Optical glass dropping information prediction method and device, electronic equipment and storage medium

Abstract

The invention provides an optical glass dropping information prediction method and device, electronic equipment and a storage medium, and relates to the technical field of optical glass dropping information prediction. According to the method, firstly, sample process data and performance data labels are obtained; secondly, based on at least one kind of associated data having influence on the performance of the optical glass drops, classifying the multiple pieces of sample process data to obtain at least one data classification set; then, for each data classification set, based on each piece of sample process data in the data classification set and the corresponding performance data label, training to form a target material dropping information prediction model; and finally, predicting the target optical glass drop description data by using the target drop information prediction model, and outputting target optical glass drop prediction data. On the basis of the content, the problem that in the prior art, the production cost of optical glass drops is high can be solved.

Description

Technical Field

[0001] The present application relates to the technical field of optical glass drop information prediction, and in particular to an optical glass drop information prediction method and device, electronic equipment and storage medium. Background Art

[0002] In the field of optoelectronic materials, small-sized lenses made of optical glass with special optical properties are widely used in optical instruments or mechanical systems. With the rapid development of industries such as smartphones, computers, new energy vehicles, and drones with imaging functions in recent years, the market demand for small-sized lenses has also increased sharply. Optical glass drips, as their preforms, can be used to obtain small-sized lenses with ideal optical properties through rapid precision molding. However, the inventors have found that in the current production method of optical glass drips, it is necessary to test the process at each stage through experiments, and adjust the process parameters by trial and error to achieve the drip size and shape requirements given by the user. In addition, the process adjustment involves parameters such as the temperature of each point of the drip tube, the nozzle type of the drip furnace, the nozzle length, the mold type, the cooling air temperature, intensity, time, and drip cycle. In this way, there is a problem of high production costs. Summary of the invention

[0003] In view of this, the purpose of the present application is to provide an optical glass drop material information prediction method and device, electronic equipment and storage medium, so as to improve the problem of high production cost of optical glass drop materials existing in the prior art.

[0004] To achieve the above purpose, this application adopts the following technical solutions: An optical glass drop material information prediction method, comprising: Acquire sample process data corresponding to the optical glass gob and performance data labels corresponding to the optical glass gob, wherein the sample process data is multiple, each of which is used to describe the production process of the corresponding optical glass gob, and the performance data labels is multiple, each of which is used to reflect the actual performance of the corresponding optical glass gob; Based on at least one associated data having an impact on the performance of the optical glass gob, a plurality of the sample process data are classified to obtain at least one data classification set corresponding to the plurality of the sample process data, wherein each of the data classification sets includes at least one sample process data; For each data classification set in the at least one data classification set, based on each sample process data in the data classification set and the performance data label corresponding to the sample process data, a target drip information prediction model corresponding to the data classification set is trained, wherein the target drip information prediction model belongs to a random forest model; The target optical glass drop description data to be predicted is obtained, and the target optical glass drop description data is predicted using the target drop information prediction model, and the target optical glass drop prediction data corresponding to the target optical glass drop description data is output.

[0005] In a preferred embodiment of the present application, in the above optical glass drop information prediction method, the step of obtaining the target optical glass drop description data to be predicted, predicting the target optical glass drop description data using the target drop information prediction model, and outputting the target optical glass drop prediction data corresponding to the target optical glass drop description data includes: Acquire target optical glass drop description data to be predicted, wherein the target optical glass drop description data belongs to performance data; Based on each of the target drop information prediction models, the target optical glass drop description data is predicted, and each target optical glass drop prediction data corresponding to the target optical glass drop description data is output; or Based on a target drop information prediction model that matches the target optical glass drop description data, the target optical glass drop description data is predicted, and target optical glass drop prediction data corresponding to the target optical glass drop description data is output, wherein the target optical glass drop prediction data belongs to process data.

[0006] In a preferred embodiment of the present application, in the above optical glass drop information prediction method, the step of predicting the target optical glass drop description data based on a target drop information prediction model matching the target optical glass drop description data, and outputting the target optical glass drop prediction data corresponding to the target optical glass drop description data includes: For each of the plurality of sample process data, including a plurality of process parameters, a plurality of candidate process parameter combinations are determined, wherein each candidate process parameter combination includes a plurality of process parameters, and the number of process parameters included in every two candidate process parameter combinations is the same; For each of the candidate process parameter combinations, based on a target drop material information prediction model that matches the target optical glass drop material description data, the candidate process parameter combination is predicted, and predicted performance data corresponding to the candidate process parameter combination is output; For each of the predicted performance data, respectively determine a first error corresponding to each performance parameter in the predicted performance data and a second error corresponding to the predicted performance data, wherein the first error is used to reflect the difference between the performance parameter in the predicted performance data and the performance parameter in the target optical glass drop description data, and the second error is used to reflect the deviation balance between the performance parameter in the predicted performance data and the performance parameter in the target optical glass drop description data; Among the predicted performance data, the predicted performance data whose first errors corresponding to the performance parameters are smaller than the predetermined first threshold and whose corresponding second errors are smaller than the predetermined second threshold are selected as candidate predicted performance data; For each candidate prediction performance data, the first errors of the performance parameters in the candidate prediction performance data are summed to obtain a summed first error, and the summed first error and the second error corresponding to the candidate prediction performance data are weighted and summed to obtain a target error corresponding to the candidate prediction performance data, wherein the weighted coefficient corresponding to the summed first error is greater than the weighted coefficient corresponding to the second error; Among the candidate prediction performance data, the candidate prediction performance data with the minimum corresponding target error is screened out, and the candidate process parameter combination corresponding to the candidate prediction performance data is used as the target optical glass drop prediction data corresponding to the target optical glass drop description data.

[0007] In a preferred embodiment of the present application, in the above optical glass drop material information prediction method, the step of determining a plurality of candidate process parameter combinations for each of the plurality of sample process data including a plurality of process parameters comprises: Based on each of the plurality of sample process data including a plurality of process parameters, respectively determining a parameter magnitude of each process parameter, and, based on the parameter magnitude of each process parameter, respectively determining a value range corresponding to each process parameter; For each process parameter, a random value processing is performed in the current stage within the value interval corresponding to the process parameter to obtain the current process parameter random value of the process parameter; Based on a target drop material information prediction model matched with the target optical glass drop material description data, predicting a parameter combination formed by a current random value of each process parameter, outputting current random prediction performance data, and determining a random first error between the current random prediction performance data and the target optical glass drop material description data; Determine whether a random first error between current random predicted performance data and the target optical glass drop description data is less than a random first error between the random predicted performance data and the target optical glass drop description data in a previous stage; When the random first error between the current random predicted performance data and the target optical glass drop description data is less than the random first error between the random predicted performance data in the previous stage and the target optical glass drop description data, a parameter combination formed by the current process parameter random value of each process parameter is used as a candidate process parameter combination; When the random first error between the current random predicted performance data and the target optical glass drop description data is not less than the random first error between the random predicted performance data in the previous stage and the target optical glass drop description data, the acceptance probability of the current random predicted performance data is determined based on the random first error, and based on the acceptance probability, it is determined whether to use the parameter combination formed by the current process parameter random values ​​of each process parameter as a candidate process parameter combination.

[0008] In a preferred embodiment of the present application, in the above optical glass drop information prediction method, the step of obtaining the target optical glass drop description data to be predicted, predicting the target optical glass drop description data using the target drop information prediction model, and outputting the target optical glass drop prediction data corresponding to the target optical glass drop description data includes: Acquire target optical glass drop description data to be predicted, wherein the target optical glass drop description data belongs to process data; Based on each of the target drop information prediction models, the target optical glass drop description data is predicted, and each target optical glass drop prediction data corresponding to the target optical glass drop description data is output; or Based on a target drop information prediction model that matches the target optical glass drop description data, the target optical glass drop description data is predicted, and target optical glass drop prediction data corresponding to the target optical glass drop description data is output, wherein the target optical glass drop prediction data belongs to performance data.

[0009] In a preferred embodiment of the present application, in the above optical glass drop information prediction method, the step of obtaining sample process data corresponding to the optical glass drop and performance data labels corresponding to the optical glass drop includes: Obtaining sample process data corresponding to the optical glass dripping material, wherein the sample process data includes at least one parameter of the nozzle length of the production equipment, the nozzle thickness of the production equipment, the mold curvature of the production equipment, the mold height of the production equipment, the mold thickness of the production equipment, the temperature at various locations of the production equipment, the cooling air temperature, the cooling air direction, the cooling air intensity, the cooling air time, and the dripping cycle; A performance data label corresponding to the optical glass gob is obtained, wherein the performance data label includes at least one parameter of the optical glass gob, including weight, center thickness, outer diameter, roundness, and upper and lower diameters.

[0010] In a preferred embodiment of the present application, in the optical glass gob information prediction method, the step of classifying the plurality of sample process data based on at least one associated data having an impact on the performance of the optical glass gob to obtain at least one data classification set corresponding to the plurality of sample process data includes: For each optical glass gob, a production location of the optical glass gob is obtained, and the production location of the optical glass gob and a cooling wind direction of the optical glass gob are used as at least one associated data having an impact on the performance of the optical glass gob; Based on at least one associated data having an impact on the performance of the optical glass gob, a plurality of sample process data corresponding to the plurality of optical glass gobs are classified to obtain at least one data classification set corresponding to the plurality of sample process data.

[0011] The present application also provides an optical glass drop information prediction device, comprising: A sample data acquisition module is used to acquire sample process data corresponding to the optical glass gob and performance data labels corresponding to the optical glass gob, wherein the sample process data is multiple, each of which is used to describe the production process of the corresponding optical glass gob, and the performance data labels are multiple, each of which is used to reflect the actual performance of the corresponding optical glass gob; A sample data classification module, configured to classify a plurality of the sample process data based on at least one associated data having an impact on the performance of the optical glass gob, and obtain at least one data classification set corresponding to the plurality of the sample process data, wherein each of the data classification sets includes at least one sample process data; A prediction model training module, for training a target drip information prediction model corresponding to each data classification set in the at least one data classification set based on each sample process data in the data classification set and a performance data label corresponding to the sample process data, wherein the target drip information prediction model belongs to a random forest model; The drop information prediction module is used to obtain the target optical glass drop description data to be predicted, and use the target drop information prediction model to predict the target optical glass drop description data, and output the target optical glass drop prediction data corresponding to the target optical glass drop description data.

[0012] Based on the above, the present application also provides an electronic device, including: Memory for storing computer programs; The processor connected to the memory is used to execute the computer program stored in the memory to implement the above-mentioned optical glass drop material information prediction method.

[0013] On the basis of the above, the present application further provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is run, each step of the above-mentioned optical glass drop material information prediction method is executed.

[0014] The optical glass drip information prediction method and device, electronic device and storage medium provided by the present application first obtain sample process data and performance data labels; secondly, based on at least one associated data that has an impact on the performance of the optical glass drip, multiple sample process data are classified to obtain at least one data classification set; then, for each data classification set, based on each sample process data and the corresponding performance data label in the data classification set, a target drip information prediction model is trained to form; finally, the target optical glass drip description data is predicted using the target drip information prediction model, and the target optical glass drip prediction data is output. Based on the above content, after the target drip information prediction model is trained to form, the target drip information prediction model formed by training can be used to directly perform corresponding predictions. Compared with the technical means in the prior art that require experimental trial and error, the solution of the present application can reduce the cost caused by experimental trial and error, and therefore, the problem of high production cost of optical glass drips existing in the prior art can be improved. In addition, efficiency can be improved, and since the target drip information prediction model is trained by classification, the reliability of the target drip information prediction model can be higher, ensuring the reliability of the prediction data. In this way, the problem of product mismatch caused by poor reliability of the prediction data can be further improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings.

[0016] Figure 1 A structural block diagram of an electronic device provided in an embodiment of the present application.

[0017] Figure 2 A schematic flow chart of a method for predicting optical glass drop information provided in an embodiment of the present application.

[0018] Figure 3 A block diagram of an optical glass drop information prediction device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0020] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0021] like Figure 1 As shown, an embodiment of the present application provides an electronic device, wherein the electronic device may include a memory, a processor, and an optical glass drop information prediction device.

[0022] In detail, the memory and the processor are electrically connected directly or indirectly to achieve data transmission or interaction. For example, the memory and the processor can be electrically connected through one or more communication buses or signal lines. The optical glass drop information prediction device includes at least one software function module stored in the memory in the form of software or firmware. The processor is used to execute an executable computer program stored in the memory, for example, the software function module and computer program included in the optical glass drop information prediction device, so as to implement the optical glass drop information prediction method provided in the embodiment of the present application.

[0023] Optionally, the memory may be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable read-only memory (EEPROM), etc.

[0024] Furthermore, the processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), a system on chip (SoC), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0025] Understandably, Figure 1 The structure shown is for illustration only, and the electronic device may also include Figure 1 More or fewer components as shown, or with Figure 1 The different configurations shown, for example, may also include a communication unit for exchanging information with other devices.

[0026] Combination Figure 2 The embodiment of the present application also provides an optical glass drop information prediction method applicable to the above electronic device, wherein the method steps defined in the process related to the optical glass drop information prediction method can be implemented by the electronic device.

[0027] The following will Figure 2 The specific process shown is explained in detail.

[0028] Step S110, obtaining sample process data corresponding to the optical glass gob and performance data labels corresponding to the optical glass gob.

[0029] In an embodiment of the present application, the electronic device can obtain sample process data corresponding to the optical glass drop and a performance data label corresponding to the optical glass drop. There are multiple sample process data, each of which is used to describe the production process of the corresponding optical glass drop, and there are multiple performance data labels, each of which is used to reflect the actual performance of the corresponding optical glass drop. There is a one-to-one correspondence between the multiple performance data labels and the multiple sample process data, that is, one optical glass drop has one sample process data and one performance data label. For example, for optical glass drop A, its production process can be reflected by sample process data 1, and its actual performance can be reflected by performance data label 1. For optical glass drop B, its production process can be reflected by sample process data 2, and its actual performance can be reflected by performance data label 2.

[0030] Step S120: classify the plurality of sample process data based on at least one associated data that has an impact on the performance of the optical glass gob, and obtain at least one data classification set corresponding to the plurality of sample process data.

[0031] In an embodiment of the present application, the electronic device can classify a plurality of the sample process data based on at least one associated data that has an impact on the performance of the optical glass drop material, and obtain at least one data classification set corresponding to the plurality of the sample process data, so that the sample process data (or optical glass drop material) that are similar in the dimension of the associated data can be classified into one set, wherein the associated data can be the sample process data itself, or other data other than the sample process data, and can also include a part of the sample process data. Each of the data classification sets includes at least one sample process data.

[0032] Step S130, for each data classification set in the at least one data classification set, based on each sample process data in the data classification set and the performance data label corresponding to the sample process data, training a target drip information prediction model corresponding to the data classification set.

[0033] In an embodiment of the present application, the electronic device can train a target drip information prediction model corresponding to each data classification set in the at least one data classification set based on each sample process data in the data classification set and the performance data label corresponding to the sample process data. The target drip information prediction model belongs to a random forest model. Based on this, since a target drip information prediction model is formed based on the training of sample process data in the same set, that is, the basis for training has a high similarity in the dimension to which the associated data belongs, the interference degree of the model training caused by the difference in the associated data can be reduced, thereby protecting the model's fitting performance better and making reliable predictions.

[0034] Step S140, obtaining target optical glass drop description data to be predicted, and using the target drop information prediction model to predict the target optical glass drop description data, and outputting target optical glass drop prediction data corresponding to the target optical glass drop description data.

[0035] In an embodiment of the present application, after training and forming a target drop information prediction model, the electronic device can obtain the target optical glass drop description data to be predicted, and use the target drop information prediction model to predict the target optical glass drop description data, and output the target optical glass drop prediction data corresponding to the target optical glass drop description data. It can be understood that the prediction can be based on all target drop information prediction models, or it can be based on a determined matching target drop information prediction model. Among them, the matching target drop information prediction model refers to a match on the dimension to which the associated data belongs (such as high similarity).

[0036] Based on the above content, after training to form the target drip information prediction model, the target drip information prediction model formed by training can be used to directly make corresponding predictions. Compared with the technical means in the prior art that require experimental trial and error, the solution of the present application can reduce the cost caused by experimental trial and error, and therefore, can improve the problem of high production cost of optical glass drips in the prior art. In addition, efficiency can also be improved, and since the target drip information prediction model is trained by classification, the reliability of the target drip information prediction model can be higher, ensuring the reliability of the prediction data, so that the problem of product mismatch caused by poor reliability of the prediction data can be further improved, thereby avoiding product waste and other problems (it can also reduce costs).

[0037] First, it should be noted that for step S110, the specific method of obtaining the sample process data and performance data labels is not limited and can be selected according to actual needs.

[0038] For example, in an alternative implementation, in order to ensure that a reliable target drip information prediction model can be trained based on the acquired sample process data and performance data labels, the above step S110 may further include the following contents: First, sample process data corresponding to the optical glass dripping material can be obtained, wherein the sample process data includes at least one parameter of the nozzle length of the production equipment, the nozzle thickness of the production equipment, the mold curvature of the production equipment, the mold height of the production equipment, the mold thickness of the production equipment, the temperature at various locations of the production equipment, the cooling air temperature, the cooling air direction, the cooling air intensity, the cooling air time and the dripping cycle. In other embodiments, other parameters may also be included; Secondly, the performance data label corresponding to the optical glass drop can be obtained, wherein the performance data label includes at least one parameter of the weight, middle thickness, outer diameter, roundness and upper and lower diameters of the optical glass drop. In other embodiments, other parameters may also be included.

[0039] It should be noted that after obtaining sample process data corresponding to multiple optical glass drop materials, the sample process data can also be processed accordingly. For example, missing value check and KNN nearest neighbor filling, 3σ (3 times the standard deviation) principle to eliminate outliers, data standardization to reduce the dimensional influence between features, etc. can be performed.

[0040] Secondly, it should be noted that for step S120, the specific method of classifying the plurality of sample process data based on at least one associated data having an impact on the performance of the optical glass gob is not limited and can be selected accordingly according to actual needs.

[0041] For example, in an alternative implementation, in order to eliminate the system error caused by the change of different workstations and cooling air angles and ensure the fitting accuracy of the trained model, the above step S120 may further include the following contents: First, for each optical glass gob, the production location of the optical glass gob can be obtained, and the production location of the optical glass gob and the cooling wind direction of the optical glass gob are used as at least one associated data that has an impact on the performance of the optical glass gob; that is, there are two types of associated data, namely, the production location and the cooling wind direction; Secondly, based on at least one associated data that has an impact on the performance of the optical glass drop, multiple sample process data corresponding to the multiple optical glass drop are classified to obtain at least one data classification set corresponding to the multiple sample process data; exemplarily, classification can be performed based on the KNN algorithm, wherein the K value in the classification process can be 2-4; based on this, the sample process data in the same data classification set can be similar in the dimensions of production position and cooling air direction, so that the corresponding target drop information prediction model can eliminate corresponding interference.

[0042] Thirdly, it should be noted that for step S130, the specific method of training the target drip information prediction model corresponding to the data classification set based on each sample process data and the performance data label corresponding to the sample process data is not limited and can be selected according to actual needs.

[0043] For example, in an alternative implementation, in order to make the target drip information prediction model formed by training have high reliability, that is, to be able to perform relatively reliable drip information prediction, during the training process, the data classification set can be divided into a training set and a test set according to 80%-20%, and the set hyperparameters include the number of random forest decision trees, the maximum number of features, the maximum depth, the minimum number of sample splits, and the minimum number of sample leaves. Among them, the hyperparameter optimization can use the random grid search algorithm Random Grid Search, and the evaluation indicator selects the mean square error MSE to obtain the optimal parameters to train the random forest model and use the test set for prediction. In addition, the performance of the model can be evaluated by the evaluation indicators R2 (coefficient of determination) and RMSE. Specifically, the RMSE error of weight, middle thickness, outer diameter, roundness, etc. does not exceed 3% of the mean, and the upper and lower diameters are determined by product requirements.

[0044] Fourthly, it should be noted that for step S140, the specific method of using the target drop information prediction model to predict the target optical glass drop description data is not limited and can be selected according to actual needs.

[0045] For example, in an alternative implementation, when performance data prediction is required based on process data, the above step S140 may further include the following contents: First, target optical glass drop description data to be predicted may be obtained, wherein the target optical glass drop description data belongs to process data, such as nozzle length of production equipment, nozzle thickness of production equipment, mold curvature of production equipment, mold height of production equipment, mold thickness of production equipment, temperature at various locations of production equipment, cooling air temperature, cooling air direction, cooling air intensity, cooling air time, and drop cycle, etc.; Secondly, the target optical glass drop description data can be predicted based on each of the target drop information prediction models, and each target optical glass drop prediction data corresponding to the target optical glass drop description data can be output; or, the target optical glass drop description data can be predicted based on a target drop information prediction model that matches the target optical glass drop description data (such as the corresponding production location and cooling air direction are similar or consistent), and the target optical glass drop prediction data corresponding to the target optical glass drop description data can be output, wherein the target optical glass drop prediction data belongs to performance data, such as the weight, middle thickness, outer diameter, roundness, upper and lower diameters of the optical glass drop.

[0046] For another example, in another alternative implementation, when it is necessary to predict process data based on performance data, the above-mentioned step S140 may further include step S141, step S142 and step S143, and the specific content of each step is described as follows.

[0047] Step S141, obtaining target optical glass drop description data to be predicted.

[0048] In the embodiment of the present application, the description data of the target optical glass drop to be predicted can be obtained, wherein the description data of the target optical glass drop is performance data, such as the weight, middle thickness, outer diameter, roundness, upper and lower diameters of the optical glass drop.

[0049] Step S142, predicting the target optical glass drop description data based on each of the target drop information prediction models, and outputting each target optical glass drop prediction data corresponding to the target optical glass drop description data.

[0050] In the embodiment of the present application, after obtaining the target optical glass drop description data, the target optical glass drop description data can be predicted based on each of the target drop information prediction models, and each target optical glass drop prediction data corresponding to the target optical glass drop description data can be output. The target optical glass drop prediction data belongs to process data, such as the nozzle length of the production equipment, the nozzle thickness of the production equipment, the mold curvature of the production equipment, the mold height of the production equipment, the mold thickness of the production equipment, the temperature of various parts of the production equipment, the cooling air temperature, the cooling air direction, the cooling air intensity, the cooling air time and the drop cycle, etc. In other words, one target drop information prediction model outputs one target optical glass drop prediction data, and when there are multiple target drop information prediction models, multiple target optical glass drop prediction data can be output.

[0051] Step S143, predicting the target optical glass drop description data based on a target drop information prediction model that matches the target optical glass drop description data, and outputting target optical glass drop prediction data corresponding to the target optical glass drop description data.

[0052] In the embodiment of the present application, after the target optical glass drop description data is obtained, the target optical glass drop description data can be predicted based on a target drop information prediction model that matches the target optical glass drop description data (such as the corresponding production location and cooling air direction are similar or consistent), and the target optical glass drop prediction data corresponding to the target optical glass drop description data is output. The target optical glass drop prediction data belongs to process data.

[0053] It can be understood that in the above-mentioned step S142 and step S143, the prediction method can be consistent, and the specific prediction method is not limited and can be selected according to actual needs.

[0054] For example, in an alternative embodiment, in order to reliably predict the target optical glass drop prediction data (i.e., reliably predict the process data based on the performance data), the above-mentioned step S143 may further include step S143a, step S143b, step S143c, step S143d, step S143e and step S143f, and the specific content of each step is described as follows.

[0055] Step S143a: for each of the plurality of sample process data including a plurality of process parameters, a plurality of candidate process parameter combinations are determined.

[0056] In the embodiment of the present application, multiple candidate process parameter combinations can be determined for each of the multiple sample process data including multiple process parameters. Each candidate process parameter combination includes multiple process parameters, and the number of process parameters included between each two candidate process parameter combinations is the same, such as the nozzle length of the production equipment, the nozzle thickness of the production equipment, the mold curvature of the production equipment, the mold height of the production equipment, the mold thickness of the production equipment, the temperature at various locations of the production equipment, the cooling air temperature, the cooling air direction, the cooling air intensity, the cooling air time, and the dripping cycle, except that the specific values ​​of at least some of the process parameters may be different.

[0057] Step S143b, for each of the candidate process parameter combinations, based on a target drop information prediction model that matches the target optical glass drop description data, predict the candidate process parameter combination and output predicted performance data corresponding to the candidate process parameter combination.

[0058] In an embodiment of the present application, after determining a plurality of candidate process parameter combinations, for each of the candidate process parameter combinations, a target drop information prediction model that matches the target optical glass drop description data can be used to predict the candidate process parameter combination and output predicted performance data corresponding to the candidate process parameter combination, i.e., predicting performance data based on process data.

[0059] Step S143c: for each predicted performance data, respectively determine a first error corresponding to each performance parameter in the predicted performance data and a second error corresponding to the predicted performance data.

[0060] In an embodiment of the present application, after outputting the predicted performance data, the first error corresponding to each performance parameter in the predicted performance data and the second error corresponding to the predicted performance data can be determined for each of the predicted performance data. The first error is used to reflect the difference between the performance parameters in the predicted performance data and the performance parameters in the target optical glass drop description data (such as the difference between the weight in the predicted performance data and the weight in the target optical glass drop description data, the difference between the middle thickness in the predicted performance data and the middle thickness in the target optical glass drop description data, etc.), and the second error is used to reflect the deviation balance between the performance parameters in the predicted performance data and the performance parameters in the target optical glass drop description data (such as first determining the difference between the weight in the predicted performance data and the weight in the target optical glass drop description data, the difference between the middle thickness in the predicted performance data and the middle thickness in the target optical glass drop description data, and performing variance calculation on the differences corresponding to each performance parameter to obtain the second error).

[0061] Step S143d, among the predicted performance data, select the predicted performance data whose first errors corresponding to the performance parameters are smaller than the predetermined first threshold and whose corresponding second errors are smaller than the predetermined second threshold as candidate predicted performance data.

[0062] In an embodiment of the present application, after determining the first error and the second error, the predictive performance data in which the first errors corresponding to the various performance parameters are less than a predetermined first threshold value (such as the percentage of the first error is less than 5%) and the corresponding second errors are less than a predetermined second threshold value (such as the percentage of the second error is less than 10%) can be screened out as candidate predictive performance data, that is, some predictive performance data with relatively small errors are first screened out.

[0063] Step S143e, for each of the candidate prediction performance data, the first errors of the performance parameters in the candidate prediction performance data are summed to obtain the summed first error, and the summed first error and the second error corresponding to the candidate prediction performance data are weightedly summed to obtain the target error corresponding to the candidate prediction performance data.

[0064] In an embodiment of the present application, after the candidate prediction performance data are screened, the first errors of the performance parameters in the candidate prediction performance data can be summed for each candidate prediction performance data to obtain the summed first error, and the summed first error and the second error corresponding to the candidate prediction performance data are weighted and summed to obtain the target error corresponding to the candidate prediction performance data. The weighted coefficient corresponding to the summed first error is greater than (can be much greater than) the weighted coefficient corresponding to the second error, that is, the target error can focus more on the first error, that is, focus on the difference with the required performance.

[0065] Step S143f, among the candidate prediction performance data, select the candidate prediction performance data whose corresponding target error has the minimum value, and combine the candidate process parameters corresponding to the candidate prediction performance data as the target optical glass drop prediction data corresponding to the target optical glass drop description data.

[0066] In an embodiment of the present application, after obtaining the target error, the candidate prediction performance data whose corresponding target error has the minimum value can be screened out from each of the candidate prediction performance data, and the candidate process parameters corresponding to the candidate prediction performance data can be combined as the target optical glass drop prediction data corresponding to the target optical glass drop description data.

[0067] It is understandable that, in the above step S143a, the specific method of determining multiple candidate process parameter combinations is not limited and can be selected according to actual needs. For example, in an alternative embodiment, in order to ensure the reliability of the determined candidate process parameter combination, the above step S143a can further include the following contents: First, based on each of the plurality of sample process data including a plurality of process parameters, the parameter magnitude of each process parameter (i.e., the magnitude of the parameter, such as 1, 10, 100, etc.) can be determined respectively, and, based on the parameter magnitude of each process parameter, the value interval corresponding to each process parameter (e.g., [0.8 ~1.2 ],in, Indicates the parameter magnitude of a process parameter); Secondly, for each process parameter, a random value processing can be performed in the value range corresponding to the process parameter at the current stage to obtain the current random value of the process parameter. For example, 500 values ​​can be randomly selected in the value range; Then, based on a target drop material information prediction model that matches the target optical glass drop material description data, a parameter combination (one combination for one stage) formed by the current random value of the process parameter of each process parameter can be predicted, and the current random predicted performance data can be output, and a random first error between the current random predicted performance data and the target optical glass drop material description data can be determined (i.e., the difference between the performance parameters in the random predicted performance data and the performance parameters in the target optical glass drop material description data, i.e., the prediction error; it should be supplemented that the "random" in the "random first error" is only used to distinguish it from the first error in the previous text, and refers to the error corresponding to the random predicted performance data, and has no other limiting effect); Afterwards, it can be determined whether the random first error between the current random prediction performance data and the target optical glass drop description data is smaller than the random first error between the random prediction performance data and the target optical glass drop description data in the previous stage, that is, it can be determined whether the current random first error is smaller than the random first error in the previous stage, that is, whether the random first error is decreasing; Further, when the random first error between the current random predicted performance data and the target optical glass drop description data is less than the random first error between the random predicted performance data in the previous stage and the target optical glass drop description data, a parameter combination formed by the current process parameter random value of each process parameter can be used as a candidate process parameter combination; Finally, when the random first error between the current random predicted performance data and the target optical glass drop description data is not less than the random first error between the random predicted performance data in the previous stage and the target optical glass drop description data, the acceptance probability of the current random predicted performance data can be determined based on the random first error, and based on the acceptance probability, it can be determined whether to use the parameter combination formed by the current process parameter random values ​​of each process parameter as a candidate process parameter combination. In this way, the determination of one stage can be completed, and then the next stage can be entered until the candidate process parameter combinations that meet the requirements are determined, such as 1000; wherein, the acceptance probability , Represents the random first error, k is an adjustment parameter that can be selected according to actual needs. () is an exponential operation related to the natural logarithm base e.

[0068] Combination Figure 3The embodiment of the present application also provides an optical glass drop information prediction device applicable to the above electronic device. The optical glass drop information prediction device includes a sample data acquisition module, a sample data classification module, a prediction model training module and a drop information prediction module.

[0069] In detail, the sample data acquisition module can be used to acquire sample process data corresponding to the optical glass drop and performance data labels corresponding to the optical glass drop, wherein the sample process data is multiple, each of which is used to describe the production process of the corresponding optical glass drop, and the performance data labels are multiple, each of which is used to reflect the actual performance of the corresponding optical glass drop. In the embodiment of the present application, the sample data acquisition module can be used to execute Figure 2 As shown in step S110, the relevant contents of the sample data acquisition module can refer to the above description of step S110.

[0070] In detail, the sample data classification module can be used to classify the plurality of sample process data based on at least one associated data that has an impact on the performance of the optical glass gob, and obtain at least one data classification set corresponding to the plurality of sample process data, wherein each of the data classification sets includes at least one sample process data. In the embodiment of the present application, the sample data classification module can be used to perform Figure 2 As shown in step S120, the relevant contents of the sample data classification module can refer to the above description of step S120.

[0071] In detail, the prediction model training module can be used to train a target drip information prediction model corresponding to each data classification set in the at least one data classification set based on each sample process data in the data classification set and the performance data label corresponding to the sample process data, wherein the target drip information prediction model belongs to a random forest model. In the embodiment of the present application, the prediction model training module can be used to perform Figure 2 As shown in step S130, for the relevant content of the prediction model training module, reference can be made to the above description of step S130.

[0072] In detail, the drop information prediction module can be used to obtain the target optical glass drop description data to be predicted, and use the target drop information prediction model to predict the target optical glass drop description data, and output the target optical glass drop prediction data corresponding to the target optical glass drop description data. In the embodiment of the present application, the drop information prediction module can be used to perform Figure 2As shown in step S140, the relevant contents of the drip information prediction module can refer to the above description of step S140.

[0073] In the embodiment of the present application, corresponding to the optical glass drop material information prediction method applied to the electronic device, a computer-readable storage medium is also provided, in which a computer program is stored, and when the computer program is run, each step of the optical glass drop material information prediction method is executed. Among them, each step executed when the aforementioned computer program is run will not be described one by one here, and reference can be made to the explanation of the optical glass drop material information prediction method in the previous text.

[0074] In summary, the optical glass drip information prediction method and device, electronic device and storage medium provided by the present application first obtain sample process data and performance data labels; secondly, based on at least one associated data that has an impact on the performance of the optical glass drip, multiple sample process data are classified to obtain at least one data classification set; then, for each data classification set, based on each sample process data and the corresponding performance data label in the data classification set, a target drip information prediction model is trained to form; finally, the target optical glass drip description data is predicted using the target drip information prediction model, and the target optical glass drip prediction data is output. Based on the above content, after the target drip information prediction model is trained to form, the target drip information prediction model formed by training can be used to directly perform corresponding predictions. Compared with the technical means in the prior art that require experimental trial and error, the solution of the present application can reduce the cost caused by experimental trial and error, and therefore, the problem of high production cost of optical glass drips existing in the prior art can be improved. In addition, efficiency can be improved. Moreover, since the target drip information prediction model is trained by classification, the reliability of the target drip information prediction model can be higher, thereby ensuring the reliability of the prediction data. In this way, the problem of product mismatch caused by poor reliability of the prediction data can be further improved, thereby avoiding product waste and other problems (it can also reduce costs).

[0075] In several embodiments provided in the embodiments of the present application, it should be understood that the disclosed device and method can also be implemented in other ways. The device and method embodiments described above are merely schematic, for example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the device, method and computer program product according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0076] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0077] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, electronic device, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code. It should be noted that in this article, the term "include", "include" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements inherent to such process, method, article or device. Without more constraints, an element defined by the phrase "comprising a..." does not exclude the existence of other identical elements in the process, method, article or apparatus comprising the element.

[0078] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for predicting optical glass drop information, characterized in that: include: Acquire sample process data corresponding to the optical glass gob and performance data labels corresponding to the optical glass gob, wherein the sample process data is multiple, each of which is used to describe the production process of the corresponding optical glass gob, and the performance data labels is multiple, each of which is used to reflect the actual performance of the corresponding optical glass gob; Based on at least one associated data having an impact on the performance of the optical glass gob, a plurality of the sample process data are classified to obtain at least one data classification set corresponding to the plurality of the sample process data, wherein each of the data classification sets includes at least one sample process data; For each data classification set in the at least one data classification set, based on each sample process data in the data classification set and the performance data label corresponding to the sample process data, a target drip information prediction model corresponding to the data classification set is trained, wherein the target drip information prediction model belongs to a random forest model; The target optical glass drop description data to be predicted is obtained, and the target optical glass drop description data is predicted using the target drop information prediction model, and the target optical glass drop prediction data corresponding to the target optical glass drop description data is output.

2. The optical glass drop information prediction method according to claim 1, characterized in that: The step of obtaining the target optical glass drop description data to be predicted, predicting the target optical glass drop description data using the target drop information prediction model, and outputting the target optical glass drop prediction data corresponding to the target optical glass drop description data comprises: Acquire target optical glass drop description data to be predicted, wherein the target optical glass drop description data belongs to performance data; Based on each of the target drop information prediction models, the target optical glass drop description data is predicted, and each target optical glass drop prediction data corresponding to the target optical glass drop description data is output; or Based on a target drop information prediction model that matches the target optical glass drop description data, the target optical glass drop description data is predicted, and target optical glass drop prediction data corresponding to the target optical glass drop description data is output, wherein the target optical glass drop prediction data belongs to process data.

3. The optical glass drop information prediction method according to claim 2, characterized in that: The step of predicting the target optical glass drop description data based on a target drop information prediction model that matches the target optical glass drop description data and outputting target optical glass drop prediction data corresponding to the target optical glass drop description data comprises: For each of the plurality of sample process data, including a plurality of process parameters, a plurality of candidate process parameter combinations are determined, wherein each candidate process parameter combination includes a plurality of process parameters, and the number of process parameters included in every two candidate process parameter combinations is the same; For each of the candidate process parameter combinations, based on a target drop material information prediction model that matches the target optical glass drop material description data, the candidate process parameter combination is predicted, and predicted performance data corresponding to the candidate process parameter combination is output; For each of the predicted performance data, respectively determine a first error corresponding to each performance parameter in the predicted performance data and a second error corresponding to the predicted performance data, wherein the first error is used to reflect the difference between the performance parameter in the predicted performance data and the performance parameter in the target optical glass drop description data, and the second error is used to reflect the deviation balance between the performance parameter in the predicted performance data and the performance parameter in the target optical glass drop description data; Among the predicted performance data, the predicted performance data whose first errors corresponding to the performance parameters are smaller than the predetermined first threshold and whose corresponding second errors are smaller than the predetermined second threshold are selected as candidate predicted performance data; For each candidate prediction performance data, the first errors of the performance parameters in the candidate prediction performance data are summed to obtain a summed first error, and the summed first error and the second error corresponding to the candidate prediction performance data are weighted and summed to obtain a target error corresponding to the candidate prediction performance data, wherein the weighted coefficient corresponding to the summed first error is greater than the weighted coefficient corresponding to the second error; Among the candidate prediction performance data, the candidate prediction performance data with the minimum corresponding target error is screened out, and the candidate process parameter combination corresponding to the candidate prediction performance data is used as the target optical glass drop prediction data corresponding to the target optical glass drop description data.

4. The optical glass drop information prediction method according to claim 3, characterized in that: The step of determining a plurality of candidate process parameter combinations for each of the plurality of sample process data including a plurality of process parameters comprises: Based on each of the plurality of sample process data including a plurality of process parameters, respectively determining a parameter magnitude of each process parameter, and, based on the parameter magnitude of each process parameter, respectively determining a value range corresponding to each process parameter; For each process parameter, a random value processing is performed in the current stage within the value interval corresponding to the process parameter to obtain the current process parameter random value of the process parameter; Based on a target drop material information prediction model matched with the target optical glass drop material description data, predicting a parameter combination formed by a current random value of each process parameter, outputting current random prediction performance data, and determining a random first error between the current random prediction performance data and the target optical glass drop material description data; Determine whether a random first error between current random predicted performance data and the target optical glass drop description data is less than a random first error between the random predicted performance data and the target optical glass drop description data in a previous stage; When the random first error between the current random predicted performance data and the target optical glass drop description data is less than the random first error between the random predicted performance data in the previous stage and the target optical glass drop description data, a parameter combination formed by the current process parameter random value of each process parameter is used as a candidate process parameter combination; When the random first error between the current random predicted performance data and the target optical glass drop description data is not less than the random first error between the random predicted performance data in the previous stage and the target optical glass drop description data, the acceptance probability of the current random predicted performance data is determined based on the random first error, and based on the acceptance probability, it is determined whether to use the parameter combination formed by the current random values ​​of each process parameter as a candidate process parameter combination.

5. The optical glass drop information prediction method according to claim 1, characterized in that: The step of obtaining the target optical glass drop description data to be predicted, predicting the target optical glass drop description data using the target drop information prediction model, and outputting the target optical glass drop prediction data corresponding to the target optical glass drop description data comprises: Acquire target optical glass drop description data to be predicted, wherein the target optical glass drop description data belongs to process data; Based on each of the target drop information prediction models, the target optical glass drop description data is predicted, and each target optical glass drop prediction data corresponding to the target optical glass drop description data is output; or Based on a target drop information prediction model that matches the target optical glass drop description data, the target optical glass drop description data is predicted, and target optical glass drop prediction data corresponding to the target optical glass drop description data is output, wherein the target optical glass drop prediction data belongs to performance data.

6. The optical glass drop information prediction method according to any one of claims 1 to 5, characterized in that: The step of obtaining sample process data corresponding to the optical glass gob and performance data labels corresponding to the optical glass gob includes: Obtaining sample process data corresponding to the optical glass dripping material, wherein the sample process data includes at least one parameter of the nozzle length of the production equipment, the nozzle thickness of the production equipment, the mold curvature of the production equipment, the mold height of the production equipment, the mold thickness of the production equipment, the temperature at various locations of the production equipment, the cooling air temperature, the cooling air direction, the cooling air intensity, the cooling air time, and the dripping cycle; A performance data label corresponding to the optical glass gob is obtained, wherein the performance data label includes at least one parameter of the optical glass gob, including weight, center thickness, outer diameter, roundness, and upper and lower diameters.

7. The optical glass drop information prediction method according to any one of claims 1 to 5, characterized in that: The step of classifying the plurality of sample process data based on at least one associated data having an impact on the performance of the optical glass gob to obtain at least one data classification set corresponding to the plurality of sample process data comprises: For each optical glass gob, a production location of the optical glass gob is obtained, and the production location of the optical glass gob and a cooling wind direction of the optical glass gob are used as at least one associated data having an impact on the performance of the optical glass gob; Based on at least one associated data having an impact on the performance of the optical glass gob, a plurality of sample process data corresponding to the plurality of optical glass gobs are classified to obtain at least one data classification set corresponding to the plurality of sample process data.

8. An optical glass drop information prediction device, characterized in that: include: A sample data acquisition module is used to acquire sample process data corresponding to the optical glass gob and performance data labels corresponding to the optical glass gob, wherein the sample process data is multiple, each of which is used to describe the production process of the corresponding optical glass gob, and the performance data labels are multiple, each of which is used to reflect the actual performance of the corresponding optical glass gob; A sample data classification module, configured to classify a plurality of the sample process data based on at least one associated data having an impact on the performance of the optical glass gob, and obtain at least one data classification set corresponding to the plurality of the sample process data, wherein each of the data classification sets includes at least one sample process data; A prediction model training module, for training a target drip information prediction model corresponding to each data classification set in the at least one data classification set based on each sample process data in the data classification set and a performance data label corresponding to the sample process data, wherein the target drip information prediction model belongs to a random forest model; The drop information prediction module is used to obtain the target optical glass drop description data to be predicted, and use the target drop information prediction model to predict the target optical glass drop description data, and output the target optical glass drop prediction data corresponding to the target optical glass drop description data.

9. An electronic device, characterized in that: include: Memory for storing computer programs; A processor connected to the memory is used to execute a computer program stored in the memory to implement the optical glass drop information prediction method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is run, the optical glass drop material information prediction method according to any one of claims 1 to 7 is executed.

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