Engineering quality supervision acceptance inspection detection system and method based on Internet of Things technology

Abstract

The invention relates to the technical field of project quality supervision acceptance detection, and discloses a project quality supervision acceptance detection system and method based on the Internet of Things technology, and the system comprises an information collection module, a data analysis module, a man-machine interaction module and a data storage library. The data analysis module comprises a point cloud preprocessing sub-module, a fitting comparison sub-module and a problem analysis sub-module; the man-machine interaction module comprises a quality early warning sub-module, a manual re-checking sub-module, a terminal display sub-module and a result output sub-module. According to the system, manual reading and recording are not needed, the data entry error can be reduced, and the data processing efficiency can be improved; a data analysis module automatically judges the wall surface abnormal condition and the quality excellent degree of the local point cloud model, abnormal data are verified through a man-machine interaction working mode, normal data are further mined and analyzed, and therefore the judgment accuracy and the working efficiency of the system are improved; and the workload of supervision personnel is reduced to a great extent.

Description

Technical Field

[0001] The present invention relates to the technical field of project quality supervision and acceptance testing, and in particular to a project quality supervision and acceptance testing system and method based on Internet of Things technology, specifically a building wall flatness supervision and acceptance testing system and method. Background Art

[0002] At present, in the "Uniform Standard for Construction Quality Acceptance of Building Engineering" (GB50300-2011), the method for detecting the flatness of building walls is to use a wedge-shaped feeler gauge with a 2m ruler, and obtain the flatness deviation by reading the vernier scale of the feeler gauge. This method not only has human operation errors, but also has uneven detection point settings and low measurement accuracy. With the rapid development and iteration of high-tech in the past two decades, 3D laser scanning technology has been proposed. This technology uses high-speed laser scanning measurement methods to quickly obtain the spatial coordinates, reflectivity, RGB color and other 3D feature information of each point on the surface of an object over a large area and with high resolution, and quickly rebuild a proportional 3D point cloud model, providing an accurate basis for subsequent internal data processing and analysis.

[0003] However, the existing wall flatness detection equipment has the following defects: 1) The data obtained is basically read and recorded manually, which is not only inefficient, but also may have data entry errors caused by human operating errors; 2) The data obtained by using a 3D scanner to detect wall flatness is redundant and usually needs to be manually imported into professional software for data analysis, resulting in a large workload for supervisors; 3) The traditional way of processing actual measured data is relatively rough, lacking in the depth of data mining, which is not conducive to the comprehensive evaluation of project quality and the guidance of supervisory behavior. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide an engineering quality supervision and acceptance detection system and method based on Internet of Things technology to solve the technical problems mentioned in the prior art.

[0005] The project quality supervision and acceptance inspection system based on the Internet of Things technology includes: An information acquisition module measures the flatness of several detection areas of the wall to be detected to obtain corresponding building information, and generates several groups of local point cloud models of the wall to be detected based on the acquired building information; A data analysis module, the data analysis module includes a point cloud preprocessing submodule, a fitting comparison submodule and a problem analysis submodule, the point cloud preprocessing submodule is configured to: preprocess several groups of local point cloud models of the wall to be detected according to a preset processing rule to generate an initial plane; the fitting comparison submodule is configured to: perform adjustment processing on the initial plane to generate a fitting plane, and obtain the unqualified point rate of the fitting plane based on a preset standard wall; the problem analysis submodule is configured to: determine whether the fitting plane is a normal wall or an abnormal wall according to the discrimination condition of the unqualified point rate of the fitting plane, and perform a quality analysis on the normal wall and a problem level analysis on the abnormal wall; the discrimination condition is: determine whether the unqualified point rate of the fitting plane exceeds a preset detection threshold, if it exceeds, determine that the fitting plane is an abnormal wall, if not, determine that it is a normal wall; A human-computer interaction module, the human-computer interaction module includes a quality warning submodule, a good judgment submodule, a manual review submodule and a result output submodule, the good judgment submodule is configured to: mark the wall surface whose quality is lower than the preset standard in the normal wall surface as a reminder of the precautions for subsequent decoration and renovation; the quality warning submodule is configured to: divide the abnormal wall surface into several levels to represent different degrees of wall surface defects, and generate corresponding acceptance instructions according to different degrees of wall surface defects and send them to the manual review submodule; the manual review submodule is configured to: review the abnormal wall surface of the corresponding level according to the received acceptance instruction and generate an acceptance report to input into the result output submodule; the result output submodule sends the acceptance report to the user end to feedback the acceptance results of the abnormal wall surface; The system configures a data repository for the information acquisition module, the data analysis module and the human-computer interaction module. The data repository is used to store the task flow and data information generated by the system during the operation phase.

[0006] Optionally, the information collection module includes: The area division submodule divides each detection area of ​​the wall to be detected into a plurality of sub-areas according to a plane grid, and configures a unique number for each sub-area and standardizes and embeds it in the data analysis module; A measuring station layout submodule is used to layout a number of measuring stations in each of the sub-areas according to the wall surface flatness acceptance requirements; The wall flatness detection submodule configures a three-dimensional laser scanner for a plurality of the measuring stations, and the three-dimensional laser scanner is used to statically or dynamically scan the building information at the measuring stations within the corresponding range to generate the corresponding local point cloud model.

[0007] Optionally, the numbers of the detection area and the sub-area are respectively composed of one or more of a building number, a floor number, a room number and a wall number.

[0008] Optionally, the processing rules preset in the point cloud preprocessing submodule are set as: A plurality of groups of local point cloud models are randomly selected from the local point cloud models acquired from the information acquisition module for splicing according to a preset combination relationship or to obtain an overall point cloud model, and the overall point cloud model is denoised, and then three detection points are arbitrarily selected from the overall point cloud model after denoising to form an initial plane.

[0009] Optionally, the fitting comparison submodule adjusts the initial plane by a weighted iteration method to obtain the fitting plane.

[0010] Optionally, the unqualified point rate of the fitting plane is set to: Based on the preset standard wall, the deviation error parameter of each detection point in the fitting plane is obtained, and the deviation error parameter is set to the shortest distance between the detection point and the standard wall. The detection points in the fitting plane whose deviation error parameters exceed the preset detection threshold are regarded as unqualified points, and the ratio of unqualified points to all detection points is calculated as the unqualified point rate of the fitting plane.

[0011] Optionally, the fitting comparison submodule constructs a heat map by setting different indications for each detection point in the fitting plane according to the size of the deviation error parameter, and the indications in the heat map have display symbols of different colors and / or shapes, and the display symbols are dynamically adjusted in real time according to changes in the deviation error parameters corresponding to the detection points.

[0012] Optionally, the human-computer interaction module further includes a terminal display submodule, which is configured as a PC terminal, a mobile phone terminal, a tablet and / or a large screen, and displays the heat map in the form of a BI dashboard.

[0013] The project quality supervision and acceptance detection method based on the Internet of Things technology is applied to the above-mentioned system, and the method includes the following steps: S1. Using the information acquisition module, measure the wall flatness of several detection areas of the wall to be detected respectively to obtain corresponding building information, and generate several groups of local point cloud models of the wall to be detected based on the acquired building information; S2, preprocessing several groups of local point cloud models of the wall to be detected through a point cloud preprocessing submodule to obtain an initial plane; S3, performing adjustment processing on the initial plane through the fitting comparison submodule to obtain a fitting plane, and obtaining the unqualified point rate of the fitting plane based on a preset standard wall; S4, the problem analysis submodule determines whether the fitting plane is a normal wall surface or an abnormal wall surface according to the discrimination condition of the unqualified point rate of the fitting plane, and performs a quality analysis on the normal wall surface and a problem level analysis on the abnormal wall surface. The discrimination condition is: Determine whether the unqualified point rate of the fitting plane exceeds a preset detection threshold; if so, determine that the fitting plane is an abnormal wall surface; if not, determine that the fitting plane is a normal wall surface; S5, the quality warning submodule divides the abnormal wall surface into several levels to represent different degrees of wall surface defects, and generates corresponding acceptance instructions according to different degrees of wall surface defects and sends them to the manual review submodule, and at the same time, the good judgment submodule marks the wall surface whose quality is lower than the preset standard among the normal wall surfaces as a reminder of the precautions for subsequent decoration and renovation; S6, the manual review submodule reviews the abnormal wall surface of the corresponding level according to the received acceptance instruction and generates an acceptance report which is input into the achievement output submodule; S7. The result output submodule sends the acceptance report to the user end to feedback the acceptance result of the abnormal wall surface.

[0014] Optionally, in S5, the abnormal wall surface is divided into general problems and serious problems; When the abnormal wall surface is a general problem, the acceptance instruction is set to conduct a random inspection and re-inspection of any one or more of the abnormal walls with all general problems; When the abnormal wall surface has a serious problem, the acceptance instruction is set to manually review all the abnormal walls with serious problems and determine whether it is a system misjudgment; If the system misjudges, the judgment condition is updated and iterated; If it is not a system misjudgment, a corresponding rectification notice will be generated and issued to the corresponding construction personnel to carry out rectification work on the abnormal wall surface and review the rectification situation at the same time.

[0015] The beneficial effects that the present invention can produce include: 1. The engineering quality supervision and acceptance inspection system and method based on the Internet of Things technology provided by the present invention can generate a local point cloud model from the measured building information without manual reading and recording, which can reduce data entry errors and improve data processing efficiency; and automatically determine the wall abnormalities and quality of the local point cloud model through the data analysis module, and verify the abnormal data through the human-computer interaction working mode, and further mine and analyze the normal data, so as to improve the accuracy of the system's judgment and the efficiency of subsequent decoration and renovation preparation work; at the same time, through the system's efficient information flow function, the construction unit and the construction unit personnel can timely grasp the acceptance results and subsequent rectification, greatly reducing the workload of the supervision personnel.

[0016] 2. In the present invention, by configuring the human-computer interaction function, when the system encounters reserved holes on the wall to be inspected during the actual inspection process using a three-dimensional laser scanner, it can gradually update the accuracy of the iterative data analysis module through manual review, ledger notes, and later adjustment of the judgment conditions of the data analysis module during the human-computer interaction process, thereby increasing the practicality and reliability of the system in actual operation.

[0017] 3. In the present invention, by configuring the system with prefabricated criteria for judging the rate of unqualified points, the standard acceptance specifications (three reliances) for supervisors in the actual acceptance process are introduced. This not only takes into account the advantages of the system's comprehensive detection, but also combines the sampling scenarios in actual applications, making the detection results more valuable for actual reference, thereby making the system more practical.

[0018] 4. In the present invention, the format and content of the acceptance report are preset in the system in advance, and the key information and test results formed by the test data flowing through each sub-module are automatically recorded and counted to form an information ledger, which is convenient for various roles on the user side to capture data according to their own needs, and related reports are automatically generated through the prefabricated templates built in the early stage, which facilitates the efficient transmission of information. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a block diagram of the system of the present invention; Figure 2 It is a schematic diagram of the distribution of the detection area of ​​the wall to be detected of the present invention; Figure 3 Schematic diagram of the process of the present invention; Figure 4 A logical schematic diagram of the present invention for performing problem level analysis on abnormal wall surfaces; Figure 5 This is a schematic diagram of the results of the quality analysis of a normal wall surface performed by the present invention; Figure 6 Schematic diagram of normal distribution of a normal wall surface in the present invention; Figure 7 Schematic diagram of right-skewed distribution of a normal wall surface in the present invention; Figure 8 Schematic diagram of left-skewed distribution of a normal wall surface in the present invention; Fig. 9 Schematic diagram of abnormal distribution of a normal wall surface in the present invention; In the figure: 1. Information collection module, 11. Area division submodule, 12. Measurement station layout submodule, 13. Wall flatness detection submodule, 2. Data analysis module, 21. Point cloud preprocessing submodule, 22. Fitting comparison submodule, 23. Problem analysis submodule, 3. Human-computer interaction module, 31. Quality warning submodule, 32. Excellent judgment submodule, 33. Manual review submodule, 33. Terminal display submodule, 35. Result output submodule, 4. Data repository, 5. Detection area, 6. Sub-area. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] like Figure 1As shown, the present invention provides a project quality supervision and acceptance inspection system based on the Internet of Things technology, the system includes an information acquisition module 1, a data analysis module 2, a human-computer interaction module 3 and a data storage library 4, the information acquisition module 1 measures the wall flatness of several detection areas 5 of the wall to be detected respectively to obtain corresponding building information, and generates several groups of local point cloud models of the wall to be detected based on the acquired building information; the data analysis module 2 includes a point cloud preprocessing submodule 21, a fitting comparison submodule 22 and a problem analysis submodule 23, the point cloud preprocessing submodule 21 is configured to: Several groups of local point cloud models of the surface are preprocessed according to the preset processing rules to generate an initial plane; the fitting comparison submodule 22 is configured to: perform adjustment processing on the initial plane to generate a fitting plane, and obtain the unqualified point rate of the fitting plane based on the preset standard wall; the problem analysis submodule 23 is configured to: determine whether the fitting plane is a normal wall or an abnormal wall according to the judgment condition of the unqualified point rate of the fitting plane, and perform a good quality analysis on the normal wall and a problem level analysis on the abnormal wall, and mark the wall with poor quality in the normal wall as a reminder for subsequent decoration and renovation Matters; wherein the discrimination condition is: judging whether the unqualified point rate of the fitting plane exceeds the preset detection threshold value, if it exceeds, the fitting plane is judged to be an abnormal wall surface, if it does not exceed, it is judged to be a normal wall surface; the human-computer interaction module 3 includes a quality warning submodule 31, an excellent judgment submodule 32, a manual review submodule 33 and a result output submodule 35, the excellent judgment submodule 32 is configured to: mark the wall surface whose quality is lower than the preset standard in the normal wall surface as a reminder of the precautions for subsequent decoration and renovation; the quality warning submodule 31 is configured to: divide the abnormal wall surface into several levels to represent different The system detects wall defects of different degrees, and generates corresponding acceptance instructions according to the wall defects of different degrees, which are sent to the manual review submodule 33; the manual review submodule 33 is configured to: review the abnormal wall of the corresponding level according to the received acceptance instructions and generate an acceptance report which is input into the result output submodule 35; the result output submodule 35 sends the acceptance report to the user end to feedback the acceptance results of the abnormal wall; the system configures the information collection module 1, the data analysis module 2 and the human-computer interaction module 3 with a data storage library 4, which is used to store the task flow and data information generated by the system during the operation stage.

[0022] In this embodiment, if Figure 4As shown, the quality warning submodule 31 divides the abnormal wall into general problems and serious problems according to the problem level analysis results. The boundary of the problem level is set to whether the wall defect degree of the abnormal wall is greater than the set threshold of the wall defect. The wall defect degree of the abnormal wall greater than the set threshold of the wall defect is classified as a serious problem, and the wall defect degree of the abnormal wall not greater than the set threshold of the wall defect is classified as a general problem. Among them, the general problem can be notified by the supervisor to the construction personnel for spot check and re-inspection, while for serious problems, manual comprehensive review and inspection are required to determine whether the serious problem is a system misjudgment; if the serious problem is a system misjudgment, the system technician is contacted to record the misjudgment situation, so as to update and iterate the system's judgment conditions; if the serious problem is true, the supervisor will edit the rectification notice according to the actual situation of the serious problem and issue it to the corresponding construction personnel, and at the same time urge the construction personnel to rectify within a time limit and review the rectification situation. It should be noted that for serious problems, during the actual on-site acceptance, it is considered that a part of the fitting plane has no quality problems, but is a system misjudgment caused by the system computer recognition error due to the reserved holes in the wall. Therefore, the situation is fed back to the system computer to update and iterate the judgment conditions, thereby realizing human-computer interaction, so as to gradually update and iterate the accuracy of the data analysis module 2 of the system, and increase the practicality and reliability of the system in actual operation.

[0023] In this embodiment, if Figure 6 , Figure 7 , Figure 8 and Fig. 9 The following are schematic diagrams of the output results of the good judgment submodule 32. When the good quality degree of a normal wall is analyzed, it can be known from mathematical statistics in practical applications that the data deviation distribution obtained by measuring the flatness of the wall mainly includes normal distribution, skewed distribution (with left-skewed distribution and right-skewed distribution) and abnormal distribution (with island distribution, bimodal distribution and sawtooth distribution); Figure 5 As shown, the present invention adopts a data distribution analysis method based on SPSS to analyze the quality of normal walls, that is, by importing the measured normal wall data into SPSS to achieve rapid processing of the wall flatness data, thereby obtaining important parameters and fitting images of data deviation distribution such as mean, variance, mode, and interquartile range. Figure 5 The X-axis in represents the distance from the wall point scanned by the above 3D laser scanner to the fitting plane, and takes its absolute value, that is, the value interval of the X-axis is (0, X); the Y-axis represents the frequency of the wall point distribution. It should be noted that, if Fig. 9 As shown in , when the deviation probability distribution is abnormal, it means that the deviation value of the wall flatness is unevenly distributed and the uniformity of the construction quality is poor; Figure 6 and Figure 7As shown in FIG. 1 , when the deviation probability distribution is a normal distribution or a left-skewed distribution, it indicates that the deviation value distribution of the wall to be detected is relatively uniform. At this time, the good judgment submodule 32 can Figure 5 The key parameters generated by SPSS in the above data are used to further judge the quality of the wall flatness. Specifically, the normal distribution can judge the concentration of the deviation values ​​of the wall flatness through the mean, where the deviation values ​​of the wall flatness represented by the mean are mainly concentrated near a certain deviation value; the normal distribution can judge the degree of dispersion of the deviation values ​​of the wall flatness through the variance, and the variance represents the dispersion of the deviation values ​​of the wall flatness relative to the mean; the smaller the mean and the smaller the variance, the deviation values ​​of the wall flatness in this area are close to 0 as a whole, and are more closely concentrated near the mean, indicating that the wall flatness is better. The skewed distribution can judge the concentration of the deviation values ​​of the wall flatness through the mode, where the deviation values ​​of the wall flatness represented by the mode are mainly concentrated near a certain deviation value; the degree of dispersion is judged by the interquartile range; similarly, the smaller the mode and the smaller the interquartile range, the better the wall flatness.

[0024] Furthermore, if Figure 2 As shown, the information acquisition module 1 includes a region division submodule 11, a measuring station layout submodule 12 and a wall flatness detection submodule 13. The region division submodule 11 divides each detection area 5 of the wall to be detected into a plurality of sub-areas 6 according to a plane grid, and configures a unique number for each sub-area 6 and standardizes and embeds it in the data analysis module 2, so as to facilitate the later operation personnel to rectify and quickly locate the abnormal wall during review, thereby improving the acceptance efficiency and accuracy; the measuring station layout submodule 12 arranges a plurality of measuring stations in each sub-area 6 according to the wall flatness acceptance requirements; the wall flatness detection submodule 13 configures a three-dimensional laser scanner for a plurality of measuring stations, and the three-dimensional laser scanner is used to statically or dynamically scan the building information at the measuring stations within the corresponding range to generate a corresponding local point cloud model; by directly acquiring and inputting the measurement data by the three-dimensional laser scanner, and forming a local point cloud model with massive information, there is no need for manual reading and transcription, which greatly improves the accuracy and efficiency of data input.

[0025] In the above, the numbers of the detection area 5 and the sub-area 6 are respectively composed of one or more of the building number, the floor number, the room number and the wall number, wherein the building number is represented by D, the floor number is represented by L, the room number is represented by R, and the wall number is represented by W. For example, D1L2R201W1 represents the detection area 5 with the wall number 1 in the room 201 on the second floor of the building 1. At the same time, in order to distinguish the wall numbers of the detection area 5 and the sub-area 6 so that the system can be more precisely positioned, the first or first two Roman numerals of the wall number are sorted and set according to the number of the detection areas 5 and in a decimal numbering method. For example, when the number of the detection areas 5 in each room is less than 10, the first Roman numeral of the wall number can be represented as the detection area 5 of the wall, and the second and subsequent Roman numerals can be represented as the sub-area 6 of the wall, such as D1L2R201W13 represents the sub-area 6 No. 3 of the first detection area 5 in the room 201 on the second floor of the building 1.

[0026] In this embodiment, when the numbering standardization is built into the data analysis module 2, in order to distinguish the processed and unprocessed local point cloud models in the system, different indicators can be added on the basis of the original numbering to indicate normal walls and abnormal walls, which is helpful for classifying and summarizing the detection results; for example, adding α to the prefix of the detected wall number indicates a normal wall, and adding β indicates an abnormal wall. The walls to be detected can be screened according to the pre-made unqualified point rate judgment standard, and the normal walls are set as the set α, and the abnormal walls are set as the set β; for example, αD1L2R201W13 indicates that the 3rd sub-area 6 of the 1st detection area 5 in room 201 on the 2nd floor of building 1 is a normal wall. It should be noted that standardization refers to unified processing or production according to certain specifications, standards or patterns.

[0027] In the above, the present invention designs three layers of detection standards, the first layer of detection standards is the qualified point rate, the second layer of detection standards is the mean (for normal distribution) or the mode (for skewed distribution), and the third layer of detection standards is the variance (for normal distribution) or the interquartile range (for skewed distribution); it should be noted that the priority judgment order of the three layers of detection standards is set to descend step by step, and the walls in the set α are ranked according to the three layers of detection standards; the mode of the skewed distribution can be compared with the mean of the normal distribution, and the interquartile range of the skewed distribution can be compared with the variance of the normal distribution. Specifically, as Figure 6 This is the normal distribution of a normal wall, with a mean of 3.97 and a variance of 1.746; Figure 7 It is a right-skewed distribution of a normal wall, with a mode of 3 and an interquartile range of 2; Figure 8 It is a left-skewed distribution of a normal wall, with a mode of 5 and an interquartile range of 2; Fig. 9The abnormal distribution of the normal wall surface. According to the priority judgment order of the above three-layer detection standards, the excellent grades of wall flatness are as follows: Figure 7 > Figure 6 > Figure 8 > Fig. 9 .

[0028] Furthermore, the processing rules preset in the point cloud preprocessing submodule 21 are set as follows: from the local point cloud model acquired by the information acquisition module 1, multiple groups of local point cloud models are selected according to the preset combination relationship or randomly to be spliced ​​to obtain the overall point cloud model, and the overall point cloud model is denoised, and then three detection points are arbitrarily selected from the overall point cloud model after denoising to form an initial plane, and then the fitting comparison submodule 22 adjusts the initial plane through the weighted iteration method to obtain the fitting plane, thereby improving the wall data accuracy of the acquired local point cloud model, so that the system can more accurately identify wall defects. Specifically, the splicing process can be spliced ​​using the ICP algorithm or through a target; the denoising process can be imported into FARO SCENE for preliminary denoising, and then imported into the geomagic qualify software for further denoising. It should be noted that the weighted iteration method is a method commonly used to eliminate or suppress gross errors in data processing, and the success of the weighted iteration method depends largely on the residual obtained from the first adjustment. The traditional approach is to determine the initial residual by least squares estimation, but since the least squares estimation has a balancing effect and insensitivity to gross errors, this may result in the residual of the observation containing gross errors not being the largest, thereby affecting the effect of the weighted iteration method. In this embodiment, in the robust estimation, the M estimation method is simple to calculate, so the weighted iteration method selects any one of Huber, Hampel or IGG3, preferably Huber.

[0029] In the above, the unqualified point rate of the fitting plane is set as follows: based on the preset standard wall, the deviation error parameter of each detection point in the fitting plane is obtained, the deviation error parameter is set as the shortest distance between the detection point and the standard wall, and the detection point whose deviation error parameter in the fitting plane exceeds the preset detection threshold is regarded as an unqualified point, and the ratio of the unqualified point to all the detection points is calculated as the unqualified point rate of the fitting plane. Specifically, for general projects that use counting inspections in large quantities, normal walls require a qualified point rate of 80% or more, and there must be no serious defects in the unqualified points that are allowed to exist less than 20%. For general projects that use counting inspections in small quantities, normal walls require a qualified point rate of 90% or more, and there must be no serious defects in the unqualified points that are allowed to exist less than 10%. The system performs acceptance operations on building walls in accordance with the above standard acceptance specifications, which is more scientific and operational.

[0030] In this embodiment, the judgment standard of the prefabricated unqualified point rate is set as follows: the problem analysis submodule compares the unqualified point rate of the wall to be tested obtained by fitting the data processing submodule with the standard acceptance specification (three references) or the acceptance requirements of the construction unit. Among them, considering that the wall flatness acceptance test method currently used by on-site supervisors is to use a ruler and a feeler gauge to perform multiple measurements on the upper left, lower right and middle parts of the wall to be tested, thereby obtaining multiple groups of measurement data, and taking the average value of the multiple groups of measurement data or recording the maximum deviation value as the wall flatness test result. Specifically, when counting the unqualified point rate of the inspected wall, on the one hand, the unqualified point rate of the overall inspection points can be calculated based on the number of unqualified points measured in the fitting plane obtained by the three-dimensional laser scanner; on the other hand, some inspection points can be extracted from the overall measurement data according to the area and shape of the wall in a certain proportion and rule, and then the unqualified point rate of these sampled inspection points is calculated, and compared with the standard acceptance specifications to determine whether the inspected wall is a normal wall or an abnormal wall; this not only takes into account the advantages of the system's comprehensive detection, but also combines the sampling inspection scenarios in actual applications, so that the detection results have more practical reference value, thereby making the system more practical.

[0031] Furthermore, the fitting comparison submodule 22 constructs a heat map by setting different indications for each detection point in the fitting plane according to the size of the deviation error parameter. The indications in the heat map have display symbols of different colors and / or shapes. The display symbols are dynamically adjusted in real time according to the changes in the deviation error parameters of the corresponding detection points to more intuitively reflect the flatness of the wall.

[0032] Furthermore, the human-computer interaction module 3 also includes a terminal display submodule 34, which is configured as a PC terminal, a mobile phone terminal, a tablet and / or a large screen, and displays the heat map in the form of a BI dashboard.

[0033] In the above, as shown in Table 1, the result output submodule sends the acceptance report and the ledger generated by the system to the corresponding role on the user side to feedback the acceptance results and overall situation of the wall flatness detection; wherein, the ledger is the key information generated and precipitated by each submodule in the system, and is composed of fields recognizable by computer language, including basic information such as the detection area number, detection personnel, acceptance personnel, and acceptance time, as well as the unqualified point rate F, unqualified point rate f, manual review results, problem description, and excellent grade.

[0034] Table 1

[0035] In this embodiment, the unqualified point rate F is: the unqualified point rate of all detection points when the system performs a comprehensive inspection. The unqualified point f is: considering the actual sampling method of supervision and acceptance, the unqualified point rate obtained by extracting some detection points from the overall data according to the above-mentioned certain proportions and rules. The manual review result is: for abnormal wall surfaces, if it is a system misjudgment, "system misjudgment" is displayed under this field; if it is not a system misjudgment, "quality warning" is displayed under this field; for normal wall surfaces, "—" is displayed under this field as no data processing. The problem description is: for the detection area that is confirmed as a system misjudgment by manual review, the description of the misjudgment after confirmation by the system technician is displayed under this field; for the detection area that is confirmed as a non-system misjudgment by manual review, the corresponding problem level of the abnormal wall is filled in under this field according to the above-mentioned judgment rules; for normal walls, "—" is displayed under this field as no data processing. The quality level is: for the detection area that is confirmed as a system misjudgment after manual review, the quality level analyzed according to the normal wall judgment conditions is displayed under this field; for the detection area that is confirmed as a non-system misjudgment after manual review, "-" is displayed under this field as no data processing; for normal walls, fill in whether to mark or not mark under this field to show the quality level of the detection area. Specifically, the wall type is finally determined as: this field displays the detection area type after being judged by each of the above submodules. For the detection area that was originally determined to be a normal wall, and the detection area that was confirmed as a system misjudgment after manual review, the number of the corresponding detection area is displayed under this field and α is added to its prefix to indicate that the wall to be detected in the corresponding detection area is a normal wall; for the detection area that is confirmed as a non-system misjudgment after manual review, the number of the corresponding detection area is displayed under this field and β is added to its prefix to indicate that the wall to be detected in the corresponding detection area is an abnormal wall. It should be noted that the format and content of the acceptance report are preset in the system in advance, and the key information and test results formed by the test data flowing through each sub-module are automatically recorded and counted to form an information ledger, which is convenient for various roles on the user side to capture data according to their own needs, and relevant reports are automatically generated through the pre-built templates built in the early stage, which facilitates the efficient transmission of information.

[0036] like Figure 3 and Figure 4 As shown, the present invention also provides a project quality supervision and acceptance detection method based on the Internet of Things technology, which is applied to the above-mentioned system and includes the following steps: Step 1: Use the information acquisition module 1 to measure the wall flatness of several detection areas 5 of the wall to be detected to obtain corresponding building information, and generate several groups of local point cloud models of the wall to be detected based on the acquired building information.

[0037] Step 2: Preprocess several groups of local point cloud models of the wall to be detected through the point cloud preprocessing submodule 21 to obtain an initial plane.

[0038] Step 3: Perform adjustment processing on the initial plane through the fitting comparison submodule 22 to obtain a fitting plane, and obtain the unqualified point rate of the fitting plane based on a preset standard wall.

[0039] Step 4, the problem analysis submodule 23 determines whether the fitting plane is a normal wall or an abnormal wall according to the judgment condition of the unqualified point rate of the fitting plane. The judgment condition is: whether the unqualified point rate of the fitting plane exceeds the preset detection threshold; if it exceeds, the fitting plane is judged to be an abnormal wall; if not, it is judged to be a normal wall.

[0040] Step 5, the quality warning submodule 31 divides the abnormal wall into several levels to represent different degrees of wall defects, and generates corresponding acceptance instructions according to the different degrees of wall defects and sends them to the manual review submodule 33. At the same time, the wall surfaces whose quality is lower than the preset standard in the normal wall surface are marked by the excellent judgment submodule 32 as a reminder of the precautions for subsequent decoration and renovation; specifically, the abnormal walls are divided into general problems and serious problems; when the abnormal wall surface is a general problem, the acceptance instruction is set to conduct a random check and re-inspection of any one or more of the abnormal walls with all general problems; when the abnormal wall surface is a serious problem, the acceptance instruction is set to manually review the abnormal walls with all serious problems, and determine whether it is a system misjudgment; if it is a system misjudgment, the judgment conditions are updated and iterated; if it is not a system misjudgment, a corresponding rectification notice is generated and issued to the corresponding construction personnel to rectify the abnormal wall surface and review the rectification situation at the same time.

[0041] Step 6: The manual review submodule 33 reviews the abnormal wall surface of the corresponding level according to the received acceptance instruction and generates an acceptance report which is input into the result output submodule 35 .

[0042] Step 7: The result output submodule 35 sends the acceptance report to the user end to feedback the acceptance result of the abnormal wall surface.

[0043] In the present invention, the system can generate a local point cloud model from the measured building information during use, without the need for manual reading and recording, which can reduce data entry errors and improve data processing efficiency; and automatically determine the wall abnormalities and quality of the local point cloud model through the data analysis module 2, and verify the abnormal data through the human-computer interaction working mode, and further mine and analyze the normal data, thereby improving the accuracy of the system's judgment and the efficiency of subsequent decoration and renovation preparation work; at the same time, through the system's efficient information flow function, the construction unit and the construction unit personnel can timely grasp the acceptance results and subsequent rectification situations, greatly reducing the workload of the supervision personnel.

Claims

1. The engineering quality supervision and acceptance inspection system based on Internet of Things technology is characterized by: The system comprises: An information acquisition module (1) measures the flatness of a plurality of detection areas (5) of a wall surface to be detected respectively to obtain corresponding building information, and generates a plurality of groups of local point cloud models of the wall surface to be detected based on the acquired building information; A data analysis module (2), the data analysis module (2) comprising a point cloud preprocessing submodule (21), a fitting comparison submodule (22) and a problem analysis submodule (23), the point cloud preprocessing submodule (21) being configured to: preprocess a plurality of groups of local point cloud models of the wall surface to be detected according to a preset processing rule to generate an initial plane; the fitting comparison submodule (22) being configured to: perform adjustment processing on the initial plane to generate a fitting plane, and obtain the unqualified point rate of the fitting plane based on a preset standard wall surface; the problem analysis submodule (23) being configured to: determine whether the fitting plane is a normal wall surface or an abnormal wall surface according to a discrimination condition of the unqualified point rate of the fitting plane, and perform a quality analysis on the normal wall surface and a problem level analysis on the abnormal wall surface; the discrimination condition is: determine whether the unqualified point rate of the fitting plane exceeds a preset detection threshold, if so, determine that the fitting plane is an abnormal wall surface, and if not, determine that it is a normal wall surface; A human-computer interaction module (3), the human-computer interaction module (3) comprising a quality warning submodule (31), a quality determination submodule (32), a manual review submodule (33) and a result output submodule (35), the quality determination submodule (32) being configured to: mark the wall surface whose quality is lower than a preset standard among the normal wall surfaces as a reminder of matters needing attention for subsequent decoration and renovation; the quality warning submodule (31) being configured to: divide the abnormal wall surface into a plurality of grades to represent wall surface defects of different degrees, and generate corresponding acceptance instructions according to the wall surface defects of different degrees and send them to the manual review submodule (33); the manual review submodule (33) being configured to: review the abnormal wall surface of the corresponding grade according to the received acceptance instructions and generate an acceptance report to be input into the result output submodule (35); the result output submodule (35) sends the acceptance report to the user end to feedback the acceptance result of the abnormal wall surface; The system is configured with a data storage library (4) for the information acquisition module (1), the data analysis module (2) and the human-computer interaction module (3), and the data storage library (4) is used to store the task flow and data information generated by the system during the operation phase.

2. According to the engineering quality supervision and acceptance detection system based on Internet of Things technology according to claim 1, it is characterized in that: The information collection module (1) comprises: A region division submodule (11) divides each of the detection regions (5) of the wall to be detected into a plurality of subregions (6) according to a plane grid, and configures a unique number for each of the subregions (6) and standardizes and embeds it in the data analysis module (2); A measuring station layout submodule (12) is configured to layout a plurality of measuring stations in each of the sub-areas (6) according to the wall surface flatness acceptance requirements; The wall flatness detection submodule (13) configures a three-dimensional laser scanner for a plurality of the measuring stations, wherein the three-dimensional laser scanner is used to statically or dynamically scan the building information at the measuring stations within a corresponding range to generate the corresponding local point cloud model.

3. According to the engineering quality supervision and acceptance detection system based on Internet of Things technology as described in claim 2, it is characterized in that: The numbers of the detection area (5) and the sub-area (6) are respectively composed of one or more of a building number, a floor number, a room number and a wall number.

4. According to the engineering quality supervision and acceptance detection system based on Internet of Things technology as claimed in claim 1, it is characterized in that: The preset processing rules of the point cloud preprocessing submodule (21) are set as follows: A plurality of groups of local point cloud models are randomly selected from the local point cloud models acquired by the information acquisition module (1) according to a preset combination relationship and spliced ​​to obtain an overall point cloud model, and the overall point cloud model is denoised, and then three detection points are randomly selected from the overall point cloud model after denoising to form an initial plane.

5. According to the engineering quality supervision and acceptance detection system based on Internet of Things technology as claimed in claim 1, it is characterized in that: The fitting comparison submodule (22) performs adjustment processing on the initial plane through a weighted iteration method to obtain the fitting plane.

6. According to claim 4, the engineering quality supervision and acceptance detection system based on Internet of Things technology is characterized in that: The unqualified point rate of the fitting plane is set to: Based on the preset standard wall, the deviation error parameter of each detection point in the fitting plane is obtained, and the deviation error parameter is set to the shortest distance between the detection point and the standard wall. The detection points in the fitting plane whose deviation error parameters exceed the preset detection threshold are regarded as unqualified points, and the ratio of unqualified points to all detection points is calculated as the unqualified point rate of the fitting plane.

7. The engineering quality supervision and acceptance detection system based on Internet of Things technology according to claim 6 is characterized in that: The fitting comparison submodule (22) constructs a heat map by setting different indications for each detection point in the fitting plane according to the size of the deviation error parameter, wherein the indication in the heat map has display symbols of different colors and / or shapes, and the display symbols are dynamically adjusted in real time according to changes in the deviation error parameters corresponding to the detection points.

8. The engineering quality supervision and acceptance detection system based on Internet of Things technology according to claim 7 is characterized in that: The human-computer interaction module (3) further comprises a terminal display submodule (34), wherein the terminal display submodule (34) is configured as a PC terminal, a mobile phone terminal, a tablet and / or a large screen, and displays the heat map in the form of a BI dashboard.

9. The project quality supervision and acceptance inspection method based on Internet of Things technology is characterized by: The method is applied to the system according to any one of claims 1 to 8, and the method comprises the following steps: S1. Using the information acquisition module (1), respectively measuring the wall flatness of a plurality of detection areas (5) of the wall to be detected to obtain corresponding building information, and generating a plurality of groups of local point cloud models of the wall to be detected based on the obtained building information; S2, preprocessing a plurality of groups of local point cloud models of the wall to be detected by a point cloud preprocessing submodule (21) to obtain an initial plane; S3, performing adjustment processing on the initial plane through the fitting comparison submodule (22) to obtain a fitting plane, and obtaining the unqualified point rate of the fitting plane based on a preset standard wall surface; S4, the problem analysis submodule (23) determines whether the fitting plane is a normal wall surface or an abnormal wall surface according to the judgment condition of the unqualified point rate of the fitting plane, and performs a quality analysis on the normal wall surface and a problem level analysis on the abnormal wall surface. The judgment condition is: Determine whether the unqualified point rate of the fitting plane exceeds a preset detection threshold; if so, determine that the fitting plane is an abnormal wall surface; if not, determine that the fitting plane is a normal wall surface; S5, the quality warning submodule (31) divides the abnormal wall surface into several levels to represent wall surface defects of different degrees, and generates corresponding acceptance instructions according to the wall surface defects of different degrees and sends them to the manual review submodule (33), and at the same time, the wall surface whose quality is lower than the preset standard among the normal wall surfaces is marked by the good judgment submodule (32) as a reminder of matters needing attention for subsequent decoration and renovation; S6, the manual review submodule (33) reviews the abnormal wall surface of the corresponding level according to the received acceptance instruction and generates an acceptance report which is input into the result output submodule (35); S7, the result output submodule (35) sends the acceptance report to the user end to feedback the acceptance result of the abnormal wall surface.

10. The project quality supervision and acceptance detection method based on Internet of Things technology according to claim 9 is characterized in that: In S5, the abnormal wall surface is divided into general problems and serious problems; When the abnormal wall surface is a general problem, the acceptance instruction is set to conduct a random inspection and re-inspection of any one or more of the abnormal walls with all general problems; When the abnormal wall surface has a serious problem, the acceptance instruction is set to manually review all the abnormal walls with serious problems and determine whether it is a system misjudgment; If the system misjudges, the judgment condition is updated and iterated; If it is not a system misjudgment, a corresponding rectification notice will be generated and issued to the corresponding construction personnel to carry out rectification work on the abnormal wall surface and review the rectification situation at the same time.

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