Lens production quality management system based on optical characteristics

Abstract

The present invention provides a lens production quality management system based on optical characteristics, which relates to the technical field of production quality management, and includes: a production node acquisition module for acquiring N lens production nodes; a state data acquisition module for acquiring N lens state data sets; an acquisition device connection module for connecting N quality data acquisition devices; an operation data acquisition module for acquiring N equipment operation data sets; a steady-state evaluation module for performing steady-state evaluation and acquiring N state stability indicators and N equipment stability indicators; a steady-state index output module for outputting N steady-state indexes; and a production quality management module for connecting a lens production quality management system to perform lens production quality management. The present invention solves the technical problem that the prior art is difficult to monitor and control the lens quality and equipment state changes in the production process in real time, resulting in quality fluctuations and low production efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of production quality management, and in particular to a lens production quality management system based on optical characteristics. Background Art

[0002] Optical lens production usually involves complex process flows and high precision requirements. The optical lenses at each production node need to maintain a high degree of precision and stability during the production process to ensure that the optical performance of the final product meets the design requirements. However, traditional production management systems often rely on experience and manual operations, making it difficult to monitor and control the lens quality and equipment status changes during the production process in real time, resulting in quality fluctuations and low production efficiency. In addition, the amount of data involved in optical lens production is huge, including multi-dimensional data. Traditional data acquisition systems are usually scattered and uncentralized, resulting in low data collection efficiency and difficult data management. Summary of the invention

[0003] This application provides a lens production quality management system based on optical properties, aiming to solve the technical problem that the existing technology is difficult to monitor and control the lens quality and equipment status changes in the production process in real time, resulting in quality fluctuations and low production efficiency.

[0004] The present application discloses a lens production quality management system based on optical characteristics, the system comprising: a production node acquisition module, the production node acquisition module is used to acquire N lens production nodes; a status data acquisition module, the status data acquisition module is used to acquire lens production data of the N lens production nodes, and acquire N lens status data sets, wherein each lens status data set corresponds to a data set of lens performance status under each production node; an acquisition device connection module, the acquisition device connection module is used to connect N quality data acquisition devices corresponding to the N lens production nodes; an operation data acquisition module, the operation data acquisition module is used to acquire the N quality data acquisition devices corresponding to the N quality data acquisition devices; N equipment operation data sets; a steady-state evaluation module, the steady-state evaluation module is used to construct a steady-state index evaluation model, and steady-state evaluation is performed on the N lens state data sets and the N equipment operation data sets according to the steady-state index evaluation model to obtain N state stability indicators and N equipment stability indicators; a steady-state index output module, the steady-state index output module is used to output N steady-state indices corresponding to the N lens production nodes according to the N state stability indicators and the N equipment stability indicators; a production quality management module, the production quality management module is used to connect to a lens production quality management system, and the lens production quality management system performs lens production quality management according to the N steady-state indices.

[0005] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0006] Through centralized data collection of N production nodes and equipment, the comprehensiveness and real-time data of lens status and equipment operation in the lens production process are ensured; the steady-state index evaluation model is introduced, combined with the lens status data set and the equipment operation data set, to achieve a multi-dimensional evaluation of lens status and equipment stability, improve the accuracy and real-time performance of the steady-state evaluation, so that potential quality problems can be quickly discovered and responded to during the production process; the intelligent management of lens production quality is realized according to the steady-state index, and based on real-time steady-state index data, it can automatically identify steady-state production nodes and non-steady-state production nodes, and adjust data collection and production strategies in a targeted manner to improve production efficiency and product quality. In summary, the system effectively solves the problems of complex data management, inaccurate steady-state evaluation, and slow quality management response in traditional lens production by integrating data collection, steady-state evaluation model, and intelligent management methods, improves production efficiency and product quality, and realizes intelligent lens production quality management.

[0007] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A schematic diagram of the structure of a lens production quality management system based on optical characteristics is provided for the embodiment of the present application;

[0009] Figure 2 A schematic diagram of a lens production quality management process in a lens production quality management system based on optical characteristics is provided for an embodiment of the present application.

[0010] Explanation of the reference numerals: production node acquisition module 10 , status data acquisition module 20 , collection device connection module 30 , operation data acquisition module 40 , steady-state evaluation module 50 , steady-state index output module 60 , production quality management module 70 . DETAILED DESCRIPTION

[0011] The embodiments of the present application provide a lens production quality management system based on optical characteristics, thereby solving the technical problem that it is difficult in the prior art to monitor and control the lens quality and equipment status changes during the production process in real time, resulting in quality fluctuations and low production efficiency.

[0012] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically described below in conjunction with the drawings in the specification.

[0013] like Figure 1As shown, the embodiment of the present application provides a lens production quality management system based on optical characteristics, the system comprising:

[0014] A production node acquisition module 10, wherein the production node acquisition module 10 is used to acquire N lens production nodes;

[0015] Lens production nodes refer to each link or process in lens production. These nodes include different machines, workstations or production lines. Determine the specific value of N, that is, the number of production nodes to be monitored, which depends on the complexity of the production process, the scale of production and the need for monitoring. Obtain the process flow chart of lens production, from raw material input to finished product output, identify each key step, and obtain the N lens production nodes.

[0016] A status data acquisition module 20, wherein the status data acquisition module 20 is used to collect lens production data of the N lens production nodes to obtain N lens status data sets, wherein each lens status data set corresponds to a data set of lens performance status at each production node;

[0017] The lens status dataset refers to the lens quality and status information recorded at each production node during the production process. The dataset includes but is not limited to changes in lens shape, such as curvature radius, center thickness, etc.; changes in lens surface, such as surface finish, number of defects, etc.; changes in lens size, such as diameter, thickness, etc.

[0018] Each production node is equipped with appropriate data acquisition equipment, which can monitor the key parameters of the lens production process in real time. For example, shape measuring instruments are used to measure the radius of curvature and thickness of the lens; surface inspection equipment is used to detect the finish and defects of the lens surface; size measurement tools are used to measure the diameter and thickness of the lens. During the production process, the data acquisition equipment continuously monitors the status data of the lens at each production node to form a complete lens status data set. It realizes accurate and real-time acquisition of lens status data at each production node, providing basic data support for subsequent quality management.

[0019] A collection device connection module 30, wherein the collection device connection module 30 is used to connect N quality data collection devices corresponding to the N lens production nodes;

[0020] An operation data acquisition module 40, the operation data acquisition module 40 is used to acquire N device operation data sets corresponding to the N quality data acquisition devices;

[0021] Each lens production node is equipped with corresponding quality data collection equipment, which is used to monitor the quality parameters of the lens production process in real time. The equipment includes shape measuring instruments, surface inspection equipment, and dimension measuring tools. The equipment is installed at the appropriate location on the production line to ensure that the equipment can accurately collect lens quality data.

[0022] The equipment operation data set includes the operation status and performance parameters of each quality data acquisition device during the working process. The operation data includes the equipment acquisition focal length, the vibration amplitude change of the equipment acquisition head, and the equipment acquisition ambient light change. During the lens production process, the operation status and performance parameters of N quality data acquisition devices are recorded in real time to generate N equipment operation data sets.

[0023] A steady-state evaluation module 50, wherein the steady-state evaluation module 50 is used to construct a steady-state index evaluation model, and to perform steady-state evaluation on the N lens state data sets and the N device operation data sets according to the steady-state index evaluation model to obtain N state stability indices and N device stability indices;

[0024] The collected lens state data set and equipment operation data set are input into the steady-state index evaluation model, and the steady-state index evaluation model is used to evaluate the input data. Specifically, the lens state data set is evaluated to calculate the state stability index of the lens at different production nodes. For example, the mean square error of lens shape, surface quality and size changes can be used as a state stability index; the equipment operation data set is evaluated to calculate the operation stability index of the equipment at different nodes, such as the mean square error of the equipment acquisition focal length change, the standard deviation of the vibration amplitude of the equipment acquisition head, etc. According to the evaluation results, the state stability index and equipment stability index of each production node are output. These indicators are used to judge the stability of the production process and guide production optimization.

[0025] Furthermore, the system further includes a state stability index output module to perform the following operation steps:

[0026] Performing steady-state evaluation on the N lens state data sets according to the steady-state index evaluation model, including lens shape change, lens surface change, and lens size change;

[0027] According to the lens shape change, the lens surface change and the lens size change, a state stability index is output.

[0028] Lens shape change refers to the change in the lens's geometric features such as radius of curvature and thickness during the production process. The lens shape data of each production node, including radius of curvature, thickness, etc., is extracted. Key features are extracted from the shape data, such as the mean and standard deviation of the radius of curvature, the mean and standard deviation of the thickness. The shape change of each node at different time points is calculated, and calculation methods such as mean square error and standard deviation are used to generate a lens shape change stability index.

[0029] Lens surface change refers to the change in surface features such as lens surface smoothness and number of defects. Lens surface data, including smoothness, number of defects, etc., are extracted. Key features are extracted from the surface data, such as the mean and standard deviation of the smoothness, the mean and standard deviation of the number of defects. The surface change of each node at different time points is calculated. The mean square error, standard deviation and other calculation methods are also used to generate a lens surface change stability index.

[0030] Lens size change refers to the change in lens diameter, thickness and other dimensional features. The lens size data, including diameter, thickness, etc., is extracted. Key features are extracted from the size data, such as the mean and standard deviation of the diameter, the mean and standard deviation of the thickness. The size change of each node at different time points is calculated. The mean square error, standard deviation and other calculation methods are also used to generate the lens size change stability index.

[0031] The lens shape change stability index, lens surface change stability index, and lens size change stability index are weighted and summed to form a comprehensive state stability index, providing a basis for subsequent quality management and optimization.

[0032] Furthermore, the system further includes an equipment stability index output module to perform the following operation steps:

[0033] Perform steady-state evaluation on the N device operation data sets according to the steady-state index evaluation model, including changes in device acquisition focal length, vibration amplitude of the device acquisition head, and changes in ambient light of the device acquisition;

[0034] The device stability index is output according to the change of the device acquisition focal length, the vibration amplitude of the device acquisition head and the change of the device acquisition environment light.

[0035] The change of equipment acquisition focal length refers to the change of equipment acquisition focal length during the production process. The equipment acquisition focal length data of each production node is extracted, including the focal length values ​​at different time points. Key features such as the mean and standard deviation of the focal length are extracted from the focal length data. The focal length change of each node at different time points is calculated. The mean square error, standard deviation and other calculation methods are also used to generate the stability index of equipment acquisition focal length change.

[0036] The vibration amplitude of the equipment acquisition head refers to the change in the vibration amplitude of the equipment acquisition head during the production process. The change in the equipment acquisition environment light refers to the change in the equipment acquisition environment light during the production process. The same method as mentioned above is used to obtain the stability index of the equipment acquisition head vibration amplitude change and the stability index of the equipment acquisition environment light change.

[0037] The weighted sum of the equipment acquisition focal length change stability index, the equipment acquisition head vibration amplitude change stability index, and the equipment acquisition ambient light change stability index are integrated into a comprehensive equipment stability index to provide a basis for subsequent quality management and optimization.

[0038] A steady-state index output module 60, the steady-state index output module 60 is used to output N steady-state indexes corresponding to the N lens production nodes according to the N state stability indicators and the N equipment stability indicators;

[0039] The production quality management module 70 is used to connect to the lens production quality management system, and the lens production quality management system performs lens production quality management according to the N steady-state indexes.

[0040] The steady-state index is a comprehensive indicator used to reflect the overall stability of the lens state and equipment operation of each production node during the production process. The steady-state index is calculated based on N state stability indicators and N equipment stability indicators. For example, the stability index is first standardized, and the weighted sum of the standardized N state stability indicators and N equipment stability indicators is performed. The inverse of the calculation result is calculated to obtain the N steady-state indices. By calculating the inverse, the larger the steady-state index is, the more stable the data of the node is.

[0041] The calculated steady-state index is transmitted to the lens production quality management system through the data interface. The quality management system evaluates and identifies the quality of each production node based on the acquired steady-state index. For example, it determines whether the steady-state index of any production node is greater than the preset steady-state threshold. Nodes greater than the threshold are determined to be steady-state production nodes, and nodes less than or equal to the threshold are determined to be non-steady-state production nodes. For steady-state production nodes, the existing production process and data collection frequency are maintained to ensure stable production. For non-steady-state production nodes, the production process and data collection timing are optimized to improve production stability.

[0042] Furthermore, the system also includes a retention collection module, which performs the following steps: Figure 2 The steps shown are:

[0043] Identify the N steady-state indexes to obtain steady-state production nodes and non-steady-state production nodes;

[0044] Among them, the steady-state index corresponding to the steady-state production node is greater than the preset steady-state index, and the steady-state index corresponding to the non-steady-state production node is less than or equal to the preset steady-state index;

[0045] The lens production quality management system retains and collects the data collection timing of the steady-state production nodes.

[0046] Define a preset steady-state index threshold value, which is used as the standard for judging steady-state production nodes and non-steady-state production nodes. For example, set the threshold value to 0.8, and compare the steady-state index of each production node with the preset steady-state index threshold value. If the steady-state index is greater than the preset steady-state index threshold value, the node is a steady-state production node. If the steady-state index is less than or equal to the preset steady-state index threshold value, the node is a non-steady-state production node. Generate a list of steady-state production nodes and a list of non-steady-state production nodes based on the judgment results.

[0047] The data collection timing includes the frequency and time interval of data collection. The data collection timing of the steady-state production nodes is retained, that is, the existing data collection timing is retained without adjustment to ensure the continuity and consistency of the data.

[0048] Furthermore, the system further comprises a quality data acquisition module to perform the following operation steps:

[0049] Obtaining the non-steady-state data collection time sequence corresponding to the non-steady-state production node;

[0050] Taking the preset steady-state index as the optimization target, optimizing the non-steady-state data acquisition timing until the output satisfies the updated data acquisition timing under the preset steady-state index;

[0051] Quality data is collected on the non-steady-state production node according to the updated data collection sequence.

[0052] Obtain the non-steady-state data collection timing of the non-steady-state production node, analyze the current data collection timing of the non-steady-state production node, and determine the key factors that cause the steady-state index to be lower than the preset steady-state index, such as equipment operating conditions, environmental impact, etc.

[0053] The preset steady-state index is used as the optimization target, and the non-steady-state data collection timing is adjusted so that the steady-state index reaches the preset steady-state index threshold. The adjustment strategy is set, such as increasing the data collection frequency, improving data quality, adjusting the data processing algorithm, etc. According to the optimization target and strategy, the non-steady-state data collection timing is adjusted and optimized until the optimization target is reached, that is, when the preset steady-state index is met, the updated data collection timing is output. According to the optimized data collection timing, the quality data of the non-steady-state production nodes is collected. By optimizing the data collection strategy and timing settings, the stability of the production nodes and the efficiency of data collection can be effectively improved, thereby supporting subsequent quality management and production optimization work.

[0054] Furthermore, the system further includes an optimization module to perform the following operation steps:

[0055] Establishing a time series-steady-state prediction model, wherein the time series-steady-state prediction model is obtained through training of time series samples, lens state sample sets distributed under the time series samples, device operation sample sets distributed under the time series samples, identification information identifying state stability index, identification information identifying device stability index, and identification information identifying steady-state index;

[0056] The time series-steady-state prediction model optimizes the non-steady-state data acquisition time series to obtain the predicted data acquisition time series under the preset steady-state index;

[0057] The predicted data collection timing is output as the updated data collection timing.

[0058] The time series samples include the time series data of the lens state sample set and the device operation sample set. The lens state sample set includes data such as the shape change, surface change, and size change of the lens. The device operation sample set includes data such as the focal length change of the device, the head vibration amplitude, and the ambient light change. The identification information of the state stability index is used to identify the state stability index under each time series sample, such as the steady-state index of the lens. The identification information of the device stability index is used to identify the device stability index under each time series sample. The identification information of the steady-state index is used to identify the steady-state index value under each time series sample.

[0059] The lens state sample set and the equipment operation sample set of the time series samples are sorted according to the time series, and the state stability index, equipment stability index and steady-state index value of each sample are determined. The time series data is feature extracted, such as statistical features, frequency domain features or time domain features, so that the model can understand and predict the change pattern. A model suitable for processing time series data, such as a recurrent neural network, a long short-term memory network, etc., is selected. The model is trained using the prepared time series sample set, and the time series data and corresponding identification information are input. The model predicts the future steady-state index by learning the patterns and change trends in the time series data and the relationship between the steady-state index. The prediction ability of the model is evaluated using a validation set or cross-validation method, such as calculating the error between the predicted steady-state index and the actual steady-state index, and adjusting the model's hyperparameters according to the evaluation results to improve the accuracy of the model. Finally, a time series-steady-state prediction model that meets the preset accuracy requirements is obtained.

[0060] Use the trained time series-steady-state prediction model to predict the current non-steady-state data collection time series. The model analyzes historical data patterns and current status to predict the data collection frequency and parameters that need to be adjusted in the future. The preset steady-state index is used as the optimization target to adjust the non-steady-state data collection time series so that the steady-state index reaches the preset steady-state index threshold. According to the results of the model prediction, an updated data collection time series is generated. The updated time series includes the adjusted data collection frequency, optimized data collection parameters, etc., to ensure that the steady-state index meets the preset requirements.

[0061] Furthermore, the system also includes a quality data continuous acquisition module to perform the following operation steps:

[0062] Setting a tolerance timing error threshold, wherein the tolerance timing error threshold includes an upper tolerance timing error and a lower tolerance timing error;

[0063] Processing the update data acquisition timing according to the tolerance timing error, and outputting an update data acquisition timing threshold;

[0064] The quality data of the non-steady-state production node is continuously collected within the update data collection timing threshold.

[0065] According to the requirements of the production environment and steady-state index, determine the appropriate tolerant timing error threshold to balance the real-time and stability requirements of data acquisition, including the upper tolerant timing error and the lower tolerant timing error. The upper tolerant timing error is the threshold that allows the data acquisition timing to fluctuate within a certain error range above the preset steady-state index, and the lower tolerant timing error is the threshold that allows the data acquisition timing to fluctuate within a certain error range below the preset steady-state index.

[0066] The predicted update data acquisition timing is compared with the set tolerance timing error threshold. If the fluctuation of the update timing is within the upper and lower tolerance timing error range, it is considered to meet the requirements of the steady-state index and can be directly applied. If the fluctuation of the update timing exceeds the tolerance timing error range, the excess part will be eliminated to ensure that the steady-state index is within an acceptable range. After processing, the update data acquisition timing threshold is output.

[0067] According to the processed update data collection timing threshold, continuous quality data collection is performed on the non-steady-state production nodes, so that the collected data meets the requirements of quality management and steady-state index, while maintaining the continuity and accuracy of data collection.

[0068] Furthermore, the system further includes a device operation data set acquisition module to perform the following operation steps:

[0069] determining whether each production node includes at least two devices for collecting lens quality data;

[0070] If at least two devices for lens quality data collection are included, an operation data set of each device is obtained, and the output is a device operation data set corresponding to each production node.

[0071] For each production node, count the number of lens quality data collection devices to determine whether the number of devices reaches or exceeds two. If the number of devices at a production node is less than two, appropriate measures need to be taken to increase the number of devices to meet the needs of quality data collection.

[0072] If the production node meets the equipment quantity requirement, for each production node, connect and obtain the operating data of all devices used for lens quality data collection, and associate the operating data set of each device with the production node to which it belongs, so as to ensure that each production node has complete equipment operating data.

[0073] In summary, the lens production quality management system based on optical characteristics provided by the embodiments of the present application has the following technical effects:

[0074] Through centralized data collection of N production nodes and equipment, the comprehensiveness and real-time data of lens status and equipment operation in the lens production process are ensured; the steady-state index evaluation model is introduced, combined with the lens status data set and the equipment operation data set, to achieve a multi-dimensional evaluation of lens status and equipment stability, improve the accuracy and real-time performance of the steady-state evaluation, so that potential quality problems can be quickly discovered and responded to during the production process; the intelligent management of lens production quality is realized according to the steady-state index, and based on real-time steady-state index data, it can automatically identify steady-state production nodes and non-steady-state production nodes, and adjust data collection and production strategies in a targeted manner to improve production efficiency and product quality. In summary, the system effectively solves the problems of complex data management, inaccurate steady-state evaluation, and slow quality management response in traditional lens production by integrating data collection, steady-state evaluation model, and intelligent management methods, improves production efficiency and product quality, and realizes intelligent lens production quality management.

[0075] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A lens production quality management system based on optical characteristics, characterized in that: The system comprises: A production node acquisition module, wherein the production node acquisition module is used to acquire N lens production nodes; A status data acquisition module, wherein the status data acquisition module is used to collect lens production data of the N lens production nodes to obtain N lens status data sets, wherein each lens status data set corresponds to a data set of lens performance status at each production node; A collection device connection module, the collection device connection module is used to connect N quality data collection devices corresponding to the N lens production nodes; An operation data acquisition module, the operation data acquisition module is used to acquire N device operation data sets corresponding to the N quality data acquisition devices; A steady-state evaluation module, wherein the steady-state evaluation module is used to construct a steady-state index evaluation model, and to perform steady-state evaluation on the N lens state data sets and the N device operation data sets according to the steady-state index evaluation model to obtain N state stability indices and N device stability indices; A steady-state index output module, the steady-state index output module is used to output N steady-state indexes corresponding to the N lens production nodes according to the N state stability indicators and the N equipment stability indicators; A production quality management module, the production quality management module is used to connect to a lens production quality management system, the lens production quality management system performs lens production quality management according to the N steady-state indexes; The system further includes a state stability index output module to perform the following operation steps: Performing steady-state evaluation on the N lens state data sets according to the steady-state index evaluation model, including lens shape change, lens surface change, and lens size change; Outputting a state stability index according to the lens shape change, the lens surface change and the lens size change; The system further includes an equipment stability index output module to perform the following operation steps: Perform steady-state evaluation on the N device operation data sets according to the steady-state index evaluation model, including changes in device acquisition focal length, vibration amplitude of the device acquisition head, and changes in ambient light of the device acquisition; The device stability index is output according to the change of the device acquisition focal length, the vibration amplitude of the device acquisition head and the change of the device acquisition environment light.

2. The system according to claim 1, characterized in that The system also includes a retention acquisition module to perform the following operation steps: Identify the N steady-state indexes to obtain steady-state production nodes and non-steady-state production nodes; Among them, the steady-state index corresponding to the steady-state production node is greater than the preset steady-state index, and the steady-state index corresponding to the non-steady-state production node is less than or equal to the preset steady-state index; The lens production quality management system retains and collects the data collection timing of the steady-state production nodes.

3. The system according to claim 2, characterized in that The system also includes a quality data acquisition module to perform the following operation steps: Obtaining the non-steady-state data collection time sequence corresponding to the non-steady-state production node; Taking the preset steady-state index as the optimization target, optimizing the non-steady-state data acquisition timing until the output satisfies the updated data acquisition timing under the preset steady-state index; Quality data is collected on the non-steady-state production node according to the updated data collection sequence.

4. The system according to claim 3, characterized in that The system also includes an optimization module to perform the following steps: Establishing a time series-steady-state prediction model, wherein the time series-steady-state prediction model is obtained through training of time series samples, lens state sample sets distributed under the time series samples, device operation sample sets distributed under the time series samples, identification information identifying state stability index, identification information identifying device stability index, and identification information identifying steady-state index; The time series-steady-state prediction model optimizes the non-steady-state data acquisition time series to obtain the predicted data acquisition time series under the preset steady-state index; The predicted data collection timing is output as the updated data collection timing.

5. The system according to claim 3, characterized in that The system also includes a quality data continuous acquisition module to perform the following operation steps: Setting a tolerance timing error threshold, wherein the tolerance timing error threshold includes an upper tolerance timing error and a lower tolerance timing error; Processing the update data acquisition timing according to the tolerance timing error, and outputting an update data acquisition timing threshold; The quality data of the non-steady-state production node is continuously collected within the update data collection timing threshold.

6. The system according to claim 1, wherein: The system also includes a device operation data set acquisition module to perform the following operation steps: determining whether each production node includes at least two devices for collecting lens quality data; If at least two devices for lens quality data collection are included, an operation data set of each device is obtained, and the output is a device operation data set corresponding to each production node.

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