Thermal insulation performance testing method based on fabricated building

Abstract

The invention relates to the technical field of thermal insulation performance testing, and discloses a prefabricated building-based thermal insulation performance testing method, which comprises the steps of data acquisition, data processing, performance analysis and result output. According to the invention, temperature, humidity and solar radiation intensity data are collected, and various influence factors are considered. Through reasonable arrangement of sensors, acquisition according to a preset interval and processing of abnormal values, data reliability is guaranteed, and a solid foundation is provided for evaluation. The temperature difference inside and outside the building, the humidity correction coefficient and the solar radiation equivalent temperature are used as analysis characteristics, the calculation process logic is rigorous, the influence of humidity and solar radiation on the temperature is fully considered, and the states of the building in different environments are accurately reflected; the first and second evaluation indexes and the comprehensive evaluation index are adopted for multi-dimensional evaluation, the limitation of a single index is overcome, each index is clearly associated with the heat insulation performance, and the performance can be visually reflected; related personnel can conveniently and comprehensively know conditions, and decision making and operation are facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal insulation performance testing, and in particular to a thermal insulation performance testing method based on prefabricated buildings. Background Art

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

[0003] Traditional building insulation performance testing methods have many limitations when facing prefabricated buildings. The structural characteristics and construction methods of prefabricated buildings are different from those of traditional buildings, and the assembly form of their various components may have a unique impact on heat transfer. For example, the connection parts of prefabricated buildings may become weak links in heat conduction, and traditional testing methods may not accurately capture the impact of these parts on the overall insulation performance.

[0004] In addition, the thermal insulation performance of a building is affected by a variety of environmental factors. Temperature, humidity and solar radiation intensity are the most critical factors. In the actual environment, changes in temperature form a temperature difference between the inside and outside of the building, which is the main driving force for heat transfer. Humidity will change the thermal conductivity of the air, thereby affecting the efficiency of heat transfer inside and outside the building. The intensity of solar radiation directly determines the amount of heat absorbed by the building surface, which has an important impact on the evaluation of the thermal insulation performance of the building. Some existing testing methods often cannot fully and accurately consider the interaction of these environmental factors, nor can they effectively process the data collected in complex environments, resulting in inaccurate evaluation of the thermal insulation performance of prefabricated buildings.

[0005] At the same time, with the construction industry increasingly focusing on energy conservation and environmental protection, a more scientific, accurate and applicable thermal insulation performance test method for prefabricated buildings is needed to better guide building design, construction and subsequent quality assessment, improve the thermal insulation level of prefabricated buildings, reduce energy consumption, and meet people's demand for a comfortable indoor environment. The present invention is a new thermal insulation performance test method based on prefabricated buildings based on the above background. Summary of the invention

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

[0007] The purpose of the present invention can be achieved through the following technical solutions:

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

[0009] Step 1: Data Collection

[0010] Collect environmental data related to building thermal insulation performance, including temperature data, humidity data, and solar radiation intensity data;

[0011] Step 2: Data Processing

[0012] Test and organize environmental data and derive analytical features 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 the corresponding evaluation indicators for evaluating the thermal insulation performance of prefabricated buildings are obtained through the evaluation and analysis;

[0015] Step 4: Result output

[0016] Present the evaluation and analysis results to relevant personnel.

[0017] As a further solution of the present invention: the environmental data is collected in the following manner:

[0018] Step K1. Temperature data collection

[0019] By setting a plurality of temperature sensors inside and outside the building, the temperature values ​​inside and outside the building are collected at a predetermined time interval T1;

[0020] StepK2, humidity data collection

[0021] By setting a plurality of humidity sensors inside and outside the building, the humidity values ​​inside and outside the building are collected at a predetermined time interval T1;

[0022] Step K3: Solar radiation intensity data collection

[0023] By installing solar radiation sensors on the facade of the building, the solar radiation intensity value outside the building is collected according to the predetermined time interval T1.

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

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

[0026] Since humidity affects the thermal conductivity of the air and thus affects heat transfer, a humidity correction factor needs to be introduced to correct the temperature difference.

[0027] When solar radiation hits the surface of a building, the building absorbs heat, causing the surface temperature to rise. The solar radiation equivalent temperature is the equivalent temperature value corresponding to it calculated based on the solar radiation intensity data.

[0028] As a further solution of the present invention: the test arrangement method is as follows:

[0029] Step Z1. Temperature difference calculation

[0030] Step Z1.1, at each collection time point within a specified period, extract the temperature value outside the building and the temperature value inside the building collected by each temperature sensor at the same collection time point;

[0031] In the specified period, according to the time trend, the building external temperature series TW is obtained i，j1 and the temperature series TN in the building i，j1 ;

[0032] Wherein, i=1, 2, ... n, n represents the number of corresponding collection time points of temperature data and humidity data in a specified period, j1=1, 2, ... m1, m1 represents the number of temperature sensors set inside and outside the building, and the number of humidity sensors set inside and outside the building, respectively. The temperature data and humidity data are collected synchronously in a specified period;

[0033] Step Z1.2. Select a collection time point, and at the same collection time point, calculate the average value of the building's external temperature values ​​collected by all temperature sensors and mark 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 collection time point and mark it as TWB i ;

[0035] Step Z1.4, extract the building external temperature value TW collected by the temperature sensor at the collection time point i，j1 ;

[0036] Then |TW i，j1 -TWP i |The value obtained is 3 times TWB i The values ​​are compared:

[0037] If|TW i，j1 -TWP i |>3TWB i , then it means the building external temperature value TW collected by the corresponding temperature sensor at the collection time pointi，j1 as outliers and then remove them;

[0038] If|TW i，j1 -TWP i |≤3TWB i , then it means the building external temperature value TW collected by the corresponding temperature sensor at the collection time point i，j1 to normal value and then keep it;

[0039] Step Z1.5: Extract the building external temperature value with normal value collected by the corresponding temperature sensor at the collection time point, then calculate its average value and re-mark it as TWPP i ;

[0040] Step Z1.6: According to the method of Step Z1.2 to Step Z1.5, extract the temperature value in the building where the corresponding temperature sensor collects the normal value at the collection time point, then calculate its average value and re-mark it as TNPP i ;

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

[0042] Step Z2, humidity correction coefficient calculation

[0043] SSepZ2.1. At each collection time point within a specified period, extract the humidity value outside the building and the humidity value inside the building collected by each humidity sensor at the same collection time point;

[0044] In the specified period, according to the time trend, the humidity sequence SW outside the building is obtained i，j1 and the humidity series SN in the building i，j1 ;

[0045] SSepZ2.2. Select a collection time point, and at the same collection time point, calculate the average value of the humidity outside the building collected by all humidity sensors and mark it as SWP i ;

[0046] SSepZ2.3. Calculate the standard deviation of the humidity values ​​outside the building collected by all humidity sensors at the collection time point and mark it as SWB i ;

[0047] SSepZ2.4, extract the humidity value SW outside the building collected by the humidity sensor at the collection time point i，j1 ;

[0048] Then |SW i，j1 -SWP i |The value obtained is equal to 3 times SWB i The values ​​are compared:

[0049] If | SW i，j1 -SWP i |>3SWB i , it means the humidity value SW outside the building collected by the corresponding humidity sensor at the collection time point i，j1 as outliers and then remove them;

[0050] If | SW i，j1 -SWP i |≤3SWB i , it means the humidity value SW outside the building collected by the corresponding humidity sensor at the collection time point i，j1 to normal value and then keep it;

[0051] SSepZ2.5. Extract the humidity value outside the building for which the corresponding humidity sensor collects normal values ​​at the collection time point, then calculate its average value and re-label it as SWPP i ;

[0052] SSepZ2.6. According to the method of SSepZ2.2 to SSepZ2.5, extract the humidity value in the building where the corresponding humidity sensor collects the normal value at the collection time point, then calculate its average value and re-label it as SNPP i ;

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

[0054] Wherein, k1 is the preset humidity influence coefficient, which is determined by experimental derivation;

[0055] Step Z3: Calculation of solar radiation equivalent temperature

[0056] SSepZ3.1. At each collection time point within a specified period, extract the solar radiation intensity value outside the building collected by each solar radiation sensor at the same collection time point;

[0057] In the specified period, according to the time trend, 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 in a specified period, j2 = 1, 2, ... m2, m2 represents the number of solar radiation sensors installed on the building facade;

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

[0059] SSepZ3.3, followed by DT i =k2×FQZ i , calculate the solar radiation equivalent temperature DT at each collection time point i ;

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

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

[0062] Step P1: Extract the temperature difference TC inside and outside the building at all acquisition time points in the specified period i , then calculate its average value, and record its average value as the first evaluation index;

[0063] Step P2: Extract the temperature difference TC inside and outside the building at all acquisition time points in the specified period i , humidity correction factor XS at all acquisition time points i And the solar radiation equivalent temperature DT at all collection time points i ;

[0064] Then through ZTC i =TC i ×XS i -DT i , calculate the comprehensive temperature difference ZTC at each acquisition time point i ;

[0065] Then calculate the comprehensive temperature difference ZTC at all acquisition time points i The average value of and record it as the second evaluation indicator;

[0066] Step P3, extract the first evaluation index and the second evaluation index, and then calculate the comprehensive evaluation index ZP through ZP = P1 × γ1 + P2 × γ2;

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

[0068] As a further solution of the present invention: wherein, the displayed evaluation and analysis results include a first evaluation index, a second evaluation index and a comprehensive evaluation index.

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

[0070] The larger the value of the first evaluation index is, the better the thermal insulation performance of the prefabricated building is;

[0071] The larger the value of the second evaluation index is, the better the thermal insulation performance of the prefabricated building is;

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

[0073] Beneficial effects of the present invention:

[0074] Multi-type data collection: By collecting environmental data related to building thermal insulation performance, such as temperature data, humidity data, and solar radiation intensity data, we can comprehensively consider the factors that affect the thermal insulation performance of prefabricated buildings. 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 increases the surface temperature of the building. Multi-dimensional data provides a basis for accurately evaluating thermal insulation performance.

[0075] Scientific data collection method: Multiple temperature sensors and humidity sensors are installed inside and outside the building to collect data at predetermined time intervals, and solar radiation sensors are installed on the building facade to collect data. This scientific and reasonable arrangement ensures the extensiveness and reliability of data sources. At the same time, outliers are identified and eliminated during the collection process, which further improves the accuracy of the data and enables subsequent analysis to be based on high-quality data.

[0076] The analysis features are well-considered: the temperature difference between the inside and outside of the building, the humidity correction coefficient, and the solar radiation equivalent temperature are introduced as analysis features. Among them, the calculation of the temperature difference has undergone strict outlier processing and average value calculation to ensure the reliability of the temperature difference data. The calculation of the humidity correction coefficient takes into account the influence of humidity on heat transfer, and accurately corrects the temperature difference through reasonable formula calculation and preset humidity influence coefficient. The calculation of the solar radiation equivalent temperature is based on the solar radiation intensity data, and the intermediate value is obtained according to a specific sorting method and combined with the preset conversion coefficient. These analysis features comprehensively and accurately reflect the state of the building under the influence of different environmental factors.

[0077] The test and collation logic is rigorous: In the calculation process of temperature difference, humidity correction coefficient and solar radiation equivalent temperature, each step has clear and rigorous logic. From data extraction, calculation of mean and standard deviation, to judgment and processing of abnormal values, to calculation of final analysis characteristic values, the whole process ensures the accuracy and scientificity of data processing, so that the analysis results can 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 capacity of buildings under different environmental conditions, avoiding the limitations of single indicator evaluation.

[0079] Clear evaluation significance: The value of each evaluation index has a clear correlation with the thermal insulation performance of the prefabricated building. The larger the index value, the better the thermal insulation performance. This makes the evaluation result intuitively meaningful. Relevant personnel can clearly understand the level of thermal insulation performance of the building, thereby providing a strong basis for building design improvement, quality assessment, etc.

[0080] The output results are clear and practical: The evaluation and analysis results displayed include the first evaluation index, the second evaluation index and the comprehensive evaluation index. This clear and concise output method allows relevant personnel to fully understand the various evaluation conditions of the building's thermal insulation performance. Whether it is a construction engineer, quality inspector or other relevant personnel, they can quickly grasp the key information of the building's thermal insulation performance based on these results, so as to make further decisions and operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] The present invention will be further described below in conjunction with the accompanying drawings.

[0082] Figure 1 It is a flow chart of a method for testing thermal insulation performance of prefabricated buildings according to the present invention.

[0083] Figure 2 It is a flow chart of data processing steps in a method for testing thermal insulation performance of prefabricated buildings according to the present invention. DETAILED DESCRIPTION

[0084] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0085] Embodiment 1

[0086] Test environment preparation: Choose the appropriate test season and weather conditions, give priority to sunny weather with stable temperature, and avoid extreme weather conditions such as strong winds and rain;

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

[0088] Install multiple temperature sensors and humidity sensors 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 test standards. For example, one sensor is set for every [X] square meters indoors, and sensors are evenly distributed in different directions on the building facade.

[0090] See also Figure 1 and Figure 2 As shown, the present 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 building thermal insulation performance, including: temperature data;

[0093] The specific collection methods are as follows:

[0094] Using a temperature sensor, collect temperature values ​​inside and outside the building at a predetermined time interval T1;

[0095] In the specified period, according to the time trend, the building external temperature series TW is obtained i，j1 and the temperature series TN in the building i，j1 ;

[0096] Among them, T1 is set to 5 minutes;

[0097] Step 2: Data Processing

[0098] Test and organize environmental data, and the test results include the temperature value outside the building and the temperature difference inside the building;

[0099] The test is organized as follows:

[0100] Step Z1. Temperature difference calculation

[0101] Step Z1.1, at each collection time point within a specified period, extract the temperature value outside the building and the temperature value inside the building collected by each temperature sensor at the same collection time point;

[0102] Step Z1.2. Select a collection time point, and at the same collection time point, calculate the average value of the building's external temperature values ​​collected by all temperature sensors and mark 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 collection time point and mark it as TWB i ;

[0104] Step Z1.4, extract the building external temperature value TW collected by the temperature sensor at the collection time point i，j1 ;

[0105] Then |TW i，j1 -TWP i |The value obtained is 3 times TWB i The values ​​are compared:

[0106] If|TW i，j1 -TWP i |>3TWB i , then it means the building external temperature value TW collected by the corresponding temperature sensor at the collection time point i，j1 as outliers and then remove them;

[0107] If|TW i，j1 -TWP i |≤3TWB i , then it means the building external temperature value TW collected by the corresponding temperature sensor at the collection time point i，j1 to normal value and then keep it;

[0108] Step Z1.5: Extract the building external temperature value with normal value collected by the corresponding temperature sensor at the collection time point, then calculate its average value and re-mark it as TWPP i ;

[0109] Step Z1.6: According to the method of Step Z1.2 to Step Z1.5, extract the temperature value in the building where the corresponding temperature sensor collects the normal value at the collection time point, then calculate its average value and re-mark it as TNPP i ;

[0110] Step Z1.7, then pass TC i =|TWPP i -TNPP i |, calculate the temperature difference TC inside and outside the building at each 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 inside and outside the building at all acquisition time points in the specified period i , then calculate its average value, and record its average value as the first evaluation index;

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

[0116] Step 4: Result output

[0117] Used to present the evaluation and analysis results to relevant personnel.

[0118] In this embodiment, temperature sensors and humidity sensors are reasonably set inside and outside the building, and the sensors are ensured to be evenly distributed and can represent the overall environmental conditions of the building according to relevant building test standards. Temperature data is collected at predetermined time intervals, and a temperature sequence is formed within a specified period to provide rich data for subsequent accurate analysis. The collected temperature data is finely processed, including calculating the temperature difference. During the processing, the relationship between the collected value of each temperature sensor and the average value and standard deviation is compared, and the average value is recalculated after the abnormal value is eliminated. This method can effectively reduce errors and improve data quality, thereby more accurately analyzing the thermal insulation performance of the building. The first evaluation index is calculated based on the processed temperature difference data, and the thermal insulation performance of the prefabricated building is intuitively evaluated through the value of the first evaluation index. The larger the value of the first evaluation index, the better the thermal insulation performance, which provides a quantitative basis for the evaluation of the thermal insulation performance of the building.

[0119] Embodiment 2

[0120] Test environment preparation: Set up multiple humidity sensors and solar radiation sensors inside and outside the building to ensure that the sensors are evenly distributed and can represent the overall environmental conditions of the building;

[0121] As the second embodiment of the present invention, when the present application is implemented, compared with the first embodiment, the technical solution of the present embodiment is different from that of the first embodiment only in that:

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

[0123] The specific collection methods are as follows:

[0124] StepK2, Humidity Data Collection

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

[0126] In the specified period, according to the time trend, the humidity sequence SW outside the building is obtained i，j1 and the humidity series SN in the building i，j1 ;

[0127] Wherein, i=1, 2, ... n, n represents the number of corresponding collection time points of temperature data and humidity data in a specified period, j1=1, 2, ... m1, m1 represents the number of temperature sensors set inside and outside the building, and the number of humidity sensors set inside and outside the building, respectively. The temperature data and humidity data are collected synchronously in a specified period;

[0128] Step K3: Solar radiation intensity data collection

[0129] Install a solar radiation sensor on the building facade to collect the solar radiation intensity value outside the building at a predetermined time interval T1;

[0130] In the specified period, according to the time trend, 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 in a specified period, j2 = 1, 2, ... m2, m2 represents the number of solar radiation sensors installed on the building facade;

[0131] In this embodiment, the solar radiation intensity has an important influence on the heat absorption of the building and is one of the key factors in analyzing the thermal insulation performance;

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

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

[0134] Step Z2, humidity correction coefficient calculation

[0135] SSepZ2.1. At each collection time point within a specified period, extract the humidity value outside the building and the humidity value inside the building collected by each humidity sensor at the same collection time point;

[0136] SSepZ2.2. Select a collection time point, and at the same collection time point, calculate the average value of the humidity outside the building collected by all humidity sensors and mark it as SWP i ;

[0137] SSepZ2.3. Calculate the standard deviation of the humidity values ​​outside the building collected by all humidity sensors at the collection time point and mark it as SWB i ;

[0138] SSepZ2.4, extract the humidity value SW outside the building collected by the humidity sensor at the collection time point i，j1 ;

[0139] Then |SW i，j1 -SWP i |The value obtained is equal to 3 times SWB i The values ​​are compared:

[0140] If | SW i，j1 -SWP i |>3SWB i , it means the humidity value SW outside the building collected by the corresponding humidity sensor at the collection time point i，j1 as outliers and then remove them;

[0141] If | SW i，j1 -SWP i |≤3SWB i , it means the humidity value SW outside the building collected by the corresponding humidity sensor at the collection time point i，j1 to normal value and then keep it;

[0142] SSepZ2.5. Extract the humidity value outside the building for which the corresponding humidity sensor collects normal values ​​at the collection time point, then calculate its average value and re-label it as SWPP i ;

[0143] SSepZ2.6. According to the method of SSepZ2.2 to SSepZ2.5, extract the humidity value in the building where the corresponding humidity sensor collects the normal value at the collection time point, then calculate its average value and re-label it as SNPP i ;

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

[0145] Wherein, k1 is the preset humidity influence coefficient, which is determined by experimental derivation;

[0146] In this step, the humidity factor needs to be corrected because humidity affects the thermal conductivity of the air and thus affects the heat transfer;

[0147] Step Z3: Calculation of solar radiation equivalent temperature

[0148] SSepZ3.1. At each collection time point within a specified period, extract the solar radiation intensity value outside the building collected by each solar radiation sensor at the same collection time point;

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

[0150] SSepZ3.3, followed by DT i =k2×FQZ i , calculate the solar radiation equivalent temperature DT at each collection time point i ;

[0151] Wherein, k2 is the preset solar radiation and 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 impact of solar radiation on the temperature of the building. For example, in the summer when the solar radiation is strong, by calculating the solar radiation equivalent temperature, we can intuitively understand how much the solar radiation increases the surface temperature of the building, and then evaluate its impact on the indoor thermal environment;

[0153] The evaluation and analysis methods are also as follows:

[0154] In the specified period, extract the temperature difference TC inside and outside the building at all sampling time points i , humidity correction factor XS at all acquisition time points i And the solar radiation equivalent temperature DT at all collection time points i ;

[0155] Then through ZTC i =TC i ×XS i -DT i , calculate the comprehensive temperature difference ZTC at each acquisition time point i ;

[0156] Then calculate the comprehensive temperature difference ZTC at all acquisition time points i The average value of and record it as the second evaluation indicator;

[0157] Among them, the larger the value of the second evaluation index is, the better the thermal insulation performance of the prefabricated building is.

[0158] On the basis of Example 1, this embodiment adds the collection of humidity data and solar radiation intensity data, and more comprehensively considers the factors affecting the thermal insulation performance of the building. Humidity affects the thermal conductivity of the air and thus affects the heat transfer. The solar radiation intensity has an important influence on the heat absorption of the building. The introduction of new data makes the evaluation results more in line with the actual situation; for humidity data and solar radiation intensity data, a similar processing method as temperature data is adopted, and the abnormal values ​​are eliminated by calculating the average value and standard deviation to ensure data quality; for humidity factors, the humidity effect is corrected by calculating the humidity correction coefficient, and the effect of humidity on the thermal conductivity of the air and heat transfer is considered to make the evaluation more accurate; by calculating the solar radiation equivalent temperature, the solar radiation intensity is converted into the corresponding temperature value, and the degree of influence of solar radiation on the temperature of the building is quantified, which can more intuitively analyze the influence of solar radiation on the indoor thermal environment. In the evaluation and analysis, the comprehensive temperature difference is calculated by combining the temperature difference, the humidity correction coefficient and the solar radiation equivalent temperature, and its average value is used as the second evaluation index. This comprehensive evaluation method can more comprehensively measure the thermal insulation performance of the prefabricated building, and the evaluation result is more scientific than that of Example 1.

[0159] Embodiment 3

[0160] As the third embodiment of the present invention, when the present application is implemented, compared with the first and second embodiments, the technical solution of this embodiment is to combine the solutions of the first and second embodiments, and the difference between the technical solution of this embodiment and the first and second embodiments is only in this embodiment;

[0161] Extract the first evaluation index and the second evaluation index in the first embodiment and the second embodiment, and then calculate the comprehensive evaluation index ZP through ZP=P1×γ1+P2×γ2;

[0162] Wherein, P1 is the first evaluation index, P2 is the second evaluation index, γ1 and γ2 are weight coefficients preset according to the first evaluation index and the second evaluation index respectively;

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

[0164] The displayed evaluation and analysis results include the first evaluation index, the second evaluation index and the comprehensive evaluation index.

[0165] This embodiment combines the evaluation indicators of embodiment 1 and embodiment 2, and evaluates the thermal insulation performance of prefabricated buildings through the comprehensive evaluation indicator ZP. The first evaluation indicator based only on temperature difference and the second evaluation indicator considering humidity and solar radiation factors are comprehensively considered, and calculated according to the preset weight coefficient, so that the evaluation results reflect the thermal insulation performance of the building more comprehensively and comprehensively. The displayed evaluation and analysis results include the first evaluation indicator, the second evaluation indicator and the comprehensive evaluation indicator, which provide multi-dimensional information for relevant personnel, help to understand the thermal insulation performance of the building from different angles, and provide richer data support for building design, improvement and other decisions.

[0166] Embodiment 4

[0167] As the fourth embodiment of the present invention, when the present application is specifically implemented, compared with the first, second and third embodiments, the technical solution of this embodiment is to combine and implement the solutions of the above-mentioned first, second and third embodiments.

[0168] This embodiment combines the solutions of Embodiment 1, Embodiment 2, and Embodiment 3 for implementation, integrating the advantages of the previous embodiments to form a more complete and comprehensive test and evaluation system for the thermal insulation performance of prefabricated buildings, which ensures the comprehensiveness of the test data, the scientific nature of the processing, and the accuracy of the evaluation results to the greatest extent, and can fully meet the needs for the evaluation of the thermal insulation performance of prefabricated buildings.

[0169] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0170] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for testing thermal insulation performance of prefabricated buildings, characterized in that: The following steps are involved: The first step is to collect environmental data related to the thermal insulation performance of the building, including temperature data, humidity data, and solar radiation intensity data; The second step is to test and organize the environmental data and derive the analytical characteristics for analyzing the thermal insulation performance of prefabricated buildings. The analytical characteristics include the temperature difference between the inside and outside of the building, the humidity correction coefficient, and the solar radiation equivalent temperature; The third step is to evaluate and analyze the thermal insulation performance of prefabricated buildings based on the test results, and obtain corresponding evaluation indicators for evaluating the thermal insulation performance of prefabricated buildings through evaluation and analysis; Step 4: Present the evaluation and analysis results to relevant personnel.

2. A method for testing thermal insulation performance of prefabricated buildings according to claim 1, characterized in that: The temperature difference is calculated as follows: Step Z1.1, within a specified period, divide it 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; In the specified period, according to the time trend, the building external temperature series TW is obtained i，j1 and the building temperature series TN i，j1 ; Wherein, i=1, 2, ... n, n represents the number of corresponding collection time points of temperature data and humidity data in a specified period, j1=1, 2, ... m1, m1 represents the number of temperature sensors set inside and outside the building, and the number of humidity sensors set inside and outside the building, respectively. The temperature data and humidity data are collected synchronously in a specified period; Step Z1.

2. Select a collection time point, and at the same collection time point, calculate the average value of the building's external temperature values ​​collected by all temperature sensors and mark it as TWP i ; Step Z1.

3. Calculate the standard deviation of the building exterior temperature values ​​collected by all temperature sensors at the collection time point and mark it as TWB i ; Step Z1.4, extract the building external temperature value TW collected by the temperature sensor at the collection time point i，j1 ; Then |TW i，j1 -TWP i |The value obtained is 3 times TWB i The values ​​are compared: If|TW i，j1 -TWP i |>3TWB i , then it means the building external temperature value TW collected by the corresponding temperature sensor at the collection time point i，j1 as outliers and then remove them; If|TW i，j1 -TWP i |≤3TWB i , then it means the building external temperature value TW collected by the corresponding temperature sensor at the collection time point i，j1 to normal value and then keep it; Step Z1.5: Extract the building external temperature value with normal value collected by the corresponding temperature sensor at the collection time point, then calculate its average value and re-mark it as TWPP i ; Step Z1.6: According to the method of Step Z1.2 to Step Z1.5, extract the temperature value in the building where the corresponding temperature sensor collects the normal value at the collection time point, then calculate its average value and re-mark it as TNPP i ; Step Z1.7, then pass TC i =|TWPP i -TNPP i |, calculate the temperature difference TC inside and outside the building at each collection time point i .

3. A method for testing thermal insulation performance of prefabricated buildings according to claim 2, characterized in that: The humidity correction factor is calculated as follows: SSepZ2.

1. Within a specified period, the predetermined time interval T1 is divided into collection time points, and the humidity values ​​outside the building and the humidity values ​​inside the building collected by each humidity sensor at the same collection time point are extracted; In the specified period, according to the time trend, the humidity sequence SW outside the building is obtained i，j1 and the humidity series SN in the building i，j1 ; SSepZ2.

2. Select a collection time point, and at the same collection time point, calculate the average value of the humidity outside the building collected by all humidity sensors and mark it as SWP i ; SSepZ2.

3. Calculate the standard deviation of the humidity values ​​outside the building collected by all humidity sensors at the collection time point and mark it as SWB i ; SSepZ2.4, extract the humidity value SW outside the building collected by the humidity sensor at the collection time point i，j1 ; Then |SW i，j1 -SWP i |The value obtained is equal to 3 times SWB i The values ​​are compared: If | SW i，j1 -SWP i |>3SWB i , it means the humidity value SW outside the building collected by the corresponding humidity sensor at the collection time point i，j1 as outliers and then remove them; If | SW i，j1 -SWP i |≤3SWB i , it means the humidity value SW outside the building collected by the corresponding humidity sensor at the collection time point i，j1 to normal value and then keep it; SSepZ2.

5. Extract the humidity value outside the building for which the corresponding humidity sensor collects normal values ​​at the collection time point, then calculate its average value and re-label it as SWPP i ; SSepZ2.

6. According to the method of SSepZ2.2 to SSepZ2.5, extract the humidity value in the building where the corresponding humidity sensor collects the normal value at the collection time point, then calculate its average value and re-label it as SNPP i ; SSepZ2.7, followed by XS i =1+k1×(SWPP i -SNPP i ), calculate the humidity correction coefficient XS at each collection time point i ; Wherein, k1 is a preset humidity influence coefficient, which is determined based on experimental derivation.

4. A method for testing thermal insulation performance of prefabricated buildings according to claim 3, characterized in that: The calculation method of solar radiation equivalent temperature is as follows: SSepZ3.

1. Within a specified period, the predetermined time interval T1 is divided into collection time points, and the solar radiation intensity values ​​outside the building collected by each solar radiation sensor at the same collection time point are extracted; In the specified period, according to the time trend, 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 in a specified period, j2 = 1, 2, ... m2, m2 represents the number of solar radiation sensors installed on the building facade; SSepZ3.

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

5. A method for testing thermal insulation performance of prefabricated buildings according to claim 4, characterized in that: in: The temperature difference between the inside and outside of a building refers to the difference between the external ambient temperature of the building and the internal temperature of the building at the same time: The humidity correction factor is used to correct for temperature differences; The solar radiation equivalent temperature is the equivalent temperature value calculated based on the solar radiation intensity data.

6. A method for testing thermal insulation performance of prefabricated buildings according to claim 5, characterized in that: The evaluation and analysis methods are as follows: In the specified period, extract the temperature difference TC inside and outside the building at all sampling time points i , and then calculate its average value, and record its average value as the first evaluation indicator.

7. A method for testing thermal insulation performance of prefabricated buildings according to claim 6, characterized in that: The evaluation and analysis methods are as follows: In the specified period, extract the temperature difference TC inside and outside the building at all sampling time points i , humidity correction factor XS at all acquisition time points i And the solar radiation equivalent temperature DT at all collection time points i ; Then through ZTC i =TC i ×XS i -DT i , calculate the comprehensive temperature difference ZTC at each acquisition time point i ; Then calculate the comprehensive temperature difference ZTC at all acquisition time points i The average value of is taken as the second evaluation indicator.

8. A method for testing thermal insulation performance of prefabricated buildings according to claim 7, characterized in that: The evaluation and analysis methods are as follows: Extract the first evaluation index and the second evaluation index, and then calculate the comprehensive evaluation index ZP through ZP=P1×γ1+P2×γ2; In the formula, P1 is the first evaluation index, P2 is the second evaluation index, γ1 and γ2 are weight coefficients preset according to the first evaluation index and the second evaluation index, respectively.

9. A method for testing thermal insulation performance of prefabricated buildings according to claim 8, characterized in that: in, The displayed evaluation and analysis results include the first evaluation index, the second evaluation index and the comprehensive evaluation index.

10. A method for testing thermal insulation performance of prefabricated buildings according to claim 8, characterized in that: in: The larger the value of the first evaluation index is, the better the thermal insulation performance of the prefabricated building is; The larger the value of the second evaluation index is, the better the thermal insulation performance of the prefabricated building is; The larger the value of the comprehensive evaluation index, the better the thermal insulation performance of the prefabricated building.

Images