On-site detection method and system for heat transfer coefficient of external wall based on infrared thermal imaging technology

Abstract

The application provides a field detection method and system for the heat transfer coefficient of an external wall based on infrared thermal imaging technology, which uses an infrared thermal imager to non-contact and fast scan the external wall, efficiently obtains an initial temperature distribution, saves time and effort, constructs a dynamic environment correction mechanism, collects 12-hour wall temperature and solar, wind speed and other data, extracts phase difference characteristics, uses amplitude and phase spectrum to correct coefficients and phase offsets in real time, accurately eliminates the lag interference of solar radiation and wind speed fluctuation, makes the temperature data more real, and calculates the heat transfer coefficient based on the corrected temperature data, so that the result is accurate and reliable, can provide key basis for building energy saving reconstruction and judging whether the standard is met, is suitable for different environments and external wall types, has strong universality, and helps building industry energy saving evaluation and quality control.

Description

Technical Field

[0001] This invention relates to the field of building wall inspection technology, and in particular to a method and system for on-site detection of the heat transfer coefficient of an exterior wall based on infrared thermal imaging technology. Background Technology

[0002] The heat transfer coefficient of building exterior walls is a core indicator for measuring building energy efficiency, and its accurate testing is of great significance for building energy consumption assessment and the formulation of energy-saving renovation plans. Currently, the testing methods for the heat transfer coefficient of exterior walls mainly fall into two categories: laboratory testing methods and on-site testing methods.

[0003] Laboratory testing methods, which involve constructing simulated environments to test wall specimens, can obtain relatively accurate results under controlled conditions, but they have obvious limitations: on the one hand, the structure and construction process of the specimens may differ from those of actual building exterior walls, leading to a disconnect between the test results and the actual situation; on the other hand, this method cannot directly test the heat transfer coefficient of the exterior walls of existing buildings, thus having a narrow applicability.

[0004] On-site testing methods mainly include the heat flux meter method and the hot box method. The heat flux meter method calculates the heat transfer coefficient by directly measuring heat flux density and temperature difference by placing heat flux meters and temperature sensors on the wall surface. However, its testing cycle is long (usually requiring more than 72 hours of continuous monitoring), and the sensor placement can damage the integrity of the wall surface, affecting the building's appearance. The hot box method creates an artificial thermal environment on both sides of the wall to form a temperature difference, and then calculates the heat transfer coefficient. This method involves bulky equipment, complex operation, and is greatly affected by environmental factors, making it difficult to guarantee testing accuracy.

[0005] With the development of infrared thermal imaging technology, it has been initially applied in building exterior wall inspection due to its advantages of non-contact and rapid acquisition of large-area temperature fields. However, existing detection methods based on infrared thermal imaging technology still have significant drawbacks: because building exterior walls are exposed to the natural environment for a long time, their surface temperature is easily affected by dynamic environmental factors such as solar radiation and wind speed, resulting in large errors in the initial temperature data acquired by the infrared thermal imager; at the same time, the impact of fluctuations in solar radiation and wind speed on wall temperature has a lag, and existing methods lack effective correction mechanisms to eliminate such interference, resulting in low accuracy of the final calculated heat transfer coefficient, which cannot meet the needs of engineering practice. Summary of the Invention

[0006] In view of the above problems, the present invention is proposed to provide a method for on-site detection of the heat transfer coefficient of an exterior wall based on infrared thermal imaging technology and a corresponding system for on-site detection of the heat transfer coefficient of an exterior wall based on infrared thermal imaging technology, which overcomes or at least partially solves the above problems.

[0007] This invention discloses a method for on-site detection of the heat transfer coefficient of external walls based on infrared thermal imaging technology, the method comprising:

[0008] Infrared thermal imagers are used to detect the temperature of the building's exterior wall surface to obtain the initial temperature of the exterior wall surface; the initial temperature of the exterior wall surface includes the initial temperature of the outer surface of the exterior wall.

[0009] Simultaneously collect exterior wall surface temperature data and environmental parameter data for a preset duration;

[0010] Based on the exterior wall surface temperature data and environmental parameter data for the preset duration, the dynamic relationship between changes in environmental parameter data and changes in exterior wall surface temperature is analyzed using frequency domain analysis methods.

[0011] Based on the dynamic relationship between changes in environmental parameter data and changes in exterior wall surface temperature, a dynamic correction factor is calculated to correct the initial temperature of the exterior wall surface. The dynamic correction factor is then used to correct the initial temperature of the exterior wall surface to obtain the corrected temperature of the exterior wall surface. The corrected temperature of the exterior wall surface includes the corrected temperature of the exterior wall surface.

[0012] The heat transfer coefficient of the building's exterior wall is calculated by collecting the temperature of the inner surface of the exterior wall, the indoor air temperature, and the outdoor air temperature, based on the corrected temperature of the outer surface of the exterior wall, the temperature of the inner surface of the exterior wall, the indoor air temperature, and the outdoor air temperature, using a machine learning algorithm.

[0013] Optionally, the environmental parameter data includes at least solar radiation intensity and wind speed; based on the exterior wall surface temperature data and environmental parameter data for the preset duration, the dynamic relationship between changes in environmental parameter data and changes in exterior wall surface temperature is analyzed using frequency domain analysis methods, including:

[0014] Based on the exterior wall surface temperature data, solar radiation intensity data, and wind speed data for a preset duration, an exterior wall surface temperature change curve is constructed. The exterior wall surface temperature change curve shows the dynamic evolution of temperature over time, and intuitively reflects the temporal correspondence between key environmental factors such as solar radiation intensity and wind speed and changes in wall temperature.

[0015] Amplitude and phase spectra of each frequency component are generated based on the temperature change curve of the exterior wall surface;

[0016] Based on the amplitude and phase spectra of each frequency component, the measured peak amplitude, the phase difference between changes in solar radiation intensity and changes in external wall surface temperature, and the phase difference between changes in wind speed and changes in external wall surface temperature are extracted.

[0017] Optionally, a dynamic correction factor for correcting the initial temperature of the exterior wall surface is calculated based on the dynamic relationship between changes in environmental parameter data and changes in the exterior wall surface temperature, and the initial temperature of the exterior wall surface is corrected using the dynamic correction factor to obtain the corrected exterior wall surface temperature, including:

[0018] Interpreting solar activity characteristics and wind speed fluctuation characteristics corresponding to frequency components based on amplitude and phase spectra;

[0019] If the solar activity characteristics are high-frequency fluctuations, then the instantaneous correction coefficient is calculated based on the measured peak amplitude and the preset wind speed fluctuation characteristic coefficient. The instantaneous correction coefficient is used to correct the initial temperature of the outer wall surface to obtain the corrected temperature of the outer wall surface. The preset wind speed fluctuation characteristic coefficient represents the leading or lagging relationship between the wind speed fluctuation phase and the solar radiation intensity phase, and corresponds to the attenuation coefficient or the enhancement coefficient.

[0020] If the solar activity is characterized by low-frequency fluctuations, the phase shift is calculated based on the phase difference between the changes in solar radiation intensity and the changes in the external wall surface temperature, and the phase difference between the changes in wind speed and the changes in the external wall surface temperature. The phase shift is then used to correct the initial temperature of the external wall surface, resulting in the corrected temperature of the external wall surface.

[0021] Optionally, based on exterior wall surface temperature data, solar radiation intensity data, and wind speed data for a preset duration, an exterior wall surface temperature change curve is constructed, including:

[0022] Outliers are removed from the exterior wall surface temperature data, solar radiation intensity data, and wind speed data for the preset duration. Short-term missing values ​​are filled using linear interpolation. The average temperature data of the exterior wall surface is obtained by averaging the temperature data of different monitoring points on the same wall.

[0023] Using time as the horizontal axis and the average temperature data of the exterior wall surface as the vertical axis, a smooth curve is generated by fitting the curve. The solar radiation intensity and wind speed peak points at the corresponding times are marked next to the curve to obtain the exterior wall surface temperature change curve.

[0024] Optionally, amplitude and phase spectra of each frequency component are generated based on the temperature change curve of the exterior wall surface, including:

[0025] Extract the surface temperature data, solar radiation intensity data, and wind speed data from the surface temperature change curve of the exterior wall, align them by timestamp, and synchronize the time series of the three data.

[0026] Fast Fourier transform is performed on the surface temperature data of the exterior wall, solar radiation intensity data, and wind speed data to convert the time-domain signals into frequency-domain signals, thereby obtaining the amplitude spectrum and phase spectrum of each frequency component.

[0027] Optionally, a fast Fourier transform is performed on the exterior wall surface temperature data, solar radiation intensity data, and wind speed data to convert the time-domain signals into frequency-domain signals, obtaining the amplitude spectrum and phase spectrum of each frequency component, including:

[0028] The surface temperature data, solar radiation intensity data, and wind speed data of the exterior wall are preprocessed. The FFT algorithm is then applied to the preprocessed data to separate the samples with even and odd indices. The FFT of the two subsequences is calculated recursively, and the results are merged to obtain the complete complex FFT result.

[0029] The modulus and phase of the complex FFT result are calculated, and the frequency axis is calculated based on the sampling frequency and data length. Based on the calculated modulus, phase, and frequency axis, amplitude and phase spectra of the external wall surface temperature data, solar radiation intensity data, and wind speed data are plotted.

[0030] Optionally, based on the amplitude and phase spectra of each frequency component, the measured peak amplitude, the phase difference between changes in solar radiation intensity and changes in external wall surface temperature, and the phase difference between changes in wind speed and changes in external wall surface temperature are extracted, including:

[0031] Identify the main frequency components from the amplitude spectrum and phase spectrum, and select the frequencies with an amplitude ratio within the preset range as characteristic frequencies. At the characteristic frequencies, extract the phase angle of the solar radiation intensity phase spectrum and the phase angle of the external wall surface temperature phase spectrum. The difference between the phase angle of the solar radiation intensity phase spectrum and the phase angle of the external wall surface temperature phase spectrum is the phase difference between the change in solar radiation intensity and the change in external wall surface temperature.

[0032] The main frequency components are identified from the amplitude and phase spectra. Frequency components with amplitude proportions within a preset range are selected as characteristic frequencies. At the characteristic frequencies, the phase angles of the wind speed phase spectrum and the external wall surface temperature phase spectrum are extracted. The difference between the phase angles of the wind speed phase spectrum and the external wall surface temperature phase spectrum is the phase difference between the wind speed change and the external wall surface temperature change. A positive phase difference indicates that the environmental parameter change precedes the external wall surface temperature change, while a negative phase difference indicates that the external wall surface temperature change precedes the environmental parameter change. The phase difference statistics are then compiled for each characteristic frequency.

[0033] Optionally, after the step of correcting the initial temperature of the exterior wall surface using a dynamic correction factor to obtain the corrected temperature of the exterior wall surface, the method further includes:

[0034] By comparing the initial temperature spectrum of the exterior wall surface before and after correction with the corrected temperature spectrum of the exterior wall surface, if the amplitude of the high-frequency band drops to the background noise level and the phase difference of the low-frequency band shrinks to within the preset phase difference threshold range, the correction is deemed effective.

[0035] Optionally, a machine learning algorithm is used to calculate the heat transfer coefficient of the building's exterior wall based on the corrected temperature of the exterior wall's outer surface, the temperature of the interior wall's inner surface, the indoor air temperature, and the outdoor air temperature, including:

[0036] Standardize the external wall surface temperature, internal wall surface temperature, indoor air temperature, and outdoor air temperature.

[0037] The standardized external wall surface temperature, internal wall surface temperature, indoor air temperature, and outdoor air temperature are input into a pre-trained artificial neural network model, which outputs the heat transfer coefficient of the building's exterior wall.

[0038] This invention also discloses an on-site detection system for the heat transfer coefficient of exterior walls based on infrared thermal imaging technology, the system comprising:

[0039] The exterior wall surface initial temperature detection module is used to detect the temperature of the building's exterior wall surface using an infrared thermal imager to obtain the initial temperature of the exterior wall surface.

[0040] The temperature and environmental parameter data acquisition module is used to synchronously collect exterior wall surface temperature data and environmental parameter data for a preset time period;

[0041] The module for analyzing the dynamic relationship between temperature and environmental parameters is used to analyze the dynamic relationship between changes in environmental parameter data and changes in external wall surface temperature based on the preset duration of external wall surface temperature data and environmental parameter data, using frequency domain analysis methods.

[0042] The initial temperature correction module is used to calculate a dynamic correction factor for correcting the initial temperature of the external wall surface based on the dynamic relationship between changes in environmental parameter data and changes in the external wall surface temperature, and to correct the initial temperature of the external wall surface using the dynamic correction factor to obtain the corrected temperature of the external wall surface.

[0043] The heat transfer coefficient calculation module is used to collect the temperature of the inner surface of the exterior wall, the indoor air temperature, and the outdoor air temperature. It uses machine learning algorithms to calculate the heat transfer coefficient of the building exterior wall based on the corrected temperature of the outer surface of the exterior wall, the temperature of the inner surface of the exterior wall, the indoor air temperature, and the outdoor air temperature.

[0044] This invention has the following advantages:

[0045] This invention presents a method for on-site detection of exterior wall heat transfer coefficient based on infrared thermal imaging technology. It uses an infrared thermal imager to rapidly scan the exterior wall non-contactly, efficiently acquiring the initial temperature distribution, saving time and effort. A dynamic environmental correction mechanism is constructed, collecting 12 hours of wall temperature data along with solar and wind speed data. Phase difference characteristics are extracted, and amplitude and phase spectra are used to instantly correct the coefficients and phase shifts, accurately eliminating the hysteresis interference from solar radiation and wind speed fluctuations, resulting in more accurate temperature data. The heat transfer coefficient calculated based on the corrected temperature data is accurate and reliable, providing crucial evidence for building energy-saving renovations and determining compliance. Furthermore, it is adaptable to different environments and exterior wall types, exhibiting strong versatility and assisting in energy-saving assessments and quality control in the building industry. Attached Figure Description

[0046] Figure 1 This is a flowchart illustrating the steps of an on-site detection method for the heat transfer coefficient of an external wall based on infrared thermal imaging technology, provided in an embodiment of the present invention. Detailed Implementation

[0047] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0048] Reference Figure 1 The diagram illustrates a flowchart of a method for on-site detection of the heat transfer coefficient of an external wall based on infrared thermal imaging technology, as provided in an embodiment of the present invention. Specifically, it may include the following steps:

[0049] S1. Use an infrared thermal imager to detect the temperature of the building's exterior wall surface to obtain the initial temperature of the exterior wall surface;

[0050] S2. Synchronously collect exterior wall surface temperature data and environmental parameter data for a preset duration;

[0051] S3. Based on the exterior wall surface temperature data and environmental parameter data of the preset duration, analyze the dynamic relationship between the changes in environmental parameter data and the changes in exterior wall surface temperature using frequency domain analysis methods;

[0052] S4. Calculate a dynamic correction factor for correcting the initial temperature of the external wall surface based on the dynamic relationship between changes in environmental parameter data and changes in the external wall surface temperature, and use the dynamic correction factor to correct the initial temperature of the external wall surface to obtain the corrected temperature of the external wall surface.

[0053] S5. Collect the temperature of the inner surface of the exterior wall, the indoor air temperature, and the outdoor air temperature. Using a machine learning algorithm, calculate the heat transfer coefficient of the building's exterior wall based on the corrected temperature of the outer surface of the exterior wall, the temperature of the inner surface of the exterior wall, the indoor air temperature, and the outdoor air temperature.

[0054] This invention effectively eliminates interference from solar radiation and wind speed through a dynamic environmental parameter correction mechanism and multi-dimensional data fusion, significantly improving detection accuracy and providing reliable data for heat transfer coefficient calculation. Relying on 12-hour monitoring data and machine learning algorithms, it reduces subjective bias and enhances the stability and reliability of results. Rapid data acquisition by an infrared thermal imager combined with automated processing shortens the detection cycle, reduces repetitive work, and improves efficiency. An accurate heat transfer coefficient can precisely assess the thermal insulation performance of external walls, providing a scientific basis for building energy-saving renovations, helping to reduce building energy consumption, and promoting the development of energy-saving technologies.

[0055] In step S1, select a sunny day for detection. Keep the infrared thermal imager perpendicular to the wall surface, align the center of the lens with the center of the detection area, and control the distance within the range of 5-20m. Divide the building exterior wall into several 10m×10m detection areas according to the facade. Take more than 3 thermal images of each area. When taking the images, focus on the wall surface. Average the initial temperature data obtained from the images and use it for later use.

[0056] The operating procedures of this invention can ensure the quality of testing from multiple aspects. Choosing a sunny day can reduce interference from light fluctuations and provide stable basic data for subsequent corrections. Dividing the area into 10m×10m sections and taking ≥3 thermal images can avoid omissions, and after averaging, random errors can be eliminated, improving the representativeness of the data. The lens being perpendicular to the wall, center-aligned, and controlled at a distance of 5-20m can reduce measurement deviations caused by angle and distance, ensuring temperature measurement accuracy. Focusing on the wall can avoid background interference and ensure clear temperature signals.

[0057] Standardized operating procedures ensure data comparability and facilitate overall analysis. Averaging also reduces the pressure of subsequent corrections. These details reduce interference through standardized processes, ensuring the authenticity and stability of initial data and providing reliable support for subsequent corrections and calculations.

[0058] In step S2, exterior wall surface temperature data and environmental parameter data are collected simultaneously for a preset duration:

[0059] Three temperature monitoring points were evenly distributed on the wall surface. Thermocouple sensors were used, and solar radiation sensors and an anemometers were installed simultaneously to collect data for 12 hours.

[0060] In step S3, based on the exterior wall surface temperature data and environmental parameter data for the preset duration, an exterior wall surface temperature change curve is constructed. The environmental parameter data change characteristics and exterior wall surface temperature change characteristics are analyzed using frequency domain analysis methods to analyze the dynamic relationship between the two and establish a dynamic environmental parameter correction mechanism.

[0061] In an optional embodiment of the present invention, based on the exterior wall surface temperature data and environmental parameter data of the preset duration, the dynamic relationship between changes in environmental parameter data and changes in exterior wall surface temperature is analyzed using a frequency domain analysis method, including:

[0062] Based on the exterior wall surface temperature data, solar radiation intensity data, and wind speed data for a preset duration, an exterior wall surface temperature change curve is constructed. The exterior wall surface temperature change curve shows the dynamic evolution of temperature over time, and intuitively reflects the temporal correspondence between key environmental factors such as solar radiation intensity and wind speed and changes in wall temperature.

[0063] Amplitude and phase spectra of each frequency component are generated based on the temperature change curve of the exterior wall surface;

[0064] Based on the amplitude and phase spectra of each frequency component, the measured peak amplitude, the phase difference between changes in solar radiation intensity and changes in external wall surface temperature, and the phase difference between changes in wind speed and changes in external wall surface temperature are extracted.

[0065] In an optional embodiment of the present invention, a surface temperature change curve for the exterior wall is constructed based on exterior wall surface temperature data, solar radiation intensity data, and wind speed data for a preset duration, including:

[0066] Outliers are removed from the exterior wall surface temperature data, solar radiation intensity data, and wind speed data for the preset duration. Short-term missing values ​​are filled using linear interpolation. The average temperature data of the exterior wall surface is obtained by averaging the temperature data of different monitoring points on the same wall.

[0067] Using time as the horizontal axis and the average temperature data of the exterior wall surface as the vertical axis, a smooth curve is generated by fitting the curve. The solar radiation intensity and wind speed peak points at the corresponding times are marked next to the curve to obtain the exterior wall surface temperature change curve.

[0068] In an optional embodiment of the present invention, generating amplitude and phase spectra of each frequency component based on the temperature change curve of the exterior wall surface includes:

[0069] Extract the surface temperature data, solar radiation intensity data, and wind speed data from the surface temperature change curve of the exterior wall, align them by timestamp, and synchronize the time series of the three data.

[0070] Fast Fourier transform is performed on the surface temperature data of the exterior wall, solar radiation intensity data, and wind speed data to convert the time-domain signals into frequency-domain signals, thereby obtaining the amplitude spectrum and phase spectrum of each frequency component.

[0071] In an optional embodiment of the present invention, a fast Fourier transform is performed on the exterior wall surface temperature data, solar radiation intensity data, and wind speed data to convert the time-domain signal into a frequency-domain signal, obtaining the amplitude spectrum and phase spectrum of each frequency component, including:

[0072] The surface temperature data, solar radiation intensity data, and wind speed data of the exterior wall are preprocessed. The FFT algorithm is then applied to the preprocessed data to separate the samples with even and odd indices. The FFT of the two subsequences is calculated recursively, and the results are merged to obtain the complete complex FFT result.

[0073] The modulus and phase of the complex FFT result are calculated, and the frequency axis is calculated based on the sampling frequency and data length. Based on the calculated modulus, phase, and frequency axis, amplitude and phase spectra of the external wall surface temperature data, solar radiation intensity data, and wind speed data are plotted.

[0074] In an optional embodiment of the present invention, the extraction of the measured amplitude peak value, the phase difference between changes in solar radiation intensity and changes in external wall surface temperature, and the phase difference between changes in wind speed and changes in external wall surface temperature based on the amplitude spectrum and phase spectrum of each frequency component includes:

[0075] Identify the main frequency components from the amplitude spectrum and phase spectrum, and select the frequencies with an amplitude ratio within the preset range as characteristic frequencies. At the characteristic frequencies, extract the phase angle of the solar radiation intensity phase spectrum and the phase angle of the external wall surface temperature phase spectrum. The difference between the phase angle of the solar radiation intensity phase spectrum and the phase angle of the external wall surface temperature phase spectrum is the phase difference between the change in solar radiation intensity and the change in external wall surface temperature.

[0076] The main frequency components are identified from the amplitude and phase spectra. Frequency components with amplitude proportions within a preset range are selected as characteristic frequencies. At the characteristic frequencies, the phase angles of the wind speed phase spectrum and the external wall surface temperature phase spectrum are extracted. The difference between the phase angles of the wind speed phase spectrum and the external wall surface temperature phase spectrum is the phase difference between the wind speed change and the external wall surface temperature change. A positive phase difference indicates that the environmental parameter change precedes the external wall surface temperature change, while a negative phase difference indicates that the external wall surface temperature change precedes the environmental parameter change. The phase difference statistics are then compiled for each characteristic frequency.

[0077] When constructing the temperature change curve of the exterior wall surface, this invention first establishes a coordinate system framework with time as the horizontal axis (accurate to the minute level, covering the complete 12-hour acquisition cycle) and the average wall temperature as the vertical axis (unit: °C, range set according to the actual monitoring range). This provides a clear dimensional benchmark for data visualization. The pre-processed average wall temperature data (with outlier removal, missing value filling, and multi-point averaging calculation completed) is imported into professional data processing software. The software will make preliminary connections based on these discrete data points to form an original curve reflecting the temperature fluctuation over time.

[0078] Since the original curve may contain high-frequency noise caused by instantaneous airflow and minor sensor vibrations (manifested as irregular sharp fluctuations in local parts of the curve), a moving average method is required for smoothing. A reasonable sliding window is set (e.g., three consecutive data points as one window), the average temperature within each window is calculated and the original value of the center data point of the window is replaced. The iterative processing of the data throughout the entire time period is completed by sliding the window point by point. This process can effectively filter out short-cycle, small-amplitude temperature fluctuations, making the curve shape smoother and highlighting the overall trend of wall temperature changes with the environment (e.g., the heating stage affected by solar radiation and the cooling stage at night).

[0079] To visually demonstrate the correlation between environmental parameters and wall temperature, solar radiation intensity (in W / m², using numerical labels or a broken line subaxis) and peak wind speed (in m / s, marked with arrows or special symbols) need to be precisely labeled next to the corresponding time points on the smooth curve. For example, at the curve position around noon, the maximum value of solar radiation intensity during this period should be simultaneously labeled; at the moment when gusts occur at 3 pm, the point of sudden change in wind speed should be marked with a peak symbol. Through this multi-parameter linkage labeling, the final external wall surface temperature change curve can not only clearly show the dynamic evolution of temperature over time, but also intuitively reflect the temporal correspondence between key environmental factors such as solar radiation intensity and wind speed and changes in wall temperature, providing a visual analytical basis for subsequent extraction of phase difference features and temperature data correction.

[0080] The steps for extracting amplitude and phase difference features are as follows:

[0081] Wall temperature, solar radiation intensity, and wind speed data are extracted from the temperature change curve and aligned by timestamps to synchronize the three time series. Fast Fourier Transform (FFT) is then performed on the three sets of data. The core function of this transformation is to convert the signal, originally presented in the time domain (with time as the variable), to the frequency domain (with frequency as the variable), thereby revealing the hidden periodic patterns in the data. Through the transformation, the amplitude spectrum and phase spectrum corresponding to each frequency component can be obtained: the amplitude spectrum reflects the energy proportion of different frequency components in the data; the larger the amplitude, the more significant the periodic change corresponding to that frequency; the phase spectrum characterizes the starting position of each frequency component in time, i.e., the phase angle, which reflects the time difference of different signals in periodic changes.

[0082] To focus on key periodic features, frequency components need to be screened. All frequency components are identified from the amplitude spectrum, and the proportion of each frequency component's amplitude to the total amplitude is calculated. The frequencies with the highest amplitude proportions are selected as characteristic frequencies. These characteristic frequencies correspond to the strongest and most representative periodic fluctuations in the data, effectively reflecting the main patterns of solar radiation, wind speed, and wall temperature changes, while eliminating interference from secondary frequency components.

[0083] At a defined characteristic frequency, the phase angles of the solar radiation intensity phase spectrum and the wall temperature phase spectrum are extracted respectively. Since the phase angle reflects the position of the signal within the period, the difference between the two is the phase difference between solar radiation and wall temperature changes at that characteristic frequency. Similarly, the phase angles of the wind speed phase spectrum and the wall temperature phase spectrum are extracted and the difference is calculated to obtain the phase difference between wind speed and wall temperature changes.

[0084] The sign of the phase difference has a clear physical meaning: when the phase difference is positive, it indicates that in the periodic changes corresponding to the characteristic frequency, the changes in environmental parameters (solar radiation or wind speed) precede the changes in wall temperature, that is, the fluctuations in environmental parameters occur first, and the wall temperature responds subsequently; when the phase difference is negative, it indicates that the changes in wall temperature precede the changes in environmental parameters, and the fluctuations in wall temperature occur before the fluctuations in environmental parameters. The phase differences between solar radiation and wall temperature, and between wind speed and wall temperature, obtained at all characteristic frequencies are statistically analyzed to form a systematic phase difference feature dataset, providing a key phase relationship basis for subsequent temperature data correction.

[0085] Before performing the Fast Fourier Transform (FFT), each set of data (wall temperature, solar radiation intensity, and wind speed data) needs to be preprocessed. This step aims to eliminate potential trend terms and DC components in the data. For example, linear fitting can be used to remove the slow drift of wall temperature data over time, or the average value of wind speed data can be subtracted to eliminate interference from constant airflow. This ensures that the subsequent transformation results can accurately reflect the periodic fluctuation characteristics of the data. If the data length does not exceed a power of 2, zero padding is required to meet the data length requirements of the FFT algorithm and improve computational efficiency and spectral resolution.

[0086] After preprocessing, the FFT algorithm is executed on each set of data: First, the data samples are split into two subsequences according to the parity of the index. For example, the original sequence x(0), x(1), x(2), ..., x(N-1) is divided into an even subsequence x(0), x(2), ..., x(N-2) and an odd subsequence x(1), x(3), ..., x(N-1). Then, the two subsequences are recursively FFT calculated until the subsequence length is reduced to 1. During the recursion, the transformation results of the two subsequences are merged using a rotation factor (based on the periodicity of the complex exponential function). The butterfly operation structure reduces repeated calculations, and finally, the complete complex form FFT result is obtained, which contains the amplitude and phase information of the data at different frequencies.

[0087] Based on the complex FFT results, three key parameters are further calculated: first, the magnitude (i.e., amplitude) of the complex result, obtained by taking the square root of the sum of the squares of the real and imaginary parts, which reflects the energy intensity of the corresponding frequency component; second, the phase of the result, calculated by the argument of the complex number, which characterizes the starting position of the frequency component on the time axis; and third, the frequency axis, calculated according to the sampling frequency (e.g., 0.00033Hz corresponding to 1 sample every 5 minutes) and the data length (the value of N after zero padding), using the formula "frequency = sampling frequency × index / N" to ensure that the horizontal axis scale of the spectrum accurately corresponds to the physical frequency.

[0088] Plot the amplitude and phase spectra of the three sets of data with frequency on the horizontal axis and amplitude / phase on the vertical axis. In the amplitude spectrum, the higher the peak value, the more significant the periodic fluctuation of the corresponding frequency (e.g., the peak value of solar radiation data around 0.0417Hz corresponds to the diurnal cycle). In the phase spectrum, the phase values ​​at different frequencies intuitively show the time offset characteristics of the signal. Through these spectra, the main frequency components and their energy distribution in the data can be clearly identified, providing a visual analysis basis for subsequent selection of characteristic frequencies and calculation of phase difference.

[0089] In an optional embodiment of the present invention, a dynamic correction factor for correcting the initial temperature of the exterior wall surface is calculated based on the dynamic relationship between changes in environmental parameter data and changes in the exterior wall surface temperature, and the initial temperature of the exterior wall surface is corrected using the dynamic correction factor to obtain the corrected temperature of the exterior wall surface, including:

[0090] Interpreting solar activity characteristics and wind speed fluctuation characteristics corresponding to frequency components based on amplitude and phase spectra;

[0091] If the solar activity characteristics are high-frequency fluctuations, then the instantaneous correction coefficient is calculated based on the measured peak amplitude and the preset wind speed fluctuation characteristic coefficient. The instantaneous correction coefficient is used to correct the initial temperature of the outer wall surface to obtain the corrected temperature of the outer wall surface. The preset wind speed fluctuation characteristic coefficient represents the leading or lagging relationship between the wind speed fluctuation phase and the solar radiation intensity phase, and corresponds to the attenuation coefficient or the enhancement coefficient.

[0092] If the solar activity is characterized by low-frequency fluctuations, the phase shift is calculated based on the phase difference between the changes in solar radiation intensity and the changes in the external wall surface temperature, and the phase difference between the changes in wind speed and the changes in the external wall surface temperature. The phase shift is then used to correct the initial temperature of the external wall surface, resulting in the corrected temperature of the external wall surface.

[0093] This invention interprets solar activity corresponding to frequency components based on amplitude and phase spectra. It uses an instant correction coefficient for high-frequency fluctuations and introduces a phase offset for low-frequency fluctuations to correct the initial temperature data of the wall surface, thereby eliminating the lag interference of solar radiation and wind speed fluctuations on the detection results.

[0094] (1) Real-time correction of high-frequency fluctuations:

[0095] To capture pulsed disturbances in solar transient activity with high-frequency fluctuations >1Hz, the relative energy ratio is calculated by continuously monitoring the amplitude peak value in this frequency band, with the correction factor being:

[0096] K = 1 + (measured amplitude / reference amplitude - 1) × 0.6;

[0097] Where K is the correction coefficient, the reference amplitude is the average peak value under the same period of calm weather in history, and the coefficient of 0.6 is used to balance the risk of overcorrection; when the wind speed fluctuation phase leads the solar radiation by more than 0.2π, the K value is multiplied by the attenuation coefficient of 0.8; otherwise, it is multiplied by the enhancement coefficient of 1.2. The real-time update frequency is once per second to correct the initial temperature data of the exterior wall surface.

[0098] This invention precisely addresses the pulse-like interference from solar transients. High-frequency fluctuations >1Hz can easily distort initial temperature data. By continuously monitoring the peak amplitude of this frequency band and calculating the relative energy ratio, the temperature data can be reasonably adjusted based on the correction coefficient formula, thus balancing the risk of overcorrection.

[0099] When the phase difference between wind speed and solar radiation is different, multiplying by an attenuation or enhancement coefficient can take into account the interaction between the two, making the correction more realistic. Real-time updates once per second can keep up with high-frequency fluctuations and ensure the timeliness of the correction, ultimately improving the accuracy of the initial temperature data and laying a reliable foundation for subsequent heat transfer coefficient calculations.

[0100] (2) Low-frequency fluctuations introduce phase shift:

[0101] Calculate the offset Δt based on the phase difference in the low-frequency band:

[0102] Δt = (measured phase difference / 2π) × period;

[0103] The period is based on a 12-hour baseline value;

[0104] Construct a temperature compensation model:

[0105] The corrected temperature T' = T_initial + (T_initial(t+Δt) - T_initial(t) × 0.7;

[0106] The cumulative error of long-term correction is weakened by a weighting coefficient of 0.7, and the offset is updated every hour for temperature correction.

[0107] The present invention features a slow-paced low-frequency fluctuation (such as a trend change within a 12-hour cycle) and updates the offset Δt once per hour. This not only responds promptly to slow changes in phase difference (such as fine-tuning of phase difference caused by changes in the angle of sunlight and the alternation of day and night), but also avoids the unnecessary computational consumption caused by high-frequency updates. This update frequency matches the time scale of the low-frequency fluctuation, ensuring that the correction parameters are always synchronized with the lag characteristics of the current environment, further improving the rationality of the correction.

[0108] This correction method precisely addresses the lag between environmental parameters and wall temperature in low-frequency fluctuations by quantifying time offset, balancing correction weights, and dynamically updating parameters. It preserves the trend characteristics of temperature data while effectively controlling the accumulation of errors in long-cycle corrections, making the corrected temperature data more consistent with the actual thermal response of the wall. This provides a crucial guarantee for the accurate calculation of the heat transfer coefficient in step S5.

[0109] In an optional embodiment of the present invention, after the step of correcting the initial temperature of the exterior wall surface using a dynamic correction factor to obtain the corrected temperature of the exterior wall surface, the method further includes:

[0110] By comparing the initial temperature spectrum of the exterior wall surface before and after correction with the corrected temperature spectrum of the exterior wall surface, if the amplitude of the high-frequency band drops to the background noise level and the phase difference of the low-frequency band shrinks to within the preset phase difference threshold range, the correction is deemed effective.

[0111] Comparing the data spectra before and after correction, if the amplitude of the high-frequency band drops to the background noise level and the phase difference of the low-frequency band shrinks to within ±0.1π, the correction is considered effective.

[0112] In an optional embodiment of the present invention, a machine learning algorithm is used to calculate the heat transfer coefficient of the building exterior wall based on the corrected temperature of the exterior wall surface, the exterior wall surface temperature, the indoor air temperature, and the outdoor air temperature, including:

[0113] Standardize the external wall surface temperature, internal wall surface temperature, indoor air temperature, and outdoor air temperature.

[0114] The standardized external wall surface temperature, internal wall surface temperature, indoor air temperature, and outdoor air temperature are input into a pre-trained artificial neural network model, which outputs the heat transfer coefficient of the building's exterior wall.

[0115] In step S5, the temperature of the inner surface of the exterior wall is scanned and collected using an infrared thermal imager to capture the overall temperature distribution of the wall facing the interior. The temperature of the outer surface of the wall is corrected to exclude interference from direct sunlight and external factors such as wind and rain. The indoor air temperature is collected at the center of the space away from doors, windows and heat sources, at a height controlled between 1.2 and 1.5 meters. The outdoor air temperature is measured in a well-ventilated and shaded area around the building.

[0116] The calculation of the wall heat transfer coefficient K value from temperature parameters is carried out using an artificial neural network. The four collected temperature variables are used as inputs, and the four temperature variables are output as the wall heat transfer coefficient K value through the process of "data standardization → feature mapping → nonlinear transformation → result output". The model is trained based on measured data.

[0117] During the data standardization phase, the four temperature variables need to be processed uniformly. Since the numerical ranges of different temperature parameters vary, for example, the temperature difference between indoor and outdoor air may be large, while the temperature difference between the inner and outer surfaces of the wall is relatively small. Directly inputting these values ​​into the model would affect the accuracy of the calculation. Standardization processes map these temperature values ​​to the same numerical range, which preserves the relative relationships between the temperatures, such as the temperature difference trend between indoor air and the inner surface, and the temperature difference characteristics between the outer surface and the outdoor air, while eliminating the interference caused by different magnitudes. This allows the model to focus more on the intrinsic relationship between the temperature parameters.

[0118] The feature mapping stage involves feeding standardized temperature data into the input layer of the neural network, and then transferring features through weighted connections between the input and hidden layers. After receiving four temperature variables, the input layer distributes the information to each neuron in the hidden layer according to a pre-defined weight matrix. This process is not simply numerical transfer, but rather a preliminary screening and reorganization of temperature features based on patterns learned during training. For example, it highlights temperature combinations that significantly affect the K value, laying the foundation for subsequent deep learning computations.

[0119] Nonlinear transformation is mainly completed in the hidden layer of the neural network. The hidden layer processes the received features through a nonlinear activation function. This processing can simulate the complex nonlinear relationship between temperature parameters and K value. In the heat transfer process, the K value does not simply fluctuate linearly with temperature changes, but is affected by the synergistic influence of multiple temperature parameters, showing a complex variation law. The multi-layer structure of the hidden layer will gradually deepen the processing of features, extracting higher-order features from the basic temperature values, such as the influence of the interaction between different temperature gradients on the K value, and continuously approximating the real heat transfer law.

[0120] After being processed by multiple hidden layers, the information is finally transmitted to the output layer, which calculates and outputs the wall heat transfer coefficient K value. The output layer will obtain the specific K value result through a linear activation function based on the feature information transmitted by the last hidden layer and the optimized weight parameters, ensuring that the output value conforms to the actual physical meaning and is within a reasonable range.

[0121] In an optional embodiment of the present invention, the initial temperature of the exterior wall surface collected includes the initial temperature of the outer surface of the exterior wall and the initial temperature of the inner surface of the exterior wall; the initial temperature of the outer surface of the exterior wall and the initial temperature of the inner surface of the exterior wall are corrected to obtain the corrected temperature of the outer surface of the exterior wall and the corrected temperature of the inner surface of the exterior wall; the heat transfer coefficient of the building exterior wall is calculated using a machine learning algorithm based on the corrected temperature of the outer surface of the exterior wall, the corrected temperature of the inner surface of the exterior wall, the indoor air temperature and the outdoor air temperature.

[0122] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0123] An embodiment of the present invention provides an on-site detection system for the heat transfer coefficient of an external wall based on infrared thermal imaging technology, which may specifically include the following modules:

[0124] The exterior wall surface initial temperature detection module is used to detect the temperature of the building's exterior wall surface using an infrared thermal imager to obtain the initial temperature of the exterior wall surface.

[0125] The temperature and environmental parameter data acquisition module is used to synchronously collect exterior wall surface temperature data and environmental parameter data for a preset time period;

[0126] The module for analyzing the dynamic relationship between temperature and environmental parameters is used to analyze the dynamic relationship between changes in environmental parameter data and changes in external wall surface temperature based on the preset duration of external wall surface temperature data and environmental parameter data, using frequency domain analysis methods.

[0127] The initial temperature correction module is used to calculate a dynamic correction factor for correcting the initial temperature of the external wall surface based on the dynamic relationship between changes in environmental parameter data and changes in the external wall surface temperature, and to correct the initial temperature of the external wall surface using the dynamic correction factor to obtain the corrected temperature of the external wall surface.

[0128] The heat transfer coefficient calculation module is used to collect the temperature of the inner surface of the exterior wall, the indoor air temperature, and the outdoor air temperature. It uses machine learning algorithms to calculate the heat transfer coefficient of the building exterior wall based on the corrected temperature of the outer surface of the exterior wall, the temperature of the inner surface of the exterior wall, the indoor air temperature, and the outdoor air temperature.

[0129] Optionally, the environmental parameter data includes at least solar radiation intensity and wind speed; the temperature and environmental parameter dynamic relationship analysis module is also used for:

[0130] Based on the exterior wall surface temperature data, solar radiation intensity data, and wind speed data for a preset duration, an exterior wall surface temperature change curve is constructed. The exterior wall surface temperature change curve shows the dynamic evolution of temperature over time, and intuitively reflects the temporal correspondence between key environmental factors such as solar radiation intensity and wind speed and changes in wall temperature.

[0131] Amplitude and phase spectra of each frequency component are generated based on the temperature change curve of the exterior wall surface;

[0132] Based on the amplitude and phase spectra of each frequency component, the measured peak amplitude, the phase difference between changes in solar radiation intensity and changes in external wall surface temperature, and the phase difference between changes in wind speed and changes in external wall surface temperature are extracted.

[0133] Optionally, the initial temperature correction module is also used for:

[0134] Interpreting solar activity characteristics and wind speed fluctuation characteristics corresponding to frequency components based on amplitude and phase spectra;

[0135] If the solar activity characteristics are high-frequency fluctuations, then the instantaneous correction coefficient is calculated based on the measured peak amplitude and the preset wind speed fluctuation characteristic coefficient. The instantaneous correction coefficient is used to correct the initial temperature of the outer wall surface to obtain the corrected temperature of the outer wall surface. The preset wind speed fluctuation characteristic coefficient represents the leading or lagging relationship between the wind speed fluctuation phase and the solar radiation intensity phase, and corresponds to the attenuation coefficient or the enhancement coefficient.

[0136] If the solar activity is characterized by low-frequency fluctuations, the phase shift is calculated based on the phase difference between the changes in solar radiation intensity and the changes in the external wall surface temperature, and the phase difference between the changes in wind speed and the changes in the external wall surface temperature. The phase shift is then used to correct the initial temperature of the external wall surface, resulting in the corrected temperature of the external wall surface.

[0137] Optionally, the module for analyzing the dynamic relationship between temperature and environmental parameters is also used for:

[0138] Outliers are removed from the exterior wall surface temperature data, solar radiation intensity data, and wind speed data for the preset duration. Short-term missing values ​​are filled using linear interpolation. The average temperature data of the exterior wall surface is obtained by averaging the temperature data of different monitoring points on the same wall.

[0139] Using time as the horizontal axis and the average temperature data of the exterior wall surface as the vertical axis, a smooth curve is generated by fitting the curve. The solar radiation intensity and wind speed peak points at the corresponding times are marked next to the curve to obtain the exterior wall surface temperature change curve.

[0140] Optionally, the module for analyzing the dynamic relationship between temperature and environmental parameters is also used for:

[0141] Extract the surface temperature data, solar radiation intensity data, and wind speed data from the surface temperature change curve of the exterior wall, align them by timestamp, and synchronize the time series of the three data.

[0142] Fast Fourier transform is performed on the surface temperature data of the exterior wall, solar radiation intensity data, and wind speed data to convert the time-domain signals into frequency-domain signals, thereby obtaining the amplitude spectrum and phase spectrum of each frequency component.

[0143] Optionally, the module for analyzing the dynamic relationship between temperature and environmental parameters is also used for:

[0144] The surface temperature data, solar radiation intensity data, and wind speed data of the exterior wall are preprocessed. The FFT algorithm is then applied to the preprocessed data to separate the samples with even and odd indices. The FFT of the two subsequences is calculated recursively, and the results are merged to obtain the complete complex FFT result.

[0145] The modulus and phase of the complex FFT result are calculated, and the frequency axis is calculated based on the sampling frequency and data length. Based on the calculated modulus, phase, and frequency axis, amplitude and phase spectra of the external wall surface temperature data, solar radiation intensity data, and wind speed data are plotted.

[0146] Optionally, the module for analyzing the dynamic relationship between temperature and environmental parameters is also used for:

[0147] Identify the main frequency components from the amplitude spectrum and phase spectrum, and select the frequencies with an amplitude ratio within the preset range as characteristic frequencies. At the characteristic frequencies, extract the phase angle of the solar radiation intensity phase spectrum and the phase angle of the external wall surface temperature phase spectrum. The difference between the phase angle of the solar radiation intensity phase spectrum and the phase angle of the external wall surface temperature phase spectrum is the phase difference between the change in solar radiation intensity and the change in external wall surface temperature.

[0148] The main frequency components are identified from the amplitude and phase spectra. Frequency components with amplitude proportions within a preset range are selected as characteristic frequencies. At the characteristic frequencies, the phase angles of the wind speed phase spectrum and the external wall surface temperature phase spectrum are extracted. The difference between the phase angles of the wind speed phase spectrum and the external wall surface temperature phase spectrum is the phase difference between the wind speed change and the external wall surface temperature change. A positive phase difference indicates that the environmental parameter change precedes the external wall surface temperature change, while a negative phase difference indicates that the external wall surface temperature change precedes the environmental parameter change. The phase difference statistics are then compiled for each characteristic frequency.

[0149] Optionally, the system further includes a validity correction module, which is used to:

[0150] By comparing the initial temperature spectrum of the exterior wall surface before and after correction with the corrected temperature spectrum of the exterior wall surface, if the amplitude of the high-frequency band drops to the background noise level and the phase difference of the low-frequency band shrinks to within the preset phase difference threshold range, the correction is deemed effective.

[0151] Optionally, the heat transfer coefficient calculation module is also used for:

[0152] Standardize the external wall surface temperature, internal wall surface temperature, indoor air temperature, and outdoor air temperature.

[0153] The standardized external wall surface temperature, internal wall surface temperature, indoor air temperature, and outdoor air temperature are input into a pre-trained artificial neural network model, which outputs the heat transfer coefficient of the building's exterior wall.

[0154] As the system implementation is basically similar to the method implementation, it is described in a relatively simple way. For relevant details, please refer to the description of the method implementation.

[0155] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus 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 apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0156] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0157] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. A method for detecting the heat transfer coefficient of an external wall in situ based on infrared thermography, characterized in that, The method comprises: The method comprises: Synchronously collecting the temperature data of the outer wall surface and the environmental parameter data for a preset time length; Based on the temperature data of the outer wall surface and the environmental parameter data for the preset time length, the dynamic relationship between the change of the environmental parameter data and the change of the temperature of the outer wall surface is analyzed by a frequency domain analysis method; Based on the dynamic relationship between the change of the environmental parameter data and the change of the temperature of the outer wall surface, a dynamic correction factor for correcting the initial temperature of the outer wall surface is calculated, and the initial temperature of the outer wall surface is corrected by using the dynamic correction factor to obtain a corrected temperature of the outer wall surface; the corrected temperature of the outer wall surface comprises a corrected temperature of the outer surface of the outer wall; The temperature of the inner surface of the outer wall, the indoor air temperature and the outdoor air temperature are collected, and a machine learning algorithm is used to calculate the heat transfer coefficient of the building outer wall based on the corrected temperature of the outer surface of the outer wall, the temperature of the inner surface of the outer wall, the indoor air temperature and the outdoor air temperature; The environmental parameter data at least comprises solar radiation intensity and wind speed; based on the temperature data of the outer wall surface and the environmental parameter data for the preset time length, the dynamic relationship between the change of the environmental parameter data and the change of the temperature of the outer wall surface is analyzed by a frequency domain analysis method, which comprises: Based on the temperature data of the outer wall surface, the solar radiation intensity data and the wind speed data for the preset time length, a temperature change curve of the outer wall surface is constructed; the temperature change curve of the outer wall surface shows the dynamic evolution law of temperature with time, and directly reflects the time sequence corresponding relationship between the key environmental factors of solar radiation intensity and wind speed and the temperature change of the wall; Based on the temperature change curve of the outer wall surface, amplitude spectrum and phase spectrum of each frequency component are generated; Based on the amplitude spectrum and phase spectrum of each frequency component, the measured amplitude peak value, the phase difference between the change of solar radiation intensity and the change of the temperature of the outer wall surface, and the phase difference between the change of wind speed and the change of the temperature of the outer wall surface are extracted; Based on the dynamic relationship between the change of the environmental parameter data and the change of the temperature of the outer wall surface, a dynamic correction factor for correcting the initial temperature of the outer wall surface is calculated, and the initial temperature of the outer wall surface is corrected by using the dynamic correction factor to obtain a corrected temperature of the outer wall surface, which comprises: Based on the amplitude spectrum and phase spectrum, the solar activity characteristics and wind speed fluctuation characteristics corresponding to the frequency component are interpreted; If the solar activity characteristics are high-frequency fluctuations, an instant correction coefficient is calculated based on the measured amplitude peak value and a preset wind speed fluctuation characteristic coefficient, and the initial temperature of the outer wall surface is corrected by using the instant correction coefficient to obtain a corrected temperature of the outer wall surface; the preset wind speed fluctuation characteristic coefficient represents the leading or lagging relationship between the wind speed fluctuation phase and the solar radiation intensity phase, which corresponds to a decay coefficient or an enhancement coefficient; If the solar activity characteristics are low-frequency fluctuations, a phase offset is calculated based on the phase difference between the change of solar radiation intensity and the change of the temperature of the outer wall surface, and the phase difference between the change of wind speed and the change of the temperature of the outer wall surface, and the initial temperature of the outer wall surface is corrected by using the phase offset to obtain a corrected temperature of the outer wall surface.

2. The method of claim 1, wherein, Based on the temperature data of the outer wall surface, the solar radiation intensity data and the wind speed data for the preset time length, a temperature change curve of the outer wall surface is constructed, which comprises: The average temperature data of the external wall surface temperature is obtained by removing outliers from the preset length of the external wall surface temperature data, the solar radiation intensity data and the wind speed data, filling in short missing values by using linear interpolation method, and averaging the temperature data of different monitoring points of the wall body; Taking time as the horizontal axis and the average temperature data of the external wall surface temperature as the vertical axis, a smooth curve is fitted to obtain the external wall surface temperature change curve, and the solar radiation intensity and the wind speed peak point at the corresponding time are marked on the curve.

3. The method of claim 1, wherein, Based on the external wall surface temperature change curve, the amplitude spectrum and the phase spectrum of each frequency component are generated, including: The external wall surface temperature data, the solar radiation intensity data and the wind speed data are extracted from the external wall surface temperature change curve, and the time series of the three are synchronized by aligning the time stamps; The external wall surface temperature data, the solar radiation intensity data and the wind speed data are subjected to fast Fourier transform to convert time domain signals into frequency domain signals, and the amplitude spectrum and the phase spectrum of each frequency component are obtained.

4. The method of claim 3, wherein, The external wall surface temperature data, the solar radiation intensity data and the wind speed data are subjected to fast Fourier transform to convert time domain signals into frequency domain signals, and the amplitude spectrum and the phase spectrum of each frequency component are obtained, including: The external wall surface temperature data, the solar radiation intensity data and the wind speed data are preprocessed, and the FFT algorithm is executed on the preprocessed data, the samples with even and odd indexes are separated, the FFT of the two sub-sequences is recursively calculated, and the complete complex FFT result is obtained by merging the results; The modulus and phase of the complex FFT result are calculated, the frequency axis is calculated based on the sampling frequency and the data length, and the amplitude spectrum and the phase spectrum of the external wall surface temperature data, the solar radiation intensity data and the wind speed data are plotted based on the calculated modulus, phase and frequency axis.

5. The method of claim 1, wherein, Based on the amplitude spectrum and the phase spectrum of each frequency component, the measured amplitude peak value, the phase difference between the solar radiation intensity change and the external wall surface temperature change, and the phase difference between the wind speed change and the external wall surface temperature change are extracted, including: The main frequency components are identified from the amplitude spectrum and the phase spectrum, and the frequencies with an amplitude ratio within a preset proportion are selected as characteristic frequencies. At the characteristic frequencies, the phase angle of the solar radiation intensity phase spectrum and the phase angle of the external wall surface temperature phase spectrum are extracted, and the difference between the phase angle of the solar radiation intensity phase spectrum and the phase angle of the external wall surface temperature phase spectrum is the phase difference between the solar radiation intensity change and the external wall surface temperature change; The main frequency components are identified from the amplitude spectrum and the phase spectrum, and the frequencies with an amplitude ratio within a preset proportion are selected as characteristic frequencies. At the characteristic frequencies, the phase angle of the wind speed phase spectrum and the phase angle of the external wall surface temperature phase spectrum are extracted, and the difference between the phase angle of the wind speed phase spectrum and the phase angle of the external wall surface temperature phase spectrum is the phase difference between the wind speed change and the external wall surface temperature change. Wherein, the phase difference is positive, indicating that the environmental parameter change leads the external wall surface temperature change; the phase difference is negative, indicating that the external wall surface temperature change leads the environmental parameter change, and the phase difference statistics of each characteristic frequency are counted.

6. The method of claim 1, wherein, After the step of correcting the initial temperature of the external wall surface using a dynamic correction factor to obtain the corrected temperature of the external wall surface, the method further includes: The initial temperature spectrum of the outer wall surface before correction and the corrected temperature spectrum of the outer wall surface are compared, and if the amplitude of the high frequency band is reduced to the background noise level and the phase difference of the low frequency band is reduced to within the preset phase difference threshold range, it is determined that the correction is effective.

7. The method of claim 1, wherein, The heat transfer coefficient of the building outer wall is calculated based on the corrected temperature of the outer surface of the outer wall, the inner surface temperature of the outer wall, the indoor air temperature and the outdoor air temperature by using a machine learning algorithm, including: The corrected temperature of the outer surface of the outer wall, the inner surface temperature of the outer wall, the indoor air temperature and the outdoor air temperature are standardized; The corrected temperature of the outer surface of the outer wall, the inner surface temperature of the outer wall, the indoor air temperature and the outdoor air temperature are standardized; 8. A system for detecting the heat transfer coefficient of an external wall in situ based on infrared thermography, applying the method for detecting the heat transfer coefficient of an external wall in situ based on infrared thermography according to any one of claims 1 to 7, characterized in that, The corrected temperature of the outer surface of the outer wall, the inner surface temperature of the outer wall, the indoor air temperature and the outdoor air temperature are standardized; The system comprises: The outer wall surface initial temperature detection module is used to detect the temperature of the outer wall surface of the building by using an infrared thermal imager to obtain the initial temperature of the outer wall surface; the initial temperature of the outer wall surface includes the initial temperature of the outer surface of the outer wall; The temperature and environmental parameter data acquisition module is used to synchronously acquire the temperature data and environmental parameter data of the outer wall surface for a preset time length; The temperature and environmental parameter dynamic relationship analysis module is used to analyze the dynamic relationship between the change of the environmental parameter data and the change of the temperature of the outer wall surface based on the temperature data and environmental parameter data of the outer wall surface for the preset time length by using a frequency domain analysis method; The initial temperature correction module is used to calculate a dynamic correction factor for correcting the initial temperature of the outer wall surface based on the dynamic relationship between the change of the environmental parameter data and the change of the temperature of the outer wall surface, and to correct the initial temperature of the outer wall surface by using the dynamic correction factor to obtain the corrected temperature of the outer wall surface; the corrected temperature of the outer wall surface includes the corrected temperature of the outer surface of the outer wall; The heat transfer coefficient calculation module is used to acquire the inner surface temperature of the outer wall, the indoor air temperature and the outdoor air temperature, and to calculate the heat transfer coefficient of the building outer wall based on the corrected temperature of the outer surface of the outer wall, the inner surface temperature of the outer wall, the indoor air temperature and the outdoor air temperature by using a machine learning algorithm.

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