Engineering project supervision quality monitoring management system based on big data

Abstract

The invention relates to the technical field of engineering project supervision quality monitoring, and discloses an engineering project supervision quality monitoring management system based on big data. The system comprises a wall information acquisition module, a building environment data acquisition module, a quality analysis and evaluation module and a quality early warning module. The wall surface information collection module is used for collecting repair material data and wall body data in the wall surface repair process to construct a wall surface repair database. According to the system, a first evaluation unit is used for preliminarily evaluating the wall surface repair condition based on the wall surface repair similarity, the wall surface repair cracking risk coefficient, the wall surface flatness, the color of wall surface crack damage repair and the reaction condition between materials obtained from a wall surface repair database; and meanwhile, when the wall surface quality is not good, the quality early warning module decides whether the first adjustment strategy or the second adjustment strategy is executed or not, the repaired wall surface is adjusted, and the efficiency of detecting the repair quality of the old community wall surface is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering project supervision quality monitoring, and in particular to an engineering project supervision quality monitoring and management system based on big data. Background Art

[0002] With the rapid development of the construction industry in recent years, various construction behaviors and construction legal systems have been gradually improved, and the importance of construction supervision has been increasingly valued by people. Supervision engineers have played an important role in promoting and ensuring the quality of projects.

[0003] For example, in the renovation project of old residential communities, the supervision engineer needs to monitor and evaluate the quality of the wall repair of the renovated building walls. At the same time, the traditional monitoring method for the renovation quality of the building walls in old residential communities is mainly to measure the flatness of the building walls in old residential communities. The traditional measurement method requires the supervision engineer to conduct random sampling measurement of the wall flatness. However, this detection method not only has low monitoring accuracy and insufficient detection density, but also is affected by subjective factors. Only the measurement of flatness will lead to a one-sided assessment. For example, the differences in hanging objects on the wall and the color of the wall under different lighting angles are not fully considered, making it difficult to achieve a comprehensive assessment of the renovation quality of the building walls in old residential communities. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the shortcomings of the prior art, the present invention provides an engineering project supervision quality monitoring and management system based on big data, which has the advantages of efficient and comprehensive detection, and solves the above-mentioned technical problems.

[0006] (II) Technical solution

[0007] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a big data-based engineering project supervision quality monitoring and management system, comprising a wall information collection module, a building environment data collection module, a quality analysis and evaluation module, and a quality early warning module;

[0008] The wall information collection module is used to collect repair material data and wall data during the wall repair process to build a wall repair database;

[0009] The building environment data collection module is used to collect the ground levelness and ground subsidence of the area where the wall to be inspected is located, forming a building environment database;

[0010] The quality analysis and evaluation module includes a first evaluation unit and a second evaluation unit. The first evaluation unit obtains the wall repair similarity QMXSD, the wall repair cracking risk coefficient QMKLFX and the wall flatness QMPZD based on the wall repair database, and inputs them into the quality warning module to calculate the first warning coefficient QMKLFX. The quality warning module decides whether to execute the first adjustment strategy or the second adjustment strategy to adjust the repaired wall.

[0011] After executing the first adjustment strategy or the second adjustment strategy, the second evaluation unit obtains the pipeline leakage risk coefficient GXFX, the building exterior surface suspension risk coefficient ZHFX and the environmental vibration risk coefficient ZDFX based on the building environment database, and inputs them into the quality warning module to calculate the second warning coefficient DEFX. The quality warning module decides whether to execute the third adjustment strategy to repair and adjust the building.

[0012] As a preferred technical solution of the present invention, the specific steps of the first evaluation unit obtaining the wall repair similarity QMXSD based on the wall repair database are as follows:

[0013] Step A1: Set up a camera to shoot the currently repaired floor, obtain images of the repaired wall at all times when the sun is shining directly on it, and store them in the wall repair database, and calculate the color difference value ZSCY between the repaired area and the non-repaired area when the sun is shining directly on it. ZS , which is expressed as follows:

[0014]

[0015] Where n represents the number of sampling times of the image of the sun directly repairing the wall, zs∈[1,n], Indicates the sum of n sampling color differences, ΔE zs It represents the color difference between the patched area and the non-patch area when the sun is directly shining during the zs-th sampling process. The expression is as follows:

[0016]

[0017] in, They represent the mean of the patched area in the Lab space during the zs-th sampling process, They represent the mean values ​​of the non-patched areas in the Lab space during the zs-th sampling process;

[0018] A2: Set up a camera to shoot the currently repaired floor, obtain images of the repaired wall at all times when the sun is not directly shining, and store them in the wall repair database, and calculate the color difference value ZSCY between the repaired area and the non-repaired area when the sun is not directly shining F , which is expressed as follows:

[0019]

[0020] Among them, f∈[1,W], Indicates the sum of W sampling color differences, ΔE f It represents the color difference between the repaired area and the non-repaired area when the sun is not directly shining during the f-th sampling process. Its calculation method is the same as the color difference ΔE between the repaired area and the non-repaired area when the sun is directly shining during the zs-th sampling process. zs Consistency;

[0021] A3: Use a surface roughness detector to obtain the roughness CCD in the repaired area and the roughness CCD in the non-repaired area 0 , and calculate the roughness difference value CCDXS:

[0022]

[0023] in, Represents the sum of the roughness of the I sub-sampling points in the patching area, CCD i represents the roughness of the ith sub-sampling point in the patch area, represents the sum of the roughness of J sub-sampling points in the non-patched area, represents the roughness of the jth sub-sampling point in the non-patched area, and |*| represents the absolute value operation;

[0024] A4: Calculate the wall repair similarity QMXSD. The specific expression is as follows:

[0025]

[0026] Among them, CCDXS represents the roughness difference value, ZSCY ZS Indicates the color difference between the patched area and the non-patch area when the sun is directly shining, ZSCY F Indicates the color difference between the patched area and the non-patch area when the sun is not directly shining.

[0027] As a preferred technical solution of the present invention, the steps for obtaining the wall repair crack risk coefficient QMKLFX are as follows:

[0028] B1: Obtain the thermal expansion value RPZ of the wall repair material and the thermal expansion value RPZ in the wall material based on the repair material data in the wall repair database 0 , and calculate the material thermal expansion deviation RPZPC, the specific expression is as follows:

[0029]

[0030] Among them, |RPZ-RPZ 0| indicates RPZ-RPZ 0 The absolute value of

[0031] B2: Get the wall vegetation growth area ZBSZ before restoration 0 And the total area of ​​the wall ZTMJ, and calculate the wall vegetation erosion coefficient ZBQXXS, the specific expression is as follows:

[0032]

[0033] Among them, e represents a natural constant;

[0034] B3: Obtain the type set of repair materials and the type set of wall materials from the wall repair database, and traverse the type set of wall materials element by element, and record the total number FYZL of exothermic or endothermic chemical reactions between the type set of wall materials and the type set of repair materials, and calculate the material reaction influence coefficient CLFYXS, which is expressed as follows:

[0035]

[0036] Among them, CLZS represents the total number of all elements in the wall material type set;

[0037] B4: Based on B1-B3, calculate the wall repair crack risk factor QMKLFX. The specific expression is as follows:

[0038] QMKLFX=(1+RPZPC)×ZBQXXS×CLFYXS

[0039] Among them, RPZPC represents the thermal expansion deviation of the material, and ZBQXXS represents the wall vegetation erosion coefficient.

[0040] As a preferred technical solution of the present invention, the steps for obtaining the wall flatness QMPZD are as follows:

[0041] C1: Divide the wall repair area into several sub-areas;

[0042] C2: Use a drone to measure distances area by area at the same distance, and obtain the distance between each sub-area and the drone, and calculate the wall flatness QMPZD. The expression is as follows:

[0043]

[0044] Among them, JL m represents the distance between the mth sub-region and the UAV, JL 0 Indicates standard distance measurement, |JL m -JL 0 | indicates JL m -JL 0 The absolute value of Indicates that for all |JL in M ​​regions m -JL 0 |To perform the summation.

[0045] As a preferred technical solution of the present invention, the specific expression of the first warning coefficient QMKLFX calculated by the quality warning module is as follows:

[0046] QMKLFX=(ω 1 *QMXSD+ω 2 *QMPZD)×(QMKLFX+1)

[0047] Among them, ω 1 ,ω 2 They represent weight coefficients that sum to 1, QMXSD represents the similarity of wall repair, QMKLFX represents the risk coefficient of wall repair cracking, and QMPZD represents the flatness of the wall.

[0048] As a preferred technical solution of the present invention, the quality warning module decides whether to execute the first adjustment strategy or the second adjustment strategy, and the specific steps of adjusting the repaired wall surface are as follows:

[0049] When the first warning coefficient QMKLFX < the first warning value YJZ, the first adjustment strategy is not executed, and the wall surface is output as qualified;

[0050] When the first warning value YJZ≤the first warning coefficient QMKLFX<the first warning value YJZ×120%, the first adjustment strategy is implemented, specifically: use 1%-2% color toner to recolor the wall repair area, and after drying, use 80-mesh or 100-mesh sandpaper to polish and level it;

[0051] When the first warning coefficient QMKLFX ≥ the first warning value YJZ×120%, the second adjustment strategy is executed, specifically: use 3%-4% color toner to recolor the wall repair area, and after drying, perform a secondary cleaning on the vegetation growth area and use 120-mesh or 150-mesh sandpaper to grind and level it.

[0052] As a preferred technical solution of the present invention, the steps for obtaining the pipeline leakage risk coefficient GXFX are as follows:

[0053] D1: Obtain the leakage times of all pipelines in the current building and the distance between the historical pipeline leakage nodes and the current wall to be detected from the building environment database, and calculate the pipeline leakage impact factor GXYZ. The specific expression is as follows:

[0054]

[0055] Among them, GZCS p represents the number of leaks of the pth leaking node, Indicates a total of P leaking nodes Perform summation;

[0056] D2: Get the overall pipeline aging factor GXLH. The specific expression is as follows:

[0057]

[0058] Among them, GXLH q represents the pipeline aging factor of the pth leaking node, and its expression is as follows:

[0059]

[0060] Among them, XSHD q represents the pipeline corrosion thickness of the pth leakage node, obtained by ultrasonic detection, YSHD q represents the initial thickness of the pipeline at the pth leakage node;

[0061] D3: Calculate the pipeline leakage risk factor GXFX. The specific expression is as follows:

[0062] GXFX=GXLH×GXYZ

[0063] Among them, GXLH represents the overall pipeline aging factor, and GXYZ represents the pipeline leakage impact factor.

[0064] As a preferred technical solution of the present invention, the steps for obtaining the hanging risk coefficient ZHFX on the outer surface of the building are as follows:

[0065] E1: Get the number of hanging objects XGWSL in the non-hanging area of ​​the current wall;

[0066] E2: Get the total area S corresponding to the hanging objects in the non-hanging area of ​​the current wall;

[0067] E3: Calculate the risk factor ZHFX of the hanging objects on the exterior surface of the building based on the total area S corresponding to the hanging objects in the non-hanging area of ​​the current wall and the number XGWSL of hanging objects in the non-hanging area of ​​the current wall. The specific expression is as follows:

[0068]

[0069] Among them, the number of hanging objects XGWSL in the non-hanging area of ​​the current wall and the total area S corresponding to the hanging objects in the non-hanging area of ​​the current wall are obtained by manual measurement. It means that when XGWSL≠0, θ=0, and when XGWSL=0, θ=1.

[0070] As a preferred technical solution of the present invention, the steps for obtaining the environmental vibration risk factor CXFX are as follows:

[0071] F1: Obtain the construction vibration factor SGZD by consulting the construction data around the currently repaired building. The specific expression is as follows:

[0072]

[0073] Among them, WSGTS represents the number of days without construction, and SGTS represents the number of days under construction;

[0074] F2: Based on the number of hanging objects XGWSL in the non-hanging area of ​​the current wall, calculate the hanging object vibration factor FGWZD. The specific expression is as follows:

[0075]

[0076] Among them, FXGWSL represents the number of hanging objects in the hanging area;

[0077] F3: Calculate the environmental vibration risk factor CXFX. The specific expression is as follows:

[0078]

[0079] Among them, SGZD represents the construction vibration factor, and XGWZD represents the hanging object vibration factor.

[0080] As a preferred technical solution of the present invention, the specific expression of the second warning coefficient DEFX calculated by the quality warning module is as follows:

[0081] DEFX=ω 3 ×GXFX+ω 4 ×ZHFX+ω 5 ×ZDFX

[0082] Among them, GXFX represents the pipeline leakage risk factor, ZHFX represents the building surface hanging risk factor, ZDFX represents the environmental vibration risk factor, ω 3 ,ω 4 ,ω 5 They represent weight coefficients whose sum is 1 respectively;

[0083] The specific steps of the quality warning module deciding whether to execute the third adjustment strategy are as follows:

[0084] When the second warning coefficient DEFX ≤ the second warning value EYJZ, the third adjustment strategy is not executed, and the wall surface is output as qualified;

[0085] When the second warning coefficient DEFX>the second warning value EYJZ, the third adjustment strategy is executed, specifically: thickening the pipeline by 10%-20%, and thickening the gaskets of the hanging object fixing nodes by 5%-7%.

[0086] Compared with the prior art, the present invention provides a project supervision quality monitoring and management system based on big data, which has the following beneficial effects:

[0087] 1. The present invention obtains the wall repair similarity, wall repair cracking risk coefficient and wall flatness from the wall repair database through the first evaluation unit, and conducts a preliminary evaluation of the wall repair situation based on the color of the wall crack damage repair and the reaction between the materials, thereby ensuring the comprehensiveness and accuracy of the wall repair. At the same time, when the wall quality is poor, the quality warning module decides whether to execute the first adjustment strategy or the second adjustment strategy, and adjusts the repaired wall, thereby improving the efficiency of the quality detection of wall repair in old communities.

[0088] 2. The present invention conducts a secondary assessment of the wall after the preliminary assessment, obtains the pipeline leakage risk coefficient, the building outer surface suspension risk coefficient and the environmental vibration risk coefficient based on the building environment database, and inputs them into the quality warning module to calculate the second warning coefficient. The quality warning module decides whether to implement the third adjustment strategy and repair and adjust the building, thereby quickly evaluating the repair quality of old communities in a more comprehensive manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 Schematic diagram of the system framework of the present invention. DETAILED DESCRIPTION

[0090] 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.

[0091] See also Figure 1 , a quality monitoring and management system for engineering project supervision based on big data, including a wall information collection module, a building environment data collection module, a quality analysis and evaluation module, and a quality early warning module;

[0092] The wall information collection module is used to collect the repair material data and wall data during the wall repair process to build a wall repair database;

[0093] The building environment data collection module is used to collect the ground levelness and ground subsidence of the area where the wall to be inspected is located, forming a building environment database;

[0094] The quality analysis and evaluation module includes a first evaluation unit and a second evaluation unit. The first evaluation unit obtains the wall repair similarity QMXSD, the wall repair cracking risk coefficient QMKLFX and the wall flatness QMPZD based on the wall repair database, and inputs them into the quality warning module to calculate the first warning coefficient QMKLFX. The quality warning module decides whether to execute the first adjustment strategy or the second adjustment strategy to adjust the repaired wall.

[0095] The specific steps of the first evaluation unit to obtain the wall repair similarity QMXSD based on the wall repair database are as follows:

[0096] Step A1: Set up a camera to shoot the currently repaired floor, obtain images of the repaired wall at all times when the sun is shining directly on it, and store them in the wall repair database, and calculate the color difference value ZSCY between the repaired area and the non-repaired area when the sun is shining directly on it. ZS , which is expressed as follows:

[0097]

[0098] Where n represents the number of sampling times of the image of the sun directly repairing the wall, zs∈[1,n], Indicates the sum of n sampling color differences, ΔE zs It represents the color difference between the patched area and the non-patch area when the sun is directly shining during the zs-th sampling process. The expression is as follows:

[0099]

[0100] in, They represent the mean of the patched area in the Lab space during the zs-th sampling process, They represent the mean values ​​of the non-patched areas in the Lab space during the zs-th sampling process;

[0101] A2: Set up a camera to shoot the currently repaired floor, obtain images of the repaired wall at all times when the sun is not directly shining, and store them in the wall repair database, and calculate the color difference value ZSCY between the repaired area and the non-repaired area when the sun is not directly shining F , which is expressed as follows:

[0102]

[0103] Among them, f∈[1,W], Indicates the sum of W sampling color differences, ΔE fIt represents the color difference between the repaired area and the non-repaired area when the sun is not directly shining during the f-th sampling process. Its calculation method is the same as the color difference ΔE between the repaired area and the non-repaired area when the sun is directly shining during the zs-th sampling process. zs Consistency;

[0104] A3: Use a surface roughness detector to obtain the roughness CCD in the repaired area and the roughness CCD in the non-repaired area 0 , and calculate the roughness difference value CCDXS:

[0105]

[0106] in, Represents the sum of the roughness of the I sub-sampling points in the patching area, CCD i represents the roughness of the ith sub-sampling point in the patch area, represents the sum of the roughness of J sub-sampling points in the non-patched area, represents the roughness of the jth sub-sampling point in the non-patched area, and |*| represents the absolute value operation;

[0107] A4: Calculate the wall repair similarity QMXSD. The specific expression is as follows:

[0108]

[0109] Among them, CCDXS represents the roughness difference value, ZSCY ZS Indicates the color difference between the patched area and the non-patch area when the sun is directly shining, ZSCY F Indicates the color difference between the patched area and the non-patch area when the sun is not directly shining.

[0110] The steps for obtaining the wall repair crack risk factor QMKLFX are as follows:

[0111] B1: Obtain the thermal expansion value RPZ of the wall repair material and the thermal expansion value RPZ in the wall material based on the repair material data in the wall repair database 0 , and calculate the material thermal expansion deviation RPZPC, the specific expression is as follows:

[0112]

[0113] Among them, |RPZ-RPZ 0 | indicates RPZ-RPZ 0 The absolute value of the wall repair material and the wall material thermal expansion value deviation is too large, which will make it easier for the repair material and the old material to crack at the connection node under the condition of large temperature difference;

[0114] B2: Get the wall vegetation growth area ZBSZ before restoration 0 And the total area of ​​the wall ZTMJ, and calculate the wall vegetation erosion coefficient ZBQXXS, the specific expression is as follows:

[0115]

[0116] Among them, e represents a natural constant;

[0117] B3: Obtain the type set of repair materials and the type set of wall materials from the wall repair database, and traverse the type set of wall materials element by element, and record the total number FYZL of exothermic or endothermic chemical reactions between the type set of wall materials and the type set of repair materials, and calculate the material reaction influence coefficient CLFYXS, which is expressed as follows:

[0118]

[0119] Among them, CLZS represents the total number of all elements in the wall material type set;

[0120] B4: Based on B1-B3, calculate the wall repair crack risk factor QMKLFX. The specific expression is as follows:

[0121] QMKLFX=(1+RPZPC)×ZBQXXS×CLFYXS

[0122] Among them, RPZPC represents the thermal expansion deviation of the material, and ZBQXXS represents the wall vegetation erosion coefficient.

[0123] The steps to obtain the wall flatness QMPZD are as follows:

[0124] C1: Divide the wall repair area into several sub-areas;

[0125] C2: Use a drone to measure distances area by area at the same distance, and obtain the distance between each sub-area and the drone, and calculate the wall flatness QMPZD. The expression is as follows:

[0126]

[0127] Among them, JL m represents the distance between the mth sub-region and the UAV, JL 0 Indicates standard distance measurement, |JL m -JL 0 | indicates JL m -JL 0 The absolute value of Indicates that for all |JL in M ​​regions m -JL 0 |To perform the summation.

[0128] The specific expression of the first warning coefficient QMKLFX calculated by the quality warning module is as follows:

[0129] QMKLFX=(ω 1 *QMXSD+ω 2 *QMPZD)×(QMKLFX+1)

[0130] Among them, ω 1 ,ω 2 They represent weight coefficients that sum to 1, QMXSD represents the similarity of wall repair, QMKLFX represents the risk coefficient of wall repair cracking, and QMPZD represents the flatness of the wall.

[0131] The quality warning module decides whether to execute the first adjustment strategy or the second adjustment strategy. The specific steps for adjusting the repaired wall are as follows:

[0132] When the first warning coefficient QMKLFX < the first warning value YJZ, the first adjustment strategy is not executed, and the wall surface is output as qualified;

[0133] When the first warning value YJZ≤the first warning coefficient QMKLFX<the first warning value YJZ×120%, the first adjustment strategy is implemented, specifically: use 1%-2% color toner to recolor the wall repair area, and after drying, use 80-mesh or 100-mesh sandpaper to polish and level it;

[0134] When the first warning coefficient QMKLFX ≥ the first warning value YJZ × 120%, the second adjustment strategy is implemented, specifically: use 3%-4% color toner to recolor the wall repair area, and after drying, perform a secondary cleaning of the vegetation growth area, and use 120 mesh or 150 mesh sandpaper to grind and level it. The secondary cleaning of the vegetation growth area includes re-grouting and painting the wall.

[0135] After executing the first adjustment strategy or the second adjustment strategy, the second assessment unit obtains the pipeline leakage risk coefficient GXFX, the building outer surface suspension risk coefficient ZHFX and the environmental vibration risk coefficient ZDFX based on the building environment database, and inputs them into the quality warning module to calculate the second warning coefficient DEFX. The quality warning module decides whether to execute the third adjustment strategy and repair and adjust the building.

[0136] The steps for obtaining the pipeline leakage risk factor GXFX are as follows:

[0137] D1: Obtain the leakage times of all pipelines in the current building and the distance between the historical pipeline leakage nodes and the current wall to be detected from the building environment database, and calculate the pipeline leakage impact factor GXYZ. The specific expression is as follows:

[0138]

[0139] Among them, GZCS p represents the number of leaks of the pth leaking node, Indicates a total of P leaking nodes Perform summation;

[0140] D2: Get the overall pipeline aging factor GXLH. The specific expression is as follows:

[0141]

[0142] Among them, GXLH q represents the pipeline aging factor of the pth leaking node, and its expression is as follows:

[0143]

[0144] Among them, XSHD q Indicates the corrosion thickness of the pipeline at the pth leakage node, obtained by ultrasonic detection, YSHD q represents the initial thickness of the pipeline at the pth leaking node;

[0145] D3: Calculate the pipeline leakage risk factor GXFX. The specific expression is as follows:

[0146] GXFX=GXLH×GXYZ

[0147] Among them, GXLH represents the overall pipeline aging factor, and GXYZ represents the pipeline leakage impact factor.

[0148] The steps to obtain the risk factor ZHFX of the hanging on the exterior surface of the building are as follows:

[0149] E1: Get the number of hanging objects XGWSL in the non-hanging area of ​​the current wall;

[0150] E2: Get the total area S corresponding to the hanging objects in the non-hanging area of ​​the current wall;

[0151] E3: Calculate the risk factor ZHFX of the hanging objects on the exterior surface of the building based on the total area S corresponding to the hanging objects in the non-hanging area of ​​the current wall and the number XGWSL of hanging objects in the non-hanging area of ​​the current wall. The specific expression is as follows:

[0152]

[0153] Among them, the number of hanging objects XGWSL in the non-hanging area of ​​the current wall and the total area S corresponding to the hanging objects in the non-hanging area of ​​the current wall are obtained by manual measurement. It means that when XGWSL≠0, θ=0, and when XGWSL=0, θ=1.

[0154] The steps to obtain the environmental vibration risk factor CXFX are as follows:

[0155] F1: Obtain the construction vibration factor SGZD by consulting the construction data around the currently repaired building. The specific expression is as follows:

[0156]

[0157] Among them, WSGTS represents the number of days without construction, and SGTS represents the number of days under construction;

[0158] F2: Based on the number of hanging objects XGWSL in the non-hanging area of ​​the current wall, calculate the hanging object vibration factor XGWZD. The specific expression is as follows:

[0159]

[0160] Among them, FXGWSL represents the number of hanging objects in the hanging area;

[0161] F3: Calculate the environmental vibration risk factor CXFX. The specific expression is as follows:

[0162]

[0163] Among them, SGZD represents the construction vibration factor, and XGWZD represents the hanging object vibration factor.

[0164] The specific expression of the second warning coefficient DEFX calculated by the quality warning module is as follows:

[0165] DEFX=ω 3 ×GXFX+ω 4 ×ZHFX+ω 5 ×ZDFX

[0166] Among them, GXFX represents the pipeline leakage risk factor, ZHFX represents the building surface hanging risk factor, ZDFX represents the environmental vibration risk factor, ω 3 ,ω 4 ,ω 5 They represent weight coefficients whose sum is 1 respectively;

[0167] The specific steps for the quality warning module to decide whether to execute the third adjustment strategy are as follows:

[0168] When the second warning coefficient DEFX ≤ the second warning value EYJZ, the third adjustment strategy is not executed, and the wall surface is output as qualified;

[0169] When the second warning coefficient DEFX>the second warning value EYJZ, the third adjustment strategy is executed, specifically: thicken the pipeline by 10%-20%, and thicken the gaskets at the fixed nodes of the suspension by 5%-7%. The thickening of the gaskets at the fixed nodes is used to replace the original gaskets that have been used for too long, thereby ensuring the stability of the fixed nodes.

[0170] Example:

[0171] The following are the specific data of the present invention during implementation;

[0172] In this embodiment, the old residential area to be repaired is oriented north-south and the repair time is summer. 1 =0.75,ω 2 =0.25,ω 3 =0.18,ω 4 =0.67,ω 5 =0.15, for images collected at all times when the sun is shining directly, including 14:00, 15:00, and 16:00 in the afternoon, and the corresponding ΔE zs See Table 1 below

[0173] Table 1

[0174] Direct shot moment <![CDATA[Delta value ΔE zs > 14:00 <![CDATA[ΔE 1 =6.20]]> 15:00 <![CDATA[ΔE 2 =6.12]]> 16:00 <![CDATA[ΔE 3 =6.16]]>

[0175] at this time,

[0176] Get images of the repaired wall at all times when the sun is not directly shining, including 8:30, 9:30, and 10:30 in the morning, and the corresponding ΔE f See Table 2 below;

[0177] Table 2

[0178] Direct shot moment <![CDATA[Delta value ΔE f > 8:30 <![CDATA[ΔE 1 =5.25]]> 9:30 <![CDATA[ΔE 2 =5.28]]> 10:30 <![CDATA[ΔE 3 =5.31]]>

[0179] at this time,

[0180] Both the repaired area and the non-repaired area are divided into 4 areas, and the corresponding roughness is shown in Table 3 below:

[0181] Table 3

[0182]

[0183]

[0184] at this time, And calculate the wall repair similarity QMXSD =

[0185]

[0186] Thermal expansion value of wall repair material RPZ = 10.0 × 10 -6 and thermal expansion value RPZ in wall material 0 =9.5×10 -6 ,at this time

[0187] Get the wall vegetation growth area ZBSZ before restoration 0 =40 and total wall area ZTMJ = 200;

[0188] Calculate the wall vegetation erosion coefficient In this embodiment, the repair material does not produce exothermic or endothermic chemical reaction with the type set of the repair material. Therefore, CLFYXS=1;

[0189] QMKLFX=(1+RPZPC)×ZBQXXS×CLFYXS=1.053×1.918×1=2.019654

[0190] In this embodiment, a drone is used to measure the distance area by area at the same distance, and the distance between each sub-area and the drone is obtained as shown in Table 4;

[0191] Table 4

[0192]

[0193] At this time, the wall flatness is calculated.

[0194] QMKLFX=(ω 1 *QMXSD+ω 2 *QMPZD)×(QMKLFX+1)

[0195] =(6.26912*0.75+1.36*0.25)*(2.019654+1)=15.224612

[0196] ≈15.22

[0197] In this embodiment, YJZ=9, QMKLFX>9×1.2=10.8, and the second adjustment strategy is implemented, specifically: use 2% color toner to recolor the wall repair area, wait for it to dry, clean the vegetation growth area for a second time, and use 120-grit sandpaper to polish and level it. The Lab value after coloring is (209, 189, 199);

[0198] In this embodiment, the leakage nodes are shown in Table 5 below.

[0199] Table 5

[0200]

[0201] at this time, GXFX=GXLH×GXYZ=13.247×0.23=3.04681≈3.05;

[0202] In this embodiment, the relevant data of the building outer surface hanging are shown in Table 6 below

[0203] Table 6

[0204] Hanging objects <![CDATA[Exceeding area m 2 > 1 2 2 5 3 8 4 3

[0205]

[0206] In this embodiment, the construction days SGTS=0, FXGWSL, and the construction vibration factor SGZD=0, DEFX=ω 3 ×3.05+ω 4 ×4.5+ω 5 ×ZDFX=3.564≤the second warning value EYJZ=5, the third adjustment strategy is not executed, and the wall surface is output as qualified.

[0207] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A project supervision quality monitoring and management system based on big data, characterized by: It includes wall information collection module, building environment data collection module, quality analysis and evaluation module and quality early warning module; The wall information collection module is used to collect repair material data and wall data during the wall repair process to build a wall repair database; The building environment data collection module is used to collect the ground levelness and ground subsidence of the area where the wall to be inspected is located, and form a building environment database; The quality analysis and evaluation module includes a first evaluation unit and a second evaluation unit. The first evaluation unit obtains the wall repair similarity QMXSD, the wall repair cracking risk coefficient QMKLFX and the wall flatness QMPZD based on the wall repair database, and inputs them into the quality warning module to calculate the first warning coefficient QMKLFX. The quality warning module decides whether to execute the first adjustment strategy or the second adjustment strategy to adjust the repaired wall. After executing the first adjustment strategy or the second adjustment strategy, the second evaluation unit obtains the pipeline leakage risk coefficient GXFX, the building exterior surface suspension risk coefficient ZHFX and the environmental vibration risk coefficient ZDFX based on the building environment database, and inputs them into the quality warning module to calculate the second warning coefficient DEFX. The quality warning module decides whether to execute the third adjustment strategy to repair and adjust the building.

2. According to the big data-based engineering project supervision quality monitoring and management system of claim 1, it is characterized by: The specific steps of the first evaluation unit obtaining the wall repair similarity QMXSD based on the wall repair database are as follows: Step A1: Set up a camera to shoot the currently repaired floor, obtain images of the repaired wall at all times when the sun is shining directly on it, and store them in the wall repair database, and calculate the color difference value ZSCY between the repaired area and the non-repaired area when the sun is shining directly on it. ZS , which is expressed as follows: Where n represents the number of sampling times of the image of the sun directly repairing the wall, zs∈[1,n], Indicates the sum of n sampling color differences, ΔE zs It represents the color difference between the patched area and the non-patch area when the sun is directly shining during the zs-th sampling process. The expression is as follows: in, They represent the mean of the patched area in the Lab space during the zs-th sampling process, They represent the mean values ​​of the non-patched areas in the Lab space during the zs-th sampling process; A2: Set up a camera to shoot the currently repaired floor, obtain images of the repaired wall at all times when the sun is not directly shining, and store them in the wall repair database, and calculate the color difference value ZSCY between the repaired area and the non-repaired area when the sun is not directly shining F , which is expressed as follows: Among them, f∈[1,W], Indicates the sum of W sampling color differences, ΔE f It represents the color difference between the repaired area and the non-repaired area when the sun is not directly shining during the f-th sampling process. Its calculation method is the same as the color difference ΔEz between the repaired area and the non-repaired area when the sun is directly shining during the zs-th sampling process. s Consistency; A3: Use a surface roughness detector to obtain the roughness CCD in the repaired area and the roughness CCD0 in the non-repaired area, and calculate the roughness difference CCDXS: in, Represents the sum of the roughness of the I sub-sampling points in the patching area, CCD i represents the roughness of the ith sub-sampling point in the patch area, represents the sum of the roughness of J sub-sampling points in the non-patched area, represents the roughness of the jth sub-sampling point in the non-patched area, and |*| represents the absolute value operation; A4: Calculate the wall repair similarity QMXSD. The specific expression is as follows: Among them, CCDXS represents the roughness difference value, ZSCY ZS Indicates the color difference between the patched area and the non-patch area when the sun is directly shining, ZSCY F Indicates the color difference between the patched area and the non-patch area when the sun is not directly shining.

3. According to the big data-based engineering project supervision quality monitoring and management system of claim 1, it is characterized by: The steps for obtaining the wall repair crack risk coefficient QMKLFX are as follows: B1: Based on the repair material data in the wall repair database, the thermal expansion value RPZ of the wall repair material and the thermal expansion value RPZ0 of the wall material are obtained, and the material thermal expansion deviation RPZPC is calculated. The specific expression is as follows: Among them, |RPZ-RPZ0| represents the absolute value of RPZ-RPZ0; B2: Obtain the wall vegetation growth area ZBSZ0 and the total wall area ZTMJ before restoration, and calculate the wall vegetation erosion coefficient ZBQXXS. The specific expression is as follows: Among them, e represents a natural constant; B3: Obtain the type set of repair materials and the type set of wall materials from the wall repair database, and traverse the type set of wall materials element by element, and record the total number FYZL of exothermic or endothermic chemical reactions between the type set of wall materials and the type set of repair materials, and calculate the material reaction influence coefficient CLFYXS, which is expressed as follows: Among them, CLZS represents the total number of all elements in the wall material type set; B4: Based on B1-B3, calculate the wall repair crack risk factor QMKLFX. The specific expression is as follows: QMKLFX=(1+RPZPC)×ZBQXXS×CLFYXS Among them, RPZPC represents the thermal expansion deviation of the material, and ZBQXXS represents the wall vegetation erosion coefficient.

4. According to the big data-based engineering project supervision quality monitoring and management system of claim 1, it is characterized by: The steps for obtaining the wall flatness QMPZD are as follows: C1: Divide the wall repair area into several sub-areas; C2: Use a drone to measure distances area by area at the same distance, and obtain the distance between each sub-area and the drone, and calculate the wall flatness QMPZD. The expression is as follows: Among them, JL m represents the distance between the mth sub-area and the drone, JL0 represents the standard distance measurement, |JL m -JL0|means JL m -The absolute value of JL0, Indicates that for all |JL in M ​​regions m -JL0| is summed, where the distance between each sub-area and the drone actually refers to the distance from the center point of each sub-area to the drone.

5. According to the big data-based engineering project supervision quality monitoring and management system of claim 1, it is characterized by: The specific expression of the first warning coefficient QMKLFX calculated by the quality warning module is as follows: QMKLFX=(ω1*QMXSD+ω2*QMPZD)×(QMKLFX+1) Among them, ω1 and ω2 represent weight coefficients whose sum is 1, QMXSD represents the wall repair similarity, QMKLFX represents the wall repair cracking risk coefficient, and QMPZD represents the wall flatness.

6. According to the big data-based engineering project supervision quality monitoring and management system of claim 1, it is characterized by: The quality warning module decides whether to execute the first adjustment strategy or the second adjustment strategy. The specific steps of adjusting the repaired wall surface are as follows: When the first warning coefficient QMKLFX < the first warning value YJZ, the first adjustment strategy is not executed, and the wall surface is output as qualified; When the first warning value YJZ≤the first warning coefficient QMKLFX<the first warning value YJZ×120%, the first adjustment strategy is implemented, specifically: use 1%-2% color toner to recolor the wall repair area, and after drying, use 80-mesh or 100-mesh sandpaper to polish and level it; When the first warning coefficient QMKLFX ≥ the first warning value YJZ×120%, the second adjustment strategy is executed, specifically: use 3%-4% color toner to recolor the wall repair area, and after drying, perform a secondary cleaning on the vegetation growth area and use 120-mesh or 150-mesh sandpaper to grind and level it.

7. According to the big data-based engineering project supervision quality monitoring and management system of claim 1, it is characterized by: The steps for obtaining the pipeline leakage risk coefficient GXFX are as follows: D1: Obtain the leakage times of all pipelines in the current building and the distance between the historical pipeline leakage nodes and the current wall to be detected from the building environment database, and calculate the pipeline leakage impact factor GXYZ. The specific expression is as follows: Among them, GZCS p represents the number of leaks of the pth leaking node, Indicates a total of P leaking nodes Perform summation; D2: Get the overall pipeline aging factor GXLH. The specific expression is as follows: Among them, GXLH q represents the pipeline aging factor of the pth leaking node, and its expression is as follows: Among them, XSHD q Indicates the corrosion thickness of the pipeline at the pth leakage node, obtained by ultrasonic detection, YSHD q represents the initial thickness of the pipeline at the pth leakage node; D3: Calculate the pipeline leakage risk factor GXFX. The specific expression is as follows: GXFX=GXLH×GXYZ Among them, GXLH represents the overall pipeline aging factor, and GXYZ represents the pipeline leakage impact factor.

8. According to the big data-based engineering project supervision quality monitoring and management system of claim 1, it is characterized by: The steps for obtaining the hanging risk coefficient ZHFX on the outer surface of the building are as follows: E1: Get the number of hanging objects XGWSL in the non-hanging area of ​​the current wall; E2: Get the total area S corresponding to the hanging objects in the non-hanging area of ​​the current wall; E3: Calculate the risk factor ZHFX of the hanging objects on the exterior surface of the building based on the total area S corresponding to the hanging objects in the non-hanging area of ​​the current wall and the number XGWSL of hanging objects in the non-hanging area of ​​the current wall. The specific expression is as follows: Among them, the number of hanging objects XGWSL in the non-hanging area of ​​the current wall and the total area S corresponding to the hanging objects in the non-hanging area of ​​the current wall are obtained by manual measurement. It means that when XGWSL≠0, θ=0, and when XGWSL=0, θ=1.

9. The engineering project supervision quality monitoring and management system based on big data according to claim 8 is characterized by: The steps for obtaining the environmental vibration risk factor CXFX are as follows: F1: Obtain the construction vibration factor SGZD by consulting the construction data around the currently repaired building. The specific expression is as follows: Among them, WSGTS represents the number of days without construction, and SGTS represents the number of days under construction; F2: Based on the number of hanging objects XGWSL in the non-hanging area of ​​the current wall, calculate the hanging object vibration factor XGWZD. The specific expression is as follows: Among them, FXGWSL represents the number of hanging objects in the hanging area; F3: Calculate the environmental vibration risk factor CXFX. The specific expression is as follows: Among them, SGZD represents the construction vibration factor, and XGWZD represents the hanging object vibration factor.

10. The engineering project supervision quality monitoring and management system based on big data according to claim 1 is characterized by: The specific expression of the second warning coefficient DEFX calculated by the quality warning module is as follows: DEFX=ω3×GXFX+ω4×ZHFX+ω5×ZDFX Among them, GXFX represents the pipeline leakage risk factor, ZHFX represents the building external surface hanging risk factor, ZDFX represents the environmental vibration risk factor, ω3, ω4, ω5 represent the weight coefficients whose sum is 1 respectively; The specific steps of the quality warning module deciding whether to execute the third adjustment strategy are as follows: When the second warning coefficient DEFX ≤ the second warning value EYJZ, the third adjustment strategy is not executed, and the wall surface is output as qualified; When the second warning coefficient DEFX>the second warning value EYJZ, the third adjustment strategy is executed, specifically: thickening the pipeline by 10%-20%, and thickening the gaskets of the hanging object fixing nodes by 5%-7%.

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