A method for testing thermal insulation performance based on fabricated building

Abstract

The present application relates to the technical field of thermal insulation performance test, and discloses a kind of heat insulation performance test method based on fabricated building, comprising steps data acquisition, data processing, performance analysis, result output;The present application considers multiple influence factors by collecting temperature, humidity, solar radiation intensity data. By arranging sensor reasonably and collecting at predetermined interval, and to abnormal value processing, guarantee data reliable, provide solid foundation for evaluation;With building inside and outside temperature difference, humidity correction coefficient, solar radiation equivalent temperature as analysis feature, the calculation process is logical, fully consider the influence of humidity and solar radiation on temperature, accurately reflect the state of building under different environment;Adopt first, second evaluation index and multi-dimensional evaluation of comprehensive evaluation index, overcome the limitation of single index, and each index is associated with heat insulation performance, can intuitively reflect performance good or bad;It is convenient for relevant personnel to understand the situation comprehensively, conducive to decision and operation.

Description

Technical Field

[0001] This invention relates to the field of thermal insulation performance testing technology, and specifically to a method for testing the thermal insulation performance of prefabricated buildings. Background Technology

[0002] With the development of the construction industry, prefabricated buildings have been widely used due to their advantages such as high efficiency and environmental friendliness. In modern building design and use, thermal insulation performance is one of the key indicators for measuring building quality, and it plays a vital role in reducing building energy consumption and improving indoor comfort.

[0003] Traditional methods for testing the thermal insulation performance of buildings have many limitations when dealing with prefabricated buildings. The structural characteristics and construction methods of prefabricated buildings differ from traditional buildings, and the assembly methods of their components may have unique effects on heat transfer. For example, the connection points in prefabricated buildings may become weak points in heat conduction, and traditional testing methods may not accurately capture the impact of these points on the overall thermal insulation performance.

[0004] Furthermore, the thermal insulation performance of a building is influenced by a combination of environmental factors. Temperature, humidity, and solar radiation intensity are among the most critical. In real-world environments, temperature variations create a temperature difference between the inside and outside of a building, which is the primary driver of heat transfer. Humidity alters the thermal conductivity of air, thus affecting the efficiency of heat transfer between the inside and outside of the building. The intensity of solar radiation directly determines the amount of heat absorbed by the building surface, having a significant impact on the assessment of its thermal insulation performance. Existing testing methods often fail to comprehensively and accurately consider the interactions of these environmental factors and cannot effectively process data collected in complex environments, resulting in inaccurate assessments of the thermal insulation performance of prefabricated buildings.

[0005] Meanwhile, with the construction industry increasingly emphasizing energy conservation and environmental protection, there is a need for a more scientific, accurate, and applicable method for testing the thermal insulation performance of prefabricated buildings. This would better guide building design, construction, and subsequent quality assessment, improve the thermal insulation level of prefabricated buildings, reduce energy consumption, and meet people's demands for a comfortable indoor environment. This invention proposes a novel method for testing the thermal insulation performance of prefabricated buildings based on the aforementioned background. Summary of the Invention

[0006] The purpose of this invention is to provide a method for testing the thermal insulation performance of prefabricated buildings, which solves the technical problems mentioned in the background art.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A method for testing the thermal insulation performance of prefabricated buildings includes the following steps:

[0009] Step 1: Data Collection

[0010] Collect environmental data related to the thermal insulation performance of buildings, including temperature data, humidity data, and solar radiation intensity data.

[0011] Step 2: Data Processing

[0012] Environmental data were tested and organized to derive analytical characteristics for analyzing the thermal insulation performance of prefabricated buildings.

[0013] Step 3: Performance Analysis

[0014] Based on the test results, the thermal insulation performance of prefabricated buildings is evaluated and analyzed, and corresponding evaluation indicators for evaluating the thermal insulation performance of prefabricated buildings are obtained through the evaluation and analysis.

[0015] Step 4: Output Results

[0016] The assessment and analysis results will be presented to relevant personnel.

[0017] As a further aspect of the present invention, the environmental data is collected in the following way:

[0018] Step K1: Temperature Data Acquisition

[0019] Multiple temperature sensors are installed inside and outside the building to collect temperature values ​​inside and outside the building at predetermined time intervals T1.

[0020] Step K2: Humidity Data Acquisition

[0021] Multiple humidity sensors are installed inside and outside the building to collect humidity values ​​inside and outside the building at predetermined time intervals T1.

[0022] Step K3: Solar Radiation Intensity Data Acquisition

[0023] By installing solar radiation sensors on the building facade, the solar radiation intensity values ​​outside the building are collected at predetermined time intervals T1.

[0024] As a further aspect of the present invention: the analytical features include the temperature difference between the inside and outside of the building, the humidity correction factor, and the equivalent temperature of solar radiation;

[0025] Among them, the temperature difference between the inside and outside of a building refers to the difference between the external ambient temperature and the internal temperature of the building at the same time;

[0026] Since humidity affects the thermal conductivity of air and thus heat transfer, a humidity correction factor is needed to correct for temperature differences.

[0027] When solar radiation hits a building surface, it causes the building to absorb heat, resulting in an increase in surface temperature. The equivalent temperature of solar radiation is a value calculated based on data on solar radiation intensity.

[0028] As a further aspect of the present invention, the testing and sorting method is as follows:

[0029] Step Z1: Temperature Difference Calculation

[0030] Step Z1.1: At each acquisition time point within the specified period, extract the temperature values ​​outside the building and the temperature values ​​inside the building collected by each temperature sensor at the same acquisition time point;

[0031] Within a specified period, the building exterior temperature sequence TW is obtained according to the time trend. i，j1 and building temperature series TN i，j1 ;

[0032] Where i = 1, 2, ..., n, n represents the number of time points corresponding to the collection of temperature and humidity data within the specified period, j1 = 1, 2, ..., m1, m1 represents the number of temperature sensors installed inside and outside the building, and the number of humidity sensors installed inside and outside the building, and the temperature and humidity data are collected synchronously within the specified period.

[0033] Step Z1.2: Select a data acquisition time point. At the same data acquisition time point, calculate the average value of the building's external temperature collected by all temperature sensors and label it as TWP. i ;

[0034] Step Z1.3: Calculate the standard deviation of the building exterior temperature values ​​collected by all temperature sensors at the acquisition time points, and label it as TWB. i ;

[0035] Step Z1.4: Extract the building exterior temperature value TW collected by the temperature sensor at this acquisition time point. i，j1 ;

[0036] Subsequently, |TW i，j1 -TWP i The obtained value is 3 times TWB. i Value comparison:

[0037] If |TW i，j1 -TWP i |>3TWB i This indicates the building exterior temperature value TW collected by the corresponding temperature sensor at that collection time point.i，j1 If a value is found to be outlier, it is then removed.

[0038] If |TW i，j1 -TWP i ≤3TWB i This indicates the building exterior temperature value TW collected by the corresponding temperature sensor at that collection time point. i，j1 The value is normal, so it is retained.

[0039] Step Z1.5: Extract the building exterior temperature values ​​that are within the normal range collected by the corresponding temperature sensor at the time of acquisition, calculate their average value, and relabel them as TWPP. i ;

[0040] Step Z1.6: Following the methods in Step Z1.2 to Step Z1.5, extract the indoor temperature values ​​of the building that are within the normal range at the corresponding temperature sensor at the acquisition time point, calculate their average value, and relabel them as TNPP. i ;

[0041] Step Z1.7, then through TC i =|TWPP i -TNPP i | Calculate the temperature difference TC inside and outside the building at each data collection time point. i ;

[0042] Step Z2: Calculation of Humidity Correction Factor

[0043] SSepZ2.1 Extracts the humidity values ​​outside the building and inside the building collected by each humidity sensor at the same collection time point within a specified period.

[0044] Within a specified period, the building's external humidity sequence SW is obtained according to the time trend. i，j1 and building humidity sequence SN i，j1 ;

[0045] SSepZ2.2. Select a data collection time point. At the same data collection time point, calculate the average value of the outdoor humidity values ​​collected by all humidity sensors and label it as SWP. i ;

[0046] SSepZ2.3 Calculate the standard deviation of the building's external humidity values ​​collected by all humidity sensors at the acquisition time points, and label it as SWB. i ;

[0047] SSepZ2.4 Extract the building exterior humidity value SW collected by the humidity sensor at this acquisition time point. i，j1 ;

[0048] Then |SW i，j1 -SWP i The obtained value is 3 times the SWB. i Value comparison:

[0049] If |SW i，j1 -SWP i |>3SWB i This indicates the outdoor humidity value SW collected by the corresponding humidity sensor at that collection time point. i，j1 If a value is found to be outlier, it is then removed.

[0050] If |SW i，j1 -SWP i |≤3SWB i This indicates the outdoor humidity value SW collected by the corresponding humidity sensor at that collection time point. i，j1 The value is normal, so it is retained.

[0051] SSepZ2.5 Extract the building's external humidity values ​​that are within the normal range at the corresponding humidity sensor collection time point, calculate their average value, and relabel them as SWPP. i ;

[0052] SSepZ2.6. Following the methods of SSepZ2.2 to SSepZ2.5, extract the indoor humidity values ​​that are within the normal range collected by the corresponding humidity sensor at the collection time point, calculate their average value, and relabel them as SNPP. i ;

[0053] SSepZ2.7, followed by XS i =1+k1×(SWPP) i -SNPP i ), calculate the humidity correction factor XS at each data collection time point. i ;

[0054] Wherein, k1 is a preset humidity influence coefficient, which is determined based on experimental derivation;

[0055] Step Z3: Calculation of Equivalent Temperature of Solar Radiation

[0056] SSepZ3.1 Extract the solar radiation intensity values ​​outside the building collected by each solar radiation sensor at the same collection time point within a specified period.

[0057] Within a specified period, following the time progression, the solar radiation intensity sequence FQ is obtained. i，j2, where i = 1, 2, ..., n, n represents the number of time points corresponding to the solar radiation intensity within the specified period, j2 = 1, 2, ..., m2, m2 represents the number of solar radiation sensors installed on the exterior of the building;

[0058] SSepZ3.2. Select a data acquisition time point, extract the solar radiation intensity values ​​outside the building collected by all solar radiation sensors at the same time point, sort them in ascending order, and obtain the median value as the calculated value of the solar radiation equivalent temperature, and label it as FQZ. i ;

[0059] SSepZ3.3, followed by DT i =k2×FQZ i The equivalent temperature of solar radiation DT at each data collection time point was calculated. i ;

[0060] Wherein, k2 is a preset solar radiation to temperature conversion coefficient, which is determined based on experimental derivation.

[0061] As a further aspect of the present invention, the evaluation and analysis method is as follows:

[0062] Step P1: Extract the temperature difference (TC) between the inside and outside of the building at all data collection points within a specified period. i Then, its average value is calculated and recorded as the first evaluation index;

[0063] Step P2: Extract the temperature difference (TC) between the inside and outside of the building at all data collection points within a specified period. i Humidity correction factor XS at all data collection points i And the equivalent temperature of solar radiation (DT) at all data collection points. i ;

[0064] Subsequently through ZTC i =TC i ×XS i -DT i The comprehensive temperature difference ZTC at each sampling time point was calculated. i ;

[0065] Then, the combined temperature difference ZTC at all data collection time points was calculated. i The average value is recorded as the second evaluation indicator;

[0066] Step P3: Extract the first and second evaluation indicators, and then calculate the comprehensive evaluation indicator ZP using ZP = P1 × γ1 + P2 × γ2.

[0067] In the formula, P1 is the first evaluation index, P2 is the second evaluation index, and γ1 and γ2 are the weight coefficients preset based on the first and second evaluation indices, respectively.

[0068] As a further aspect of the present invention, the evaluation and analysis results shown include a first evaluation index, a second evaluation index, and a comprehensive evaluation index.

[0069] As a further aspect of the present invention, wherein:

[0070] The higher the value of the first evaluation index, the better the thermal insulation performance of the prefabricated building.

[0071] The higher the value of the second evaluation index, the better the thermal insulation performance of the prefabricated building.

[0072] The higher the value of the comprehensive evaluation index, the better the thermal insulation performance of the prefabricated building.

[0073] The beneficial effects of this invention are:

[0074] Multi-type data acquisition: By collecting environmental data related to building thermal insulation performance, such as temperature, humidity, and solar radiation intensity, a comprehensive consideration of factors affecting the thermal insulation performance of prefabricated buildings can be achieved. Temperature data reflects the thermal state inside and outside the building, humidity data considers the impact of humidity on air thermal conductivity and heat transfer, and solar radiation intensity data reflects the key factor that solar radiation raises the building surface temperature. This multi-dimensional data provides a foundation for accurately assessing thermal insulation performance.

[0075] Scientific data collection methods: Multiple temperature and humidity sensors are installed both inside and outside the building, collecting data at predetermined time intervals. Solar radiation sensors are also installed on the building's exterior facade to collect data. This scientifically sound arrangement ensures the breadth and reliability of data sources. Furthermore, outliers are identified and removed during the data collection process, further improving data accuracy and enabling subsequent analysis to be based on high-quality data.

[0076] The analysis features are comprehensive: temperature difference between the building's interior and exterior, humidity correction factor, and equivalent solar radiation temperature are incorporated as analytical features. Specifically, the temperature difference calculation underwent rigorous outlier handling and average value calculation to ensure the reliability of the temperature difference data. The humidity correction factor calculation takes into account the impact of humidity on heat transfer, accurately correcting the temperature difference through a reasonable formula and a preset humidity influence coefficient. The equivalent solar radiation temperature is calculated based on solar radiation intensity data, obtaining intermediate values ​​according to a specific sorting method and combining them with preset conversion coefficients. These analytical features comprehensively and accurately reflect the building's condition under the influence of various environmental factors.

[0077] The testing and data processing adheres to a rigorous logical framework: each step in the calculation of temperature difference, humidity correction factor, and equivalent temperature of solar radiation follows a clear and meticulous logic. From data extraction, calculation of average and standard deviation, to the identification and handling of outliers, and finally to the calculation of analytical characteristic values, the entire process ensures the accuracy and scientific rigor of data processing, enabling the analysis results to truly reflect the thermal insulation performance of prefabricated buildings.

[0078] Multi-dimensional evaluation indicators: The thermal insulation performance of prefabricated buildings is evaluated by calculating the first evaluation indicator, the second evaluation indicator, and the comprehensive evaluation indicator. This multi-dimensional evaluation method can more comprehensively measure the thermal insulation capability of buildings under different environmental conditions, avoiding the limitations of single-indicator evaluation.

[0079] Clear assessment significance: The value of each assessment indicator is clearly related to the thermal insulation performance of prefabricated buildings. The larger the indicator value, the better the thermal insulation performance, making the assessment results intuitive. Relevant personnel can clearly understand the level of thermal insulation performance of the building, thus providing a strong basis for building design improvement, quality assessment, etc.

[0080] The results are clear and practical: the displayed evaluation and analysis results include the first evaluation indicator, the second evaluation indicator, and the comprehensive evaluation indicator. This clear and concise output allows relevant personnel to fully understand the various evaluation aspects of the building's thermal insulation performance. Whether it's building engineers, quality inspectors, or other relevant personnel, they can quickly grasp the key information about the building's thermal insulation performance based on these results, enabling further decision-making and action. Attached Figure Description

[0081] The invention will now be further described with reference to the accompanying drawings.

[0082] Figure 1 This is a flowchart illustrating a method for testing the thermal insulation performance of prefabricated buildings according to the present invention.

[0083] Figure 2 This is a flowchart illustrating the data processing steps in a method for testing the thermal insulation performance of prefabricated buildings according to the present invention. Detailed Implementation

[0084] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0085] Example 1

[0086] Test environment preparation: Choose a suitable test season and weather conditions, prioritizing sunny weather with stable temperatures, and avoiding extreme weather conditions such as strong winds and rain;

[0087] Extreme weather can interfere with the accuracy of test data. For example, wind can accelerate heat transfer, causing test results to deviate from the building's actual thermal insulation performance.

[0088] Multiple temperature and humidity sensors are installed inside and outside the building to ensure that the sensors are evenly distributed and can represent the overall environmental conditions of the building.

[0089] The arrangement of sensors must comply with relevant building testing standards. For example, one sensor should be installed every [X] square meters indoors, and sensors should be evenly distributed on the exterior facade of the building according to different orientations.

[0090] Please see Figure 1 and Figure 2 As shown, this invention is a method for testing the thermal insulation performance of prefabricated buildings, comprising the following steps:

[0091] Step 1: Data Collection

[0092] Collect environmental data related to the thermal insulation performance of buildings, including temperature data;

[0093] The specific data collection method is as follows:

[0094] Temperature sensors are used to collect temperature values ​​inside and outside the building at predetermined time intervals T1.

[0095] Within a specified period, the building exterior temperature sequence TW is obtained according to the time trend. i，j1 and building temperature series TN i，j1 ;

[0096] T1 is set to 5 minutes.

[0097] Step 2: Data Processing

[0098] Environmental data is tested and processed, and the results include the temperature value outside the building and the temperature difference between different areas inside the building.

[0099] The test organization method is as follows:

[0100] Step Z1: Temperature Difference Calculation

[0101] Step Z1.1: At each acquisition time point within the specified period, extract the temperature values ​​outside the building and the temperature values ​​inside the building collected by each temperature sensor at the same acquisition time point;

[0102] Step Z1.2: Select a data acquisition time point. At the same data acquisition time point, calculate the average value of the building's external temperature collected by all temperature sensors and label it as TWP. i ;

[0103] Step Z1.3: Calculate the standard deviation of the building exterior temperature values ​​collected by all temperature sensors at the acquisition time points, and label it as TWB. i ;

[0104] Step Z1.4: Extract the building exterior temperature value TW collected by the temperature sensor at this acquisition time point. i，j1 ;

[0105] Subsequently, |TW i，j1 -TWP i The obtained value is 3 times TWB. i Value comparison:

[0106] If |TW i，j1 -TWP i |>3TWB i This indicates the building exterior temperature value TW collected by the corresponding temperature sensor at that collection time point. i，j1 If a value is found to be outlier, it is then removed.

[0107] If |TW i，j1 -TWP i ≤3TWB i This indicates the building exterior temperature value TW collected by the corresponding temperature sensor at that collection time point. i，j1 The value is normal, so it is retained.

[0108] Step Z1.5: Extract the building exterior temperature values ​​that are within the normal range collected by the corresponding temperature sensor at the time of acquisition, calculate their average value, and relabel them as TWPP. i ;

[0109] Step Z1.6: Following the methods in Step Z1.2 to Step Z1.5, extract the indoor temperature values ​​of the building that are within the normal range at the corresponding temperature sensor at the acquisition time point, calculate their average value, and relabel them as TNPP. i ;

[0110] Step Z1.7, then through TC i =|TWPP i -TNPP i | Calculate the temperature difference TC inside and outside the building at each data collection time point. i ;

[0111] Step 3: Performance Analysis

[0112] Based on the test results, the thermal insulation performance of prefabricated buildings is evaluated and analyzed.

[0113] The evaluation and analysis methods are as follows:

[0114] Step P1: Extract the temperature difference (TC) between the inside and outside of the building at all data collection points within a specified period. i Then, its average value is calculated and recorded as the first evaluation index;

[0115] Among them, the larger the value of the first evaluation index, the better the thermal insulation performance of the prefabricated building;

[0116] Step 4: Output Results

[0117] It is used to present the evaluation and analysis results to relevant personnel.

[0118] This embodiment rationally places temperature and humidity sensors inside and outside the building, ensuring uniform sensor distribution and representativeness of the overall building environment according to relevant building testing standards. Temperature data is collected at predetermined time intervals, forming a temperature sequence within a specified period, providing rich data for subsequent accurate analysis. The collected temperature data undergoes refined processing, including calculating temperature differences. During processing, the relationship between each temperature sensor's collected value and its average value and standard deviation is compared. Outliers are removed, and the average value is recalculated. This method effectively reduces errors, improves data quality, and thus more accurately analyzes the building's thermal insulation performance. A first evaluation index is calculated based on the processed temperature difference data. The value of this first evaluation index directly assesses the thermal insulation performance of the prefabricated building; a higher value indicates better thermal insulation performance, providing a quantitative basis for evaluating building thermal insulation performance.

[0119] Example 2

[0120] Test environment preparation: Multiple humidity sensors and solar radiation sensors were installed inside and outside the building to ensure that the sensors were evenly distributed and could represent the overall environmental conditions of the building.

[0121] As a second embodiment of the present invention, in specific implementation, the technical solution of this embodiment differs from that of embodiment one only in that:

[0122] Environmental data also includes humidity data and solar radiation intensity data;

[0123] The specific data collection method is as follows:

[0124] Step K2: Humidity Data Acquisition

[0125] Using a humidity sensor, humidity values ​​inside and outside the building are collected at predetermined time intervals T1;

[0126] Within a specified period, the building's external humidity sequence SW is obtained according to the time trend. i，j1 and building humidity sequence SN i，j1 ;

[0127] Where i = 1, 2, ..., n, n represents the number of time points corresponding to the collection of temperature and humidity data within the specified period, j1 = 1, 2, ..., m1, m1 represents the number of temperature sensors installed inside and outside the building, and the number of humidity sensors installed inside and outside the building, and the temperature and humidity data are collected synchronously within the specified period.

[0128] Step K3: Solar Radiation Intensity Data Acquisition

[0129] A solar radiation sensor is installed on the exterior of the building to collect the solar radiation intensity value outside the building at a predetermined time interval T1.

[0130] Within a specified period, following the time progression, the solar radiation intensity sequence FQ is obtained. i，j2 , where i = 1, 2, ..., n, n represents the number of time points corresponding to the solar radiation intensity within the specified period, j2 = 1, 2, ..., m2, m2 represents the number of solar radiation sensors installed on the exterior of the building;

[0131] In this embodiment, solar radiation intensity has a significant impact on the heat absorption of a building and is one of the key factors in analyzing its thermal insulation performance.

[0132] Furthermore, solar radiation intensity data, temperature data, and humidity data are collected synchronously within a specified period;

[0133] The test results also include humidity correction factor and solar radiation equivalent temperature;

[0134] Step Z2: Calculation of Humidity Correction Factor

[0135] SSepZ2.1 Extracts the humidity values ​​outside the building and inside the building collected by each humidity sensor at the same collection time point within a specified period.

[0136] SSepZ2.2. Select a data collection time point. At the same data collection time point, calculate the average value of the outdoor humidity values ​​collected by all humidity sensors and label it as SWP. i ;

[0137] SSepZ2.3 Calculate the standard deviation of the building's external humidity values ​​collected by all humidity sensors at the acquisition time points, and label it as SWB. i ;

[0138] SSepZ2.4 Extract the building exterior humidity value SW collected by the humidity sensor at this acquisition time point. i，j1 ;

[0139] Then |SW i，j1 -SWP i The obtained value is 3 times the SWB. i Value comparison:

[0140] If |SW i，j1 -SWP i |>3SWB i This indicates the outdoor humidity value SW collected by the corresponding humidity sensor at that collection time point. i，j1 If a value is found to be outlier, it is then removed.

[0141] If |SW i，j1 -SWP i |≤3SWB i This indicates the outdoor humidity value SW collected by the corresponding humidity sensor at that collection time point. i，j1 The value is normal, so it is retained.

[0142] SSepZ2.5 Extract the building's external humidity values ​​that are within the normal range at the corresponding humidity sensor collection time point, calculate their average value, and relabel them as SWPP. i ;

[0143] SSepZ2.6. Following the methods of SSepZ2.2 to SSepZ2.5, extract the indoor humidity values ​​that are within the normal range collected by the corresponding humidity sensor at the collection time point, calculate their average value, and relabel them as SNPP. i ;

[0144] SSepZ2.7, followed by XS i =1+k1×(SWPP) i -SNPP i ), calculate the humidity correction factor XS at each data collection time point. i ;

[0145] Wherein, k1 is a preset humidity influence coefficient, which is determined based on experimental derivation;

[0146] In this step, since humidity affects the thermal conductivity of air, thus affecting heat transfer, it is necessary to correct for the humidity factor.

[0147] Step Z3: Calculation of Equivalent Temperature of Solar Radiation

[0148] SSepZ3.1 Extract the solar radiation intensity values ​​outside the building collected by each solar radiation sensor at the same collection time point within a specified period.

[0149] SSepZ3.2. Select a data acquisition time point, extract the solar radiation intensity values ​​outside the building collected by all solar radiation sensors at the same time point, sort them in ascending order, and obtain the median value as the calculated value of the solar radiation equivalent temperature, and label it as FQZ. i ;

[0150] SSepZ3.3, followed by DT i =k2×FQZ i The equivalent temperature of solar radiation DT at each data collection time point was calculated. i ;

[0151] Wherein, k2 is a preset solar radiation to temperature conversion coefficient, which is determined based on experimental derivation;

[0152] Among them, the solar radiation equivalent temperature converts the solar radiation intensity into the corresponding temperature value through a certain conversion coefficient, thereby quantifying the degree of influence of solar radiation on building temperature. For example, when solar radiation is strong in summer, by calculating the solar radiation equivalent temperature, we can intuitively understand how much the solar radiation has increased the building surface temperature, and then assess its impact on the indoor thermal environment.

[0153] The evaluation and analysis methods also include the following:

[0154] At a specified period, extract the temperature difference (TC) between the inside and outside of the building at all data collection points. i Humidity correction factor XS at all data collection points i And the equivalent temperature of solar radiation (DT) at all data collection points. i ;

[0155] Subsequently through ZTC i =TC i ×XS i -DT i The comprehensive temperature difference ZTC at each sampling time point was calculated. i ;

[0156] Then, the combined temperature difference ZTC at all data collection time points was calculated. i The average value is recorded as the second evaluation indicator;

[0157] The higher the value of the second evaluation index, the better the thermal insulation performance of the prefabricated building.

[0158] This embodiment, based on Embodiment 1, adds the collection of humidity and solar radiation intensity data, taking into account factors affecting the thermal insulation performance of buildings more comprehensively. Humidity affects air thermal conductivity and thus heat transfer, while solar radiation intensity has a significant impact on building heat absorption. The introduction of new data makes the evaluation results more consistent with reality. For humidity and solar radiation intensity data, a similar processing method as for temperature data is used, calculating the average value and standard deviation to remove outliers and ensure data quality. For the humidity factor, a humidity correction coefficient is calculated to correct for the humidity effect, taking into account the role of humidity in air thermal conductivity and heat transfer, making the evaluation more accurate. By calculating the equivalent temperature of solar radiation, solar radiation intensity is converted into a corresponding temperature value, quantifying the degree of influence of solar radiation on building temperature and providing a more intuitive analysis of the impact of solar radiation on the indoor thermal environment. In the evaluation analysis, a comprehensive temperature difference is calculated by combining the temperature difference, humidity correction coefficient, and equivalent temperature of solar radiation, and its average value is used as the second evaluation index. This comprehensive evaluation method can more comprehensively measure the thermal insulation performance of prefabricated buildings, and the evaluation results are more scientific than those of Embodiment 1.

[0159] Example 3

[0160] As a third embodiment of the present invention, in specific implementation, compared with embodiments one and two, the technical solution of this embodiment is to combine the solutions of embodiments one and two. The difference between the technical solution of this embodiment and embodiments one and two is only in this embodiment.

[0161] The first and second evaluation indicators from Example 1 and Example 2 are extracted, and then the comprehensive evaluation indicator ZP is calculated by ZP = P1×γ1 + P2×γ2.

[0162] In the formula, P1 is the first evaluation index, P2 is the second evaluation index, and γ1 and γ2 are the weight coefficients preset based on the first and second evaluation indices, respectively.

[0163] Among them, the higher the value of the comprehensive evaluation index, the better the thermal insulation performance of the prefabricated building;

[0164] The evaluation and analysis results presented include the first evaluation indicator, the second evaluation indicator, and the comprehensive evaluation indicator.

[0165] This embodiment combines the evaluation indicators of Embodiment 1 and Embodiment 2, using the comprehensive evaluation indicator ZP to assess the thermal insulation performance of prefabricated buildings. It comprehensively considers both the first evaluation indicator based solely on temperature difference and the second evaluation indicator considering humidity and solar radiation, and calculates the results according to preset weighting coefficients, making the evaluation results more comprehensive and reflecting the building's thermal insulation performance. The presented evaluation analysis results include the first evaluation indicator, the second evaluation indicator, and the comprehensive evaluation indicator, providing relevant personnel with multi-dimensional information, helping to understand the building's thermal insulation performance from different perspectives, and providing richer data support for building design, improvement, and other decisions.

[0166] Example 4

[0167] As a fourth embodiment of the present invention, in specific implementation, compared with embodiments one, two and three, the technical solution of this embodiment is to combine the solutions of embodiments one, two and three above.

[0168] This embodiment combines the solutions of Embodiment 1, Embodiment 2, and Embodiment 3, integrating the advantages of each previous embodiment to form a more complete and comprehensive testing and evaluation system for the thermal insulation performance of prefabricated buildings. This system maximizes the comprehensiveness of test data, the scientific nature of processing, and the accuracy of evaluation results, and can comprehensively meet the needs for evaluating the thermal insulation performance of prefabricated buildings.

[0169] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0170] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for testing the thermal insulation performance of prefabricated buildings, characterized in that, Includes the following steps: The first step is to collect environmental data related to the building's thermal insulation performance, including temperature data, humidity data, and solar radiation intensity data. The second step is to test and organize the environmental data, and derive analytical features for analyzing the thermal insulation performance of prefabricated buildings. These analytical features include the temperature difference between the inside and outside of the building, the humidity correction factor, and the equivalent temperature of solar radiation. The third step is to evaluate and analyze the thermal insulation performance of prefabricated buildings based on the test results, and obtain the corresponding evaluation indicators for evaluating the thermal insulation performance of prefabricated buildings through the evaluation and analysis. The fourth step is to present the assessment and analysis results to relevant personnel. The temperature difference is calculated as follows: Step Z1.1: Within a specified period, divide the time into multiple collection time points according to a predetermined time interval T1, and extract the temperature values ​​outside the building and inside the building collected by each temperature sensor at the same collection time point. Within a specified period, the building exterior temperature sequence TW is obtained according to the time trend. i，j1 and building temperature series TN i，j1 ; Where i = 1, 2, ..., n, n represents the number of time points corresponding to the collection of temperature and humidity data within the specified period, j1 = 1, 2, ..., m1, m1 represents the number of temperature sensors installed inside and outside the building, and the number of humidity sensors installed inside and outside the building, and the temperature and humidity data are collected synchronously within the specified period. Step Z1.2: Select a data acquisition time point. At the same data acquisition time point, calculate the average value of the building's external temperature collected by all temperature sensors and label it as TWP. i ; Step Z1.3: Calculate the standard deviation of the building exterior temperature values ​​collected by all temperature sensors at the acquisition time points, and label it as TWB. i ; Step Z1.4: Extract the building exterior temperature value TW collected by the temperature sensor at this acquisition time point. i，j1 ; Subsequently, |TW i，j1 -TWP i The obtained value is 3 times TWB i Value comparison: If |TW i，j1 -TWP i |>3TWB i This indicates the building exterior temperature value TW collected by the corresponding temperature sensor at that collection time point. i，j1 If a value is found to be outlier, it is then removed. If |TW i，j1 -TWP i ≤3TWB i This indicates the building exterior temperature value TW collected by the corresponding temperature sensor at that collection time point. i，j1 The value is normal, so it is retained. Step Z1.5: Extract the building exterior temperature values ​​that are within the normal range collected by the corresponding temperature sensor at the time of acquisition, calculate their average value, and relabel them as TWPP. i ; Step Z1.6: Following the methods in Step Z1.2 to Step Z1.5, extract the indoor temperature values ​​of the building that are within the normal range at the corresponding temperature sensor at the acquisition time point, calculate their average value, and relabel them as TNPP. i ; Step Z1.7, then through TC i =|TWPP i -TNPP i | Calculate the temperature difference TC inside and outside the building at each data collection time point. i ; The humidity correction factor is calculated as follows: SSepZ2.1: Within a specified period, the data collection time points are divided according to a predetermined time interval T1, and the humidity values ​​outside the building and inside the building collected by each humidity sensor at the same time point are extracted. Within a specified period, the building's external humidity sequence SW is obtained according to the time trend. i，j1 and building humidity sequence SN i，j1 ; SSepZ2.

2. Select a data collection time point. At the same data collection time point, calculate the average value of the outdoor humidity values ​​collected by all humidity sensors and label it as SWP. i ; SSepZ2.3 Calculate the standard deviation of the building's external humidity values ​​collected by all humidity sensors at the acquisition time points, and label it as SWB. i ; SSepZ2.4 Extract the building exterior humidity value SW collected by the humidity sensor at this acquisition time point. i，j1 ; Then |SW i，j1 -SWP i The obtained value is 3 times the SWB. i Value comparison: If |SW i，j1 -SWP i |>3SWB i This indicates the outdoor humidity value SW collected by the corresponding humidity sensor at that collection time point. i，j1 If a value is found to be outlier, it is then removed. If |SW i，j1 -SWP i |≤3SWB i This indicates the outdoor humidity value SW collected by the corresponding humidity sensor at that collection time point. i，j1 The value is normal, so it is retained. SSepZ2.5 Extract the building's external humidity values ​​that are within the normal range collected by the corresponding humidity sensor at the time of acquisition, calculate their average value, and relabel them as SWPP. i ; SSepZ2.

6. Following the methods of SSepZ2.2 to SSepZ2.5, extract the indoor humidity values ​​that are within the normal range collected by the corresponding humidity sensor at the collection time point, calculate their average value, and relabel them as SNPP. i ; SSepZ2.7, followed by XS i =1+k1×(SWPP i -SNPP i ), calculate the humidity correction factor XS at each data collection time point. i ; Wherein, k1 is a preset humidity influence coefficient, which is determined based on experimental derivation; The equivalent temperature of solar radiation is calculated as follows: SSepZ3.

1. Within a specified period, the solar radiation intensity values ​​outside the building are collected at the same time point by dividing the time into collection time points according to a predetermined time interval T1. Within a specified period, following the time progression, the solar radiation intensity sequence FQ is obtained. i，j2 , where i=1, 2, ..., n, n represents the number of time points corresponding to the solar radiation intensity within the specified period, j2=1, 2, ..., m2, m2 represents the number of solar radiation sensors installed on the exterior of the building; SSepZ3.

2. Select a data acquisition time point, extract the solar radiation intensity values ​​outside the building collected by all solar radiation sensors at the same time point, sort them in ascending order, and obtain the median value as the calculated value of the solar radiation equivalent temperature, and label it as FQZ. i ; SSepZ3.3, followed by DT i =k2×FQZ i The equivalent temperature of solar radiation DT at each data collection time point was calculated. i ; Wherein, k2 is a preset solar radiation to temperature conversion coefficient, which is determined based on experimental derivation.

2. The method for testing the thermal insulation performance of prefabricated buildings according to claim 1, characterized in that, in: The temperature difference between the inside and outside of a building refers to the difference between the external ambient temperature and the internal temperature of the building at the same time: The humidity correction factor is used to correct for temperature differences; The equivalent temperature of solar radiation is calculated based on solar radiation intensity data to obtain the corresponding equivalent temperature value.

3. The method for testing the thermal insulation performance of prefabricated buildings according to claim 2, characterized in that, The evaluation and analysis methods are as follows: At a specified period, extract the temperature difference (TC) between the inside and outside of the building at all data collection points. i Then, its average value is calculated and recorded as the first evaluation index.

4. The method for testing the thermal insulation performance of prefabricated buildings according to claim 3, characterized in that, The evaluation and analysis methods also include the following: At a specified period, extract the temperature difference (TC) between the inside and outside of the building at all data collection points. i Humidity correction factor XS at all data collection points i And the equivalent temperature of solar radiation (DT) at all data collection points. i ; Subsequently through ZTC i =TC i ×XS i -DT i The comprehensive temperature difference ZTC at each sampling time point was calculated. i ; Then, the combined temperature difference ZTC at all data collection time points was calculated. i The average value is recorded as the second evaluation indicator.

5. The method for testing the thermal insulation performance of prefabricated buildings according to claim 4, characterized in that, The evaluation and analysis methods also include the following: Extract the first and second evaluation indicators, and then calculate the comprehensive evaluation indicator ZP using ZP=P1×γ1+P2×γ2. In the formula, P1 is the first evaluation index, P2 is the second evaluation index, and γ1 and γ2 are the weight coefficients preset based on the first and second evaluation indices, respectively.

6. The method for testing the thermal insulation performance of prefabricated buildings according to claim 5, characterized in that, in, The evaluation and analysis results presented include the first evaluation indicator, the second evaluation indicator, and the comprehensive evaluation indicator.

7. The method for testing the thermal insulation performance of prefabricated buildings according to claim 5, characterized in that, in: The higher the value of the first evaluation index, the better the thermal insulation performance of the prefabricated building. The higher the value of the second evaluation index, the better the thermal insulation performance of the prefabricated building. The higher the value of the comprehensive evaluation index, the better the thermal insulation performance of the prefabricated building.

Images