A prefabricated building quality assessment system based on data analysis

Abstract

The present invention relates to the field of prefabricated buildings, and in particular to a prefabricated building quality assessment system based on data analysis, which solves the problem that traditional building quality assessment methods mostly rely on the experience and subjective judgment of construction supervisors, have the disadvantages of incomplete data collection and lack of scientificity, resulting in inaccurate analysis results and inability to conduct effective quality assessment of prefabricated buildings; the system can timely discover and eliminate potential quality risks through fine quality analysis of each building accessory, thereby avoiding potential quality risks from causing more serious safety accidents in subsequent construction, and can adjust construction plans according to real-time data to avoid unnecessary rework and waste, and can also monitor and manage the comprehensive quality of prefabricated buildings, greatly improving the quality and safety level of the entire prefabricated building project, improving the quality level of construction projects, and promoting the green development of the construction industry.

Description

Technical Field

[0001] The present invention relates to the field of prefabricated buildings, and in particular to a prefabricated building quality assessment system based on data analysis. Background Art

[0002] With the acceleration of urbanization and the popularization of the concept of sustainable development, prefabricated buildings have gradually become an important development direction of the construction industry due to their advantages such as fast construction speed, controllable quality, and resource conservation. However, the construction quality and safety of prefabricated buildings are directly related to the overall performance and service life of the building, and comprehensive monitoring and evaluation of its construction process is particularly important. Traditional building quality assessment methods mostly rely on the experience and subjective judgment of construction supervisors, and have the shortcomings of incomplete data collection and lack of scientificity, resulting in inaccurate analysis results, making it impossible to conduct effective quality assessment of prefabricated buildings, and it is difficult to meet the needs of modern prefabricated buildings for high-quality monitoring. Therefore, the development of a prefabricated building quality assessment system based on data analysis is of great significance to improving the quality management level of prefabricated buildings. Summary of the invention

[0003] In order to overcome the above-mentioned technical problems, the purpose of the present invention is to provide a prefabricated building quality assessment system based on data analysis, which solves the problem that traditional building quality assessment methods rely heavily on the experience and subjective judgment of construction supervisors, have the shortcomings of incomplete data collection and lack of scientificity, resulting in inaccurate analysis results, and cannot effectively assess the quality of prefabricated buildings, making it difficult to meet the needs of modern prefabricated buildings for high-quality monitoring.

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] A prefabricated building quality assessment system based on data analysis, comprising:

[0006] A building detection module, which is used to obtain building detection parameters of the analysis site i after receiving the building detection instruction, and send the building detection parameters to the data analysis module; wherein the building detection parameters include width information KD, angle information JD and vibration information ZD;

[0007] A data analysis module is used to obtain a building detection coefficient JZ according to the building detection parameters, and send the building detection coefficient JZ to the quality assessment module;

[0008] The specific process of the data analysis module obtaining the building detection coefficient JZ is as follows:

[0009] The width information KD, the angle information JD and the vibration information ZD are normalized, and the values ​​of the width information KD, the angle information JD and the vibration information ZD are quantified according to a preset building detection model to obtain a building detection coefficient JZ;

[0010] Among them, the building detection model is as follows:

[0011] Where:

[0012] Both π and e are mathematical constants;

[0013] λ1, λ2 and λ3 are preset weight factors corresponding to the set width information KD, angle information JD and vibration information ZD respectively, and λ1, λ2 and λ3 satisfy λ3>λ2>λ1>2.017, and λ1=2.41, λ2=3.01, and λ3=3.83;

[0014] The quality assessment module is used to generate a building abnormality instruction according to the building detection coefficient JZ, and send the building abnormality instruction to the abnormality reminder module;

[0015] The abnormality reminder module is used to ring the building abnormality bell after receiving the building abnormality instruction.

[0016] As a further solution of the present invention: the specific process of the building detection module obtaining the building detection parameters is as follows:

[0017] After receiving the building inspection instruction, all the building accessories connection points in the assembled prefabricated building are marked as analysis sites i in sequence, i=1, ..., u, u is a positive integer, i is the number of any analysis site, and u is the total number of analysis sites;

[0018] Obtain the width of the assembly connection gap in the analysis site i and the width of the assembly connection gap in the corresponding assembly drawing, obtain the difference between the two, and mark it as the width difference KCi, sum up all the width difference KCi, and mark it as the width information KD;

[0019] Obtain the angle between adjacent building accessories in the analysis site i and the angle between adjacent building accessories in the corresponding assembly drawing, obtain the difference between the two, and mark it as the angle value JJi, sum up all the angle values ​​JJi, and mark it as angle information JD;

[0020] Obtain the number of vibrations and the maximum vibration amplitude per unit time at the analysis site i, and mark them as vibration value ZS and amplitude value ZF respectively, quantify the vibration value ZS and amplitude value ZF, extract the values ​​of the vibration value ZS and amplitude value ZF, and substitute them into the formula for calculation. Get the amplitude value SFi, obtain the maximum amplitude value SFi and the average value of all amplitude values ​​SFi, and mark them as the maximum amplitude value SZ and the average amplitude value SJ, respectively; obtain the product of the maximum amplitude value SZ and the average amplitude value SJ, and mark it as vibration information ZD; where e is a mathematical constant, z1 and z2 are the preset proportional coefficients corresponding to the set vibration value ZS and amplitude value ZF, respectively, z1 and z2 satisfy z1+z2=1, 0<z2<z1<1, and z1=0.67 and z2=0.33;

[0021] The width information KD, angle information JD and vibration information ZD are sent to the data analysis module.

[0022] As a further solution of the present invention: the specific process of the quality assessment module generating a building abnormality instruction is as follows:

[0023] The building detection coefficient JZ is compared with the preset building detection threshold JZy, and the comparison results are as follows:

[0024] If the building detection coefficient JZ≥building detection threshold JZy, a building abnormality instruction is generated and sent to the abnormality reminder module.

[0025] As a further solution of the present invention: the prefabricated building quality assessment system based on data analysis also includes:

[0026] The accessory detection module is used to obtain and analyze the accessory detection parameters of the accessory o, and send the accessory detection parameters to the data analysis module; wherein the accessory detection parameters include defect information QX, pit information AK and deviation information PC.

[0027] As a further solution of the present invention: the specific process of the accessory detection module obtaining the accessory detection parameters is as follows:

[0028] The building accessories of the prefabricated building are marked as analysis accessories o, o=1, ..., v, v is a positive integer, o is the number of any analysis accessory, and v is the total number of analysis accessories;

[0029] Get the average number of cracks and the average crack area per unit surface area of ​​the analysis accessory o, and mark them as the texture value WS and the texture volume value WJ, respectively. Get the average number of holes and the average hole area per unit surface area of ​​the analysis accessory o, and mark them as the hole value DS and the hole volume value DJ, respectively. Quantify the texture value WS, the texture volume value WJ, the hole value DS and the hole volume value DJ, extract the values ​​of the texture value WS, the texture volume value WJ, the hole value DS and the hole volume value DJ, and substitute them into the formula for calculation. According to the formula Obtain defect information QX; wherein, e and π are both mathematical constants, q1, q2, q3 and q4 are preset proportional coefficients corresponding to the grain value WS, grain volume value WJ, hole value DS and hole volume value DJ respectively, q1, q2, q3 and q4 satisfy 0<q2<q4<q3<q1<1, and q1 and q2 satisfy q1+q2=1, q3 and q4 satisfy q3+q4=1, and q1=0.79, q2=0.21, q3=0.55, q4=0.45;

[0030] Obtain the average total number of pits and protrusions and the average total area of ​​pits and protrusions per unit surface area of ​​the analyzed accessory o, and mark them as the concave-convex value AS and the concave-convex product value AJ, quantify the concave-convex value AS and the concave-convex product value AJ, extract the values ​​of the concave-convex value AS and the concave-convex product value AJ, and substitute them into the formula for calculation. Obtain pit information AK; wherein a1 and a2 are preset proportional coefficients corresponding to the concave-convex value AS and the concave-convex product value AJ, respectively, and a1 and a2 satisfy a1+a2=1, 0<a1<a2<1, and a1=0.31, a2=0.69;

[0031] Obtain the difference between the mass of the analysis accessory o and the preset mass, and mark it as the mass difference value ZC; obtain the non-overlapping area between the surface contour of the analysis accessory o and the preset surface contour, and mark it as the non-overlapping value WH; obtain the difference between the volume of the analysis accessory o and the preset volume, and mark it as the volume difference value TC; quantify the mass difference value ZC, the non-overlapping value WH, and the volume difference value TC, extract the values ​​of the mass difference value ZC, the non-overlapping value WH, and the volume difference value TC, and substitute them into the formula for calculation; according to the formula Obtain deviation information PC; wherein c1, c2 and c3 are preset proportional coefficients corresponding to the quality difference value ZC, the uncombined value WH and the volume difference value TC, respectively, c1, c2 and c3 satisfy c1+c2+c3=1, 0<c1<c2<c3<1, and c1=0.22, c2=0.30, c3=0.48;

[0032] The defect information QX, pit information AK and deviation information PC are sent to the data analysis module.

[0033] As a further solution of the present invention: the data analysis module is also used to obtain the accessory detection coefficient PJo according to the accessory detection parameters, and send the accessory detection coefficient PJo to the quality assessment module.

[0034] As a further solution of the present invention: the specific process of the data analysis module obtaining the accessory detection coefficient PJo is as follows:

[0035] The defect information QX, the pit information AK and the deviation information PC are normalized, and the values ​​of the defect information QX, the pit information AK and the deviation information PC are quantified according to a preset accessory detection model to obtain an accessory detection coefficient PJo;

[0036] Among them, the accessory detection model is as follows:

[0037] Where:

[0038] Both π and e are mathematical constants;

[0039] κ1, κ2 and κ3 are preset weight factors corresponding to the set defect information QX, pit information AK and deviation information PC respectively, and κ1, κ2 and κ3 satisfy κ3>κ2>κ1>1.237, and κ1=1.52, κ2=1.96, κ3=2.35 are taken;

[0040] The accessory detection coefficient PJo is sent to the quality assessment module.

[0041] As a further solution of the present invention: the quality assessment module is also used to generate an accessory abnormality instruction or a building detection instruction according to the accessory detection coefficient PJo, and send the accessory abnormality instruction to the abnormal reminder module, and send the building detection instruction to the building detection module.

[0042] As a further solution of the present invention: the specific process of the quality assessment module generating the accessory abnormality instruction or the building detection instruction is as follows:

[0043] The accessory detection coefficient PJo is compared with the preset accessory detection threshold PJy. The comparison results are as follows:

[0044] If the accessory detection coefficient PJo ≥ the accessory detection threshold PJy, the analysis accessory o corresponding to the accessory detection coefficient PJo is marked as an abnormal accessory, and an accessory abnormal instruction is generated, and the abnormal accessory and the accessory abnormal instruction are sent to the abnormal reminder module;

[0045] If the accessory detection coefficient PJo is less than the accessory detection threshold PJy, a building detection instruction is generated and sent to the building detection module.

[0046] As a further solution of the present invention: the abnormality reminder module is also used to sound an accessory abnormality ringtone after receiving an accessory abnormality instruction.

[0047] Beneficial effects of the present invention:

[0048] The present invention provides a prefabricated building quality assessment system based on data analysis. First, data collection is performed on building accessories to obtain accessory detection parameters. Then, the accessory detection coefficient obtained according to the accessory detection parameters using data analysis technology can comprehensively measure the abnormality of the building accessories. The larger the accessory detection coefficient, the higher the abnormality. Then, data collection is performed on the assembled prefabricated building to obtain building detection parameters. Then, the building detection coefficient obtained according to the building detection parameters using data analysis technology can comprehensively measure the abnormality of the prefabricated building. The larger the building detection coefficient, the higher the abnormality. Finally, an abnormality reminder is performed.

[0049] The prefabricated building quality assessment system based on data analysis of the present invention can timely discover and eliminate potential quality risks through detailed quality analysis of each building accessory, thereby preventing potential quality risks from causing more serious safety accidents in subsequent construction. The construction plan can be adjusted according to real-time data to avoid unnecessary rework and waste. The comprehensive quality monitoring and management of prefabricated buildings can also be carried out, which greatly improves the quality and safety level of the entire prefabricated building project, improves the quality level of the construction project, and promotes the green development of the construction industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The present invention will be further described below in conjunction with the accompanying drawings.

[0051] Figure 1 It is a structural schematic diagram of a prefabricated building quality assessment system based on data analysis in the present invention;

[0052] Figure 2 It is a flow chart of a working method of a prefabricated building quality assessment system based on data analysis in the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with 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.

[0054] Embodiment 1:

[0055] See also Figure 1 As shown, this embodiment is a prefabricated building quality assessment system based on data analysis, including the following modules: an accessory detection module, a data analysis module, a quality assessment module, an abnormality reminder module and a building detection module;

[0056] The accessory detection module is used to obtain and analyze the accessory detection parameters of the accessory o, and send the accessory detection parameters to the data analysis module; wherein the accessory detection parameters include defect information QX, pit information AK and deviation information PC;

[0057] The data analysis module is used to obtain the accessory detection coefficient PJo according to the accessory detection parameters, and send the accessory detection coefficient PJo to the quality assessment module; it is also used to obtain the building detection coefficient JZ according to the building detection parameters, and send the building detection coefficient JZ to the quality assessment module;

[0058] The quality assessment module is used to generate an accessory abnormality instruction or a building detection instruction according to the accessory detection coefficient PJo, and send the accessory abnormality instruction to the abnormality reminder module, and send the building detection instruction to the building detection module; it is also used to generate a building abnormality instruction according to the building detection coefficient JZ, and send the building abnormality instruction to the abnormality reminder module;

[0059] The abnormality reminder module is used to sound an abnormal accessory ring tone after receiving an abnormal accessory instruction; and is also used to sound an abnormal building ring tone after receiving an abnormal building instruction;

[0060] Among them, the building detection module is used to obtain the building detection parameters of the analysis site i after receiving the building detection instruction, and send the building detection parameters to the data analysis module; wherein the building detection parameters include width information KD, angle information JD and vibration information ZD.

[0061] Embodiment 2:

[0062] See also Figure 2 As shown, this embodiment is a working method of a prefabricated building quality assessment system based on data analysis, comprising the following steps:

[0063] Step 1: The accessory detection module obtains and analyzes the accessory detection parameters of the accessory o, wherein the accessory detection parameters include defect information QX, pit information AK and deviation information PC, and sends the accessory detection parameters to the data analysis module;

[0064] Step 2: The data analysis module obtains the accessory detection coefficient PJo according to the accessory detection parameters, and sends the accessory detection coefficient PJo to the quality assessment module;

[0065] Step 3: The quality assessment module generates an accessory abnormality instruction or a building detection instruction according to the accessory detection coefficient PJo, and sends the accessory abnormality instruction to the abnormality reminder module and sends the building detection instruction to the building detection module;

[0066] Step 4: After receiving the accessory abnormality instruction, the abnormality reminder module sounds an accessory abnormality ringtone;

[0067] Step 5: After receiving the building detection instruction, the building detection module obtains the building detection parameters of the analysis site i, where the building detection parameters include width information KD, angle information JD and vibration information ZD, and sends the building detection parameters to the data analysis module;

[0068] Step 6: The data analysis module obtains the building detection coefficient JZ according to the building detection parameters, and sends the building detection coefficient JZ to the quality assessment module;

[0069] Step 7: The quality assessment module generates a building abnormality instruction according to the building detection coefficient JZ, and sends the building abnormality instruction to the abnormality reminder module;

[0070] Step 8: After receiving the building abnormality instruction, the abnormality reminder module sounds a building abnormality bell.

[0071] Embodiment 3:

[0072] Based on any of the above embodiments, Embodiment 3 of the present invention is an accessory detection module. The function of the accessory detection module is to obtain accessory detection parameters, wherein the accessory detection parameters include defect information QX, pit information AK and deviation information PC. The specific process is as follows:

[0073] The accessory detection module marks the building accessories of the prefabricated building as analysis accessories o in sequence, where o=1, ..., v, v is a positive integer, o is the number of any analysis accessory, and v is the total number of analysis accessories;

[0074] The accessory detection module obtains the average number of cracks and the average crack area per unit surface area of ​​the analyzed accessory o, and marks them as the texture value WS and the texture volume value WJ respectively, obtains the average number of holes and the average hole area per unit surface area of ​​the analyzed accessory o, and marks them as the hole value DS and the hole volume value DJ respectively, quantifies the texture value WS, the texture volume value WJ, the hole value DS and the hole volume value DJ, extracts the values ​​of the texture value WS, the texture volume value WJ, the hole value DS and the hole volume value DJ, and substitutes them into the formula for calculation. According to the formula Obtain defect information QX; wherein, e and π are both mathematical constants, q1, q2, q3 and q4 are preset proportional coefficients corresponding to the grain value WS, grain volume value WJ, hole value DS and hole volume value DJ respectively, q1, q2, q3 and q4 satisfy 0<q2<q4<q3<q1<1, and q1 and q2 satisfy q1+q2=1, q3 and q4 satisfy q3+q4=1, and q1=0.79, q2=0.21, q3=0.55, q4=0.45;

[0075] The accessory detection module obtains the average total number of pits and convexities and the average total area of ​​pits and convexities within the unit surface area of ​​the accessory o, and marks them as the concave-convex value AS and the concave-convex product value AJ respectively, quantifies the concave-convex value AS and the concave-convex product value AJ, extracts the values ​​of the concave-convex value AS and the concave-convex product value AJ, and substitutes them into the formula for calculation. Obtain pit information AK; wherein a1 and a2 are preset proportional coefficients corresponding to the concave-convex value AS and the concave-convex product value AJ, respectively, and a1 and a2 satisfy a1+a2=1, 0<a1<a2<1, and a1=0.31, a2=0.69;

[0076] The accessory detection module obtains the difference between the mass of the analysis accessory o and the preset mass, and marks it as the mass difference value ZC, obtains the non-overlapping area between the surface contour of the analysis accessory o and the preset surface contour, and marks it as the non-overlapping value WH, obtains the difference between the volume of the analysis accessory o and the preset volume, and marks it as the volume difference value TC, quantifies the mass difference value ZC, the non-overlapping value WH and the volume difference value TC, extracts the values ​​of the mass difference value ZC, the non-overlapping value WH and the volume difference value TC, and substitutes them into the formula for calculation. According to the formula Obtain deviation information PC; wherein c1, c2 and c3 are preset proportional coefficients corresponding to the quality difference value ZC, the uncombined value WH and the volume difference value TC, respectively, c1, c2 and c3 satisfy c1+c2+c3=1, 0<c1<c2<c3<1, and c1=0.22, c2=0.30, c3=0.48;

[0077] The accessory detection module sends the defect information QX, pit information AK and deviation information PC to the data analysis module.

[0078] Embodiment 4:

[0079] Based on any of the above embodiments, Embodiment 4 of the present invention is a data analysis module, and the data analysis module has two functions:

[0080] One of the functions is to obtain the accessory detection coefficient PJo, the specific process is as follows:

[0081] The data analysis module normalizes the defect information QX, the pit information AK and the deviation information PC, and quantifies the values ​​of the defect information QX, the pit information AK and the deviation information PC according to a preset accessory detection model to obtain an accessory detection coefficient PJo;

[0082] Among them, the accessory detection model is as follows:

[0083] Where:

[0084] Both π and e are mathematical constants;

[0085] κ1, κ2 and κ3 are preset weight factors corresponding to the set defect information QX, pit information AK and deviation information PC respectively, and κ1, κ2 and κ3 satisfy κ3>κ2>κ1>1.237, and κ1=1.52, κ2=1.96, κ3=2.35 are taken;

[0086] The data analysis module sends the accessory detection coefficient PJo to the quality assessment module;

[0087] The second function is to obtain the building detection coefficient JZ. The specific process is as follows:

[0088] The data analysis module normalizes the width information KD, the angle information JD and the vibration information ZD, and quantifies the values ​​of the width information KD, the angle information JD and the vibration information ZD according to a preset building detection model to obtain a building detection coefficient JZ;

[0089] Among them, the building detection model is as follows:

[0090] Where:

[0091] Both π and e are mathematical constants;

[0092] λ1, λ2 and λ3 are preset weight factors corresponding to the set width information KD, angle information JD and vibration information ZD respectively, and λ1, λ2 and λ3 satisfy λ3>λ2>λ1>2.017, and λ1=2.41, λ2=3.01, and λ3=3.83;

[0093] The data analysis module sends the building inspection coefficient JZ to the quality assessment module.

[0094] Embodiment 5:

[0095] Based on any of the above embodiments, Embodiment 5 of the present invention is a quality assessment module, and the quality assessment module has two functions:

[0096] One of them is to generate accessory abnormality instructions or building detection instructions. The specific process is as follows:

[0097] The quality assessment module compares the accessory detection coefficient PJo with the preset accessory detection threshold PJy. The comparison results are as follows:

[0098] If the accessory detection coefficient PJo ≥ the accessory detection threshold PJy, the analysis accessory o corresponding to the accessory detection coefficient PJo is marked as an abnormal accessory, and an accessory abnormal instruction is generated, and the abnormal accessory and the accessory abnormal instruction are sent to the abnormal reminder module;

[0099] If the accessory detection coefficient PJo < the accessory detection threshold PJy, a building detection instruction is generated and sent to the building detection module;

[0100] The second function is to generate building exception instructions. The specific process is as follows:

[0101] The quality assessment module compares the building detection coefficient JZ with the preset building detection threshold JZy. The comparison results are as follows:

[0102] If the building detection coefficient JZ≥building detection threshold JZy, a building abnormality instruction is generated and sent to the abnormality reminder module.

[0103] Embodiment 6:

[0104] Based on any of the above embodiments, Embodiment 6 of the present invention is an abnormality reminder module, and the abnormality reminder module has two functions:

[0105] One function is to ring an abnormal accessory ring tone after receiving an abnormal accessory command, and re-dispatch the same model of building accessories according to the abnormal accessories;

[0106] The second function is to sound a building abnormality bell after receiving a building abnormality command.

[0107] Embodiment 7:

[0108] Based on any of the above embodiments, Embodiment 7 of the present invention is a building detection module. The function of the building detection module is to obtain building detection parameters, wherein the building detection parameters include width information KD, angle information JD and vibration information ZD. The specific process is as follows:

[0109] After receiving the building inspection instruction, the building inspection module marks all the building accessory connections in the assembled prefabricated building as analysis sites i in sequence, where i=1, ..., u, where u is a positive integer, i is the number of any one of the analysis sites, and u is the total number of the analysis sites;

[0110] The building detection module obtains the width of the assembly connection gap in the analysis site i and the width of the assembly connection gap in the corresponding assembly drawing, obtains the difference between the two, and marks it as the width difference KCi, sums up all the width difference KCi, and marks it as the width information KD;

[0111] The building detection module obtains the angle between adjacent building accessories in the analysis site i and the angle between adjacent building accessories in the corresponding assembly drawing, obtains the difference between the two, and marks it as the angle value JJi, sums up all the angle values ​​JJi, and marks it as angle information JD;

[0112] The building detection module obtains the number of vibrations and the maximum vibration amplitude per unit time at the analysis site i, and marks them as vibration value ZS and amplitude value ZF respectively, quantifies the vibration value ZS and amplitude value ZF, extracts the values ​​of the vibration value ZS and amplitude value ZF, and substitutes them into the formula for calculation. Get the amplitude value SFi, obtain the maximum amplitude value SFi and the average value of all amplitude values ​​SFi, and mark them as the maximum amplitude value SZ and the average amplitude value SJ, respectively; obtain the product of the maximum amplitude value SZ and the average amplitude value SJ, and mark it as vibration information ZD; where e is a mathematical constant, z1 and z2 are the preset proportional coefficients corresponding to the set vibration value ZS and amplitude value ZF, respectively, z1 and z2 satisfy z1+z2=1, 0<z2<z1<1, and z1=0.67 and z2=0.33;

[0113] The building detection module sends the width information KD, the angle information JD and the vibration information ZD to the data analysis module.

[0114] Based on the above embodiments 1-7, the working principle of the present invention is as follows:

[0115] The present invention provides a prefabricated building quality assessment system based on data analysis, which obtains the accessory detection parameters of the analyzed accessories through the accessory detection module, wherein the accessory detection parameters include defect information, pit information and deviation information, obtains the accessory detection coefficient according to the accessory detection parameters through the data analysis module, generates the accessory abnormality instruction or the building detection instruction according to the accessory detection coefficient through the quality assessment module, sounds the accessory abnormality ringtone after receiving the accessory abnormality instruction through the abnormal reminder module, obtains the building detection parameters of the analyzed location after receiving the building detection instruction through the building detection module, wherein the building detection parameters include width information, angle information and vibration information, obtains the building detection coefficient according to the building detection parameters through the data analysis module, generates the building abnormality instruction according to the building detection coefficient through the quality assessment module, and sounds the building abnormality ringtone after receiving the building abnormality instruction through the abnormal reminder module; the system first collects data on the building accessories to obtain the accessory detection parameters, and then Then, the accessory detection coefficient obtained according to the accessory detection parameters using data analysis technology can comprehensively measure the degree of abnormality of the building accessories, and the larger the accessory detection coefficient, the higher the degree of abnormality. Then, data collection is carried out on the assembled prefabricated buildings to obtain the building detection parameters. Then, the building detection coefficient obtained according to the building detection parameters using data analysis technology can comprehensively measure the degree of abnormality of the prefabricated buildings, and the larger the building detection coefficient, the higher the degree of abnormality. Finally, an abnormal reminder is issued; through the fine quality analysis of each building accessory, the system can promptly discover and eliminate potential quality risks, thereby avoiding potential quality risks from causing more serious safety accidents in subsequent construction, and can adjust the construction plan according to real-time data to avoid unnecessary rework and waste. It can also monitor and manage the comprehensive quality of prefabricated buildings, greatly improving the quality and safety level of the entire prefabricated building project, improving the quality level of construction projects, and promoting green development of the construction industry.

[0116] It should be further explained that the above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The coefficients in the formula are set by technical personnel in this field according to actual conditions.

[0117] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0118] The above contents are merely examples and explanations of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the invention or exceed the scope defined in this application, they shall all fall within the protection scope of the present invention.

Claims

1. A prefabricated building quality assessment system based on data analysis, characterized in that: include: A building detection module, which is used to obtain building detection parameters of the analysis site i after receiving the building detection instruction, and send the building detection parameters to the data analysis module; wherein the building detection parameters include width information KD, angle information JD and vibration information ZD; The specific process of the building detection module obtaining building detection parameters is as follows: After receiving the building inspection instruction, all the building accessories connection points in the assembled prefabricated building are marked as analysis sites i in sequence, i=1, ..., u, u is a positive integer, i is the number of any analysis site, and u is the total number of analysis sites; Obtain the width of the assembly connection gap in the analysis site i and the width of the assembly connection gap in the corresponding assembly drawing, obtain the difference between the two, and mark it as the width difference KCi, sum up all the width difference KCi, and mark it as the width information KD; Obtain the angle between adjacent building accessories in the analysis site i and the angle between adjacent building accessories in the corresponding assembly drawing, obtain the difference between the two, and mark it as the angle value JJi, sum up all the angle values ​​JJi, and mark it as angle information JD; Obtain the number of vibrations and the maximum vibration amplitude per unit time at the analysis site i, and mark them as vibration value ZS and amplitude value ZF respectively. The vibration value ZS and amplitude value ZF are quantified according to the formula Get the amplitude value SFi, obtain the maximum amplitude value SFi and the average value of all amplitude values ​​SFi, and mark them as the maximum amplitude value SZ and the average amplitude value SJ, respectively; obtain the product of the maximum amplitude value SZ and the average amplitude value SJ, and mark it as vibration information ZD; where e is a mathematical constant, and z1 and z2 are the preset proportional coefficients corresponding to the set vibration value ZS and amplitude value ZF, respectively; Send the width information KD, angle information JD and vibration information ZD to the data analysis module; A data analysis module is used to obtain a building detection coefficient JZ according to the building detection parameters, and send the building detection coefficient JZ to the quality assessment module; The specific process of the data analysis module obtaining the building detection coefficient JZ is as follows: The values ​​of the width information KD, the angle information JD and the vibration information ZD are quantified according to a preset building detection model to obtain a building detection coefficient JZ; Among them, the building detection model is as follows: Where: Both π and e are mathematical constants; λ1, λ2 and λ3 are preset weight factors corresponding to the set width information KD, angle information JD and vibration information ZD respectively; The quality assessment module is used to generate a building abnormality instruction according to the building detection coefficient JZ, and send the building abnormality instruction to the abnormality reminder module; The abnormality reminder module is used to ring the building abnormality bell after receiving the building abnormality instruction.

2. According to claim 1, a prefabricated building quality assessment system based on data analysis is characterized in that: The specific process of the quality assessment module generating a building abnormality instruction is as follows: The building detection coefficient JZ is compared with the preset building detection threshold JZy, and the comparison results are as follows: If the building detection coefficient JZ≥building detection threshold JZy, a building abnormality instruction is generated and sent to the abnormality reminder module.

3. The prefabricated building quality assessment system based on data analysis according to claim 1 is characterized in that: Also includes: The accessory detection module is used to obtain and analyze the accessory detection parameters of the accessory o, and send the accessory detection parameters to the data analysis module; wherein the accessory detection parameters include defect information QX, pit information AK and deviation information PC.

4. The prefabricated building quality assessment system based on data analysis according to claim 3 is characterized in that: The specific process of the accessory detection module obtaining the accessory detection parameters is as follows: The building accessories of the prefabricated building are marked as analysis accessories o, o=1, ..., v, v is a positive integer, o is the number of any analysis accessory, and v is the total number of analysis accessories; Get the average number of cracks and the average crack area per unit surface area of ​​the analysis accessory o, and mark them as the texture value WS and the texture volume value WJ respectively. Get the average number of holes and the average hole area per unit surface area of ​​the analysis accessory o, and mark them as the hole value DS and the hole volume value DJ respectively. Quantify the texture value WS, the texture volume value WJ, the hole value DS and the hole volume value DJ. According to the formula Obtain defect information QX; wherein e and π are mathematical constants, and q1, q2, q3 and q4 are preset proportional coefficients corresponding to the texture value WS, the texture volume value WJ, the hole value DS and the hole volume value DJ respectively; Obtain the average total number of concave and convex pits and the average total area of ​​concave and convex pits per unit surface area of ​​the analyzed accessory o, and mark them as concave and convex numerical value AS and concave and convex product value AJ, respectively. Quantify the concave and convex numerical value AS and concave and convex product value AJ according to the formula Obtain pit information AK; wherein a1 and a2 are preset proportional coefficients corresponding to the concave-convex value AS and the concave-convex product value AJ respectively; Obtain the difference between the mass of the analysis accessory o and the preset mass, and mark it as the mass difference value ZC; obtain the non-overlapping area between the surface contour of the analysis accessory o and the preset surface contour, and mark it as the non-overlapping value WH; obtain the difference between the volume of the analysis accessory o and the preset volume, and mark it as the volume difference value TC; quantify the mass difference value ZC, the non-overlapping value WH, and the volume difference value TC according to the formula Obtain deviation information PC; wherein c1, c2 and c3 are preset proportional coefficients corresponding to the quality difference value ZC, the uncombined value WH and the volume difference value TC respectively; The defect information QX, pit information AK and deviation information PC are sent to the data analysis module.

5. The prefabricated building quality assessment system based on data analysis according to claim 4 is characterized in that: The data analysis module is also used to obtain the accessory detection coefficient PJo according to the accessory detection parameters, and send the accessory detection coefficient PJo to the quality assessment module.

6. The prefabricated building quality assessment system based on data analysis according to claim 5 is characterized in that: The specific process of the data analysis module obtaining the accessory detection coefficient PJo is as follows: The values ​​of the defect information QX, the pit information AK and the deviation information PC are quantified according to a preset accessory detection model to obtain an accessory detection coefficient PJo; Among them, the accessory detection model is as follows: Where: Both π and e are mathematical constants; κ1, κ2 and κ3 are preset weight factors corresponding to the set defect information QX, pit information AK and deviation information PC respectively; The accessory detection coefficient PJo is sent to the quality assessment module.

7. The prefabricated building quality assessment system based on data analysis according to claim 6 is characterized in that: The quality assessment module is also used to generate an accessory abnormality instruction or a building detection instruction according to the accessory detection coefficient PJo, and send the accessory abnormality instruction to the abnormality reminder module and send the building detection instruction to the building detection module.

8. The prefabricated building quality assessment system based on data analysis according to claim 7 is characterized in that: The specific process of the quality assessment module generating an accessory abnormality instruction or a building detection instruction is as follows: The accessory detection coefficient PJo is compared with the preset accessory detection threshold PJy. The comparison results are as follows: If the accessory detection coefficient PJo ≥ the accessory detection threshold PJy, the analysis accessory o corresponding to the accessory detection coefficient PJo is marked as an abnormal accessory, and an accessory abnormal instruction is generated, and the abnormal accessory and the accessory abnormal instruction are sent to the abnormal reminder module; If the accessory detection coefficient PJo is less than the accessory detection threshold PJy, a building detection instruction is generated and sent to the building detection module.

9. The prefabricated building quality assessment system based on data analysis according to claim 1, characterized in that: The abnormality reminder module is also used to sound an accessory abnormality ringtone after receiving an accessory abnormality instruction.

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