CN117437220A - Building engineering quality detection method and system based on big data - Google Patents
Building engineering quality detection method and system based on big data Download PDFInfo
- Publication number
- CN117437220A CN117437220A CN202311734360.XA CN202311734360A CN117437220A CN 117437220 A CN117437220 A CN 117437220A CN 202311734360 A CN202311734360 A CN 202311734360A CN 117437220 A CN117437220 A CN 117437220A
- Authority
- CN
- China
- Prior art keywords
- construction
- building
- point cloud
- quality
- engineering
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 98
- 238000010276 construction Methods 0.000 claims abstract description 120
- 238000000034 method Methods 0.000 claims abstract description 62
- 230000007547 defect Effects 0.000 claims abstract description 30
- 230000008569 process Effects 0.000 claims abstract description 20
- 238000005516 engineering process Methods 0.000 claims abstract description 17
- 230000008859 change Effects 0.000 claims abstract description 6
- 238000006243 chemical reaction Methods 0.000 claims description 21
- 238000009435 building construction Methods 0.000 claims description 14
- 238000012545 processing Methods 0.000 claims description 13
- 238000004458 analytical method Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 9
- 238000010586 diagram Methods 0.000 claims description 8
- 238000001931 thermography Methods 0.000 claims description 8
- 238000012544 monitoring process Methods 0.000 claims description 7
- 238000001914 filtration Methods 0.000 claims description 6
- 238000013461 design Methods 0.000 claims description 5
- 230000008030 elimination Effects 0.000 claims description 5
- 238000003379 elimination reaction Methods 0.000 claims description 5
- 238000007781 pre-processing Methods 0.000 claims description 4
- 239000002994 raw material Substances 0.000 claims description 4
- 230000002950 deficient Effects 0.000 claims description 3
- 230000007613 environmental effect Effects 0.000 claims description 3
- 230000001939 inductive effect Effects 0.000 claims description 2
- 238000007689 inspection Methods 0.000 claims 3
- 230000009471 action Effects 0.000 description 3
- 230000001276 controlling effect Effects 0.000 description 3
- 230000006399 behavior Effects 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000005728 strengthening Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06395—Quality analysis or management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/08—Construction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
- G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
- G06T2207/10012—Stereo images
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10048—Infrared image
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Geometry (AREA)
- Educational Administration (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Computer Graphics (AREA)
- Software Systems (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a construction engineering quality detection method and system based on big data, wherein the method comprises the following steps: step one: converting the construction environment into point cloud data by using a special three-dimensional laser scanner for construction engineering; step two: performing change detection on point cloud data obtained from construction site scanning and point cloud data obtained by converting a BIM model by using an algorithm; step three: the temperature of the surface of an object is visually presented by utilizing the difference of the temperature distribution of the surface of the building object in the building engineering, and the part with weak thermal performance or quality defect is detected; step four: based on the data standard and the report format of the unified quality detection process, the construction quality detection service is effectively fed back in real time through a data exchange technology, so that relevant departments can timely and accurately acquire various data related to the construction quality detection service.
Description
Technical Field
The invention relates to the technical field of engineering quality detection, in particular to a construction engineering quality detection method and system based on big data.
Background
The quality detection of the building engineering utilizes the related service industry to acquire accurate data, so that the quality and the safety of the building engineering are effectively regulated by related government departments, the progress of the whole building industry is promoted by strengthening management and control on the safety and the quality of the engineering project, the system is continuously perfected, the supervision capability is continuously improved, the supervision experience is continuously accumulated, but more supervision staff show a state of 'tired life', various resources cannot be effectively integrated, the increasingly huge construction scale is faced, the supervision resources are obviously insufficient, the engineering height, the number of layers and the complexity are increasingly increased along with the continuous development of the economic technology, the position of the engineering construction project is continuously radiated outwards along with the expansion of the city, the inherent supervision resources obviously keep up with the increasingly huge construction scale, the supervision cost is relatively high, and the coverage rate is very small from the aspect of the overall efficiency of spot check. Therefore, it is necessary to design a method and a system for detecting the quality of construction engineering based on big data, which improve the detection precision and the detection efficiency of the quality of construction engineering.
Disclosure of Invention
The invention aims to provide a construction engineering quality detection method and system based on big data, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a building engineering quality detection method based on big data comprises the following steps:
step one: converting the construction environment into point cloud data by using a special three-dimensional laser scanner for construction engineering;
step two: the method comprises the steps of performing change detection on point cloud data obtained through construction site scanning and point cloud data obtained through BIM conversion by means of an algorithm, and judging whether a certain building element in an engineering is built or not according to whether the offset distance of the point cloud data exceeds a set distance threshold value;
step three: the temperature of the surface of an object is visually presented in a pseudo-color image mode by utilizing the difference of the temperature distribution of the surface of the building object in the building engineering, and the part with weak thermal performance or quality defect is detected;
step four: based on the data standard and the report format of the unified quality detection process, the real-time effective feedback is carried out on the construction engineering quality detection service through the data exchange technology, so that the related departments can timely and accurately acquire various data related to the construction engineering quality detection service.
According to the technical scheme, the method for acquiring the point cloud data in the building construction process comprises the following steps:
step 11: site planning is carried out according to the building plan obtained through BIM conversion, so that scanning sites capable of obtaining complete environment data are obtained, and a special three-dimensional laser scanner is utilized to move to each site to collect original point cloud data of a construction site;
step 12: splitting the BIM model according to the types of the building elements, classifying the split building elements, putting the building elements into respective component pools, and realizing three-dimensional point cloud data representation of the building elements through uniform point cloud filling;
step 13: the method comprises the steps of automatically classifying point clouds by using existing software, filtering ground points, then filtering the point clouds again based on building characteristics to obtain point cloud data of a construction building, generating an octree map based on the point cloud data acquired by primary scanning, and judging whether a building missing part exists;
step 14: after judging that the position of the missing part of the building exists, the construction site of the building is a region with a fixed range, carrying out second building data acquisition on the basis of the known construction building object, and carrying out difference processing on the position and range information of the missing part and a door and window position diagram extracted by BIM to obtain the determined position and range of the missing wall;
step 15: the laser scanner carried by the mobile platform enters a scanning range corresponding to the missing part of the point cloud data of the engineering building again, the laser scanner is enabled to execute scanning within a specific angle range until all the missing parts are complemented, the whole scanning process is completed, the point cloud data of the missing part of the engineering building is extracted by synchronously adopting a manual intervention mode in the second acquisition process, and the complete acquisition of the environmental point cloud is realized;
step 16: and performing coarse difference elimination pretreatment on the obtained original point cloud data, calculating the distance from each point to a certain set, judging the relation between the distance and a given threshold value, judging the coarse difference point when the distance is larger than the given threshold value, and eliminating part of coarse difference.
According to the above technical solution, the step of performing change detection on the point cloud data obtained by scanning the construction site and the point cloud data obtained by converting the BIM model by using an algorithm includes:
establishing an association relation between a determined construction progress plan and an original BIM model;
adding start time and completion time information for each item in the schedule to a corresponding building element attribute table in the BIM model;
according to the construction site data acquisition time, the plan progress information of the construction project under the corresponding time stage is found in the progress plan, such as the current construction layer and the current construction project content;
setting engineering quality detection projects, which respectively comprise: judging whether the quality of engineering products and building raw materials meets the specified requirements or design standards, judging whether working procedures are normal, measuring working procedure capacity, and controlling the quality of the working procedures.
According to the above technical scheme, the step of judging whether a certain building element in the project has been built includes:
based on the construction engineering site scanning point cloud, the point cloud obtained by BIM conversion is used for detecting the construction progress of a single building element in order to obtain the construction element which is not constructed: for the construction progress of a single building element, the building element is segmented and extracted, the proportion of the volume of the constructed wall body to the monitoring target wall body is obtained through volume calculation, the STL model with the equivalent volume of the scanning point cloud and the STL model of the detection target wall body are utilized for volume calculation, the corresponding proportion is calculated, and the building construction progress monitoring and quality detection are completed;
aiming at the quality detection of the construction progress, the percentage result of the construction progress is obtained by comparing the point cloud volume obtained by utilizing the point cloud projection rasterization with the original building element volume, the plane fitting is firstly carried out by utilizing the extracted wall surface point cloud in the construction quality detection part, and the quality detection of the main body size, the indoor practical area and the indoor yin and yang angle of the house is further realized by utilizing the obtained fitting equation through the correlation calculation of space geometry.
According to the above technical solution, the step of visually presenting the temperature of the surface of the object includes:
and detecting quality defects of hollows, leakage and cracks of the building engineering component by using an infrared thermal imaging detection technology, judging a temperature difference region by using an image processing technology through an infrared thermal imaging chart, and calculating the area of the quality defect region.
According to the technical scheme, the method for detecting the quality defect part comprises the following steps:
step 31: adopting an infrared thermal imager to detect the leakage of a plastering hollow wall surface of a building and the quality defect phenomenon of a cold-hot bridge, and when a defective area is detected, adopting the infrared thermal imager and a three-dimensional laser scanner to simultaneously acquire data of the area;
step 32: using an infrared thermal imager to capture temperature image information with laser calibration points at the empty drum position, and performing color model conversion on the temperature image by reading the temperature image to perform binarization processing;
step 33: the two-dimensional temperature distribution diagram acquired by the infrared thermal imager is processed, the area size of a quality defect area is calculated, the three-dimensional laser scanner is utilized to acquire point cloud data of the whole building including the quality defect area, data processing is completed, and an accurate point cloud model of a target object is established;
step 34: and selecting the same-name characteristic lines or encapsulating the same-name characteristic line region point clouds into polygonal grids by using boundaries for the point cloud model constructed based on the point cloud data, and fusing the two-dimensional images after matching the same-name characteristic points and the same-name characteristic lines to finish the three-dimensional model for detecting the quality defects of the building engineering.
According to the technical scheme, the construction engineering quality detection system comprises:
the building construction environment conversion module is used for converting the building construction environment into point cloud data by utilizing a special three-dimensional laser scanner for building engineering;
the engineering quality analysis detection module is used for carrying out quality analysis detection by utilizing an algorithm according to the point cloud data and the point cloud data obtained by converting the BIM model;
and the real-time feedback module of the detection result is used for effectively feeding back the quality detection service of the building engineering in real time through a data exchange technology.
Compared with the prior art, the invention has the following beneficial effects: in the present invention, the number of the components,
by establishing an association relation between a determined construction progress plan and an original BIM model, adding the starting time and the finishing time information of each item in the progress plan into a corresponding building element attribute table in the BIM model, and finding the plan progress information of a construction project under a corresponding time stage in the progress plan according to the construction site data acquisition time, wherein the project quality detection project is set according to the current construction layer and the current construction project content, and the project quality detection project comprises the following steps: judging whether the quality of engineering products and building raw materials meets the specified requirements or design standards, judging whether the working procedure is normal, measuring the working procedure capacity, and controlling the quality of the working procedure, wherein compared with the existing common manual monitoring and detecting method, the method can realize the rapid, accurate and visual analysis;
the infrared thermal imager can be used for rapidly identifying the differences of uneven materials, shrinkage, hollowing and the like of the internal structure of the building engineering due to the influence of various reasons, so that the heat transfer speed of the surface of the structure is inconsistent, the phenomenon of uneven surface temperature occurs, the quality defect detection of hollowing, leakage and cracks of building engineering components is carried out, the convenience and the accuracy are higher, and the engineering quality detection accuracy and the detection efficiency can be improved to a greater extent.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of a method for detecting quality of construction engineering based on big data according to a first embodiment of the invention;
fig. 2 is a schematic diagram of module composition of a construction quality detection system based on big data according to a second embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one: fig. 1 is a flowchart of a construction quality detection method based on big data, which is provided in an embodiment of the present invention, and the method may be implemented by a construction quality detection system based on big data, as shown in fig. 1, and specifically includes the following steps:
step one: converting the construction environment into point cloud data by using a special three-dimensional laser scanner for construction engineering;
in the embodiment of the invention, based on the bottom-up construction sequence of the main structure of the engineering building, after the construction of the main structure below the top layer is completed, the construction environment of the top layer of the current building is acquired, a laser scanner is fixed on a mobile platform, and each scanning site of the engineering building is scanned through the laser scanner carried by the mobile platform, and as the laser scanner needs to be kept stationary at a certain site when acquiring the environment point cloud, the point cloud acquisition is required to be carried out at different scanning sites so as to ensure that the complete point cloud of the construction environment of the building is obtained; acquiring a local more accurate construction progress condition of a building based on the complete point cloud;
exemplary, the method for acquiring the point cloud data in the building construction process comprises the following steps:
step 11: site planning is carried out according to the building plan obtained through BIM conversion, so that scanning sites capable of obtaining complete environment data are obtained, and a special three-dimensional laser scanner is utilized to move to each site to collect original point cloud data of a construction site;
step 12: splitting the BIM model according to the types of the building elements, classifying the split building elements, putting the building elements into respective component pools, and realizing three-dimensional point cloud data representation of the building elements through uniform point cloud filling;
step 13: the method comprises the steps of automatically classifying point clouds by using existing software, filtering ground points, then filtering the point clouds again based on building characteristics to obtain point cloud data of a construction building, generating an octree map based on the point cloud data acquired by primary scanning, and judging whether a building missing part exists;
step 14: after judging that the position of the missing part of the building exists, the construction site of the building is a region with a fixed range, carrying out second building data acquisition on the basis of the known construction building object, and carrying out difference processing on the position and range information of the missing part and a door and window position diagram extracted by BIM to obtain the determined position and range of the missing wall;
step 15: the laser scanner carried by the mobile platform enters a scanning range corresponding to the missing part of the point cloud data of the engineering building again, the laser scanner is enabled to execute scanning within a specific angle range until all the missing parts are complemented, the whole scanning process is completed, the point cloud data of the missing part of the engineering building is extracted by synchronously adopting a manual intervention mode in the second acquisition process, and the complete acquisition of the environmental point cloud is realized;
step 16: and performing coarse difference elimination pretreatment on the obtained original point cloud data, calculating the distance from each point to a certain set, judging the relation between the distance and a given threshold value, judging the coarse difference point when the distance is larger than the given threshold value, and eliminating part of coarse difference.
Step two: the method comprises the steps of performing change detection on point cloud data obtained through construction site scanning and point cloud data obtained through BIM conversion by means of an algorithm, and judging whether a certain building element in an engineering is built or not according to whether the offset distance of the point cloud data exceeds a set distance threshold value;
in the embodiment of the invention, an association relation between a determined construction progress plan and an original BIM model is established, the starting time and the finishing time information of each item in the progress plan are added into a corresponding building component attribute table in the BIM model, the plan progress information of a construction item in a corresponding time stage is found in the progress plan according to the construction site data acquisition time, such as a current construction layer and current construction item content, and engineering quality detection items are set, wherein the steps respectively comprise: judging whether the quality of engineering products and building raw materials meets the specified requirements or design standards, judging whether working procedures are normal, measuring the working procedure capacity, and controlling the quality of the working procedures;
exemplary, based on the construction engineering site scanning point cloud, the point cloud obtained by BIM conversion is used for detecting the construction progress of a single building element in order to obtain an unworked building element: for the construction progress of a single building element, the building element is segmented and extracted, the proportion of the volume of the constructed wall body to the monitoring target wall body is obtained through volume calculation, the STL model with the equivalent volume of the scanning point cloud and the STL model of the detection target wall body are utilized for volume calculation, the corresponding proportion is calculated, and the building construction progress monitoring and quality detection are completed;
for the quality detection of the construction progress, the percentage result of the construction progress is obtained by comparing the point cloud volume obtained by utilizing the point cloud projection rasterization with the original building element volume, the plane fitting is firstly carried out by utilizing the extracted wall surface point cloud in the construction quality detection part, and the quality detection of the main body size, the indoor practical area and the indoor internal and external corners of the house is further realized by utilizing the obtained fitting equation through the correlation calculation of space geometry.
Step three: the temperature of the surface of an object is visually presented in a pseudo-color image mode by utilizing the difference of the temperature distribution of the surface of the building object in the building engineering, and the part with weak thermal performance or quality defect is detected;
in the embodiment of the invention, the enclosure structure in the building engineering is influenced by various reasons, the internal structure of the enclosure structure generates differences such as uneven materials, shrinkage, hollowness and the like, so that the heat transfer speed of the surface of the structure is inconsistent, and the phenomenon of uneven surface temperature occurs, therefore, the phenomenon can be rapidly identified by using an infrared thermal imaging instrument, the quality defect detection of hollowness, leakage and cracks of building engineering components is carried out by using an infrared thermal imaging detection technology, the temperature difference area is judged by using an image processing technology on an infrared thermal imaging chart, the area of the quality defect area is calculated, and the infrared thermal imaging detection method is used as a non-contact detection means, has convenience and high precision, and can realize remote measurement, analysis and detection;
exemplary, the method for detecting the quality defect part comprises the following steps:
step 31: adopting an infrared thermal imager to detect the leakage of a plastering hollow wall surface of a building and the quality defect phenomenon of a cold-hot bridge, and when a defective area is detected, adopting the infrared thermal imager and a three-dimensional laser scanner to simultaneously acquire data of the area;
step 32: using an infrared thermal imager to capture temperature image information with laser calibration points at the empty drum position, and performing color model conversion on the temperature image by reading the temperature image to perform binarization processing;
step 33: the two-dimensional temperature distribution diagram acquired by the infrared thermal imager is processed, the area size of a quality defect area is calculated, the three-dimensional laser scanner is utilized to acquire point cloud data of the whole building including the quality defect area, data processing is completed, and an accurate point cloud model of a target object is established;
step 34: and selecting the same-name characteristic lines or encapsulating the same-name characteristic line region point clouds into polygonal grids by using boundaries for the point cloud model constructed based on the point cloud data, and fusing the two-dimensional images after matching the same-name characteristic points and the same-name characteristic lines to finish the three-dimensional model for detecting the quality defects of the building engineering.
Step four: based on the data standard and the report format of the unified quality detection process, the real-time effective feedback is carried out on each construction engineering quality detection service through a data exchange technology, so that the related departments can timely and accurately acquire each item of data related to the construction engineering quality detection service;
in the embodiment of the invention, the detection data are summarized, the logic rule is formulated, the detection conclusion is deduced in the software detection environment and is judged, the correctness of the conclusion can be verified by an expert or a user, the system automatically judges the detection conclusion according to the condition limit set by the user, and in the daily detection behaviors, the repeated complex judgment of a large amount of data by detection personnel is reduced;
by means of the processing information provided on the construction engineering quality detection supervision platform, behavior supervision on relevant unit quality such as construction engineering responsibility bodies such as construction units and supervision companies and quality detection mechanisms is extended, quality information of construction units and accessories and site construction quality are timely known and mastered, the construction engineering quality and safety are ensured in multiple aspects and multiple layers, and influences caused by human factors in the construction engineering quality detection process are effectively reduced to the greatest extent.
Embodiment two: the second embodiment of the present invention provides a system for detecting quality of construction engineering based on big data, and fig. 2 is a schematic diagram of module composition of the system for detecting quality of construction engineering based on big data, as shown in fig. 2, the system includes:
the building construction environment conversion module is used for converting the building construction environment into point cloud data by utilizing a special three-dimensional laser scanner for building engineering;
the engineering quality analysis detection module is used for carrying out quality analysis detection by utilizing an algorithm according to the point cloud data and the point cloud data obtained by converting the BIM model;
and the real-time feedback module of the detection result is used for effectively feeding back the quality detection service of the building engineering in real time through a data exchange technology.
In some embodiments of the present invention, a building construction environment conversion module includes:
the three-dimensional laser scanner module is used for scanning each scanning site of the engineering building through a laser scanner carried by the mobile platform;
the point cloud data acquisition module is used for acquiring point cloud data in the construction process of the building;
the original point cloud data preprocessing module is used for performing rough difference elimination preprocessing on the obtained original point cloud data.
In some embodiments of the invention, the engineering quality analysis detection module comprises:
the BIM conversion module is used for obtaining point cloud data through conversion by the BIM;
the association relation establishing module is used for establishing an association relation between the established construction progress plan and the original BIM model;
the construction progress quality detection module is used for detecting the construction progress of a single building element;
and the quality defect part detection module is used for detecting parts with weak thermal performance and quality defects in engineering.
In some embodiments of the present invention, the real-time feedback module for detection results includes:
the quality detection data reporting module is used for unifying the data standard and reporting format of the quality detection process;
the data exchange module is used for effectively feeding back the construction engineering quality detection service in real time through a data exchange technology;
the logic rule making module is used for making logic rules by inducing the detection data;
and the engineering quality real-time knowing module is used for knowing and grasping quality information of the accessory structure and site construction quality in engineering projects in real time.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A construction engineering quality detection method based on big data is characterized in that: the method comprises the following steps:
step one: converting the construction environment into point cloud data by using a special three-dimensional laser scanner for construction engineering;
step two: the method comprises the steps of performing change detection on point cloud data obtained through construction site scanning and point cloud data obtained through BIM conversion by means of an algorithm, and judging whether a certain building element in an engineering is built or not according to whether the offset distance of the point cloud data exceeds a set distance threshold value;
step three: the temperature of the surface of an object is visually presented in a pseudo-color image mode by utilizing the difference of the temperature distribution of the surface of the building object in the building engineering, and the part with weak thermal performance or quality defect is detected;
step four: based on the data standard and the report format of the unified quality detection process, the real-time effective feedback is carried out on the construction engineering quality detection service through the data exchange technology, so that the related departments can timely and accurately acquire various data related to the construction engineering quality detection service.
2. The method for detecting the quality of construction engineering based on big data according to claim 1, wherein the method comprises the following steps: the method for acquiring the point cloud data in the building construction process comprises the following steps:
step 11: site planning is carried out according to the building plan obtained through BIM conversion, so that scanning sites capable of obtaining complete environment data are obtained, and a special three-dimensional laser scanner is utilized to move to each site to collect original point cloud data of a construction site;
step 12: splitting the BIM model according to the types of the building elements, classifying the split building elements, putting the building elements into respective component pools, and realizing three-dimensional point cloud data representation of the building elements through uniform point cloud filling;
step 13: the method comprises the steps of automatically classifying point clouds by using existing software, filtering ground points, then filtering the point clouds again based on building characteristics to obtain point cloud data of a construction building, generating an octree map based on the point cloud data acquired by primary scanning, and judging whether a building missing part exists;
step 14: after judging that the position of the missing part of the building exists, the construction site of the building is a region with a fixed range, carrying out second building data acquisition on the basis of the known construction building object, and carrying out difference processing on the position and range information of the missing part and a door and window position diagram extracted by BIM to obtain the determined position and range of the missing wall;
step 15: the laser scanner carried by the mobile platform enters a scanning range corresponding to the missing part of the point cloud data of the engineering building again, the laser scanner is enabled to execute scanning within a specific angle range until all the missing parts are complemented, the whole scanning process is completed, the point cloud data of the missing part of the engineering building is extracted by synchronously adopting a manual intervention mode in the second acquisition process, and the complete acquisition of the environmental point cloud is realized;
step 16: and performing coarse difference elimination pretreatment on the obtained original point cloud data, calculating the distance from each point to a certain set, judging the relation between the distance and a given threshold value, judging the coarse difference point when the distance is larger than the given threshold value, and eliminating part of coarse difference.
3. The method for detecting the quality of construction engineering based on big data according to claim 2, wherein the method comprises the following steps: the step of detecting the change of the point cloud data obtained from the construction site scanning and the point cloud data obtained by converting the BIM model by utilizing an algorithm comprises the following steps:
establishing an association relation between a determined construction progress plan and an original BIM model;
adding start time and completion time information for each item in the schedule to a corresponding building element attribute table in the BIM model;
according to the construction site data acquisition time, the plan progress information of the construction project under the corresponding time stage is found in the progress plan, such as the current construction layer and the current construction project content;
setting engineering quality detection projects, which respectively comprise: judging whether the quality of engineering products and building raw materials meets the specified requirements or design standards, judging whether working procedures are normal, measuring working procedure capacity, and controlling the quality of the working procedures.
4. A method for detecting the quality of construction works based on big data according to claim 3, wherein: the step of judging whether a certain building element in the project is built or not comprises the following steps:
based on the construction engineering site scanning point cloud, the point cloud obtained by BIM conversion is used for detecting the construction progress of a single building element in order to obtain the construction element which is not constructed: for the construction progress of a single building element, the building element is segmented and extracted, the proportion of the volume of the constructed wall body to the monitoring target wall body is obtained through volume calculation, the STL model with the equivalent volume of the scanning point cloud and the STL model of the detection target wall body are utilized for volume calculation, the corresponding proportion is calculated, and the building construction progress monitoring and quality detection are completed;
aiming at the quality detection of the construction progress, the percentage result of the construction progress is obtained by comparing the point cloud volume obtained by utilizing the point cloud projection rasterization with the original building element volume, the plane fitting is firstly carried out by utilizing the extracted wall surface point cloud in the construction quality detection part, and the quality detection of the main body size, the indoor practical area and the indoor yin and yang angle of the house is further realized by utilizing the obtained fitting equation through the correlation calculation of space geometry.
5. The method for detecting the quality of the constructional engineering based on the big data as defined in claim 4, wherein the method comprises the following steps of: the step of visually presenting the temperature of the surface of the object comprises the following steps:
and detecting quality defects of hollows, leakage and cracks of the building engineering component by using an infrared thermal imaging detection technology, judging a temperature difference region by using an image processing technology through an infrared thermal imaging chart, and calculating the area of the quality defect region.
6. The method for detecting the quality of the constructional engineering based on the big data according to claim 5, wherein the method comprises the following steps: the method for detecting the quality defect part comprises the following steps:
step 31: adopting an infrared thermal imager to detect the leakage of a plastering hollow wall surface of a building and the quality defect phenomenon of a cold-hot bridge, and when a defective area is detected, adopting the infrared thermal imager and a three-dimensional laser scanner to simultaneously acquire data of the area;
step 32: using an infrared thermal imager to capture temperature image information with laser calibration points at the empty drum position, and performing color model conversion on the temperature image by reading the temperature image to perform binarization processing;
step 33: the two-dimensional temperature distribution diagram acquired by the infrared thermal imager is processed, the area size of a quality defect area is calculated, the three-dimensional laser scanner is utilized to acquire point cloud data of the whole building including the quality defect area, data processing is completed, and an accurate point cloud model of a target object is established;
step 34: and selecting the same-name characteristic lines or encapsulating the same-name characteristic line region point clouds into polygonal grids by using boundaries for the point cloud model constructed based on the point cloud data, and fusing the two-dimensional images after matching the same-name characteristic points and the same-name characteristic lines to finish the three-dimensional model for detecting the quality defects of the building engineering.
7. The utility model provides a building engineering quality detecting system based on big data which characterized in that: the system comprises:
the building construction environment conversion module is used for converting the building construction environment into point cloud data by utilizing a special three-dimensional laser scanner for building engineering;
the engineering quality analysis detection module is used for carrying out quality analysis detection by utilizing an algorithm according to the point cloud data and the point cloud data obtained by converting the BIM model;
and the real-time feedback module of the detection result is used for effectively feeding back the quality detection service of the building engineering in real time through a data exchange technology.
8. The big data based construction quality inspection system of claim 7, wherein: the building construction environment conversion module comprises:
the three-dimensional laser scanner module is used for scanning each scanning site of the engineering building through a laser scanner carried by the mobile platform;
the point cloud data acquisition module is used for acquiring point cloud data in the construction process of the building;
the original point cloud data preprocessing module is used for performing rough difference elimination preprocessing on the obtained original point cloud data.
9. The big data based construction quality inspection system of claim 8, wherein: the engineering quality analysis detection module comprises:
the BIM conversion module is used for obtaining point cloud data through conversion by the BIM;
the association relation establishing module is used for establishing an association relation between the established construction progress plan and the original BIM model;
the construction progress quality detection module is used for detecting the construction progress of a single building element;
and the quality defect part detection module is used for detecting parts with weak thermal performance and quality defects in engineering.
10. The big data based construction quality inspection system of claim 9, wherein: the real-time feedback module of the detection result comprises:
the quality detection data reporting module is used for unifying the data standard and reporting format of the quality detection process;
the data exchange module is used for effectively feeding back the construction engineering quality detection service in real time through a data exchange technology;
the logic rule making module is used for making logic rules by inducing the detection data;
and the engineering quality real-time knowing module is used for knowing and grasping quality information of the accessory structure and site construction quality in engineering projects in real time.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311734360.XA CN117437220A (en) | 2023-12-18 | 2023-12-18 | Building engineering quality detection method and system based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311734360.XA CN117437220A (en) | 2023-12-18 | 2023-12-18 | Building engineering quality detection method and system based on big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117437220A true CN117437220A (en) | 2024-01-23 |
Family
ID=89551830
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311734360.XA Pending CN117437220A (en) | 2023-12-18 | 2023-12-18 | Building engineering quality detection method and system based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117437220A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118018035A (en) * | 2024-04-09 | 2024-05-10 | 毛茸茸(南通)智能科技有限公司 | Information transmission method of intelligent supervision system for decoration progress |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112633657A (en) * | 2020-12-16 | 2021-04-09 | 中冶建筑研究总院有限公司 | Construction quality management method, device, equipment and storage medium |
| US20220005332A1 (en) * | 2018-10-29 | 2022-01-06 | Hexagon Technology Center Gmbh | Facility surveillance systems and methods |
-
2023
- 2023-12-18 CN CN202311734360.XA patent/CN117437220A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20220005332A1 (en) * | 2018-10-29 | 2022-01-06 | Hexagon Technology Center Gmbh | Facility surveillance systems and methods |
| CN112633657A (en) * | 2020-12-16 | 2021-04-09 | 中冶建筑研究总院有限公司 | Construction quality management method, device, equipment and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| 谢桐亮: "基于建筑信息模型的机器人建筑施工进度监测和质量检测", 万方数据, 11 November 2022 (2022-11-11), pages 1 - 84 * |
| 陈文: "基于 BIM 技术的工程质量缺陷检测方法研究及应用", 中国优秀硕士学位论文全文数据库工程科技Ⅱ辑(月刊), no. 12, 15 December 2019 (2019-12-15), pages 1 - 58 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118018035A (en) * | 2024-04-09 | 2024-05-10 | 毛茸茸(南通)智能科技有限公司 | Information transmission method of intelligent supervision system for decoration progress |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Tan et al. | Mapping and modelling defect data from UAV captured images to BIM for building external wall inspection | |
| Duque et al. | Bridge deterioration quantification protocol using UAV | |
| CN105045950B (en) | A kind of bridge security assessment system based on 3 D laser scanning | |
| Lins et al. | Automatic crack detection and measurement based on image analysis | |
| Zhang et al. | Automated progress control using laser scanning technology | |
| CN102135236B (en) | Automatic non-destructive testing method for internal wall of binocular vision pipeline | |
| CN106777680A (en) | A kind of existing bridge fast B IM modelings and method | |
| CN117437220A (en) | Building engineering quality detection method and system based on big data | |
| Lin et al. | Applications of computer vision on tile alignment inspection | |
| CN113920266A (en) | Artificial intelligence generation method and system for semantic information of city information model | |
| Ham et al. | Rapid 3D energy performance modeling of existing buildings using thermal and digital imagery | |
| Sedek et al. | Development of new system for detection of bridges construction defects using terrestrial laser remote sensing technology | |
| CN113222937A (en) | Detection method and detection device for appearance peripheral surface defects of nuclear fuel pellet | |
| Li et al. | A deep learning-based indoor acceptance system for assessment on flatness and verticality quality of concrete surfaces | |
| Arias et al. | 3D modeling and section properties of ancient irregular timber structures by means of digital photogrammetry | |
| Wang et al. | Structured-light three-dimensional scanning for process monitoring and quality control in precast concrete production. | |
| Motayyeb et al. | Fusion of UAV-based infrared and visible images for thermal leakage map generation of building facades | |
| KR20230086087A (en) | Construction site supervision apparatus and supervision method using 3D scanning | |
| Dědič | Evaluation of the processes of creating a project documentation of an existing building using a 3D scanner | |
| González-Aguilera et al. | Accuracy assessment of vehicles surface area measurement by means of statistical methods | |
| CN116381726A (en) | Unmanned aerial vehicle laser point cloud precision self-evaluation method based on data | |
| CN116226965A (en) | Building waste estimation algorithm based on BIM | |
| Xiong et al. | Reality capture and site-scanning techniques for material reuse planning | |
| Jang et al. | Structural evaluation by reverse engineering with 3D laser scanner | |
| Puri et al. | ’Dimensional quality control of precast concrete slabs using3D laser scan point clouds’ |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |