Water oxygen permeability measurement method, device, equipment and medium

Abstract

The application discloses a kind of water oxygen permeability measurement method, device, equipment and medium, for measuring the water oxygen permeability numerical value of to-be-measured film based on numerical simulation and particle swarm algorithm.The method comprises: obtaining real-time monitoring data detected by mass spectrometer;According to the kind of the to-be-measured film, determine the corresponding physical model;Based on the physical model, the real-time detection data is preprocessed using numerical simulation algorithm, to obtain the estimated value of relevant parameters and training samples;According to the range of parameter sample space obtained according to the relevant parameters, and according to the training sample and the physical model, obtain test sample, and according to the range of parameter sample space, the training sample and the test sample, parameter fitting is carried out using particle swarm algorithm, to obtain the water oxygen permeability numerical value of the to-be-measured film at different times.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of permeability detection, and in particular to a water-oxygen permeability measurement method, device, equipment and medium. BACKGROUND

[0002] With the continuous development of flexible display panels, organic light-emitting diode (OLED) display devices are widely used due to their self-emitting, short response time, high contrast, wide viewing angle, and low power consumption. However, OLED display screens are sensitive to water vapor and oxygen in the air, and a barrier film is needed to prevent the organic light-emitting layer from being corroded by water and oxygen in the air. Therefore, for testing OLED display screens, detecting the water-oxygen permeability of the barrier film is a key technology.

[0003] However, the measurement process of the water-oxygen permeability of the barrier film may have a long measurement period. Since the permeability is not stable at the beginning, but increases slowly and eventually reaches a stable state, the period of this process may be as long as 60 days. The traditional water-oxygen permeability measurement method is based on a theoretical formula and uses the least squares method to fit the early signal data.

[0004] However, this method is not effective in shortening the water-oxygen permeability calculation time of the barrier film, and often needs to reach 2 / 3 of the period to have the best results, and this method is not suitable for barrier films with complex structures, and has a small application range. SUMMARY

[0005] Therefore, it is necessary to provide a water-oxygen permeability measurement method, device, equipment and medium to effectively shorten the water-oxygen permeability calculation period and have a wider application range.

[0006] To solve the above technical problems, the first aspect of the present application provides a water-oxygen permeability measurement method for measuring the water-oxygen permeability value of a to-be-measured film in a test chamber based on numerical simulation and particle swarm optimization. The to-be-measured film separates the test chamber into a gas inlet chamber and an accumulation chamber. The test gas in the gas inlet chamber penetrates through the film into the accumulation chamber. The method comprises:

[0007] Obtaining real-time monitoring data detected by a mass spectrometer connected to the accumulation chamber via a detection chamber;

[0008] Determining a corresponding physical model according to the type of the to-be-measured film;

[0009] Using a numerical simulation algorithm to preprocess the real-time detection data based on the physical model to obtain estimated values of related parameters and training samples;

[0010] According to the estimated value of the related parameters, the range of the parameter sample space is obtained, the test sample is obtained according to the training sample and the physical model, and the parameter fitting is performed by using the particle swarm algorithm according to the range of the parameter sample space, the training sample and the test sample, so as to predict the water and oxygen permeability value of the to-be-measured film.

[0011] In the water and oxygen permeability measurement method of the above embodiment, the real-time detection data of the to-be-measured film detected by the mass spectrometer is obtained, the corresponding physical model is established according to the type of the to-be-measured film, and the real-time detection data is processed based on the physical model by using numerical simulation and particle swarm algorithm, so as to predict the water and oxygen permeability value of the to-be-measured film. This method effectively shortens the water and oxygen permeability measurement time of the to-be-measured film by using numerical simulation algorithm and particle swarm algorithm, and can be applied to the measurement of water and oxygen permeability of more types of films.

[0012] In one of the embodiments, the types of the to-be-measured film include laminated film, organic film and inorganic film, and the corresponding physical model is determined according to the type of the to-be-measured film, including:

[0013] A function relationship between the concentration of the test gas at different positions in the film and time is established:

[0014]

[0015] In the above formula, D(C, x) is the diffusion coefficient, x represents the position, and C represents the concentration of the test gas at a certain position in the film. The diffusion coefficient is a binary function of the concentration and the position.

[0016] In one of the embodiments, the related parameters include the diffusion coefficient and the delay time.

[0017] The real-time detection data is preprocessed based on the physical model by using numerical simulation algorithm, so as to obtain the estimated value of the related parameters and the training sample, including:

[0018] The length of time of the real-time detection data is obtained;

[0019] The matching matrix, the benchmark value and the cycle number are obtained based on the physical model and the length of time, wherein the benchmark value is the cycle value corresponding to the preset value of the related parameters;

[0020] The sampling time corresponding to each data in the matching matrix is calculated according to the cycle number, the sampling data value corresponding to the real-time detection data is obtained according to the sampling time, the corresponding sampling data value matrix is generated, and the correlation coefficient of the matching matrix and the sampling data matrix is calculated, so as to obtain the maximum value of each correlation coefficient corresponding to each cycle number, and determine the cycle number corresponding to the maximum value.

[0021] determining whether a maximum value in the correlation coefficient is greater than or equal to a preset threshold value;

[0022] If yes, the diffusion coefficient and the delay time are calculated according to a cycle number corresponding to the maximum value.

[0023] In one of the embodiments, the real-time detection data is preprocessed based on the physical model by using a numerical simulation algorithm to obtain an estimated value of a correlation parameter and a training sample, and the method further comprises:

[0024] According to a preset sampling period, values on an actual data curve are equally spaced sampled to obtain the training sample including a preset number of sampling data values.

[0025] In one of the embodiments, the range of the parameter sample space is obtained according to the correlation parameter, and the method further comprises:

[0026] A binary parameter is generated according to the diffusion coefficient and the delay time.

[0027] The range of the parameter sample space is determined with the binary parameter as a center point.

[0028] In one of the embodiments, the test sample is obtained according to the training sample and the physical model, and the method further comprises:

[0029] A sampling time corresponding to each sampling data value in the training sample is obtained.

[0030] Theoretical values corresponding to each sampling time are calculated based on the physical model, and the test sample is generated according to each theoretical value.

[0031] In one of the embodiments, the parameter fitting is performed by using a particle swarm algorithm according to the range of the parameter sample space, the training sample and the test sample to predict a water-oxygen permeability value of the to-be-tested thin film, and the method further comprises:

[0032] The preset number of particle points are initialized according to the range of the parameter sample space, and a parameter number is determined, the parameter number being associated with the preset number.

[0033] An initial speed is randomly assigned to each particle point according to a preset speed range, and an initial parameter is randomly assigned to each particle point in the parameter sample space according to the parameter number.

[0034] A calculation precision value and a maximum iteration number are obtained.

[0035] A matching degree of each particle point in each iteration to the test sample and the training sample is calculated.

[0036] acquire a maximum value of the matching degree in each iteration of the particle point, and determine the parameter corresponding to the maximum value as a local target matching parameter corresponding to the particle point;

[0037] acquire a maximum value of the matching degree in each iteration of the particle point, and determine the parameter corresponding to the maximum value as a local target matching parameter corresponding to the particle point;

[0038] determine whether the global target parameter is greater than or equal to the precision value;

[0039] if yes, perform fitting on the real-time detection data according to the global target matching parameter to predict the water and oxygen permeability value of the to-be-measured film; if no, update the moving speed and parameter of each particle based on an update formula.

[0040] The second aspect of the present application proposes a water and oxygen permeability measuring device for a film, which is used to measure the water and oxygen permeability value of a to-be-measured film in a test cavity based on numerical simulation and particle swarm algorithm. The to-be-measured film separates the test cavity into a gas inlet chamber and an accumulation chamber. The test gas in the gas inlet chamber penetrates through the film into the accumulation chamber. The device comprises:

[0041] a real-time detection data acquisition module, which is used to acquire real-time detection data detected by a mass spectrometer located in a detection chamber in communication with the accumulation chamber;

[0042] a physical model determination module, which is used to determine a corresponding physical model according to the type of the to-be-measured film;

[0043] a related parameter estimated value and training sample acquisition module, which is used to preprocess the real-time detection data based on the physical model by using a numerical simulation algorithm to acquire a related parameter estimated value and a training sample;

[0044] a water and oxygen permeability value acquisition module, which is used to acquire the range of a parameter sample space according to the related parameters, acquire a test sample according to the training sample and the physical model, and perform parameter fitting by using a particle swarm algorithm according to the range of the parameter sample space, the training sample and the test sample to acquire the water and oxygen permeability value of the to-be-measured film at different times.

[0045] In the water and oxygen permeability measuring device of the above embodiment, the physical model determination module, the related parameter estimated value and training sample acquisition module, and the water and oxygen permeability value acquisition module are cooperated with each other. The real-time detection data of a known to-be-measured film is processed based on numerical simulation and particle swarm algorithm to acquire the water and oxygen permeability value of the to-be-measured film at different times. The water and oxygen permeability measuring device can effectively shorten the calculation time of the water and oxygen permeability of the to-be-measured film, and can be applied to more types of film calculation.

[0046] The third aspect of the present application provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method described above when executing the computer program.

[0047] The fourth aspect of the present application provides a computer readable storage medium, which stores a computer program, and the computer program implements the steps of the method described above when executed by a processor.

[0048] The above description is only a summary of the technical solutions of the present application. In order to make the technical means of the present application clearer and can be implemented according to the content of the specification, the following will describe the preferred embodiments of the present application in detail with the help of the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings of other embodiments can also be obtained without creative labor on the basis of these drawings.

[0050] Figure 1 A schematic diagram of a process for measuring water and oxygen permeability of a barrier film by a mass spectrometer provided in the present application;

[0051] Figure 2 A schematic diagram of an apparatus for measuring water and oxygen permeability of a barrier film provided in the present application;

[0052] Figure 3 A schematic diagram of a process for measuring water and oxygen permeability provided in an embodiment of the present application;

[0053] Figure 4 A schematic diagram of a process for measuring water and oxygen permeability provided in another embodiment of the present application;

[0054] Figure 5 A schematic diagram of a parameter sample space provided in an embodiment of the present application;

[0055] Figure 6 A schematic diagram of a numerical solution process taking an organic film diffusion model as an example provided in an embodiment of the present application;

[0056] Figure 7 A schematic diagram of a process for measuring water and oxygen permeability provided in another embodiment of the present application;

[0057] Figure 8An effect schematic diagram of fitting real-time detection data of water-oxygen permeability based on numerical simulation and particle swarm algorithm provided in an embodiment of the present application is shown in FIG. 1.

[0058] Figure 9 An effect schematic diagram of fitting real-time detection data of heat transfer process based on numerical simulation and particle swarm algorithm provided in an embodiment of the present application is shown in FIG. 2. DETAILED DESCRIPTION

[0059] In order to facilitate the understanding of the present application, the present application will be described more fully below with reference to the accompanying drawings. The preferred embodiments of the present application are shown in the drawings. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive.

[0060] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terminology used in the specification of the present application is only for the purpose of describing specific embodiments and is not intended to limit the present application. The term "and / or" used herein includes any and all combinations of one or more of the associated listed items.

[0061] In the case of using "include", "have", and "contain" described herein, unless a clear limiting term is used, such as "only", "consisting of", etc., another component can be added. Unless otherwise mentioned, the singular form of the term can include the plural form and cannot be understood as one in number.

[0062] It should be understood that although the terms "first", "second", etc. can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of the present application, a first element can be referred to as a second element, and similarly, a second element can be referred to as a first element.

[0063] In the present application, unless otherwise explicitly specified and limited, the terms "connected", "connected" and the like should be understood broadly, for example, it can be directly connected or indirectly connected through an intermediate medium, it can be internal communication of two elements or interaction relationship of two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0064] There are many methods for measuring water-oxygen permeability, such as weighing method, storage experiment method and mass spectrometer method. Taking the process of measuring water-oxygen permeability of barrier film by mass spectrometer as an example, please refer to Figure 1, the barrier film is placed in the middle of the upper and lower chambers, the upper chamber is connected to high pressure water vapor or other gas, and the lower chamber is in a high vacuum state. The gas slowly penetrates through the film and enters the lower chamber. The mass spectrometer measures the pressure in the lower chamber and obtains the gas permeability using a certain correspondence. This value gradually increases over time, and when the pressure stabilizes, it corresponds to the steady-state permeability. However, this process takes a long time, which can take up to 60 days. Therefore, it is necessary to predict the permeability at steady state based on the obtained permeability data to shorten the water and oxygen permeability measurement time of the barrier film. The existing technology is based on the Fick's law model and uses the least squares method to fit the early data to predict the final steady-state value. However, the least squares method has poor fitting results, and often requires 2 / 3 of the cycle to achieve the best fitting result, which is not obvious in shortening the water and oxygen permeability measurement time of the barrier film. In addition, this method is only applicable to ideal cases, i.e., cases where the theoretical formula of the physical model corresponding to the barrier film has an analytical solution. However, in reality, most complex barrier films (such as three-layer films) or some hydrophilic films often deviate from the theoretical formula, and the theoretical formula does not have an analytical solution, so the least squares method cannot be used for fitting. Therefore, the scope of application of this accelerated water and oxygen permeability measurement method is limited.

[0065] To solve the above problems, the present application provides a water and oxygen permeability measurement method that can effectively shorten the water and oxygen permeability measurement period and is applicable to a wider range of barrier films. As shown in Figure 2 , the water and oxygen permeability measurement data is based on the detection device, but is not limited to this device. Specifically, the device includes three chambers: a gas inlet chamber, an accumulation chamber, and a detection chamber, each chamber is provided with a barometer, wherein the gas inlet chamber, the accumulation chamber, and the detection chamber are connected to a vacuum pump through respective valves, the accumulation chamber is connected to the detection chamber through a valve, and the detection chamber is connected to a mass spectrometer. During detection, the sample film is placed between the gas inlet chamber and the accumulation chamber. The gas enters the gas inlet chamber through a certain buffer device, penetrates through the film into the accumulation chamber, and after a certain period of time, the gas enters the detection chamber and is detected by the mass spectrometer. By obtaining the barometer values of each chamber and analyzing the detected gas using the mass spectrometer, the water and oxygen permeability of the film to be measured is obtained. After detection, the accumulation chamber and the detection chamber are evacuated for the next cycle, and the detection data is collected.

[0066] To illustrate the technical solution of the water and oxygen permeability measurement method of the present application, the following specific embodiments are used for illustration.

[0067] In one embodiment, as shown in Figure 3As shown, a water and oxygen permeability measurement method is provided for measuring the water and oxygen permeability value of a to-be-measured film in a test cavity based on numerical simulation and particle swarm algorithm, the to-be-measured film separating the test cavity into a gas inlet chamber and an accumulation chamber, a test gas in the gas inlet chamber permeating through the film into the accumulation chamber, the method comprising:

[0068] Step S10: obtaining real-time monitoring data detected by a mass spectrometer connected to the accumulation chamber via a detection chamber;

[0069] Wherein, the real-time detection data can be detected by a detection device as shown, and specifically, the above process can be referred to for the description. Figure 2

[0070] Step S20: determining a corresponding physical model according to the type of the to-be-measured film;

[0071] Wherein, the type of the to-be-measured film includes laminated film, organic film, inorganic film, etc., and according to different types of films, corresponding physical models can be established by referring to historical data and theoretical formulas.

[0072] Step S30: pre-processing the real-time detection data based on the physical model using a numerical simulation algorithm to obtain estimated values of related parameters and training samples;

[0073] Wherein, numerical simulation, also known as computer simulation, relies on electronic computers, combines the concepts of finite elements or finite volumes, and achieves the purpose of studying engineering problems, physical problems, and even various problems in nature through numerical calculation and image display; the related parameters are parameters that affect the water and oxygen permeability value of the to-be-measured film in the detection process, which need to be introduced into the theoretical formula to revise the theoretical formula, so that the physical model is more consistent with the data; the training sample is a sample composed of data selected according to the real-time detection data according to a certain rule for fitting calculation.

[0074] Step S40: obtaining the range of the parameter sample space according to the estimated values of the related parameters, obtaining test samples according to the training samples and the physical model, and performing parameter fitting using a particle swarm algorithm according to the range of the parameter sample space, the training samples, and the test samples to predict the water and oxygen permeability value of the to-be-measured film.

[0075] ​The principle of the particle swarm algorithm is to randomly generate a plurality of random points in a sample space, and then constantly iterate and update the parameters of the random points until the optimal solution is found. In each iteration and parameter update, the particles update themselves in two "directions". The first direction is towards the optimal point encountered by the particle itself in the previous parameter update, which is a local optimal point, and the other direction is towards the optimal point found by all particles, which is a global optimal point. Through the above process of constant updating, the best matching scheme is finally obtained.

[0076] In the water and oxygen permeability measurement method of the above embodiment, by obtaining the real-time detection data of the to-be-measured film detected by the mass spectrometer, a corresponding physical model is established according to the type of the to-be-measured film, and the real-time detection data is processed based on the physical model by using numerical simulation and particle swarm algorithm to predict the water and oxygen permeability value of the to-be-measured film. The method effectively shortens the water and oxygen permeability calculation time of the to-be-measured film by using numerical simulation algorithm and particle swarm algorithm, and can be applied to more types of film water and oxygen permeability calculation.

[0077] In one of the embodiments, the types of the to-be-measured film include laminated film, organic film and inorganic film, and the corresponding physical model is determined according to the type of the to-be-measured film, including:

[0078] A function relationship between the concentration of the test gas at different positions in the film and time is established:

[0079]

[0080] In the above formula, D(C, x) is the diffusion coefficient, x represents the position, and C represents the concentration of the test gas in the film. The diffusion coefficient is a binary function of the concentration and the position.

[0081] The function relationship between the concentration of the test gas at different positions in the accumulation chamber and time is a nonlinear Fick's diffusion law. The mathematical expression of the first law of Fick's diffusion is: J is the diffusion flux, that is, the diffusion material flux per unit area perpendicular to the diffusion direction per unit time, with a unit of g·cm -2 ·s -1 D is the diffusion coefficient, and the negative sign indicates that the material always migrates from the place with high concentration to the place with low concentration. Fick's first law can be directly used to process steady-state diffusion problems, that is, the concentration distribution does not change with time, and after the boundary conditions are determined, it can be easily solved according to the formula. The mathematical expression of the second law is: The physical meaning is that the rate of change of concentration in diffusion is proportional to the rate of change of concentration gradient along the diffusion direction with the change of diffusion distance, which describes the change of species concentration with time in the diffusion process. When the concentration of material distribution changes with time, the flux of diffusing material is also different because the concentration at different positions at different times is different. In the water and oxygen permeability measurement method of the embodiment of the application, the Fick's law is extended to be nonlinear for the measurement equipment. For example, water vapor can cause hydrolysis of the organic film, and the pressure difference between the upper and lower surfaces causes the film to deform to different degrees. At this time, the diffusion coefficient is no longer a constant, but a function that changes with concentration and distance. The function relationship between the concentration of the test gas at different positions in the film and time is: In the above formula, D(C, x) is the diffusion coefficient, x represents the position, and C represents the concentration of the test gas at a certain position in the film, which has multiple forms.

[0082] In one of the embodiments, the type of the film to be measured is a polyethylene naphthalate (PEN) organic film, and the test gas is nitrogen, which does not react with the film, so the gas concentration in the film does not affect the diffusion coefficient. The function expression of the diffusion coefficient of the PEN organic film is D(C, x) = D0*(1+kx), where k is introduced for correction when the pressure is large, and the value is related to the pressure difference. D0 is the diffusion coefficient of the film when it does not deform. Since the physical model is a nonlinear partial differential equation, a general analytical solution cannot be obtained, so the water and oxygen permeability measurement method of the embodiment of the application can be applied to complex barrier films.

[0083] In one of the embodiments, the related parameters include the diffusion coefficient and the delay time.

[0084] Specifically, the gas measurement process is divided into two steps: one is the process of gas permeating the film, according to the expression of the physical model established in the embodiment of the application: The permeability of the gas permeating the film is related to the diffusion coefficient, and the diffusion coefficient may no longer be a constant, but may change with the position. The second is the process of gas from the accumulation chamber to the detection chamber and then to the mass spectrometer for testing. In this process, the gas is initially adsorbed on the surface of the cavity, and the vacuum pump is used to pump away the gas, resulting in a certain delay of the mass spectrometer signal. Therefore, the delay time is introduced to further correct the physical formula, so that the physical model is more consistent with the data, and the predicted water and oxygen permeability value of the film to be measured is more accurate.

[0085] Step 30: based on the physical model, using a numerical simulation algorithm to preprocess the real-time detection data to obtain the estimated value of the related parameters and the training sample, such as Figure 4 As shown in the figure, it includes:

[0086] Step S302: Obtain the time length of the real-time detection data;

[0087] As an example, the time length of the obtained real-time detection data is T.

[0088] Step S304: Obtain a matching matrix, a benchmark value and a cycle number based on the physical model and the time length, wherein the benchmark value is a cycle value corresponding to a preset value of the related parameter;

[0089] The physical model can represent a curve of water-oxygen permeability of the measured film changing with time by using a numerical simulation method, and the curve is a predicted water-oxygen permeability signal curve. Different model parameters correspond to different permeation curves, and a set of data can be selected according to a rule to form a matching matrix based on the curve. Specifically, according to experience, the 1 / 6 cycle data point of such a data curve is special, which is the inflection point of the second derivative. Therefore, the data before 1 / 6 cycle can be uniformly sampled, and ten points or more can be selected, but generally not less than five. These data points form a matching matrix. The matrix data is generally the value of the 4 / 60 cycle, 5 / 60 cycle, 6 / 60 cycle, …, 10 / 60 cycle data point of the ideal data. The reason for not selecting 1 / 60, 2 / 60 and 3 / 60 cycle is that the actual permeability value has high uncertainty and large fluctuation, and the use of these three points will greatly reduce the matching success rate. Thus, a 1*7 matrix data is formed. As an example, the related parameter D0 is taken as 10 -13 , the diffusion coefficient correction parameter k is taken as 0, and the delay time is taken as 0. Thus, the matching matrix is obtained as [S4, S5, S6, S7, S8, S9, S10], and the corresponding time T0 is calculated as the benchmark value, and the cycle number is rounded down.

[0090] Step S306: Calculate the sampling time corresponding to each data in the matching matrix according to the cycle number, and generate a corresponding sampling data value matrix according to the sampling time and the corresponding sampling data value in the real-time detection data, and calculate the correlation coefficient of the matching matrix and the sampling data matrix to obtain the maximum value in each correlation coefficient corresponding to each cycle number, and determine the cycle number corresponding to the maximum value.

[0091] Specifically, the matching matrix [S4, S5, S6, S7, S8, S9, S10] is the value of the data points corresponding to the 4 / 60 period, 5 / 60 period, 6 / 60 period, …, 10 / 60 period of the predicted water oxygen permeability signal curve, which is the curve of the water oxygen permeability of the film changing with time in an ideal state. The data fitting effect tends to be stable as the data goes further back. Therefore, if the corresponding real-time detection time is less than one sixth of the entire detection period, the data amount of the real-time detection data does not reach the preset value, and the fitting effect is poor. Therefore, the real-time detection data can be matched according to the matching matrix, and whether the data amount of the real-time detection data reaches the preset threshold value can be determined according to the matching degree. Thus, the amount of fitting calculation is reduced, and the prediction time is shortened to one sixth of the original actual measurement time, greatly optimizing the fitting effect.

[0092] How to determine whether the data amount of the real-time detection data reaches the preset threshold value. The process is as follows: obtain the fitting curve of the real-time detection data, calculate the sampling time corresponding to each data in the matching matrix according to the number of cycles, determine the sampling data values corresponding to each sampling time on the fitting curve, and generate a corresponding sampling data value matrix, and calculate the correlation coefficients of the matching matrix and the sampling data matrix to obtain the maximum value in each correlation coefficient corresponding to each cycle. Obtain the detection signal fitting curve of the real-time detection data, and generate a corresponding sampling data value matrix according to the matching matrix. As an example, first calculate the time nodes [T-6*n*5, T-5*n*5, T-4*n*5, T-3*n*5, T-2*n*5, T-1*n*5, T] of the matrix matching data points, where n = 1, 2, 3…N. Then take the data values [F4, F5, F6, F7, F8, F9, F10] of the above time nodes on the actual signal curve. In actual selection, experimental data often cannot collect data points exactly, so points can be taken according to the interpolation method, and the correlation coefficients of the matrix [S4, S5, S6, S7, S8, S9, S10] and [F4, F5, F6, F7, F8, F9, F10] corresponding to each cycle are sequentially solved. Then the maximum correlation coefficient under this data amount is obtained, and the cycle number corresponding to the maximum value is obtained.

[0093] Step S308: Determine whether the maximum value in the correlation coefficient is greater than or equal to the preset threshold value.

[0094] Step S3010: If yes, calculate the diffusion coefficient and the delay time according to the cycle number corresponding to the maximum value.

[0095] As an example, if the kind of the to-be-tested thin film is polyethylene naphthalate (PEN) organic thin film, the to-be-tested gas is nitrogen, and the influence of the deformation amount can be ignored when the pressure difference is small, then D(C, x) = D0, and the correlation coefficient can be preset as 0.95. If the number of times of positioning with the maximum correlation coefficient is the nth time in N cycles, then the diffusion coefficient estimation value D0 corresponding to the nth cycle is calculated and output as D0 = 10 -13 T0 / (n*5*10), the delay time estimation value T d = T-n*5*10.

[0096] In one of the embodiments, the step S30 of pre-processing the real-time detection data based on the physical model to obtain the estimation value of the related parameters and the training sample further includes:

[0097] The step S3012 of sampling the values on the measured data curve at equal intervals according to a preset sampling period to obtain the training sample including a preset number of sampling data values.

[0098] The measured data curve is a curve graph formed according to the actual measurement data at each sampling moment. In the actual water oxygen permeability measurement, the actual sampling process is often to collect a data point every 1 s, so there can be hundreds of thousands of signal data points in the measurement process. Therefore, part of the data points need to be selected as the training sample. As an example, 100 data points are obtained at equal intervals from the actual signal by using the interpolation method, and the data values [S1, S2, S3 …… S100] of the data points form the training sample.

[0099] In one of the embodiments, the step S40 of obtaining the range of the parameter sample space according to the related parameters further includes:

[0100] The step S402 of generating a binary parameter according to the diffusion coefficient and the delay time.

[0101] As an example, the diffusion coefficient D and the delay time T d have been calculated according to the cycle number corresponding to the maximum value in the step S308, and the binary parameter is generated according to the diffusion coefficient and the delay time, that is, the binary parameter is (D, T d ).

[0102] The step S404 of determining the range of the parameter sample space with the binary parameter as the center point.

[0103] As an example, please refer to Figure 5 , and assume that the binary parameter is (D, T dThe value of the parameter pair (P1, P2) is (P1, P2), and the selected spatial range is 0.2*P~3*P. The parameter sample space is determined as a rectangle surrounded by four points (0.2*P1, 0.2*P2), (0.2*P1, 3*P2), (3*P1, 0.2*P2), and (3*P1, 3*P2).

[0104] In one of the embodiments, the step S40 of obtaining the test sample according to the training sample and the physical model comprises:

[0105] The step S406 of obtaining the sampling time corresponding to each sampling data value in the training sample.

[0106] As an example, 100 data points are selected as training samples on the actual data curve in the step S3010, and the sampling time corresponding to each data point [t1, t2, t3…t100] is recorded.

[0107] The step S408 of calculating the theoretical value corresponding to each sampling time based on the physical model and generating the test sample according to each theoretical value.

[0108] Specifically, according to the sampling time [t1, t2, t3…t100], the theoretical value corresponding to each sampling time is calculated based on the physical model, and the test sample [F1, F2, F3…F100] is generated according to each theoretical value.

[0109] As an example, the numerical solution process will have different methods according to different theoretical models. Taking the simplest Fick model as an example:

[0110] The physical model is The physical meaning is that the concentration at different positions and different times in the film is not the same, so the concentration is a function of time and position, and the partial derivative of the concentration with respect to time at each point satisfies the above equal relationship, and further, the equation can be expanded as:

[0111]

[0112] According to the relationship, please refer to Figure 6 That is, the concentration at a certain position at the next time can be solved by the surrounding points at the previous time, and the permeability value at different times can be solved by continuous iteration.

[0113] In one of the embodiments, the step S40 of obtaining the test sample according to the training sample and the physical model comprises: Figure 7As shown, comprising:

[0114] Step S4010: initializing the preset number of particle points according to the range of the parameter sample space and determining the parameter number, the parameter number being associated with the preset number;

[0115] Specifically, when the parameter number is n, the preset number is usually 10 n , as an example: when the relevant parameters are the diffusion coefficient and the delay time, the parameter number is 2, and the preset number is 100.

[0116] Step S4012: randomly assigning an initial speed to each of the particle points according to a preset speed range;

[0117] Specifically, the preset speed range can be set artificially according to historical experience and iteration requirements, and the moving speed of the particle points is initialized, and the particle point speed is randomly assigned, such as the moving speed of the i-th particle V i .

[0118] Step S4014: randomly assigning an initial parameter to each of the particle points in the parameter sample space according to the parameter number;

[0119] Specifically, as an example: when the parameter number is n and the particle number is 10 n , each particle randomly assigns a parameter in the parameter sample space range, and the parameter of the m-th particle is (P m1 , P m2 , P m3 ……P mn ).

[0120] Step S4016: obtaining a calculation precision value and a maximum iteration number;

[0121] Specifically, the calculation precision value and the maximum iteration number are set artificially according to actual detection needs.

[0122] Step S4018: calculating the matching degree of the test sample and the training sample corresponding to each of the particle points;

[0123] Specifically, as an example, when the m-th particle corresponds to the parameters (P m1 , P m2 , P m3 ……P mn ), the test sample [F1, F2, F3……F100] is obtained by using a numerical solution method according to the sampling time [t1, t2, t3……t100], and the matching degree of the test sample [F1, F2, F3……F100] and the training sample [S1, S2, S3……S100] is calculated as ε m .

[0124] Step S4020: obtaining the maximum value of the matching degree of each particle point in each iteration, and determining the parameter corresponding to the maximum value as the local target matching parameter of the particle point;

[0125] Specifically, after N iterations, each particle corresponds to N matching degrees (ε1, ε2, ε3……ε N , and the maximum value in the matching degrees is selected. i , and the parameter corresponding to ε i is P Ni =(P′ i1 , P′ i2 , ……P′ in ), which is the local target matching parameter of the particle. Ni

[0126] Step S4022: obtaining the maximum value of the matching degree in the local target matching parameter corresponding to each particle point as the global best matching degree, and the parameter corresponding to the maximum value as the global target matching parameter;

[0127] Specifically, each particle corresponds to a local maximum matching degree, and the local maximum matching degrees of all particles are The maximum value in the local maximum matching degrees is selected. gmax , and the parameter corresponding to ε gmax is P Ng =(P′ g1 , P′ g2 , ……P′ gn ), which is the global target matching parameter corresponding to the particle point. Ng

[0128] Step S4024: determining whether the global target parameter is greater than or equal to the precision value.

[0129] As an example, the precision value is set to e, and it is determined whether ε gmax is greater than or equal to e.

[0130] Step S4026: if yes, fitting the real-time detection data according to the global target matching parameter to obtain the water and oxygen permeability value of the to-be-measured thin film; and if no, updating the moving speed and parameter of each particle based on an update formula.

[0131] If yes, the water and oxygen permeability value of the to-be-measured thin film is finally predicted based on the physical model and numerical simulation according to the P Ng =(P′ g1 , P′ g2 , ……P′ gn ) parameter value.

[0132] ​​If not, the moving speed and parameters of each particle are updated based on an update formula, and steps S4018 and S4026 are repeated. Specifically, the update formula is as follows:

[0133]

[0134] P i = P i + av i ;

[0135] wherein, wherein is the last obtained moving speed of the i-th particle, v i is the speed of the i-th particle obtained by this update, P i is the current parameter of the i-th particle, W is an inertia factor, C1 and C2 are weight factors, and the three values can control the weights of self-experience and social experience, r1 and r2 are random numbers in [0, 1] and are randomly updated each time the calculation is performed. a is a constraint factor, and the purpose is also to control the speed. Through the above two formulas, each particle moves along the comprehensive result of the previous motion direction, the local self-experience direction, and the global optimal direction. In this way, the parameters are constantly updated, and finally the parameter value that meets the condition is output to obtain the predicted water and oxygen permeability value of the film.

[0136] In the water and oxygen permeability measurement method of the above embodiment, the real-time detection data is fitted and calculated based on numerical simulation and particle swarm algorithm. Please refer to Figure 8 , the data points represent the real-time acquired water and oxygen permeability detection data of the film, the solid curve is the prediction signal curve output after fitting the real-time acquired water and oxygen permeability data based on numerical simulation and particle swarm algorithm, and the dashed curve is the relationship curve of the water and oxygen permeability of the film with time in actual measurement. Obviously, by using the water and oxygen permeability measurement method in the present application, the water and oxygen permeability of the actual film can be predicted using a small amount of measurement data, effectively shortening the water and oxygen permeability calculation time of the film to be measured.

[0137] The calculation method based on numerical simulation and particle swarm algorithm in the present application independently establishes the model and the fitting algorithm, so that the applicability of the algorithm is wider. It can not only be used in the calculation of the water and oxygen permeability of the barrier film, but also can be introduced into the fitting and estimation of the heat transfer parameter.

[0138] Specifically, in the heat transfer process, the heat flow value is difficult to be directly obtained, and correspondingly, the temperature of different points can be monitored in real time in the time scale to realize the estimation of the heat flow value. In the case where the boundary condition is unknown, the temperature time information of two points is collected at will, and the to-be-determined parameter is obtained by data fitting.

[0139] As an example, please refer to Figure 9, based on numerical simulation and particle swarm algorithm, the real-time data of the heat transfer process are fitted, and the corresponding parameter data are output. The fitting error is 0.1% under 2% noise data, and the fitting error is 0% under noiseless data, which has good anti-interference ability. Compared with using Gaussian distribution to solve the parameter fitting of the nonlinear partial differential equation, the fitting error is 1.7% under 1% noise data, and the fitting error is 2.8% under noiseless data, which proves that the measurement method based on numerical simulation and particle swarm algorithm has excellent performance in partial differential equation fitting.

[0140] The application provides a water and oxygen permeability measurement device for measuring water and oxygen permeability of a to-be-measured film in a test cavity based on numerical simulation and a particle swarm algorithm. The to-be-measured film separates the test cavity into a gas inlet chamber and an accumulation chamber. Test gas in the gas inlet chamber penetrates through the film into the accumulation chamber. The device comprises the following modules.

[0141] A real-time detection data acquisition module is configured to acquire real-time detection data detected by a mass spectrometer in a detection chamber in communication with the accumulation chamber.

[0142] A physical model determination module is configured to determine a corresponding physical model according to the type of the to-be-measured film.

[0143] A related parameter estimated value and training sample acquisition module is configured to acquire a related parameter estimated value and a training sample by preprocessing the real-time detection data based on the physical model and using a numerical simulation algorithm.

[0144] A water and oxygen permeability value acquisition module is configured to acquire a range of a parameter sample space according to the related parameters, acquire a test sample according to the training sample and the physical model, and perform parameter fitting by using a particle swarm algorithm according to the range of the parameter sample space, the training sample and the test sample, so as to acquire water and oxygen permeability values of the to-be-measured film at different times.

[0145] In the water and oxygen permeability measurement device provided in the above embodiment, the physical model determination module, the related parameter estimated value and training sample acquisition module and the water and oxygen permeability value acquisition module are matched with each other, the real-time detection data of the known to-be-measured film are processed based on numerical simulation and a particle swarm algorithm, so as to acquire water and oxygen permeability values of the to-be-measured film at different times. The water and oxygen permeability measurement device can effectively shorten the water and oxygen permeability measurement time of the to-be-measured film, and can be applied to more types of film measurement.

[0146] In an embodiment of the application, a computer device is also provided, which comprises a memory and a processor. The memory stores a computer program, and the processor implements the steps of the method described in any embodiment of the application when executing the computer program.

[0147] In one embodiment of the present application, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program. The computer program is executed by a processor to implement the steps of the method described in any embodiment of the present application.

[0148] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, the computer program can include the processes of the above-mentioned embodiments. Any reference to memory, storage, database or other medium used in the embodiments provided by the present application can include at least one of non-volatile and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory. The volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0149] Please note that the above-mentioned embodiments are only for illustrative purposes and do not mean to limit the present application.

[0150] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described, but as long as the combinations of the technical features do not exist, they should be considered as the scope of the present application.

[0151] The above-mentioned embodiments only express several embodiments of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be pointed out that for those of ordinary skill in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of protection of the patent of the present application should be subject to the appended claims.

Claims

1. A method for measuring the water oxygen permeability of a thin film, characterized in that, For measuring the water and oxygen permeability of a thin film under test located in a test chamber based on numerical simulation and particle swarm optimization algorithm, wherein the thin film under test divides the test chamber into an inlet chamber and an accumulation chamber, and the test gas in the inlet chamber permeates through the thin film into the accumulation chamber, the method includes: Real-time detection data from a mass spectrometer are acquired, wherein the mass spectrometer is connected to the accumulation chamber via a detection chamber; The corresponding physical model is determined based on the type of thin film to be tested; Based on the physical model, a numerical simulation algorithm is used to preprocess the real-time detection data to obtain estimated values ​​of relevant parameters and training samples. The range of the parameter sample space is obtained based on the estimated values ​​of the relevant parameters. Test samples are obtained based on the training samples and the physical model. Then, the parameters are fitted using a particle swarm optimization algorithm based on the range of the parameter sample space, the training samples, and the test samples to predict the water and oxygen permeability of the membrane under test.

2. The method for measuring the water and oxygen permeability of a thin film according to claim 1, characterized in that, The types of films to be tested include laminated films, organic films, and inorganic films. Determining the corresponding physical model based on the type of film to be tested includes: Establish the functional relationship between the concentration of the test gas at different locations within the thin film and time: ； In the above formula, Where is the diffusion coefficient. Indicates location, The diffusion coefficient represents the concentration of the test gas at a certain location on the thin film, and is a binary function of the concentration and the location.

3. The method for measuring the water and oxygen permeability of a thin film according to claim 2, characterized in that, The relevant parameters include the diffusion coefficient and the delay time; Based on the physical model, a numerical simulation algorithm is used to preprocess the real-time detection data to obtain estimated values ​​of relevant parameters and training samples, including: The duration of time for acquiring the real-time detection data; Based on the physical model and the time length, a matching matrix, a benchmark value, and a number of iterations are obtained, wherein the benchmark value is the period value corresponding to the preset value of the relevant parameters; The sampling time corresponding to each data in the matching matrix is ​​calculated based on the number of iterations, and the corresponding sampling data value in the real-time detection data is obtained based on the sampling time to generate a corresponding sampling data value matrix. The correlation coefficient between the matching matrix and the sampling data value matrix is ​​calculated to obtain the maximum value among the correlation coefficients corresponding to each number of iterations, and the number of iterations corresponding to the maximum value is determined. Determine whether the maximum value of the correlation coefficient is greater than or equal to a preset threshold; If so, the diffusion coefficient and the delay time are calculated based on the number of cycles corresponding to the maximum value.

4. The method for measuring the water and oxygen permeability of a thin film according to claim 3, characterized in that, Based on the physical model, a numerical simulation algorithm is used to preprocess the real-time detection data to obtain estimated values ​​of relevant parameters and training samples, which also includes: According to a preset sampling period, the values ​​on the measured data curve are sampled at equal intervals to obtain the training samples, which include a preset number of sampled data values.

5. The method for measuring the water and oxygen permeability of a thin film according to claim 4, characterized in that, The range of the parameter sample space obtained based on the estimated values ​​of the relevant parameters includes: A binary parameter is generated based on the diffusion coefficient and the delay time; The range of the parameter sample space is determined with the binary parameter as the center point.

6. The method for measuring the water and oxygen permeability of a thin film according to claim 4, characterized in that, The step of obtaining test samples based on the training samples and the physical model includes: Obtain the sampling time corresponding to each sampled data value in the training sample; The theoretical values ​​corresponding to each sampling time are calculated based on the physical model, and the test samples are generated based on each theoretical value.

7. The method for measuring the water and oxygen permeability of a thin film according to any one of claims 4-6, characterized in that, The step of using a particle swarm optimization algorithm to fit parameters based on the range of the parameter sample space, the training samples, and the test samples to predict the water and oxygen permeability of the membrane under test includes: The preset number of particle points are initialized according to the range of the parameter sample space, and the number of parameters is determined, wherein the number of parameters is related to the preset number; An initial velocity is randomly assigned to each particle point according to a preset velocity range; Initial parameters are randomly assigned to each particle point in the parameter sample space according to the number of parameters; Obtain the calculation precision value and the maximum number of iterations; Calculate the matching degree between the test sample and the training sample corresponding to each particle point; Obtain the maximum value of the matching degree of each particle point in each iteration, and determine the parameter corresponding to the maximum value as the local target matching parameter of the particle point; The maximum value of the matching degree among the local target matching parameters corresponding to each particle point is the global best matching degree, and the parameter corresponding to the maximum value is the global target matching parameter. Determine whether the global target matching parameter is greater than or equal to the precision value; If yes, the real-time detection data is fitted according to the global target matching parameters to predict the water and oxygen permeability of the film under test; if no, the movement speed and parameters of each particle are updated based on the update formula.

8. A thin-film water oxygen permeability measuring device, characterized in that, For measuring the water and oxygen permeability of a thin film under test located in a test chamber based on numerical simulation and particle swarm optimization algorithm, wherein the thin film under test divides the test chamber into an inlet chamber and an accumulation chamber, and the test gas in the inlet chamber permeates through the thin film into the accumulation chamber, the device includes: A real-time detection data acquisition module is used to acquire real-time detection data detected by a mass spectrometer, wherein the mass spectrometer is located in a detection chamber connected to the accumulation chamber; The physical model determination module is used to determine the corresponding physical model according to the type of the thin film to be tested; The module for obtaining the estimated values ​​of relevant parameters and training samples is used to preprocess the real-time detection data based on the physical model using a numerical simulation algorithm to obtain the estimated values ​​of relevant parameters and training samples. The water and oxygen permeability value acquisition module is used to obtain the range of the parameter sample space based on the estimated values ​​of the relevant parameters, obtain test samples based on the training samples and the physical model, and perform parameter fitting using a particle swarm optimization algorithm based on the range of the parameter sample space, the training samples and the test samples to obtain the water and oxygen permeability values ​​of the film under test at different times.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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