Methods, systems, terminals, and storage media for diagnosing thermal defects and optimizing low-carbon practices in prefabricated building window joints.

Abstract

This invention relates to the field of digital technology for building energy conservation, and discloses a method, system, terminal, and storage medium for diagnosing thermal defects and optimizing low-carbon processes at window joints in prefabricated buildings. The method includes: preprocessing image data to obtain a target image; using a defect recognition algorithm to identify defects in the target image to obtain the defect area; locating defects and walls based on the defect area; constructing a thermal bridge coefficient based on the temperatures of the defects and walls to calculate hidden carbon emissions; constructing an initial assembly scheme based on the hidden carbon emissions; optimizing the initial assembly scheme in multiple dimensions to obtain a Pareto optimal solution set; and determining a target assembly scheme from the Pareto optimal solution set. This invention enables precise location of thermal defects in the exterior envelope of prefabricated buildings, quantification of hidden carbon emissions, and generation of low-carbon retrofit schemes, effectively improving the efficiency of the entire lifecycle management of design, construction, and operation and maintenance.

Description

Technical Field

[0001] This invention relates to the field of digital technology for building energy conservation, and in particular to a method, system, terminal, and computer-readable storage medium for diagnosing thermal defects and optimizing low-carbon practices in the joints of exterior windows in prefabricated buildings. Background Technology

[0002] Quality inspection of exterior window joints in prefabricated buildings is crucial for ensuring energy efficiency, waterproofing, airtightness, and structural durability. With the rapid development of prefabricated buildings, the joints between exterior windows and walls have become a key weak point affecting overall performance, and their quality directly relates to living comfort and building lifespan.

[0003] However, traditional manual infrared scanning cannot quantify the defect area (error > 30%), and the thermal bridging effect caused by metal connectors is difficult to capture dynamically; the static LCI database (Life Cycle Inventory Database) does not associate the hidden energy consumption caused by thermal defects (such as window leakage increasing heating energy consumption by 15-25%), and the carbon emission calculation error exceeds 35%; at this time, the modification of the design scheme can only rely on the engineer's experience, and this process cannot balance the constraints of multiple objectives such as carbon emissions, cost, and construction period.

[0004] Therefore, existing technologies still need to be improved and developed. Summary of the Invention

[0005] The main objective of this invention is to provide a method, system, terminal, and computer-readable storage medium for diagnosing thermal defects and optimizing low-carbon practices in the joints of exterior windows of prefabricated buildings. This aims to solve the problems in the prior art where the inspection process for the joints of exterior windows of prefabricated buildings is inaccurate, and the lack of correlation between multi-dimensional information leads to unsuitable assembly schemes.

[0006] To achieve the above objectives, the present invention provides a method for diagnosing thermal defects and optimizing low-carbon practices in the joints of prefabricated building exterior windows. The method includes the following steps: Image data acquired by an infrared thermal imager is obtained, the image data is preprocessed to obtain a target image of the target area, and the defect area of ​​the target area is calculated based on the resolution of the infrared thermal imager. Based on the defect area, a laser rangefinder is used to locate the defects and walls in the target area. A thermal bridge coefficient is constructed based on the temperature of the defects and the walls. The hidden carbon emissions of the target area are then calculated based on the thermal bridge coefficient. An initial assembly scheme is constructed based on the implicit carbon emissions, and the MO-LPHGS-SDE algorithm is used to optimize the initial assembly scheme in multiple dimensions to obtain the optimal solution set in terms of carbon emissions, cost, and cycle. The target assembly scheme is then determined from the Pareto optimal solution set according to the user's personalized needs.

[0007] Optionally, the method for diagnosing thermal defects and optimizing low-carbon practices in prefabricated building window joints, wherein acquiring image data collected by an infrared thermal imager, preprocessing the image data to obtain a target image of the target area, and calculating the defect area of ​​the target area based on the resolution of the infrared thermal imager, specifically includes: The image data acquired by the infrared thermal imager is obtained, and the image data is read in grayscale mode to obtain a grayscale image; The gray image is subjected to Gaussian filtering for noise reduction to obtain a smooth image, and the smooth image is subjected to histogram equalization to obtain a target image with enhanced contrast. The number of defect pixels at the defect location in the target region is determined based on the target image, and the defect area of ​​the target region is calculated based on the number of defect pixels and the resolution of the infrared thermal imager.

[0008] Optionally, in the method for diagnosing thermal defects and optimizing low-carbon features of prefabricated building exterior window joints, the lens axis of the infrared thermal imager is set to be perpendicular to the joint surface of the target area. The expression for the defect area is: ; in, Indicates the area of ​​the defect. Indicates the first Number of defective pixels, This represents the area corresponding to each pixel. This indicates the total number of pixels.

[0009] Optionally, the method for diagnosing thermal defects and optimizing low-carbon emissions in prefabricated building exterior window joints, wherein the step of locating the defects and walls in the target area using a laser rangefinder based on the defect area, constructing a thermal bridge coefficient based on the temperatures of the defects and the walls, and calculating the hidden carbon emissions of the target area based on the thermal bridge coefficient, specifically includes: The defect area is scanned using a laser rangefinder to obtain the location of the defect and the location of the wall in the target area; Collect the defect temperature at the defect location and the wall temperature at the wall location, and construct a thermal bridge coefficient based on the temperature gradient between the defect temperature and the wall temperature. ; in, Indicates the thermal bridge coefficient. This indicates the temperature gradient at the seam. This indicates the temperature gradient of the main wall structure; A large carbon emission factor is constructed based on the thermal bridge coefficient, and the hidden carbon emissions of the target area are calculated by combining the additional energy consumption caused by the defects in the target area.

[0010] Optionally, the method for diagnosing thermal defects and optimizing low-carbon practices in prefabricated building window joints, wherein constructing a large carbon emission factor based on the thermal bridge coefficient and calculating the hidden carbon emissions of the target area in conjunction with the additional energy consumption generated by the defects in the target area, specifically includes: A carbon emission factor is constructed based on the aforementioned thermal bridge coefficient: ; in, Indicates the major carbon emission factor; Obtain the additional energy consumption generated at the defect in the target area and the energy carbon factor of the target area. Calculate the latent carbon emissions of the target area based on the carbon emission factor, the additional energy consumption, and the energy carbon factor. ; in, This indicates hidden carbon emissions. Indicates the energy carbon factor, This indicates additional energy consumption.

[0011] Optionally, the method for diagnosing thermal defects and optimizing low-carbon features of prefabricated building exterior window joints, wherein the step of constructing an initial assembly scheme based on the hidden carbon emissions and using a multi-objective inverse optimization algorithm to optimize the initial assembly scheme in multiple dimensions to obtain a Pareto optimal solution set specifically includes: Obtain the budget information input by the user, and construct an optimization target model based on the budget information and the hidden carbon emissions: ; in, This indicates the target model to be optimized. For hidden carbon emissions, Indicates the budget, Indicates time, Indicates carbon emission materials, Indicates the major factors of carbon emissions. Energy consumption that represents carbon emissions Indicates the material budget. Indicates labor costs, Indicates the construction time; The MO-LPHGS-SDE algorithm is used to perform multi-dimensional optimization of the target model within the scope of the budget information to obtain the Pareto optimal solution set.

[0012] Optionally, the method for diagnosing thermal defects and optimizing low-carbon features of prefabricated building exterior window joints, wherein the initial assembly scheme is constructed based on the implicit carbon emissions, and the initial assembly scheme is optimized in multiple dimensions using the MO-LPHGS-SDE algorithm to obtain the optimal solution set in terms of carbon emission, cost, and cycle dimensions, and the target assembly scheme is determined from the Pareto optimal solution set according to the user's personalized needs, further includes: The target assembly scheme was subjected to thermal verification, airtightness verification, and carbon emission verification, and the thermal verification results, airtightness verification results, and carbon emission verification results of the target assembly scheme were obtained respectively.

[0013] Furthermore, to achieve the above objectives, the present invention also provides a system for diagnosing thermal defects and optimizing low-carbon practices in the joints of prefabricated building exterior windows, wherein the system comprises: The defect location module is used to acquire image data collected by an infrared thermal imager, preprocess the image data to obtain a target image of the target area, and calculate the defect area of ​​the target area according to the resolution of the infrared thermal imager. A carbon emission calculation module is used to locate the defects and walls in the target area using a laser rangefinder based on the defect area, construct a thermal bridge coefficient based on the temperature of the defects and the walls, and calculate the hidden carbon emissions of the target area based on the thermal bridge coefficient. The scheme design module is used to construct an initial assembly scheme based on the implicit carbon emissions, and to optimize the initial assembly scheme in multiple dimensions using the MO-LPHGS-SDE algorithm to obtain the optimal solution set in terms of carbon emission, cost and cycle dimensions. Based on the user's personalized needs, the target assembly scheme is determined from the Pareto optimal solution set.

[0014] Furthermore, to achieve the above objectives, the present invention also provides a terminal, wherein the terminal includes: a memory, a processor, and a prefabricated building exterior window joint thermal defect diagnosis and low-carbon optimization program stored in the memory and executable on the processor, wherein when the prefabricated building exterior window joint thermal defect diagnosis and low-carbon optimization program is executed by the processor, the steps of the prefabricated building exterior window joint thermal defect diagnosis and low-carbon optimization method as described above are implemented.

[0015] Furthermore, to achieve the above objectives, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a prefabricated building exterior window joint thermal defect diagnosis and low-carbon optimization program, which, when executed by a processor, implements the steps of the prefabricated building exterior window joint thermal defect diagnosis and low-carbon optimization method as described above.

[0016] In this invention, image data acquired by an infrared thermal imager is preprocessed to obtain a target image of the target area. The defect area of ​​the target area is calculated based on the resolution of the infrared thermal imager. Based on the defect area, a laser rangefinder is used to locate the defects and walls in the target area. A thermal bridge coefficient is constructed based on the temperatures of the defects and walls, and the hidden carbon emissions of the target area are calculated based on the thermal bridge coefficient. An initial assembly scheme is constructed based on the hidden carbon emissions, and the MO-LPHGS-SDE algorithm is used to optimize the initial assembly scheme in multiple dimensions to obtain the optimal solution set in terms of carbon emissions, cost, and cycle time. The target assembly scheme is determined from the Pareto optimal solution set according to the user's personalized needs. This invention can achieve precise location of thermal defects in the exterior envelope of prefabricated buildings, quantification of hidden carbon emissions, and generation of low-carbon retrofit schemes, effectively improving the efficiency of the entire life cycle management of design, construction, and operation and maintenance. Attached Figure Description

[0017] Figure 1 This is a flowchart of a preferred embodiment of the method for diagnosing thermal defects and optimizing low-carbon features at the joints of prefabricated building exterior windows according to the present invention. Figure 2 This is a flowchart of a preferred embodiment of the method for diagnosing thermal defects and optimizing low-carbon features at the joints of prefabricated building windows according to the present invention. Figure 3 This is a schematic diagram of the thermal-carbon emission correlation model of a preferred embodiment of the method for diagnosing thermal defects and optimizing low-carbon features of exterior window joints in prefabricated buildings according to the present invention. Figure 4 This is a schematic diagram of the multi-objective optimization Pareto front of a preferred embodiment of the method for diagnosing thermal defects and optimizing low-carbon features of exterior window joints in prefabricated buildings according to the present invention. Figure 5 This is an infrared comparison image before and after modification of a preferred embodiment of the method for diagnosing thermal defects and optimizing low-carbon features of exterior window joints in prefabricated buildings according to the present invention. Figure 6 This is a structural diagram of a preferred embodiment of the prefabricated building exterior window joint thermal defect diagnosis and low-carbon optimization system of the present invention; Figure 7 This is a structural diagram of a preferred embodiment of the terminal of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] Current methods for inspecting the quality of exterior window joints in prefabricated buildings suffer from three major deficiencies: insufficient inspection accuracy, distorted carbon emission calculations, and isolated optimization decisions. To effectively address these issues, this invention discloses a method for diagnosing defects and controlling carbon emissions in exterior window joints that integrates infrared thermal imaging technology, thermal parameter inversion algorithms, and multi-objective inverse optimization decision-making. This method is applicable to the precise location of thermal defects in the exterior envelope of prefabricated buildings, the quantification of hidden carbon emissions, and the generation of low-carbon retrofitting schemes, covering the entire lifecycle management from design and construction to operation and maintenance.

[0020] The preferred embodiment of the present invention describes a method for diagnosing thermal defects and optimizing low-carbon features at the joints of prefabricated building exterior windows. Figure 1 As shown, the method for diagnosing thermal defects and optimizing low-carbon features at the joints of prefabricated building exterior windows includes the following steps: Step S10: Acquire image data collected by the infrared thermal imager, preprocess the image data to obtain the target image of the target area, and calculate the defect area of ​​the target area according to the resolution of the infrared thermal imager.

[0021] Specifically, image data acquired by the infrared thermal imager is obtained, and the image data is read in grayscale mode to obtain a grayscale image; The gray image is subjected to Gaussian filtering for noise reduction to obtain a smooth image, and the smooth image is subjected to histogram equalization to obtain a target image with enhanced contrast. The number of defect pixels at the defect location in the target region is determined based on the target image, and the defect area of ​​the target region is calculated based on the number of defect pixels and the resolution of the infrared thermal imager.

[0022] In the embodiments disclosed herein, environmental requirements are defined for testing: the indoor-outdoor temperature difference is not less than 10°C, the wind speed is not higher than 3 m / s, and the humidity is maintained at 70% or above; Figure 2 As shown, an infrared thermal imager is then used to acquire images. The thermal sensitivity of the infrared thermal imager used in this invention is 0.03℃. When acquiring images, a three-dimensional coordinate system is established with the center of the window frame as the origin (where the horizontal direction is the horizontal axis, the vertical direction is the vertical axis, and the depth is the vertical axis).

[0023] Specifically, the lens axis of the infrared thermal imager is set to be perpendicular to the seam surface of the target area (the tilt angle cannot exceed 5°), and the resolution is controlled at 640×480 pixels (i.e., each pixel corresponds to an actual size of 0.5mm). Under normal hardware performance, this can achieve fine quantitative calculation of the defect area (each pixel represents 0.25mm²), laying the foundation for subsequent accurate carbon emission calculation, and balancing hardware cost (avoiding high-resolution equipment), data processing efficiency (ensuring real-time algorithm operation), and on-site operational feasibility (high fault tolerance).

[0024] The expression for the defect area is: ; in, Indicates the area of ​​the defect. Indicates the first Number of defective pixels, This represents the area corresponding to each pixel. This indicates the total number of pixels.

[0025] Furthermore, in the embodiments disclosed in this invention, after the image is acquired, it is first read: "img=cv2.imread("window_seam.jpg",cv2.IMREAD_GRAYSCALE)"; Use the cv2.imread function to read the image file named "window_seam.jpg" and read the image in grayscale mode using the "cv2.IMREAD_GRAYSCALE" parameter. This simplifies subsequent processing steps and eliminates the interference of color on the processing results.

[0026] Then, Gaussian filtering is applied to the grayscale image for noise reduction: "img_blur=cv2.GaussianBlur(img,(3,3),sigmaX=1.5)"; Here, (3,3) represents the size of the Gaussian kernel, which must be a positive odd number, determining the smoothness of the filter; sigmaX=1.5 represents the standard deviation along the horizontal axis, used to control the shape of the Gaussian distribution, set to 1.5 here. OpenCV (OpenSource Computer Vision Library, a cross-platform computer vision library) will automatically calculate the standard deviation along the vertical axis (which defaults to the same as the horizontal axis). The purpose of Gaussian filtering is to remove noise from the image while smoothing it.

[0027] Finally, histogram equalization is used to enhance contrast. "img_eq=cv2.equalizeHist(img_blur)"; Histogram equalization of an image after Gaussian filtering can increase the dynamic range of grayscale, thereby enhancing image contrast and making image details more apparent.

[0028] Furthermore, based on the processed image, a defect area detection algorithm is used. In this process, the Roberts operator can be used, and the adaptive gradient threshold range can be dynamically calculated based on the OTSU algorithm (Otsu's method, maximum inter-class variance method). In the embodiment of the present invention, sub-pixel positioning accuracy is used for identification, and the defect area of ​​the target region is finally obtained.

[0029] Step S20: Based on the defect area, use a laser rangefinder to locate the defect and the wall in the target area, construct a thermal bridge coefficient based on the temperature of the defect and the wall, and calculate the hidden carbon emissions of the target area based on the thermal bridge coefficient.

[0030] Specifically, a laser rangefinder is used to scan the defect area to obtain the location of the defect and the location of the wall in the target area; Collect the defect temperature at the defect location and the wall temperature at the wall location, and construct a thermal bridge coefficient based on the temperature gradient between the defect temperature and the wall temperature. ; in, Indicates the thermal bridge coefficient. This indicates the temperature gradient at the seam. This indicates the temperature gradient of the main wall structure; A large carbon emission factor is constructed based on the thermal bridge coefficient, and the hidden carbon emissions of the target area are calculated by combining the additional energy consumption caused by the defects in the target area.

[0031] In the embodiments disclosed in this invention, a cumulative temperature difference ratio algorithm is adopted, which quantifies the persistence and range of thermal bridge effects (by calculating temperature deviation through integration). Compared with traditional single temperature judgment, it can more scientifically identify high-risk nodes, realize dual judgment, improve the accuracy of defect diagnosis, and support the reliability of carbon emission quantification models.

[0032] Furthermore, a large carbon emission factor is constructed based on the aforementioned thermal bridge coefficient: ; in, Indicates the major carbon emission factor; Obtain the additional energy consumption generated at the defect in the target area and the energy carbon factor of the target area. Calculate the latent carbon emissions of the target area based on the carbon emission factor, the additional energy consumption, and the energy carbon factor. ; in, This indicates hidden carbon emissions. Indicates the energy carbon factor, This indicates additional energy consumption.

[0033] Among them, such as Figure 3 As shown, in Figure 3 Figure (a) shows the fitting curve of thermal bridge coefficient and carbon emission factor. Figure 3 In (a), ① represents the thermal bridge coefficient, and ② represents the significant thermal bridge determination line; Figure 3 (b) shows a heat map of carbon emissions. Figure 3 In (b), ③ represents a high-risk area, which in the embodiments disclosed in this invention can be represented as a corner, the corner of the window structure. Due to its special structure, this area is prone to abnormal heat transfer and belongs to a high-risk area with high carbon emission intensity. ⑦ represents a glass glue point, which refers to the glass glue part connecting the window glass and the frame. The sealing and heat conduction performance of this part will affect the overall carbon emission intensity. In the embodiments disclosed in this invention, a dynamic carbon emission factor is first established, and then the implicit carbon emission is calculated. In this way, the dynamic correlation between thermal defects and carbon emissions is realized, which can effectively solve the distortion problem of the static LCI database (the error is reduced from >35% to ≤8%), and the reliability of the model is confirmed by closed-loop verification (actual carbon emission error of 1.7%).

[0034] Specifically, firstly, a thermal parameter inversion model is constructed based on the results of thermal parameter inversion. The thermal bridge coefficient is calculated using the thermal parameter inversion model, and the dynamic carbon emission large factor formula is applied to the thermal bridge coefficient. Then, the hidden carbon emissions are calculated by combining the additional energy consumption caused by defects (such as the increased heating energy consumption due to window leakage) and the energy carbon factor. In another embodiment of the present invention, taking a low-carbon renovation project of the window joints of a prefabricated office building in North China as an example, the final calculated annual hidden carbon emission was 21 kg of carbon dioxide, while the actual monitored value was 18.2 kg of carbon dioxide. The error of this result is only 1.7%; while the error calculated using traditional methods is more than 35%. The thermal parameter inversion model disclosed in this invention improves this accuracy to 77%, which is sufficient to prove the effectiveness of the present invention.

[0035] Furthermore, this invention solves the problem of hidden energy consumption caused by the lack of association between static LCI database and defects by dynamically quantifying the impact of thermal defects on carbon emissions through large carbon emission factors (e.g., carbon emissions increase by 104% when the thermal bridge coefficient is 6.2).

[0036] Step S30: Construct an initial assembly scheme based on the hidden carbon emissions, and use the MO-LPHGS-SDE algorithm to optimize the initial assembly scheme in multiple dimensions to obtain the optimal solution set in terms of carbon emission, cost, and cycle dimensions. Determine the target assembly scheme from the Pareto optimal solution set according to the user's personalized needs.

[0037] In addition to generating hidden carbon emissions, the thermal parameter inversion model can also directly support optimization decisions (such as generating Pareto optimal solutions), achieving a carbon emission reduction of 15% to 28% in the case study and improving the efficiency of full-cycle management.

[0038] Specifically, the budget information input by the user is obtained, and an optimization target model is constructed based on the budget information and the implicit carbon emissions: ; in, This indicates the target model to be optimized. For hidden carbon emissions, Indicates the budget, Indicates time, Indicates carbon emission materials, Indicates the major factors of carbon emissions. Energy consumption that represents carbon emissions Indicates the material budget. Indicates labor costs, Indicates the construction time; The MO-LPHGS-SDE algorithm is used to perform multi-dimensional optimization of the target model within the scope of the budget information to obtain the Pareto optimal solution set.

[0039] In the embodiments disclosed in this invention, the focus is not only on hidden carbon emissions. Based on this, the invention constructs a multi-objective vector by combining user-inputted budget and time. The constraints include thermal constraints and construction constraints. The thermal constraints require the thermal bridge coefficient to be no higher than 1.8 W / (m²·K), while the construction constraints require the joint width to be no less than 15 mm and the compaction density to be no less than 95%. Then, the MO-LPHGS-SDE algorithm (Multi-Objective Learning-Predation and Hunger Games Algorithm with Shift-Based Density Estimation) is used to generate a Pareto optimal solution set, specifically as follows: Figure 4 As shown, Figure 4 Option (A) requires carbon emissions to be no higher than 28%, costs to be no lower than 35%, and a cycle of no more than 250 days; Figure 4Option (B) requires carbon emissions to be no less than 15%, costs to be no more than 20%, and a cycle of no more than 350 days; Figure 4 The solution required by scheme (C) in the middle needs to be... Figure 4 Scheme (A) and Figure 4 The balance between options (B) is achieved, which balances multiple objectives such as carbon emissions, cost, and construction period, rather than just optimizing hidden carbon emissions.

[0040] Furthermore, thermal verification, airtightness verification, and carbon emission verification are performed on the target assembly scheme to obtain the thermal verification results, airtightness verification results, and carbon emission verification results of the target assembly scheme, respectively.

[0041] In the embodiments disclosed in this invention, multi-dimensional closed-loop verification is performed based on the final selected target assembly scheme. For example, thermal verification requires that the peak value of the infrared remeasured heat flux density is not greater than 12.26 W / m², and its reduction rate is not less than 81.6%. In the airtightness verification, no side leakage occurs even after a 15-minute water spray test using a water pressure of 0.3 MPa. Most importantly, the final actual energy consumption data and prediction error are not greater than 8%. Under the condition of ensuring accuracy, this invention can construct the target assembly scheme in no more than 5 minutes, while in the traditional manual generation method, it takes two to three days.

[0042] Furthermore, taking the low-carbon renovation project of the exterior window joints of a prefabricated office building in North China disclosed in this invention as an example, such as... Figure 5 The figure shows the assembly result constructed by the present invention. Based on the target assembly scheme disclosed in the present invention, the joint is filled with 30mm thick polyurethane foam (density of 35kg / m³). During the verification, the peak heat flux density of the infrared re-measurement result is 12.3 W / m², which is 81.7% lower than that of the traditional method. The actual carbon emission of the energy consumption monitoring result is 18.2kg of carbon dioxide, which is only 1.7% lower than the predicted 17.9kg.

[0043] This invention enables precise location of thermal defects in the exterior envelope of prefabricated buildings, quantification of hidden carbon emissions, and generation of low-carbon retrofit schemes. It is applicable to the entire lifecycle management of design, construction, and operation and maintenance, and can achieve refined quantitative calculation of defect area, laying the foundation for accurate carbon emission calculation. It balances hardware cost (avoiding high-resolution equipment), data processing efficiency (ensuring real-time algorithm operation), and on-site operational feasibility (high fault tolerance). By dynamically quantifying the impact of thermal defects on carbon emissions through large carbon emission factors (e.g., carbon emission increase of 104% when the thermal bridge coefficient is 6.2), it effectively solves the problem of hidden energy consumption caused by defects not being associated with static LCI databases.

[0044] Furthermore, such as Figure 6As shown, based on the above-mentioned method for diagnosing thermal defects and optimizing low-carbon features of prefabricated building window joints, this invention also provides a system for diagnosing thermal defects and optimizing low-carbon features of prefabricated building window joints, wherein the system includes: The defect location module 51 is used to acquire image data collected by the infrared thermal imager, preprocess the image data to obtain a target image of the target area, and calculate the defect area of ​​the target area according to the resolution of the infrared thermal imager. Carbon emission calculation module 52 is used to locate the defects and walls in the target area using a laser rangefinder based on the defect area, construct a thermal bridge coefficient based on the temperature of the defects and the walls, and calculate the hidden carbon emissions of the target area based on the thermal bridge coefficient. The scheme design module 53 is used to construct an initial assembly scheme based on the implicit carbon emissions, and to optimize the initial assembly scheme in multiple dimensions using the MO-LPHGS-SDE algorithm to obtain the optimal solution set in terms of carbon emission dimension, cost dimension and cycle dimension. Based on the user's personalized needs, the target assembly scheme is determined from the Pareto optimal solution set.

[0045] Furthermore, such as Figure 7 As shown, based on the above-mentioned method and system for diagnosing thermal defects and optimizing low-carbon features of exterior window joints in prefabricated buildings, the present invention also provides a terminal, which includes a processor 10, a memory 20, and a display 30. Figure 7 Only some of the terminal components are shown; however, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0046] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as a hard disk or memory. In other embodiments, the memory 20 may be an external storage device of the terminal, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the terminal. Further, the memory 20 may include both internal and external storage devices. The memory 20 is used to store application software and various types of data installed on the terminal, such as the program code installed on the terminal. The memory 20 may also be used to temporarily store data that has been output or will be output. In one embodiment, the memory 20 stores a prefabricated building exterior window joint thermal defect diagnosis and low-carbon optimization program 40, which can be executed by the processor 10 to realize the prefabricated building exterior window joint thermal defect diagnosis and low-carbon optimization method of this application.

[0047] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor, or other data processing chip, used to run program code stored in the memory 20 or process data, such as executing the method for diagnosing thermal defects in the joints of prefabricated building windows and optimizing low carbon emissions.

[0048] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display 30 is used to display information on the terminal and to display a visual user interface. The components of the terminal communicate with each other via a system bus.

[0049] In one embodiment, when the processor 10 executes the prefabricated building exterior window joint thermal defect diagnosis and low-carbon optimization program 40 in the memory 20, the following steps are performed: Image data acquired by an infrared thermal imager is obtained, the image data is preprocessed to obtain a target image of the target area, and the defect area of ​​the target area is calculated based on the resolution of the infrared thermal imager. Based on the defect area, a laser rangefinder is used to locate the defects and walls in the target area. A thermal bridge coefficient is constructed based on the temperature of the defects and the walls. The hidden carbon emissions of the target area are then calculated based on the thermal bridge coefficient. An initial assembly scheme is constructed based on the implicit carbon emissions, and the MO-LPHGS-SDE algorithm is used to optimize the initial assembly scheme in multiple dimensions to obtain the optimal solution set in terms of carbon emissions, cost, and cycle. The target assembly scheme is then determined from the Pareto optimal solution set according to the user's personalized needs.

[0050] The steps of acquiring image data collected by an infrared thermal imager, preprocessing the image data to obtain a target image of the target area, and calculating the defect area of ​​the target area based on the resolution of the infrared thermal imager specifically include: The image data acquired by the infrared thermal imager is obtained, and the image data is read in grayscale mode to obtain a grayscale image; The gray image is subjected to Gaussian filtering for noise reduction to obtain a smooth image, and the smooth image is subjected to histogram equalization to obtain a target image with enhanced contrast. The number of defect pixels at the defect location in the target region is determined based on the target image, and the defect area of ​​the target region is calculated based on the number of defect pixels and the resolution of the infrared thermal imager.

[0051] The lens axis of the infrared thermal imager is set to be perpendicular to the seam surface of the target area; The expression for the defect area is: ; in, Indicates the area of ​​the defect. Indicates the first Number of defective pixels, This represents the area corresponding to each pixel. This indicates the total number of pixels.

[0052] Specifically, the process of locating defects and walls in the target area using a laser rangefinder based on the defect area, constructing a thermal bridge coefficient based on the temperatures of the defects and walls, and calculating the hidden carbon emissions of the target area based on the thermal bridge coefficient includes: The defect area is scanned using a laser rangefinder to obtain the location of the defect and the location of the wall in the target area; Collect the defect temperature at the defect location and the wall temperature at the wall location, and construct a thermal bridge coefficient based on the temperature gradient between the defect temperature and the wall temperature. ; in, Indicates the thermal bridge coefficient. This indicates the temperature gradient at the seam. This indicates the temperature gradient of the main wall structure; A large carbon emission factor is constructed based on the thermal bridge coefficient, and the hidden carbon emissions of the target area are calculated by combining the additional energy consumption caused by the defects in the target area.

[0053] Specifically, the step of constructing a large carbon emission factor based on the thermal bridge coefficient and calculating the hidden carbon emissions of the target area in conjunction with the additional energy consumption generated by the defects in the target area includes: A carbon emission factor is constructed based on the aforementioned thermal bridge coefficient: ; in, Indicates the major carbon emission factor; Obtain the additional energy consumption generated at the defect in the target area and the energy carbon factor of the target area. Calculate the latent carbon emissions of the target area based on the carbon emission factor, the additional energy consumption, and the energy carbon factor. ; in, This indicates hidden carbon emissions. Indicates the energy carbon factor, This indicates additional energy consumption.

[0054] Specifically, the step of constructing an initial assembly scheme based on the hidden carbon emissions and then using a multi-objective inverse optimization algorithm to optimize the initial assembly scheme in multiple dimensions to obtain a Pareto optimal solution set includes: Obtain the budget information input by the user, and construct an optimization target model based on the budget information and the hidden carbon emissions: ; in, This indicates the target model to be optimized. For hidden carbon emissions, Indicates the budget, Indicates time, Indicates carbon emission materials, Indicates the major factors of carbon emissions. Energy consumption that represents carbon emissions Indicates the material budget. Indicates labor costs, Indicates the construction time; The MO-LPHGS-SDE algorithm is used to perform multi-dimensional optimization of the target model within the scope of the budget information to obtain the Pareto optimal solution set.

[0055] The process includes: constructing an initial assembly scheme based on the implicit carbon emissions; optimizing the initial assembly scheme in multiple dimensions using the MO-LPHGS-SDE algorithm to obtain optimal solution sets in terms of carbon emissions, cost, and cycle time; determining the target assembly scheme from the Pareto optimal solution set based on the user's personalized needs; and further including: The target assembly scheme was subjected to thermal verification, airtightness verification, and carbon emission verification, and the thermal verification results, airtightness verification results, and carbon emission verification results of the target assembly scheme were obtained respectively.

[0056] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a prefabricated building exterior window joint thermal defect diagnosis and low-carbon optimization program, which, when executed by a processor, implements the steps of the prefabricated building exterior window joint thermal defect diagnosis and low-carbon optimization method as described above.

[0057] In summary, this invention provides a method and related equipment for diagnosing thermal defects and optimizing low-carbon practices in the joints of exterior windows of prefabricated buildings. The method includes: acquiring image data collected by an infrared thermal imager; preprocessing the image data to obtain a target image of a target area; calculating the defect area of ​​the target area based on the resolution of the infrared thermal imager; locating the defects and walls in the target area using a laser rangefinder based on the defect area; constructing a thermal bridge coefficient based on the temperatures of the defects and walls; calculating the hidden carbon emissions of the target area based on the thermal bridge coefficient; constructing an initial assembly scheme based on the hidden carbon emissions; optimizing the initial assembly scheme in multiple dimensions using the MO-LPHGS-SDE algorithm to obtain the optimal solution set in the carbon emission, cost, and cycle dimensions; and determining the target assembly scheme from the Pareto optimal solution set according to the user's personalized needs. This invention can achieve accurate location of thermal defects in the exterior envelope of prefabricated buildings, quantification of hidden carbon emissions, and generation of low-carbon retrofit schemes, effectively improving the efficiency of the entire life cycle management of design, construction, and operation and maintenance.

[0058] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal that includes that element.

[0059] Of course, those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.). The program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The computer-readable storage medium can be a memory, magnetic disk, optical disk, etc.

[0060] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A method for diagnosing thermal defects and optimizing low-carbon practices in the joints of exterior windows in prefabricated buildings, characterized in that... The method for diagnosing thermal defects and optimizing low-carbon features of prefabricated building exterior window joints includes: Image data acquired by an infrared thermal imager is obtained, the image data is preprocessed to obtain a target image of the target area, and the defect area of ​​the target area is calculated based on the resolution of the infrared thermal imager. Based on the defect area, a laser rangefinder is used to locate the defects and walls in the target area. A thermal bridge coefficient is constructed based on the temperature of the defects and the walls. The hidden carbon emissions of the target area are then calculated based on the thermal bridge coefficient. An initial assembly scheme is constructed based on the implicit carbon emissions, and the MO-LPHGS-SDE algorithm is used to optimize the initial assembly scheme in multiple dimensions to obtain the optimal solution set in terms of carbon emissions, cost, and cycle. The target assembly scheme is then determined from the Pareto optimal solution set according to the user's personalized needs.

2. The method for diagnosing thermal defects and optimizing low-carbon features of prefabricated building exterior window joints according to claim 1, characterized in that, The process of acquiring image data collected by an infrared thermal imager, preprocessing the image data to obtain a target image of the target area, and calculating the defect area of ​​the target area based on the resolution of the infrared thermal imager specifically includes: The image data acquired by the infrared thermal imager is obtained, and the image data is read in grayscale mode to obtain a grayscale image; The gray image is subjected to Gaussian filtering for noise reduction to obtain a smooth image, and the smooth image is subjected to histogram equalization to obtain a target image with enhanced contrast. The number of defect pixels at the defect location in the target region is determined based on the target image, and the defect area of ​​the target region is calculated based on the number of defect pixels and the resolution of the infrared thermal imager.

3. The method for diagnosing thermal defects and optimizing low-carbon features of prefabricated building exterior window joints according to claim 2, characterized in that, The lens axis of the infrared thermal imager is set to be perpendicular to the seam surface of the target area; The expression for the defect area is: ； in, Indicates the area of ​​the defect. Indicates the first Number of defective pixels, This represents the area corresponding to each pixel. This indicates the total number of pixels.

4. The method for diagnosing thermal defects and optimizing low-carbon features of prefabricated building exterior window joints according to claim 1, characterized in that, Based on the defect area, a laser rangefinder is used to locate the defects and walls in the target area. A thermal bridge coefficient is constructed based on the temperatures of the defects and walls. The hidden carbon emissions of the target area are then calculated based on the thermal bridge coefficient. Specifically, this includes: The defect area is scanned using a laser rangefinder to obtain the location of the defect and the location of the wall in the target area; Collect the defect temperature at the defect location and the wall temperature at the wall location, and construct a thermal bridge coefficient based on the temperature gradient between the defect temperature and the wall temperature. ； in, Indicates the thermal bridge coefficient. This indicates the temperature gradient at the seam. This indicates the temperature gradient of the main wall structure; A large carbon emission factor is constructed based on the thermal bridge coefficient, and the hidden carbon emissions of the target area are calculated by combining the additional energy consumption caused by the defects in the target area.

5. The method for diagnosing thermal defects and optimizing low-carbon features of prefabricated building exterior window joints according to claim 1, characterized in that, The process of constructing a large carbon emission factor based on the thermal bridge coefficient and calculating the hidden carbon emissions of the target area in conjunction with the additional energy consumption generated by the defects in the target area specifically includes: A carbon emission factor is constructed based on the aforementioned thermal bridge coefficient: ； in, Indicates the major carbon emission factor; Obtain the additional energy consumption generated at the defect in the target area and the energy carbon factor of the target area. Calculate the latent carbon emissions of the target area based on the carbon emission factor, the additional energy consumption, and the energy carbon factor. ； in, This indicates hidden carbon emissions. Indicates the energy carbon factor, This indicates additional energy consumption.

6. The method for diagnosing thermal defects and optimizing low-carbon features of prefabricated building exterior window joints according to claim 1, characterized in that, The process involves constructing an initial assembly scheme based on the implicit carbon emissions, and then using the MO-LPHGS-SDE algorithm to optimize the initial assembly scheme in multiple dimensions to obtain the optimal solution set in terms of carbon emissions, cost, and cycle time. Specifically, this includes: Obtain the budget information input by the user, and construct an optimization target model based on the budget information and the hidden carbon emissions: ； in, This indicates the target model to be optimized. For hidden carbon emissions, Indicates the budget, Indicates time, Indicates carbon emission materials, Indicates the major factors of carbon emissions. Energy consumption that represents carbon emissions Indicates the material budget. Indicates labor costs, Indicates the construction time; The MO-LPHGS-SDE algorithm is used to perform multi-dimensional optimization of the target model within the scope of the budget information to obtain the Pareto optimal solution set.

7. The method for diagnosing thermal defects and optimizing low-carbon features of prefabricated building exterior window joints according to claim 1, characterized in that, The process involves constructing an initial assembly scheme based on the implicit carbon emissions, optimizing the initial assembly scheme in multiple dimensions using the MO-LPHGS-SDE algorithm to obtain the optimal solution set in terms of carbon emissions, cost, and cycle time. Then, based on the user's personalized needs, a target assembly scheme is determined from the Pareto optimal solution set. The process further includes: The target assembly scheme was subjected to thermal verification, airtightness verification, and carbon emission verification, and the thermal verification results, airtightness verification results, and carbon emission verification results of the target assembly scheme were obtained respectively.

8. A system for diagnosing thermal defects and optimizing low-carbon practices in the joints of prefabricated building exterior windows, characterized in that... The prefabricated building exterior window joint thermal defect diagnosis and low-carbon optimization system is used to implement the prefabricated building exterior window joint thermal defect diagnosis and low-carbon optimization method as described in any one of claims 1-7, wherein the prefabricated building exterior window joint thermal defect diagnosis and low-carbon optimization system includes: The defect location module is used to acquire image data collected by an infrared thermal imager, preprocess the image data to obtain a target image of the target area, and use the infrared thermal imager to identify defects in the target image to obtain the defect area of ​​the target area. A carbon emission calculation module is used to locate the defects and walls in the target area using a laser rangefinder based on the defect area, construct a thermal bridge coefficient based on the temperature of the defects and the walls, and calculate the hidden carbon emissions of the target area based on the thermal bridge coefficient. The scheme design module is used to construct an initial assembly scheme based on the implicit carbon emissions, and to optimize the initial assembly scheme in multiple dimensions using the MO-LPHGS-SDE algorithm to obtain the optimal solution set in terms of carbon emission, cost and cycle dimensions. Based on the user's personalized needs, the target assembly scheme is determined from the Pareto optimal solution set.

9. A terminal, characterized in that, The terminal includes: a memory, a processor, and a prefabricated building exterior window joint thermal defect diagnosis and low-carbon optimization program stored in the memory and executable on the processor. When the prefabricated building exterior window joint thermal defect diagnosis and low-carbon optimization program is executed by the processor, it implements the steps of the prefabricated building exterior window joint thermal defect diagnosis and low-carbon optimization method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a prefabricated building exterior window joint thermal defect diagnosis and low-carbon optimization program, which, when executed by a processor, implements the steps of the prefabricated building exterior window joint thermal defect diagnosis and low-carbon optimization method as described in any one of claims 1-7.

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