A method, system, terminal and storage medium for detecting exterior windows of a house

Abstract

The present invention provides a method, device, terminal, and storage medium for detecting exterior windows of a house. The method comprises: obtaining an exterior window thermal image generated by scanning the exterior windows of the current house with a thermal imaging device, and extracting corresponding temperature distribution information from the exterior window thermal image; obtaining an exterior window image generated by photographing the exterior windows of the current house with a camera device, and identifying the exterior window image to determine the structural characteristics of the exterior window and the safety hazards of the exterior window; constructing a three-dimensional model of the exterior window based on the exterior window thermal image and the exterior window image; and detecting and evaluating the exterior window based on the three-dimensional model, temperature distribution information, structural characteristics of the exterior window, and safety hazards of the exterior window, obtaining a detection and evaluation result reflecting the thermal performance and safety performance of the exterior window. The present invention achieves a comprehensive evaluation of the thermal performance and safety performance of exterior windows through the combination of thermal imaging and machine vision detection, and realizes automated detection of exterior windows of a house, thereby improving detection efficiency and the accuracy of detection results.

Description

Technical Field

[0001] The present invention relates to the technical field of building detection, and in particular to a method, system, terminal and storage medium for detecting exterior windows of a house. Background Art

[0002] At present, the inspection of exterior windows of houses mostly relies on manual operation. This method is not only inefficient but also easily affected by human factors, making it difficult to ensure the accuracy and comprehensiveness of the inspection results. In particular, in the inspection of thermal and safety performance, there is a lack of efficient automated means, making it difficult to meet the high standards of safety and energy conservation required by modern buildings. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method, system, terminal and storage medium for detecting exterior windows of a house in response to the above-mentioned defects of the prior art, aiming to solve the problems in the prior art that the detection of exterior windows of a house relies on manual detection, resulting in low efficiency, low detection accuracy, and inability to achieve automated detection.

[0004] The technical solutions adopted by the present invention to solve the technical problems are as follows:

[0005] A method for detecting exterior windows of a house, wherein the method comprises:

[0006] Obtaining a thermal image of an exterior window generated by scanning an exterior window of a current house with a thermal imaging device, and extracting corresponding temperature distribution information from the thermal image of the exterior window;

[0007] Acquire an exterior window image generated by photographing an exterior window of a current house using a camera device, and identify the exterior window image to determine structural features of the exterior window of the current house and potential safety hazards of the exterior window;

[0008] Constructing a three-dimensional model of the exterior windows corresponding to the current house based on the exterior window thermal image and the exterior window image;

[0009] The exterior windows of the current house are inspected and evaluated based on the exterior window three-dimensional model, the temperature distribution information, the exterior window structural characteristics and the exterior window safety hazards, and an inspection and evaluation result reflecting the thermal performance and safety performance of the exterior windows of the current house is obtained.

[0010] In one implementation, obtaining a thermal image of an exterior window generated by scanning an exterior window of a current house using a thermal imaging device includes:

[0011] Obtain a thermal image of the exterior windows of the current house generated by scanning the exterior windows with an infrared thermal imager.

[0012] In one implementation, extracting corresponding temperature distribution information from the exterior window thermal image includes:

[0013] The exterior window thermal image is analyzed using a preset thermal image processing algorithm to extract corresponding temperature gradient information and hot spot information.

[0014] In one implementation, before identifying the exterior window image to determine the exterior window structural features and exterior window safety hazards of the current house, the method further includes:

[0015] Preprocessing the exterior window image according to a preset RGB image processing method to obtain a corresponding target exterior window image;

[0016] The identifying of the exterior window image to determine the exterior window structural features and exterior window safety hazards of the current house includes:

[0017] The target exterior window image is identified using a preset image recognition algorithm to determine the exterior window structural features and exterior window safety hazards of the current house.

[0018] In one implementation, preprocessing the external window image according to a preset RGB image processing method to obtain a corresponding target external window image includes:

[0019] The exterior window image is subjected to noise removal and contrast enhancement processing according to a preset RGB image processing method to obtain a corresponding target exterior window image.

[0020] In one implementation, the housing exterior window detection method further includes:

[0021] Monitor the state changes of the exterior windows of the current house in real time, and determine whether there is any abnormality in the exterior windows of the current house based on the state changes of the exterior windows;

[0022] If there is an abnormality in the external window of the current house, an alarm is triggered and corresponding alarm information is pushed to the user.

[0023] In one implementation, determining whether the external window of the current house has an abnormality based on the change in the external window state includes:

[0024] When the change in the state of the exterior window indicates that the thermal performance of the exterior window has dropped to a preset threshold, it is determined that there is an abnormality in the exterior window of the current house;

[0025] Alternatively, when the change in the state of the exterior window indicates that the structural damage of the exterior window has reached a preset degree, it is determined that there is an abnormality with the exterior window of the current house.

[0026] The present invention also discloses a house exterior window detection system, wherein the system comprises:

[0027] A thermal imaging detection module is used to obtain a thermal image of an exterior window generated by scanning an exterior window of a current house with a thermal imaging device, and to extract corresponding temperature distribution information from the thermal image of the exterior window;

[0028] A machine vision detection module is used to obtain an exterior window image generated by a camera device shooting an exterior window of the current house, and identify the exterior window image to determine the structural characteristics of the exterior window of the current house and the safety hazards of the exterior window;

[0029] The data processing and analysis module is used to construct a three-dimensional model of the exterior windows corresponding to the current house based on the exterior window thermal image and the exterior window image, and to detect and evaluate the exterior windows of the current house based on the exterior window three-dimensional model, the temperature distribution information, the exterior window structural characteristics and the exterior window safety hazards, so as to obtain a detection and evaluation result reflecting the thermal performance and safety performance of the exterior windows of the current house.

[0030] The present invention also discloses a terminal, which includes: a memory, a processor, and a house exterior window detection program stored in the memory and runnable on the processor. When the house exterior window detection program is executed by the processor, the steps of the house exterior window detection method described above are implemented.

[0031] The present invention also discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program can be executed to implement the steps of the above-mentioned method for detecting exterior windows of a house.

[0032] The present invention provides a method, system, terminal, and storage medium for detecting exterior windows of a house. The method comprises: obtaining an exterior window thermal image generated by scanning an exterior window of a current house with a thermal imaging device, and extracting corresponding temperature distribution information from the exterior window thermal image; obtaining an exterior window image generated by photographing an exterior window of the current house with a camera device, and identifying the exterior window image to determine the structural characteristics and safety hazards of the exterior window of the current house; constructing a three-dimensional exterior window model corresponding to the current house based on the exterior window thermal image and the exterior window image; and detecting and evaluating the exterior window of the current house based on the exterior window three-dimensional model, the temperature distribution information, the exterior window structural characteristics, and the exterior window safety hazards, to obtain a detection and evaluation result reflecting the thermal performance and safety performance of the exterior window of the current house. It can be seen that the present invention realizes automated detection of exterior windows of a house by performing corresponding data processing on the exterior window thermal image generated by the thermal imaging device and the exterior window image generated by the camera device, significantly improving detection efficiency and reducing labor costs. In other words, through the combination of thermal imaging and machine vision detection, a comprehensive evaluation of the thermal performance and safety performance of the exterior window is achieved, improving the accuracy of the detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a flow chart of a preferred embodiment of the housing exterior window detection method of the present invention;

[0034] Figure 2 It is a thermal imaging image of the exterior windows of a house in winter and summer in the present invention;

[0035] Figure 3 It is a thermal imaging diagram for observing single-layer glass indoors in the present invention;

[0036] Figure 4 This is a schematic diagram of heat transfer from an exterior window in the present invention;

[0037] Figure 5 This is a flow chart of a preferred embodiment of a specific housing exterior window detection method of the present invention;

[0038] Figure 6 It is a schematic diagram of a framework of an exterior window thermal performance evaluation process in the present invention;

[0039] Figure 7 Schematic diagram of a quad-rotor drone and a six-rotor drone in the present invention;

[0040] Figure 8 This is a schematic diagram of a thermal defect of an exterior window detected by a thermal imaging image in the present invention;

[0041] Figure 9 It is a schematic diagram of a thermal defect recognition algorithm in the present invention;

[0042] Figure 10 This is a basic network structure diagram of a convolutional neural network in the present invention;

[0043] Figure 11 This is a schematic diagram of the DeepLabV3+ network structure in the present invention;

[0044] Figure 12 This is a schematic diagram of a UAV-IRT system framework in the present invention;

[0045] Figure 13 This is a schematic diagram of the flight distance of a drone in the present invention;

[0046] Figure 14 This is a schematic diagram of the flight path of a UAV in the present invention;

[0047] Figure 15 This is a functional principle block diagram of a preferred embodiment of the house exterior window detection system of the present invention;

[0048] Figure 16 This is a schematic diagram of a specific house exterior window detection system in the present invention;

[0049] Figure 17It is a functional principle block diagram of a preferred embodiment of the terminal in the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of the present invention more clear and distinct, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0051] See Figure 1 , Figure 1 This is a flow chart of the housing exterior window detection method of the present invention. Figure 1 As shown, the housing exterior window detection method according to the embodiment of the present invention includes:

[0052] Step S11 : obtaining a thermal image of an exterior window generated by scanning an exterior window of a current house with a thermal imaging device, and extracting corresponding temperature distribution information from the thermal image of the exterior window.

[0053] In this embodiment, a thermal imaging device deployed in the current house, such as a thermal imaging camera, specifically an infrared thermal imager, performs contactless scanning of the exterior windows to generate corresponding thermal images of the exterior windows. The thermal images of the exterior windows generated by the thermal imaging device are then acquired, and corresponding temperature distribution information is extracted from the thermal images of the exterior windows. Specifically, the thermal images of the exterior windows are analyzed using a preset thermal image processing algorithm to extract corresponding temperature gradient information and hotspot information. It is understood that capturing the thermal images of the exterior windows, analyzing the thermal images, and determining the temperature distribution information therein can subsequently be used to evaluate the thermal performance of the exterior windows based on this temperature distribution information, such as evaluating the thermal insulation performance of the exterior windows and determining whether there are problems such as thermal bridges.

[0054] Among them, thermal bridge describes a situation in a building where the exterior and interior are directly connected through one or more elements with a higher thermal conductivity than the rest of the building envelope. Specifically, it refers to reinforced concrete or metal beams, columns, ribs and other parts in the envelope structure such as exterior walls and roofs. These parts have strong heat transfer capacity, dense heat flow and low internal surface temperature, so they are called thermal bridges.

[0055] It should be noted that thermal images are a special type of image that records the heat or temperature of the object itself or radiated outward. In thermal images, temperature distribution information such as temperature gradient and hot spots can be determined. Among them, the temperature gradient is a physical quantity that describes the direction and rate of the fastest temperature change in a specific area; hot spots refer to areas with higher temperatures in thermal images. In thermal images, hot spots are usually displayed in red or pink, representing higher temperatures. Conversely, blue and green represent lower temperatures.

[0056] For example, see Figure 2As shown in Figure 2, there are temperature differences on the exterior window glass surface in different seasons, and this difference can be clearly reflected in the thermal imaging diagram. For example, in summer and winter, a single-layer glass curtain wall is scanned by a thermal imaging device to generate thermal imaging diagrams. From these thermal imaging diagrams, we can see the heat loss of the single-layer glass in winter and the heat gain in summer. Figure 3 As shown in the figure, when observing the thermal image of a single layer of glass from indoors, part of the glass is shaded internally. The high temperature in the unshaded part shows the radiation heat gain and glare problems. Therefore, when performing infrared thermal imaging measurement, corresponding temperature compensation can also be performed to improve the imaging quality. Among them, commonly used temperature compensation methods include least squares method and machine learning algorithm. The least squares method performs correction by establishing the relationship between the factors affecting temperature measurement and the temperature value.

[0057] Step S12: obtaining an exterior window image generated by photographing the exterior window of the current house with a camera device, and identifying the exterior window image to determine the exterior window structural features and safety hazards of the exterior window of the current house.

[0058] In this embodiment, a camera, such as a high-definition camera, deployed in the current house captures the exterior windows of the current house, generating corresponding RGB images of the exterior windows. The images of the exterior windows captured by the camera are then acquired and recognized to determine the structural characteristics and safety hazards of the exterior windows of the current house. It is understood that the high-definition camera captures high-definition images of the exterior windows, which are then processed for recognition to identify the structural characteristics of the exterior windows and safety hazards such as cracks and damage.

[0059] Step S13: constructing a three-dimensional model of the exterior windows corresponding to the current house based on the exterior window thermal image and the exterior window image.

[0060] In this embodiment, after obtaining thermal images of exterior windows generated by a thermal imaging device and images of exterior windows captured by a high-definition camera, a three-dimensional model of the exterior windows corresponding to the current building is constructed based on the thermal and machine vision data. It will be appreciated that the fusion of thermal imaging and machine vision data to construct a three-dimensional model of the exterior windows allows for performance analysis in terms of thermal energy, lighting, and ventilation, thereby helping to optimize window design and improve the building's energy efficiency and comfort.

[0061] Step S14: Based on the exterior window three-dimensional model, the temperature distribution information, the exterior window structural characteristics, and the exterior window safety hazards, the exterior windows of the current house are inspected and evaluated to obtain inspection and evaluation results reflecting the thermal performance and safety performance of the exterior windows of the current house.

[0062] In this embodiment, a thermal image of an exterior window captured by a thermal imaging device and an image of an exterior window taken by a camera are acquired. The thermal image of the exterior window is then analyzed to determine corresponding temperature distribution information, and the structural characteristics and safety hazards of the exterior window are identified from the exterior window image. Based on the three-dimensional model of the exterior window, the temperature distribution information, the structural characteristics, and the safety hazards of the exterior window, the exterior windows of the current house are then inspected and evaluated, resulting in an inspection and evaluation result reflecting the thermal performance and safety performance of the exterior windows of the current house. It is understood that by fusing thermal imaging data with machine vision data to construct a three-dimensional model of the exterior window, and utilizing a data analysis algorithm to comprehensively consider the temperature distribution information, the structural characteristics, and the safety hazards of the exterior window, a comprehensive evaluation of the exterior window is performed, and a corresponding evaluation report is generated. This achieves a comprehensive evaluation of the thermal performance and safety performance of the exterior window, improving the accuracy of the inspection results. Based on the evaluation report, the thermal performance of the exterior window can then be optimized, thereby helping to improve the energy efficiency of the house, reduce energy consumption, and thus promote energy conservation and environmental protection.

[0063] For example, the performance of window systems can be evaluated using the WINDOW software and THERM software. Figure 4 As shown, the thermal performance of the window system is calculated, so that thermal characteristics such as U factor, shading coefficient, solar heat gain coefficient and other glass center optical parameters can be calculated. The window system is presented as a one-dimensional steady-state model, and then an iterative method is used to perform accurate temperature distribution information analysis, so that any performance index can be derived from the temperature distribution information.

[0064] For example, see the above Figure 4 As shown, by calculating the total product area-weighted properties, WINDOW software can examine window products composed of various partitions, gaskets, gaskets, frames, and glass layers in any climate and at any angle. After determining the U-factor of the glass frame and edge, WINDOW software can determine the area-weighted total product value. The U-factor is calculated by multiplying the properties of each component by the respective component area to calculate the area-weighted sum of the properties of each component, and then dividing this total by the total projected area of ​​the product, that is:

[0065]

[0066] Where U is the U-factor of the edge, A is the area, cg is the center of the glass, such as the edge of the glass, f is the frame, and pf is the size of the window product in the rough opening of the wall or roof.

[0067] THERM software complements WINDOW by solving the two-dimensional energy equation using the finite element method. It is important to note that THERM software integrates WINDOW’s frame and glazing components, allowing boundary conditions to be applied to create a more detailed model of the door and window system, or boundary conditions to be assigned to each boundary surface to define the numerical model.

[0068] In this embodiment, the state changes of the exterior windows of the current house can also be monitored in real time, and based on the state changes of the exterior windows, it can be determined whether the exterior windows of the current house have any abnormalities. If an abnormality is found in the exterior windows of the current house, an alarm is triggered and a corresponding alarm message is pushed to the user. It is understood that when the state changes of the exterior windows indicate that the thermal performance of the exterior windows has degraded to a preset threshold, the exterior windows of the current house are determined to have an abnormality; or when the state changes of the exterior windows indicate that the structural damage of the exterior windows has reached a preset level, the exterior windows of the current house are determined to have an abnormality. In other words, by monitoring the changes in the temperature distribution information of the exterior windows and the changes in the safety status of the exterior windows in real time, real-time monitoring of exterior window state changes is achieved. Once an abnormality is detected, such as a decrease in thermal insulation performance or increased structural damage, an alarm is triggered, prompting the user to take timely measures to repair or maintain the exterior windows, thereby enabling timely detection and early warning of potential safety hazards.

[0069] It can be seen that in the embodiments of the present invention, by performing corresponding data processing on the thermal images of the exterior windows generated by the thermal imaging equipment and the images of the exterior windows generated by the camera equipment, automated detection of the exterior windows of the house is achieved, which significantly improves the detection efficiency and reduces labor costs. That is, through the combined detection of thermal imaging and machine vision, a comprehensive evaluation of the thermal performance and safety performance of the exterior windows is achieved, thereby improving the accuracy of the detection results.

[0070] See also Figure 5 As shown, the embodiment of the present invention discloses a specific method for detecting exterior windows of a house. Compared with the previous embodiment, this embodiment further illustrates and optimizes the technical solution.

[0071] Step S21 : obtaining a thermal image of an exterior window generated by scanning an exterior window of a current house with a thermal imaging device, and extracting corresponding temperature distribution information from the thermal image of the exterior window.

[0072] Step S22: obtaining an exterior window image generated by photographing the exterior window of the current house by a camera device;

[0073] Step S23: pre-process the exterior window image according to a preset RGB image processing method to obtain a corresponding target exterior window image, and use a preset image recognition algorithm to recognize the target exterior window image to determine the exterior window structural features and exterior window safety hazards of the current house.

[0074] In this embodiment, an image of an exterior window captured by a camera device is obtained, and the exterior window image is preprocessed according to a preset RGB image processing method to obtain a corresponding target exterior window image. Specifically, noise removal and contrast enhancement are performed on the exterior window image according to the preset RGB image processing method to obtain a corresponding target exterior window image. Then, a preset image recognition algorithm is used to perform recognition processing on the preprocessed target exterior window image to identify safety hazards such as structural features, cracks, and damage of the exterior window.

[0075] Step S24: constructing a three-dimensional model of the exterior windows corresponding to the current house based on the exterior window thermal image and the exterior window image.

[0076] Step S25: Based on the exterior window three-dimensional model, the temperature distribution information, the exterior window structural characteristics, and the exterior window safety hazards, the exterior windows of the current house are inspected and evaluated to obtain inspection and evaluation results reflecting the thermal performance and safety performance of the exterior windows of the current house.

[0077] For the specific contents of the above steps S21 to S22 and steps S24 to S25, reference may be made to the corresponding contents disclosed in the aforementioned embodiments, which will not be repeated here.

[0078] It can be seen that in the embodiments of the present invention, by performing corresponding data processing on the thermal images of the exterior windows generated by the thermal imaging equipment and the images of the exterior windows generated by the camera equipment, automated detection of the exterior windows of the house is achieved, which significantly improves the detection efficiency and reduces labor costs. That is, through the combined detection of thermal imaging and machine vision, a comprehensive evaluation of the thermal performance and safety performance of the exterior windows is achieved, thereby improving the accuracy of the detection results.

[0079] For example, the thermal performance of building envelopes (such as exterior windows) can be evaluated by installing an infrared thermal imaging system on a drone. Figure 6 As shown in Figure 2, the main stages of using an infrared thermal imaging system built on a drone to evaluate the thermal performance of building exterior windows include: instrument selection and site survey, flight parameter configuration, and data analysis. During the instrument selection and site survey stage, thermal imaging equipment can be installed on a drone, such as a thermal infrared camera (infrared imager) installed on the drone to observe the surrounding weather environment of the drone. The drone can be a quad-rotor drone or a six-rotor drone, see [1]. Figure 7As shown, rotary-wing drones offer advantages in stable image transmission and operational flexibility. During the flight parameter configuration phase, also known as the parameter selection phase, on-site testing can be performed to test locations such as windows, rooftops, and around walls; focus positions and flight paths can be tested; and appropriate flight speeds, altitudes, and flight paths can be determined. During the data analysis phase, the analysis can be qualitative or quantitative. The image formats involved in the thermal and safety performance assessment of exterior windows are primarily thermal infrared images generated by thermal imaging equipment and RGB images generated by cameras. The associated image processing methods can be semantic segmentation or machine learning. For example, combining traditional image processing and machine learning techniques can identify and locate thermal defects in building exterior windows through steps such as image segmentation, feature extraction, and classification.

[0080] For example, see Figure 8 As shown in the figure, the thermal defects of building exterior windows that can be detected by the infrared thermal imaging system mainly include cracks in the insulation layer, delamination of the insulation layer, holes, adhesive quality defects in the insulation layer, thermal bridges and air leakage, etc., which helps to identify and evaluate problems such as thermal bridges and voids.

[0081] Specifically, thermal defects can be detected by using a defect recognition algorithm on the thermal image obtained by infrared thermal imaging, wherein see Figure 9 As shown in the figure, the thermal defect recognition algorithm mainly involves image classification, target detection (edge ​​detection), semantic segmentation (threshold segmentation) algorithm and deep learning algorithm, such as convolutional neural network (CNN), which can better realize the automatic extraction and classification of image features. The basic network structure of convolutional neural network can be seen in Figure 10 As shown, the DeepLabV3+ model can be used specifically. The DeepLabV3+ network structure can be found in Figure 11 As shown in the figure, it has a high recognition accuracy in the detection of thermal defects in building walls.

[0082] In the quantitative analysis of thermal performance evaluation, temperature data (temperature distribution information) obtained through infrared thermal imaging can be used to calculate the heat loss and thermal conductivity of building exterior windows. After post-processing and correction, these data can be used to determine whether the building exterior windows meet energy efficiency standards. For example, when evaluating the thermal performance of building exterior windows, the steady-state heat conduction theory can be used to calculate the thermal conductivity (U value). The basic heat conduction equation is:

[0083]

[0084] Where K represents the thermal conductivity of the wall (unit: W / (m 2 K), q represents the heat flow through the wall (unit: W / m2 ), Τ in and Τ ext Respectively represents the air temperature inside and outside (unit: ℃)

[0085] Taking into account factors such as the internal and external surface temperatures of the wall, the radiation temperature, and the radiation heat transfer coefficient, the overall thermal conductivity of the wall is calculated as follows:

[0086]

[0087] Where ε represents the surface emissivity of the wall, T refl represents the radiation temperature, T s,in Indicates the inner surface temperature of the wall, T s,out represents the outer surface temperature of the wall, and σ represents the radiation heat transfer coefficient.

[0088] In addition, the wall surface emissivity ε has a significant impact on the accuracy of infrared temperature measurement, that is, accurate measurement of emissivity is crucial for thermal performance testing. The emissivity is calculated as follows:

[0089]

[0090] Where ε represents the emissivity, T0 is the actual surface temperature, T0' is the radiation temperature measured by the infrared thermal imager, and T u is the ambient temperature.

[0091] Specifically, the emissivity of the wall surface is calculated using known temperature measurements, namely:

[0092]

[0093] Also, see Figure 12 As shown in FIG, the UAV-IRT (Infrared Reflow Technology) system mainly consists of a UAV platform, a thermal infrared camera (thermal imaging equipment), an optical camera (video equipment), a control terminal, and other parts. Specifically, the UAV pilot controls the UAV equipped with an infrared thermal imager through a remote command terminal to fly to a predetermined location around the building to scan and photograph the exterior windows. A corresponding ground control station can also be built on the ground to control the UAV. The image data can then be transmitted via satellite and processed by a computer to evaluate the thermal performance and safety performance of the building's exterior windows.

[0094] It should be noted that the flight distance of different drones from the building exterior wall has an impact on the quality of thermal imaging images, so during the field test phase, see Figure 13As shown, the appropriate flight distance of the drone can be determined by testing different flight distances to ensure the quality of thermal imaging images, and see Figure 14 As shown in the figure, the commonly used flight paths of drones in the thermal performance evaluation of building exterior windows may include but are not limited to the horizontal strip method, vertical strip method, grid method, circular method, spiral flight method and field angle method.

[0095] In one embodiment, if Figure 15 As shown, based on the above-mentioned house exterior window detection method, the present invention also provides a house exterior window detection system, including:

[0096] The thermal imaging detection module 100 is used to obtain a thermal image of an exterior window generated by scanning an exterior window of a current house using a thermal imaging device, and to extract corresponding temperature distribution information from the thermal image of the exterior window;

[0097] The machine vision detection module 200 is used to obtain an exterior window image generated by a camera device shooting an exterior window of the current house, and identify the exterior window image to determine the structural characteristics of the exterior window of the current house and the safety hazards of the exterior window;

[0098] The data processing and analysis module 300 is used to construct a three-dimensional model of the exterior windows corresponding to the current house based on the exterior window thermal image and the exterior window image, and to detect and evaluate the exterior windows of the current house based on the exterior window three-dimensional model, the temperature distribution information, the exterior window structural characteristics and the exterior window safety hazards, to obtain a detection and evaluation result reflecting the thermal performance and safety performance of the exterior windows of the current house.

[0099] For example, see Figure 16As shown in FIG, the house exterior window detection system is composed of a thermal imaging detection module, a machine vision detection module, and a data processing and analysis module. The thermal imaging detection module generates a corresponding thermal image of the exterior window by performing contactless scanning of the exterior window by a thermal imaging camera deployed in the current house, obtains the thermal image of the exterior window generated by the thermal imaging camera, and analyzes the temperature gradient information and hot spot information in the thermal image of the exterior window, so as to evaluate the thermal insulation performance of the exterior window according to the temperature gradient information and hot spot information; the machine vision detection module uses a high-definition camera to collect high-definition images of the exterior window, and then removes noise and enhances contrast of the exterior window image, and uses an image recognition algorithm to identify the processed exterior window image to identify the structural features and safety hazards of the exterior window; the data processing and analysis module The module mainly integrates thermal imaging data and machine vision data to construct a three-dimensional model of the exterior windows, that is, based on the thermal image and the image of the exterior windows, a three-dimensional model of the exterior windows corresponding to the current house is constructed, and the thermal performance and safety performance of the exterior windows are further comprehensively evaluated, and a detailed evaluation report is generated. In addition, an intelligent monitoring and early warning module can also be constructed in the exterior window detection system of the house. The intelligent monitoring and early warning module realizes real-time monitoring of the state changes of the exterior windows by real-time monitoring of the temperature distribution information changes of the exterior windows and the changes in the safety conditions of the exterior windows. Once an abnormal situation is found, such as a decrease in thermal insulation performance, increased structural damage, etc., the intelligent monitoring and early warning module can automatically issue an alarm to remind users to take timely measures to repair or maintain the exterior windows, so as to timely discover and warn of potential safety hazards.

[0100] Figure 17 This is a schematic diagram of the structure of a terminal provided in an embodiment of the present application. The terminal may include:

[0101] Memory 501 , processor 502 , and computer programs stored in the memory 501 and executable on the processor 502 .

[0102] When the processor 502 executes the program, the housing exterior window detection method provided in the above embodiment is implemented.

[0103] Furthermore, the terminal further includes:

[0104] The communication interface 503 is used for communication between the memory 501 and the processor 502 .

[0105] The memory 501 is used to store computer programs that can be run on the processor 502 .

[0106] The memory 501 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0107] If the memory 501, processor 502, and communication interface 503 are implemented independently, the communication interface 503, memory 501, and processor 502 can be interconnected via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be divided into address buses, data buses, control buses, etc. For ease of illustration, the figure shows only one line, but this does not mean that there is only one bus or only one type of bus.

[0108] Optionally, in a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.

[0109] The processor 502 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0110] This embodiment further provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the above-mentioned method for detecting exterior windows of a house is implemented.

[0111] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0112] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this application, "N" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0113] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing a custom logical function or process step, and the scope of the preferred embodiments of the present application includes additional implementations in which the order shown or discussed may not be followed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by a person skilled in the art to which the embodiments of the present application belong.

[0114] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can read and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or N wires (electronic devices), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting, or otherwise processing in a suitable manner as necessary, and then storing it in a computer memory.

[0115] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiment, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0116] Those skilled in the art will understand that all or part of the steps in the method for implementing the above-mentioned embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0117] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0118] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

[0119] It should be understood that the application of the present invention is not limited to the above examples. For those skilled in the art, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A method for detecting exterior windows of a house, characterized in that: The method comprises: Obtaining a thermal image of an exterior window of a current house generated by scanning the exterior windows of the house using a thermal imaging device, and analyzing the exterior window thermal image using a preset thermal image processing algorithm to extract corresponding temperature gradient information and hotspot information; the temperature gradient information is used to describe the direction and rate of temperature change within the target area, and the hotspot information is used to describe the colors corresponding to different temperatures; Acquire an exterior window image generated by photographing an exterior window of a current house using a camera device, and identify the exterior window image to determine structural features of the exterior window of the current house and potential safety hazards of the exterior window; Constructing a three-dimensional model of the exterior windows corresponding to the current house based on the exterior window thermal image and the exterior window image; Performing an inspection and evaluation of the exterior windows of the current house based on the exterior window three-dimensional model, the temperature gradient information, the hot spot information, the exterior window structural characteristics, and the exterior window safety hazards, to obtain an inspection and evaluation result reflecting the thermal performance and safety performance of the exterior windows of the current house; The exterior windows of the current house are inspected and evaluated based on the exterior window three-dimensional model, the temperature gradient information, the hot spot information, the exterior window structural characteristics, and the exterior window safety hazards, to obtain inspection and evaluation results reflecting the thermal performance and safety performance of the exterior windows of the current house, including: Calculating the heat loss and heat transfer coefficient of the exterior window according to the temperature gradient information and the hot spot information; determining an energy efficiency of the exterior window based on the heat loss and the heat transfer coefficient; The housing exterior window detection method further includes: Using the DeepLabV3+ model to perform thermal defect detection on the exterior window thermal image obtained by infrared thermal imaging; Calculating the thermal performance of the exterior window based on the exterior window three-dimensional model, the temperature gradient information, and the hotspot information using WINDOW software and THERM software; wherein the WINDOW software is used to examine window products consisting of partitions, gaskets, gas layers, frames and glass layers under any climatic conditions and at any angle, and to determine the area-weighted total product value while determining the U-factor; The U-factor is a parameter calculated by multiplying the attribute of each component of the window product by its corresponding component area to obtain the area-weighted sum of the attribute of each component, and dividing the area-weighted sum by the total projected area of ​​the window product; The calculation formula of the U factor is: ； Where U represents the U factor and A represents the area; Furthermore, the THERM software is integrated with window frame and glass components to create a door and window system model or assign boundary conditions to each boundary surface to define a numerical model.

2. The housing exterior window detection method according to claim 1, characterized in that: The step of obtaining a thermal image of an exterior window generated by scanning an exterior window of a current house with a thermal imaging device includes: Obtain a thermal image of the exterior windows of the current house generated by scanning the exterior windows with an infrared thermal imager.

3. The housing exterior window detection method according to claim 1, characterized in that: Before identifying the exterior window image to determine the exterior window structural features and exterior window safety hazards of the current house, the method further includes: Preprocessing the exterior window image according to a preset RGB image processing method to obtain a corresponding target exterior window image; The identifying of the exterior window image to determine the exterior window structural features and exterior window safety hazards of the current house includes: The target exterior window image is identified using a preset image recognition algorithm to determine the exterior window structural features and exterior window safety hazards of the current house.

4. The housing exterior window detection method according to claim 3, characterized in that: The preprocessing of the external window image according to a preset RGB image processing method to obtain a corresponding target external window image includes: The exterior window image is subjected to noise removal and contrast enhancement processing according to a preset RGB image processing method to obtain a corresponding target exterior window image.

5. The method for detecting exterior windows of a house according to any one of claims 1 to 4, characterized in that: Also includes: Monitor the state changes of the exterior windows of the current house in real time, and determine whether there is any abnormality in the exterior windows of the current house based on the state changes of the exterior windows; If there is an abnormality in the external window of the current house, an alarm is triggered and corresponding alarm information is pushed to the user.

6. The housing exterior window detection method according to claim 5, characterized in that: The determining whether there is an abnormality with the exterior window of the current house based on the change in the exterior window state includes: When the change in the state of the exterior window indicates that the thermal performance of the exterior window has dropped to a preset threshold, it is determined that there is an abnormality in the exterior window of the current house; Alternatively, when the change in the state of the exterior window indicates that the structural damage of the exterior window has reached a preset degree, it is determined that there is an abnormality with the exterior window of the current house.

7. A house exterior window detection system, characterized in that: The system comprises: A thermal imaging detection module is configured to obtain thermal images of exterior windows of a current house generated by scanning the exterior windows with a thermal imaging device, and analyze the thermal images using a preset thermal image processing algorithm to extract corresponding temperature gradient information and hotspot information; the temperature gradient information is used to describe the direction and rate of temperature change within a target area, and the hotspot information is used to describe the colors corresponding to different temperatures; A machine vision detection module is used to obtain an exterior window image generated by a camera device shooting an exterior window of the current house, and identify the exterior window image to determine the structural characteristics of the exterior window of the current house and the safety hazards of the exterior window; a data processing and analysis module, configured to construct a three-dimensional model of the exterior windows corresponding to the current house based on the exterior window thermal image and the exterior window image, and to detect and evaluate the exterior windows of the current house based on the exterior window three-dimensional model, the temperature gradient information, the hotspot information, the exterior window structural characteristics, and the exterior window safety hazards, to obtain a detection and evaluation result reflecting the thermal performance and safety performance of the exterior windows of the current house; The data processing and analysis module is specifically used to: Calculating the heat loss and heat transfer coefficient of the exterior window according to the temperature gradient information and the hot spot information; determining an energy efficiency of the exterior window based on the heat loss and the heat transfer coefficient; The housing exterior window detection system is further specifically used for: Using the DeepLabV3+ model to perform thermal defect detection on the exterior window thermal image obtained by infrared thermal imaging; Calculating the thermal performance of the exterior window based on the exterior window three-dimensional model, the temperature gradient information, and the hotspot information using WINDOW software and THERM software; wherein the WINDOW software is used to examine window products consisting of partitions, gaskets, gas layers, frames and glass layers under any climatic conditions and at any angle, and to determine the area-weighted total product value while determining the U-factor; The U-factor is a parameter calculated by multiplying the attribute of each component of the window product by its corresponding component area to obtain the area-weighted sum of the attribute of each component, and dividing the area-weighted sum by the total projected area of ​​the window product; The calculation formula of the U factor is: ； Where U represents the U factor and A represents the area; Furthermore, the THERM software is integrated with window frame and glass components to create a door and window system model or assign boundary conditions to each boundary surface to define a numerical model.

8. A terminal, characterized in that: include: A memory, a processor, and a house exterior window detection program stored in the memory and executable on the processor, wherein the house exterior window detection program, when executed by the processor, implements the steps of the house exterior window detection method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and the computer program can be executed to implement the steps of the house exterior window detection method according to any one of claims 1 to 6.

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