Intelligent detection method and system for quality of heat insulation material yoga column

Abstract

The present invention relates to the technical field of thermal insulation performance measurement, specifically to an intelligent quality detection method and system for a yoga column made of thermal insulation material, comprising the following steps: collecting meteorological data, including average temperature, humidity range and heat flow direction, combining user feedback on use in different geographical locations, simulating the use environment conditions of the yoga column, and obtaining an environmental simulation data set. In the present invention, by comprehensively applying the initial value and boundary condition setting of the heat conduction equation, variance analysis and short-time Fourier transform technology, dynamic simulation and in-depth analysis of the thermal insulation performance of the yoga column are achieved. According to actual meteorological data and user feedback, an environment close to the actual use conditions can be simulated to accurately evaluate the thermal insulation performance of the yoga column. The time-frequency analysis results are used to analyze the variation amplitude of thermal conductivity and the spectrum energy distribution, which can capture the key factors affecting thermal stability, provide a scientific basis for improving the thermal stability of the yoga column, and greatly improve the accuracy and detection capability of thermal insulation performance detection.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal insulation performance determination, and in particular to an intelligent detection method and system for the quality of a thermal insulation material yoga column. Background Art

[0002] The technical field of thermal insulation performance measurement refers to a series of methods and processes that focus on evaluating and measuring the ability of materials or systems to prevent heat transfer. Thermal insulation performance is directly related to the energy efficiency of materials, environmental comfort, and energy consumption. Thermal insulation materials are widely used in industries such as construction, aviation, automobile manufacturing, clothing, and sports equipment. Excellent thermal insulation materials can effectively reduce energy consumption, improve the comfort of the use environment, and have a positive impact on environmental protection. The methods for measuring thermal insulation performance include but are not limited to the heat flow meter method, steady-state method, transient method, etc., which aim to determine its thermal resistance or thermal conductivity by accurately measuring the heat flow through the material under known temperature difference conditions, thereby evaluating its thermal insulation performance.

[0003] The intelligent detection method for the quality of heat-insulating materials yoga columns is an advanced technology used to evaluate the performance of heat-insulating materials used in yoga columns. The purpose is to ensure that the yoga column can effectively isolate the heat and cold of the ground while maintaining the user's body temperature comfort. This not only helps to improve the comfort and effectiveness of yoga practice, but also ensures the health and safety of users. Through the intelligent detection method, the heat-insulating performance of yoga columns can be quickly and accurately evaluated, thereby ensuring product quality, meeting consumers' high standards for health and comfort, optimizing the heat-insulating performance of yoga columns, and ensuring that they can maintain good heat-insulating effects in various usage environments.

[0004] Traditional technologies usually rely on static measurements under specific conditions, which makes it difficult to fully reflect the thermal insulation performance and thermal stability of materials in changing environments. As a result, when faced with complex or changing usage environments, the evaluation results may deviate from the actual performance, affecting the accuracy of the selection of thermal insulation materials and the optimization of product design. The lack of in-depth analysis and comprehensive evaluation of the simulation of actual usage conditions limits the ability to fully understand and improve the performance of thermal insulation materials, and also reduces the accuracy of predicting future performance changes, which to a certain extent restricts the application development and performance optimization of thermal insulation materials in different fields. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an intelligent detection method and system for the quality of a yoga column made of thermal insulation material.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: a method for intelligently detecting the quality of a heat-insulating material yoga column, comprising the following steps:

[0007] S1: Collect meteorological data, including average temperature, humidity range, and heat flow direction, and combine it with user feedback in different geographical locations to simulate the environmental conditions for using yoga columns and obtain an environmental simulation data set;

[0008] S2: according to each environmental condition in the environmental simulation data set, set the initial value and boundary conditions of the corresponding heat conduction equation, run the simulation program, simulate the temperature change process of the yoga column under different environments, and obtain the thermal response analysis results;

[0009] S3: inputting the temperature change data in the thermal response analysis results into statistical analysis software, using variance analysis method to perform statistical analysis on the temperature change trend of the yoga column, revealing the thermal stability of the yoga column under different simulated environments, and generating thermal stability evaluation results;

[0010] S4: According to the thermal stability evaluation results, the thermal conductivity data of the yoga column is collected, time series sampling is performed, and time-frequency analysis is performed using short-time Fourier transform to identify the variation characteristics of thermal conductivity in different time periods, reveal the thermal conductivity variation pattern in the time series, and construct the time-frequency analysis results;

[0011] S5: using the time-frequency analysis results, analyzing the variation amplitude of thermal conductivity and the spectrum energy distribution, capturing the key factors affecting thermal stability, analyzing the core indicators affecting the time stability of thermal conductivity of the yoga column, and obtaining a thermal stability indicator set;

[0012] S6: Combine the thermal stability index set and the environmental simulation data set to perform data statistical analysis, predict the future change trend of the thermal conductivity of the yoga column, reveal the change range of the thermal conductivity of the yoga column in future use, and establish a thermal conductivity change trend prediction model.

[0013] As a further solution of the present invention, the environmental simulation data set includes a simulated indoor temperature range, a relative humidity percentage, and a heat flow rate between the ground and the air; the thermal response analysis results include a temperature change curve on the surface and inside of a yoga column, and time series data of a dynamic response of heat flow; the thermal stability evaluation results include a temperature stability classification of a yoga column under different environmental conditions, and a maximum and minimum temperature fluctuation range; the time-frequency analysis results are specifically a spectrum diagram of thermal conductivity changing over time, and thermal conductivity change characteristics at key time points; the thermal stability index set includes an average change amplitude of thermal conductivity, and energy distribution at key frequencies; the thermal conductivity change trend prediction model specifically refers to a future thermal conductivity change trend prediction curve obtained based on historical data analysis and a predicted thermal stability change interval.

[0014] As a further solution of the present invention, meteorological data including average temperature, humidity range and heat flow direction are collected, and the environmental conditions of the yoga column are simulated in combination with the user feedback in different geographical locations. The steps of obtaining the environmental simulation data set are as follows:

[0015] S101: Collect average temperature data of a region, record the temperature range including the highest temperature and the lowest temperature, and obtain a temperature data set;

[0016] S102: Based on the temperature data set, collect humidity range data of the corresponding area, including maximum humidity and minimum humidity, and construct a humidity data set;

[0017] S103: Integrate the temperature data set and the humidity data set, combine the feedback of users using the yoga column, and combine the direction and intensity of heat flow between the ground and the air to simulate the environment conditions for using the yoga column, and obtain an environment simulation data set.

[0018] As a further solution of the present invention, according to each environmental condition in the environmental simulation data set, the initial value and boundary conditions of the corresponding heat conduction equation are set, and the simulation program is run to simulate the temperature change process of the yoga column under different environments. The steps of obtaining the thermal response analysis results are as follows:

[0019] S201: Based on the environmental simulation data set, setting the initial value of the heat conduction equation for each simulated environmental condition, including setting the initial temperature and the initial heat flow, to obtain the initial condition setting value;

[0020] S202: According to the initial condition setting, corresponding boundary conditions are set for each simulation environment, including an environment temperature limit and a heat flow limit, to ensure the accuracy of the simulation and obtain the boundary condition configuration;

[0021] S203: combining the boundary condition configuration, simulating the temperature change process of the yoga column under different environmental conditions, and obtaining a thermal response analysis result.

[0022] As a further solution of the present invention, the temperature change data in the thermal response analysis result is input into the statistical analysis software, and the temperature change trend of the yoga column is statistically analyzed by variance analysis to reveal the thermal stability of the yoga column under the differential simulation environment. The step of generating the thermal stability evaluation result is as follows:

[0023] S301: extracting temperature change data of the yoga column according to the thermal response analysis result, including temperature records under each simulation environment, to obtain a temperature change data set;

[0024] S302: inputting the temperature change data set into statistical analysis software, setting the analysis item to variance analysis, identifying and comparing the temperature change differences under the difference simulation environment, and obtaining variance analysis preparation data;

[0025] S303: preparing data according to the variance analysis, analyzing the temperature change trend by using the variance analysis method, revealing the difference in thermal stability of the yoga column under all simulated environments, and obtaining a thermal stability evaluation result;

[0026] The variance analysis method is according to the formula:

[0027]

[0028] The improved statistics are calculated to obtain the thermal stability evaluation results of the yoga column, where: is the improved statistic, is the ambient temperature change rate, is the thermal conductivity coefficient of the yoga column material, Use time for the yoga column; is the ambient humidity, To consider the ambient temperature change rate And the thermal conductivity of the yoga column material The mean square difference between the groups after is the ambient temperature change rate And the thermal conductivity of the yoga column material The weight coefficient of To consider the use time of yoga column and ambient humidity The mean squared difference within the group is Time to use the yoga column and ambient humidity The weight coefficient of .

[0029] As a further solution of the present invention, according to the thermal stability evaluation results, the thermal conductivity data of the yoga column is collected, time series sampling is performed, and time-frequency analysis is performed using short-time Fourier transform to identify the change characteristics of thermal conductivity in different time periods and reveal the thermal conductivity change pattern in the time series. The steps of constructing the time-frequency analysis results are as follows:

[0030] S401: According to the thermal stability evaluation result, collecting thermal conductivity data of the yoga column associated with the evaluation result, including thermal conductivity measurement values ​​under different environmental conditions, to obtain a thermal conductivity data set;

[0031] S402: Based on the thermal conductivity data set, perform time series sampling, determine the sampling frequency to capture the change pattern of thermal conductivity over time, and generate time series sampling data;

[0032] S403: performing short-time Fourier transform according to the time series sampling data, analyzing the frequency components of thermal conductivity in the difference time period, revealing the pattern of thermal conductivity change, and obtaining time-frequency analysis results;

[0033] Perform the short-time Fourier transform according to the formula:

[0034]

[0035] Calculate the time-frequency distribution of the thermal conductivity time series and generate the time-frequency analysis results, where: is the thermal conductivity signal that varies with time, According to the shape parameter and time variation factor Adjusted window function, is the shape parameter, is the time variation factor, is the time shift variable, Represents the conversion of the signal from the time domain to the frequency domain, is the frequency weighting factor, It is a nonlinear adjustment term.

[0036] As a further solution of the present invention, the time-frequency analysis results are used to analyze the variation amplitude of thermal conductivity and the spectrum energy distribution, capture the key factors affecting thermal stability, analyze the core indicators affecting the time stability of thermal conductivity of yoga columns, and obtain the steps of obtaining a thermal stability indicator set,

[0037] S501: According to the time-frequency analysis result, the usage data of the yoga column under different temperatures are collected, including temperature changes, usage time and frequency, and the data are screened and summarized to obtain a basic data set;

[0038] S502: Based on the basic data set, analyzing the thermal conductivity variation amplitude and the spectrum energy distribution, analyzing the energy distribution under the difference frequency, identifying the key frequency range affecting the thermal stability, and obtaining the analysis results of the factors affecting the thermal stability;

[0039] S503: Based on the analysis results of the factors affecting thermal stability, the core indicators affecting the time stability of thermal conductivity of the yoga column are quantitatively analyzed, including the energy value and the change rate of the key frequency, to obtain a thermal stability indicator set.

[0040] As a further solution of the present invention, the thermal stability index set and the environmental simulation data set are combined to perform data statistical analysis to predict the future trend of thermal conductivity of the yoga column, reveal the range of thermal conductivity changes in the future use of the yoga column, and establish a thermal conductivity change trend prediction model in the following steps:

[0041] S601: Integrate the thermal stability index set and the environmental simulation data set, including temperature, humidity and usage frequency, and fuse the two to obtain a comprehensive data set;

[0042] S602: Based on the comprehensive data set, statistical analysis of the data is performed to reveal the correlation between the thermal stability index and the environmental factors, capture key influencing factors, and obtain key influencing factor analysis results;

[0043] S603: Based on the analysis results of the key influencing factors and the influence of key environmental factors on thermal conductivity, by simulating the change of thermal conductivity under future environmental conditions, the change range of thermal conductivity of the yoga column in future use is revealed, and a thermal conductivity change trend prediction model is obtained.

[0044] An intelligent quality detection system for heat-insulating yoga columns, the system comprising a data collection module, an environmental simulation module, a heat conduction simulation module, a thermal stability analysis module, a thermal conductivity time-frequency analysis module, a thermal stability index integration module, a data statistical analysis module, and a trend prediction module;

[0045] The data collection module collects meteorological data, including average temperature, humidity range and heat flow direction, and generates meteorological and user feedback data sets in combination with user feedback in different geographical locations;

[0046] The environmental simulation module simulates the environmental conditions for using the yoga column based on the meteorological and user feedback data sets, combined with the direction and intensity of heat flow between the ground and the air, and generates an environmental simulation data set;

[0047] The heat conduction simulation module sets the initial value and boundary conditions of the heat conduction equation based on the environmental simulation data set, including the environmental temperature limit and the heat flow limit, simulates the temperature change process of the yoga column under different environmental conditions, and generates thermal response analysis results;

[0048] The thermal stability analysis module uses variance analysis method based on the thermal response analysis results to analyze the temperature change trend of the yoga column under the different simulation environments, reveal the thermal stability, and generate thermal stability evaluation results;

[0049] The thermal conductivity time-frequency analysis module collects the thermal conductivity data of the yoga column for time series sampling based on the thermal stability evaluation results, uses the short-time Fourier transform method to analyze the thermal conductivity change characteristics, and generates time-frequency analysis results;

[0050] The thermal stability index integration module analyzes the thermal conductivity variation amplitude and spectrum energy distribution based on the time-frequency analysis results, captures key factors, analyzes core indicators, and generates a thermal stability index set;

[0051] The data statistical analysis module combines the thermal stability index set and the environmental simulation data set to perform data fusion and statistical analysis, capture key influencing factors that will affect thermal conductivity in the future, and generate key influencing factor analysis results;

[0052] The trend prediction module predicts the future change trend and range of the thermal conductivity of the yoga column based on the key influencing factor analysis results and the influence of key environmental factors on thermal conductivity, and generates a thermal conductivity change trend prediction model.

[0053] Compared with the prior art, the advantages and positive effects of the present invention are:

[0054] In the present invention, by comprehensively applying the initial value and boundary condition setting of the heat conduction equation, variance analysis and short-time Fourier transform technology, dynamic simulation and in-depth analysis of the thermal insulation performance of the yoga column are achieved. According to actual meteorological data and user feedback, an environment closer to the actual use conditions can be simulated, so as to make a more accurate evaluation of the thermal insulation performance of the yoga column. The time-frequency analysis results are used to analyze the variation amplitude of thermal conductivity and the spectrum energy distribution, which can capture the key factors affecting thermal stability, provide a scientific basis for improving the thermal stability of the yoga column, and greatly improve the accuracy and detection capability of thermal insulation performance detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0056] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0057] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0058] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0059] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0060] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0061] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0062] Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0064] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined. Embodiment 1

[0065] See also Figure 1 The present invention provides a technical solution: an intelligent detection method for the quality of a heat-insulating material yoga column, comprising the following steps:

[0066] S1: Collect meteorological data, including average temperature, humidity range, and heat flow direction, and combine it with user feedback in different geographical locations to simulate the environmental conditions for using yoga columns and obtain an environmental simulation data set;

[0067] S2: According to each environmental condition in the environmental simulation data set, set the initial value and boundary conditions of the corresponding heat conduction equation, run the simulation program, simulate the temperature change process of the yoga column under different environments, and obtain the thermal response analysis results;

[0068] S3: Input the temperature change data in the thermal response analysis results into the statistical analysis software, use the variance analysis method to perform statistical analysis on the temperature change trend of the yoga column, reveal the thermal stability of the yoga column under different simulation environments, and generate thermal stability evaluation results;

[0069] S4: According to the results of thermal stability assessment, the thermal conductivity data of the yoga column is collected, time series sampling is performed, and time-frequency analysis is performed using short-time Fourier transform to identify the variation characteristics of thermal conductivity in different time periods, reveal the thermal conductivity variation pattern in the time series, and construct the time-frequency analysis results;

[0070] S5: Using the time-frequency analysis results, analyze the variation amplitude of thermal conductivity and the spectrum energy distribution, capture the key factors affecting thermal stability, analyze the core indicators that affect the time stability of thermal conductivity of yoga columns, and obtain a set of thermal stability indicators;

[0071] S6: Combine the thermal stability index set and the environmental simulation data set to conduct data statistical analysis, predict the future trend of thermal conductivity of the yoga column, reveal the range of thermal conductivity changes in the future use of the yoga column, and establish a thermal conductivity change trend prediction model.

[0072] The environmental simulation data set includes the simulated indoor temperature range, relative humidity percentage, and heat flow rate between the ground and the air. The thermal response analysis results include the temperature change curves on the surface and inside of the yoga column, and the time series data of the dynamic response of the heat flow. The thermal stability evaluation results include the temperature stability classification of the yoga column under different environmental conditions, and the maximum and minimum temperature fluctuation ranges. The time-frequency analysis results are specifically the spectrum diagram of thermal conductivity changing with time and the thermal conductivity change characteristics at key time points. The thermal stability index set includes the average change amplitude of thermal conductivity and the energy distribution at key frequencies. The thermal conductivity change trend prediction model specifically refers to the future thermal conductivity change trend prediction curve obtained based on historical data analysis and the predicted thermal stability change range.

[0073] In step S1, an environmental simulation data set is constructed by collecting and organizing meteorological data and user feedback. Specific operations include collecting data on regional average temperature, humidity range, and heat flow direction, and integrating user feedback information from different geographical locations. The data is mapped spatially and temporally through the Geographic Information System (GIS) and meteorological models to simulate the various environmental conditions for the use of yoga columns. The generated environmental simulation data set records in detail the simulated indoor temperature range, relative humidity percentage, and heat flow rate between the ground and the air, effectively constructing a multi-dimensional, high-precision use environment simulation, which provides basic data for the performance analysis of yoga columns.

[0074] In step S2, the environmental simulation data set is processed through the heat conduction equation model to obtain the thermal response analysis results. This process involves using each environmental condition in the environmental simulation data set as an input parameter of the heat conduction equation, including the setting of initial values ​​and boundary conditions. The simulation is performed using finite element analysis (FEA) software to simulate in detail the temperature change process of the yoga column under different environmental conditions. The thermal response analysis results are saved in the form of charts and data files, including the temperature change curves on the surface and inside of the yoga column, and the time series data of the dynamic response of the heat flow, which deeply reveals the thermal behavior characteristics of the yoga column in different environments and lays the foundation for further thermal stability evaluation.

[0075] In step S3, the thermal response analysis results are processed by variance analysis to generate thermal stability evaluation results. First, the temperature change data is imported into the statistical analysis software, and the variance analysis project is set to analyze the temperature change trend under different simulation environments. By calculating the statistical significance of temperature changes in each environment, the thermal stability level and temperature fluctuation range of the yoga column under each simulation environment are revealed. The thermal stability evaluation results include temperature stability classification, maximum and minimum temperature fluctuation ranges, which provide a basis for material selection and design improvement of yoga columns.

[0076] In step S4, the thermal stability evaluation results are deeply analyzed through time series sampling and short-time Fourier transform to construct time-frequency analysis results. In the operation, the thermal conductivity data is first sampled in time series, and the sampling frequency is determined to capture the change pattern of thermal conductivity over time. Then, the short-time Fourier transform is applied to analyze the sampled data to obtain the spectrum of thermal conductivity over time and the characteristics of thermal conductivity change at key time points, revealing the dynamic changes of thermal conductivity in time series, providing an in-depth perspective for understanding the thermal stability of yoga columns.

[0077] In step S5, the thermal stability index set is obtained by comprehensively analyzing the time-frequency analysis results. This operation involves evaluating the variation of thermal conductivity and the energy distribution at key frequencies, and describing in detail the key influencing factors and core indicators of thermal stability in the form of a mathematical model. The thermal stability index set includes the average variation of thermal conductivity and the energy distribution at key frequencies, which provides key indicators for performance optimization and material improvement of yoga columns.

[0078] In step S6, by combining the thermal stability index set and the environmental simulation data set, data statistical analysis was performed to establish a thermal conductivity change trend prediction model. Regression analysis and machine learning techniques were used to analyze the relationship between thermal stability indicators and environmental factors, and a thermal conductivity change trend prediction model was constructed to predict the change trend of thermal conductivity and the thermal stability change range of yoga columns under different conditions in the future, providing a scientific basis for long-term product performance assurance and market positioning.

[0079] See also Figure 2 ,Collect meteorological data, including average temperature, humidity range and heat flow direction, and combine the user feedback in different geographical locations to simulate the environmental conditions of yoga column use. The steps to obtain the environmental simulation data set are as follows,

[0080] S101: Collect average temperature data of a region, record the temperature range including the highest temperature and the lowest temperature, and obtain a temperature data set;

[0081] In sub-step S101, the regional average temperature data is obtained by collecting API interfaces, and the Python requests library is used to send HTTP requests to the meteorological data platform. The json library is used to parse the temperature range information in the response content, including the maximum temperature and the minimum temperature. The data is organized into a DataFrame format through the Pandas library. Each row of data includes three fields: date, maximum temperature, and minimum temperature, and a temperature data set is generated.

[0082] S102: Based on the temperature data set, collect humidity range data of the corresponding area, including maximum humidity and minimum humidity, and construct a humidity data set;

[0083] In sub-step S102, based on the same meteorological data platform, the same requests library is used to send a request to obtain the humidity range data, and the response content is parsed through the json library to extract the maximum and minimum humidity at each time point. The Pandas library is also used to organize these data into a DataFrame format. Each row of data includes three fields: date, maximum humidity, and minimum humidity, to generate a humidity data set.

[0084] S103: Integrate the temperature data set and the humidity data set, combine the feedback from users using the yoga column, and combine the direction and intensity of heat flow between the ground and the air to simulate the environment conditions for using the yoga column, and obtain an environment simulation data set;

[0085] In sub-step S103, the merge function of the Pandas library is used to merge the temperature dataset and the humidity dataset according to the date field to obtain a comprehensive environmental dataset containing temperature and humidity information. Then, based on user feedback, the temperature and humidity values ​​in the dataset are adjusted to simulate the actual use environment of the yoga column. The numpy library is used to calculate the direction and intensity of the heat flow between the ground and the air. By setting a preset value for the heat flow rate, the heat flow value of each data point is adjusted according to the linear equation, and finally an environmental simulation dataset containing the simulated indoor temperature range, relative humidity percentage, and heat flow rate between the ground and the air is generated.

[0086] See also Figure 3 ,According to each environmental condition in the environmental simulation data set, the initial value and boundary conditions of the corresponding heat conduction equation are set, and the simulation program is run to simulate the temperature change process of the yoga column under different environments. The steps to obtain the thermal response analysis results are as follows,

[0087] S201: Based on the environmental simulation data set, setting the initial value of the heat conduction equation for each simulated environmental condition, including setting the initial temperature and the initial heat flow, to obtain the initial condition setting value;

[0088] In sub-step S201, based on the environmental simulation data set, the initial value of the heat conduction equation is set for each simulated environmental condition using the principles of thermodynamics. The specific operation includes using the solve_ivp function in Python's SciPy library, which is used to solve the initial value problem. The initial temperature is determined by the average temperature in the environmental simulation data set, and the initial heat flow is preset according to the heat flow direction and intensity parameters. The preset operation is performed by setting the function parameter boundary_conditions, which contains the boundary conditions of temperature and heat flow. These conditions are adjusted according to actual meteorological data and user feedback to generate the initial condition setting value.

[0089] S202: according to the initial condition setting, corresponding boundary conditions are set for each simulation environment, including the ambient temperature limit and the heat flow limit, to ensure the accuracy of the simulation and obtain the boundary condition configuration;

[0090] In sub-step S202, according to the initial condition setting, the corresponding boundary conditions are set for each simulation environment using the finite element analysis method. This process is executed through the pdepe function of MATLAB, which is specifically used to solve partial differential equations with boundary conditions. The ambient temperature limit is preset by the actual collected maximum and minimum temperature data, and the heat flow limit is set based on the heat flow rate between the ground and the air to ensure the accuracy of the simulation and obtain the boundary condition configuration.

[0091] S203: combining boundary condition configuration, simulating the temperature change process of the yoga column under different environmental conditions, and obtaining thermal response analysis results;

[0092] In sub-step S203, COMSOL Multiphysics software is used to simulate the temperature change process of the yoga column under different environmental conditions in combination with the boundary condition configuration. The software provides a dedicated module for simulating the heat transfer process. By setting specific parameters of the simulation environment such as ambient temperature, humidity, and heat flow direction, the simulation calculation is performed. The internal algorithm of the software automatically performs iterative calculations according to the set boundary conditions and initial conditions until convergence, and generates thermal response analysis results, including temperature change curves on the surface and inside of the yoga column, and time series data of the dynamic response of heat flow.

[0093] See also Figure 4 ,The temperature change data in the thermal response analysis results are input into the statistical analysis software, and the temperature change trend of the yoga column is statistically analyzed using the variance analysis method to reveal the thermal stability of the yoga column under different simulation environments. The steps to generate the thermal stability evaluation results are as follows,

[0094] S301: extracting temperature change data of the yoga column according to the thermal response analysis results, including temperature records under each simulation environment, to obtain a temperature change data set;

[0095] In sub-step S301, based on the results of thermal response analysis, Python programming language is used in combination with the Pandas library to extract and process data. First, the CSV file generated by the thermal response analysis is read using the Pandas DataFrame structure. The file records the temperature records of the yoga column in each simulation environment, including timestamps and corresponding temperature values. In the step, the index of the DataFrame is set to the timestamp, and the column name is set to the corresponding temperature record to generate a temperature change data set, ensuring that the data is organized in a form that facilitates subsequent analysis.

[0096] S302: Input the temperature change data set into the statistical analysis software, set the analysis project to variance analysis, identify and compare the temperature change differences under the difference simulation environment, and obtain variance analysis preparation data;

[0097] In sub-step S302, the temperature change data set is input into the statistical analysis software R, and variance analysis is performed using the aov function in the R language. This function belongs to the stats package of the R language and is specifically used to perform variance analysis. By specifying the formula parameter as temperature~environment, temperature is used as the response variable and environmental conditions are used as factors for analysis. The execution of this step depends on the data frame structure of the R language, in which environmental conditions are used as categorical variables and temperature change data are used as continuous variables. Before performing variance analysis, the lm function is used to fit the linear model to provide necessary preprocessing for variance analysis and obtain data for variance analysis preparation.

[0098] S303: Prepare data according to variance analysis, analyze the temperature change trend by variance analysis, reveal the difference of thermal stability of yoga column in all simulation environments, and obtain the thermal stability evaluation result;

[0099] ANOVA method, according to the formula:

[0100]

[0101] The improved statistics are calculated to obtain the thermal stability evaluation results of the yoga column, where: is an improved statistic used to evaluate the significance of differences in thermal stability of yoga columns. is the ambient temperature change rate, is the thermal conductivity coefficient of the yoga column material, Use time for the yoga column; is the ambient humidity, To consider the ambient temperature change rate And the thermal conductivity of the yoga column material The mean square difference between the groups after is the ambient temperature change rate And the thermal conductivity of the yoga column material The weight coefficient adjusts the influence of the two parameters on the mean square difference between groups. To consider the use time of yoga column and ambient humidity The mean squared difference within the group is Time to use the yoga column and ambient humidity The weight coefficients are used to adjust the impact of these two parameters on the mean square difference within the group.

[0102] The execution process is as follows:

[0103] Parameter measurement and calculation, measurement of ambient temperature change rate , Thermal conductivity of yoga column material , Yoga column usage time and ambient humidity ;

[0104] The weight coefficient is determined by using historical data or expert opinions to conduct correlation analysis or regression analysis to determine , , , The specific degree of influence on thermal stability, based on the analysis results, assign weight coefficients to each parameter and .

[0105] Adjust the mean square difference calculation, adjust and The calculation method combines the weight coefficient with the influence of the corresponding parameter.

[0106] Calculate the improved The improved formula is used to calculate the new value, so as to evaluate the difference in thermal stability of yoga columns under different simulation environments;

[0107] The results were interpreted using the improved The values ​​were statistically significant tested to determine whether the effects of various environmental conditions on the thermal stability of the yoga column were significant.

[0108] Through the above improved execution process, the thermal stability of the yoga column under different simulation environments can be evaluated more accurately, providing a scientific basis for design and improvement. By introducing additional parameters and weight coefficients, various influencing factors can be considered more carefully, thus making a more accurate evaluation.

[0109] See also Figure 5 ,According to the results of thermal stability evaluation, the thermal conductivity data of yoga columns are collected, time series sampling is performed, and time-frequency analysis is performed using short-time Fourier transform to identify the variation characteristics of thermal conductivity in different time periods and reveal the variation pattern of thermal conductivity in the time series. The steps to construct the time-frequency analysis results are as follows,

[0110] S401: According to the thermal stability evaluation result, thermal conductivity data of the yoga column associated with the evaluation result is collected, including thermal conductivity measurement values ​​under different environmental conditions, to obtain a thermal conductivity data set;

[0111] In sub-step S401, based on the thermal stability evaluation results, Python language combined with NumPy library is used for data processing. First, the thermal conductivity data files of the yoga column related to the thermal stability evaluation are read. These data files include thermal conductivity measurement values ​​under different environmental conditions. The data are read using NumPy's loadtxt function, and the measurement values ​​are classified and sorted according to environmental conditions. A multidimensional array is generated as a thermal conductivity data set, with environmental conditions as the index and thermal conductivity measurement values ​​as the content, providing a basic data structure for subsequent analysis.

[0112] S402: Based on the thermal conductivity data set, time series sampling is performed to determine the sampling frequency to capture the change pattern of thermal conductivity over time, and generate time series sampling data;

[0113] In sub-step S402, based on the thermal conductivity data set, Python's Pandas library is used to perform time series sampling. By setting the parameters of the resample method, such as 'S' representing sampling by second, the sampling frequency of the time series is established to capture the change pattern of thermal conductivity over time. The to_csv function of Pandas is used to save the sampled time series data as a CSV file to generate time series sampling data, which records in detail the changes in the thermal conductivity of the yoga column at the set sampling frequency and prepares the original data for time-frequency analysis.

[0114] S403: performing short-time Fourier transform according to the time series sampling data, analyzing the frequency components of thermal conductivity in the difference time period, revealing the pattern of thermal conductivity change, and obtaining time-frequency analysis results;

[0115] Perform a short-time Fourier transform according to the formula:

[0116]

[0117] Calculate the time-frequency distribution of the thermal conductivity time series and generate the time-frequency analysis results, where: is the thermal conductivity signal that varies with time, According to the shape parameter and time variation factor Adjusted window function, is the shape parameter, is the time variation factor, is the time shift variable, Represents the conversion of the signal from the time domain to the frequency domain, is the frequency weighting factor, which is used to emphasize the influence of specific frequency components. It is a nonlinear adjustment term used to adjust the response of the STFT according to the frequency to adapt to the signal characteristics and analysis objectives.

[0118] The execution process is as follows:

[0119] The window function is adjusted and multiplied with the signal, using the window function with adjusted shape , considering the signal characteristics and analysis requirements, through the shape parameters and time variation factor Customized to accommodate local characteristics and temporal variations of the signal;

[0120] The time-frequency domain is converted, and then the signal processed by the window function is transformed by Fourier transform. Convert from the time domain to the frequency domain to examine its performance at different frequencies;

[0121] Frequency weighting and nonlinear adjustment, based on the conversion, through the frequency weighting factor and nonlinear adjustment terms , further adjusting the importance and response of each frequency component to reflect the specific characteristics of the signal and the analysis goals;

[0122] Integration processing integrates the entire signal to obtain the performance of the signal at different times and frequencies, and construct a detailed time-frequency distribution diagram, thereby achieving in-depth analysis of the signal's time and frequency characteristics.

[0123] By introducing the shape parameter , time variation factor , frequency weighting factor and nonlinear adjustment terms , which not only improves the accuracy of the calculation, but also increases the flexibility and depth of analyzing the thermal conductivity variation pattern.

[0124] See also Figure 6 ,Using the time-frequency analysis results, we analyze the variation amplitude of thermal conductivity and the spectrum energy distribution, capture the key factors affecting thermal stability, analyze the core indicators that affect the time stability of thermal conductivity of yoga columns, and obtain the steps of thermal stability indicator set as follows,

[0125] S501: According to the time-frequency analysis results, the usage data of the yoga column under different temperatures are collected, including temperature changes, usage time and frequency, and the data are screened and summarized to obtain a basic data set;

[0126] In sub-step S501, based on the results of time-frequency analysis, Python's Pandas library is used to filter and summarize the usage data of yoga columns at different temperatures. Specifically, the read_csv function is used to read the CSV file containing temperature changes, usage time and frequency information, and then the query method of DataFrame is used to filter out data entries related to the time-frequency analysis results. The filtering conditions are set according to the key time periods and frequency ranges identified in the time-frequency analysis. Finally, a basic data set is generated, which records in detail the relationship between the thermal conductivity of the yoga column and the temperature, usage time and frequency in the key time period, providing accurate original data for further analysis.

[0127] S502: Based on the basic data set, the thermal conductivity variation amplitude and spectrum energy distribution are analyzed, the energy distribution under the difference frequency is analyzed, the key frequency range affecting the thermal stability is identified, and the analysis results of the factors affecting the thermal stability are obtained;

[0128] In sub-step S502, based on the basic data set, Python's SciPy library is used to analyze the thermal conductivity change amplitude and spectral energy distribution. Specifically, the short-time Fourier transform analysis is performed through the signal.spectrogram function. This function takes the thermal conductivity data as input, analyzes the energy distribution at different frequencies by setting the window function and window size parameters, and identifies the key frequency range that affects thermal stability. This analysis takes into account the impact of temperature changes on thermal conductivity during the use of the yoga column, generates the analysis results of factors affecting thermal stability, and displays in detail the energy distribution of thermal conductivity in the key frequency range, providing a basis for identifying factors that affect thermal stability.

[0129] S503: Based on the analysis results of factors affecting thermal stability, the core indicators affecting the time stability of thermal conductivity of the yoga column are quantitatively analyzed, including the energy value and change rate of key frequencies, to obtain a thermal stability indicator set;

[0130] In sub-step S503, based on the analysis results of factors affecting thermal stability, the NumPy library is used to perform quantitative analysis of core indicators. First, the np.mean function is used to calculate the average change of thermal conductivity at each key frequency, and then the np.std function is used to calculate the standard deviation of the change rate. The standard deviation is determined based on the energy distribution in the key frequency range, and finally a thermal stability indicator set is obtained. The indicator set includes information such as the average change of thermal conductivity, the energy value and the change rate at the key frequency, which provides a quantitative reference indicator for evaluating and improving the thermal stability of the yoga column.

[0131] See also Figure 7,Combined with the thermal stability index set and the environmental simulation data set, data statistical analysis is performed to predict the future trend of thermal conductivity of yoga columns, revealing the range of thermal conductivity changes in future use of yoga columns. The steps to establish a thermal conductivity change trend prediction model are as follows,

[0132] S601: Integrate the thermal stability index set and the environmental simulation data set, including temperature, humidity and frequency of use, and fuse the two to obtain a comprehensive data set;

[0133] In sub-step S601, the thermal stability index set and the environmental simulation data set are integrated through Python's Pandas library. The specific operation includes using the merge method of DataFrame to merge the thermal stability index data and the environmental simulation data with the environmental conditions as the key connection column, and obtain a comprehensive data set containing information such as temperature, humidity, frequency of use, thermal conductivity change amplitude, key frequency energy distribution, etc. This process ensures that all relevant variables are taken into account, providing a complete data basis for statistical analysis of the data and the establishment of subsequent prediction models.

[0134] S602: Based on the comprehensive data set, statistical analysis of the data is performed to reveal the correlation between thermal stability indicators and environmental factors, capture key influencing factors, and obtain key influencing factor analysis results;

[0135] In sub-step S602, based on the comprehensive data set, the lm function in R language is used to perform linear regression analysis. By establishing a linear model between the thermal stability index and the environmental factors, the construction of the model considers temperature, humidity and frequency of use as independent variables, and the thermal stability index as the dependent variable. This step aims to reveal the correlation between the thermal stability index and the environmental factors. The results of the regression model are analyzed in detail through the summary function to obtain statistical information such as the coefficients, R-square values ​​and p-values ​​of the model, and generate key influencing factor analysis results, which reveal the significant relationship between the thermal stability index and each environmental factor, and provide a basis for identifying the key environmental factors that affect the thermal conductivity of the yoga column.

[0136] S603: Based on the analysis results of key influencing factors and the influence of key environmental factors on thermal conductivity, by simulating the change of thermal conductivity under future environmental conditions, the change range of thermal conductivity of the yoga column in future use is revealed, and a prediction model for the change trend of thermal conductivity is obtained;

[0137] In sub-step S603, based on the results of the key influencing factor analysis, the LinearRegression model in Python's SciKit-Learn library is used in combination with the predicted data of future environmental conditions to perform linear regression analysis. The model is trained using the fit method, and then the future thermal conductivity changes are predicted using the predict method. In this process, the input variables of the prediction model are the future predicted environmental condition data, such as temperature, humidity, and frequency of use, and the output variable is the predicted value of thermal conductivity. A thermal conductivity change trend prediction model is generated, which shows the changing trend and range of the thermal conductivity of the yoga column in future use, and provides scientific predictions for the design optimization and usage guidelines of the yoga column.

[0138] See also Figure 8 The intelligent quality detection system of the heat-insulating material yoga column includes data collection module, environmental simulation module, heat conduction simulation module, thermal stability analysis module, thermal conductivity time-frequency analysis module, thermal stability index integration module, data statistical analysis module, and trend prediction module;

[0139] The data collection module collects meteorological data, including average temperature, humidity range, and heat flow direction, and combines it with user feedback in different geographical locations to generate meteorological and user feedback data sets;

[0140] The environmental simulation module simulates the environmental conditions for using yoga columns based on meteorological and user feedback data sets, combined with the direction and intensity of heat flow between the ground and the air, and generates an environmental simulation data set;

[0141] The heat conduction simulation module sets the initial value and boundary conditions of the heat conduction equation based on the environmental simulation data set, including the environmental temperature limit and the heat flow limit, simulates the temperature change process of the yoga column under different environmental conditions, and generates thermal response analysis results;

[0142] The thermal stability analysis module uses variance analysis method based on the thermal response analysis results to analyze the temperature change trend of the yoga column under different simulation environments, reveal the thermal stability, and generate thermal stability evaluation results;

[0143] The thermal conductivity time-frequency analysis module collects the thermal conductivity data of the yoga column for time series sampling based on the thermal stability evaluation results, uses the short-time Fourier transform method to analyze the thermal conductivity change characteristics, and generates time-frequency analysis results;

[0144] The thermal stability index integration module analyzes the thermal conductivity variation and spectrum energy distribution based on the time-frequency analysis results, captures key factors, analyzes core indicators, and generates a thermal stability index set;

[0145] The data statistical analysis module combines the thermal stability index set and the environmental simulation data set to perform data fusion and statistical analysis, capture the key influencing factors that will affect thermal conductivity in the future, and generate key influencing factor analysis results;

[0146] The trend prediction module predicts the future trend and range of thermal conductivity of yoga columns based on the analysis results of key influencing factors and the influence of key environmental factors on thermal conductivity, and generates a thermal conductivity change trend prediction model.

[0147] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. An intelligent detection method for the quality of a yoga column made of heat-insulating materials, characterized in that: The following steps are included: Collect meteorological data, including average temperature, humidity range, and heat flow direction, and combine it with user feedback in different geographical locations to simulate the environmental conditions for yoga column use and obtain an environmental simulation data set; According to each environmental condition in the environmental simulation data set, the initial value and boundary conditions of the corresponding heat conduction equation are set, and the simulation program is run to simulate the temperature change process of the yoga column under different environments to obtain the thermal response analysis results; The temperature change data in the thermal response analysis results are input into statistical analysis software, and the temperature change trend of the yoga column is statistically analyzed using variance analysis to reveal the thermal stability of the yoga column under different simulated environments and generate thermal stability evaluation results; According to the thermal stability evaluation results, the thermal conductivity data of the yoga column is collected, time series sampling is performed, and time-frequency analysis is performed using short-time Fourier transform to identify the variation characteristics of thermal conductivity in different time periods, reveal the thermal conductivity variation pattern in the time series, and construct the time-frequency analysis results; Using the time-frequency analysis results, the variation amplitude of thermal conductivity and the spectrum energy distribution are analyzed to capture the key factors affecting thermal stability, analyze the core indicators that affect the time stability of thermal conductivity of the yoga column, and obtain a thermal stability indicator set; Combined with the thermal stability index set and the environmental simulation data set, data statistical analysis is performed to predict the future trend of thermal conductivity of the yoga column, reveal the range of thermal conductivity changes in the future use of the yoga column, and establish a thermal conductivity change trend prediction model; The environmental simulation data set includes the simulated indoor temperature range, relative humidity percentage, and heat flow rate between the ground and the air; the thermal response analysis results include the temperature change curves of the surface and interior of the yoga column, and the time series data of the dynamic response of the heat flow; the thermal stability evaluation results include the temperature stability classification of the yoga column under different environmental conditions, and the maximum and minimum temperature fluctuation ranges; the time-frequency analysis results are specifically the spectrum of thermal conductivity over time and the thermal conductivity change characteristics at key time points; the thermal stability index set includes the average change amplitude of thermal conductivity and the energy distribution at key frequencies; the thermal conductivity change trend prediction model specifically refers to the future thermal conductivity change trend prediction curve obtained based on historical data analysis and the predicted thermal stability change range; According to the thermal stability evaluation results, the thermal conductivity data of the yoga column is collected, time series sampling is performed, and time-frequency analysis is performed using short-time Fourier transform to identify the change characteristics of thermal conductivity in different time periods and reveal the thermal conductivity change pattern in the time series. The steps for constructing the time-frequency analysis results are as follows: According to the thermal stability evaluation results, thermal conductivity data of the yoga column associated with the evaluation results are collected, including thermal conductivity measurement values ​​under different environmental conditions, to obtain a thermal conductivity data set; Based on the thermal conductivity data set, time series sampling is performed, and a sampling frequency is determined to capture a change pattern of thermal conductivity over time, thereby generating time series sampling data; According to the time series sampling data, a short-time Fourier transform is performed to analyze the frequency components of the thermal conductivity in the difference time period, reveal the pattern of thermal conductivity change, and obtain a time-frequency analysis result; Perform the short-time Fourier transform according to the formula: ； Calculate the time-frequency distribution of the thermal conductivity time series and generate the time-frequency analysis results, where: is the thermal conductivity signal that varies with time, According to the shape parameter and time variation factor Adjusted window function, is the shape parameter, is the time variation factor, is the time shift variable, Represents the conversion of the signal from the time domain to the frequency domain, is the frequency weighting factor, is a nonlinear adjustment term; Using the time-frequency analysis results, the variation amplitude of thermal conductivity and the spectrum energy distribution are analyzed to capture the key factors affecting thermal stability, analyze the core indicators that affect the time stability of thermal conductivity of yoga columns, and obtain the steps of thermal stability indicator set as follows: According to the time-frequency analysis results, the usage data of the yoga column under different temperatures are collected, including temperature changes, usage time and frequency, and the data are screened and summarized to obtain a basic data set; Based on the basic data set, the thermal conductivity variation amplitude and spectrum energy distribution are analyzed, the energy distribution under the difference frequency is analyzed, the key frequency range affecting the thermal stability is identified, and the analysis results of the factors affecting the thermal stability are obtained; Based on the analysis results of the factors affecting thermal stability, the core indicators affecting the time stability of thermal conductivity of the yoga column are quantitatively analyzed, including the energy value and change rate of key frequencies, to obtain a thermal stability indicator set.

2. The intelligent quality detection method of the heat-insulating material yoga column according to claim 1 is characterized in that: Collect meteorological data, including average temperature, humidity range, and heat flow direction, and combine user feedback in different geographical locations to simulate the environmental conditions of yoga columns. The steps to obtain the environmental simulation data set are as follows: Collect the average temperature data of the region, record the temperature range including the highest temperature and the lowest temperature, and obtain the temperature data set; Based on the temperature data set, collect humidity range data of the corresponding area, including maximum humidity and minimum humidity, and construct a humidity data set; The temperature data set and the humidity data set are integrated, combined with user feedback on the yoga column, combined with the direction and intensity of heat flow between the ground and the air, to simulate the environmental conditions for the use of the yoga column and obtain an environmental simulation data set.

3. The intelligent quality detection method of the heat-insulating material yoga column according to claim 1 is characterized in that: According to each environmental condition in the environmental simulation data set, the initial value and boundary conditions of the corresponding heat conduction equation are set, and the simulation program is run to simulate the temperature change process of the yoga column under different environments. The steps of obtaining the thermal response analysis results are as follows: Based on the environmental simulation data set, setting the initial value of the heat conduction equation for each simulated environmental condition, including setting the initial temperature and the initial heat flow, to obtain the initial condition setting value; According to the initial condition setting, corresponding boundary conditions are set for each simulation environment, including the ambient temperature limit and the heat flow limit, to ensure the accuracy of the simulation and obtain the boundary condition configuration; Combined with the boundary condition configuration, the temperature change process of the yoga column under different environmental conditions is simulated to obtain the thermal response analysis results.

4. The intelligent quality detection method of the heat-insulating material yoga column according to claim 1 is characterized in that: The temperature change data in the thermal response analysis results are input into statistical analysis software, and the temperature change trend of the yoga column is statistically analyzed using variance analysis to reveal the thermal stability of the yoga column under different simulated environments. The steps of generating thermal stability evaluation results are as follows: According to the thermal response analysis results, extracting temperature change data of the yoga column, including temperature records under each simulation environment, to obtain a temperature change data set; Input the temperature change data set into statistical analysis software, set the analysis project to variance analysis, identify and compare the temperature change differences under different simulation environments, and obtain variance analysis preparation data; According to the ANOVA prepared data, the temperature variation trend was analyzed using the ANOVA method to reveal the difference in thermal stability of the yoga column under all simulated environments and obtain the thermal stability evaluation results; The variance analysis method is according to the formula: ； The improved statistics are calculated to obtain the thermal stability evaluation results of the yoga column, where: is the improved statistic, is the ambient temperature change rate, is the thermal conductivity coefficient of the yoga column material, Use time for the yoga column; is the ambient humidity, To consider the ambient temperature change rate And the thermal conductivity of the yoga column material The mean square difference between groups after is the rate of change of ambient temperature And the thermal conductivity of the yoga column material The weight coefficient of To consider the use time of yoga column and ambient humidity The mean squared difference within the group after Time to use the yoga column and ambient humidity The weight coefficient of .

5. The intelligent quality detection method of the heat-insulating material yoga column according to claim 1 is characterized in that: Combined with the thermal stability index set and the environmental simulation data set, data statistical analysis is performed to predict the future trend of thermal conductivity of the yoga column, reveal the range of thermal conductivity changes in the future use of the yoga column, and the steps of establishing a thermal conductivity change trend prediction model are as follows: Integrate the thermal stability index set and the environmental simulation data set, including temperature, humidity and frequency of use, and fuse the two to obtain a comprehensive data set; Based on the comprehensive data set, statistical analysis of the data is performed to reveal the correlation between thermal stability indicators and environmental factors, capture key influencing factors, and obtain key influencing factor analysis results; Based on the analysis results of the key influencing factors and combined with the impact of key environmental factors on thermal conductivity, by simulating the changes in thermal conductivity under future environmental conditions, the range of changes in thermal conductivity of the yoga column in future use is revealed, and a thermal conductivity change trend prediction model is obtained.

6. Intelligent quality detection system for heat insulation material yoga column, characterized by: According to the intelligent detection method for the quality of the heat-insulating material yoga column according to any one of claims 1 to 5, the system comprises a data collection module, an environmental simulation module, a heat conduction simulation module, a thermal stability analysis module, a thermal conductivity time-frequency analysis module, a thermal stability index integration module, a data statistical analysis module, and a trend prediction module; The data collection module collects meteorological data, including average temperature, humidity range and heat flow direction, and generates meteorological and user feedback data sets in combination with user feedback in different geographical locations; The environmental simulation module simulates the environmental conditions for using the yoga column based on the meteorological and user feedback data sets, combined with the direction and intensity of heat flow between the ground and the air, and generates an environmental simulation data set; The heat conduction simulation module sets the initial value and boundary conditions of the heat conduction equation based on the environmental simulation data set, including the environmental temperature limit and the heat flow limit, simulates the temperature change process of the yoga column under different environmental conditions, and generates thermal response analysis results; The thermal stability analysis module uses variance analysis method based on the thermal response analysis results to analyze the temperature change trend of the yoga column under the different simulation environments, reveal the thermal stability, and generate thermal stability evaluation results; The thermal conductivity time-frequency analysis module collects the thermal conductivity data of the yoga column for time series sampling based on the thermal stability evaluation results, uses the short-time Fourier transform method to analyze the thermal conductivity change characteristics, and generates time-frequency analysis results; The thermal stability index integration module analyzes the thermal conductivity variation amplitude and spectrum energy distribution based on the time-frequency analysis results, captures key factors, analyzes core indicators, and generates a thermal stability index set; The data statistical analysis module combines the thermal stability index set and the environmental simulation data set to perform data fusion and statistical analysis, capture key influencing factors that will affect thermal conductivity in the future, and generate key influencing factor analysis results; The trend prediction module predicts the future change trend and range of the thermal conductivity of the yoga column based on the key influencing factor analysis results and the influence of key environmental factors on thermal conductivity, and generates a thermal conductivity change trend prediction model.

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