Optical glass data prediction method and device, electronic equipment and storage medium

Abstract

The invention provides an optical glass data prediction method and device, electronic equipment and a storage medium, and relates to the technical field of optical glass data prediction. The method comprises the following steps: firstly, acquiring sample formula data and a transmittance label; secondly, through a candidate prediction network, transmittance prediction is carried out on the sample formula data, transmittance prediction data is output, through the candidate prediction network, superimposed effects among formula components in the sample formula data are predicted, and superimposed effect characterization values corresponding to the sample formula data are output; then, based on the transmittance label, the transmittance prediction data and the superimposed effect characterization value, parameters of the candidate prediction network are optimized, and a target prediction network is formed; and finally, predicting the target optical glass data by using the target prediction network, and outputting predicted optical glass data. On the basis of the content, the problem that in the prior art, the determination cost of optical glass data is high can be solved.

Description

Technical Field

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

[0002] Functional glass has high optical transmittance, high uniformity in chemistry and physics (structure and performance), and specific and precise optical constant characteristics. It has extremely important applications in various optical instruments, information communications, luminous displays, etc., and is the foundation and important part of the optoelectronic information technology industry. Among them, functional glass such as insulation glass, laser glass, photochromic glass, etc., has special optical properties, and transmittance is a key indicator to measure its optical performance. Therefore, the relationship between its component formula and transmittance is very important.

[0003] In the existing technology, the best combination of components can be found through trial-and-error glass formula experiments and statistical glass performance (transmittance, etc.) results. However, the number of experimental factor types and factor level values ​​of the formula will affect the number of experimental formulas, and there is an explosion of glass performance data for formula component combinations, which leads to a long formula development cycle and high costs. Summary of the invention

[0004] In view of this, the purpose of the present application is to provide an optical glass data prediction method and device, an electronic device and a storage medium, so as to improve the problem of high cost of determining optical glass data existing in the prior art.

[0005] To achieve the above objectives, this application adopts the following technical solutions: An optical glass data prediction method, comprising: Acquire sample formula data corresponding to the optical glass and a transmittance label corresponding to the optical glass, wherein the sample formula data includes each formula component and the component content of each formula component; The transmittance of the sample formula data is predicted by a pre-constructed candidate prediction network, and transmittance prediction data corresponding to the sample formula data is outputted. The sample formula data including the superposition effect between the formula components is predicted by the candidate prediction network, and a superposition effect characterization value corresponding to the sample formula data is outputted, wherein the superposition effect characterization value is used to reflect the matching degree between the formula components and the target linear constraint relationship, and the target linear constraint relationship is at least used to reflect that the sum of the molar percentages of the component contents corresponding to the formula components is 100%; Based on the transmittance label, the transmittance prediction data and the superposition effect characterization value, optimizing the parameters of the candidate prediction network to form a target prediction network; The target prediction network is used to predict the target optical glass data, and the predicted optical glass data corresponding to the target optical glass data is output, wherein when the target optical glass data includes formula data, the predicted optical glass data includes transmittance data, and when the target optical glass data includes transmittance data, the predicted optical glass data includes formula data.

[0006] In a preferred embodiment of the present application, in the above optical glass data prediction method, the steps of predicting transmittance of the sample formula data through a pre-constructed candidate prediction network, outputting transmittance prediction data corresponding to the sample formula data, and predicting the sample formula data including the superposition effect between each formula component through the candidate prediction network, and outputting the superposition effect characterization value corresponding to the sample formula data, include: Performing feature mining on the sample recipe data through a feature mining unit included in a pre-built candidate prediction network, and outputting sample recipe features corresponding to the sample recipe data; The sample formula feature is predicted and outputted by a first output unit included in the candidate prediction network to obtain transmittance prediction data corresponding to the sample formula data; Matrixing the recipe components included in the sample recipe data by a projection unit included in the candidate prediction network to form at least one projection matrix corresponding to the sample recipe data, wherein each row of each projection matrix corresponds to a linear constraint relationship between the recipe components; The at least one projection matrix is ​​processed by a second output unit included in the candidate prediction network to output a superposition effect representation value corresponding to the sample recipe data.

[0007] In a preferred embodiment of the present application, in the optical glass data prediction method, the step of processing the at least one projection matrix through the second output unit included in the candidate prediction network and outputting the superposition effect characterization value corresponding to the sample formula data includes: The at least one projection matrix is ​​weighted and summed by the weight coefficients of each projection matrix in the second output unit included in the candidate prediction network, and the superposition effect characterization value corresponding to the sample formula data is output, wherein the weighted summation calculation formula includes: ; in, is the characterization value of the superposition effect, represents the first linear constraint relationship in the target linear constraint relationship, represents the second linear constraint relationship in the target linear constraint relationship, are the weight coefficients corresponding to the projection matrix, , The projection matrix composed of each recipe component, , They are the configured constants respectively.

[0008] In a preferred embodiment of the present application, in the above optical glass data prediction method, the step of optimizing the parameters of the candidate prediction network based on the transmittance label, the transmittance prediction data and the superposition effect characterization value to form a target prediction network includes: Determining a first error index of the candidate prediction network based on a difference between the transmittance label and the transmittance prediction data; Determining a second error indicator of the candidate prediction network based on a difference between the stacking effect characterization value and a predetermined stacking effect target value; Based on the first error indicator and the second error indicator, the parameters of the candidate prediction network are optimized so that the first error indicator converges and the second error indicator converges, and when converged, a corresponding target prediction network is determined based on the candidate prediction network.

[0009] In a preferred embodiment of the present application, in the above optical glass data prediction method, the step of using the target prediction network to predict the target optical glass data and outputting the predicted optical glass data corresponding to the target optical glass data includes: Performing feature mining on the target optical glass data through a feature mining unit included in the target prediction network, and outputting target formula features corresponding to the target optical glass data, wherein the target optical glass data belongs to formula data; The target formula feature is predicted and outputted by the first output unit included in the target prediction network to obtain transmittance prediction data corresponding to the target optical glass data, wherein the transmittance prediction data is used as the predicted optical glass data corresponding to the target optical glass data; Matrixing the formula components included in the target optical glass data through a projection unit included in the target prediction network to form at least one projection matrix corresponding to the target optical glass data; Through the second output unit included in the target prediction network, at least one projection matrix corresponding to the target optical glass data is processed, and a superposition effect characterization value corresponding to the target optical glass data is output, wherein the superposition effect characterization value is used to reflect the degree of matching between each formula component in the target optical glass data and the target linear constraint relationship, and the degree of matching is positively correlated with the reliability of the predicted optical glass data corresponding to the target optical glass data.

[0010] In a preferred embodiment of the present application, in the above optical glass data prediction method, the step of using the target prediction network to predict the target optical glass data and outputting the predicted optical glass data corresponding to the target optical glass data includes: For each formula component included in the sample formula data, determine the order of magnitude of the component content corresponding to the formula component, and determine the value interval corresponding to the formula component based on the order of magnitude, and perform random value processing based on the value interval to obtain multiple random component contents corresponding to the formula component, wherein each of the random component contents belongs to the corresponding value interval; Combining and processing multiple random component contents corresponding to each formula component included in the sample formula data to form multiple random formula data, wherein each of the random formula data includes each formula component and the random component content of each formula component; The target prediction network is used to determine a random formula data that best matches the target optical glass data among the multiple random formula data, and the random formula data is determined as the predicted optical glass data corresponding to the target optical glass data, wherein the target optical glass data belongs to transmittance data.

[0011] In a preferred embodiment of the present application, in the optical glass data prediction method, the step of using the target prediction network to determine a random formula data that best matches the target optical glass data among the multiple random formula data, and determining the random formula data as the predicted optical glass data corresponding to the target optical glass data includes: For each of the multiple random formula data, feature mining is performed on the random formula data through the feature mining unit included in the target prediction network, and the random formula features corresponding to the random formula data are outputted, and the random formula features are predicted and outputted through the first output unit included in the target prediction network to obtain transmittance prediction data corresponding to the random formula data, and each formula component included in the random formula data is matrixed through the projection unit included in the target prediction network to form at least one projection matrix corresponding to the random formula data, and at least one projection matrix corresponding to the random formula data is processed through the second output unit included in the target prediction network to output the superposition effect characterization value corresponding to the random formula data, wherein the superposition effect characterization value is used to reflect the matching degree between each formula component in the random formula data and the target linear constraint relationship, and the matching degree is positively correlated with the reliability of the transmittance prediction data corresponding to the random formula data; Based on the transmittance prediction data and the corresponding superposition effect characterization value corresponding to each of the random formula data, a random formula data that best matches the target optical glass data is determined among the multiple random formula data, and the random formula data is determined as the predicted optical glass data corresponding to the target optical glass data.

[0012] The present application also provides an optical glass data prediction device, comprising: A sample label acquisition module, used to acquire sample formula data corresponding to the optical glass and a transmittance label corresponding to the optical glass, wherein the sample formula data includes each formula component and the component content of each formula component; A sample transmittance prediction module, used to predict the transmittance of the sample formula data through a pre-constructed candidate prediction network, output the transmittance prediction data corresponding to the sample formula data, and predict the sample formula data including the superposition effect between the formula components through the candidate prediction network, and output the superposition effect representation value corresponding to the sample formula data, wherein the superposition effect representation value is used to reflect the matching degree between the formula components and the target linear constraint relationship, and the target linear constraint relationship is at least used to reflect that the sum of the molar percentages of the component contents corresponding to the formula components is 100%; A prediction network optimization module, configured to optimize the parameters of the candidate prediction network based on the transmittance label, the transmittance prediction data and the superposition effect characterization value to form a target prediction network; An optical glass data prediction module is used to predict the target optical glass data using the target prediction network, and output predicted optical glass data corresponding to the target optical glass data, wherein when the target optical glass data includes formula data, the predicted optical glass data includes transmittance data, and when the target optical glass data includes transmittance data, the predicted optical glass data includes formula data.

[0013] 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 optical glass data prediction method.

[0014] 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 optical glass data prediction method is executed.

[0015] The optical glass data prediction method and device, electronic device and storage medium provided by the present application first obtain sample formula data and transmittance labels; secondly, transmittance prediction is performed on the sample formula data through a candidate prediction network, transmittance prediction data is output, and the sample formula data including the superposition effect between the formula components is predicted through the candidate prediction network, and the superposition effect characterization value corresponding to the sample formula data is output; then, based on the transmittance label, transmittance prediction data and superposition effect characterization value, the parameters of the candidate prediction network are optimized to form a target prediction network; finally, the target prediction network is used to predict the target optical glass data and output the predicted optical glass data. Based on the above content, since the candidate prediction network can be trained using samples, the corresponding prediction can be directly made based on the target prediction network formed by the training. Compared with the conventional technical solution in the prior art that requires experimental trial and error, the trial and error cost can be effectively reduced, and the problem of high cost of determining optical glass data in the prior art can be improved. In addition, after the target prediction network is formed through training, prediction can be made directly, and the efficiency can be fully improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] 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.

[0017] Figure 1 A structural block diagram of an electronic device provided in an embodiment of the present application.

[0018] Figure 2 A schematic flow chart of the optical glass data prediction method provided in an embodiment of the present application.

[0019] Figure 3 A network architecture diagram of a candidate prediction network provided in an embodiment of the present application.

[0020] Figure 4 A block diagram of an optical glass data prediction device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] 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.

[0022] 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.

[0023] 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 data prediction device.

[0024] 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 data 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 data prediction device, so as to implement the optical glass data prediction method provided in the embodiment of the present application.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Combination Figure 2 The embodiment of the present application also provides an optical glass data prediction method applicable to the above electronic device, wherein the method steps defined in the process related to the optical glass data prediction method can be implemented by the electronic device.

[0029] The following will Figure 2 The specific process shown is explained in detail.

[0030] Step S110, obtaining sample formula data corresponding to the optical glass and a transmittance label corresponding to the optical glass.

[0031] In the embodiment of the present application, the electronic device can obtain sample formula data corresponding to the optical glass and a transmittance label (i.e., actual transmittance) corresponding to the optical glass. The sample formula data includes each formula component (such as iron oxide, titanium oxide, lithium oxide, potassium oxide, sodium oxide, and silicon oxide, etc.) and the content of each formula component (such as 0.5%, 1%, etc., in molar percentage).

[0032] Step S120, predicting the transmittance of the sample formula data through a pre-constructed candidate prediction network, outputting the transmittance prediction data corresponding to the sample formula data, and predicting the sample formula data including the superposition effect between the formula components through the candidate prediction network, and outputting the superposition effect characterization value corresponding to the sample formula data.

[0033] In an embodiment of the present application, after obtaining the sample formula data and the transmittance label, the electronic device can predict the transmittance of the sample formula data through a pre-constructed candidate prediction network (which can be a convolutional neural network), output the transmittance prediction data corresponding to the sample formula data (i.e., the predicted transmittance), and predict the sample formula data including the superposition effect between the formula components through the candidate prediction network, and output the superposition effect characterization value corresponding to the sample formula data. Wherein, the superposition effect characterization value is used to reflect the degree of matching between each formula component and the target linear constraint relationship, and the target linear constraint relationship is at least used to reflect that the sum of the molar percentages of the component contents corresponding to each formula component is 100%. Exemplarily, it can include only one linear constraint relationship, or it can include multiple linear constraint relationships. For example, it can also be used to reflect the relationship between the component contents of two components, such as for iron oxide and titanium oxide with synergistic effects, the sum of the molar percentages of the component contents can be less than 2%. It should be noted that the transmittance can refer to the transmittance at the H line 404.7nm, or, in other embodiments, it can also be the transmittance under other indicator parameters.

[0034] Step S130 , based on the transmittance label, the transmittance prediction data and the superposition effect representation value, the parameters of the candidate prediction network are optimized to form a target prediction network.

[0035] In an embodiment of the present application, after obtaining the transmittance prediction data and the superposition effect characterization value, the electronic device can optimize the parameters of the candidate prediction network based on the transmittance label, the transmittance prediction data and the superposition effect characterization value to form a target prediction network. For example, the candidate prediction network after parameter optimization can be used as the target prediction network, or a target prediction network can be constructed based on the optimized parameters.

[0036] Step S140: using the target prediction network to predict the target optical glass data, and outputting predicted optical glass data corresponding to the target optical glass data.

[0037] In an embodiment of the present application, after the target prediction network is formed, the electronic device can use the target prediction network to predict the target optical glass data and output the predicted optical glass data corresponding to the target optical glass data. Wherein, when the target optical glass data includes formula data, the predicted optical glass data includes transmittance data, and when the target optical glass data includes transmittance data, the predicted optical glass data includes formula data. In other words, the target prediction network can be used not only to predict the corresponding transmittance through the formula, but also to predict the corresponding formula through the transmittance. In this way, the utilization rate of the target prediction network can be improved to a large extent, and the maximum utilization of resources can be achieved.

[0038] Based on the above content, since the candidate prediction network can be trained using samples, the corresponding prediction can be directly made based on the target prediction network formed by training. Compared with the conventional technical solution in the prior art that requires experimental trial and error, the trial and error cost can be effectively reduced, and the problem of high cost of determining optical glass data in the prior art can be improved. In addition, after the target prediction network is formed through training, the prediction can be made directly, and the efficiency can be fully improved.

[0039] First, it should be noted that the sample formula data obtained in step S110 may be multiple. Based on this, in order to ensure the reliability of the data, the multiple sample formula data obtained may be preprocessed, such as missing value check, 3σ (3 times the standard deviation) principle to eliminate outliers, etc. In addition, the correlation analysis of each formula component in the sample formula data can be performed, and the formula component with a close relationship with the transmittance can be determined. In addition, each component can be standardized so that the dimensional influence between the components can be reduced.

[0040] Secondly, it should be noted that for step S120, the specific method of performing corresponding prediction through the candidate prediction network is not limited and can be selected according to actual needs.

[0041] For example, in an alternative implementation, in order to enable effective prediction through the candidate prediction network so that the reliability of the candidate optimization process can be guaranteed, the above-mentioned step S120 may further include step S121, step S122, step S123 and step S124, and the specific content of each step is described as follows.

[0042] Step S121 , performing feature mining on the sample recipe data through a feature mining unit included in a pre-built candidate prediction network, and outputting sample recipe features corresponding to the sample recipe data.

[0043] In the embodiment of the present application, the feature mining unit included in the pre-built candidate prediction network can be used to perform feature mining on the sample recipe data and output the sample recipe features corresponding to the sample recipe data. Exemplarily, the feature mining unit can include an input layer, a convolution layer, an activation layer, and a fully connected layer, such as Figure 3 shown.

[0044] Step S122: predict and output the sample recipe feature through the first output unit included in the candidate prediction network to obtain transmittance prediction data corresponding to the sample recipe data.

[0045] In an embodiment of the present application, after the sample formula features are mined, the sample formula features can be predicted and output through the first output unit included in the candidate prediction network to obtain transmittance prediction data corresponding to the sample formula data. For example, the first output unit can include a linear regression function for outputting transmittance prediction data.

[0046] Step S123: matrixing each recipe component included in the sample recipe data through a projection unit included in the candidate prediction network to form at least one projection matrix corresponding to the sample recipe data.

[0047] In an embodiment of the present application, the projection unit included in the candidate prediction network can also be used to matrix the recipe components included in the sample recipe data to form at least one projection matrix corresponding to the sample recipe data. Each row of each projection matrix corresponds to a linear constraint relationship between the recipe components. Specifically, a linear constrained optimization projection method can be used as an example. For example, if there are n recipe components, the relationship between the recipe components may be affected by linear constraints, such as , G is an m*n matrix, representing the coefficients of the constraint conditions, x is an n-dimensional column vector, representing the content of each formula component, and b is an m-dimensional column vector, representing the right-hand constant of the constraint. Both G and b can be configured according to actual needs. In this way, we have , further, there is =0, where, according to the analysis In a certain glass system, it is fixed. For at least one linear constraint relationship, at least one projection matrix can be formed.

[0048] Step S124: Processing the at least one projection matrix through a second output unit included in the candidate prediction network, and outputting a superposition effect representation value corresponding to the sample recipe data.

[0049] In the embodiment of the present application, after forming the at least one projection matrix, the at least one projection matrix can be processed by the second output unit included in the candidate prediction network to output the superposition effect representation value corresponding to the sample formula data. In other words, the linear constraint relationship represented by each projection matrix is ​​fused to determine the matching of various content relationships between the formula components, that is, to obtain the corresponding superposition effect representation value.

[0050] It can be understood that, in the above step S124, the specific method of determining the superposition effect characterization value is not limited and can be selected according to actual needs. For example, in an alternative implementation, in order to ensure that multiple linear constraint relationships can be fully characterized, the above step S124 can further include the following specific implementation process: The at least one projection matrix may be weighted and summed by the weight coefficients of the projection matrices in the second output unit included in the candidate prediction network, and the superposition effect characterization value corresponding to the sample formula data may be output, wherein the weighted and summed calculation formula includes: ; in, is the characterization value of the superposition effect, represents the first linear constraint relationship in the target linear constraint relationship, represents the second linear constraint relationship in the target linear constraint relationship (in other implementations, more linear constraint relationships may be included, such as the third linear constraint relationship and the fourth linear constraint relationship), are the weight coefficients corresponding to the projection matrix, , The projection matrix composed of each recipe component (for example, combined with the relevant description in the previous article, q = , , are respectively the configuration constants (i.e. In different linear constraint relationships). Based on this, in theory, =0, then, ideally =0, =0. Therefore, The ideal value should also be 0.

[0051] Thirdly, it should be noted that for step S130, the specific method of optimizing the parameters of the candidate prediction network is not limited and can be selected according to actual needs.

[0052] For example, in an alternative implementation, in order to improve the reliability of the optimized target prediction network, the above step S130 may further include the following specific implementation process: First, based on the difference between the transmittance label and the transmittance prediction data, a first error index of the candidate prediction network, such as mean square error MSE, can be determined; Secondly, based on the difference between the superposition effect characterization value and the predetermined superposition effect target value (such as difference calculation), the second error index of the candidate prediction network can be determined; illustratively, The ideal value of should be 0, so the target value of the superposition effect can be 0; Then, the parameters of the candidate prediction network can be optimized based on the first error index and the second error index, so that the first error index converges and the second error index converges, and when converged, the corresponding target prediction network is determined based on the candidate prediction network. Exemplarily, the optimization can be performed based on the first error index and the second error index respectively, specifically, the parameters of the feature mining unit can be optimized in the direction of reducing the first error index. In addition, the parameters in the projection unit and the second output unit can be optimized in the direction of reducing the second error index, such as the weight coefficient mentioned above.

[0053] Fourthly, it should be noted that for step S140, the specific method of predicting the target optical glass data is not limited and can be selected according to actual needs.

[0054] For example, in an alternative implementation, in order to achieve reliable prediction of transmittance through the target prediction network, the above step S140 may further include the following contents: First, the target optical glass data may be subjected to feature mining by a feature mining unit included in the target prediction network, and target formula features corresponding to the target optical glass data may be output, wherein the target optical glass data belongs to formula data; Secondly, the target formula feature can be predicted and outputted by the first output unit included in the target prediction network to obtain transmittance prediction data corresponding to the target optical glass data, wherein the transmittance prediction data is used as the predicted optical glass data corresponding to the target optical glass data; Then, each formula component included in the target optical glass data may be matrixed by a projection unit included in the target prediction network to form at least one projection matrix corresponding to the target optical glass data; Finally, at least one projection matrix corresponding to the target optical glass data is processed through the second output unit included in the target prediction network, and the superposition effect characterization value corresponding to the target optical glass data is output, wherein the superposition effect characterization value is used to reflect the matching degree between each formula component in the target optical glass data and the target linear constraint relationship, and the matching degree is positively correlated with the reliability of the predicted optical glass data corresponding to the target optical glass data; that is, the closer the superposition effect characterization value is to 0, the more the relationship between the content of each formula component matches the target linear constraint relationship, thus, the higher the reliability of the content of each formula component is, and the more reliable the predicted transmittance prediction data is. Conversely, the further the superposition effect characterization value is from 0, the less the relationship between the content of each formula component matches the target linear constraint relationship, thus, the lower the reliability of the content of each formula component is, and the less reliable the predicted transmittance prediction data is.

[0055] For example, in an alternative embodiment, in order to achieve reliable prediction of the recipe through the target prediction network, 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.

[0056] Step S141, for each formula component included in the sample formula data, determine the order of magnitude of the component content corresponding to the formula component, and determine the value range corresponding to the formula component based on the order of magnitude, and perform random value processing based on the value range to obtain multiple random component contents corresponding to the formula component.

[0057] In the embodiment of the present application, for each formula component included in the sample formula data, the order of magnitude of the component content corresponding to the formula component can be determined (for a formula component, the corresponding order of magnitude can also be determined based on the component content of the formula component in multiple sample formula data), and the value range corresponding to the formula component can be determined based on the order of magnitude (such as [0.8 ~1.2 ] ,in, represents an order of magnitude), and, based on the value interval, a random value processing is performed to obtain a plurality of random component contents corresponding to the formula component, such as randomly selecting 200 random component contents. Each of the random component contents belongs to a corresponding value interval.

[0058] Step S142: performing combination processing based on the contents of multiple random components corresponding to each formula component included in the sample formula data to form multiple random formula data.

[0059] In the embodiment of the present application, after obtaining the multiple random component contents corresponding to each formula component, the multiple random component contents corresponding to each formula component included in the sample formula data can be combined and processed to form multiple random formula data. Each of the random formula data includes each formula component and the random component content of each formula component.

[0060] Step S143, using the target prediction network, determining a random formula data that best matches the target optical glass data among the multiple random formula data, and determining the random formula data as the predicted optical glass data corresponding to the target optical glass data.

[0061] In the embodiment of the present application, after obtaining a plurality of random formula data, the target prediction network can be used to determine a random formula data that best matches the target optical glass data among the plurality of random formula data, and determine the random formula data as the predicted optical glass data corresponding to the target optical glass data. Wherein, the target optical glass data is transmittance data, that is, the random formula data corresponding to the transmittance that best matches the transmittance data is determined.

[0062] It can be understood that, in the above step S143, the specific method of determining a random formula data that best matches the target optical glass data from the plurality of random formula data is not limited and can be selected according to actual needs. For example, in an alternative implementation, in order to ensure the reliability of the determined formula data, the above step S143 can further include: First, for each of the multiple random formula data, feature mining is performed on the random formula data through the feature mining unit included in the target prediction network, and the random formula features corresponding to the random formula data are outputted, and the random formula features are predicted and outputted through the first output unit included in the target prediction network to obtain transmittance prediction data corresponding to the random formula data, and each formula component included in the random formula data is matrixed through the projection unit included in the target prediction network to form at least one projection matrix corresponding to the random formula data, and at least one projection matrix corresponding to the random formula data is processed through the second output unit included in the target prediction network to output the superposition effect representation value corresponding to the random formula data, wherein the superposition effect representation value is used to reflect the matching degree between each formula component in the random formula data and the target linear constraint relationship, and the matching degree is positively correlated with the reliability of the transmittance prediction data corresponding to the random formula data; Secondly, based on the transmittance prediction data and the corresponding superposition effect characterization value corresponding to each of the random formula data, a random formula data that best matches the target optical glass data can be determined among the multiple random formula data, and the random formula data can be determined as the predicted optical glass data corresponding to the target optical glass data; illustratively, some random formula data with relatively small superposition effect characterization values ​​(such as the difference between the superposition effect target value "0" does not exceed a threshold, etc.) can be selected first, and then, among these random formula data, a random formula data with the smallest difference from the target optical glass data can be selected.

[0063] Combination Figure 4 The embodiment of the present application also provides an optical glass data prediction device applicable to the above electronic device. The optical glass data prediction device may include a sample label acquisition module, a sample transmittance prediction module, a prediction network optimization module and an optical glass data prediction module.

[0064] In detail, the sample label acquisition module can be used to acquire sample formula data corresponding to the optical glass and a transmittance label corresponding to the optical glass, wherein the sample formula data includes each formula component and the component content of each formula component. In the embodiment of the present application, the sample label acquisition module can be used to execute Figure 2 As shown in step S110, the relevant contents of the sample label acquisition module can refer to the above description of step S110.

[0065] In detail, the sample transmittance prediction module can be used to predict the transmittance of the sample formula data through a pre-constructed candidate prediction network, output the transmittance prediction data corresponding to the sample formula data, and predict the sample formula data including the superposition effect between the formula components through the candidate prediction network, and output the superposition effect characterization value corresponding to the sample formula data, wherein the superposition effect characterization value is used to reflect the degree of matching between each formula component and the target linear constraint relationship, and the target linear constraint relationship is at least used to reflect that the sum of the molar percentages of the component contents corresponding to each formula component is 100%. In an embodiment of the present application, the sample transmittance prediction module can be used to perform Figure 2 As shown in step S120 , the relevant contents of the sample transmittance prediction module can refer to the above description of step S120 .

[0066] In detail, the prediction network optimization module can be used to optimize the parameters of the candidate prediction network based on the transmittance label, the transmittance prediction data and the superposition effect characterization value to form a target prediction network. In the embodiment of the present application, the prediction network optimization module can be used to perform Figure 2As shown in step S130, the relevant contents of the predictive network optimization module can refer to the above description of step S130.

[0067] In detail, the optical glass data prediction module can be used to predict the target optical glass data using the target prediction network, and output the predicted optical glass data corresponding to the target optical glass data, wherein when the target optical glass data includes formula data, the predicted optical glass data includes transmittance data, and when the target optical glass data includes transmittance data, the predicted optical glass data includes formula data. In an embodiment of the present application, the optical glass data prediction module can be used to perform Figure 2 As shown in step S140, the relevant contents of the optical glass data prediction module can refer to the above description of step S140.

[0068] In the embodiment of the present application, corresponding to the optical glass data 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 data 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 data prediction method in the previous text.

[0069] In summary, the optical glass data prediction method and device, electronic device and storage medium provided by the present application first obtain sample formula data and transmittance labels; secondly, transmittance prediction is performed on the sample formula data through the candidate prediction network, transmittance prediction data is output, and the sample formula data including the superposition effect between the formula components is predicted through the candidate prediction network, and the superposition effect characterization value corresponding to the sample formula data is output; then, based on the transmittance label, transmittance prediction data and superposition effect characterization value, the parameters of the candidate prediction network are optimized to form a target prediction network; finally, the target prediction network is used to predict the target optical glass data and output the predicted optical glass data. Based on the above content, since the candidate prediction network can be trained using samples, the corresponding prediction can be directly made based on the target prediction network formed by the training. Compared with the conventional technical solution that requires experimental trial and error in the prior art, the trial and error cost can be effectively reduced, and the problem of high cost of determining optical glass data in the prior art can be improved. In addition, after the target prediction network is formed through training, the prediction can be made directly, and the efficiency can be fully improved, which also improves the problem of long experimental trial and error cycle in the prior art.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] 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 data, characterized in that: include: Acquire sample formula data corresponding to the optical glass and a transmittance label corresponding to the optical glass, wherein the sample formula data includes each formula component and the component content of each formula component; The transmittance of the sample formula data is predicted by a pre-constructed candidate prediction network, and transmittance prediction data corresponding to the sample formula data is outputted. The sample formula data including the superposition effect between the formula components is predicted by the candidate prediction network, and a superposition effect characterization value corresponding to the sample formula data is outputted, wherein the superposition effect characterization value is used to reflect the matching degree between the formula components and the target linear constraint relationship, and the target linear constraint relationship is at least used to reflect that the sum of the molar percentages of the component contents corresponding to the formula components is 100%; Based on the transmittance label, the transmittance prediction data and the superposition effect characterization value, optimizing the parameters of the candidate prediction network to form a target prediction network; The target prediction network is used to predict the target optical glass data, and the predicted optical glass data corresponding to the target optical glass data is output, wherein when the target optical glass data includes formula data, the predicted optical glass data includes transmittance data, and when the target optical glass data includes transmittance data, the predicted optical glass data includes formula data.

2. The optical glass data prediction method according to claim 1, characterized in that: The steps of predicting transmittance of the sample formula data by using a pre-constructed candidate prediction network, outputting transmittance prediction data corresponding to the sample formula data, and predicting the sample formula data including the superposition effect between each formula component by using the candidate prediction network, and outputting the superposition effect characterization value corresponding to the sample formula data, include: Performing feature mining on the sample recipe data through a feature mining unit included in a pre-built candidate prediction network, and outputting sample recipe features corresponding to the sample recipe data; The sample formula feature is predicted and outputted by a first output unit included in the candidate prediction network to obtain transmittance prediction data corresponding to the sample formula data; Matrixing the recipe components included in the sample recipe data by a projection unit included in the candidate prediction network to form at least one projection matrix corresponding to the sample recipe data, wherein each row of each projection matrix corresponds to a linear constraint relationship between the recipe components; The at least one projection matrix is ​​processed by a second output unit included in the candidate prediction network to output a superposition effect representation value corresponding to the sample recipe data.

3. The optical glass data prediction method according to claim 2, characterized in that: The step of processing the at least one projection matrix through the second output unit included in the candidate prediction network and outputting the superposition effect representation value corresponding to the sample formula data includes: The at least one projection matrix is ​​weighted and summed by the weight coefficients of each projection matrix in the second output unit included in the candidate prediction network, and the superposition effect characterization value corresponding to the sample formula data is output, wherein the weighted summation calculation formula includes: ； in, is the characterization value of the superposition effect, represents the first linear constraint relationship in the target linear constraint relationship, represents the second linear constraint relationship in the target linear constraint relationship, are the weight coefficients corresponding to the projection matrix, , are the projection matrices of each recipe component, , They are the configured constants respectively.

4. The optical glass data prediction method according to claim 1, characterized in that: The step of optimizing the parameters of the candidate prediction network based on the transmittance label, the transmittance prediction data and the superposition effect characterization value to form a target prediction network includes: Determining a first error index of the candidate prediction network based on a difference between the transmittance label and the transmittance prediction data; Determining a second error indicator of the candidate prediction network based on a difference between the stacking effect characterization value and a predetermined stacking effect target value; Based on the first error indicator and the second error indicator, the parameters of the candidate prediction network are optimized so that the first error indicator converges and the second error indicator converges, and when converged, a corresponding target prediction network is determined based on the candidate prediction network.

5. The optical glass data prediction method according to any one of claims 1 to 4, characterized in that: The step of using the target prediction network to predict target optical glass data and outputting predicted optical glass data corresponding to the target optical glass data comprises: Performing feature mining on the target optical glass data through a feature mining unit included in the target prediction network, and outputting target formula features corresponding to the target optical glass data, wherein the target optical glass data belongs to formula data; The target formula feature is predicted and outputted by the first output unit included in the target prediction network to obtain transmittance prediction data corresponding to the target optical glass data, wherein the transmittance prediction data is used as the predicted optical glass data corresponding to the target optical glass data; Matrixing the formula components included in the target optical glass data through a projection unit included in the target prediction network to form at least one projection matrix corresponding to the target optical glass data; Through the second output unit included in the target prediction network, at least one projection matrix corresponding to the target optical glass data is processed, and a superposition effect characterization value corresponding to the target optical glass data is output, wherein the superposition effect characterization value is used to reflect the degree of matching between each formula component in the target optical glass data and the target linear constraint relationship, and the degree of matching is positively correlated with the reliability of the predicted optical glass data corresponding to the target optical glass data.

6. The optical glass data prediction method according to any one of claims 1 to 4, characterized in that: The step of using the target prediction network to predict target optical glass data and outputting predicted optical glass data corresponding to the target optical glass data comprises: For each formula component included in the sample formula data, determine the order of magnitude of the component content corresponding to the formula component, and determine the value interval corresponding to the formula component based on the order of magnitude, and perform random value processing based on the value interval to obtain multiple random component contents corresponding to the formula component, wherein each of the random component contents belongs to the corresponding value interval; Combining and processing multiple random component contents corresponding to each formula component included in the sample formula data to form multiple random formula data, wherein each of the random formula data includes each formula component and the random component content of each formula component; The target prediction network is used to determine a random formula data that best matches the target optical glass data among the multiple random formula data, and the random formula data is determined as the predicted optical glass data corresponding to the target optical glass data, wherein the target optical glass data belongs to transmittance data.

7. The optical glass data prediction method according to claim 6, characterized in that: The step of using the target prediction network to determine a random formula data that best matches the target optical glass data among the multiple random formula data, and determining the random formula data as the predicted optical glass data corresponding to the target optical glass data includes: For each of the multiple random formula data, feature mining is performed on the random formula data through the feature mining unit included in the target prediction network, and the random formula features corresponding to the random formula data are outputted, and the random formula features are predicted and outputted through the first output unit included in the target prediction network to obtain transmittance prediction data corresponding to the random formula data, and each formula component included in the random formula data is matrixed through the projection unit included in the target prediction network to form at least one projection matrix corresponding to the random formula data, and at least one projection matrix corresponding to the random formula data is processed through the second output unit included in the target prediction network to output the superposition effect characterization value corresponding to the random formula data, wherein the superposition effect characterization value is used to reflect the matching degree between each formula component in the random formula data and the target linear constraint relationship, and the matching degree is positively correlated with the reliability of the transmittance prediction data corresponding to the random formula data; Based on the transmittance prediction data and the corresponding superposition effect characterization value corresponding to each of the random formula data, a random formula data that best matches the target optical glass data is determined among the multiple random formula data, and the random formula data is determined as the predicted optical glass data corresponding to the target optical glass data.

8. An optical glass data prediction device, characterized in that: include: A sample label acquisition module, used to acquire sample formula data corresponding to the optical glass and a transmittance label corresponding to the optical glass, wherein the sample formula data includes each formula component and the component content of each formula component; A sample transmittance prediction module, used to predict the transmittance of the sample formula data through a pre-constructed candidate prediction network, output the transmittance prediction data corresponding to the sample formula data, and predict the sample formula data including the superposition effect between the formula components through the candidate prediction network, and output the superposition effect representation value corresponding to the sample formula data, wherein the superposition effect representation value is used to reflect the matching degree between the formula components and the target linear constraint relationship, and the target linear constraint relationship is at least used to reflect that the sum of the molar percentages of the component contents corresponding to the formula components is 100%; A prediction network optimization module, configured to optimize the parameters of the candidate prediction network based on the transmittance label, the transmittance prediction data and the superposition effect characterization value to form a target prediction network; An optical glass data prediction module is used to predict the target optical glass data using the target prediction network, and output predicted optical glass data corresponding to the target optical glass data, wherein when the target optical glass data includes formula data, the predicted optical glass data includes transmittance data, and when the target optical glass data includes transmittance data, the predicted optical glass data includes formula data.

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

Images