Construction and detection integrated method for building curtain wall with low heat transfer coefficient

Abstract

The invention relates to the technical field of curtain wall detection, in particular to a construction and detection integrated method for a low heat transfer coefficient building curtain wall, and the method comprises the steps: detecting the realization quality of a thermal bridge cutoff structure of a curtain wall through a thermal bridge cutoff detection model in a pre-construction stage; detecting the continuity and leakproofness of the curtain wall thermal insulation system through a thermal insulation and leakproofness field detection model matched with the thermal bridge cutoff detection model in the construction stage; the root cause of the curtain wall defect is diagnosed according to the quality, continuity and leakproofness of the thermal bridge cutoff structure; and dynamically optimizing parameters of the thermal bridge cutoff detection model and the thermal insulation and leakproofness field detection model according to the obtained defect root cause, and predicting machining process abnormality and construction process abnormality by using machining data and construction data.

Description

Technical Field

[0001] This invention relates to the field of curtain wall testing technology, and in particular to an integrated method for the construction and testing of building curtain walls with low heat transfer coefficients. Background Technology

[0002] The heat transfer coefficient is a core indicator for measuring the thermal insulation performance of building materials (or components, such as curtain walls). It is typically expressed using the symbol K-value (commonly used in Chinese standards) or U-value (internationally accepted, commonly used in European and American standards), with units of W / (m²·K). As a large-area external envelope structure of modern buildings, the thermal performance of curtain walls has a significant impact on the building's heating and cooling energy consumption. Low heat transfer coefficient curtain walls can significantly reduce indoor and outdoor heat exchange, lowering the load and energy consumption of HVAC systems. Although the initial investment in low heat transfer coefficient curtain walls may be higher, the long-term energy-saving benefits (reduced operating costs) can usually recover costs within a reasonable period and continue to generate economic benefits.

[0003] To ensure that low heat transfer coefficient curtain wall systems truly achieve the energy-saving effects expected in the design, rigorous and systematic testing and quality control are essential. These tests are conducted throughout the entire process, from fabrication and assembly to on-site installation and final acceptance, focusing on key aspects such as thermal bridging, airtightness, watertightness, and insulation continuity. Existing technologies mostly concentrate on testing during the final acceptance process, which can be costly to address if defects are found. Therefore, we propose a method that combines testing during both the fabrication and construction processes. Summary of the Invention

[0004] This invention involves detecting thermal break structure during curtain wall fabrication and establishing an on-site monitoring model for insulation and airtightness that coordinates with the construction process. The insulation and airtightness of the curtain wall are tested during construction to detect defects in advance and reduce defect handling costs.

[0005] The technical solution proposed in this invention is: an integrated method for construction and testing of building curtain walls with low heat transfer coefficients, the method comprising:

[0006] In the pre-construction stage, the thermal breakage structure of the curtain wall is tested using a thermal breakage detection model to ensure quality.

[0007] During the construction phase, the continuity and airtightness of the curtain wall insulation system are tested using an on-site insulation and airtightness testing model in conjunction with a thermal break testing model.

[0008] Based on the thermal break structure to achieve quality, continuity and airtightness, diagnose the root causes of curtain wall defects;

[0009] Based on the obtained root causes of defects, the parameters of the thermal breakage detection model and the insulation and airtightness field detection model are dynamically optimized, and processing data and construction data are used to predict processing abnormalities and construction abnormalities.

[0010] Preferably, the step of detecting the thermal break structure of the curtain wall using a thermal break detection model during the pre-construction stage to ensure quality includes:

[0011] Infrared thermal images of the curtain wall surface are acquired using an infrared imager to obtain the temperature field distribution on the curtain wall surface. Combined with environmental parameters and material properties, abnormal thermal zones, i.e., thermal bridges, are identified, and the degree of thermal defects is quantified, including:

[0012] The temperature field is acquired, calibrated, and normalized to eliminate the influence of environmental reflections and obtain the true surface temperature. ;in, Indicates the surface nodes of the curtain wall Temperature field, Indicates surface emissivity, Indicates the reference background temperature;

[0013] Thermal bridge region detection is performed through image segmentation to identify anomalous hot areas, including:

[0014] Calculate temperature difference ;in, This represents the average temperature of the reference background area.

[0015] Binarization segmentation: thermal bridge region ;in, Indicates the threshold for thermal bridge detection;

[0016] Filtering thermal infrared images smaller than The connected components, where, Indicates the minimum filtration area;

[0017] Quantifying thermal bridge characteristics includes:

[0018] Establish the thermal bridge strength index for the thermal bridge region. ; Indicates indoor temperature. Indicates ambient temperature; This indicates the average temperature of the thermal bridge region.

[0019] Calculate the equivalent heat transfer coefficient increment to quantitatively assess the degree of degradation of the overall curtain wall U-value by thermal bridges:

[0020] Equivalent heat transfer coefficient increment ;in, Indicates the indoor convective heat transfer coefficient;

[0021] Calculate the area ratio affected by thermal bridges: ;

[0022] The thermal bridge strength index, the equivalent heat transfer coefficient increment, and the temperature difference matrix are input into the pre-trained thermal bridge breakage detection model, which outputs the thermal bridge strength index level and the rectification priority sequence; the thermal bridge breakage monitoring model is built based on a CNN network.

[0023] The thermal bridge strength index level is: ;

[0024] The rectification priority sequence is generated by arranging the thermal bridge strength index and the importance weight of the thermal bridge location in descending order to represent the degree of thermal defects.

[0025] Preferably, the rectification priority sequence is generated by arranging the thermal bridge strength index and the importance weight of the thermal bridge location in descending order to represent the degree of thermal defect, including:

[0026] Calculate the rectification index That is, the product of the thermal bridge strength index and the importance weight of the thermal bridge location: ;in, The importance weight of the thermal bridge location is indicated. Indicates the number of thermal bridge regions;

[0027] Sort all rectification indices in descending order to obtain the rectification priority sequence:

[0028] ;in, This indicates the quantity of the rectification index.

[0029] Preferably, the step of detecting the thermal break structure of the curtain wall using a thermal break detection model during the pre-construction stage to ensure quality further includes:

[0030] Thermal bridge types can be classified as follows:

[0031] Multiple features are extracted from infrared thermal images to form a multi-dimensional feature vector. ;in, Indicates elongation length. ; , Let x and y represent the moments of inertia of the thermal bridge region, respectively. ; ;in, Indicates the dimension of the thermal bridge region; Indicates the relevance of the profile. ;;

[0032] Spatial positioning is performed, that is, by using a CAD-BIM alignment algorithm, points in the infrared thermal image ( , Align it to the corresponding position on the curtain wall:

[0033] ;

[0034] in, Indicates the scaling factor. Indicates the rotation angle. This indicates the translation along the x-axis and the translation in the opposite direction along the y-axis;

[0035] The multi-dimensional feature vector is input into a pre-trained separator (classification decision tree), and the output type of the hot bridge region is as follows:

[0036] Determine the elongation Is the thermal bridge region greater than the pre-examination elongation threshold? If so, the thermal bridge region is a linear thermal bridge. Otherwise, determine whether the fractal dimension is greater than the pre-examination fractal dimension threshold. If so, the thermal bridge region is a point thermal bridge; otherwise, it is a surface thermal bridge.

[0037] Output the type of the thermal bridge region and label the corresponding thermal bridge region.

[0038] Preferably, the on-site insulation and airtightness testing model, used in conjunction with the thermal breakage testing model, is used during the construction phase to test the continuity and airtightness of the curtain wall insulation system, including:

[0039] Inspecting the continuity of the curtain wall insulation system includes:

[0040] Obtain the average temperature difference ; Calculate the ideal surface temperature ;

[0041] in express Midpoint of frame infrared thermal image Temperature difference at the location;

[0042] Therefore, the target curtain wall temperature deviation during the construction phase ;in, , These represent the internal surface heat transfer resistance and the external surface heat transfer resistance, respectively.

[0043] Based on the temperature deviation of the target curtain wall, continuous defect detection is performed, including:

[0044] if and Then the judgment point There is a continuous defect at this point; among them, This indicates that the Laplacian operator is used to detect edges; Represents the Laplace operator;

[0045] Quantifying continuous defects includes:

[0046] Calculate and obtain the insulation deficiency index ;in, Indicates the area of ​​the defect. Indicates the total area of ​​the detection zone;

[0047] Testing the airtightness of the curtain wall insulation system includes:

[0048] Dynamic air pressure is applied to the curtain wall, and a pressure sensor array is used to identify pressure leakage paths and seal failure points, including:

[0049] Construct a pressure gradient field. ;in Indicates the first Pressure value measured by a pressure sensor; Indicates the first Pressure sensor and The spacing of the pressure sensors on the y-axis;

[0050] Identify the leak point, including:

[0051] point Leakage status value ; Indicates the leakage coefficient;

[0052] if Point As a leak point, multiple leak points connect within the detection time period to form a leak path;

[0053] The airtightness of the curtain wall is quantified, including:

[0054] Define the permeation equivalent area ;in, For flow coefficient, Indicates the amount of gas leakage; This indicates the density of the leaked gas.

[0055] Preferably, the step of diagnosing the root causes of curtain wall defects based on the thermal break structure to achieve quality, continuity, and airtightness includes: constructing a defect feature vector. ;in, This indicates the deviation between the measured thickness and the designed thickness of the insulation layer. Indicates the design thickness of the insulation layer; This indicates the maximum value of the thermal bridge strength index; Indicates the coverage ratio of the sealant; Indicates the thermal bridge morphology index; for point-like thermal bridges, When there is a linear thermal bridge, When there is a planar thermal bridge, ;

[0056] Building a root cause inference model based on Bayesian networks:

[0057] ; where, prior probability The likelihood function represents the probability that a defect is caused by a root cause. This indicates that when the root cause is known to be true, the observed defect feature vector is... The probability of; total probability expansion This indicates that all possible causes are considered. The probabilities are weighted and summed. This represents the defect feature vector after normalization; the possible causes include broken insulation strips, missing sealant, and voids in the insulation layer.

[0058] Find the root cause of the defect:

[0059] Root cause in, Represents the dynamic weights of feature importance;

[0060] if If the probability is greater than the preset root cause probability threshold, then determine... The root cause of the defect.

[0061] Preferably, the step of achieving quality, continuity, and airtightness based on the thermal break structure and diagnosing the root causes of curtain wall defects further includes:

[0062] To trace the process, including:

[0063] Defect identification during processing:

[0064] ;in, Indicates the profile design area; This represents the sum of the areas where all thermal bridge zones overlap with the profile design zone; This represents the area of ​​all thermal bridge regions;

[0065] if If the condition is positive, it indicates that the defect is a manufacturing defect; otherwise, proceed to the construction process defect judgment step.

[0066] Defect assessment during construction includes the following steps:

[0067] ;in, Represents the Heaviside jump function; This indicates the allowable installation deviation threshold; At that time, the defect is a defect in the manufacturing process; Indicates the weight of continuous defects; This indicates the deviation between the actual installation location and the design location;

[0068] if If the value is 1, then the defect is determined to be a defect in the construction process.

[0069] Preferably, the step of dynamically optimizing the parameters of the thermal breakage detection model and the insulation and airtightness field detection model based on the obtained defect root causes includes:

[0070] Model parameter optimization includes:

[0071] ;in This represents the new parameters of the broken bridge monitoring model; Represents the old parameters of the broken bridge monitoring model; gradient of the loss function. ;

[0072] in, ; Represents the gradient coefficients; Indicates the model confidence level. This represents the data acquisition time decay factor. .

[0073] Preferably, the method of predicting processing and construction anomalies using processing and construction data includes:

[0074] Obtain processing data and construct the processing data input feature vector:

[0075] Among them, among them , ;

[0076] Predicting the probability of abnormalities in the processing:

[0077] ; This represents the Sigmoid function. This represents the input feature weight vector; If this is the case, it is determined that an abnormality is about to occur in the processing; among them, Indicates the anomaly probability threshold;

[0078] Obtain construction data and construct the construction data input feature matrix:

[0079] ;in , , , , , These represent the ambient temperature, ultraviolet radiation intensity, wind speed, construction duration, construction interruption duration, and construction curtain wall length during construction, respectively.

[0080] Predicting the probability of abnormalities during construction. ;in, Represents radial basis functions; , Indicates model parameters, Indicates the activation function; if there is a risk of leakage at the window corner. If the leakage risk exceeds the preset threshold, it is determined that an abnormality in the construction process is about to occur.

[0081] An electronic device includes a processor, a communication module connected to the processor, and a memory, the electronic device being used to implement the integrated construction and inspection method for a low heat transfer coefficient building curtain wall.

[0082] The beneficial effects of this invention are:

[0083] 1. This invention combines the detection of the curtain wall processing process with the detection of the on-site construction process to detect curtain wall defects in advance and trace the source of the defects; it determines whether the defects are caused by the processing process or the construction process, providing a basis for later determining which process model parameters to update.

[0084] 2. In this invention, after a defect is discovered, the cause of the defect is determined, and the parameters of the thermal break detection model and the insulation and airtightness field detection model are updated using the root cause and defect tracing results to improve the accuracy of the detection. Attached Figure Description

[0085] Figure 1 This is a flowchart of an integrated construction and testing method for a low heat transfer coefficient building curtain wall according to the present invention. Detailed Implementation

[0086] The following description is intended to disclose the present invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious modifications will occur to those skilled in the art. The basic principles of the invention defined in the following description can be applied to other embodiments, modifications, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the invention.

[0087] It is understood that the term "a" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element can be one, while in another embodiment, the number of the element can be multiple, and the term "a" should not be understood as a limitation on the number.

[0088] Example 1:

[0089] refer to Figure 1 The technical solution provided by this invention is: an integrated method for the construction and testing of building curtain walls with low heat transfer coefficients, the method comprising:

[0090] Step 1: In the pre-construction stage, the thermal breakage structure of the curtain wall is inspected using a thermal breakage testing model to ensure quality. This includes the following steps:

[0091] Infrared thermal images of the curtain wall surface are acquired using an infrared imager to obtain the temperature field distribution on the curtain wall surface. Combined with environmental parameters and material properties, abnormal thermal zones, i.e., thermal bridges, are identified, and the degree of thermal defects is quantified, including:

[0092] The temperature field is acquired, calibrated, and normalized to eliminate the influence of environmental reflections and obtain the true surface temperature. ;in, Indicates the surface nodes of the curtain wall Temperature field, Indicates surface emissivity, Indicates the reference background temperature;

[0093] Thermal bridge region detection is performed through image segmentation to identify anomalous hot areas, including:

[0094] Calculate temperature difference ;in, This indicates the average temperature of the reference background area (such as the central area of ​​the curtain wall);

[0095] Binarization segmentation: thermal bridge region ;in, Indicates the threshold for thermal bridge detection;

[0096] Filtering thermal infrared images smaller than The connected components, where, Indicates the minimum filtration area;

[0097] Quantifying thermal bridge characteristics includes:

[0098] Establish a thermal bridge strength index for the thermal bridge region, which represents the proportion of additional heat loss caused by thermal bridging. ; Indicates indoor temperature. Indicates ambient temperature; This indicates the average temperature of the thermal bridge region.

[0099] Calculate the equivalent heat transfer coefficient increment to quantitatively assess the degree of degradation of the overall curtain wall U-value by thermal bridges:

[0100] Equivalent heat transfer coefficient increment ;in, Indicates the indoor convective heat transfer coefficient;

[0101] Calculate the area ratio affected by thermal bridges: ;

[0102] The thermal bridge strength index, the equivalent heat transfer coefficient increment, and the temperature difference matrix are input into the pre-trained thermal bridge breakage detection model, which outputs the thermal bridge strength index level and the rectification priority sequence; the thermal bridge breakage monitoring model is built based on a CNN network.

[0103] The thermal bridge strength index level is: ;

[0104] The rectification priority sequence is generated by arranging the thermal bridge strength index and the importance weight of the thermal bridge location in descending order to represent the degree of thermal defect, specifically:

[0105] Calculate the rectification index That is, the product of the thermal bridge strength index and the importance weight of the thermal bridge location: ;in, The importance weight of the thermal bridge location is indicated. This indicates the number of thermal bridge regions.

[0106] Sort all rectification indices in descending order to obtain the rectification priority sequence:

[0107] ;in, This indicates the quantity of the rectification index.

[0108] Importance weight of thermal bridge location ;in This represents the normalized distance from the thermal bridge area to the corner of the curtain wall. Indicates the curtain wall orientation factor; This represents the height factor of the thermal bridge region. The weights are: corner distance weight, orientation weight, and height weight.

[0109] For example, when the curtain wall faces south When the curtain wall faces north, When the curtain wall faces east When the curtain wall faces west, When the curtain wall is located on the bottom floor of the building When located in the middle layer 1.2; When located at the top layer 1.5.

[0110] Finally, the types of thermal bridges need to be classified, including:

[0111] Multiple features are extracted from infrared thermal images to form a multi-dimensional feature vector. ;in, Indicates elongation length. ; , Let x and y represent the moments of inertia of the thermal bridge region, respectively. ; ;in, The dimensions of the thermal bridge region are represented (the thermal bridge region is a two-dimensional image). ); Indicates the relevance of the profile. (Used to reflect the probability of material processing defects);

[0112] Spatial positioning is performed, that is, by using a CAD-BIM alignment algorithm, points in the infrared thermal image ( , Align it to the corresponding position on the curtain wall:

[0113] ;

[0114] in, Indicates the scaling factor. Indicates the rotation angle. This indicates the translation along the x-axis and the translation in the opposite direction along the y-axis;

[0115] The multi-dimensional feature vector is input into a pre-trained separator (classification decision tree), and the output type of the hot bridge region is as follows:

[0116] Determine the elongation Is the elongation length greater than the pre-examination threshold (0.7)? If yes, the thermal bridge region is a linear thermal bridge; otherwise, determine whether the fractal dimension is greater than the pre-examination fractal dimension threshold (1.65). If yes, the thermal bridge region is a point thermal bridge; otherwise, it is a surface thermal bridge.

[0117] Output the type of the thermal bridge region and label the corresponding thermal bridge region.

[0118] Step 2: During the construction phase, the continuity and airtightness of the curtain wall insulation system are tested using an on-site insulation and airtightness testing model in conjunction with a thermal break testing model. This includes the following steps:

[0119] Inspecting the continuity of the curtain wall insulation system includes:

[0120] Obtain the average temperature difference ; Calculate the ideal surface temperature ;

[0121] in express Midpoint of frame infrared thermal image Temperature difference at the location;

[0122] Therefore, the target curtain wall temperature deviation during the construction phase ;in, , These represent the internal surface heat transfer resistance and the external surface heat transfer resistance, respectively.

[0123] Based on the temperature deviation of the target curtain wall, continuous defect detection is performed, including:

[0124] if and Then the judgment point There is a continuous defect at this point; among them, This indicates that the Laplacian operator is used to detect edges; Represents the Laplace operator;

[0125] Quantifying continuous defects includes:

[0126] Calculate and obtain the insulation deficiency index ;in, Indicates the area of ​​the defect. Indicates the total area of ​​the detection zone;

[0127] Testing the airtightness of the curtain wall insulation system includes:

[0128] Dynamic air pressure is applied to the curtain wall, and a pressure sensor array is used to identify pressure leakage paths and seal failure points, including:

[0129] Construct a pressure gradient field. ;in Indicates the first Pressure value measured by a pressure sensor; Indicates the first Pressure sensor and The spacing of the pressure sensors on the y-axis;

[0130] Identify the leak point, including:

[0131] point Leakage status value ; Indicates the leakage coefficient;

[0132] if Point As a leak point, multiple leak points connect within the detection time period to form a leak path;

[0133] The airtightness of the curtain wall is quantified, including:

[0134] Define the permeation equivalent area ;in, For flow coefficient, Indicates the amount of gas leakage; This indicates the density of the leaked gas.

[0135] Step 3: Based on the thermal break structure to achieve quality, continuity, and airtightness, diagnose the root causes of curtain wall defects. This specifically includes the following steps: constructing a defect feature vector. ;in, This indicates the deviation between the measured thickness and the designed thickness of the insulation layer. Indicates the design thickness of the insulation layer; This indicates the maximum value of the thermal bridge strength index; Indicates the coverage ratio of the sealant; Indicates the thermal bridge morphology index; for point-like thermal bridges, When there is a linear thermal bridge, When there is a planar thermal bridge, ;

[0136] Building a root cause inference model based on Bayesian networks: ; where, prior probability The likelihood function represents the probability that a defect is caused by a root cause. This indicates that when the root cause is known to be true, the observed defect feature vector is... The probability of; total probability expansion This indicates that all possible causes are considered. The probabilities are weighted and summed. This represents the defect feature vector after normalization; the possible causes include broken insulation strips, missing sealant, and voids in the insulation layer.

[0137] Find the root cause of the defect:

[0138] Root cause in, Represents the dynamic weights of feature importance;

[0139] if If the probability is greater than the preset root cause probability threshold, then determine... The root cause of the defect.

[0140] Step 4: Based on the obtained root causes of defects, dynamically optimize the parameters of the thermal breakage detection model and the insulation and airtightness field detection model, and use processing data and construction data to predict processing anomalies and construction anomalies. This specifically includes the following steps:

[0141] Model parameter optimization includes: ;in This represents the new parameters of the broken bridge monitoring model; Represents the old parameters of the broken bridge monitoring model; gradient of the loss function. ;in, ; Represents the gradient coefficients; Indicates the model confidence level. Indicates the data acquisition time decay factor, for example sky.

[0142] Anomaly prediction:

[0143] Obtain processing data and construct the processing data input feature vector:

[0144] Among them, among them , ;

[0145] Predicting the probability of abnormalities in the processing:

[0146] ; This represents the Sigmoid function. This represents the input feature weight vector; If this is the case, it is determined that an abnormality is about to occur in the processing; among them, Indicates the anomaly probability threshold;

[0147] Obtain construction data and construct the construction data input feature matrix:

[0148] ;in , , , , , These represent the ambient temperature, ultraviolet radiation intensity, wind speed, construction duration, construction interruption duration, and construction curtain wall length during construction, respectively.

[0149] Predicting the probability of abnormalities during construction. ;in, Represents radial basis functions; , Indicates model parameters, Indicates the activation function; if there is a risk of leakage at the window corner. If the leakage risk exceeds the preset threshold, it is determined that an abnormality in the construction process is about to occur.

[0150] By identifying the root causes of defects, dynamically optimizing model parameters, and predicting construction anomalies, a closed-loop quality optimization system for low heat transfer curtain wall systems is achieved.

[0151] Example 2:

[0152] To achieve adaptive optimization of the two models, it is necessary to determine which model parameters need to be optimized. Therefore, based on Example 1, we propose the following technical solution:

[0153] It is necessary to identify and trace the source of defect types. Defect types can be classified based on the stage at which the defect occurs or based on the characteristics of the defect itself. Specifically:

[0154] To trace the process, including:

[0155] Defect identification during processing: ;in, Indicates the profile design area; This represents the sum of the areas where all thermal bridge zones overlap with the profile design zone; This represents the area of ​​all thermal bridge regions;

[0156] if If the condition is positive, it indicates that the defect is a manufacturing defect; otherwise, proceed to the construction process defect judgment step.

[0157] Defect assessment during construction includes the following steps:

[0158] ;in, Represents the Heaviside jump function; This indicates the allowable installation deviation threshold (3mm). At that time, the defect is a defect in the manufacturing process; Indicates the weight of continuous defects; This indicates the deviation between the actual installation location and the design location. If If the value is 1, the defect is determined to be a construction process defect. When the defect is determined to be a manufacturing process defect, the thermal breakage detection model parameters are updated; if the defect is determined to be a construction process defect, the insulation and airtightness field detection model parameters are updated.

[0159] The present invention also provides an electronic device, including a processor, a communication module and a memory connected to the processor, the electronic device being used to implement the aforementioned integrated construction and testing method for a low heat transfer coefficient building curtain wall.

[0160] The processes described above with reference to the flowcharts in the embodiments disclosed in this invention can be implemented as computer software programs. The embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), it performs the functions defined in the methods of this application. It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wire segments, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless segments, wire segments, optical fibers, RF, etc., or any suitable combination thereof.

[0161] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0162] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are merely examples and do not limit the present invention. The purpose of the present invention has been fully and effectively achieved. The functions and structural principles of the present invention have been shown and explained in the embodiments. Without departing from the principles described, the implementation of the present invention may have any changes or modifications.

Claims

1. An integrated construction and testing method for building curtain walls with low heat transfer coefficients, characterized in that, The method includes: In the pre-construction stage, the thermal breakage structure of the curtain wall is tested using a thermal breakage detection model to ensure quality. During the construction phase, the continuity and airtightness of the curtain wall insulation system are tested using an on-site insulation and airtightness testing model in conjunction with a thermal break testing model. Based on the thermal break structure to achieve quality, continuity and airtightness, diagnose the root causes of curtain wall defects; Based on the obtained root causes of defects, the parameters of the thermal breakage detection model and the insulation and airtightness field detection model are dynamically optimized, and processing data and construction data are used to predict processing abnormalities and construction abnormalities.

2. The integrated construction and testing method for low heat transfer coefficient building curtain walls according to claim 1, characterized in that, The quality assurance achieved by detecting the thermal breakage structure of the curtain wall using a thermal breakage detection model during the pre-construction phase includes: Infrared thermal images of the curtain wall surface are acquired using an infrared imager to obtain the temperature field distribution on the curtain wall surface. Combined with environmental parameters and material properties, abnormal thermal zones, i.e., thermal bridges, are identified, and the degree of thermal defects is quantified, including: The temperature field is acquired, calibrated, and normalized to eliminate the influence of environmental reflections and obtain the true surface temperature. ;in, Indicates the surface nodes of the curtain wall Temperature field, Indicates surface emissivity, Indicates the reference background temperature; Thermal bridge region detection is performed through image segmentation to identify abnormal hot areas, including: Calculate temperature difference ;in, This represents the average temperature of the reference background area; Binarization segmentation: thermal bridge region ;in, This indicates the threshold for determining thermal bridges. Filtering thermal infrared images smaller than The connected components, where, Indicates the minimum filtration area; Quantifying thermal bridge characteristics includes: Establish the thermal bridge strength index for the thermal bridge region. ; Indicates indoor temperature. Indicates ambient temperature; This indicates the average temperature of the thermal bridge region. Calculate the equivalent heat transfer coefficient increment to quantitatively assess the degree of degradation of the overall curtain wall U-value by thermal bridges: Equivalent heat transfer coefficient increment ;in, Indicates the indoor convective heat transfer coefficient; Calculate the area ratio affected by thermal bridges: ; The thermal bridge strength index, the equivalent heat transfer coefficient increment, and the temperature difference matrix are input into the pre-trained thermal bridge breakage detection model, which outputs the thermal bridge strength index level and the rectification priority sequence; the thermal bridge breakage monitoring model is built based on a CNN network. The thermal bridge strength index level is: ; The rectification priority sequence is generated by arranging the thermal bridge strength index and the importance weight of the thermal bridge location in descending order to represent the degree of thermal defects.

3. The integrated construction and testing method for low heat transfer coefficient building curtain walls according to claim 2, characterized in that, The rectification priority sequence is generated by sorting the thermal bridges in descending order based on the product of the thermal bridge strength index and the importance weight of the thermal bridge location, to represent the degree of thermal defects, including: Calculate the rectification index That is, the product of the thermal bridge strength index and the importance weight of the thermal bridge location: ;in, The importance weight of the thermal bridge location is indicated. Indicates the number of thermal bridge regions; Sort all rectification indices in descending order to obtain the rectification priority sequence: ;in, This indicates the quantity of the rectification index.

4. The integrated construction and testing method for low heat transfer coefficient building curtain walls according to claim 3, characterized in that, The method of detecting the thermal break structure of the curtain wall using a thermal break testing model during the pre-construction stage to ensure quality also includes: Thermal bridge types can be classified as follows: Multiple features are extracted from infrared thermal images to form a multi-dimensional feature vector. ;in, Indicates elongation length. ; , Let x and y represent the moments of inertia of the thermal bridge region, respectively. ; ;in, Indicates the dimension of the thermal bridge region; Indicates the relevance of the profile. ;; Spatial positioning is performed, that is, by using a CAD-BIM alignment algorithm, points in the infrared thermal image ( , Align it to the corresponding position on the curtain wall: ； in, Indicates the scaling factor. Indicates the rotation angle. This indicates the translation along the x-axis and the translation in the opposite direction along the y-axis; The multi-dimensional feature vector is input into a pre-trained separator (classification decision tree), and the output type of the hot bridge region is as follows: Determine the elongation Is the thermal bridge region greater than the pre-examination elongation threshold? If so, the thermal bridge region is a linear thermal bridge. Otherwise, determine whether the fractal dimension is greater than the pre-examination fractal dimension threshold. If so, the thermal bridge region is a point thermal bridge; otherwise, it is a surface thermal bridge. Output the type of the thermal bridge region and label the corresponding thermal bridge region.

5. The integrated construction and testing method for low heat transfer coefficient building curtain walls according to claim 4, characterized in that, The aforementioned on-site insulation and airtightness testing model, used in conjunction with a thermal breakage testing model during the construction phase, is used to test the continuity and airtightness of the curtain wall insulation system, including: Inspecting the continuity of the curtain wall insulation system includes: Obtain the average temperature difference ; Calculate the ideal surface temperature ; in express Midpoint of frame infrared thermal image Temperature difference at the location; Therefore, the target curtain wall temperature deviation during the construction phase ;in, , These represent the heat transfer resistance of the inner surface and the heat transfer resistance of the outer surface, respectively. Based on the temperature deviation of the target curtain wall, continuous defect detection is performed, including: if and Then the judgment point There is a continuous defect at this point; among them, This indicates that the Laplacian operator is used to detect edges; Represents the Laplace operator; Quantifying continuous defects includes: Calculate and obtain the insulation deficiency index ;in, Indicates the area of ​​the defect. Indicates the total area of ​​the detection zone; Testing the airtightness of the curtain wall insulation system includes: Dynamic air pressure is applied to the curtain wall, and a pressure sensor array is used to identify pressure leakage paths and seal failure points, including: Construct a pressure gradient field. ;in Indicates the first Pressure value measured by a pressure sensor; Indicates the first Pressure sensor and The spacing of the pressure sensors on the y-axis; Identify the leak point, including: point Leakage status value ; Indicates the leakage coefficient; if Point As a leak point, multiple leak points connect within the detection time period to form a leak path; The airtightness of the curtain wall is quantified, including: Define the permeation equivalent area ;in, For flow coefficient, Indicates the amount of gas leakage; This indicates the density of the leaked gas.

6. The integrated construction and testing method for low heat transfer coefficient building curtain walls according to claim 5, characterized in that, The method of achieving quality, continuity, and airtightness based on thermal break construction, and diagnosing the root causes of curtain wall defects, includes: Constructing defect feature vectors ;in, This indicates the deviation between the measured thickness and the designed thickness of the insulation layer. Indicates the design thickness of the insulation layer; This indicates the maximum value of the thermal bridge strength index; Indicates the coverage ratio of the sealant; Indicates the thermal bridge morphology index; for point-like thermal bridges, When there is a linear thermal bridge, When there is a planar thermal bridge, ; Building a root cause inference model based on Bayesian networks: Among them, prior probability The likelihood function represents the probability that a defect is caused by a root cause. This indicates that when the root cause is known to be true, the observed defect feature vector is... The probability of; total probability expansion This indicates that all possible causes are considered. The probabilities are weighted and summed. This represents the defect feature vector after normalization; the possible causes include broken insulation strips, missing sealant, and voids in the insulation layer. Find the root cause of the defect: Root cause in, Represents the dynamic weights of feature importance; if If the probability is greater than the preset root cause probability threshold, then determine... The root cause of the defect.

7. The integrated construction and testing method for low heat transfer coefficient building curtain walls according to claim 6, characterized in that, The method of achieving quality, continuity, and airtightness based on thermal break structure, and diagnosing the root causes of curtain wall defects, also includes: Process traceability includes: Defect identification during processing: ;in, Indicates the profile design area; This represents the sum of the areas where all thermal bridge zones overlap with the profile design zone; This represents the area of ​​all thermal bridge regions; if If the condition is positive, it indicates that the defect is a manufacturing defect; otherwise, proceed to the construction process defect judgment step. Defect assessment during construction includes the following steps: ;in, Represents the Heaviside step function; This indicates the allowable installation deviation threshold; At that time, the defect is a defect in the manufacturing process; Indicates the weight of continuous defects; This indicates the deviation between the actual installation location and the design location; if If the value is 1, then the defect is determined to be a defect in the construction process.

8. The integrated construction and testing method for low heat transfer coefficient building curtain walls according to claim 7, characterized in that, The process of dynamically optimizing the parameters of the thermal breakage detection model and the insulation and airtightness field detection model based on the obtained defect root causes includes: Model parameter optimization includes: ;in This represents the new parameters of the broken bridge monitoring model; Represents the old parameters of the broken bridge monitoring model; gradient of the loss function. ; in, ; Represents the gradient coefficients; Indicates the model confidence level. This represents the data acquisition time decay factor. .

9. The integrated construction and testing method for low heat transfer coefficient building curtain walls according to claim 8, characterized in that, The method of predicting processing and construction anomalies using processing and construction data includes: Obtain processing data and construct the processing data input feature vector: Among them, among them , ; Predicting the probability of abnormalities in the processing: ; This represents the Sigmoid function. This represents the input feature weight vector; If this is the case, it is determined that an abnormality is about to occur in the processing; among them, Indicates the anomaly probability threshold; Obtain construction data and construct the construction data input feature matrix: ;in , , , , , These represent the ambient temperature, ultraviolet radiation intensity, wind speed, construction duration, construction interruption duration, and construction curtain wall length during construction, respectively. Predicting the probability of abnormalities during construction. ;in, Represents radial basis functions; , Indicates model parameters, Indicates the activation function; if there is a risk of leakage at the window corner. If the leakage risk exceeds the preset threshold, it is determined that an abnormality in the construction process is about to occur.

10. An electronic device, comprising a processor, a communication module connected to the processor, and a memory, characterized in that, The electronic device is used to implement the integrated construction and testing method for a low heat transfer coefficient building curtain wall as described in any one of claims 1-9.

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