Glass product production process data optimization control method

Abstract

The invention relates to the technical field of glass production, and discloses a glass product production process data optimization control method, which comprises the following steps: identifying a stress concentration region caused by the temperature change rate difference of a local glass region; establishing tuyere wear evolution characteristics representing the current cooling output capacity of the tuyere; deducing actual cooling airflow distribution states of different areas of the glass surface according to the action rule of the tuyere wear evolution characteristics on the stress concentration area; according to the corresponding influence relation between the actual cooling air flow distribution state and the stress concentration area, determining an air grid system target air pressure distribution scheme for eliminating the stress concentration area on the glass surface; the problem that the surface temperature distribution is not uniform in the cooling process of the glass product is effectively solved, and the quality stability and the production efficiency of the glass product are improved.

Description

Technical Field

[0001] This invention relates to the field of glass production technology, and more specifically, to a method for optimizing and controlling process data in glass product manufacturing. Background Technology

[0002] In the production of glass products, especially tempered glass, the process control during the cooling stage directly affects the stress distribution on the glass surface, thus determining the glass's strength, stability, and finished product quality. Current technology commonly employs an air duct system that uses multiple nozzles to inject airflow, achieving rapid cooling of the glass surface to achieve the desired strengthening effect. However, as the nozzles wear down or their flow rate decreases after prolonged use, the actual cooling output gradually deviates from the design value, causing uneven cooling in localized areas of the glass surface. This leads to localized thermal stress concentration, reducing the yield rate and production stability of the finished product.

[0003] Existing cooling control schemes for glass products are mainly based on unified control of initial design parameters, failing to fully consider the dynamic changes in wear of the air nozzles during actual operation and the real-time fluctuations in the temperature gradient of the glass surface. This results in a lack of effective real-time adaptability in the control schemes, making it difficult to accurately eliminate stress concentration problems. Furthermore, existing methods generally employ empirical or static air pressure distribution strategies, failing to establish a scientific quantitative relationship between the glass surface temperature distribution and the actual cooling capacity of the air nozzles. This prevents effective cooling regulation of localized areas of the glass, limiting further improvements in product quality.

[0004] Therefore, there is an urgent need to propose a more targeted, precise cooling control optimization method that takes into account both the actual operating status of the air nozzle and the dynamic changes in the glass surface temperature, in order to improve the adaptability and precision control of glass production processes, ultimately ensuring product quality and production stability, and enhancing the overall manufacturing level of the tempered glass industry. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a method for optimizing and controlling glass product manufacturing process data.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A method for optimizing and controlling glass product manufacturing process data, the method comprising: When the glass enters the cooling section, based on the real-time collected glass surface temperature distribution data and the initial wind pressure setting of the wind grid system, stress concentration areas caused by the difference in the rate of temperature change in local areas of the glass are identified. Based on the correlation and evolution between the historical air output data of the nozzle in different operating cycles and the actual cooling effect of the corresponding glass surface area, the wear evolution characteristics of the nozzle that characterize the current cooling output capacity of the nozzle are established. Based on the effect of the wear evolution characteristics of the air nozzle on the stress concentration area, the actual cooling airflow distribution in different areas of the glass surface is derived. Based on the corresponding influence relationship between the actual cooling airflow distribution and the stress concentration area, a target wind pressure distribution scheme for the wind grid system to eliminate the stress concentration area on the glass surface is determined.

[0007] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention analyzes the temperature distribution characteristics of the glass surface in real time and identifies stress concentration areas, which can accurately capture the abnormal local temperature difference of the glass during the cooling process. This effectively avoids the problem of local thermal stress accumulation caused by the inability to detect and deal with temperature differences in time, and improves the dynamic monitoring accuracy and production stability of the glass cooling process.

[0008] This invention establishes a quantitative correlation between the wear evolution characteristics of the air nozzle and the actual cooling effect, which can reflect the true cooling capacity of the air nozzle in a timely and accurate manner. It effectively eliminates the problem of deviation between cooling output and design value caused by air nozzle wear, ensures the precise control of glass surface temperature by the air grid system under actual operation, and significantly reduces the risk of quality fluctuations in glass products.

[0009] This invention achieves precise control of the cooling intensity of local areas on the glass surface by constructing a quantitative mapping relationship between the actual cooling airflow distribution and the target wind pressure. This completely solves the problem of local overcooling or undercooling in stress concentration areas, significantly improves the uniformity of surface temperature distribution of glass products, and enhances the qualification rate of finished products and the overall control level of the production process. Attached Figure Description

[0010] Figure 1 The flowchart illustrates a method for optimizing and controlling glass product manufacturing process data, as provided by this invention. Detailed Implementation

[0011] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0012] Example 1 Please see Figure 1As shown in the figure, this embodiment discloses a method for optimizing and controlling glass product manufacturing process data, the method comprising: S101: When the glass enters the cooling section, based on the real-time collected glass surface temperature distribution data and the initial wind pressure setting of the wind grid system, stress concentration areas caused by the difference in the rate of temperature change in local areas of the glass are identified. Specifically, identifying stress concentration areas caused by differences in the rate of temperature change in localized areas of the glass includes: Based on the real-time collected glass surface temperature distribution data, a spatial gradient field of glass surface temperature is constructed. In practice, the process of constructing the spatial gradient field of glass surface temperature aims to establish a mathematical model describing the non-uniformity of heat distribution on the glass surface.

[0013] The construction of the spatial temperature gradient field on the glass surface includes: Collect glass surface temperature data over multiple consecutive periods, and obtain the temperature change rate in different regions using time series analysis. In practical implementation, the sampling frequency of the infrared thermal imaging device is set to... (For example, (Value taken as 10Hz), acquiring two-dimensional temperature matrix data along the glass transport direction. Defined at time... Glass surface position coordinates The temperature value at that location is Select a time length of... A sliding time window containing N sampling points (for example, The time is set to 0.5 seconds, and the corresponding N value is 5.

[0014] Linear regression analysis of the temperature data within this time window was performed using the least squares method to calculate the rate of temperature change at that location. The calculation formula is as follows: In the formula, For the i-th sampling time within the time window, For the corresponding time Measured temperature value The slope calculated using this formula represents the rate of temperature change in that region at the current moment, reflecting the speed at which the glass loses heat at that location.

[0015] The temperature change rate is smoothed by a two-dimensional Gaussian kernel function to construct a continuous and stable spatial temperature gradient field on the glass surface. In practical implementation, to eliminate the gradient field discretization problem caused by measurement noise, a two-dimensional Gaussian kernel function is used. The calculated temperature change rate matrix V is then convolved. The two-dimensional Gaussian kernel function is defined as follows: In the formula, This is the scale parameter (for example, a value of 1.0). This represents the distance between the centers of the convolution kernels.

[0016] Smoothed rate of temperature change for: ; Based on the smoothed temperature change rate The gradient modulus in two-dimensional space is calculated to construct the spatial temperature gradient field of the glass surface. : The gradient field The value represents the degree of difference in the rate of temperature change between a point on the glass surface and its neighborhood. The larger the value, the more significant the difference in the rate of temperature change between that location and its surrounding area, and the higher the risk of thermal stress.

[0017] The initial stress concentration region on the glass surface is obtained by using the mapping relationship between the spatial temperature gradient field and the stress concentration region of the glass. In practical implementation, the threshold for determining stress concentration is set as follows: (For example, The value is taken as 2.5 ℃ / s / cm. Construct a binarization mapping function. To determine the initial stress concentration region:

[0018] when When, determine the coordinates This belongs to the initial stress concentration region on the glass surface. This set is denoted as... .

[0019] By utilizing the initial stress concentration area on the glass surface and the initial wind pressure setting state of the wind grid system, the temperature gradient field in the local area of ​​the glass is corrected, and the corrected stress concentration area is output. In practical implementation, considering the higher stress risk associated with the temperature gradient generated under strong wind cooling (i.e., a large temperature gradient still exists under high wind pressure, indicating high thermal inertia or severe stress accumulation in the region), a wind pressure coupling correction coefficient is introduced. The coordinate system of the wind grating is then obtained. The corresponding initial wind pressure value and the rated maximum wind pressure of the wind grid system (For example, (15000Pa) Construct the corrected gradient field decision value The calculation formula is as follows: In the formula, This is the wind pressure influence weighting factor (exemplarily set to 0.4). This formula shows that, under the same temperature change rate gradient, the greater the wind pressure currently experienced by the area, the more amplified its corresponding stress risk assessment value will be.

[0020] Based on the revised judgment value Again with the determination threshold By comparing the results, the final corrected stress concentration areas can be determined. This stress concentration area Used as a spatial basis for subsequent wind pressure optimization and allocation.

[0021] S102: Based on the correlation and evolution law between the historical air output data of the nozzle in different operating cycles and the actual cooling effect of the corresponding glass surface area, establish the nozzle wear evolution characteristics that characterize the current cooling output capacity of the nozzle. Specifically, the establishment of the wear evolution characteristics of the nozzle, which characterizes the current cooling output capacity of the nozzle, includes: Extract the attenuation trend of airflow velocity at the nozzle from historical airflow data and actual cooling effect data of the corresponding area; In practice, the system database stores the commanded air pressure of the air nozzle at different historical moments t. and the actual cooling rate of the glass in the area covered by the air nozzle at the corresponding moment. To quantify the degradation, a reference cooling rate is introduced. That is, applying the same commanded air pressure when the nozzle is brand new and unworn. The standard cooling rate that should be achieved (this value is obtained through the equipment's factory calibration).

[0022] Define the airflow velocity attenuation coefficient at time t. The coefficient This is a dimensionless numerical value, typically ranging from (0, 1]. A time series dataset is constructed by collecting data from a series of discrete time points throughout the entire lifespan of the nozzle. .

[0023] The sequence was denoised using a moving average algorithm to extract the airflow velocity decay trend curve of the nozzle over time. .

[0024] By utilizing the attenuation trend of the airflow velocity at the nozzle, a function relating the wear degree of the nozzle to the attenuation rate of the airflow velocity at the nozzle is fitted. The function for fitting the relationship between the wear degree of the nozzle and the attenuation rate of the outlet airflow velocity includes: Collect actual cooling air velocity data of multiple air nozzles under different wear conditions to obtain a sample set of cooling air velocity corresponding to the wear degree of the air nozzles; In practice, M samples of air nozzles with different service lives are selected (for example, M=50). For each air nozzle, its current physical wear W (e.g., the increase in nozzle outlet diameter relative to the standard diameter) and the actual cooling air velocity decay rate under this wear condition are measured. .

[0025] Among them, the actual cooling wind speed attenuation rate Defined as velocity loss per unit wind pressure: In the formula, For standard flow rate, To measure the flow rate, To test wind pressure.

[0026] Therefore, a sample set of cooling air velocities corresponding to the wear degree of the air nozzle is constructed. .

[0027] Based on the cooling air velocity sample set, a quantitative relationship function between the wear degree of the nozzle and the airflow velocity attenuation rate is established using a multinomial regression algorithm; In practical implementation, the wear degree W of the nozzle is set as the independent variable, and the airflow velocity attenuation rate is set as the independent variable. The dependent variable is an nth-degree polynomial function. Perform regression fitting on the sample set S (for example, initially assume n=3): In the formula, Let be the undetermined regression coefficients. We use the least squares method to solve for these regression coefficients, minimizing the mean squared error E: .

[0028] Through model cross-validation, the quantitative relationship function with the smallest error is selected as the relationship function characterizing the current wear state of the nozzle; In practice, the sample set S is divided into a training set and a validation set (exemplarily, in an 8:2 ratio). Polynomial models of different orders (e.g., n=1, 2, 3, 4) are trained and validated separately. The root mean square error (RMSE) of each order model on the validation set is calculated. In the formula, k is the number of samples in the validation set. For the true value, These are the model's predicted values.

[0029] Compare the RMSE values ​​of models of different orders, and select the polynomial function corresponding to the smallest RMSE as the final quantitative relationship function. For example, if the error is minimized when n=2, then a quadratic function model is selected.

[0030] Based on the aforementioned relationship function, a wear evolution characteristic representing the cooling output capability of the nozzle is established; In practice, the current wear level is estimated by combining the cumulative operating data of the air nozzles. Substitute into the relational function The current nozzle flow rate attenuation correction factor is calculated. In the formula, k is the normalization coefficient.

[0031] Finally, wear evolution characteristics representing the current cooling output capacity of the nozzle were established. This feature is represented by the actual output efficiency matrix of the nozzle under the current wear condition:

[0032] In the formula, Let be the velocity attenuation correction factor for the i-th nozzle, and N be the total number of nozzles in the air grating system. This characteristic matrix will be directly used for the subsequent derivation and calculation of the cooling airflow field.

[0033] S103: Based on the effect of the wear evolution characteristics of the air nozzle on the stress concentration area, deduce the actual cooling airflow distribution in different areas of the glass surface; Specifically, the derivation of the actual cooling airflow distribution in different areas of the glass surface includes: The actual velocity field of the air outlet of the nozzle is corrected by using the wear evolution characteristics of the nozzle, and the corrected actual velocity field of the nozzle is obtained. In practical implementation, the system calls the nozzle wear evolution feature matrix obtained in S102. Let the theoretical design outlet velocity of the i-th nozzle in the air duct system be... (This value is determined by the current fan frequency and nozzle design parameters, for example, .

[0034] The theoretical velocity is corrected point by point based on wear characteristics, and the actual outlet velocity of the i-th nozzle after correction is calculated. In the formula, Characteristic matrix The formula corresponds to the velocity attenuation correction factor for the i-th nozzle. This formula directly maps the physical wear state of the nozzle to the inlet boundary conditions of the fluid dynamics, thereby obtaining a set of actual velocity field data that closely reflects the current state of the equipment. .

[0035] Based on the corrected actual velocity field of the nozzle and the spatial geometry of the glass surface, a flow field model is established to show the effect of the actual airflow from the nozzle on different areas of the glass surface. The establishment of a flow field model for the actual airflow from the vent nozzle affecting different areas of the glass surface includes: Based on the corrected actual velocity field of the nozzle, obtain the area of ​​cooling airflow action corresponding to the glass surface region; The step of obtaining the cooling airflow area corresponding to the glass surface region includes: The actual velocity field of the corrected nozzle was numerically simulated using computational fluid dynamics (CFD) to determine the effective boundary of the airflow after diffusion on the glass surface. In practice, the finite element volume method (FVM) is used to numerically solve the process of a single jet impacting a plate. The inlet boundary conditions are set as described above. Turbulence model selected Model. The glass surface is set as a solid boundary, and the vertical height of the nozzle outlet from the glass surface is H (for example, H = 50 mm). The radial diffusion velocity distribution after the airflow contacts the glass surface is calculated. , where r is the radial distance from the center point of the jet.

[0036] Define the velocity threshold of the effective action boundary as (For example, = 5). When the tangential flow velocity at a certain point on the glass surface When this point is within the effective range, it is determined that it is located within the effective range. The effective range boundary radius is then determined accordingly. .

[0037] Based on the effective action boundary, determine the cooling airflow action area corresponding to each glass surface region; In practical implementation, the effective action boundary radius is based on the calculation. Calculate the effective cooling circular area of ​​the i-th air nozzle on the glass surface. This area represents the spatial range within which the nozzle can produce a substantial convective heat transfer effect.

[0038] By utilizing the cooling airflow area and the spatial relative position between the nozzle and the glass surface, a spatial influence coefficient matrix is ​​established to describe the effect of the actual airflow from the nozzle on different areas of the glass surface. In a specific implementation, the glass surface is discretized into M micro-grid regions (exemplarily, the grid size is M). Define the spatial influence coefficient matrix W, with dimensions of . , of which elements This represents the weight of the cooling contribution of the i-th air nozzle to the j-th glass micro-element region.

[0039] Calculate the projection center of the air nozzle With glass micro-element center Euclidean distance between Based on the Gaussian jet distribution theory, a weight calculation formula is constructed:

[0040] In the formula, The jet diffusion distribution parameters are... Related (e.g., This formula quantifies the spatial distribution of airflow energy attenuation with distance.

[0041] Based on the aforementioned spatial influence coefficient matrix, a flow field model is constructed to illustrate the effect of the actual airflow from the nozzle on different areas of the glass surface. In practical implementation, a flow field interaction model is constructed. The model is a linear superposition operation system. Its input is the actual outlet velocity vector of each nozzle. The output is the arrival velocity vector at each infinitesimal element on the glass surface. ; That is, for the j-th glass micro-element region, the total cooling airflow velocity it receives. The sum of contributions from all nozzles that effectively cover the area: .

[0042] The actual cooling airflow distribution in different regions of the glass surface was derived based on the flow field interaction model. In practice, the corrected actual velocity field of the nozzle is substituted into the constructed flow field model, and matrix multiplication is performed to calculate the velocity distribution matrix covering the entire glass surface. .

[0043] Mapping this velocity distribution matrix back onto the two-dimensional coordinate system of the glass surface forms the actual cooling airflow distribution state field. Each value in this state field directly reflects the actual cooling airflow intensity obtained at the corresponding coordinate point on the glass surface under the current wear condition and spatial layout of the air nozzle. This distribution state serves as the basis for subsequent calculations of the heat exchange coefficient in S104.

[0044] S104: Based on the corresponding influence relationship between the actual cooling airflow distribution state and the stress concentration area, determine the target wind pressure distribution scheme of the wind grid system to eliminate the stress concentration area on the glass surface; Specifically, the method for determining the target wind pressure distribution scheme of the wind grid system to eliminate stress concentration areas on the glass surface includes: Calculate the actual heat exchange coefficient of each area on the glass surface based on the actual cooling airflow distribution. In practical implementation, the system is based on the actual cooling airflow distribution state field on the glass surface derived in S103. This state field provides the positions of each micro-element on the glass surface. Actual tangential velocity at the location .

[0045] Calculate the local convective heat transfer coefficient at this location using empirical formulas for forced convection heat transfer (e.g., for impingement jet cooling models). Among them, Nusselt number The calculation is based on the following correlation: The actual calculation formula after expansion is: In the formula: The thermal conductivity of air (determined based on experimental data, for example, 0.026); The hydraulic diameter of the nozzle; The Reynolds number is determined by the actual tangential flow velocity. Decide; It is the Prandtl number (e.g., 0.71). The constants for the experimental fitting are (e.g., A=0.15, m=0.7, n=0.33).

[0046] This formula maps the flow velocity distribution to a thermodynamic heat exchange capacity distribution matrix. Each element in the matrix is ​​the corresponding position. value.

[0047] By utilizing the corresponding influence relationship between the actual heat exchange coefficient of each region on the glass surface and the corresponding stress concentration region, a set of constraint equations for the wind pressure of the wind grid and the temperature uniformity of the glass surface is established. In practical implementation, the core objective to eliminate stress concentration is to make the temperature field on the glass surface tend to be uniform, that is, to ensure that at the next moment... Temperature in each region Minimize the spatial gradient.

[0048] First, establish the air pressure setting for the nozzle. With local heat transfer coefficient The physical relationship between them is established by considering that wind pressure is proportional to the square root of the outlet velocity, and the heat transfer coefficient is proportional to the power function of the velocity, as follows: In the formula, The spatial influence coefficient established in S103, The transformation constant, The wind pressure-heat transfer index (determined based on experimental data, for example, taking...) = 0.35).

[0049] Secondly, the thermal equilibrium constraint equations are established. Based on Newton's law of cooling and the specific heat capacity formula of glass, the thermal equilibrium constraint equations for a certain infinitesimal region are... Temperature change over time In the formula, The current surface temperature, To cool the air temperature, For the density of glass, For specific heat capacity, This refers to the thickness of the glass.

[0050] Finally, a set of constraint equations to eliminate stress concentration is constructed. For regions identified as stress concentration areas by S101... any point within The target cooling rate must be such that the temperature at that point is equal to the average temperature of other non-stress concentration regions. Maintain consistency (or gradient within the allowed threshold) Inside):

[0051] This set of equations clarifies the wind pressure of each air nozzle. Mathematical conditions must be met to ensure that the heat exchange is sufficient to smooth out temperature differences, while being limited by the physical operating range of the equipment. .

[0052] By solving the set of constraint equations, a target wind pressure distribution scheme for the wind grid system that eliminates stress concentration areas on the glass surface can be obtained. In practice, a nonlinear programming algorithm (such as Sequential Quadratic Programming, SQP) is used to solve the above constraint equations. The objective function is defined. To minimize the variance of the temperature gradient across the entire field: .

[0053] Using the constraint equations from above as limitations, we solve for the optimal wind pressure vector. ; For example, suppose that, based on calculations, the current wind pressure for nozzle k, located in the stress concentration zone at the glass edge, is 5000 Pa. To eliminate this area... The abnormal temperature difference was used to solve the system of equations to obtain the target wind pressure of the nozzle. The pressure should be adjusted to 6200 Pa; and the corresponding target wind pressure for the m-th air nozzle in the central area is... The pressure was then adjusted from 5000 Pa to 4800 Pa.

[0054] The system ultimately outputs a vector containing all the target settings for the nozzles. As the target wind pressure distribution scheme of the wind grid system, it is directly transmitted to the PLC control system to perform wind pressure regulation.

[0055] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters, weights, and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0056] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0057] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing and controlling glass product manufacturing process data, characterized in that, The method includes: When the glass enters the cooling section, based on the real-time collected glass surface temperature distribution data and the initial wind pressure setting of the wind grid system, stress concentration areas caused by the difference in the rate of temperature change in local areas of the glass are identified. Based on the correlation and evolution between the historical air output data of the nozzle in different operating cycles and the actual cooling effect of the corresponding glass surface area, the wear evolution characteristics of the nozzle that characterize the current cooling output capacity of the nozzle are established. Based on the effect of the wear evolution characteristics of the air nozzle on the stress concentration area, the actual cooling airflow distribution in different areas of the glass surface is derived. Based on the corresponding influence relationship between the actual cooling airflow distribution and the stress concentration area, a target wind pressure distribution scheme for the wind grid system to eliminate the stress concentration area on the glass surface is determined.

2. The method for optimizing and controlling glass product manufacturing process data according to claim 1, characterized in that, The identification of stress concentration areas caused by differences in the rate of temperature change in localized areas of the glass includes: Based on the real-time collected glass surface temperature distribution data, a spatial gradient field of glass surface temperature is constructed. The initial stress concentration region on the glass surface is obtained by using the mapping relationship between the spatial temperature gradient field and the stress concentration region of the glass. By utilizing the initial stress concentration area on the glass surface and the initial wind pressure setting of the wind grid system, the temperature gradient field in the local area of ​​the glass is corrected, and the corrected stress concentration area is output.

3. The method for optimizing and controlling glass product manufacturing process data according to claim 2, characterized in that, The construction of the spatial temperature gradient field on the glass surface includes: Collect glass surface temperature data over multiple consecutive periods, and obtain the temperature change rate in different regions using time series analysis. The temperature change rate is smoothed by a two-dimensional Gaussian kernel function to construct a continuous and stable spatial temperature gradient field on the glass surface.

4. The method for optimizing and controlling glass product manufacturing process data according to claim 3, characterized in that, The establishment of the wear evolution characteristics of the nozzle to characterize the current cooling output capacity includes: Extract the attenuation trend of airflow velocity at the nozzle from historical airflow data and actual cooling effect data of the corresponding area; By utilizing the attenuation trend of the airflow velocity at the nozzle, a function relating the wear degree of the nozzle to the attenuation rate of the airflow velocity at the nozzle is fitted. Based on the aforementioned relationship function, a wear evolution characteristic representing the cooling output capability of the nozzle is established.

5. The method for optimizing and controlling glass product manufacturing process data according to claim 4, characterized in that, The fitting function obtained to determine the relationship between the wear degree of the nozzle and the attenuation rate of the outlet airflow velocity includes: Collect actual cooling air velocity data of multiple air nozzles under different wear conditions to obtain a sample set of cooling air velocity corresponding to the wear degree of the air nozzles; Based on the cooling air velocity sample set, a quantitative relationship function between the wear degree of the nozzle and the airflow velocity attenuation rate is established using a multinomial regression algorithm; Through model cross-validation, the quantitative relationship function with the smallest error is selected as the relationship function characterizing the current wear state of the nozzle.

6. The method for optimizing and controlling glass product manufacturing process data according to claim 5, characterized in that, The derivation of the actual cooling airflow distribution in different areas of the glass surface includes: The actual velocity field of the air outlet of the nozzle is corrected by using the wear evolution characteristics of the nozzle, and the corrected actual velocity field of the nozzle is obtained. Based on the corrected actual velocity field of the nozzle and the spatial geometry of the glass surface, a flow field model is established to show the effect of the actual airflow from the nozzle on different areas of the glass surface. The actual cooling airflow distribution in different regions of the glass surface was derived based on the flow field model.

7. The method for optimizing and controlling glass product manufacturing process data according to claim 6, characterized in that, The establishment of a flow field model for the actual airflow from the nozzle on different areas of the glass surface includes: Based on the corrected actual velocity field of the nozzle, obtain the area of ​​cooling airflow action corresponding to the glass surface region; By utilizing the cooling airflow area and the spatial relative position between the nozzle and the glass surface, a spatial influence coefficient matrix is ​​established to describe the effect of the actual airflow from the nozzle on different areas of the glass surface. Based on the aforementioned spatial influence coefficient matrix, a flow field model is constructed to demonstrate the effect of the actual airflow from the nozzle on different areas of the glass surface.

8. The method for optimizing and controlling glass product manufacturing process data according to claim 7, characterized in that, The acquisition of the cooling airflow area corresponding to the glass surface region includes: The actual velocity field of the corrected nozzle was numerically simulated using computational fluid dynamics (CFD) to determine the effective boundary of the airflow after diffusion on the glass surface. Based on the effective action boundary, determine the cooling airflow action area corresponding to each glass surface region.

9. The method for optimizing and controlling glass product manufacturing process data according to claim 8, characterized in that, The target wind pressure distribution scheme for the wind grid system that determines the stress concentration areas on the glass surface includes: Calculate the actual heat exchange coefficient of each area on the glass surface based on the actual cooling airflow distribution. By utilizing the corresponding influence relationship between the actual heat exchange coefficient of each region on the glass surface and the corresponding stress concentration region, a set of constraint equations for the wind pressure of the wind grid and the temperature uniformity of the glass surface is established. By solving the set of constraint equations, a target wind pressure distribution scheme for the wind grid system that eliminates stress concentration areas on the glass surface is obtained.

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