A method, device, equipment and storage medium for predicting the service life of vacuum glass

Abstract

This application provides a method, apparatus, device, and storage medium for predicting the service life of vacuum glass. The method includes: acquiring measured surface temperature and heat flow of the vacuum glass, vacuum glass structural data, support column structural data, leakage rate, and typical annual meteorological data of the vacuum glass's location; calculating the initial intracavity gas thermal resistance based on the measured surface temperature, heat flow, structural data, and support column structural data; determining the initial vacuum level corresponding to the initial intracavity gas thermal resistance based on the mapping relationship between vacuum level and gas thermal resistance; and predicting the service life of the vacuum glass using the initial vacuum level, glass structural data, leakage rate, typical annual meteorological data of the vacuum glass's location, and a service life prediction mathematical model. This application provides a model basis for predicting the service life of vacuum glass, and the prediction results are more consistent with actual service conditions, effectively improving prediction accuracy.

Description

Technical Field

[0001] The present application relates to the field of vacuum glass detection technology, and in particular to a method, device, equipment and storage medium for predicting the service life of vacuum glass. Background Art

[0002] As a technology with mature preparation methods, excellent thermal insulation performance and significant energy-saving effects, vacuum glass has attracted much attention in the construction field in recent years.

[0003] Vacuum glass is composed of two layers of low-emissivity glass, a medium vacuum interlayer and a getter between the two layers of glass. The vacuum degree of the vacuum glass interlayer is very high, which can effectively reduce heat transfer by air conduction and convection, and is a highly efficient thermal insulation material. At present, vacuum glass on the market can only be tested online during the factory process. During the storage and use of the product, the service life of the vacuum glass will be reduced due to the outgassing of the material itself or due to insufficient degassing during the processing and improper installation. If the vacuum glass cannot be tested during use, it may break accidentally due to aging of the glass and reduced strength.

[0004] Testing the service life of vacuum glass is of great significance in ensuring safety, improving energy saving, achieving economic benefits and environmental friendliness. Therefore, how to test the service life of vacuum glass during use is an urgent problem to be solved. Summary of the invention

[0005] In view of this, the purpose of this application is to provide a method, device, equipment and storage medium for predicting the service life of vacuum glass, so as to realize the life prediction of vacuum glass in service, make the predicted service life more consistent with the actual service conditions, and improve the prediction accuracy. The specific technical scheme is as follows:

[0006] In a first aspect, the present application provides a method for predicting the service life of vacuum glass, the method comprising:

[0007] Obtain the measured surface temperature and measured surface heat flux of the vacuum glass, glass structure data, support column structure data, leakage rate and typical meteorological data of the vacuum glass location;

[0008] Calculating the initial intracavity gas thermal resistance according to the measured surface temperature, the measured surface heat flux, the glass structure data, and the support column structure data;

[0009] Determine the initial vacuum degree corresponding to the initial thermal resistance of the gas in the cavity according to the mapping relationship between the vacuum degree and the gas thermal resistance;

[0010] The service life prediction is performed using the initial vacuum degree, the glass structure data, the leakage rate, the typical meteorological year data of the location of the vacuum glass, and a service life prediction mathematical model to obtain the predicted service life of the vacuum glass. The service life prediction mathematical model is generated based on the leakage rate, gas desorption rate and gas permeation rate. The gas desorption rate is obtained through the typical meteorological year data, and the gas permeation rate is obtained through the typical meteorological year data.

[0011] In a possible implementation, the service life prediction is performed using the initial vacuum degree, the glass structure data, the leakage rate, typical meteorological data of the location of the vacuum glass, and a service life prediction mathematical model to obtain the predicted service life of the vacuum glass, including:

[0012] Obtaining the temperature and solar radiation intensity at each moment from typical meteorological data of the location of the vacuum glass;

[0013] Simulating the gas permeation rate at each moment by molecular dynamics simulation method based on the temperature at each moment and the initial vacuum degree;

[0014] Determine the gas desorption rate corresponding to the solar radiation intensity at each moment from the gas desorption rate mapping relationship;

[0015] The initial vacuum degree, the glass structure data, the leakage rate, the gas permeation rate at each moment, and the gas desorption rate corresponding to the solar radiation intensity at each moment are input into the service life prediction mathematical model to obtain the predicted service life of the vacuum glass.

[0016] In a possible implementation, inputting the initial vacuum degree, the glass structure data, the leakage rate, the gas permeation rate at each moment, and the gas desorption rate corresponding to the solar radiation intensity at each moment into the service life prediction mathematical model to obtain the predicted service life of the vacuum glass includes:

[0017] Input the initial vacuum degree, the glass structure data, the leakage rate, the gas permeation rate at each moment, and the gas desorption rate corresponding to the solar radiation intensity at each moment into the service life prediction mathematical model to obtain the vacuum degree of the vacuum glass after N years, where N is a positive integer;

[0018] If the vacuum degree of the vacuum glass after N years is greater than the failure threshold of the location of the vacuum glass, (N-1) years is determined as the predicted service life of the vacuum glass.

[0019] In a possible implementation, the process of generating the mapping relationship between vacuum degree and gas thermal resistance includes:

[0020] Obtain multiple experimental surface temperatures of the vacuum glass sample under multiple experimental vacuum degrees, where different experimental vacuum degrees correspond to different experimental surface temperatures;

[0021] Acquiring a plurality of experimental surface heat fluxes of the vacuum glass sample under a plurality of the experimental vacuum degrees, wherein different experimental vacuum degrees correspond to different experimental surface heat fluxes;

[0022] For any first experimental vacuum degree among the plurality of experimental vacuum degrees, a thermal resistance value is calculated using a first experimental surface temperature, a first experimental surface heat flux, support column structure data and glass structure data of the vacuum glass sample to obtain a first experimental gas thermal resistance corresponding to the first experimental vacuum degree, the first experimental surface temperature corresponds to the first experimental vacuum degree, the first experimental surface heat flux corresponds to the first experimental vacuum degree, the first experimental surface temperature is one of the plurality of experimental surface temperatures, and the first experimental surface heat flux is one of the plurality of experimental surface heat fluxes;

[0023] Traversing a plurality of the experimental vacuum degrees to obtain the experimental gas thermal resistances corresponding to the plurality of the experimental vacuum degrees;

[0024] The mapping relationship between the vacuum degree and the gas thermal resistance is generated based on the plurality of experimental vacuum degrees and the experimental gas thermal resistances corresponding to the plurality of experimental vacuum degrees.

[0025] In a possible implementation, the calculating the initial intracavity gas thermal resistance according to the measured surface temperature, the measured surface heat flux, the glass structure data, and the support column structure data includes:

[0026] Calculating the total thermal resistance of the central area of ​​the vacuum glass using the measured surface temperature, the glass structure data, and the support column structure data to obtain the total thermal resistance of the central area of ​​the vacuum glass;

[0027] The unknown gas thermal resistance is calculated using the measured surface heat flux and the total thermal resistance of the central area of ​​the vacuum glass, and the calculated unknown gas thermal resistance is determined as the initial intra-cavity gas thermal resistance.

[0028] In a second aspect, the present application also provides a vacuum glass service life prediction device, the device comprising:

[0029] A data acquisition module is used to obtain the measured surface temperature and measured surface heat flux of the vacuum glass, glass structure data, support column structure data, leakage rate and typical meteorological data of the vacuum glass location;

[0030] A thermal resistance calculation module, used for calculating the initial thermal resistance of the gas in the cavity according to the measured surface temperature, the measured surface heat flux, the glass structure data and the support column structure data;

[0031] A vacuum degree determination module, used to determine an initial vacuum degree corresponding to the initial thermal resistance of the gas in the cavity according to a mapping relationship between the vacuum degree and the gas thermal resistance;

[0032] The service life prediction module is used to predict the service life of the vacuum glass by using the initial vacuum degree, the glass structure data, the leakage rate, the typical meteorological data of the year where the vacuum glass is located, and a service life prediction mathematical model, so as to obtain the predicted service life of the vacuum glass. The service life prediction mathematical model is generated based on the leakage rate, the gas desorption rate and the gas permeation rate. The gas desorption rate is obtained by the typical meteorological data of the year, and the gas permeation rate is obtained by the typical meteorological data of the year.

[0033] In a possible implementation, the life prediction module includes:

[0034] A parameter acquisition unit, used to obtain the temperature and solar radiation intensity at each moment from typical meteorological data of the location of the vacuum glass;

[0035] A permeation rate determination unit, used to simulate the gas permeation rate at each moment by a molecular dynamics simulation method based on the temperature at each moment and the initial vacuum degree;

[0036] A desorption rate determination unit, used to determine the gas desorption rate corresponding to the solar radiation intensity at each moment from the gas desorption rate mapping relationship;

[0037] The life prediction unit is used to input the initial vacuum degree, the glass structure data, the leakage rate, the gas permeation rate at each moment and the gas desorption rate corresponding to the solar radiation intensity at each moment into the service life prediction mathematical model to obtain the predicted service life of the vacuum glass.

[0038] In a possible implementation manner, the device further includes:

[0039] An experimental temperature acquisition module is used to obtain multiple experimental surface temperatures of the vacuum glass sample under multiple experimental vacuum degrees, and different experimental vacuum degrees correspond to different experimental surface temperatures;

[0040] An experimental heat flow acquisition module, used to acquire a plurality of experimental surface heat flows of the vacuum glass sample under a plurality of experimental vacuum degrees, wherein different experimental vacuum degrees correspond to different experimental surface heat flows;

[0041] an experimental thermal resistance calculation module, for calculating the thermal resistance value of any first experimental vacuum degree among the plurality of experimental vacuum degrees by using a first experimental surface temperature, a first experimental surface heat flux, support column structure data and glass structure data of the vacuum glass sample, to obtain a first experimental gas thermal resistance corresponding to the first experimental vacuum degree, the first experimental surface temperature corresponds to the first experimental vacuum degree, the first experimental surface heat flux corresponds to the first experimental vacuum degree, the first experimental surface temperature is one of the plurality of experimental surface temperatures, and the first experimental surface heat flux is one of the plurality of experimental surface heat fluxes;

[0042] A traversal module, used for traversing a plurality of the experimental vacuum degrees to obtain the experimental gas thermal resistance corresponding to each of the plurality of the experimental vacuum degrees;

[0043] A generating module is used to generate the mapping relationship between the vacuum degree and the gas thermal resistance based on the multiple experimental vacuum degrees and the experimental gas thermal resistances corresponding to the multiple experimental vacuum degrees.

[0044] In a third aspect, the present application further provides a computer device, including: a memory and a processor;

[0045] Wherein, the memory is used to store computer programs;

[0046] The processor is used to execute the computer program in the memory to implement the method described in the first aspect above.

[0047] In a fourth aspect, the present application further provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute the method described in the first aspect above.

[0048] In this application, the measured surface temperature and measured surface heat flow of the vacuum glass, the glass structure data, the support column structure data, the leakage rate and the typical meteorological data of the location of the vacuum glass are obtained; the initial gas thermal resistance in the cavity is calculated according to the measured surface temperature, the measured surface heat flow, the glass structure data and the support column structure data; the initial vacuum degree corresponding to the initial gas thermal resistance in the cavity is determined from the mapping relationship between the vacuum degree and the gas thermal resistance; the service life is predicted using the initial vacuum degree, the glass structure data, the leakage rate, the typical meteorological data of the location of the vacuum glass and the service life prediction mathematical model to obtain the predicted service life of the vacuum glass. Based on the mapping relationship between the vacuum degree and the gas thermal resistance, this application can quickly and accurately determine the gas thermal resistance of the vacuum glass, and then quantify the thermal performance of the vacuum glass, providing a data basis for predicting the service life of the vacuum glass. The prediction of the service life of the vacuum glass based on the typical meteorological data of the location of the vacuum glass, the leakage rate, the gas desorption rate and the gas permeation rate is more in line with the actual service conditions and improves the accuracy of the prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application, and for ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0050] Figure 1 A flow chart of a method for predicting the service life of vacuum glass provided in an embodiment of the present application is shown;

[0051] Figure 2 A schematic diagram of a vacuum glass sample provided in an embodiment of the present application is shown;

[0052] Figure 3 A schematic diagram of an experimental platform provided in an embodiment of the present application is shown;

[0053] Figure 4 A schematic diagram showing the position arrangement of vacuum glass samples during a solar radiation aging experiment provided in an embodiment of the present application is shown;

[0054] Figure 5 A schematic structural diagram of a vacuum glass service life prediction device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of this application.

[0056] Vacuum glass is a glass product that is made by sealing two sheets of flat glass, evacuating the gap between them and sealing the exhaust holes, so that the gap between the two sheets of glass is close to a vacuum state. It has excellent energy-saving, heat-insulating, and sound-insulating properties. With the continuous advancement of technology and the continuous expansion of applications, vacuum glass will play its unique role in more fields.

[0057] The performance of vacuum glass mainly depends on the pressure in the vacuum chamber, that is, the vacuum degree. The vacuum degree is used to indicate the rarefaction of the gas in a vacuum state, and the unit is Pa. High vacuum and low vacuum usually refer to the degree close to vacuum. The thinner the gas, the smaller the pressure, the lower the vacuum degree, and the closer to the ideal vacuum state, it is called high vacuum.

[0058] During long-term service, the pressure (i.e., vacuum degree) in the internal cavity of vacuum glass will gradually increase due to processes such as penetration, leakage, and desorption, which will cause the performance of the vacuum glass to deteriorate or even fail. By testing the life of vacuum glass, it is possible to promptly discover and replace glass that is about to reach its service life limit, avoid accidental breakage due to glass aging and strength reduction, thereby ensuring the safety of users, and also helping to assess the use risk of vacuum glass in the future, providing a basis for taking necessary protective measures. Therefore, how to predict the effective life of vacuum glass is an urgent problem to be solved.

[0059] At present, the research on the prediction of the effective life of vacuum glass mainly adopts a combination of indirect measurement and data fitting. First, the problem of measuring the vacuum degree of vacuum glass is solved, and then a performance degradation model is established. The indirect measurement method is adopted because the vacuum cavity of vacuum glass is a relatively narrow space with a thickness of only about 0.3 mm. It is impossible to arrange a vacuum gauge inside the vacuum cavity to directly measure the vacuum degree. Indirect measurement usually causes certain physical phenomena in the gas, and then measures the physical quantities related to the gas pressure in the process, thereby indirectly determining the gas pressure. The current methods for measuring the vacuum degree inside vacuum glass include:

[0060] Based on the characteristic that alternating electromagnetic fields are prone to generate inductively coupled plasma in a low-pressure environment, the area of ​​the luminous area formed by the cavity inside the vacuum glass to be tested is used to indicate the vacuum degree of the vacuum glass to be tested. However, the corresponding relationship between the area of ​​the luminous area and the vacuum degree has not been verified, and the accuracy of this detection method needs to be discussed.

[0061] The portable photoelastic instrument is moved on the surface of the vacuum glass to be tested, and the vacuum degree of the vacuum glass is inferred by observing the size and distribution of the stress spot at the supporting point of the vacuum glass. However, the accuracy of the mathematical relationship between the spot size and the vacuum degree has not yet been determined.

[0062] The correspondence between the detection physical quantities such as the luminous area, spot size and other parameters used in the above-mentioned indirect detection method and the vacuum degree has not been effectively verified, so the accuracy of the detection remains to be discussed.

[0063] A glass vacuum degree online detection device is used to quickly detect the vacuum degree of vacuum glass by quantitatively analyzing the energy attenuation of ultrasonic wave transmission in different media. However, the measurement conversion of the detection device is complicated, the post-processing is cumbersome, and the detection efficiency is low.

[0064] The above detection method has the problems of complex detection device and difficult operation.

[0065] Regarding the long-term evolution of the thermal performance of vacuum glass, the existing technology placed a 200mm×200mm vacuum glass outdoors to explore the impact of solar radiation on the thermal performance of vacuum glass. It was found that after 180 days, the internal pressure of the vacuum glass increased by 1.33Pa. The existing technology conducted a 120-hour extreme thermal cycle experiment (-35°C~50°C) on a 400mm×400mm vacuum glass and found that the heat transfer coefficient of the vacuum glass center increased from 1.18 watts per square meter per Kelvin ( ) increased to 1.30 . The above experimental studies have demonstrated that the thermal performance of vacuum glass will gradually degrade under the influence of outdoor temperature and solar radiation, but no thermal performance degradation model has been established to analyze the performance degradation law of vacuum glass in the actual service environment and predict its effective life. In addition, the inventors found that most of the existing studies on the effective life of vacuum glass only considered the impact of photothermal desorption on the vacuum degree inside the glass, without considering the impact of glass penetration and sealing leakage. Therefore, the existing research on the effective life of vacuum glass is difficult to meet the evaluation needs of actual service conditions.

[0066] Based on this, an embodiment of the present application provides a method for predicting the service life of vacuum glass, establishes a mapping relationship between vacuum degree and gas thermal resistance based on experimental data; uses a helium mass spectrometer to measure the leakage rate of vacuum glass; establishes a molecular structure model of glass with different chemical components, and uses the grand canonical Monte Carlo method and molecular dynamics simulation method to explore the penetration of gas molecules in the air in the glass model, and then calculates the penetration rate of each gas molecule in different glass models; conducts solar radiation aging experiments, and determines the gas desorption rate corresponding to different radiation intensities based on experimental data; finally, establishes a mathematical model for predicting the service life of vacuum glass under variable operating conditions.

[0067] See also Figure 1 , shows a flow chart of a method for predicting the service life of vacuum glass provided in an embodiment of the present application, and the embodiment of the present application at least includes the following steps:

[0068] S11, obtaining the measured surface temperature and measured surface heat flux of the vacuum glass, glass structure data, support column structure data, leakage rate, and typical meteorological year data of the location of the vacuum glass.

[0069] When it is necessary to predict the service life of vacuum glass, the measured surface temperature and measured surface heat flux of the vacuum glass to be tested, glass structure data, support column structure data, leakage rate and typical meteorological data of the location of the vacuum glass are obtained.

[0070] The measured surface temperature of the vacuum glass can be measured by temperature sensors arranged on both sides of the vacuum glass, and the measured surface heat flux of the vacuum glass can be measured by a heat flux meter arranged on the surface of the vacuum glass.

[0071] The glass structure data may include the type of vacuum glass, the size of the vacuum glass, the coating of the vacuum glass and the thermal conductivity of the vacuum glass, wherein the type of vacuum glass reflects the chemical composition of the vacuum glass, and the size of the vacuum glass may include the length, width and thickness of the vacuum glass.

[0072] The support column structure data may include the material of the support column, the spacing of the support column, the radius of the support column, the height of the support column and the thermal conductivity of the support column. In the glass structure, the support column plays a key role in load bearing and stability.

[0073] The leakage rate of vacuum glass, i.e., vacuum leakage rate, refers to the rate at which the internal pressure of vacuum glass increases per unit time due to gas penetration, diffusion, drift, etc. In the embodiment of the present application, the leakage rate refers to the factory leakage rate of vacuum glass, which can be measured by a helium mass spectrometer.

[0074] Typical meteorological data for the vacuum glass location may include hourly temperature and solar radiation intensity for the year.

[0075] Through the typical meteorological data of the vacuum glass location, the change in the vacuum degree of the vacuum glass in one year can be calculated, and then the service life of the vacuum glass can be predicted.

[0076] S12, calculating the initial cavity gas thermal resistance according to the measured surface temperature, the measured surface heat flux, the glass structure data and the support column structure data.

[0077] After obtaining the measured surface temperature, the measured surface heat flux, the glass structure data and the support column structure data, the initial cavity gas thermal resistance can be calculated based on the measured surface temperature, the measured surface heat flux, the glass structure data and the support column structure data. An example is given below. In one implementation, the initial cavity gas thermal resistance can be calculated based on the thermal resistance mathematical model and the heat flux mathematical model.

[0078] The thermal resistance mathematical model includes:

[0079] ;

[0080] in, is the total thermal resistance of the central area of ​​the vacuum glass, in square meters per watt ( ), is the thermal resistance of glass, in units of , is the total thermal resistance of the vacuum chamber, in units of , is the thickness of the vacuum glass, is the thermal conductivity of vacuum glass, in units of , is the thermal resistance of the support column, in units of , is the radiation heat transfer resistance of the vacuum cavity surface, in units of , is the thermal resistance of the gas in the vacuum chamber, λ is the spacing between the support columns, h is the height of the support column, is the thermal conductivity of the support column, in units of , is the surface effective emissivity, σ is the Stefan-Boltzmann constant, , and are the surface temperatures on both sides of the vacuum glass, in Kelvin ( K ). The surface effective emissivity is related to the coating of the vacuum glass, and the corresponding surface effective emissivity is obtained according to the coating of the vacuum glass.

[0081] The heat flow mathematical model includes:

[0082] ;

[0083] in, is the surface heat flux of vacuum glass, in watts per square meter ( ), U is the heat transfer coefficient of vacuum glass under the working conditions, the unit is , is the temperature difference between the two chambers, in units of K , is the total thermal resistance in , is the heat transfer resistance of the outer surface, in units of , is the heat transfer resistance of the inner surface, in units of Generally speaking, the thermal resistance of the inner surface is 0.11 The heat transfer resistance of the outer surface is 0.04 in winter. , 0.05 in summer .

[0084] The calculation formula for the external surface heat transfer thermal resistance includes:

[0085] ;

[0086] in, is the external surface heat transfer coefficient, in units of , is the surface convection heat transfer coefficient, in units of , is the external surface radiation heat transfer coefficient, in units of , is the outdoor wind speed in meters per second (m / s), σ is the Stefan-Boltzmann constant, , is the external surface emissivity, generally taken as 0.84, is the outdoor air temperature, is the air temperature on the outside of the glass.

[0087] The calculation formula for the internal surface heat transfer thermal resistance includes:

[0088] ;

[0089] in, is the inner surface heat transfer coefficient, in units of , is the inner surface convection heat transfer coefficient, in units of , is the inner surface radiation heat transfer coefficient, in units of , is the indoor wind speed in m / s, is the internal surface emissivity, generally taken as 0.84, is the indoor air temperature, is the air temperature on the inner surface of the glass.

[0090] When calculating the initial cavity gas thermal resistance, the thermal resistance mathematical model This is the initial thermal resistance of the gas in the cavity. The embodiment of the present application combines the thermal resistance mathematical model with the heat flow mathematical model to calculate the initial thermal resistance of the gas in the cavity.

[0091] Specifically, according to the measured surface temperature, the measured surface heat flux, the glass structure data and the support column structure data, one implementation method for calculating the initial cavity gas thermal resistance may include:

[0092] S121, calculating the total thermal resistance of the central area of ​​the vacuum glass using the measured surface temperature, the glass structure data, and the support column structure data, to obtain the total thermal resistance of the central area of ​​the vacuum glass.

[0093] By inputting the measured surface temperature, glass structure data and support column structure data into the thermal resistance mathematical model, the total thermal resistance of the central area including the unknown gas thermal resistance can be obtained.

[0094] S122, using the measured surface heat flux and the total thermal resistance of the central area of ​​the vacuum glass to infer the unknown gas thermal resistance, and determining the inferred unknown gas thermal resistance as the initial intra-cavity gas thermal resistance.

[0095] The measured surface heat flux and the total thermal resistance of the central area including the gas thermal resistance unknown are input into the heat flow mathematical model. Since there is only one unknown in the heat flow mathematical model, the value of the gas thermal resistance unknown can be obtained.

[0096] S13, determining an initial vacuum degree corresponding to the initial thermal resistance of the gas in the cavity according to a mapping relationship between the vacuum degree and the gas thermal resistance.

[0097] The embodiment of the present application establishes the mapping relationship between the vacuum degree of vacuum glass and the gas thermal resistance in advance through experiments. After obtaining the initial gas thermal resistance in the cavity, the initial vacuum degree corresponding to the initial gas thermal resistance in the cavity can be determined from the mapping relationship between the vacuum degree and the gas thermal resistance.

[0098] S14, predicting the service life of the vacuum glass using the initial vacuum degree, glass structure data, leakage rate, typical meteorological data of the location of the vacuum glass, and a service life prediction mathematical model to obtain a predicted service life of the vacuum glass.

[0099] In the embodiment of the present application, the service life prediction mathematical model is generated based on the leakage rate, gas desorption rate and gas permeation rate, taking into account the influence of vacuum glass permeation and seal leakage. The gas desorption rate of vacuum glass refers to the speed at which gas molecules inside the vacuum glass break away from the solid surface or tiny pores and are released into the vacuum. The gas permeation rate of vacuum glass refers to the rate at which gas molecules enter or leave the vacuum space through the vacuum glass material (including its boundary seal and the tiny pores that may exist inside).

[0100] After obtaining the initial vacuum degree and the typical meteorological annual data of the location of the vacuum glass, the gas permeation rate and gas desorption rate corresponding to each moment in a year can be determined based on the initial vacuum degree and the typical meteorological annual data of the location of the vacuum glass. Then, the initial vacuum degree, glass structure data, leakage rate, gas permeation rate and gas desorption rate corresponding to each moment are input into a service life prediction mathematical model to obtain the predicted service life of the vacuum glass.

[0101] Specifically, a service life prediction method for obtaining the predicted service life of the vacuum glass may include:

[0102] S141, obtain the temperature and solar radiation intensity at each moment from the typical meteorological data of the location of the vacuum glass;

[0103] S142, simulating the gas permeation rate at each moment by molecular dynamics simulation method based on the temperature and initial vacuum degree at each moment;

[0104] S143, determining the gas desorption rate corresponding to the solar radiation intensity at each moment from the gas desorption rate mapping relationship;

[0105] S144, input the initial vacuum degree, glass structure data, leakage rate, gas permeation rate at each moment, and gas desorption rate corresponding to the solar radiation intensity at each moment into a service life prediction mathematical model to obtain the predicted service life of the vacuum glass.

[0106] The service life prediction mathematical model includes:

[0107] ;

[0108] in, is the vacuum degree after N years, in Pa, N is a positive integer, is the initial vacuum degree, in Pa, is the annual change of vacuum degree, in Pa, is the gas permeation rate of the ith gas at time n, is the temperature at time n, is the vacuum degree at time n-1, A is the area of ​​the vacuum glass in square meters ( ), is the gas desorption rate at time n, is the solar radiation intensity at time n, is the leakage rate, L is the circumference of the vacuum glass in meters (m).

[0109] By inputting the initial vacuum degree, glass structure data, leakage rate, gas permeation rate at each moment, and gas desorption rate at each moment into the service life prediction mathematical model, the vacuum degree of the vacuum glass after N years can be obtained. The vacuum degree after N years is compared with the failure threshold of the location of the vacuum glass. If the vacuum degree after N years is greater than the failure threshold of the location of the vacuum glass, it means that the probability of failure of the vacuum glass after N years is high, and (N-1) years is determined as the predicted service life of the vacuum glass.

[0110] The embodiment of the present application determines the gas permeation rate and gas desorption rate at various times of the year through typical meteorological data of the location of the vacuum glass, so as to more specifically predict the service life of the vacuum glass and improve the accuracy of the prediction.

[0111] In the embodiment of the present application, the Python programming language can be used to establish the mapping relationship between vacuum degree and gas thermal resistance, the mapping relationship between gas desorption rate, the thermal resistance mathematical model, the heat flow mathematical model and the service life prediction mathematical model, so as to complete the prediction of the service life of the thermal performance of vacuum glass under variable working conditions. The Python programming language can be used to quickly and accurately predict the service life of vacuum glass of different sizes and different chemical compositions when used in different climate zones, and can also optimize the design of vacuum glass with the goal of improving the service life of vacuum glass.

[0112] In the embodiment of the present application, the measured surface temperature and measured surface heat flow of the vacuum glass, the glass structure data, the support column structure data, the leakage rate and the typical meteorological data of the location of the vacuum glass are obtained; the initial gas thermal resistance in the cavity is calculated according to the measured surface temperature, the measured surface heat flow, the glass structure data and the support column structure data; the initial vacuum degree corresponding to the initial gas thermal resistance in the cavity is determined from the mapping relationship between the vacuum degree and the gas thermal resistance; the service life is predicted using the initial vacuum degree, the glass structure data, the leakage rate, the typical meteorological data of the location of the vacuum glass and the service life prediction mathematical model to obtain the predicted service life of the vacuum glass. The embodiment of the present application can quickly and accurately determine the gas thermal resistance of the vacuum glass based on the mapping relationship between the vacuum degree and the gas thermal resistance, and then quantify the thermal performance of the vacuum glass, providing a data basis for predicting the service life of the vacuum glass. The service life of the vacuum glass is predicted based on the typical meteorological data of the location of the vacuum glass, the leakage rate, the gas desorption rate and the gas permeation rate, which is more in line with the actual service conditions and improves the accuracy of the prediction.

[0113] Next, the simulated gas permeation rates at each moment mentioned above are introduced.

[0114] In the embodiment of the present application, the molecular structure of glass with different chemical compositions (such as quartz glass, soda-lime glass, high magnesium glass, borosilicate glass, etc.) can be constructed by using software capable of performing molecular dynamics simulation, and multiple glass models can be obtained. The Grand Canonical Monte Carlo method (GCMC) and the molecular dynamics simulation method (MD) are used to explore the permeation of neon (Ne), helium (He), hydrogen (H2) and oxygen (O2) in the glass model under different working conditions, and the solubility coefficient of each gas is calculated based on Henry's law:

[0115] ;

[0116] in, is the solubility coefficient of the i-th gas, is the solubility, which indicates the amount of gas dissolved in the material when the material reaches dynamic equilibrium. p is the ambient gas pressure, for vacuum glass, p It refers to the pressure of the vacuum chamber faced by the vacuum glass, that is, the vacuum degree.

[0117] The solubility coefficient can be calculated using the adsorption isotherm method. By performing a Grand Canonical Monte Carlo simulation within a certain pressure range, a curve of concentration versus pressure is obtained, and then the limiting slope when the pressure is 0 is calculated to obtain the solubility coefficient. Therefore, the solubility coefficient of each gas at different set temperatures can be obtained.

[0118] The Grand Canonical Monte Carlo method is a computational chemistry method that combines the advantages of the Grand Canonical Ensemble and Monte Carlo simulation, and is widely used in the fields of gas adsorption, ion adsorption, solvent diffusion, etc. The molecular dynamics simulation method is a computer simulation experimental method based on classical mechanics (mainly Newtonian mechanics), which is widely used in the fields of materials science, biophysics, drug design, etc. The molecular dynamics simulation method solves the motion equation of each particle in the glass model and simulates the motion trajectory of the particles under specific conditions, thereby studying the various properties of the glass model.

[0119] Based on Einstein's diffusion law, the diffusion coefficient of each gas under different temperature and pressure conditions is calculated. Different temperatures and pressures are set for isothermal and isobaric molecular dynamics calculations. First, the structure of the glass model with adsorbed gas is optimized, the temperature is controlled by direct velocity calibration method, and the pressure is controlled by Berendsen method. After the glass model is balanced, the root mean square displacement of the gas is calculated, and then the gas diffusion coefficient and the root mean square displacement are related:

[0120] ;

[0121] in, is the diffusion coefficient of the i-th gas, M is the number of gases in the glass model, t is the diffusion time, is the position vector of the i-th gas at the initial moment, is the position vector of the i-th gas at time t.

[0122] Einstein's diffusion law, also known as Einstein's diffusion law, was proposed by Albert Einstein in 1905. Einstein proposed the concept that the mean square displacement of Brownian particle motion is proportional to time, and the proportionality coefficient (i.e., diffusion coefficient) is a constant. He also provided a formula for calculating the diffusion coefficient, i.e., the Stokes-Einstein equation. The Stokes-Einstein equation is a mathematical formula used to describe the motion behavior of tiny particles (such as Brownian particles) suspended in a fluid. The formula is based on the principles of fluid mechanics and statistical physics and can predict the diffusion and drift phenomena of tiny particles in a fluid.

[0123] Direct Velocity Scaling is a commonly used temperature control method in MD simulation. This method controls the temperature by directly adjusting the velocity of particles in the MD simulation system. Its basic principle is based on the direct relationship between temperature and particle kinetic energy. The Berendsen method, also known as the Berendsen heat bath method or the Berendsen weak coupling method, is mainly used to regulate the temperature and pressure in the MD simulation system to reach the desired value and maintain stability. This method controls the temperature and pressure by coupling the simulation system with a constant temperature heat bath or barostat with set temperature and / or pressure.

[0124] The calculation formula of permeation rate is:

[0125] ;

[0126] in, is the permeation rate of the ith gas.

[0127] From the above content, it can be seen that the gas permeation rate is related to the temperature and vacuum degree at each moment. The corresponding solubility coefficient is obtained by the temperature at moment n, and the diffusion coefficient is determined by the temperature at moment n and the vacuum degree at moment n-1. The gas permeation rate at moment n is obtained by multiplying the two. The gas permeation rate is expressed as:

[0128] ;

[0129] in, , is the total change of vacuum degree at the n-1th moment, .

[0130] Combining the service life prediction mathematical model, it can be understood that the service life prediction mathematical model Part of it is to calculate the change in vacuum caused by penetration, Part of it is to calculate the change in vacuum degree caused by desorption, Part of this is calculating the amount of vacuum change caused by the leak.

[0131] At the first moment (i.e., n=1), the permeation rate can be determined based on the temperature at the first moment and the initial vacuum degree (i.e., the vacuum degree at n=0) as .

[0132] The vacuum degree changes caused by penetration, desorption and leakage at the first moment are superimposed to determine the total vacuum degree change at the first moment. .

[0133] Then when calculating the penetration rate at the second moment, the penetration rate is ,in, .

[0134] After iterating for 8760 hours in a year, we can get the vacuum degree change in a year. .

[0135] After obtaining the glass structure data, the temperature at each moment and the initial vacuum degree, the glass model is determined according to the type of vacuum glass, and molecular dynamics simulation is performed based on the determined glass model to obtain the permeation rate at each moment.

[0136] Next, the generation process of the mapping relationship between vacuum degree and gas thermal resistance and the mapping relationship between gas desorption rate mentioned above is introduced.

[0137] The mapping relationship between vacuum degree and gas thermal resistance in the embodiment of the present application is established based on experimental data, and the vacuum glass sample is placed in the experimental platform provided in the embodiment of the present application. The left and right chambers of the experimental platform are built with 8mm aluminum plate insulation material.

[0138] Since the vacuum cavity of vacuum glass is a small narrow slit, the internal flow resistance is large, so the pressure at the vacuum glass exhaust hole cannot represent the average pressure of the entire vacuum cavity. Therefore, the embodiment of the present application generates the following based on the preparation process of vacuum glass "two-step method" Figure 2 The vacuum glass sample shown in the figure. Holes are opened on the diagonal lines of the vacuum glass sample to form exhaust holes and detection holes, and the vacuum glass is sealed around with a sealant. The sealed but unexhausted vacuum glass sample is placed as shown in the figure. Figure 3 The experimental platform shown in the figure includes a vacuum molecular pump unit, a compound vacuum gauge, a temperature sensor and a heat flow meter.

[0139] The vacuum hole of the vacuum glass sample is connected to the vacuum molecular pump unit to obtain vacuum, and the detection hole is connected to the compound vacuum gauge to detect the far-end pressure of the vacuum glass sample. Temperature sensors are arranged on the left and right chambers and the surface of the vacuum glass sample to measure the chamber air temperature and the glass surface temperature. In addition, a heat flow meter is arranged at the center of the vacuum glass sample surface to collect the heat flow through the vacuum glass sample surface. The vacuum molecular pump unit and the compound vacuum gauge are turned on to continuously test the pressure of the vacuum glass sample's vacuum hole and detection hole, the chamber air temperature, the glass surface temperature and the heat flow. Finally, the mapping relationship between the vacuum degree of vacuum glass and the gas thermal resistance is established based on the experimental data.

[0140] Specifically, the process of generating the mapping relationship between vacuum degree and gas thermal resistance may include:

[0141] S21, obtaining multiple experimental surface temperatures of the vacuum glass sample under multiple experimental vacuum degrees, where different experimental vacuum degrees correspond to different experimental surface temperatures; the experimental surface temperatures include surface temperatures on both sides of the vacuum glass sample;

[0142] S22, obtaining a plurality of experimental surface heat fluxes of the vacuum glass sample under a plurality of experimental vacuum degrees, wherein different experimental vacuum degrees correspond to different experimental surface heat fluxes;

[0143] S23, for any first experimental vacuum degree among the multiple experimental vacuum degrees, calculate the thermal resistance value by using the first experimental surface temperature, the first experimental surface heat flux, the support column structure data and the glass structure data of the vacuum glass sample to obtain the first experimental gas thermal resistance corresponding to the first experimental vacuum degree, the first experimental surface temperature corresponds to the first experimental vacuum degree, the first experimental surface heat flux corresponds to the first experimental vacuum degree, the first experimental surface temperature is one of the multiple experimental surface temperatures, and the first experimental surface heat flux is one of the multiple experimental surface heat fluxes;

[0144] S24, traversing multiple experimental vacuum degrees to obtain experimental gas thermal resistances corresponding to the multiple experimental vacuum degrees;

[0145] S25, generating a mapping relationship between vacuum degree and gas thermal resistance based on a plurality of experimental vacuum degrees and the experimental gas thermal resistances corresponding to the plurality of experimental vacuum degrees.

[0146] The thermal resistance mathematical model and the heat flow mathematical model are used to calculate the experimental gas thermal resistance corresponding to multiple experimental vacuum degrees, and then the mapping relationship between vacuum degree and gas thermal resistance is established, that is:

[0147] ;

[0148] Wherein, P is the average value of the pressure measured at the exhaust hole and the detection hole, in Pa.

[0149] Based on the mapping relationship between vacuum degree and gas thermal resistance, the internal pressure of the vacuum glass can be inferred by measuring the surface heat flow of the newly manufactured or in-service vacuum glass, the initial vacuum degree of the vacuum glass can be determined, and then the service life can be predicted.

[0150] The gas desorption rate mapping relationship of the embodiment of the present application is also established based on experimental data. The vacuum glass sample is placed in a solar radiation aging test platform. The solar radiation aging test platform includes a solar radiation simulator. The solar radiation intensity required for the experiment can be controlled by the distance from the solar radiation simulator. During the solar radiation aging test, the vacuum glass sample can be placed in a Figure 4 7 positions are shown to achieve different solar radiation intensities.

[0151] Although the total annual solar radiation in different climate zones varies significantly, the hourly solar radiation intensity is generally around 0 watts per square meter (W / m 2 )~1000W / m 2 In addition, too low solar radiation intensity will not cause photodesorption of gas on the glass surface. Therefore, the range of solar radiation intensity can be set to 400W / m 2 ~ 1000W / m 2 The same vacuum glass samples were exposed to 400W / m 2 , 500W / m 2 、600W / m 2 , 700W / m 2 、800W / m 2 , 900W / m 2 and 1000W / m 2 In the environment chamber, the heat flux density of each vacuum glass sample was collected by using a heat flux meter, and the test was conducted continuously for 30 days. The gas desorption rate of the vacuum glass under different solar radiation intensities was determined based on the experimental data.

[0152] According to the experimental data, the hourly heat flux density curve corresponding to different solar radiation intensities can be obtained.

[0153] If the hourly heat flux density curve is a horizontal line, it means that the internal pressure of the vacuum glass has not changed, and the gas desorption rate of the vacuum glass under the solar radiation intensity condition is zero.

[0154] If the hourly heat flux density curve is a gradually decreasing curve, determine the measured heat flux value at the beginning of the experiment and the measured heat flux value at the end of the experiment, and also determine the measured surface temperature value of the vacuum glass sample at the beginning of the experiment and the measured surface temperature value at the end of the experiment. Determine the pressure corresponding to the measured heat flux value through the thermal resistance mathematical model and the heat flow mathematical model, and calculate the pressure change. The gas desorption rate is calculated using the formula: pressure change / (experimental days*24 hours / day) = gas desorption rate. Among them, the measured surface temperature value includes the surface temperature values ​​on both sides of the vacuum glass sample.

[0155] In this way, the gas desorption rates corresponding to different solar radiation intensities can be obtained, thereby generating a gas desorption rate mapping relationship.

[0156] Next, a vacuum glass service life prediction device provided in an embodiment of the present application is introduced. The vacuum glass service life prediction device introduced below and the vacuum glass service life prediction method introduced above can correspond to each other.

[0157] See also Figure 5 , shows a schematic diagram of the structure of a vacuum glass service life prediction device provided in an embodiment of the present application, the device comprising:

[0158] The data acquisition module 501 is used to acquire the measured surface temperature and measured surface heat flux of the vacuum glass, the glass structure data, the support column structure data, the leakage rate and the typical meteorological data of the vacuum glass location;

[0159] A thermal resistance calculation module 502 is used to calculate the initial cavity gas thermal resistance according to the measured surface temperature, the measured surface heat flux, the glass structure data and the support column structure data;

[0160] A vacuum degree determination module 503 is used to determine an initial vacuum degree corresponding to the initial thermal resistance of the gas in the cavity according to a mapping relationship between the vacuum degree and the gas thermal resistance;

[0161] The life prediction module 504 is used to perform service life prediction using the initial vacuum degree, the glass structure data, the leakage rate, the typical meteorological year data of the location of the vacuum glass, and a service life prediction mathematical model to obtain a predicted service life of the vacuum glass. The service life prediction mathematical model is generated based on the leakage rate, the gas desorption rate, and the gas permeation rate. The gas desorption rate is obtained through the typical meteorological year data, and the gas permeation rate is obtained through the typical meteorological year data.

[0162] In the embodiment of the present application, the life prediction module 504 includes:

[0163] A parameter acquisition unit, used to obtain the temperature and solar radiation intensity at each moment from typical meteorological data of the location of the vacuum glass;

[0164] A permeation rate determination unit, used to simulate the gas permeation rate at each moment by a molecular dynamics simulation method based on the temperature at each moment and the initial vacuum degree;

[0165] A desorption rate determination unit, used to determine the gas desorption rate corresponding to the solar radiation intensity at each moment from the gas desorption rate mapping relationship;

[0166] The life prediction unit is used to input the initial vacuum degree, the glass structure data, the leakage rate, the gas permeation rate at each moment and the gas desorption rate corresponding to the solar radiation intensity at each moment into the service life prediction mathematical model to obtain the predicted service life of the vacuum glass.

[0167] In the embodiment of the present application, the life prediction unit is specifically used to:

[0168] Input the initial vacuum degree, the glass structure data, the leakage rate, the gas permeation rate at each moment, and the gas desorption rate corresponding to the solar radiation intensity at each moment into the service life prediction mathematical model to obtain the vacuum degree of the vacuum glass after N years, where N is a positive integer;

[0169] If the vacuum degree of the vacuum glass after N years is greater than the failure threshold of the location of the vacuum glass, (N-1) years is determined as the predicted service life of the vacuum glass.

[0170] In the embodiment of the present application, the device further includes:

[0171] An experimental temperature acquisition module is used to obtain multiple experimental surface temperatures of the vacuum glass sample under multiple experimental vacuum degrees, and different experimental vacuum degrees correspond to different experimental surface temperatures;

[0172] An experimental heat flow acquisition module, used to acquire a plurality of experimental surface heat flows of the vacuum glass sample under a plurality of experimental vacuum degrees, wherein different experimental vacuum degrees correspond to different experimental surface heat flows;

[0173] an experimental thermal resistance calculation module, for calculating the thermal resistance value of any first experimental vacuum degree among the plurality of experimental vacuum degrees by using a first experimental surface temperature, a first experimental surface heat flux, support column structure data and glass structure data of the vacuum glass sample, to obtain a first experimental gas thermal resistance corresponding to the first experimental vacuum degree, the first experimental surface temperature corresponds to the first experimental vacuum degree, the first experimental surface heat flux corresponds to the first experimental vacuum degree, the first experimental surface temperature is one of the plurality of experimental surface temperatures, and the first experimental surface heat flux is one of the plurality of experimental surface heat fluxes;

[0174] A traversal module, used for traversing a plurality of the experimental vacuum degrees to obtain the experimental gas thermal resistance corresponding to each of the plurality of the experimental vacuum degrees;

[0175] A generating module is used to generate the mapping relationship between the vacuum degree and the gas thermal resistance based on the multiple experimental vacuum degrees and the experimental gas thermal resistances corresponding to the multiple experimental vacuum degrees.

[0176] In the embodiment of the present application, the thermal resistance calculation module 502 includes:

[0177] A central thermal resistance calculation unit, used to calculate the total thermal resistance of the central area of ​​the vacuum glass by using the measured surface temperature, the glass structure data and the support column structure data, so as to obtain the total thermal resistance of the central area of ​​the vacuum glass;

[0178] The cavity thermal resistance estimation unit is used to estimate the unknown gas thermal resistance by using the measured surface heat flux and the total thermal resistance of the central area of ​​the vacuum glass, and determine the estimated unknown gas thermal resistance as the initial cavity gas thermal resistance.

[0179] The embodiment of the present application also provides a computer device, including: a memory and a processor;

[0180] Wherein, the memory is used to store computer programs;

[0181] The processor is used to execute the computer program in the memory to implement the method described in the above method embodiment.

[0182] The embodiment of the present application also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute the method described in the above method embodiment.

[0183] In the embodiment of the present application, the data acquisition module acquires the measured surface temperature and measured surface heat flow of the vacuum glass, the glass structure data, the support column structure data, the leakage rate and the typical meteorological data of the location of the vacuum glass; the thermal resistance calculation module calculates the initial gas thermal resistance in the cavity according to the measured surface temperature, the measured surface heat flow, the glass structure data and the support column structure data; the vacuum degree determination module determines the initial vacuum degree corresponding to the initial gas thermal resistance in the cavity from the mapping relationship between the vacuum degree and the gas thermal resistance; the life prediction module predicts the service life using the initial vacuum degree, the glass structure data, the leakage rate, the typical meteorological data of the location of the vacuum glass and the service life prediction mathematical model to obtain the predicted service life of the vacuum glass. The embodiment of the present application can quickly and accurately determine the gas thermal resistance of the vacuum glass based on the mapping relationship between the vacuum degree and the gas thermal resistance, and then quantify the thermal performance of the vacuum glass, providing a data basis for predicting the service life of the vacuum glass. The service life of the vacuum glass is predicted based on the typical meteorological data of the location of the vacuum glass, the leakage rate, the gas desorption rate and the gas permeation rate, which is more in line with the actual service conditions and improves the accuracy of the prediction.

[0184] It should be noted that the same or similar parts between the various embodiments can be referred to each other. As for the device embodiment and the system embodiment, since they are basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0185] For the above-mentioned embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present application is not limited by the order of the actions described, because according to the present application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0186] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0187] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0188] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for predicting the service life of vacuum glass, characterized in that: The method comprises: Obtain the measured surface temperature and measured surface heat flux of the vacuum glass, glass structure data, support column structure data, leakage rate and typical meteorological data of the vacuum glass location; Calculating the initial intracavity gas thermal resistance according to the measured surface temperature, the measured surface heat flux, the glass structure data, and the support column structure data; Determine the initial vacuum degree corresponding to the initial thermal resistance of the gas in the cavity according to the mapping relationship between the vacuum degree and the gas thermal resistance; The service life of the vacuum glass is predicted by using the initial vacuum degree, the glass structure data, the leakage rate, the typical meteorological data of the year at the location of the vacuum glass, and a service life prediction mathematical model, to obtain a predicted service life of the vacuum glass, wherein the service life prediction mathematical model is generated based on the leakage rate, the gas desorption rate, and the gas permeation rate, wherein the gas desorption rate is obtained by the typical meteorological data of the year, and the gas permeation rate is obtained by the typical meteorological data of the year; The service life prediction mathematical model includes: ； in, is the thermal resistance of the gas in the vacuum chamber, is the vacuum degree after N years, N is a positive integer, is the initial vacuum degree, is the annual change of vacuum degree, is the gas permeation rate of the ith gas at time n, is the temperature at time n, is the vacuum degree at time n-1, A is the area of ​​the vacuum glass, is the gas desorption rate at time n, is the solar radiation intensity at time n, is the leakage rate, and L is the circumference of the vacuum glass.

2. The method according to claim 1, characterized in that The service life prediction is performed by using the initial vacuum degree, the glass structure data, the leakage rate, the typical meteorological data of the location of the vacuum glass, and the service life prediction mathematical model to obtain the predicted service life of the vacuum glass, including: Obtaining the temperature and solar radiation intensity at each moment from typical meteorological data of the location of the vacuum glass; Simulating the gas permeation rate at each moment by molecular dynamics simulation method based on the temperature at each moment and the initial vacuum degree; Determine the gas desorption rate corresponding to the solar radiation intensity at each moment from the gas desorption rate mapping relationship; The initial vacuum degree, the glass structure data, the leakage rate, the gas permeation rate at each moment, and the gas desorption rate corresponding to the solar radiation intensity at each moment are input into the service life prediction mathematical model to obtain the predicted service life of the vacuum glass.

3. The method according to claim 2, characterized in that The step of inputting the initial vacuum degree, the glass structure data, the leakage rate, the gas permeation rate at each moment, and the gas desorption rate corresponding to the solar radiation intensity at each moment into the service life prediction mathematical model to obtain the predicted service life of the vacuum glass comprises: Input the initial vacuum degree, the glass structure data, the leakage rate, the gas permeation rate at each moment, and the gas desorption rate corresponding to the solar radiation intensity at each moment into the service life prediction mathematical model to obtain the vacuum degree of the vacuum glass after N years, where N is a positive integer; If the vacuum degree of the vacuum glass after N years is greater than the failure threshold of the location of the vacuum glass, (N-1) years is determined as the predicted service life of the vacuum glass.

4. The method according to any one of claims 1 to 3, characterized in that: The process of generating the mapping relationship between vacuum degree and gas thermal resistance includes: Obtain multiple experimental surface temperatures of the vacuum glass sample under multiple experimental vacuum degrees, where different experimental vacuum degrees correspond to different experimental surface temperatures; Acquiring a plurality of experimental surface heat fluxes of the vacuum glass sample under a plurality of the experimental vacuum degrees, wherein different experimental vacuum degrees correspond to different experimental surface heat fluxes; For any first experimental vacuum degree among the plurality of experimental vacuum degrees, a thermal resistance value is calculated using a first experimental surface temperature, a first experimental surface heat flux, support column structure data and glass structure data of the vacuum glass sample to obtain a first experimental gas thermal resistance corresponding to the first experimental vacuum degree, the first experimental surface temperature corresponds to the first experimental vacuum degree, the first experimental surface heat flux corresponds to the first experimental vacuum degree, the first experimental surface temperature is one of the plurality of experimental surface temperatures, and the first experimental surface heat flux is one of the plurality of experimental surface heat fluxes; Traversing a plurality of the experimental vacuum degrees to obtain the experimental gas thermal resistances corresponding to the plurality of the experimental vacuum degrees; The mapping relationship between the vacuum degree and the gas thermal resistance is generated based on the plurality of experimental vacuum degrees and the experimental gas thermal resistances corresponding to the plurality of experimental vacuum degrees.

5. The method according to any one of claims 1 to 3, characterized in that: The calculating the initial cavity gas thermal resistance according to the measured surface temperature, the measured surface heat flux, the glass structure data and the support column structure data comprises: Calculating the total thermal resistance of the central area of ​​the vacuum glass using the measured surface temperature, the glass structure data, and the support column structure data to obtain the total thermal resistance of the central area of ​​the vacuum glass; The unknown gas thermal resistance is calculated using the measured surface heat flux and the total thermal resistance of the central area of ​​the vacuum glass, and the calculated unknown gas thermal resistance is determined as the initial intra-cavity gas thermal resistance.

6. A vacuum glass service life prediction device, characterized in that: The device comprises: A data acquisition module is used to obtain the measured surface temperature and measured surface heat flux of the vacuum glass, glass structure data, support column structure data, leakage rate and typical meteorological data of the vacuum glass location; A thermal resistance calculation module, used for calculating the initial thermal resistance of the gas in the cavity according to the measured surface temperature, the measured surface heat flux, the glass structure data and the support column structure data; A vacuum degree determination module, used to determine an initial vacuum degree corresponding to the initial thermal resistance of the gas in the cavity according to a mapping relationship between the vacuum degree and the gas thermal resistance; A life prediction module, used to predict the service life of the vacuum glass by using the initial vacuum degree, the glass structure data, the leakage rate, the typical meteorological data of the year where the vacuum glass is located, and a service life prediction mathematical model, so as to obtain the predicted service life of the vacuum glass, wherein the service life prediction mathematical model is generated based on the leakage rate, the gas desorption rate and the gas permeation rate, wherein the gas desorption rate is obtained by the typical meteorological data of the year, and the gas permeation rate is obtained by the typical meteorological data of the year; The service life prediction mathematical model includes: ； in, is the thermal resistance of the gas in the vacuum chamber, is the vacuum degree after N years, N is a positive integer, is the initial vacuum degree, is the annual change of vacuum degree, is the gas permeation rate of the ith gas at time n, is the temperature at time n, is the vacuum degree at time n-1, A is the area of ​​the vacuum glass, is the gas desorption rate at time n, is the solar radiation intensity at time n, is the leakage rate, and L is the circumference of the vacuum glass.

7. The device according to claim 6, characterized in that The life prediction module comprises: A parameter acquisition unit, used to obtain the temperature and solar radiation intensity at each moment from typical meteorological data of the location of the vacuum glass; A permeation rate determination unit, used to simulate the gas permeation rate at each moment by a molecular dynamics simulation method based on the temperature at each moment and the initial vacuum degree; A desorption rate determination unit, used to determine the gas desorption rate corresponding to the solar radiation intensity at each moment from the gas desorption rate mapping relationship; The life prediction unit is used to input the initial vacuum degree, the glass structure data, the leakage rate, the gas permeation rate at each moment and the gas desorption rate corresponding to the solar radiation intensity at each moment into the service life prediction mathematical model to obtain the predicted service life of the vacuum glass.

8. The device according to claim 6 or 7, characterized in that The device also includes: An experimental temperature acquisition module is used to obtain multiple experimental surface temperatures of the vacuum glass sample under multiple experimental vacuum degrees, and different experimental vacuum degrees correspond to different experimental surface temperatures; An experimental heat flow acquisition module, used to acquire a plurality of experimental surface heat flows of the vacuum glass sample under a plurality of experimental vacuum degrees, wherein different experimental vacuum degrees correspond to different experimental surface heat flows; an experimental thermal resistance calculation module, for calculating the thermal resistance value of any first experimental vacuum degree among the plurality of experimental vacuum degrees by using a first experimental surface temperature, a first experimental surface heat flux, support column structure data and glass structure data of the vacuum glass sample, to obtain a first experimental gas thermal resistance corresponding to the first experimental vacuum degree, the first experimental surface temperature corresponds to the first experimental vacuum degree, the first experimental surface heat flux corresponds to the first experimental vacuum degree, the first experimental surface temperature is one of the plurality of experimental surface temperatures, and the first experimental surface heat flux is one of the plurality of experimental surface heat fluxes; A traversal module, used for traversing a plurality of the experimental vacuum degrees to obtain the experimental gas thermal resistance corresponding to each of the plurality of the experimental vacuum degrees; A generating module is used to generate the mapping relationship between the vacuum degree and the gas thermal resistance based on the multiple experimental vacuum degrees and the experimental gas thermal resistances corresponding to the multiple experimental vacuum degrees.

9. A computer device, characterized in that: include: Memory and processor; The memory is used to store computer programs; The processor is configured to execute the computer program in the memory to implement the method according to any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that: The device stores instructions which, when executed on a computer, cause the computer to execute the method according to any one of claims 1 to 5.

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