component of the prefabricated house when the quality of each building component of the prefabricated house is qualified, obtain the actual building model of the spliced prefabricated house, and then judge whether the actual building model of the spliced prefabricated house is qualified.

[0010] Prefabricated house qualification determination module: Used to assemble the prefabricated house on-site and perform concrete pouring when the actual building model of the spliced prefabricated house is qualified. Collect images of each angle of the prefabricated house after concrete pouring by drones, analyze the pollution degree index and defect degree index of the wall surface of the prefabricated house after concrete pouring respectively, and then analyze the comprehensive quality qualification index of the prefabricated house after concrete pouring, judge whether the quality of the prefabricated house after concrete pouring is qualified, and issue a warning.

[0011] Database: Used to store the standard dimensions of each building component of the prefabricated house, store the standard bending angles, standard hook lengths and standard straight section lengths of various steel bars in the building components, store the standard anchorage lengths, standard protective layer thicknesses of steel bars at each installation position in each type of each building component, and the standard spacing between the corresponding steel bars and their adjacent steel bars.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) By collecting the dimension data of each building component of the prefabricated house, creating the building models of each building component of the prefabricated house, and then analyzing the area qualification index of each building component of the prefabricated house, the present invention can identify in advance those problems that may only be exposed during the actual assembly process. Through this preliminary analysis, these potential problems can be discovered during the component production stage or before entering the site, and timely adjustments or replacements can be made to avoid situations such as delaying the construction period and increasing costs at the construction site.

[0013] (2) By means of the building models of the various building components of the prefabricated house, the present invention respectively analyzes the distribution qualification index and the defect degree index of the steel bars in the various building components of the prefabricated house, and further analyzes the quality qualification index of the steel bars in the various building components of the prefabricated house, so as to accurately grasp the actual condition of the steel bars in the components from multiple dimensions. It is no longer limited to the detection of only a few indicators such as the conventional strength of the steel bars, but fully considers key factors such as whether the distribution is reasonable and whether there are defects, so as to more accurately obtain the quality qualification index of the steel bars.

[0014] (3) By simulating and splicing the building models of the various building components of the prefabricated house, the present invention obtains the building model of the spliced prefabricated house, and then judges whether the building model of the spliced prefabricated house is qualified, reducing the on-site operations such as temporary adjustment, cutting, and reprocessing due to problems such as component mismatch and unreasonable design.

[0015] (4) When the building model of the spliced prefabricated house is qualified, the present invention assembles the various building components of the prefabricated house on-site and conducts concrete filling, and then collects the images of the filled prefabricated house, analyzes its pollution degree index and defect degree index to obtain the comprehensive quality qualification index, and judges whether the quality is qualified and issues an early warning accordingly. This method can avoid delivering unqualified prefabricated houses through timely quality analysis and early warning, thus greatly reducing the high costs caused by operations such as rework and repair due to quality problems. Description of the Drawings

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0017] Figure 1 It is a schematic diagram of the connection of each module of the system of the present invention.

[0018] Figure 2 It is the specific steps for analyzing the area qualification index of the building components of the prefabricated house in the system of the present invention.

[0019] Figure 3 It is the specific steps for analyzing the defect degree index of the steel bars in the building components of the prefabricated house in the system of the present invention. Detailed Embodiments

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0021] Please refer to Figure 1 As shown, the present invention provides an intelligent management system for building automation construction sites based on big data, including: a building component size qualification analysis module, a steel bar quality qualification determination module, a building component quality qualification judgment module, a building component model splicing module, a prefabricated house qualification determination module, and a database.

[0022] The steel bar quality qualification determination module is respectively connected to the building component size qualification analysis module, the building component quality qualification judgment module, and the database. The building component quality qualification judgment module is respectively connected to the steel bar quality qualification determination module and the building component model splicing module. The building component model splicing module is respectively connected to the building component quality qualification judgment module and the prefabricated house qualification determination module. The database is respectively connected to the building component size qualification analysis module, the steel bar quality qualification determination module, and the prefabricated house qualification determination module.

[0023] Building component size qualification analysis module: used to collect the size data of each building component of a prefabricated house, create the actual building models of each building component of the prefabricated house in building information model software based on the size data of each building component of the prefabricated house, and then analyze the area qualification degree index of each building component of the prefabricated house.

[0024] As a preferred embodiment, the specific process of collecting the size data of each building component of the prefabricated house is: scanning each building component of the prefabricated house with a three-dimensional laser scanner to obtain the size data of each building component of the prefabricated house.

[0025] The specific process of creating the building models of each building component of the prefabricated house in building information model software based on the size data of each building component of the prefabricated house is: creating the basic templates of each building component in BIM software based on the size data of each building component of the prefabricated house. For example, for column components, according to their cross-sectional dimensions, heights, and other data, draw the two-dimensional contours of the columns in the software's modeling tools, and then convert them into three-dimensional solid models through operations such as stretching. The same method applies to other components such as beams, slabs, and walls, and the corresponding basic three-dimensional models are created respectively according to their respective size data.

[0026] After creating the basic component templates, further add detailed features and related attributes to each component. For example, for beam components, in addition to the basic dimensions, attributes such as the reinforcement information of the beam and the concrete strength grade can be added. These attributes and details can be completed through the parameter setting function provided by the software or a dedicated attribute editing panel, thereby creating the building models of the various building components of the prefabricated house.

[0027] Please refer to Figure 2 shown. Further, the method for analyzing the area qualification degree index of the various building components of the prefabricated house includes: A1. Extract the standard dimensions of the various building components of the prefabricated house from the database, and create the standard building models of the various building components of the prefabricated house in the building information model software based on the standard dimensions of the various building components of the prefabricated house;

[0028] A2. Calculate the surface profile areas of the actual building models and the standard building models of the various building components of the prefabricated house through the area calculation function of the building information model software, obtain the surface profile areas of the actual building models and the standard building models, compare them, and obtain the difference between the surface profile areas of the actual building models of the various building components of the prefabricated house and the corresponding surface profile areas of the corresponding standard building models, and use it as the area difference of the various surface profiles of the various building components of the prefabricated house, denoted as Δarea jb , where j represents the number corresponding to the jth building component of the prefabricated house, j = 1, 2, 3,... G, and b represents the number corresponding to the bth surface profile of the building component of the prefabricated house, b = 1, 2, 3... B.

[0029] A3. Analyze the area qualification degree index dim of the various building components of the prefabricated house j .

[0030] As a preferred embodiment, the specific calculation formula for analyzing the area qualification degree index of the various building components of the prefabricated house is: where Δarea′ represents the allowable area difference of the surface profiles of the building components of the prefabricated house, and B represents the total number of surface profiles of the building components of the prefabricated house.

[0031] The allowable area difference of the surface profiles of the building components of the prefabricated house is determined by professionals based on professional knowledge and experience.

[0032] By collecting the dimension data of each building component of the prefabricated house, creating the building models of each building component of the prefabricated house, and then analyzing the area qualification degree index of each building component of the prefabricated house, it is possible to identify in advance those problems that may only be exposed during the actual assembly process. Through this preliminary analysis, these potential problems can be discovered during the component production stage or before entering the site, and timely adjustments or replacements can be made to avoid situations such as delaying the construction period and increasing costs at the construction site.

[0033] Steel bar quality qualification determination module: used to respectively analyze the distribution qualification degree index and defect degree index of steel bars in each building component of the prefabricated house based on the actual building models of each building component of the prefabricated house, and then analyze the quality qualification degree index of steel bars in each building component of the prefabricated house.

[0034] Further, the specific process of analyzing the distribution qualification degree index of steel bars in each building component of the prefabricated house is as follows: extract the spacing, anchorage length, and cover thickness between each steel bar and its adjacent steel bars of each type in each building component of the prefabricated house from the actual building models of each building component of the prefabricated house, and record them as sp jgi , an jgi and pr jgi , where g represents the number corresponding to the gth type of steel bar, g = 1, 2, 3,... J, and i represents the number corresponding to the ith steel bar, i = 1, 2, 3,... I.

[0035] Extract the standard anchorage length, standard cover thickness, and standard spacing between the corresponding steel bar and its adjacent steel bar at each installation position of each type of steel bar in each building component of the prefabricated house from the database, and then, according to the positions of each type of steel bar in each building component of the prefabricated house, screen out the standard anchorage length, standard cover thickness, and standard spacing between the corresponding steel bar and its adjacent steel bar of each type in each building component of the prefabricated house, and record them as an′ jgi , pr′ jgi and sp′ jgi .

[0036] Analyze the distribution qualification degree index dist j ,

[0037] , ι 1 , ι 2 and ι 3 respectively represent the weights corresponding to the spacing, anchorage length, and cover thickness of the steel bar of the type in the building component of the prefabricated house between the steel bar and its adjacent steel bar, and ι 1 +ι 2 +ι 3= 1, where e represents the natural constant, J represents the total number of types of steel bars in the building components of prefabricated houses, and I represents the total number of steel bars.

[0038] As a preferred embodiment, the ι 1 , ι 2 and ι 3 can be set to 0.4, 0.3, and 0.3 respectively. The steel bar anchorage length is crucial for transmitting stress and exerting bearing capacity in the concrete structure. The cover thickness can protect the steel bars from external environmental erosion, and the steel bar spacing affects the quality of concrete pouring. Therefore, in the building components of prefabricated houses, the weight of the steel bar anchorage length is higher than that of the steel bar spacing and the steel bar cover thickness.

[0039] Each type of steel bar in each building component of the prefabricated house includes: horizontal steel bars and vertical steel bars.

[0040] The specific process of extracting the spacing between each type of steel bar and its adjacent steel bar in each building component of the prefabricated house is as follows: Extract the center point coordinates of each type of steel bar in each building component of the prefabricated house through building information modeling software, compare the center point coordinates of each steel bar with the center point coordinates of its adjacent steel bars, obtain the spacing between each type of steel bar and its adjacent steel bars of the corresponding type in each building component of the prefabricated house, and select the shortest spacing as the spacing between each type of steel bar and its adjacent steel bar in each building component of the prefabricated house.

[0041] The specific process of extracting the anchorage length of each type of steel bar in each building component of the prefabricated house is as follows: Based on the building model of each building component of the prefabricated house, extract the anchorage starting point coordinates and anchorage ending point coordinates of each type of steel bar in each building component of the prefabricated house, substitute them into the Euclidean distance formula to obtain the straight-line distance from the anchorage starting point to the ending point of each type of steel bar in each building component of the prefabricated house, and use it as the anchorage length of each type of steel bar in each building component of the prefabricated house.

[0042] The anchorage starting point coordinates of each type of steel bar in each building component of the prefabricated house refer to the position point where the steel bar starts to exert the anchorage effect.

[0043] For example, the anchorage starting point of the vertical steel bars in a column is often at the top surface of the foundation or the bottom of the floor beam, etc., where the steel bars start to extend into the column and exert the anchorage effect. Taking the same spatial coordinate system as an example, assuming that the column is located at a specific position in the building, the coordinates of the anchorage starting point of its vertical steel bars (x 2 , y 2 , z 2 ), where x 2 and y 2 will be determined according to the position of the column in the building plan and the arrangement of the steel bars in the column section, and z2 It is the height value corresponding to the top surface of the foundation or the bottom of the floor beam.

[0044] The coordinate of the anchorage termination point of various steel bars in each building component of the prefabricated house refers to the position point where the steel bar completes the anchorage function.

[0045] For example, the anchorage termination point of the longitudinal steel bar of the column, such as at the top of the floor beam and other parts. When the steel bar extends into the beam and reaches the specified anchorage length, this position is the anchorage termination point. Its coordinates (x 3 , y 3 , z 3 ) are determined according to the position of the column in the building plan, the arrangement of the steel bar in the column section, and the specified anchorage length, etc. x 3 and y 3 will be determined according to the specific situation, and z 3 is the height value corresponding to the top of the floor beam.

[0046] The specific process of extracting the protective layer thickness of various steel bars in each building component of the prefabricated house is as follows: Based on the building model of each building component of the prefabricated house, extract the coordinates of each outer edge point of various steel bars in each building component of the prefabricated house and each point on the inner surface of the corresponding building component, substitute them into the Euclidean distance formula, obtain the distances from each outer edge point of various steel bars in each building component of the prefabricated house to each point on the inner surface of the corresponding building component, screen out the shortest distance from the outer edge point of various steel bars in each building component of the prefabricated house to the point on the inner surface of the corresponding building component, and take it as the protective layer thickness of various steel bars in each building component of the prefabricated house.

[0047] Please refer to Figure 3 shown. Further, the specific process of analyzing the defect degree index of the steel bars in each building component of the prefabricated house is as follows: D1. Extract the standard bending angle, standard hook length, and standard straight segment length of various steel bars in each building component of the prefabricated house from the database.

[0048] D2. Extract the bending angle, hook length, and straight segment length of various steel bars in each building component of the prefabricated house from the building model of each building component of the prefabricated house.

[0049] D3. Compare the bending angle of various steel bars in each building component of the prefabricated house with the standard bending angle of the corresponding steel bar, obtain the bending angle difference of various steel bars in each building component of the prefabricated house, and denote it as Δangle jg .

[0050] D4. According to the analysis method of the difference in the bending angle of various types of steel bars in each building component of the prefabricated house, in the same way, the differences in the hook length and the straight section length of various types of steel bars in each building component of the prefabricated house are obtained, and are respectively denoted as Δhook jg and Δline jg .

[0051] D5. Analyze the defect degree index degree of the steel bars in each building component of the prefabricated house j .

[0052] As a preferred embodiment, the bending angle of the steel bar is specifically: the included angle between both ends during the bending process of the steel bar; the hook length of the steel bar is specifically: the length of the hooked part at the end of the steel bar; the straight section length of the steel bar is specifically: the length of the continuous straight part before or after the bending of the steel bar.

[0053] The specific calculation formula for analyzing the defect degree index of the steel bars in each building component of the prefabricated house is as follows:

[0054] where Δangle′, Δhook′ and Δline′ respectively represent the allowable difference in the bending angle, the allowable difference in the hook length, and the allowable difference in the straight section length of the steel bars in the building components of the prefabricated house set, and ε 1 , ε 2 and ε 3 respectively represent the weights corresponding to the difference in the bending angle, the difference in the hook length, and the difference in the straight section length of the steel bars in the building components of the prefabricated house, and ε 1 +ε 2 +ε 3 = 1.

[0055] The ε 1 , ε 2 and ε 3 can be respectively set to 0.4, 0.3 and 0.3. In terms of the quality control of steel bars, the bending angle is the key factor, because non-compliance will affect the anchoring effect in concrete and the mechanical properties of building components; although the hook length and the straight section length are important for the anchoring and force transmission of steel bars, their direct impact on structural safety is slightly weaker than that of the bending angle. Therefore, in the building components of prefabricated houses, the weight corresponding to the difference in the bending angle of the steel bar is greater than the weights corresponding to the differences in the hook length and the straight section length of the steel bar.

[0056] Based on the building models of the various building components of the prefabricated house, the present invention analyzes the distribution qualification index and defect degree index of the steel bars in each building component of the prefabricated house, and further analyzes the quality qualification index of the steel bars in each building component of the prefabricated house, so as to accurately grasp the actual situation of the steel bars in the components from multiple dimensions. It is no longer limited to the detection of only a few indicators such as the conventional strength of the steel bars, but fully considers key factors such as whether the distribution is reasonable and whether there are defects, so as to more accurately obtain the quality qualification index of the steel bars.

[0057] Building component quality qualification judgment module: used to analyze the quality qualification index of each building component of the prefabricated house based on the area qualification index of each building component of the prefabricated house and the quality qualification index of the steel bars, judge whether the quality of each building component of the prefabricated house is qualified, and then issue a warning.

[0058] Furthermore, the specific calculation formula for analyzing the quality qualification index of the steel bars in each building component of the prefabricated house is: where γ 1 and γ 2 respectively represent the weights corresponding to the distribution qualification index and defect degree index of the steel bars in the building components of the prefabricated house, and γ 1 +γ 2 = 1.

[0059] As a preferred embodiment, γ 1 and γ 2 can be set to 0.6 and 0.4 respectively. The distribution of steel bars in the building components of the prefabricated house (including aspects such as anchorage length, cover thickness, and steel bar spacing) is crucial for the safety, durability, and normal service performance of the building components and the entire prefabricated house structure from a macroscopic level. The steel bar defect degree index mainly concerns its own form deviation (such as differences in bending angle, hook length, straight section length, etc.). Although it will affect the mechanical properties of the steel bars and their role in the structure, compared with the distribution situation, its influence range is more local, mostly affecting the performance of single or local steel bar combinations. Therefore, in the building components of the prefabricated house, the weight corresponding to the steel bar distribution qualification index is greater than the weight corresponding to the defect degree index.

[0060] Furthermore, the specific calculation process for analyzing the quality qualification index of each building component of the prefabricated house is: qualified j = dim j *β 1 + complete j *β 2 where β 1 and β 2respectively represent the weight factors corresponding to the area qualification degree index of the building components of the prefabricated house and the quality qualification degree index of the steel bars, and β 1 +β 2 = 1.

[0061] As a preferred embodiment, β 1 and β 2 can be set to 0.4 and 0.6 respectively. In the building components of the prefabricated house, the steel bars play a key role in bearing and reinforcement, and their quality is related to the overall mechanical properties and structural safety of the building components. Although the area qualification degree of the building components also has an impact on the dimensional accuracy and assembly accuracy and is very important, the potential structural safety hazards caused by unqualified steel bar quality are more serious. Therefore, the weight factor corresponding to the area qualification degree index of the building components of the prefabricated house should be greater than the weight factor corresponding to the quality qualification degree index of the steel bars.

[0062] Furthermore, the specific process of determining whether the quality of each building component of the prefabricated house is qualified is as follows: comparing the qualification degree index of each building component of the prefabricated house with a preset qualification degree index threshold. If the qualification degree index of a certain building component of the prefabricated house is less than the preset qualification degree index threshold, then the building component of the prefabricated house is unqualified. Furthermore, each unqualified building component of the prefabricated house is numbered and then a warning is issued.

[0063] As a preferred embodiment, after issuing the warning, the production personnel of the building components are notified to re-customize.

[0064] The present invention analyzes the quality qualification degree index of each building component of the prefabricated house based on the area qualification degree index and the quality qualification degree index of the steel bars of each building component of the prefabricated house, determines whether the quality of each building component of the prefabricated house is qualified, and then issues a warning, realizing a comprehensive evaluation of the component quality from multiple key elements. The size and the quality of the steel bars are important factors affecting the performance of the building components. Combining the two for analysis can more accurately reflect the actual quality status of the components, avoiding the one-sidedness of judging the quality only relying on a single index, and thus more accurately determining whether the quality of each building component is qualified.

[0065] Building component model splicing module: used for when the quality of each building component of the prefabricated house is qualified, simulating and splicing the actual building models of each building component of the prefabricated house to obtain the actual building model of the spliced prefabricated house, and then determining whether the actual building model of the spliced prefabricated house is qualified.

[0066] As a preferred embodiment, the specific process of determining whether the assembled building model of the prefabricated house is qualified is as follows: First, use a laser rangefinder to measure multiple times at different positions (such as both ends, middle, etc.) of the splicing gaps between each building component of the prefabricated house in the building model and its adjacent components, obtain the width data of the splicing gaps between each building component of the prefabricated house and its adjacent components at different positions, screen out the maximum width of the splicing gaps between each building component of the prefabricated house and its adjacent components, and compare it with the set maximum allowable width of the splicing gaps between the building component and its adjacent components. When the maximum width of the splicing gap between a certain building component of the prefabricated house and its adjacent component is less than or equal to the set maximum allowable width of the splicing gap between the building component and its adjacent components, then this building component of the prefabricated house is qualified; otherwise, it is unqualified; when the maximum widths of the splicing gaps between all building components of the prefabricated house and their adjacent components are less than or equal to the set maximum allowable width of the splicing gaps between the building component and its adjacent components, then the actual building model of the assembled prefabricated house is qualified.

[0067] The maximum allowable width is usually set according to industry standards.

[0068] In the present invention, by simulating the splicing of the building models of each building component of the prefabricated house to obtain the building model of the assembled prefabricated house, and then determining whether the building model of the assembled prefabricated house is qualified, it reduces the operations such as temporary adjustment, cutting, and reprocessing required at the construction site due to problems such as component mismatch and unreasonable design.

[0069] Prefabricated house qualification determination module: When the actual building model of the assembled prefabricated house is qualified, it is used to assemble the prefabricated house on site and perform concrete filling on it, collect images of each angle of the prefabricated house after concrete filling through a drone, respectively analyze the pollution degree index and defect degree index of the wall surface of the prefabricated house after concrete filling, and then analyze the comprehensive quality qualification degree index of the prefabricated house after concrete filling, determine whether the quality of the prefabricated house after concrete filling is qualified, and issue a warning.

[0070] Further, the specific process of analyzing the pollution degree index of the wall surface of the prefabricated house after concrete filling is as follows: perform grayscale threshold segmentation on the images of each angle of the prefabricated house after concrete filling to obtain the grayscale values of each pixel point in the images of each angle of the prefabricated house after concrete filling, and compare the grayscale values of each pixel point in the images of each angle with the standard grayscale value range of the corresponding pixel points of the standard image of the surface of the corresponding building components of the prefabricated house. If the grayscale value of a certain pixel point does not belong to the standard grayscale value range of the corresponding pixel points of the standard image of the surface of the corresponding building components of the prefabricated house, then mark this pixel point as an abnormal pixel point, screen and count the number of abnormal pixel points in the images of each angle of the prefabricated house after concrete filling, denoted as stains d where d represents the number corresponding to the d-th angle image of the prefabricated house after concrete filling, d = 1, 2, 3,... D. Furthermore, obtain the total number of pixel points in the images of each angle of the prefabricated house after concrete filling, denoted as pixel d and analyze the pollution degree index of the prefabricated house after concrete filling.

[0071] As a preferred embodiment, the specific process of obtaining the standard grayscale value range of the corresponding pixel points of the standard image of the surface of the corresponding building components of the prefabricated house is as follows: extract the standard image of the surface of the corresponding building components of the prefabricated house from the database, grayscale it to obtain the grayscale values corresponding to the standard image of the surface of the corresponding building components of the prefabricated house, and then compare and count to obtain the corresponding grayscale range, which is then used as the standard grayscale value range of the corresponding pixel points of the standard image of the surface of the corresponding building components of the prefabricated house.

[0072] The images of each angle of the prefabricated house after concrete filling are specifically: the front image, side image, and back image of the prefabricated house after concrete filling.

[0073] Further, the specific process of analyzing the defect degree index of the wall surface of the prefabricated house after concrete filling is as follows: based on the images of each angle of the prefabricated house after concrete filling, identify various defect types of the prefabricated house after concrete filling through image processing technology, and obtain the characteristic values of various defect types of the prefabricated house after concrete filling through the measurement tool in the image processing software, denoted as length t where t represents the number corresponding to the t-th defect type of the prefabricated house after concrete filling, t = 1, 2, 3,... T. Furthermore, count the total number of defects in various defect types of the prefabricated house after concrete filling, denoted as Total t .

[0074] Analyze the defect degree index crack of the prefabricated house after concrete filling. where length′ t and Total′ t respectively represent the allowable characteristic value and the total allowable number of defects of the t-th type of defect of the prefabricated house after concrete filling, ρ 1 and ρ 2 respectively represent the characteristic value of the defect type of the prefabricated house after concrete filling and the weight corresponding to the total number of defects, and ρ 1 +ρ 2 = 1, T represents the total number of defect types.

[0075] The ρ 1 and ρ 2 can be set to 0.7 and 0.3 respectively. The characteristic value of the defect type has a direct and crucial impact on the quality of the house. It can more accurately reflect the substantial damage degree of each defect to aspects such as structural performance, service function, and appearance. However, the total number of defects is also a factor that cannot be ignored. Therefore, the weight corresponding to the characteristic value of the defect type of the prefabricated house after concrete filling is greater than the weight corresponding to the total number of defects.

[0076] The specific process of identifying various defect types of the prefabricated house after concrete filling through image processing technology is as follows: Based on the building construction quality acceptance standards and relevant specifications, clarify various possible defects (such as honeycombing, pitting, cracks, etc.) and their typical characteristics after concrete filling. On this basis, determine the possible defect areas through various feature extraction methods. For example, use color feature extraction, set the color threshold range according to the color difference between the defect area (such as the honeycombing area is relatively darker in color, the inside of the hole is mostly black or dark color, etc.) and the normal concrete surface color, and extract the candidate areas that may have defects; with the help of shape feature extraction, use algorithms such as Canny edge detection for cracks to extract their edge shapes to obtain information such as length and orientation, and refine the shape features of holes, honeycombing, etc. through morphological operations; use texture feature extraction, adopt methods such as gray-level co-occurrence matrix to analyze the texture parameters of different areas, and compare the texture parameter differences between the normal concrete surface and the suspected defect area to assist in defect identification.

[0077] The characteristic value of honeycombing is the area of honeycombing; the characteristic value of the hole is the depth of the hole; the characteristic value of the crack is the length of the crack.

[0078] First, according to the extracted defect features such as color, shape, and texture, select appropriate classification methods such as support vector machines, artificial neural networks, and decision trees to establish a classification model. Taking the support vector machine as an example, a large number of image samples with known defect type annotations need to be collected as training data. The extracted feature vectors are used as inputs, and the corresponding defect type annotations are used as outputs for model training. After training, evaluate the model performance through methods such as cross-validation. If it is not ideal, optimize it by adjusting model parameters, increasing the quantity and quality of training data, improving the feature extraction method, etc. Finally, input the preprocessed and feature-extracted image to be recognized into the classification model evaluated and optimized, and the model determines the defect types existing in the image, thus completing the recognition of various defect types of the prefabricated house after concrete filling.

[0079] Furthermore, the specific process of judging whether the quality of the concrete filling of the prefabricated house after concrete filling is qualified is as follows: Analyze the comprehensive quality qualification index quality of the prefabricated house after concrete filling. where δ 1 and δ 2 respectively represent the weights corresponding to the pollution degree index and the defect degree index of the wall surface of the prefabricated house after concrete filling, and δ 1 +δ 2 = 1.

[0080] As a preferred embodiment, the δ 1 and δ 2 can be set to 0.3 and 0.7 respectively. The pollution degree of the house after concrete filling seems to be a surface factor, but it will affect the building appearance and long-term durability. For example, residual stains and debris affect the beauty and may accelerate the aging and damage of concrete; while the defect degree of concrete filling is directly related to the structural safety and performance of the prefabricated house. Defects such as holes, cracks, and non-compaction will weaken the strength, stiffness, and integrity of concrete, affect the bearing capacity and stability, and even large cracks will endanger safety. Therefore, the weight corresponding to the defect degree index of the prefabricated house after concrete filling is greater than the weight corresponding to the pollution degree index.

[0081] Compare the comprehensive quality qualification index of the prefabricated house after concrete filling with the preset comprehensive quality qualification index threshold. If the comprehensive quality qualification index of the prefabricated house after concrete filling is greater than the preset comprehensive quality qualification index threshold, the comprehensive quality of the prefabricated house after concrete filling is qualified.

[0082] In the present invention, when the building model of the assembled residential building is qualified after splicing, each building component of the assembled residential building is assembled on-site and concrete pouring is carried out. Then, images of the assembled residential building after pouring are collected, and the pollution degree index and defect degree index are analyzed to obtain a comprehensive quality qualification degree index. Based on this, it is judged whether the quality is qualified and early warning is carried out. This method can avoid delivering and using unqualified assembled residential buildings through timely quality analysis and early warning, thereby greatly reducing the high costs generated by operations such as rework and maintenance due to quality problems.

[0083] Database: used to store the standard dimensions of each building component of the assembled residential building, store the standard bending angles, standard hook lengths, and standard straight segment lengths of various types of steel bars in the building components, and store the standard anchorage lengths, standard cover thicknesses, and standard spacings between the corresponding steel bars and their adjacent steel bars at each installation position under each type of each building component.

[0084] The above content is only an example and illustration of the concept of the present invention. Those skilled in the art of this technology make various modifications or supplements or use similar methods to replace the specific embodiments described, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should fall within the protection scope of the present invention.

Claims

1. A building automation construction site intelligent management system based on big data, characterized by: include: Building construction size qualification analysis module: used to collect the size data of each building component of the prefabricated house, create the actual building model of each building component of the prefabricated house in the building information model software based on the size data of each building component of the prefabricated house, and then analyze the area qualification index of each building component of the prefabricated house; Steel bar quality qualification judgment module: used to analyze the distribution qualification index and defect degree index of steel bars in each building component of the prefabricated house based on the actual building model of each building component of the prefabricated house, and then analyze the quality qualification index of steel bars in each building component of the prefabricated house; Building construction quality qualification judgment module: used to analyze the quality qualification index of each building component of the prefabricated house based on the area qualification index of each building component of the prefabricated house and the quality qualification index of the steel bar, judge whether the quality of each building component of the prefabricated house is qualified, and then issue an early warning; Building construction model splicing module: when the quality of each building component of the prefabricated house is qualified, the actual building model of each building component of the prefabricated house is simulated and spliced ​​to obtain the actual building model of the prefabricated house after splicing, and then judge whether the actual building model of the prefabricated house after splicing is qualified; Prefabricated housing qualification judgment module: when the actual building model of the assembled prefabricated housing is qualified, the prefabricated housing is assembled on site and concrete is filled therein, and images of the prefabricated housing after concrete filling are collected by drone from various angles, and the pollution degree index and defect degree index of the wall surface of the prefabricated housing after concrete filling are analyzed respectively, and then the comprehensive quality qualification degree index of the prefabricated housing after concrete filling is analyzed, and whether the quality of the prefabricated housing after concrete filling is qualified is judged, and an early warning is issued; Database: used to store the standard dimensions of each building structure of prefabricated housing, the standard bending angles, standard hook lengths and standard straight segment lengths of various types of steel bars in building components, the standard anchorage lengths, standard protective layer thicknesses and standard spacing between corresponding steel bars and their adjacent steel bars at each installation position of each type of building component.

2. According to the big data-based building automation construction site intelligent management system of claim 1, it is characterized by: The analysis of the area qualification index of each building component of the prefabricated house includes: A1. extracting standard sizes of various building components of the prefabricated house from a database, and creating standard building models of various building components of the prefabricated house in a building information modeling software based on the standard sizes of various building components of the prefabricated house; A2. Calculate the surface contour area of ​​the actual building model and the standard building model of each building component of the prefabricated house through the area calculation function of the building information model software, obtain the surface contour area of ​​the actual building model and the standard building model, compare them, and obtain the difference between the surface contour area of ​​the actual building model of each building component of the prefabricated house and the corresponding surface contour area of ​​the corresponding standard building model, and use it as the area difference of each surface contour of each building component of the prefabricated house, recorded as Δarea jb , where j represents the number corresponding to the j-th building component of the prefabricated house, j=1,2,3,...G, and b represents the number corresponding to the b-th surface profile of the building component of the prefabricated house, b=1,2,3...B; A3. Analyze the area qualification index dim of each building component of the prefabricated house j .

3. The intelligent management system for building automation construction sites based on big data according to claim 2 is characterized by: The specific process of analyzing the distribution qualification index of the steel bars in each building component of the prefabricated house is as follows: The spacing, anchorage length and protective layer thickness between each steel bar and its adjacent steel bars of each type in each building component of the prefabricated house are extracted from the actual building model of each building component of the prefabricated house, and are recorded as sp jgi 、an jgi and pr jgi , where g represents the number corresponding to the g-th type of steel bar, g = 1, 2, 3, ... J, i represents the number corresponding to the i-th steel bar, i = 1, 2, 3, ... I; The standard anchorage length, standard protective layer thickness and standard spacing between the corresponding steel bar and its adjacent steel bar at each installation position of each type of building components of the prefabricated house are extracted from the database, and then the standard anchorage length, standard protective layer thickness and standard spacing between the corresponding steel bar and its adjacent steel bar of each type of building components of the prefabricated house are screened out according to the position of each steel bar of each type in each building component of the prefabricated house, which are recorded as an′ jgi pr′ jgi and sp′ jgi ; Analyze the distribution qualification index of steel bars in each building component of prefabricated housing j , , ι1, ι2 and ι3 respectively represent the weights corresponding to the spacing between the steel bars and their adjacent steel bars of the type in the building components of the prefabricated house, the anchorage length of the steel bars and the thickness of the protective layer of the steel bars, and ι1+ι2+ι3=1, e represents a natural constant, J represents the total number of steel bar types in the building components of the prefabricated house, and I represents the total number of steel bars.

4. The intelligent management system for building automation construction sites based on big data according to claim 3 is characterized by: The specific process of analyzing the defect index of the steel bars in each building component of the prefabricated house is as follows: D1. Extract the standard bending angle, standard hook length and standard straight segment length of various types of steel bars in various building components of prefabricated houses from the database; D2. Extracting the bending angles, hook lengths and straight segment lengths of various types of steel bars in various building components of the prefabricated house from the actual building models of the building components of the prefabricated house; D3. Compare the bending angles of various steel bars in various building components of the prefabricated house with the standard bending angles of the corresponding steel bars to obtain the difference in bending angles of various steel bars in various building components of the prefabricated house, recorded as Δangle jg ; D4. According to the analysis method of the bending angle difference of each type of steel bar in each building component of the prefabricated house, the hook length difference and straight segment length difference of each type of steel bar in each building component of the prefabricated house are obtained in the same way, which are recorded as Δhook jg and Δline jg ; D5. Analyze the degree of defect index of steel bars in each building component of prefabricated housing j .

5. The intelligent management system for building automation construction sites based on big data according to claim 4 is characterized by: The specific calculation formula for analyzing the quality qualification index of the steel bars in each building component of the prefabricated house is: Wherein γ1 and γ2 represent the weights corresponding to the distribution qualification index and defect index of the steel bars in the building components of the prefabricated house, respectively, and γ1+γ2=1.

6. The intelligent management system for building automation construction sites based on big data according to claim 5 is characterized by: The specific calculation process of analyzing the quality qualification index of each building component of the prefabricated house is as follows: j =dim j *β1+complete j *β2, where β1 and β2 represent the weight factors corresponding to the area qualification index of the building components of the prefabricated house and the quality qualification index of the steel bars, respectively, and β1+β2=1.

7. The intelligent management system for building automation construction sites based on big data according to claim 1 is characterized by: The specific process of judging whether the quality of each building component of the prefabricated house is qualified is as follows: The qualification index of each building component of the prefabricated house is compared with the preset qualification index threshold. If the qualification index of a building component of the prefabricated house is less than the preset qualification index threshold, the building component of the prefabricated house is unqualified, and then the unqualified building components of the prefabricated house are obtained, and the unqualified building components of the prefabricated house are numbered, and then an early warning is issued.

8. The intelligent management system for building automation construction sites based on big data according to claim 1 is characterized by: The specific process of analyzing the pollution degree index of the wall surface of the prefabricated house after concrete filling is as follows: The grayscale threshold segmentation process is performed on the images of each angle of the prefabricated house after concrete filling to obtain the grayscale value of each pixel in the images of each angle of the prefabricated house after concrete filling, and the grayscale value of each pixel in the image of each angle is compared with the standard grayscale value interval of the pixel corresponding to the standard image of the surface of the building component of the prefabricated house. If the grayscale value of a pixel does not belong to the standard grayscale value interval of the pixel corresponding to the standard image of the surface of the building component of the prefabricated house, the pixel is marked as an abnormal pixel. The number of abnormal pixels in the images of each angle of the prefabricated house after concrete filling is screened and counted, which is recorded as stains. d , where d represents the number corresponding to the d-th angle image of the prefabricated house after concrete filling, d = 1, 2, 3, ... D, and then the total number of pixels in each angle image of the prefabricated house after concrete filling is obtained, recorded as pixel d , analyze the pollution index of prefabricated houses after concrete filling, 9. The big data-based building automation construction site intelligent management system according to claim 8, characterized in that: The specific process of analyzing the defect degree index of the wall surface of the prefabricated house after concrete filling is as follows: Based on the images of the prefabricated house at various angles after concrete filling, the various defect types of the prefabricated house after concrete filling are identified by image processing technology, and the characteristic values ​​of the various defect types of the prefabricated house after concrete filling are obtained by the measurement tools in the image processing software, which are recorded as length t , where t represents the number corresponding to the tth defect type of the prefabricated house after concrete filling, t = 1, 2, 3, ... T, and then the total number of defects in each defect type of the prefabricated house after concrete filling is counted, recorded as Total t ; Analyze the defect index crack of prefabricated houses after concrete filling. where length′ t and Total′ t They respectively represent the allowable characteristic value and the total number of allowable defects of the tth defect type of the prefabricated house after concrete filling, ρ1 and ρ2 respectively represent the weights corresponding to the characteristic value and the total number of defects of the defect type of the prefabricated house after concrete filling, and ρ1+ρ2=1, T represents the total number of defect types.

10. The big data-based building automation construction site intelligent management system according to claim 9, characterized in that: The specific process of judging whether the quality of concrete filling of the prefabricated house after concrete filling is qualified is as follows: Analyze the comprehensive quality index of prefabricated housing after concrete filling. Wherein δ1 and δ2 represent the weights corresponding to the pollution degree index and defect degree index of the wall surface of the prefabricated house after concrete filling, respectively, and δ1+δ2=1; The comprehensive quality qualification index of the prefabricated house after concrete filling is compared with the preset comprehensive quality qualification index threshold. If the comprehensive quality qualification index of the prefabricated house after concrete filling is greater than the preset comprehensive quality qualification index threshold, the comprehensive quality of the prefabricated house after concrete filling is qualified.

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