Data-driven lens hardening process adaptive optimization method and system

Abstract

The application relates to the technical field of data processing, and provides a lens hardening process adaptive optimization method and system based on data driving. The method comprises the following steps: collecting a plurality of process parameters in a lens hardening process in real time; calling a hardening process prediction model, performing lens hardness prediction based on the plurality of process parameters, comparing a predicted lens hardness parameter with a predetermined hardness value, and generating a predicted hardness deviation; performing adaptive optimization of the plurality of process parameters based on the predicted hardness deviation, and generating optimized process parameters; feeding back the optimized process parameters to a production control end of the lens hardening process, and performing regulation and control of the lens hardening process parameters. The application solves the technical problems of uneven hardness and low production efficiency caused by unstable process parameters in the lens hardening process, achieves the technical effects of ensuring lens hardness consistency, improving production efficiency and product quality by performing real-time prediction and adaptive optimization of process parameters.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a data-driven adaptive optimization method and system for lens hardening processes. Background Technology

[0002] Lens hardening, a crucial method for strengthening the surface of materials such as glass and plastics, is widely used in the production of optical lenses, display screens, and various functional lenses. This process increases the surface hardness of the lens through physicochemical processes like heating and cooling, thereby enhancing its wear resistance, scratch resistance, and lifespan. However, traditional lens hardening processes face several technical challenges, particularly regarding the stability and precision of process parameters. Currently, process control in lens hardening relies heavily on empirical rules and manual adjustments. Differences in material properties between batches and limitations in equipment precision can easily lead to uneven hardness distribution in the lenses, affecting product quality. Furthermore, minute variations in process parameters such as temperature, pressure, and time can cause hardness deviations, which are often not detected and corrected in time during production, resulting in low production efficiency and high scrap rates. Especially in large-scale production, the lack of real-time feedback mechanisms and optimization methods makes product quality unstable and unable to meet the stringent requirements of high-end markets for lens hardness uniformity and precision. Summary of the Invention

[0003] This application provides a data-driven adaptive optimization method and system for lens hardening processes, aiming to solve the technical problems of uneven hardness and low production efficiency caused by unstable process parameters in lens hardening processes.

[0004] The first aspect disclosed in this application provides a data-driven adaptive optimization method for lens hardening processes. The method includes: real-time acquisition of multiple process parameters during the lens hardening process; invoking a hardening process prediction model, predicting lens hardness based on the multiple process parameters, comparing the predicted lens hardness parameters with predetermined hardness values ​​to generate a predicted hardness deviation; adaptively optimizing the multiple process parameters using the predicted hardness deviation to generate optimized process parameters; and feeding the optimized process parameters back to the production control terminal of the lens hardening process to execute the adjustment of the lens hardening process parameters.

[0005] Another aspect of this application discloses a data-driven adaptive optimization system for lens hardening processes. The system includes: a data acquisition module for real-time acquisition of multiple process parameters during the lens hardening process; a hardness prediction module for calling a hardening process prediction model, predicting lens hardness based on the multiple process parameters, comparing the predicted lens hardness parameters with predetermined hardness values, and generating a predicted hardness deviation; an adaptive optimization module for adaptively optimizing the multiple process parameters based on the predicted hardness deviation, generating optimized process parameters; and a process parameter control module for feeding back the optimized process parameters to the production control terminal of the lens hardening process, executing the control of the lens hardening process parameters.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The aforementioned data-driven adaptive optimization method for lens hardening first collects various process parameters (such as temperature, pressure, and time) in real time during the lens hardening process and uses a hardening process prediction model to predict the lens hardness based on these parameters. Then, the predicted hardness value is compared with a predetermined target hardness value to calculate the hardness deviation. Next, based on this deviation, relevant process parameters are automatically adjusted to optimize the hardness level and generate new process parameters. Finally, these optimized process parameters are fed back to the production control system for appropriate adjustments and execution, thereby ensuring precise control of the lens hardening process and stable product quality.

[0008] The above description is merely an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a flowchart illustrating a data-driven adaptive optimization method for lens hardening processes in one embodiment.

[0011] Figure 2 This is a system architecture diagram of an adaptive optimization system for a data-driven lens hardening process in one embodiment.

[0012] Figure labeling: Data acquisition module 11, hardness prediction module 12, adaptive optimization module 13, process parameter control module 14. Detailed Implementation

[0013] This application provides a data-driven adaptive optimization method and system for lens hardening processes, which solves the technical problems of uneven hardness and low production efficiency caused by unstable process parameters in lens hardening processes.

[0014] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0015] It should be noted that the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such process, method, product, or device.

[0016] Example 1, as Figure 1 As shown, this application provides a data-driven adaptive optimization method for lens hardening processes, the method comprising:

[0017] Multiple process parameters are collected in real time during the lens hardening process.

[0018] In this embodiment, to achieve precise control and optimization of process parameters during the lens hardening process, various high-precision sensors are first installed in key parts of the lens hardening production line, such as the hardening tank, curing oven, and conveying device. These sensors include, but are not limited to, temperature sensors, pressure sensors, time recording devices, solution concentration detectors, and pH meters. Specifically, the temperature sensor monitors the temperature change of the solution in the hardening tank in real time, ensuring that the lens hardening process is always within the optimal range required by the process. The pressure sensor accurately measures the pressure applied to the lens surface during the hardening process to ensure the stability and uniformity of the pressure. The time recording device accurately records the residence time of each lens in the hardening process, avoiding the impact of time deviation on hardness. The solution concentration detector continuously monitors the concentration of the hardening solution and can provide timely feedback when the concentration changes due to evaporation or chemical reaction. The pH meter monitors the acidity or alkalinity of the solution in real time to ensure that it is within a suitable range to maintain the stable progress of the hardening reaction. These sensors transmit the collected data back to the system, forming multiple process parameters such as temperature, pressure, solution concentration, and pH value. These process parameters not only provide a basis for subsequent process optimization and prediction, but also provide real-time feedback for quality control throughout the entire production process, ensuring that each step is carried out under optimal process conditions, thereby improving production efficiency, reducing scrap rate, and ensuring the stability and consistency of product quality.

[0019] The hardening process prediction model is invoked to predict the hardness of the lens based on the multiple process parameters. The predicted lens hardness parameters are compared with the predetermined hardness values ​​to generate the predicted hardness deviation.

[0020] In one embodiment, during the lens hardening process, multiple process parameters are first input into a pre-trained hardening process prediction model. This model is a machine learning model built based on historical data and regression algorithms. By learning from the process parameters and hardness data of previous hardening processes, the model can predict the hardness value of the lens under the current hardening conditions. Based on the received process parameters, the hardening process prediction model uses its learned knowledge to calculate a predicted hardness value. This predicted value reflects the possible hardness achievable on the lens surface under the current parameters. After calculating the predicted hardness value, it is compared with a predetermined target hardness value to calculate a predicted hardness deviation. This deviation reflects the difference between the current process conditions and the expected process effect. The magnitude of the deviation directly affects subsequent process optimization and adjustment. If the predicted hardness deviation is large, an adaptive optimization process will be triggered to adjust the process parameters to ensure that the hardness of the final product meets the predetermined standard, thereby guaranteeing the quality and performance of the lens.

[0021] Furthermore, this application provides a method for calling a hardening process prediction model, predicting lens hardness based on the multiple process parameters, comparing the predicted lens hardness parameters with predetermined hardness values, and generating a predicted hardness deviation, including:

[0022] Collect historical hardening process test datasets, train the hardening process prediction model based on regression learning; input the multiple process parameters into the hardening process prediction model to predict lens hardness, and output the predicted lens hardness parameters; calculate the difference between the predicted lens hardness parameters and the predetermined hardness value to generate the predicted hardness deviation.

[0023] Preferably, firstly, a large amount of historical hardening process data is collected. This data includes various process parameters (such as temperature, pressure, time, etc.) and corresponding lens hardness values ​​from previous production processes. Each set of data represents a complete hardening process, recording all key parameters and the final measured hardness value at that time. By summarizing this data, a complete historical hardening process detection dataset is formed for subsequent model training. Subsequently, a regression neural network is used to train the hardening process prediction model. Regression neural networks are a machine learning method that uses process parameters from historical data as input features and the corresponding lens hardness values ​​as target outputs. Through forward propagation, loss calculation, backpropagation, parameter optimization, and other steps, the relationship between process parameters and hardness is learned. Then, a portion of data not used for training is used as a validation set to evaluate the trained model. When the evaluation results show that the prediction accuracy meets the preset accuracy, the current regression neural network is adopted as the hardening process prediction model. Next, the real-time collected process parameters are input into the trained hardening process prediction model. Based on the learned mapping relationship between process parameters and hardness, the hardening process prediction model outputs a predicted lens hardness parameter. This predicted lens hardness parameter represents the estimated value of the lens surface hardness under the current process conditions. Then, the predicted lens hardness parameter is compared with a predetermined hardness value. This predetermined hardness value is an ideal hardness value determined based on product standards or design requirements. By calculating the difference between the predicted lens hardness parameter and the predetermined hardness value, the predicted hardness deviation is obtained. The magnitude and direction of this predicted hardness deviation reflect the error in lens hardness under the current process conditions. If the deviation is small, it indicates that the current process parameters are relatively accurate and the lens hardness is close to the predetermined value; if the deviation is large, it indicates that the current process parameters need to be optimized and adjusted to ensure the quality of the final product. Through this process, intelligent prediction and feedback of the lens hardening process can be achieved, providing data support for subsequent adaptive optimization and ensuring that hardness control during the production process is always maintained within the predetermined standard range.

[0024] The predicted hardness deviation is used to adaptively optimize the multiple process parameters to generate optimized process parameters.

[0025] In one embodiment, after obtaining the predicted hardness deviation, it is determined whether the predicted hardness deviation exceeds a preset deviation threshold. If it is less than the deviation threshold, it indicates that the current process parameters basically meet the requirements and no adjustment is needed. However, if the deviation exceeds the deviation threshold, it indicates that the process parameters need to be optimized. At this time, the relationship between the predicted hardness deviation and each process parameter is analyzed using historical data and statistical analysis methods to evaluate the influence weight of each process parameter on the hardness deviation. For example, changes in some process parameters may have a significant impact on hardness, while changes in other parameters may have a smaller impact. Subsequently, adaptive adjustment is performed based on the weight determined for each parameter. For example, some process parameters may require significant adjustments, while others only require minor adjustments. The optimization process gradually adjusts the process parameters until the predicted hardness deviation approaches the predetermined hardness value, thereby generating the final optimized process parameters. These optimized process parameters can effectively reduce hardness deviation, ensuring that the lens hardness reaches the predetermined target. Furthermore, these parameters will be used as control parameters in subsequent production processes and applied in actual production, thereby improving product quality consistency and production efficiency.

[0026] Furthermore, this application provides adaptive optimization of the multiple process parameters based on the predicted hardness deviation to generate optimized process parameters, including:

[0027] Determine whether the predicted hardness deviation exceeds a preset deviation threshold; if so, analyze the influence weights of the predicted hardness deviation and the deviations of the multiple process parameters, and construct a variable optimization weight distribution; based on the variable optimization weight distribution, adaptively optimize the multiple process parameters to generate the optimized process parameters.

[0028] Preferably, after obtaining the predicted hardness deviation, the predicted hardness deviation is first compared with a preset deviation threshold, which is determined based on product standards and production requirements. If the predicted hardness deviation exceeds this preset deviation threshold, it indicates a significant deviation in the current process parameters. In this case, based on historical data, statistical analysis methods are used to analyze the influence of each process parameter on the predicted deviation, identifying key factors leading to the deviation, such as temperature, pressure, and time. A deviation influence weight is assigned to each parameter, representing its contribution to the hardness deviation; parameters with greater influence have higher weights, meaning they will play a more important role in the optimization process. After analyzing the influence of each process parameter, these deviation influence weights are normalized to ensure that the sum of the weights of all process parameters is 1, thus forming a variable optimization weight distribution. This variable optimization weight distribution reflects the degree to which different process parameters should be considered during the optimization process. Subsequently, based on the constructed variable optimization weight distribution, adaptive optimization is performed on multiple process parameters. Specifically, the value of each process parameter is adjusted according to its optimization weight, and through multiple iterations, the hardness deviation is minimized to approach the predetermined hardness value. After adaptive optimization, optimized process parameters are generated. These optimized process parameters reflect how to adjust various process parameters under the current production conditions to ensure that the lens hardness reaches the predetermined standard, thereby significantly improving the automation level of the production line and the stability of product quality, and reducing the scrap rate.

[0029] Furthermore, this application provides a method for adaptively optimizing the multiple process parameters based on the weight distribution of the aforementioned variables to generate the optimized process parameters, including:

[0030] Based on the optimized weight distribution of the variables and the predicted hardness deviation, the multiple process parameters are initialized and adjusted to generate a first set of adjustment parameters and a second set of adjustment parameters. The hardening process prediction model is then invoked to predict the hardness of the first set of adjustment parameters and the second set of adjustment parameters, generating a first hardness prediction result and a second hardness prediction result. The first hardness prediction result and the second hardness prediction result are used to determine the hardness optimization direction and the temporary optimal adjustment parameter. The temporary optimal adjustment parameter is then adjusted according to the hardness optimization direction, iterated multiple times, until the adjustment parameter that meets the convergence condition is obtained, generating the optimized process parameters.

[0031] Optionally, during adaptive optimization, the first step is to adjust multiple process parameters based on the constructed variable optimization weight distribution and the current predicted hardness deviation. The purpose of this initial adjustment is to quickly identify process parameters requiring significant adjustment based on the hardness deviation and the influence weights of each process parameter, and then make preliminary adjustments accordingly. For process parameters that have a significant impact on the hardness deviation (i.e., parameters with larger weights), a larger adjustment step size will be used for initial adjustment; for process parameters with a smaller impact (i.e., parameters with smaller weights), a smaller adjustment step size will be used for initial adjustment. Through two adjustments in random directions, a first set of adjustment parameters and a second set of adjustment parameters can be generated. These two sets of parameters will be compared and optimized in subsequent hardness predictions. Subsequently, the first set of adjustment parameters and the second set of adjustment parameters are input into the hardening process prediction model, respectively, to predict the lens hardness corresponding to each set of parameters. This generates a first hardness prediction result for the first set of adjustment parameters and a second hardness prediction result for the second set of adjustment parameters. These two prediction results represent the estimated values ​​of lens hardness under different parameter adjustment conditions. Next, the two prediction results are compared, and the difference between them is used to determine the direction of hardness optimization and the temporary optimal adjustment parameters. The temporary optimal adjustment parameters are then adjusted according to the hardness optimization direction. After each adjustment, the hardening process prediction model is called to predict hardness and calculate the new hardness deviation. This process continues until the hardness deviation converges, meaning the deviation gradually decreases and reaches the preset deviation threshold. Finally, through multiple iterations, a set of optimized process parameters is obtained. These optimized process parameters can effectively adjust the lens hardness to the predetermined hardness value, thereby ensuring that the lens hardness always meets the predetermined standard, improving the accuracy and efficiency of the production process, and reducing the scrap rate.

[0032] Furthermore, after generating the first hardness prediction result and the second hardness prediction result, this application also includes:

[0033] The differences between the first hardness prediction result, the second hardness prediction result and the predetermined hardness value are calculated respectively to generate a first difference and a second difference; if either the first difference or the second difference satisfies the preset deviation threshold, the optimized process parameters are directly generated.

[0034] Optionally, after obtaining the first hardness prediction result and the second hardness prediction result, the first hardness prediction result and the second hardness prediction result are compared with the predetermined hardness value, respectively, and a first difference and a second difference are calculated. The first difference represents the error between the predicted hardness and the predetermined hardness under the first set of adjustment parameters; the larger the difference, the more significant the impact of the currently adjusted process parameters on the hardness, and the larger the deviation. The second difference reflects the error between the predicted hardness and the target hardness under the second set of adjustment parameters. Subsequently, the calculated first difference and the second difference are compared with a preset deviation threshold. If either the first difference or the second difference is less than or equal to the preset deviation threshold, it indicates that the current process parameters are sufficiently close to the target hardness value and no further adjustment is needed. In this case, the currently adjusted process parameters are directly used as the optimized process parameters, thereby achieving efficient and precise production process control.

[0035] Furthermore, this application provides methods for analyzing the influence weights of the predicted hardness deviation and the deviations of the multiple process parameters, and constructing a variable optimization weight distribution, including:

[0036] Based on historical hardening process test datasets, statistical analysis methods are used to evaluate the influence of each process parameter on hardness deviation and determine multiple influence weights of the multiple process parameters; the multiple influence weights are normalized so that the sum of the weights is 1, and the variable optimization weight distribution is generated.

[0037] Optionally, before optimization, a historical hardening process testing dataset is obtained from a historical database. This dataset contains process parameters (such as temperature, pressure, and time) and corresponding hardness measurements for different production batches. This data will be used to analyze the relationship between each process parameter and hardness deviation. Subsequently, process parameters and corresponding hardness values ​​for each production cycle are extracted from the historical hardening process testing dataset, including the specific value of each process parameter and the hardness measured after each production run. Then, statistical analysis methods such as the Pearson correlation coefficient are used to calculate the correlation coefficient between each process parameter and hardness, determining the strength of the relationship between parameter changes and hardness deviation. The larger the correlation coefficient, the greater the influence of the process parameter on the hardness deviation. By using the correlation coefficient of each process parameter as its weight, multiple influence weights are obtained for each process parameter. These influence weights reflect the relative importance of each process parameter in the optimization process. Finally, by dividing each influence weight by the sum of influence weights, the multiple influence weights are normalized to a total weight of 1, ensuring that the optimization adjustment of each process parameter is relatively balanced and avoiding any single parameter excessively affecting the process optimization. After normalization, the multiple influence weights will be organized into variable optimization weight distribution to reflect the relative importance of each process parameter in the optimization process. Process parameters with larger weights will be adjusted more during the optimization process, while parameters with smaller weights will be adjusted less or even not adjusted at all, thereby ensuring that the hardness deviation is minimized and the ideal production effect is achieved.

[0038] Furthermore, this application provides a method for locating the hardness optimization direction and temporary optimal adjustment parameters using the first hardness prediction result and the second hardness prediction result, including:

[0039] Compare the first hardness prediction result and the second hardness prediction result, take the adjustment parameter with lower hardness as the starting point and the adjustment parameter with higher hardness as the ending point, analyze the direction of change of each parameter from the starting point to the ending point, and generate the hardness optimization direction; compare the first hardness prediction result and the second hardness prediction result, and generate the temporary optimal adjustment parameter with the adjustment parameter with higher hardness.

[0040] Optionally, after obtaining the first and second hardness prediction results, these two predictions are compared. The prediction with lower hardness is used as the starting point of the optimization process, and the prediction with higher hardness is used as the ending point. Then, the direction of parameter change from the starting point to the ending point is analyzed. That is, the adjustment parameter values ​​corresponding to the two prediction results are compared to determine how to adjust the parameters from the lower hardness adjustment parameter (starting point) to the higher hardness adjustment parameter (ending point). For example, if comparison shows that increasing the temperature from a small to a large value increases the hardness from a small value to a large value, then increasing the temperature is the direction of temperature change. By summarizing the directions of these parameter changes, a hardness optimization direction is formed. Furthermore, the adjustment parameter corresponding to the higher hardness prediction result is selected as the temporary optimal adjustment parameter to further reduce the hardness deviation and approach the predetermined hardness value. This temporary optimal adjustment parameter will serve as the basis for subsequent optimization adjustments, ensuring more precise hardness control and improving production efficiency.

[0041] The optimized process parameters are fed back to the production control terminal of the lens hardening process to adjust the lens hardening process parameters.

[0042] In one embodiment, after generating optimized process parameters, these optimized process parameters are transmitted to the production control terminal of the lens hardening process. After receiving the optimized process parameters, the production control terminal adjusts the hardening process according to these new parameters, specifically including adjusting temperature, pressure, time, etc., to ensure that the hardening process is always in the optimal state, thereby improving production efficiency and reducing scrap rate.

[0043] In summary, the embodiments of this application have at least the following technical effects:

[0044] This application embodiment first collects multiple process parameters in real time during the lens hardening process; then, it calls the hardening process prediction model to predict the lens hardness based on the multiple process parameters, compares the predicted lens hardness parameters with predetermined hardness values, and generates a predicted hardness deviation; subsequently, it uses the predicted hardness deviation to adaptively optimize the multiple process parameters, generating optimized process parameters; finally, it feeds the optimized process parameters back to the production control terminal of the lens hardening process to execute the adjustment of the lens hardening process parameters. These technical effects collectively solve the technical problems of uneven hardness and low production efficiency caused by unstable process parameters in the lens hardening process, achieving the technical effect of ensuring lens hardness consistency and improving production efficiency and product quality through real-time prediction and adaptive optimization of process parameters.

[0045] Example 2, based on the same inventive concept as the data-driven adaptive optimization method for lens hardening process in the foregoing examples, such as... Figure 2As shown, this application provides a data-driven adaptive optimization system for lens hardening processes. The system includes: a data acquisition module 11 for real-time acquisition of multiple process parameters during the lens hardening process; a hardness prediction module 12 for calling a hardening process prediction model, predicting lens hardness based on the multiple process parameters, comparing the predicted lens hardness parameters with predetermined hardness values, and generating a predicted hardness deviation; an adaptive optimization module 13 for adaptively optimizing the multiple process parameters based on the predicted hardness deviation, generating optimized process parameters; and a process parameter control module 14 for feeding back the optimized process parameters to the production control terminal of the lens hardening process, and executing the control of the lens hardening process parameters.

[0046] Furthermore, the hardness prediction module 12 is also used to perform the following method:

[0047] Collect historical hardening process test datasets, train the hardening process prediction model based on regression learning; input the multiple process parameters into the hardening process prediction model to predict lens hardness, and output the predicted lens hardness parameters; calculate the difference between the predicted lens hardness parameters and the predetermined hardness value to generate the predicted hardness deviation.

[0048] Furthermore, the adaptive optimization module 13 is also used to perform the following method:

[0049] Determine whether the predicted hardness deviation exceeds a preset deviation threshold; if so, analyze the influence weights of the predicted hardness deviation and the deviations of the multiple process parameters, and construct a variable optimization weight distribution; based on the variable optimization weight distribution, adaptively optimize the multiple process parameters to generate the optimized process parameters.

[0050] Furthermore, the adaptive optimization module 13 is also used to perform the following method:

[0051] Based on the optimized weight distribution of the variables and the predicted hardness deviation, the multiple process parameters are initialized and adjusted to generate a first set of adjustment parameters and a second set of adjustment parameters. The hardening process prediction model is then invoked to predict the hardness of the first set of adjustment parameters and the second set of adjustment parameters, generating a first hardness prediction result and a second hardness prediction result. The first hardness prediction result and the second hardness prediction result are used to determine the hardness optimization direction and the temporary optimal adjustment parameter. The temporary optimal adjustment parameter is then adjusted according to the hardness optimization direction, iterated multiple times, until the adjustment parameter that meets the convergence condition is obtained, generating the optimized process parameters.

[0052] Furthermore, the adaptive optimization module 13 is also used to perform the following method:

[0053] The differences between the first hardness prediction result, the second hardness prediction result and the predetermined hardness value are calculated respectively to generate a first difference and a second difference; if either the first difference or the second difference satisfies the preset deviation threshold, the optimized process parameters are directly generated.

[0054] Furthermore, the adaptive optimization module 13 is also used to perform the following method:

[0055] Based on historical hardening process test datasets, statistical analysis methods are used to evaluate the influence of each process parameter on hardness deviation and determine multiple influence weights of the multiple process parameters; the multiple influence weights are normalized so that the sum of the weights is 1, and the variable optimization weight distribution is generated.

[0056] Furthermore, the adaptive optimization module 13 is also used to perform the following method:

[0057] Compare the first hardness prediction result and the second hardness prediction result, take the adjustment parameter with lower hardness as the starting point and the adjustment parameter with higher hardness as the ending point, analyze the direction of change of each parameter from the starting point to the ending point, and generate the hardness optimization direction; compare the first hardness prediction result and the second hardness prediction result, and generate the temporary optimal adjustment parameter with the adjustment parameter with higher hardness.

[0058] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0059] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0060] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A data-driven adaptive optimization method for lens hardening processes, characterized in that, include: Real-time acquisition of multiple process parameters during the lens hardening process; Call the hardening process prediction model, predict the lens hardness based on the multiple process parameters, compare the predicted lens hardness parameters with the predetermined hardness values, and generate the predicted hardness deviation. The predicted hardness deviation is used to adaptively optimize the multiple process parameters to generate optimized process parameters. The optimized process parameters are fed back to the production control terminal of the lens hardening process to execute the adjustment of the lens hardening process parameters; Adaptive optimization of the multiple process parameters based on the predicted hardness deviation is performed to generate optimized process parameters, including: Determine whether the predicted hardness deviation exceeds a preset deviation threshold; If so, analyze the influence weights of the predicted hardness deviation and the deviations of the multiple process parameters, and construct a variable optimization weight distribution; Based on the optimized weight distribution of the variables, adaptive optimization is performed on the multiple process parameters to generate the optimized process parameters, including: Based on the optimized weight distribution of the variables and the predicted hardness deviation, the multiple process parameters are initialized and adjusted to generate a first set of adjustment parameters and a second set of adjustment parameters. The hardening process prediction model is invoked to predict the hardness of the first set of adjustment parameters and the second set of adjustment parameters, generating a first hardness prediction result and a second hardness prediction result. The first hardness prediction result and the second hardness prediction result are used to determine the hardness optimization direction and the temporary optimal adjustment parameters; The temporary optimal adjustment parameter is adjusted according to the hardness optimization direction, and iterated multiple times until the adjustment parameter that meets the convergence condition is obtained, thereby generating the optimized process parameter. The first hardness prediction result and the second hardness prediction result are used to determine the direction of hardness optimization and the temporary optimal adjustment parameters, including: By comparing the first hardness prediction result and the second hardness prediction result, taking the adjustment parameter with lower hardness as the starting point and the adjustment parameter with higher hardness as the ending point, the direction of change of each parameter from the starting point to the ending point is analyzed to generate the hardness optimization direction. The first hardness prediction result and the second hardness prediction result are compared, and the temporary optimal adjustment parameter is generated with the adjustment parameter that has the greater hardness.

2. The data-driven adaptive optimization method for lens hardening process as described in claim 1, characterized in that, The lens hardness is predicted by calling the hardening process prediction model and based on the multiple process parameters. The predicted lens hardness parameters are compared with the predetermined hardness values ​​to generate the predicted hardness deviation, including: Collect historical hardening process inspection datasets and train the hardening process prediction model based on regression learning; The multiple process parameters are input into the hardening process prediction model to predict the lens hardness, and the predicted lens hardness parameters are output. The difference between the predicted lens hardness parameter and the predetermined hardness value is calculated to generate the predicted hardness deviation.

3. The data-driven adaptive optimization method for lens hardening process as described in claim 1, characterized in that, After generating the first hardness prediction result and the second hardness prediction result, the following is also included: The differences between the first hardness prediction result, the second hardness prediction result, and the predetermined hardness value are calculated respectively to generate a first difference and a second difference; If either the first difference or the second difference satisfies the preset deviation threshold, the optimized process parameters are directly generated.

4. The data-driven adaptive optimization method for lens hardening process as described in claim 1, characterized in that, Analyze the influence weights of the predicted hardness deviation and the deviations of the multiple process parameters, and construct a variable optimization weight distribution, including: Based on historical hardening process test datasets, statistical analysis methods are used to evaluate the degree of influence of each process parameter on hardness deviation and determine the influence weights of the multiple process parameters. The multiple influence weights are normalized so that the sum of the weights is 1, thereby generating the variable optimized weight distribution.

5. A data-driven adaptive optimization system for lens hardening processes, characterized in that, The system is used to execute the data-driven adaptive optimization method for lens hardening process according to any one of claims 1-4, including: Data acquisition module: Real-time acquisition of multiple process parameters during the lens hardening process; Hardness prediction module: calls the hardening process prediction model, predicts the hardness of the lens based on the multiple process parameters, compares the predicted lens hardness parameters with the predetermined hardness value, and generates the predicted hardness deviation. Adaptive optimization module: Adaptively optimizes the multiple process parameters based on the predicted hardness deviation to generate optimized process parameters; Process parameter control module: Feeds back the optimized process parameters to the production control terminal of the lens hardening process and performs control of the lens hardening process parameters.

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