Building external wall thermal insulation performance evaluation method and system

Abstract

The invention discloses a building external wall thermal insulation performance evaluation method and system, and the method comprises the steps: obtaining the temperature and humidity data of a building external wall according to the data collection frequency; preprocessing the temperature and humidity data of the building outer wall to obtain target temperature and humidity data; performing feature extraction processing on the target temperature and humidity data to obtain target features; according to the target characteristics, the environmental factors, the material characteristics, the temperature data and the humidity data, calculating a temperature and humidity change trend through a preset time sequence analysis method; according to the target features and the target temperature and humidity data, heat preservation performance information is calculated and predicted through a preset prediction method; calculating a thermal insulation performance index value according to the target temperature and humidity data, the thermal insulation material thickness, the outer wall area and the inside and outside temperature difference; and according to the temperature and humidity change trend, predicting thermal insulation performance information and thermal insulation performance index values, and performing thermal insulation performance evaluation to obtain a thermal insulation performance evaluation result. The thermal insulation performance evaluation is realized, and the accuracy is improved. The invention can be widely applied to the technical field of building insulation.

Description

Technical Field

[0001] The present invention relates to the technical field of building thermal insulation, and in particular to a method and system for evaluating thermal insulation performance of building exterior walls. Background Art

[0002] With the increasing prominence of energy crisis and environmental issues, building energy conservation has become a focus of attention. Rock wool thin plastered exterior walls have excellent thermal insulation performance and are environmentally friendly. However, thermal insulation performance is affected by many factors, including the quality of rock wool materials, the sophistication of construction technology, and changes in environmental conditions. At present, traditional evaluation methods only monitor single temperature data, and the analysis of evaluation indicators is limited. It is difficult to generate key indicators and accurately reflect the thermal insulation performance of rock wool thin plastered exterior walls, and the accuracy is low.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the invention

[0004] The embodiment of the present invention provides a method and system for evaluating the thermal insulation performance of a building exterior wall, which effectively improves the accuracy.

[0005] On the one hand, an embodiment of the present invention provides a method for evaluating the thermal insulation performance of a building exterior wall, comprising the following steps:

[0006] Obtain temperature and humidity data of building exterior walls according to data collection frequency;

[0007] Preprocessing the building exterior wall temperature and humidity data to obtain target temperature and humidity data, wherein the target temperature and humidity data includes temperature data and humidity data;

[0008] Performing feature extraction processing on the target temperature and humidity data to obtain target features, wherein the target features include time domain features, frequency domain features or statistical features;

[0009] Calculate the temperature and humidity change trend by a preset time series analysis method according to the target characteristics, environmental factors, material properties, the temperature data and the humidity data, wherein the environmental factors include solar radiation, wind speed or wind direction, the material properties include thermal conductivity, heat capacity or density of the exterior wall material, and the temperature and humidity change trend includes a temperature seasonal change pattern, a humidity seasonal change pattern, a temperature prediction trend or a humidity prediction trend;

[0010] According to the target characteristics and the target temperature and humidity data, predictive thermal insulation performance information is calculated by a preset prediction method, wherein the predictive thermal insulation performance information includes a linear relationship of thermal insulation performance, a nonlinear relationship of thermal insulation performance, nonlinear pattern data or data boundaries;

[0011] Calculate the thermal insulation performance index value according to the target temperature and humidity data, the thickness of the thermal insulation material, the exterior wall area and the temperature difference between the interior and the exterior, wherein the thermal insulation performance index value includes a thermal resistance value or a thermal insulation efficiency;

[0012] According to the temperature and humidity change trend, the predicted thermal insulation performance information and the thermal insulation performance index value, a thermal insulation performance evaluation is performed to obtain a thermal insulation performance evaluation result.

[0013] In some embodiments, the preprocessing of the building exterior wall temperature and humidity data to obtain target temperature and humidity data includes:

[0014] De-noising the temperature and humidity data of the building exterior wall by filtering to obtain first temperature and humidity data;

[0015] Performing standardization processing on the first temperature and humidity data to obtain second temperature and humidity data;

[0016] The target temperature and humidity data are obtained by performing missing value processing on the second temperature and humidity data through interpolation.

[0017] In some embodiments, the time domain feature includes an average value, a maximum value or a standard deviation, and the feature extraction process is performed on the target temperature and humidity data to obtain the target feature, including:

[0018] Extracting time series data from the target temperature and humidity data;

[0019] Calculate the average value according to the time series data, where the average value is used to reflect the trend of the data set;

[0020] Extracting the maximum value from the time series data, wherein the maximum value includes a maximum value or a minimum value, and the maximum value is used to reflect the data fluctuation range;

[0021] The standard deviation is calculated based on the time series data, and the standard deviation is used to measure the degree of data fluctuation.

[0022] In some embodiments, the step of performing feature extraction processing on the target temperature and humidity data to obtain target features includes:

[0023] Extracting a time domain signal from the target temperature and humidity data;

[0024] Performing frequency domain conversion processing on the time domain signal by Fourier transform method to obtain the frequency domain feature, wherein the frequency domain feature includes frequency component, power spectrum density or phase information;

[0025] The power spectrum density is used to measure the energy of the multiple frequency components, and the phase information is used to reflect the phase relationship between the multiple frequency components.

[0026] In some embodiments, the statistical features include correlation data or principal component data, and the feature extraction process is performed on the target temperature and humidity data to obtain the target features, including:

[0027] Performing correlation analysis on the target temperature and humidity data and calculating the Pearson correlation coefficient, where the Pearson correlation coefficient is used to characterize the strength of the linear relationship between variables;

[0028] According to the Pearson correlation coefficient, the target temperature and humidity data are de-redundanted to obtain the correlation data;

[0029] The target temperature and humidity data are subjected to principal component analysis through orthogonal transformation to obtain the principal component data, so as to achieve data dimensionality reduction.

[0030] In some embodiments, the preset time series analysis method includes an autoregressive integrated moving average model analysis method or a seasonal decomposition time series prediction method, and the temperature and humidity change trend is calculated by the preset time series analysis method according to the target characteristics, environmental factors, material properties, the temperature data and the humidity data, including:

[0031] Calculate the temperature prediction trend according to the target characteristics, the environmental factors, the material properties and the temperature data by using the autoregressive integrated moving average model analysis method;

[0032] Calculate the humidity prediction trend according to the target characteristics, the environmental factors, the material properties and the humidity data by using the autoregressive integrated moving average model analysis method;

[0033] Calculate the temperature seasonal variation pattern according to the target characteristics and the temperature data by using the seasonal decomposition time series prediction method;

[0034] According to the target characteristics and the humidity data, the humidity seasonal variation pattern is calculated by using the seasonal decomposition time series prediction method.

[0035] In some embodiments, the preset prediction method includes linear regression, decision tree, neural network or support vector machine, and the thermal insulation performance information is calculated and predicted by the preset prediction method according to the target characteristics and the target temperature and humidity data, including:

[0036] Calculating the thermal insulation performance linear relationship through the linear regression according to the target characteristics and the target temperature and humidity data;

[0037] Calculating the nonlinear relationship of thermal insulation performance through the decision tree according to the target characteristics and the target temperature and humidity data;

[0038] According to the target characteristics and the target temperature and humidity data, nonlinear relationship modeling is performed through the neural network to calculate the nonlinear pattern data;

[0039] The data boundary is obtained by performing classification through the support vector machine according to the target features and the target temperature and humidity data.

[0040] In some embodiments, the calculation of the thermal insulation performance index value according to the target temperature and humidity data, the thickness of the thermal insulation material, the exterior wall area, and the temperature difference between the interior and the exterior includes:

[0041] Calculating a target thermal conductivity coefficient according to the target temperature and humidity data and the thermal conductivity coefficient of the exterior wall material;

[0042] Calculating the thermal resistance value according to the target thermal conductivity and the thickness of the thermal insulation material;

[0043] Calculating the heat loss of the outer wall according to the target heat transfer coefficient, the outer wall area and the inner and outer temperature difference;

[0044] The insulation efficiency is calculated based on a heat balance equation, the heat loss of the exterior wall and the heat loss in a non-insulated state.

[0045] In some embodiments, the method further comprises:

[0046] Based on the thermal insulation performance evaluation results and alarm thresholds, a safety warning for the building's exterior walls is issued.

[0047] On the other hand, an embodiment of the present invention provides a building exterior wall thermal insulation performance evaluation system, comprising:

[0048] A data acquisition module, wherein the data acquisition module is used to obtain temperature and humidity data of the building exterior wall according to the data acquisition frequency;

[0049] A data processing module, the data processing module is used to calculate the thermal insulation performance evaluation result according to the temperature and humidity data of the building exterior wall;

[0050] A data transmission module, the data transmission module is used to transmit the temperature and humidity data of the building exterior wall to the data processing module;

[0051] An output display module is used to display the thermal insulation performance evaluation result on a user interface and to issue a safety warning for the building exterior wall.

[0052] The beneficial effects of the present invention are as follows:

[0053] The embodiment of the present invention first obtains the temperature and humidity data of the building's exterior wall according to the data collection frequency, and pre-processes the temperature and humidity data of the building's exterior wall to obtain target temperature and humidity data, and then performs feature extraction processing on the target temperature and humidity data to obtain target features, and calculates the temperature and humidity change trend through a preset time series analysis method according to the target features, environmental factors, material properties, temperature data and humidity data, and then calculates the predicted thermal insulation performance information through a preset prediction method according to the target features and the target temperature and humidity data, and calculates the thermal insulation performance index value according to the target temperature and humidity data, the thickness of the insulation material, the exterior wall area and the temperature difference between the inside and the outside, and finally performs thermal insulation performance evaluation according to the temperature and humidity change trend, the predicted thermal insulation performance information and the thermal insulation performance index value to obtain the thermal insulation performance evaluation result, thereby realizing the thermal insulation performance evaluation and improving the accuracy.

[0054] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0056] Figure 1 This is a flow chart of a method for evaluating thermal insulation performance of building exterior walls according to an embodiment of the present invention;

[0057] Figure 2 This is an overall flow chart of an embodiment of the present invention for alarming the thermal insulation effect of a building exterior wall;

[0058] Figure 3 This is a schematic diagram of the structure of a building exterior wall thermal insulation performance evaluation system according to an embodiment of the present invention;

[0059] Figure 4 A schematic diagram of the component structure of an evaluation system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0061] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0062] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0063] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0064] Before describing the embodiments of the present application in detail, some nouns and terms involved in the embodiments of the present application are first described. The nouns and terms involved in the embodiments of the present application are subject to the following explanations.

[0065] External wall insulation rock wool board: It is specially designed and produced for the external wall insulation thin plaster system of buildings. It is suitable for external wall insulation or energy-saving renovation of new or existing buildings with dense structures such as concrete or brick walls.

[0066] In the related technologies, with the increasing prominence of the global energy crisis and environmental problems, building energy conservation has become the focus of attention in the world today. As an efficient insulation method, the rock wool thin plaster exterior wall insulation system is widely used in the construction industry due to its excellent insulation performance and environmental protection characteristics. However, the actual effect of this insulation system does not always meet expectations. It is affected by many factors, including the quality of rock wool materials, the exquisiteness of construction technology, and changes in environmental conditions. At present, although there are some devices on the market for monitoring the insulation effect of buildings, these devices can often only provide a single temperature or humidity data, which cannot fully reflect the insulation effect of the rock wool thin plaster exterior wall. In addition, the data processing and analysis capabilities of these devices are relatively weak, making it difficult to provide users with valuable insulation effect evaluation indicators, and the accuracy is low. Therefore, there is an urgent need for a method and system that can comprehensively and real-time monitor and evaluate the insulation effect of rock wool thin plaster exterior walls. Not only does it need to have high-precision data acquisition capabilities, but it also needs to have powerful data processing and analysis functions to provide users with accurate and timely insulation effect feedback. At the same time, in order to facilitate user use and management, it should also have intuitive output display and convenient interactive interface.

[0067] In view of this, this embodiment integrates high-precision equipment such as temperature sensors and humidity sensors to obtain temperature and humidity data, as well as powerful data processing and analysis modules, to provide users with comprehensive and accurate insulation effect evaluation indicators, and conduct a comprehensive evaluation of the insulation effect, to help users better understand and manage the insulation effect of rock wool thin-plastered exterior walls, so as to meet the market demand for comprehensive and real-time monitoring and evaluation of insulation effects.

[0068] A method for evaluating the thermal insulation performance of a building exterior wall provided in an embodiment of the present application relates to the field of building thermal insulation technology. A method for evaluating the thermal insulation performance of a building exterior wall provided in an embodiment of the present application can be applied to a terminal, can be applied to a server, or can be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application that implements a method for evaluating the thermal insulation performance of a building exterior wall, etc., but is not limited to the above forms.

[0069] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0070] The following is a detailed explanation of the embodiments of the present application in conjunction with the accompanying drawings:

[0071] Figure 1 is an optional flow chart of a method for evaluating thermal insulation performance of building exterior walls provided in an embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S107.

[0072] Step S101, acquiring temperature and humidity data of the building exterior wall according to the data collection frequency;

[0073] Step S102, preprocessing the temperature and humidity data of the building exterior wall to obtain target temperature and humidity data, where the target temperature and humidity data includes temperature data and humidity data;

[0074] Step S103, performing feature extraction processing on the target temperature and humidity data to obtain target features, where the target features include time domain features, frequency domain features or statistical features;

[0075] Step S104, calculating the temperature and humidity change trend by a preset time series analysis method according to the target characteristics, environmental factors, material properties, temperature data and humidity data, the environmental factors include solar radiation, wind speed or wind direction, the material properties include the thermal conductivity, heat capacity or density of the exterior wall material, the temperature and humidity change trend includes the temperature seasonal change pattern, the humidity seasonal change pattern, the temperature prediction trend or the humidity prediction trend;

[0076] Step S105, calculating predicted thermal insulation performance information by a preset prediction method according to the target characteristics and the target temperature and humidity data, the predicted thermal insulation performance information including a thermal insulation performance linear relationship, a thermal insulation performance nonlinear relationship, nonlinear pattern data or data boundaries;

[0077] Step S106, calculating the thermal insulation performance index value according to the target temperature and humidity data, the thickness of the thermal insulation material, the exterior wall area and the temperature difference between the interior and the exterior, the thermal insulation performance index value including the thermal resistance value or the thermal insulation efficiency;

[0078] Step S107: perform thermal insulation performance evaluation according to the temperature and humidity change trend, the predicted thermal insulation performance information and the thermal insulation performance index value to obtain a thermal insulation performance evaluation result.

[0079] Steps S101 to S107 shown in the embodiment of the present application realize the thermal insulation performance evaluation and improve the accuracy.

[0080] In step S101 of some embodiments, parameters such as data collection frequency and alarm threshold can be set to obtain temperature and humidity data of the building exterior wall through temperature sensors and humidity sensors. The building exterior wall temperature and humidity data can also be obtained by other means, not limited thereto. Among them, the building exterior wall can include a rock wool thin plaster exterior wall covered with an exterior wall insulation rock wool board.

[0081] In some embodiments, in step S102, preprocessing the building exterior wall temperature and humidity data to obtain target temperature and humidity data may include but is not limited to the following steps:

[0082] De-noising the temperature and humidity data of the building exterior wall by filtering to obtain first temperature and humidity data;

[0083] Performing standardization processing on the first temperature and humidity data to obtain second temperature and humidity data;

[0084] The missing values ​​of the second temperature and humidity data are processed by interpolation method to obtain the target temperature and humidity data.

[0085] In some embodiments, the temperature and humidity data of the building exterior wall can be first denoised by filtering to obtain the first temperature and humidity data, so as to remove data anomalies caused by environmental factors or sensor noise, and then the first temperature and humidity data can be standardized and converted into a unified metric to obtain the second temperature and humidity data for subsequent analysis, and finally the second temperature and humidity data can be processed for missing values ​​by interpolation to fill in the missing data in the second temperature and humidity data due to sensor failure or other reasons to obtain the target temperature and humidity data. The target temperature and humidity data include temperature data and humidity data. It can be understood that after the collected building exterior wall temperature and humidity data are cleaned, denoised, standardized and missing value processed, the accuracy and consistency of the data can be improved.

[0086] In some embodiments, in step S103, the time domain feature includes an average value, a maximum value or a standard deviation, and feature extraction processing is performed on the target temperature and humidity data to obtain the target feature, which may include but is not limited to the following steps:

[0087] Extract time series data from target temperature and humidity data;

[0088] Based on the time series data, the average value is calculated, and the average value is used to reflect the trend of the data set;

[0089] Extract the maximum value from the time series data. The maximum value includes the maximum value or the minimum value. The maximum value is used to reflect the fluctuation range of the data.

[0090] Based on time series data, the standard deviation is calculated, which is used to measure the degree of data fluctuation.

[0091] In some embodiments, when the target feature is a time domain feature, the time domain feature related to the thermal insulation effect can be extracted from the preprocessed target temperature and humidity data, wherein the time domain feature may include an average value, an extreme value or a standard deviation. The time series data may be first extracted from the target temperature and humidity data, and then the average value is calculated based on the time series data, wherein the average value is used to reflect the trend in the data set. The extreme value may be extracted from the time series data, wherein the extreme value includes a maximum value or a minimum value, and the extreme value is used to reflect the range of data fluctuations. The standard deviation may be calculated based on the time series data, wherein the standard deviation is used to measure the degree of data fluctuation. It can be understood that the time domain feature can help understand the basic statistical characteristics of the data and provide a basis for further analysis.

[0092] In some embodiments, in step S103, feature extraction processing is performed on the target temperature and humidity data to obtain target features, which may include but is not limited to the following steps:

[0093] Extracting time domain signals from target temperature and humidity data;

[0094] The time domain signal is converted into the frequency domain by Fourier transform method to obtain frequency domain features, which include frequency components, power spectrum density or phase information;

[0095] Among them, the power spectrum density is used to measure the energy of multiple frequency components, and the phase information is used to reflect the phase relationship between multiple frequency components.

[0096] In some embodiments, when the target feature is a frequency domain feature, the time domain signal can be first extracted from the target temperature and humidity data, and then the time domain signal is converted into the frequency domain by Fourier transform to obtain the frequency domain feature. Among them, the frequency domain feature can include frequency components, power spectrum density or phase information. Among them, the power spectrum density is used to measure the energy of multiple frequency components, and the phase information is used to reflect the phase relationship between multiple frequency components. It can be understood that the frequency domain feature can help understand the behavior of the data at different frequencies and identify periodic or repetitive patterns.

[0097] In some embodiments, in step S103, the statistical features include correlation data or principal component data, and feature extraction processing is performed on the target temperature and humidity data to obtain the target features, which may include but is not limited to the following steps:

[0098] Perform correlation analysis on the target temperature and humidity data and calculate the Pearson correlation coefficient, which is used to characterize the strength of the linear relationship between variables;

[0099] According to the Pearson correlation coefficient, the target temperature and humidity data are de-redundanted to obtain correlation data;

[0100] The target temperature and humidity data are subjected to principal component analysis through orthogonal transformation to obtain principal component data to achieve data dimensionality reduction.

[0101] In some embodiments, when the target feature is a statistical feature, the target temperature and humidity data can be reduced in dimension or de-redundant, wherein the statistical feature can include correlation data or principal component data. The target temperature and humidity data can be first subjected to correlation analysis to calculate the Pearson correlation coefficient, wherein the Pearson correlation coefficient is used to determine the strength of the linear relationship between two or more variables, and the value of the Pearson correlation coefficient is between -1 and 1, and the closer the value is to ±1, the stronger the linear relationship between the variables. Then, according to the Pearson correlation coefficient, the target temperature and humidity data is de-redundantly processed to obtain correlation data to achieve data de-redundancy. The target temperature and humidity data can be subjected to principal component analysis by orthogonal transformation, and the target temperature and humidity data can be converted into a new coordinate system to obtain principal component data to reduce the data dimension, while retaining the variability of the original target temperature and humidity data as much as possible to achieve data dimension reduction. Among them, the new coordinate axes (principal components) are arranged in order of decreasing variance. It can be understood that principal component analysis is a dimensionality reduction technique that converts potentially related variables into a set of linearly unrelated variables, called principal components, through orthogonal transformation. Dimensionality reduction usually refers to reducing the number of features in a data set, while de-redundancy refers to removing duplicate or unimportant information from the data. Correlation analysis can help identify and remove linearly correlated features, while principal component analysis reduces dimensionality by transforming data to a new coordinate system while removing redundant information. Moreover, in applications, correlation analysis and principal component analysis can be used in combination to improve the efficiency and accuracy of data analysis. For example, before performing principal component analysis, correlation analysis can be used to identify and remove highly correlated features, thereby simplifying the model and avoiding multicollinearity problems.

[0102] In some embodiments, in step S104, the preset time series analysis method includes an autoregressive integrated moving average model analysis method or a seasonal decomposition time series prediction method. According to target characteristics, environmental factors, material properties, temperature data and humidity data, the temperature and humidity change trend is calculated by the preset time series analysis method, which may include but is not limited to the following steps:

[0103] According to the target characteristics, environmental factors, material properties and temperature data, the temperature prediction trend is calculated through the autoregressive integral moving average model analysis method;

[0104] According to the target characteristics, environmental factors, material properties and humidity data, the humidity prediction trend is calculated through the autoregressive integrated moving average model analysis method;

[0105] According to the target characteristics and temperature data, the seasonal variation pattern of temperature is calculated by seasonal decomposition time series prediction method;

[0106] According to the target characteristics and humidity data, the seasonal variation pattern of humidity is calculated by seasonal decomposition time series prediction method.

[0107] In some embodiments, the temperature and humidity change trend can be calculated by a preset time series analysis method, wherein the preset time series analysis method may include an autoregressive integral moving average model analysis method or a seasonal decomposition time series prediction method. The temperature and humidity change trend may include a temperature seasonal change pattern, a humidity seasonal change pattern, a temperature prediction trend or a humidity prediction trend. The temperature prediction trend may be calculated by an autoregressive integral moving average model analysis method according to target characteristics, environmental factors, material properties and temperature data; the humidity prediction trend may be calculated by an autoregressive integral moving average model analysis method according to target characteristics, environmental factors, material properties and humidity data; the temperature seasonal change pattern may be calculated by a seasonal decomposition time series prediction method according to target characteristics and temperature data; the humidity seasonal change pattern may be calculated by a seasonal decomposition time series prediction method according to target characteristics and humidity data. Among them, environmental factors may include solar radiation, wind speed or wind direction, and environmental factors may affect the thermal insulation effect of the exterior wall. Material properties may include thermal conductivity, heat capacity or density of the exterior wall material, and material properties have a direct impact on the thermal insulation effect.

[0108] In some embodiments, in step S105, the preset prediction method includes linear regression, decision tree, neural network or support vector machine. According to the target characteristics and target temperature and humidity data, the thermal insulation performance information is calculated and predicted by the preset prediction method, which may include but is not limited to the following steps:

[0109] According to the target characteristics and target temperature and humidity data, the linear relationship of thermal insulation performance is calculated by linear regression;

[0110] According to the target characteristics and target temperature and humidity data, the nonlinear relationship of thermal insulation performance is calculated through the decision tree;

[0111] According to the target characteristics and target temperature and humidity data, nonlinear relationship modeling is performed through neural network to calculate nonlinear pattern data;

[0112] According to the target features and target temperature and humidity data, the support vector machine is used for classification to obtain the data boundary.

[0113] In some embodiments, the thermal insulation performance information can be predicted by a preset prediction method, wherein the preset prediction method may include linear regression, decision tree, neural network or support vector machine, and the predicted thermal insulation performance information may include a linear relationship of thermal insulation performance, a nonlinear relationship of thermal insulation performance, nonlinear pattern data or data boundary. The thermal insulation performance linear relationship can be calculated by linear regression according to the target characteristics and the target temperature and humidity data to evaluate the linear influence of different factors on the thermal insulation effect. The thermal insulation performance nonlinear relationship can be calculated by a decision tree according to the target characteristics and the target temperature and humidity data to process multiple input variables and evaluate the nonlinear relationship on the thermal insulation effect. Nonlinear relationship modeling can be performed by a neural network according to the target characteristics and the target temperature and humidity data, and nonlinear pattern data can be calculated. It is understood that neural networks are suitable for complex nonlinear relationship modeling and can capture complex patterns in data. Data boundaries can be obtained by classification through a support vector machine according to the target characteristics and the target temperature and humidity data. It is understood that support vector machines are used for classification and regression problems, and the optimal boundaries in the data can be found.

[0114] In some embodiments, in step S106, the thermal insulation performance index value is calculated according to the target temperature and humidity data, the thickness of the thermal insulation material, the exterior wall area, and the temperature difference between the interior and the exterior, which may include but is not limited to the following steps:

[0115] Calculate the target thermal conductivity coefficient based on the target temperature and humidity data and the thermal conductivity coefficient of the exterior wall material;

[0116] Calculate the thermal resistance value based on the target thermal conductivity and the thickness of the insulation material;

[0117] Calculate the heat loss of the exterior wall based on the target heat transfer coefficient, exterior wall area and the temperature difference between inside and outside;

[0118] The insulation efficiency is calculated based on the heat balance equation, heat loss from the exterior wall, and heat loss in the non-insulated state.

[0119] In some embodiments, the thermal insulation performance index value can be calculated based on the target temperature and humidity data, the thickness of the insulation material, the exterior wall area and the temperature difference between the inside and outside, wherein the thermal insulation performance index value includes thermal resistance value or thermal insulation efficiency. The target thermal conductivity coefficient can be calculated based on the target temperature and humidity data and the thermal conductivity coefficient of the exterior wall material, taking into account environmental factors (such as ambient temperature and ambient humidity). It can be understood that environmental factors will affect the thermal conductivity coefficient of the material, and the increase in humidity may reduce the thermal resistance value of some materials. The thermal conductivity coefficient of the exterior wall material can be adjusted to reflect the current heat transfer situation. Then, according to the target thermal conductivity coefficient and the thickness of the insulation material, the thermal resistance value is calculated, wherein the calculation formula of the thermal resistance value is: R=L / K, where R is the thermal resistance value, L is the thickness of the insulation material (in meters), and K is the target thermal conductivity coefficient, that is, the heat passing through the material per unit area per unit time under unit temperature difference. It can be understood that the thermal resistance value (R) is a parameter to measure the thermal insulation performance of the material, which indicates the ability of the material to resist heat transfer. The larger the thermal resistance value, the better the thermal insulation performance of the material. More importantly, the calculation of insulation efficiency is another key indicator in the evaluation of insulation performance. Insulation efficiency is usually defined as the ability of the insulation system to reduce heat loss, which can be calculated by the heat balance equation. The heat loss of the exterior wall can be calculated by Fourier's law based on the target thermal conductivity coefficient, the exterior wall area and the temperature difference between the inside and the outside. The calculation formula for the heat loss of the exterior wall is: Q = -K*A*ΔT, where Q is the heat loss of the exterior wall, K is the target thermal conductivity coefficient, A is the exterior wall area, and ΔT is the temperature difference between the inside and the outside. Then calculate the insulation efficiency based on the heat balance equation, the heat loss of the exterior wall and the heat loss in the non-insulated state. The calculation formula for the heat balance equation is: Q in -Q out =ΔE, where Q in is the heat entering the system (e.g. through solar radiation or indoor heating system), Q out is the heat lost through the exterior wall, and ΔE is the change in the system's internal energy. The calculation formula for thermal insulation efficiency is: η=1-Q out / Q outmax , where η is the insulation efficiency, Q outmax It is the heat loss in the non-insulated state, that is, the maximum possible heat loss when there are no insulation measures. It is understandable that in the calculation process, it is also necessary to consider the influence of environmental factors (such as wind speed, solar radiation) and material properties (such as heat capacity, humidity effect) on the insulation efficiency. Moreover, the calculated insulation efficiency can be verified by actual measurement values ​​to ensure its accuracy and reliability. The model parameters or optimization algorithm can be adjusted according to the verification results to improve the prediction accuracy. The calculated insulation performance index value can be displayed to the user through the user interface, and explanations and suggestions can be provided. This embodiment can quantitatively evaluate the insulation performance of the insulation system and provide a scientific basis for building design, energy-saving transformation and energy management.

[0120] In some embodiments, in step S107, thermal insulation performance evaluation can be performed based on the temperature and humidity change trend, predicted thermal insulation performance information and thermal insulation performance index value to obtain a thermal insulation performance evaluation result. Exemplarily, when the temperature seasonal change pattern and the humidity seasonal change pattern are periodic changes, and the temperature prediction trend and the humidity prediction trend are both stable, the thermal insulation performance evaluation result can be obtained as high thermal insulation performance. When the thermal insulation performance linear relationship, the thermal insulation performance nonlinear relationship, the nonlinear pattern data or the data boundary does not exist within the preset range, the thermal insulation performance evaluation result can be obtained as low thermal insulation performance. When the thermal resistance value and the thermal insulation efficiency are both greater than the preset threshold value, the thermal insulation performance evaluation result can be obtained as high thermal insulation performance.

[0121] In some embodiments, the method further comprises:

[0122] Based on the thermal insulation performance evaluation results and alarm thresholds, a safety warning for the building's exterior walls is issued.

[0123] In some embodiments, a safety warning of the building exterior wall can be performed based on the thermal insulation performance evaluation results and the alarm threshold. For example, when one or more thermal insulation performance index values ​​are lower than or higher than the preset alarm threshold, the alarm system is triggered to promptly notify the user to take corresponding measures. Furthermore, data collection, processing and display can be continuously performed, and the calculation parameters and alarm thresholds can be adjusted according to actual needs.

[0124] The beneficial effects of implementing the embodiments of the present invention include: the embodiments of the present invention first obtain the temperature and humidity data of the building's exterior wall according to the data collection frequency, and pre-process the temperature and humidity data of the building's exterior wall to obtain target temperature and humidity data, and then perform feature extraction processing on the target temperature and humidity data to obtain target features, and calculate the temperature and humidity change trend through a preset time series analysis method based on the target features, environmental factors, material properties, temperature data and humidity data, and then calculate the predicted thermal insulation performance information through a preset prediction method based on the target features and target temperature and humidity data, and calculate the thermal insulation performance index value based on the target temperature and humidity data, the thickness of the insulation material, the exterior wall area and the temperature difference between the inside and the outside, and finally perform thermal insulation performance evaluation based on the temperature and humidity change trend, the predicted thermal insulation performance information and the thermal insulation performance index value to obtain the thermal insulation performance evaluation result, thereby realizing the thermal insulation performance evaluation and improving the accuracy.

[0125] In some embodiments, the overall process of alarming the thermal insulation effect of the building exterior wall is as follows: Figure 2 As shown, the temperature signal and humidity signal can be collected first, and then data processing can be performed, and the evaluation index can be calculated and the evaluation result can be generated, and then it can be determined whether the evaluation result exceeds the preset threshold. If the evaluation result exceeds the preset threshold, an alarm operation is performed. Finally, it is determined whether to stop monitoring. If monitoring is not stopped, the exterior wall of the building is inspected and maintained.

[0126] like Figure 3 As shown, the embodiment of the present invention also provides a building exterior wall thermal insulation performance evaluation system, including:

[0127] The data acquisition module 801 is used to obtain the temperature and humidity data of the building exterior wall according to the data acquisition frequency;

[0128] Data processing module 802, the data processing module is used to calculate the thermal insulation performance evaluation result according to the temperature and humidity data of the building exterior wall;

[0129] Data transmission module 803, the data transmission module is used to transmit the temperature and humidity data of the building exterior wall to the data processing module;

[0130] The output display module 804 is used to display the thermal insulation performance evaluation results on the user interface and issue a safety warning for the building exterior wall.

[0131] In some embodiments, a building exterior wall thermal insulation performance evaluation system provided in this embodiment includes a data acquisition module, a data processing module, a data transmission module and an output display module. The temperature and humidity data of the building exterior wall can be obtained through the data acquisition module according to the data acquisition frequency, wherein the data acquisition module can include multiple sensors, and the sensors are used to monitor the temperature, humidity and other parameters of the exterior wall in real time. The sensors include high-precision temperature sensors and humidity sensors, which are specially designed to withstand the harsh conditions of the exterior wall environment, such as high temperature, low temperature, rain, wind and dust. The sensor uses wireless transmission to send data to the data collector in real time, avoiding the problems of complex wiring and vulnerability to damage caused by wired connections. In addition, in signal acquisition, signal acquisition is mainly achieved through high-precision sensors. The temperature sensor uses thermistor or thermocouple principle to accurately measure the temperature of the exterior wall, while the humidity sensor determines the humidity by measuring the water vapor pressure or capacitance change in the air. These sensors can continuously capture changes in environmental parameters and convert these changes into electrical signals for subsequent processing. The thermal insulation performance evaluation result can be calculated through the data processing module according to the temperature and humidity data of the building's exterior wall, wherein the data processing module may include a data processor, and the data processor adopts a high-performance computing unit, which can quickly process a large amount of data. The processor has built-in advanced algorithms, which can calculate the thermal insulation effect evaluation index of the exterior wall according to the collected temperature and humidity data and the characteristics of the exterior wall materials. These indicators include but are not limited to thermal insulation efficiency, thermal resistance value, humidity change rate, etc. In data processing, after receiving the original signal, the data processing module first performs decoding and decryption operations, and then uses the built-in algorithm to clean and smooth the signal, and calculates the relevant thermal insulation effect evaluation index. These indicators include but are not limited to the average temperature of the exterior wall, the humidity change rate, the thermal resistance value, and the comprehensive thermal insulation performance index. The processed data is finally displayed to the user in the form of a chart or a number through the output display module, helping the user to intuitively understand the thermal insulation effect of the exterior wall. The temperature and humidity data of the building's exterior wall can be transmitted to the data processing module through the data transmission module, wherein the data transmission module may include a data collector, which is designed to receive data from multiple sensors and perform preliminary data screening and sorting. The collector has a built-in large-capacity storage device that can record data for a long time and transmit the data to the data processor in real time through a wireless network. During signal transmission, the signal transmission module transmits the collected signals to the data processing module safely and quickly through wired (such as cables, optical fibers) or wireless technologies (such as Wi-Fi, Bluetooth, ZigBee, etc.). To ensure the integrity and security of the data, the signal will be encrypted and verified during the transmission process. The thermal insulation performance evaluation results can be displayed on the user interface through the output display module, and a safety warning of the building exterior wall can be issued, wherein the output display module can include a display and an alarm system.The display is designed as a touch screen operation interface, which can intuitively display the real-time data and historical curves of various insulation effect evaluation indicators. At the same time, the display can also provide a visual display of alarm information to help users quickly locate and solve problems. The alarm system can automatically trigger an alarm based on preset alarm conditions, such as temperature and humidity exceeding the set range or insulation effect evaluation indicators falling below a certain threshold. Alarm methods include sound and light alarms, SMS notifications, etc., to ensure that users can promptly discover and handle abnormal situations.

[0132] In some embodiments, the component structure of the evaluation system is as follows Figure 4 As shown, data can be collected through multiple temperature sensors and multiple humidity sensors, and then the temperature data can be integrated through data collector A, and the humidity data can be integrated through data collector B. Then, the integrated temperature data and humidity data can be analyzed through the data processor to obtain the evaluation results, and finally the evaluation results can be displayed through the human-machine interface, and the alarm can be issued through the alarm system. In addition, the energy consumption can be monitored through the power supply system, and energy-saving measures can be provided.

[0133] The beneficial effects of implementing the embodiments of the present invention include: a building exterior wall thermal insulation performance evaluation system provided by the present embodiment includes a data acquisition module, a data processing module, a data transmission module and an output display module. The temperature and humidity data of the building exterior wall can be obtained through the data acquisition module according to the data acquisition frequency; the thermal insulation performance evaluation result can be calculated through the data processing module according to the temperature and humidity data of the building exterior wall; the temperature and humidity data of the building exterior wall can be transmitted to the data processing module through the data transmission module; the thermal insulation performance evaluation result can be displayed on the user interface through the output display module, and a safety warning of the building exterior wall can be performed, thereby realizing the thermal insulation performance evaluation and result display, and improving the evaluation accuracy and data intuitive visibility.

[0134] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A method for evaluating the thermal insulation performance of a building exterior wall, characterized in that: The following steps are involved: Obtain temperature and humidity data of building exterior walls according to data collection frequency; Preprocessing the building exterior wall temperature and humidity data to obtain target temperature and humidity data, wherein the target temperature and humidity data includes temperature data and humidity data; Performing feature extraction processing on the target temperature and humidity data to obtain target features, wherein the target features include time domain features, frequency domain features or statistical features; Calculate the temperature and humidity change trend by a preset time series analysis method according to the target characteristics, environmental factors, material properties, the temperature data and the humidity data, wherein the environmental factors include solar radiation, wind speed or wind direction, the material properties include thermal conductivity, heat capacity or density of the exterior wall material, and the temperature and humidity change trend includes a temperature seasonal change pattern, a humidity seasonal change pattern, a temperature prediction trend or a humidity prediction trend; According to the target characteristics and the target temperature and humidity data, predictive thermal insulation performance information is calculated by a preset prediction method, wherein the predictive thermal insulation performance information includes a linear relationship of thermal insulation performance, a nonlinear relationship of thermal insulation performance, nonlinear pattern data or data boundaries; Calculate the thermal insulation performance index value according to the target temperature and humidity data, the thickness of the thermal insulation material, the exterior wall area and the temperature difference between the interior and the exterior, wherein the thermal insulation performance index value includes a thermal resistance value or a thermal insulation efficiency; According to the temperature and humidity change trend, the predicted thermal insulation performance information and the thermal insulation performance index value, a thermal insulation performance evaluation is performed to obtain a thermal insulation performance evaluation result.

2. The method according to claim 1, characterized in that The preprocessing of the building exterior wall temperature and humidity data to obtain target temperature and humidity data includes: De-noising the temperature and humidity data of the building exterior wall by filtering to obtain first temperature and humidity data; Performing standardization processing on the first temperature and humidity data to obtain second temperature and humidity data; The target temperature and humidity data are obtained by performing missing value processing on the second temperature and humidity data through interpolation.

3. The method according to claim 1, characterized in that The time domain feature includes an average value, a maximum value or a standard deviation, and the feature extraction process is performed on the target temperature and humidity data to obtain the target feature, including: Extracting time series data from the target temperature and humidity data; Calculate the average value according to the time series data, where the average value is used to reflect the trend of the data set; Extracting the maximum value from the time series data, wherein the maximum value includes a maximum value or a minimum value, and the maximum value is used to reflect the data fluctuation range; The standard deviation is calculated based on the time series data, and the standard deviation is used to measure the degree of data fluctuation.

4. The method according to claim 1, characterized in that: The step of performing feature extraction processing on the target temperature and humidity data to obtain target features includes: Extracting a time domain signal from the target temperature and humidity data; Performing frequency domain conversion processing on the time domain signal by Fourier transform method to obtain the frequency domain feature, wherein the frequency domain feature includes frequency component, power spectrum density or phase information; The power spectrum density is used to measure the energy of the multiple frequency components, and the phase information is used to reflect the phase relationship between the multiple frequency components.

5. The method according to claim 1, characterized in that The statistical features include correlation data or principal component data, and the feature extraction process is performed on the target temperature and humidity data to obtain the target features, including: Performing correlation analysis on the target temperature and humidity data and calculating the Pearson correlation coefficient, where the Pearson correlation coefficient is used to characterize the strength of the linear relationship between variables; According to the Pearson correlation coefficient, the target temperature and humidity data are de-redundanted to obtain the correlation data; The target temperature and humidity data are subjected to principal component analysis through orthogonal transformation to obtain the principal component data, so as to achieve data dimensionality reduction.

6. The method according to claim 1, characterized in that The preset time series analysis method includes an autoregressive integrated moving average model analysis method or a seasonal decomposition time series prediction method. The temperature and humidity change trend is calculated by the preset time series analysis method according to the target characteristics, environmental factors, material properties, the temperature data and the humidity data, including: Calculate the temperature prediction trend according to the target characteristics, the environmental factors, the material properties and the temperature data by using the autoregressive integrated moving average model analysis method; Calculate the humidity prediction trend according to the target characteristics, the environmental factors, the material properties and the humidity data by using the autoregressive integrated moving average model analysis method; Calculate the temperature seasonal variation pattern according to the target characteristics and the temperature data by using the seasonal decomposition time series prediction method; According to the target characteristics and the humidity data, the humidity seasonal variation pattern is calculated by using the seasonal decomposition time series prediction method.

7. The method according to claim 1, characterized in that The preset prediction method includes linear regression, decision tree, neural network or support vector machine, and the thermal insulation performance information is calculated and predicted by the preset prediction method according to the target characteristics and the target temperature and humidity data, including: Calculating the thermal insulation performance linear relationship through the linear regression according to the target characteristics and the target temperature and humidity data; Calculating the nonlinear relationship of thermal insulation performance through the decision tree according to the target characteristics and the target temperature and humidity data; According to the target characteristics and the target temperature and humidity data, nonlinear relationship modeling is performed through the neural network to calculate the nonlinear pattern data; The data boundary is obtained by performing classification through the support vector machine according to the target features and the target temperature and humidity data.

8. The method according to claim 1, characterized in that The calculation of the thermal insulation performance index value according to the target temperature and humidity data, the thickness of the thermal insulation material, the exterior wall area and the temperature difference between the interior and the exterior includes: Calculating a target thermal conductivity coefficient according to the target temperature and humidity data and the thermal conductivity coefficient of the exterior wall material; Calculating the thermal resistance value according to the target thermal conductivity and the thickness of the thermal insulation material; Calculating the heat loss of the outer wall according to the target heat transfer coefficient, the outer wall area and the inner and outer temperature difference; The insulation efficiency is calculated based on a heat balance equation, the heat loss of the exterior wall and the heat loss in a non-insulated state.

9. The method according to claim 1, characterized in that: The method further comprises: Based on the thermal insulation performance evaluation results and alarm thresholds, a safety warning for the building's exterior walls is issued.

10. A building exterior wall thermal insulation performance evaluation system, characterized in that: include: A data acquisition module, wherein the data acquisition module is used to obtain temperature and humidity data of the building exterior wall according to the data acquisition frequency; A data processing module, the data processing module is used to calculate the thermal insulation performance evaluation result according to the temperature and humidity data of the building exterior wall; A data transmission module, the data transmission module is used to transmit the temperature and humidity data of the building exterior wall to the data processing module; An output display module is used to display the thermal insulation performance evaluation result on a user interface and to issue a safety warning for the building exterior wall.

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