Wall thermal insulation performance detection system and method based on building design

Abstract

The invention relates to a wall thermal insulation performance detection system and method based on building design, and relates to the technical field of building energy saving and thermotechnical testing, and the system comprises a building information acquisition module, a detection module, a data processing module, a simulation analysis module, and an evaluation and feedback module. The building information acquisition module extracts wall layered structure and thermotechnical design parameters. The detection module is composed of various sensors and a thermal infrared imager. And the data processing module processes the original data. And the simulation analysis module establishes a heat conduction numerical model for the corresponding parameters, and calculates and designs wall temperature fields and heat flux distribution under different working conditions. And the evaluation and feedback module inverts the overall U value and the local thermal bridge index of the wall body to carry out difference analysis on the data result, and generates a corresponding feedback detection data report. According to the method, the BIM design information and the field detection data are seamlessly connected, and the detection result has traceability. And numerical simulation and field measurement are combined, so that the overall thermal insulation performance can be quantitatively evaluated, and the thermal bridge or thermal insulation defect can be accurately positioned.

Description

Technical Field

[0001] The present invention relates to the technical field of building energy conservation and thermal engineering testing, and particularly to a wall thermal insulation performance detection system and method based on building design. Background Art

[0002] With the continuous improvement of the country's requirements for building energy conservation, the thermal insulation performance of walls has become an important indicator for building design, construction, and acceptance. The existing methods for detecting the thermal insulation performance of walls mainly include: 1. Heat flow meter method - It is necessary to install heat flow sensors on the wall surface and collect data for a long time, with a large workload and a long construction period; 2. Infrared thermal imaging method - It can quickly capture the wall surface temperature field, but it is difficult to quantitatively obtain the heat flux distribution at thermal bridges and material joints; 3. Instrument point measurement method - It can only obtain data at several discrete measurement points and is difficult to comprehensively reflect the thermal performance of the overall wall.

[0003] The above methods are mostly based on on-site experience and do not fully utilize the BIM model and thermal engineering calculation parameters established in the building design stage, resulting in a lack of directness in comparing the detection results with the design indicators and being unable to provide timely feedback to the design, construction, or maintenance departments during the detection. Summary of the Invention

[0004] Object of the Invention: To provide a wall thermal insulation performance detection system based on building design, and further provide a detection method based on the above wall thermal insulation performance detection system based on building design to solve the above problems existing in the prior art.

[0005] Technical Solution: A wall thermal insulation performance detection system based on building design includes five components: a building information acquisition module, a detection module, a data processing module, a simulation analysis module, and an evaluation and feedback module.

[0006] Among them, the building information acquisition module is used to extract the wall layered structure and thermal engineering design parameters.

[0007] The detection module is composed of various sensors and an infrared thermal imager, and is used to collect original thermal engineering data.

[0008] The data processing module is used to perform time series synchronization, noise reduction, and calibration processing on the original data collected by the detection module.

[0009] The simulation analysis module is used to establish a heat conduction numerical model for the parameters output by the building information acquisition module, and calculate the wall temperature field and heat flux distribution under different designed working conditions.

[0010] The evaluation and feedback module performs a difference analysis on the data results of the simulation analysis module by inversely calculating the overall U-value of the wall and local thermal bridge indicators, and generates a corresponding feedback detection data report.

[0011] In a further embodiment, the data processing module further includes an environmental compensation unit for dynamically compensating the collected values for the indoor and outdoor environments.

[0012] In a further embodiment, the simulation analysis module includes at least one of a finite element simulation unit and a network model simulation unit. The evaluation and feedback module uses least squares fitting or a machine learning model trained based on historical data for thermal parameter inversion.

[0013] In a further embodiment, the detection module further includes a movable thermocouple scanning device for obtaining a high-resolution temperature distribution in a local area of the wall surface.

[0014] A method for detecting the wall insulation performance based on building design includes the following steps: S1. Obtain the wall layer structure parameters and indoor and outdoor boundary conditions in the BIM model through the building information acquisition module; S2. Determine the number of measurement points, sensor layout, and sampling frequency according to the total length, height difference, and external window position of the wall to be detected; S3. Install the corresponding sensors according to the planned measurement points, and collect the original temperature, heat flux, and infrared image data of the wall; S4. Denoise, synchronize the time series, and correct the environment for the collected data, and establish a heat conduction model in the simulation analysis module based on the design parameters in step S1, and calculate the design thermal performance; S5. Perform a difference analysis on the measured data in step S4 and the simulation results in step S5, and inversely calculate the actual wall U-value and local thermal bridge effect; S6. Generate a detection report including thermal performance comparison, energy conservation compliance assessment, and optimization suggestions, and feedback it to the BIM collaboration platform.

[0015] In a further embodiment, in step S4, the collected original data is filtered, and the filters used include a low-pass filter and a median filter.

[0016] In a further embodiment, the inversion algorithms applied in step S5 include the least squares method or support vector regression.

[0017] In a further embodiment, after step S2, the infrared thermal imager data is calibrated and photographed to assist the evaluation and feedback module in locating the thermal bridge.

[0018] In a further embodiment, when formulating the detection scheme in step S2, measurement points are set at intervals of 0.5 - 1.5 m in the height direction of the wall. The duration of on-site data collection is 24 - 72 hours. Beneficial effects

[0019] 1. Seamlessly connect BIM design information with on-site detection data, and the detection results are traceable.

[0020] 2. Combine numerical simulation with on-site measurement, which can not only quantitatively evaluate the overall heat insulation performance, but also accurately locate heat bridges or insulation defects.

[0021] 3. The detection process is standardized and modular, and can be applied to walls with different structures and materials.

[0022] 4. The detection report can be real-time fed back to the design and operation and maintenance platforms, facilitating subsequent optimized design or maintenance management. Description of the drawings

[0023] Figure 1 It is a schematic diagram of the system framework of the present invention.

[0024] Figure 2 It is a schematic flow diagram of the detection method of the present invention. Specific implementation manners

[0025] In the following description, a large number of specific details are given to provide a more thorough understanding of the present invention. However, it is obvious to those skilled in the art that the present invention can be implemented without one or more of these details. In other examples, in order to avoid confusion with the present invention, some well-known technical features in the art are not described.

[0026] The applicant believes that traditional wall insulation performance detection does not fully utilize the BIM model and thermal calculation parameters established in the building design stage, resulting in a lack of directness in comparing the detection results with the design indicators, and it is also impossible to timely feed back to the design, construction or maintenance departments during the detection.

[0027] Therefore, the applicant designs a wall insulation performance detection system and method based on building design. By constructing an integrated system of "BIM information - simulation calculation - on-site collection - data fusion - feedback optimization", it realizes efficient, quantitative and traceable detection of the thermal performance of the wall, and the detection results can be directly returned to the design or operation and maintenance platform.

[0028] The wall insulation performance detection system based on building design involved in the present invention mainly includes: A building information acquisition module, which is used to extract wall structure hierarchy information (including wall material type, thickness, thermal conductivity, node practice, etc.) and boundary conditions from the BIM model or design database.

[0029] The detection module is used to deploy on-site and collect raw thermal data from devices such as temperature sensors, surface heat flux sensors, ambient humidity sensors and infrared thermal imagers.

[0030] The data processing module is used to perform time synchronization, noise filtering, and benchmark calibration on the collected raw data, and to associate and calibrate it with building information.

[0031] The simulation analysis module is used to calculate the wall temperature field and heat flux distribution under the design state based on the design parameters output by the building information acquisition module by calling the numerical simulation engine (such as the finite element heat conduction model or the network model).

[0032] The performance evaluation module is used to compare the actually measured thermal parameters with the simulation calculation results, and to invert the overall U value of the wall and the local thermal bridge effect using deviation analysis, least squares fitting or machine learning algorithms.

[0033] The report generation and feedback module is used to generate structured inspection reports and feed back the results to design, construction or operation and maintenance personnel through the BIM collaboration platform.

[0034] On the basis of the above-mentioned wall thermal insulation performance detection system based on architectural design, the present invention proposes a wall thermal insulation performance detection method based on architectural design, and the specific steps are as follows: Step 1: Acquisition of architectural design parameters - reading the thermal properties of wall layered materials, node construction, and indoor and outdoor boundary conditions from the BIM model; Step 2: Develop a detection plan - determine the number of measurement points, sensor layout and sampling frequency based on the total length of the wall, height difference and external window position; Step 3: Field data collection - install temperature sensors, heat flow meters and infrared cameras to continuously collect temperature and heat flux time series for at least 24-72 hours; Step 4: Data preprocessing - denoising, temperature drift correction, clock synchronization, and elimination of abnormal fluctuation points on the collected data; Step 5: Thermal simulation calculation - Based on the design parameters in step 1, a two-dimensional or three-dimensional heat conduction model is established in the simulation analysis module to calculate the steady-state and dynamic thermal responses under ideal conditions; Step 6: Data fusion and performance analysis - perform difference analysis on the measured data in step 4 and the simulation results in step 5, and use the inversion algorithm or fitting algorithm to calculate the actual wall U value and local thermal bridge conversion coefficient; Step 7: Report output and feedback - Generate a test report containing design values, measured values, error analysis, compliance and improvement suggestions, and upload it to the BIM collaboration platform.

[0035] In a further preferred embodiment, the following is the detailed content of each step of the wall thermal insulation performance detection method based on building design: Step 1: Obtaining building design parameters - Reading the thermal physical properties of wall layered materials, joint details, and indoor and outdoor boundary conditions from the BIM model; (1) Preparation of the BIM model: Ensure that the BIM model is the latest version and complete and accurate, covering the detailed information of the walls of the target building. Check the integrity of the model, including whether the geometric shape, layered structure, window and door positions, etc. of the walls are consistent with the actual building.

[0036] Confirm the compatibility of the BIM software and the data reading tool to ensure that the required data can be successfully extracted from the BIM model.

[0037] (2) Reading the thermal physical properties of wall layered materials: Extract the thermal physical property parameters such as the name, thickness, thermal conductivity, specific heat capacity, etc. of each layered material of the wall from the BIM model. For complex wall structures, there may be combinations of multiple different materials, and the relevant information of each layer of material needs to be accurately recorded.

[0038] Conduct a preliminary check on the read thermal physical property parameters to ensure the rationality and accuracy of the data. For example, the thermal conductivity should be within the common value range of this material.

[0039] (3) Reading joint details: Identify the key joints of the wall, such as the practices at the corners, around window and door openings, expansion joints, etc. Record the information such as the material connection method and sealing measures at the joints.

[0040] For special joint practices, such as the use of additional thermal insulation materials or strengthening measures, record their specific parameters and construction methods in detail.

[0041] (4) Reading indoor and outdoor boundary conditions: Obtain the boundary condition information such as the designed indoor and outdoor temperatures, relative humidities, wind speeds, etc. These information are usually related to the use function of the building and the local climate conditions, etc.

[0042] Check the integrity and accuracy of the boundary condition data to ensure that it meets the requirements of the actual project.

[0043] Step 2: Formulating the detection plan - Determine the number of measurement points, sensor layout, and sampling frequency according to the total length, height difference of the wall, and the position of the external window; (1) Analysis of wall characteristics: Accurately measure the total length and height difference of the wall, and record the position, size, and quantity of the external windows. Analyze the structural characteristics of the wall, such as whether there are different orientations, whether there are protruding or recessed parts, etc.

[0044] According to the characteristics of the wall, the wall is divided into different areas, such as walls with different orientations, areas near doors and windows, etc., so as to arrange measuring points targeted.

[0045] (2) Determination of the number of measuring points: Determine the number of measuring points based on the area, structural complexity and heat transfer characteristics of the wall. Generally speaking, the larger the wall area and the more complex the structure, the more measuring points are required.

[0046] For uniform walls, measuring points can be arranged at a certain spacing; for areas with thermal bridges, such as corners and around door and window openings, the number of measuring points should be appropriately increased.

[0047] (3) Design of sensor layout: Design the specific layout of the sensors according to the number of measuring points and the wall characteristics. Temperature sensors should be arranged at different depths of the wall to measure the temperature distribution inside the wall; heat flux meters should be installed on the wall surface to measure the heat flux of the wall.

[0048] The shooting position of the infrared camera should be able to cover the entire wall surface to comprehensively detect the temperature distribution of the wall. At the same time, avoid the influence of external factors such as direct sunlight and rain erosion on the sensors.

[0049] (4) Determination of sampling frequency: Consider the thermal response characteristics of the wall and the detection purpose to determine the sampling frequency. For rapidly changing thermal processes, such as temperature changes caused by solar radiation during the day, the sampling frequency should be higher; for slowly changing thermal processes, such as heat transfer at night, the sampling frequency can be appropriately reduced.

[0050] Generally speaking, the sampling frequency should not be less than once per hour to ensure that the changes in the thermal performance of the wall can be captured.

[0051] Step 3: On-site data collection - Install temperature sensors, heat flux meters and infrared cameras, and continuously collect temperature and heat flux time series for at least 24 - 72h; (1) Sensor installation: Install temperature sensors, heat flux meters and infrared cameras on the wall accurately according to the design of the detection plan. During the installation process, ensure that the sensors are in close contact with the wall surface to ensure the accuracy of the measurement data.

[0052] Number and mark the installed sensors, record their installation positions and the corresponding wall areas. At the same time, check whether the connection lines of the sensors are firm to avoid looseness or poor contact.

[0053] (2) Commissioning of data acquisition system: Connect data acquisition equipment, such as data acquisition instruments, computers, etc., and debug the data acquisition system. Set parameters such as sampling frequency and data storage format to ensure that the system can work normally.

[0054] Before officially collecting data, conduct a trial run for a period of time to check whether the collected data is normal. If there is any abnormality, find out the cause and make adjustments in time.

[0055] (3) Data collection process monitoring: During data collection, regularly check the working status of sensors and data acquisition equipment to ensure the continuity and stability of data collection.

[0056] Record the environmental conditions during the collection process, such as weather conditions, indoor and outdoor temperature changes, etc., for reference in subsequent data analysis.

[0057] (4) Data collection duration control: Continuously collect temperature and heat flux time series data for at least 24-72 hours to cover different diurnal changes and environmental conditions. During the collection process, ensure the integrity of the data and avoid data loss or interruption.

[0058] Step 4: Data preprocessing - denoising, temperature drift correction, clock synchronization, and elimination of abnormal fluctuation points on the collected data; (1) Denoising: Filtering algorithms, such as moving average filtering and median filtering, are used to denoise the collected temperature and heat flux data, removing high-frequency noise caused by external interference, sensor noise and other factors.

[0059] According to the characteristics of the data and the noise level, appropriate filtering parameters are selected to remove the noise while retaining the true characteristics of the data.

[0060] (2) Temperature drift correction: Analyze the temperature drift characteristics of the temperature sensor, use linear correction or nonlinear correction method to correct the temperature drift of the temperature data. Determine the temperature drift coefficient by comparing with the standard temperature source, and correct the collected temperature data.

[0061] For heat flow meters, it is also necessary to consider possible temperature drift problems and take corresponding correction measures.

[0062] (3) Clock synchronization: Since the data collection time of different sensors may have slight differences, clock synchronization is required. Using a unified time base, the data collected by each sensor is time-calibrated to ensure the time consistency of the data.

[0063] Clock synchronization can be achieved through the clock synchronization function of the data acquisition device or an external clock source.

[0064] (4)Eliminating abnormal fluctuation points: Set a reasonable threshold to identify abnormal fluctuation points in the collected data. Abnormal fluctuation points may be caused by sensor failures, sudden external interferences, etc.

[0065] Adopt statistical analysis methods, such as the method based on standard deviation, to determine whether a data point is an outlier. For abnormal fluctuation points, remove them from the data and mark them.

[0066] Step Five: Thermal simulation calculation - Based on the design parameters in Step One, establish a two-dimensional or three-dimensional heat conduction model in the simulation analysis module to calculate the steady-state and dynamic heat responses under ideal conditions; (1)Selection of simulation software: According to the detection requirements and the complexity of the wall, select a suitable thermal simulation software, such as EnergyPlus, ANSYS Fluent, etc. Ensure that the selected software can accurately simulate the heat conduction process of the wall.

[0067] (2)Model establishment: Based on the thermal physical properties of the wall layer materials, node practices, and indoor and outdoor boundary conditions obtained in Step One, establish a two-dimensional or three-dimensional heat conduction model in the simulation analysis module. The model should accurately reflect the geometric shape, material distribution, and boundary conditions of the wall.

[0068] Perform mesh division on the model and reasonably determine the density and accuracy of the mesh. For areas with large heat transfer changes, such as corners and around door and window openings, the mesh should be appropriately refined.

[0069] (3)Parameter setting: Input the thermal physical property parameters of the wall layer materials, indoor and outdoor boundary conditions, etc. into the simulation model. Set parameters such as the time step and calculation duration of the simulation to ensure that the steady-state and dynamic heat responses of the wall can be accurately simulated.

[0070] For dynamic heat response calculation, it is necessary to consider the changes in environmental conditions in different time periods, such as solar radiation, indoor and outdoor temperature fluctuations, etc.

[0071] (4)Simulation calculation: Start the simulation calculation program to perform steady-state and dynamic heat response calculations. During the calculation process, monitor the convergence situation and calculation time of the calculation to ensure the accuracy and reliability of the calculation results.

[0072] Conduct a preliminary analysis of the calculation results to check whether there are unreasonable results, such as too high or too low temperatures, etc. If there are abnormalities, adjust the model parameters or calculation settings in a timely manner.

[0073] Step 6: Data Fusion and Performance Analysis - Conduct a difference analysis on the measured data in Step 4 and the simulation results in Step 5, and calculate the actual U-value of the wall and the conversion coefficient of local thermal bridges using an inversion algorithm or a fitting algorithm; (1) Data Fusion: Match the preprocessed measured data and the simulation calculation results in terms of time and space. Ensure that the measured data and the simulation results correspond to the same wall location and time point.

[0074] Conduct a comparative analysis on the matched data, plot the comparison curve of the measured data and the simulation results, and visually display the differences between the two.

[0075] (2) Difference Analysis: Calculate the difference between the measured data and the simulation results, and analyze the distribution and change trend of the differences. Areas with large differences may have thermal bridges or other thermal performance problems.

[0076] Adopt statistical analysis methods, such as mean, standard deviation, etc., to conduct a quantitative analysis on the differences, and evaluate the consistency between the measured data and the simulation results.

[0077] (3) U-value Calculation: Use an inversion algorithm or a fitting algorithm to calculate the U-value of the actual wall based on the measured data and the simulation results. The inversion algorithm adjusts the model parameters to make the simulation results as close as possible to the measured data, thereby obtaining the thermal performance parameters of the actual wall; the fitting algorithm fits the measured data to establish the relationship between the thermal performance parameters and the measurement data.

[0078] Conduct an uncertainty analysis on the calculated U-value, and evaluate the reliability of the calculation results.

[0079] (4) Calculation of Conversion Coefficient of Local Thermal Bridges: For areas with thermal bridges, such as corners and around door and window openings, calculate the conversion coefficient of local thermal bridges. By comparing the heat fluxes in the thermal bridge area and the normal wall area, determine the influence degree of local thermal bridges on the wall insulation performance.

[0080] Analyze the influencing factors of the conversion coefficient of local thermal bridges, such as the geometric shape of the thermal bridge, material properties, etc., to provide a basis for improving the wall insulation performance.

[0081] Step 7: Report Output and Feedback - Generate an inspection report containing design values, measured values, error analysis, whether it meets the standards and improvement suggestions, and upload it to the BIM collaborative platform; (1) Report Content Arrangement: Collect and organize various data and analysis results during the detection process, including wall design parameters, measured data, simulation calculation results, U-value, local thermal bridge conversion coefficient, etc.

[0082] Conduct statistical analysis and chart drawing on the data to visually display the detection results. For example, draw temperature distribution curves, heat flux change curves, etc.

[0083] (2)Report writing: Write the detection report according to the standard report format. The report content should include parts such as detection purpose, detection method, detection results, error analysis, whether it meets the standards and improvement suggestions.

[0084] In the report, elaborate on the differences between the design values and the measured values, and analyze the causes of errors. Based on the detection results, judge whether the wall insulation performance meets the relevant standards and design requirements.

[0085] (3)Proposing improvement suggestions: According to the detection results and error analysis, put forward targeted improvement suggestions. For the situation where the wall insulation performance does not meet the standards, it is recommended to take measures such as increasing the thickness of the insulation material and improving the thermal bridge treatment.

[0086] Evaluate the feasibility and effectiveness of the improvement measures to provide reference for building design and construction.

[0087] (4)Report uploading and feedback: Upload the generated detection report to the BIM collaboration platform for relevant personnel to view and share. Set permissions on the BIM collaboration platform to ensure that only authorized personnel can access and modify the report content.

[0088] Timely feedback the detection results and improvement suggestions to relevant departments such as building design, construction, and operation management to promote the continuous improvement of the wall insulation performance.

[0089] In a further preferred embodiment, the present invention is further illustrated by specific embodiments, but not limited thereto.

[0090] In this embodiment, based on the exterior wall design of a residential project, the following process is adopted: (1)The building information acquisition module extracts the layering and thermal conductivity coefficients of the exterior wall from the BIM model established by Revit from the inside out as "20mm putty layer + 240mm aerated concrete + 20mm bonding mortar + 100mm extruded polystyrene board + 20mm plaster layer"; the indoor and outdoor boundary conditions are taken as the design conditions of 20℃ / -5℃; (2)The detection module arranges 2 groups of heat flux meters and temperature sensors at the exterior wall elevations of 1.0m, 1.8m, and 2.5m respectively, with a sampling interval of 10 minutes, and continuously collects data for 48 hours; at the same time, use an infrared thermal imager to photograph the wall temperature distribution; (3) The data processing module performs low-pass filtering and zero drift correction on the temperature and heat flux meter signals, and conducts spatial registration with the infrared image; (4) The simulation analysis module uses COMSOL Multiphysics to establish a two-dimensional heat transfer model and calculates the theoretical value of the steady-state U as 0.28 ; (5) The performance evaluation module compares the measured U value of 0.32 0.03 , and through the least squares fitting method, it is identified that the local thermal bridge at the node is enhanced, and the thermal bridge coefficient is 12% higher than the theoretical value; (6) The report generation module outputs the detection report, marks the position of the thermal bridge on the BIM platform, and suggests increasing the thickness of the insulation layer at this place or improving the node structure.

[0091] As described above, although the present invention has been shown and described with reference to specific preferred embodiments, it should not be construed as a limitation of the present invention itself. Various changes in form and detail may be made without departing from the spirit and scope of the present invention as defined by the appended claims.

Claims

1. A wall thermal insulation performance detection system based on architectural design, characterized by include: Building information acquisition module, used to extract wall layered structure and thermal design parameters; The detection module, which consists of a variety of sensors and infrared thermal imagers, is used to collect raw thermal data; A data processing module, used for performing time series synchronization, denoising and calibration processing on the raw data collected by the detection module; A simulation analysis module, used to establish a heat conduction numerical model for the parameters output by the building information acquisition module, and calculate the wall temperature field and heat flux distribution under different design conditions; The evaluation and feedback module performs difference analysis on the data results of the simulation analysis module by inverting the overall U value of the wall and the local thermal bridge index, and generates a corresponding feedback detection data report.

2. The wall thermal insulation performance detection system based on architectural design according to claim 1 is characterized by: The data processing module also includes an environment compensation unit for dynamically compensating the collected values ​​for the indoor and outdoor environments.

3. The wall thermal insulation performance detection system based on architectural design according to claim 1 is characterized by: The simulation analysis module includes at least one of a finite element simulation unit and a network model simulation unit; The evaluation and feedback module uses least squares fitting or a machine learning model trained based on historical data to perform thermal parameter inversion.

4. The wall thermal insulation performance detection system based on architectural design according to claim 1 is characterized by: The detection module also includes a movable thermocouple scanning device for obtaining high-resolution temperature distribution in a local area of ​​the wall.

5. A wall thermal insulation performance detection method based on architectural design, characterized in that: Using the system as described in any one of claims 1 to 4, the method comprises the steps of: S1. Obtain the wall layered structural parameters and indoor and outdoor boundary conditions in the BIM model through the building information acquisition module; S2. Determine the number of measurement points, sensor layout and sampling frequency according to the total length, height difference and external window position of the wall to be tested; S3. Install corresponding sensors according to the planned measurement points, and collect the original temperature, heat flow and infrared image data of the wall; S4, denoising, timing synchronization and environmental correction are performed on the collected data, and a heat conduction model is established in the simulation analysis module based on the design parameters of step S1, and the design thermal performance is calculated; S5, performing difference analysis between the measured data of step S4 and the simulation results of step S5, and inverting the actual wall U value and the local thermal bridge effect; S6. Generate a test report including thermal performance comparison, energy-saving compliance assessment and optimization suggestions, and feed it back to the BIM collaboration platform.

6. A wall thermal insulation performance detection method based on architectural design according to claim 5, characterized in that: In step S4, the collected raw data is filtered, and the filters used include a low-pass filter and a median filter.

7. The wall thermal insulation performance detection method based on architectural design according to claim 5 is characterized by: The inversion algorithm used in step S5 includes least squares method or support vector regression.

8. The wall thermal insulation performance detection method based on architectural design according to claim 5 is characterized by: After step S2, the infrared thermal imager data is calibrated and photographed to assist the evaluation and feedback module in locating the thermal bridge.

9. The wall thermal insulation performance detection system based on architectural design according to claim 5, characterized in that: When the detection plan is formulated in step S2, a measurement point is set every 0.5-1.5m in the height direction of the wall; The duration of field data collection was 24-72 hours.

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