Coating optimization method and system for new radiation-proof resin lenses

Abstract

The application provides a coating optimization method and system for a new type of radiation-proof resin lens, relates to the technical field of lens coating optimization, and comprises the following steps: tracking and analyzing the working environment of a lens user to obtain a radiation protection requirement set; obtaining a set of K coating schemes; performing enumeration combination to obtain a plurality of composite coating strategies; performing light transmittance fitting to obtain a plurality of composite light transmittance coefficients; obtaining a target light transmittance constraint interactively, and combining the plurality of composite light transmittance coefficients to perform light transmittance optimization layer configuration to obtain a plurality of light transmittance coating strategies; performing coating fitness analysis on the plurality of composite coating strategies and the plurality of light transmittance coating strategies to obtain a target coating optimization strategy, and performing multi-layer composite coating on a resin lens substrate. Through the application, the technical problem that the existing radiation-proof resin lens cannot effectively protect against multi-dimensional radiation in a complex radiation scene can be solved, and the technical effect of improving the protection effect of the lens in the complex radiation scene can be achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of lens coating optimization, and particularly relates to a coating optimization method and system for a new type of anti-radiation resin lens. BACKGROUND

[0002] The anti-radiation resin lens reflects and absorbs electronic product radiation, effectively filters out radiation, and reduces the harm of radiation to the eyes.

[0003] At present, existing resin lenses are exposed to complex radiation scenarios for a long time, such as blue light emitted by computer screens, ultraviolet rays from the sun, etc., and have not been effectively protected, which may cause eye fatigue, dryness, vision decline and other health problems of users. Certain types of radiation, such as ultraviolet rays, can also cause damage to the skin and increase the risk of skin cancer. Therefore, a method is needed to solve the above problems.

[0004] In summary, the existing anti-radiation resin lens has the defect that it cannot effectively protect against multi-dimensional radiation in complex radiation scenarios, which causes long-term effects on the user's vision and further affects the user's health. SUMMARY

[0005] The purpose of the present application is to provide a coating optimization method and system for a new type of anti-radiation resin lens to solve the technical problem that the existing anti-radiation resin lens cannot effectively protect against multi-dimensional radiation in complex radiation scenarios, which causes long-term effects on the user's vision and further affects the user's health.

[0006] In view of the above problems, the present application provides a coating optimization method and system for a new type of anti-radiation resin lens.

[0007] In a first aspect, the application provides a coating optimization method for a new type of radiation-resistant resin lens, which is implemented by a coating optimization system for the new type of radiation-resistant resin lens, wherein the method comprises: performing work environment tracking analysis on a lens user to obtain a radiation protection requirement set, wherein the radiation protection requirement set comprises K radiation protection requirements of K radiation types; obtaining K coating scheme sets, wherein the K coating scheme sets are generated by synchronizing the radiation protection requirement set to a pre-constructed coating scheme database and performing coating scheme matching; performing enumeration combination on the K coating scheme sets to obtain a plurality of composite coating strategies; performing light transmittance fitting on the plurality of composite coating strategies to obtain a plurality of composite light transmittance coefficients; interactively obtaining a target light transmittance constraint, and combining the plurality of composite light transmittance coefficients to perform light transmittance optimization layer configuration to obtain a plurality of light transmittance coating strategies; performing coating fitness analysis on the plurality of composite coating strategies and the plurality of light transmittance coating strategies to obtain a target coating optimization strategy, and performing multi-layer composite coating on a resin lens substrate of the lens user based on the target coating optimization strategy.

[0008] In a second aspect, the application also provides a coating optimization system for a new type of radiation-resistant resin lens, which is used to perform the coating optimization method for the new type of radiation-resistant resin lens as described in the first aspect, wherein the system comprises: a radiation protection requirement set obtaining module, which is used to perform work environment tracking analysis on a lens user to obtain a radiation protection requirement set, wherein the radiation protection requirement set comprises K radiation protection requirements of K radiation types; a coating scheme set obtaining module, which is used to obtain K coating scheme sets, wherein the K coating scheme sets are generated by synchronizing the radiation protection requirement set to a pre-constructed coating scheme database and performing coating scheme matching; a composite coating strategy obtaining module, which is used to perform enumeration combination on the K coating scheme sets to obtain a plurality of composite coating strategies; a composite light transmittance coefficient obtaining module, which is used to perform light transmittance fitting on the plurality of composite coating strategies to obtain a plurality of composite light transmittance coefficients; a light transmittance coating strategy obtaining module, which is used to interactively obtain a target light transmittance constraint, and combine the plurality of composite light transmittance coefficients to perform light transmittance optimization layer configuration to obtain a plurality of light transmittance coating strategies; a multi-layer composite coating module, which is used to perform coating fitness analysis on the plurality of composite coating strategies and the plurality of light transmittance coating strategies to obtain a target coating optimization strategy, and perform multi-layer composite coating on a resin lens substrate of the lens user based on the target coating optimization strategy.

[0009] The one or more technical solutions provided in the application have at least the following technical effects or advantages:

[0010] The radiation protection requirement set is obtained by tracking and analyzing the working environment of the lens user, wherein the radiation protection requirement set includes K radiation protection requirements of K radiation types; K coating scheme sets are obtained, wherein the K coating scheme sets are obtained by synchronizing the radiation protection requirement set to a pre-constructed coating scheme database, performing coating scheme matching generation; enumeration combination is performed on the K coating scheme sets to obtain a plurality of composite coating strategies; a target light transmittance constraint is obtained interactively, and light transmittance optimization layer configuration is performed in combination with the plurality of composite light transmittance coefficients to obtain a plurality of light transmittance coating strategies; coating fitness analysis is performed on the plurality of composite coating strategies and the plurality of light transmittance coating strategies to obtain a target coating optimization strategy, and multi-layer composite coating is performed on the resin lens substrate of the lens user based on the target coating optimization strategy, so as to achieve the technical goal of improving the protection effect of the lens under complex radiation, and achieve the technical effect of protecting the health of the user.

[0011] The above description is only a summary of the technical solutions of the present application. In order to more clearly understand the technical means of the present application, the specific embodiments of the present application can be implemented according to the content of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only exemplary, and other drawings can be obtained by the provided drawings without creative labor for those skilled in the art.

[0013] Figure 1 Flowchart of the coating optimization method for the new type of anti-radiation resin lens of the present application;

[0014] Figure 2 Structure diagram of the coating optimization system for the new type of anti-radiation resin lens of the present application.

[0015] Explanation of reference signs:

[0016] Radiation protection requirement set obtaining module 11, coating scheme set obtaining module 12, composite coating strategy obtaining module 13, composite light transmittance coefficient obtaining module 14, light transmittance coating strategy obtaining module 15, multi-layer composite coating module 16. DETAILED DESCRIPTION

[0017] The application provides a coating optimization method and system for a new type of radiation-proof resin lens, solves the technical problem that the existing radiation-proof resin lens cannot effectively prevent multi-dimensional radiation in a complex radiation scene, thereby affecting the user's vision for a long time and further affecting the user's health, achieves the technical goal of improving the protection effect of the lens in a complex radiation scene, and achieves the technical effect of protecting the user's health.

[0018] The technical solutions in the application will be described clearly and completely below with reference to the drawings. Obviously, the described embodiments are only some of the embodiments of the application, rather than all the embodiments of the application. It should be understood that the application is not limited to the example embodiments described herein. Based on the embodiments of the application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the scope of protection of the application. In addition, it should be noted that, for convenience of description, only parts related to the application are shown in the drawings, rather than all parts.

[0019] Embodiment one

[0020] Please refer to the drawings Figure 1 The application provides a coating optimization method for a new type of radiation-proof resin lens, wherein the method is applied to a coating optimization system for the new type of radiation-proof resin lens, and the method specifically comprises the following steps.

[0021] Step one: tracking analysis is performed on the working environment of a lens user to obtain a radiation protection demand set, wherein the radiation protection demand set comprises K radiation protection demands of K radiation types.

[0022] Specifically, the working environment of the lens user is tracked and analyzed to understand the radiation types to which the lens user is exposed. According to the working environment analysis, K radiation protection demands of K radiation types required by the lens user are determined, including ultraviolet rays, infrared rays, blue light and the like. At least one radiation type to which the lens user is exposed is included, and therefore K is a positive integer.

[0023] Step two: K coating scheme sets are obtained, wherein the K coating scheme sets are generated by synchronizing the radiation protection demand set to a pre-constructed coating scheme database and performing coating scheme matching.

[0024] Specifically, the radiation protection demand set is synchronized to the pre-constructed coating scheme database. Coating scheme matching is performed in the database, a corresponding coating scheme is generated for each radiation protection demand, and K coating scheme sets are formed.

[0025] Step three: enumeration combination is performed on the K coating scheme sets to obtain a plurality of composite coating strategies.

[0026] Specifically, the K plating layer scheme sets are enumerated and combined to generate a plurality of possible composite plating film strategies, including the combination mode and sequence between different plating layers.

[0027] Step four: performing light transmittance fitting on the plurality of composite plating film strategies to obtain a plurality of composite light transmittance coefficients.

[0028] Specifically, light transmittance fitting analysis is performed on each composite plating film strategy to simulate the light transmittance performance under different light conditions. According to the fitting results, the composite light transmittance coefficient of each composite plating film strategy is calculated and obtained, reflecting the influence of the plating film strategy on the light transmittance performance of the lens.

[0029] Step five: interactively obtaining target light transmittance constraints, and combining the plurality of composite light transmittance coefficients to perform light transmittance optimization layer configuration to obtain a plurality of light transmittance plating film strategies.

[0030] Specifically, the constraint conditions of the target light transmittance performance of the lens user are interactively obtained. Combined with the plurality of composite light transmittance coefficients and the target light transmittance constraints of the user, the plating cost of the plating layer that does not meet the plurality of composite light transmittance coefficients is evaluated, and then the light transmittance optimization layer configuration is performed to generate a plurality of light transmittance plating film strategies that meet the user's requirements. The light transmittance performance requirements of the plating layer only include refractive index and light transmittance. A multi-layer thin film structure is designed, some of which are used to provide anti-radiation function, and some of which are used to offset the influence of the radiation function layer on blocking light. Further, by precisely controlling the thickness and refractive index of each layer of thin film, a higher light transmittance can be achieved.

[0031] Step six: performing plating fitness analysis on the plurality of composite plating film strategies and the plurality of light transmittance plating film strategies to obtain a target plating optimization strategy, and performing multi-layer composite plating on the resin lens substrate of the lens user based on the target plating optimization strategy.

[0032] Specifically, the plating fitness analysis is performed on the plurality of composite plating film strategies and the plurality of light transmittance plating film strategies, including the evaluation of plating cost, production feasibility, actual use effect and the like. According to the fitness analysis result, the target plating optimization strategy is determined. Based on the target plating optimization strategy, the multi-layer composite plating is performed on the resin lens substrate of the lens user. It is ensured that the plating process meets the relevant process requirements, and the quality and performance of the plating are guaranteed.

[0033] The plating optimization method for the new anti-radiation resin lens is applied to a plating optimization system for the new anti-radiation resin lens, which can achieve the technical goal of improving the protection effect of the lens under complex radiation, and achieve the technical effect of protecting the user's health.

[0034] Further, the present application also includes:

[0035] interactively obtaining a position parameter of the working environment, and demarcating a radiation tracking space according to the position parameter; presetting a radiation tracking time domain and a radiation tracking index set; taking the radiation tracking time domain and the radiation tracking space as spatiotemporal constraints, and taking the radiation tracking index set as an object constraint, performing working environment radiation tracking collection on the lens user to obtain a radiation time-varying sequence set; interactively obtaining a radiation protection limit set as a constraint of the radiation tracking index set; and traversing the radiation time-varying sequence set by using the radiation protection limit set to obtain the radiation protection requirement set.

[0036] Specifically, the position parameter of the working environment is interactively obtained to determine the working environment of the lens user, the possible position of the radiation source and the area possibly affected by radiation, and the radiation tracking space is demarcated according to the position parameter as the basis for subsequent radiation tracking collection.

[0037] Then, the radiation tracking time domain refers to the time period for performing radiation tracking, indicating the duration and frequency of data collection. The radiation tracking index set is a parameter for measuring and evaluating the characteristics of radiation, such as radiation intensity, frequency, direction, etc. The radiation tracking time domain and the radiation tracking index set are preset.

[0038] Next, in the demarcated radiation tracking space and the preset radiation tracking time domain, the working environment of the lens user is collected using the pre-set radiation tracking index set, and the obtained radiation data is formed into a radiation time-varying sequence set, reflecting the radiation changes in the working environment.

[0039] Next, according to the requirements of the radiation tracking index set, combined with the radiation protection performance and design requirements of the lens, a set of radiation protection limit set is formulated as the constraint condition for coating optimization, to ensure that the coating can meet the specific radiation protection requirements.

[0040] Then, the radiation protection limit set is used to traverse and analyze the radiation time-varying sequence set, to determine the radiation protection requirements of the lens under different conditions according to the radiation exposure of the lens at different times and positions, forming a radiation protection requirement set to provide guidance for subsequent coating optimization. Excessive reflection or absorption of specific wavelengths of light can cause color distortion. For example, if blue light is reflected excessively, it may make the blue objects in the environment appear too dark or discolored. Based on this, the embodiment determines the radiation protection requirements according to the actual radiation.

[0041] For example, the protection against blue light radiation is to accurately reflect blue light in the wavelength range of 450 nanometers to 480 nanometers.

[0042] By obtaining the radiation protection requirement set, the radiation conditions of the working environment of the lens user are accurately tracked and analyzed, providing strong data support for the coating optimization of the lens.

[0043] Further, the present application also includes:

[0044] Interactively obtain a plurality of sample radiation types; perform network data call on the first sample radiation type to obtain a first sample protection scheme set, wherein the first sample protection scheme set includes a sample absorption coating scheme set, a sample reflection coating scheme set, and a sample filtering coating scheme set, wherein the first sample radiation type is any one of the plurality of sample radiation types; in the same way, obtain a plurality of sample protection scheme sets of the plurality of sample radiation types; perform mapping storage of the plurality of sample radiation types and the plurality of sample protection scheme sets based on the knowledge graph to generate the coating scheme database; synchronize the radiation protection requirement set to the coating scheme database to perform coating scheme matching to generate the K coating scheme sets, wherein each coating scheme set includes a matching absorption coating scheme, a matching reflection coating scheme, and a matching filtering coating scheme.

[0045] Specifically, a plurality of different sample radiation types are input by a user and stored in a list to obtain the plurality of sample radiation types.

[0046] Then, for a first sample radiation type randomly extracted from the list, a networked data is called to obtain a related protection scheme set, including a plurality of coating design parameters of a coating layer that filters blue light in different wavelength ranges. The obtained first sample protection scheme set is divided into a sample absorption coating scheme set, a sample reflection coating scheme set, and a sample filtering coating scheme set. According to the sample filtering coating scheme set, taking blue light radiation protection as an example, the lens coating layer can be designed to reflect blue light in a specific wavelength range, the lens coating layer can also be designed to absorb blue light in a specific wavelength range, and the lens coating layer can also be designed to filter blue light in a specific wavelength range.

[0047] Next, the scheme set corresponding to each sample radiation type is stored to obtain a plurality of sample protection scheme sets.

[0048] Next, the knowledge graph is a graph structure used to represent the relationship between entities. Based on the knowledge graph, the plurality of sample radiation types and the plurality of sample protection scheme sets are mapped and stored to generate a coating scheme database.

[0049] Then, for each radiation protection requirement, the related relationship and entity are queried in the knowledge graph, and the matching protection scheme set is found in the coating scheme database. According to the matching result, a coating scheme set is generated for each radiation protection requirement, including a matching absorption coating scheme, a reflection coating scheme, and a filtering coating scheme. The generated coating scheme set is output for the user to refer to or use.

[0050] Through the radiation type and the protection scheme set, the query performance of the database and the knowledge graph is optimized to improve the matching efficiency.

[0051] Further, the present application also includes:

[0052] The K plating scheme sets are subjected to enumeration combination to obtain a plurality of plating scheme combinations; the plurality of plating scheme combinations are subjected to plating sequence enumeration to obtain a plurality of plating scheme sequence groups; the standard plating sequence rules are obtained through interaction, and after the plurality of plating scheme sequence groups are subjected to depacketization processing, the abnormal plating sequence identification screening is performed based on the standard plating sequence rules to obtain the plurality of composite plating film strategies.

[0053] Specifically, the K plating scheme sets are subjected to enumeration combination, that is, all possible combination modes are considered to generate a plurality of plating scheme combinations. Each plating scheme combination contains one or more schemes in the K plating scheme sets. According to actual requirements, the generated combinations are optimized, for example, screened based on cost, efficiency, protection effect, and other factors.

[0054] Then, the plurality of plating scheme combinations are subjected to sequence enumeration, that is, the execution sequence of different plating schemes in each combination is obtained. By enumerating all possible sequences, a plurality of plating scheme sequence groups are obtained. Each plating scheme sequence group includes a plurality of plating scheme sequences, and the plating scheme sequence is the plating sequence of the plating layer on the resin lens base layer.

[0055] Next, the composite plating layer needs to follow certain plating rules to ensure that the required optical performance is achieved, for example, the reflective plating layer is usually located at the top layer of the composite plating layer, the filtering plating layer is usually located at the middle layer of the plating layer accumulation, and the absorbing plating layer is usually located at the bottom of the plating layer accumulation. Through user interaction or other ways, the standard plating sequence rules are obtained. The standard plating sequence rules may be based on industry experience, technical requirements, process standards, etc. The validity and applicability of the input standard plating sequence rules are verified to ensure that the plating scheme sequencing and screening can be correctly guided.

[0056] Next, the multiple sets of plating layer scheme sequences are de-grouped, i.e., treated as a whole rather than grouped according to the original combination, to facilitate subsequent identification of abnormal sequences based on the standard plating layer sequence rules. According to the standard plating layer sequence rules, the de-grouped plating layer scheme sequences are identified for abnormal sequences. It is checked whether the sequence of the plating layer scheme in each sequence meets the requirements of the rules, and abnormal sequences that do not meet the rules are identified. For example, if an absorbing plating layer is located at the bottom layer of the plating layer accumulation in a certain plating layer scheme sequence, then the plating layer scheme sequence has some plating layers that are affected and cannot effectively protect the radiation protection object, resulting in a lack of implementability. The identified abnormal sequences are filtered out from the multiple sets of plating layer scheme sequences, and sequences that meet the standard plating layer sequence rules are retained. After the above steps of screening and optimization, the final plating layer scheme sequences that meet the standard plating layer sequence rules are obtained, which are the multiple composite plating film strategies. Each strategy contains a series of ordered plating layer schemes, which can guide the actual plating operation.

[0057] By obtaining multiple composite plating film strategies, users can refer to or use them to improve the accuracy and efficiency of the plating process.

[0058] Further, the present application also includes:

[0059] The multiple composite plating film strategies are synchronized to a pre-constructed plating layer prediction analysis network to perform plating layer optical performance prediction, and multiple plating layer performance sequences are obtained. A plating layer accumulation prediction model is pre-constructed, and the plating layer accumulation prediction model is used to perform recursive analysis on the multiple plating layer performance sequences, and the multiple composite light transmission coefficients are obtained.

[0060] Specifically, the multiple composite plating film strategies are synchronized to a pre-constructed plating layer prediction analysis network. The plating layer prediction analysis network is a machine learning model trained based on a neural network, which can predict the optical performance of a plating film strategy based on parameters such as material, thickness, and structure. The plating layer prediction analysis network is used to predict the optical performance of each composite plating film strategy, such as reflectivity, transmissivity, and absorptivity. According to the prediction results, multiple plating layer performance sequences are formed, each containing optical performance data of the plating film strategy under different wavelengths or conditions.

[0061] Then, a prediction model for coating stack analysis is pre-built. Among them, the historical coating combination records are used as sample coating combinations, and the corresponding optical performance data are used to train the coating stack prediction model. The model training process may include data collection, preprocessing, feature extraction, and model training steps, which are used to process the interaction and optical superposition effect between multiple layers of coating films. A plurality of coating performance sequences are input into the coating stack prediction model, and recursive analysis is performed to calculate the optical performance of the superposition of multiple layers of coating films according to the influence of each layer of coating film on the overall optical performance. Based on the results of the recursive analysis, the composite transmittance coefficient of each composite coating film strategy is calculated. The composite transmittance coefficient is a key indicator for describing the overall transmission performance of multiple layers of coating films, according to the interaction and optical superposition effect between all coatings.

[0062] Through the coating prediction analysis network and the coating stack prediction model, the model is verified using an independent validation data set to ensure the accuracy and reliability of its prediction results.

[0063] Further, the present application also includes:

[0064] The plurality of sample absorption coating schemes and the plurality of sample first coating optical performances are obtained interactively, and the plurality of sample absorption coating schemes and the plurality of sample first coating optical performances are used to build an absorption coating prediction branch; the plurality of sample reflection coating schemes and the plurality of sample first coating optical performances are obtained interactively, and the plurality of sample reflection coating schemes and the plurality of sample first coating optical performances are used to build a reflection coating prediction branch; the plurality of sample filter coating schemes and the plurality of sample first coating optical performances are obtained interactively, and the plurality of sample filter coating schemes and the plurality of sample first coating optical performances are used to build a filter coating prediction branch; the absorption coating prediction branch, the reflection coating prediction branch and the filter coating prediction branch are connected in parallel to build the coating prediction analysis network.

[0065] Specifically, a plurality of sample absorption coating schemes are obtained interactively through user input, experimental data or other data sources. The optical performance data of a plurality of sample first coatings corresponding to the sample absorption coating schemes are collected. For example, the plurality of sample first coating optical performances include refractive index, reflectivity and coating thickness. The features capable of describing the coating scheme and the first coating optical performance are extracted from the collected data. The absorption coating prediction branch is built using machine learning or deep learning algorithm, taking the first coating optical performance as input to predict the optimal absorption coating design scheme.

[0066] Then, a plurality of sample reflective coating schemes are obtained through user input, experimental data or other data sources interaction. A plurality of sample first coating optical performance data corresponding to the sample reflective coating schemes are collected. For example, the plurality of sample first coating optical performance includes refractive index, reflectivity and coating thickness. Features capable of describing the coating scheme and the first coating optical performance are extracted from the collected data. A reflective coating prediction branch is constructed using a machine learning or deep learning algorithm, taking the first coating optical performance as input to predict the optimal reflective coating design scheme.

[0067] Next, a plurality of sample filter coating schemes are obtained through user input, experimental data or other data sources interaction. A plurality of sample first coating optical performance data corresponding to the sample filter coating schemes are collected. For example, the plurality of sample first coating optical performance includes refractive index, reflectivity and coating thickness. Features capable of describing the coating scheme and the first coating optical performance are extracted from the collected data. A filter coating prediction branch is constructed using a machine learning or deep learning algorithm, taking the first coating optical performance as input to predict the optimal filter coating design scheme.

[0068] Next, each prediction branch is trained through a loss function and an optimization algorithm to adjust the parameters of the model to minimize the prediction error. The trained absorption coating prediction branch, reflective coating prediction branch and filter coating prediction branch are connected in parallel to construct a complete coating prediction analysis network, which can simultaneously process different types of coating prediction tasks.

[0069] By constructing the generated coating prediction analysis network, the prediction accuracy, stability and generalization ability of the model are further improved.

[0070] Further, the present application also includes:

[0071] The optical performance of the pre-plated layers of multiple samples, the optical performance of the post-plated layers of multiple samples, and the optical performance of the composite coated films of multiple samples are obtained interactively. The plated layer accumulation prediction model is constructed based on the back propagation neural network, and the plated layer accumulation prediction model is trained using the optical performance of the pre-plated layers of multiple samples, the optical performance of the post-plated layers of multiple samples, and the optical performance of the composite coated films of multiple samples until the output of the plated layer accumulation prediction model meets the preset requirements. A first plated layer performance sequence is obtained based on the multiple plated layer performance sequences, wherein the first plated layer performance sequence includes K plated layer optical performances, and the K plated layer optical performances have K sequence position identifiers. The first plated layer optical performance and the second plated layer optical performance are called from the K plated layer optical performances based on the K sequence position identifiers, and plated layer accumulation performance prediction is performed based on the plated layer accumulation prediction model to obtain a first accumulated plated layer optical performance. The third plated layer optical performance is called from the K plated layer optical performances based on the K sequence position identifiers, and plated layer accumulation performance prediction is performed based on the plated layer accumulation prediction model on the first accumulated plated layer optical performance and the third plated layer optical performance to obtain a second accumulated plated layer optical performance. In this way, the K-1th accumulated plated layer optical performance is obtained, and the K-1th accumulated plated layer optical performance is taken as the first composite transmittance coefficient of the first plated layer performance sequence. In this way, the plated layer accumulation prediction model is used to perform recursive analysis on the multiple plated layer performance sequences to obtain the multiple composite transmittance coefficients.

[0072] Specifically, the optical performance of the pre-plated layers of multiple samples, the optical performance of the post-plated layers of multiple samples, and the optical performance of the composite coated films of multiple samples are obtained interactively as the input and output of the model.

[0073] Then, a plated layer accumulation prediction model is constructed using a back propagation neural network. The plated layer accumulation prediction model is trained using the collected optical performance data of the pre-plated layers, the post-plated layers, and the composite coated films until the output of the plated layer accumulation prediction model meets the preset accuracy or performance requirements. The preset requirement is the plated layer accumulation of the pre-plated layer-post-plated layer-lens base layer.

[0074] Next, a first plated layer performance sequence is obtained based on the multiple plated layer performance sequences, wherein the first plated layer performance sequence includes K plated layer optical performances, and the K plated layer optical performances have K sequence position identifiers. For each plated layer performance sequence, for example, the multiple plated layers of any plated layer stack, K plated layer optical performances are extracted and assigned K sequence position identifiers.

[0075] Next, two plated layer optical performances are randomly selected from the K plated layer optical performances as the first plated layer optical performance and the second plated layer optical performance. The plated layer accumulation performance prediction is performed using the trained plated layer accumulation prediction model to obtain the first accumulated plated layer optical performance.

[0076] Then, a third coating optical property is randomly selected based on the K sequence position identifiers from the K coating optical properties, together with the first stack coating optical property, the coating stack prediction model is used again to make a prediction, and a second stack coating optical property is obtained.

[0077] The stacking process is repeated, and one coating optical property is added each time until all K coating optical properties are considered, and finally a K-1 stack coating optical property is obtained, which is regarded as the first composite transmittance coefficient of the entire coating performance sequence.

[0078] The recursive analysis process is repeated for multiple coating performance sequences to obtain multiple composite transmittance coefficients.

[0079] For example, the fifth coating in the five coatings is the coating connected to the lens substrate. After fitting the first refractive index and the first transmittance of the first composite coating formed by the first coating and the second coating, the first coating and the second coating are taken as a whole, and the second refractive index and the second transmittance of the second composite coating formed by the first-second coating and the third coating are fitted, and so on. The overall refractive index and the overall transmittance of the composite coating formed by the five coatings are continuously added.

[0080] Through recursive analysis, the composite transmittance coefficient of each coating performance sequence is obtained, which reflects the optical performance of different coating stacks, and provides an important reference for designing and optimizing the coating structure.

[0081] In summary, the coating optimization method for the new radiation-resistant resin lens provided by the present application has the following technical effects:

[0082] Through tracking analysis of the working environment of the lens user, a radiation protection requirement set is obtained, wherein the radiation protection requirement set includes K radiation protection requirements of K radiation types; a K coating scheme set is obtained, wherein the K coating scheme set is generated by synchronizing the radiation protection requirement set to a pre-constructed coating scheme database and performing coating scheme matching; enumeration combination is performed on the K coating scheme set to obtain a plurality of composite coating strategies; transmittance fitting is performed on the plurality of composite coating strategies to obtain a plurality of composite transmittance coefficients; a target transmittance constraint is obtained interactively, and transmittance optimization layer configuration is performed in combination with the plurality of composite transmittance coefficients to obtain a plurality of transmittance coating strategies; coating fitness analysis is performed on the plurality of composite coating strategies and the plurality of transmittance coating strategies to obtain a target coating optimization strategy; and based on the target coating optimization strategy, multi-layer composite coating is performed on the resin lens substrate of the lens user, thereby achieving the technical goal of improving the protection effect of the lens under complex radiation scenarios, and achieving the technical effect of protecting the user's health.

[0083] Embodiment Two

[0084] Based on the coating optimization method for the new type of radiation protection resin lens in the foregoing embodiments, the application also provides a coating optimization system for the new type of radiation protection resin lens, please refer to the attached Figure 2 , the system comprises:

[0085] a radiation protection requirement set obtaining module 11, which is used for performing work environment tracking analysis on a lens user to obtain a radiation protection requirement set, wherein the radiation protection requirement set comprises K radiation protection requirements of K radiation types;

[0086] a coating scheme set obtaining module 12, which is used for obtaining K coating scheme sets by synchronizing the radiation protection requirement set to a pre-constructed coating scheme database to perform coating scheme matching generation;

[0087] a composite coating strategy obtaining module 13, which is used for performing enumeration combination on the K coating scheme sets to obtain a plurality of composite coating strategies;

[0088] a composite transmittance coefficient obtaining module 14, which is used for performing transmittance fitting on the plurality of composite coating strategies to obtain a plurality of composite transmittance coefficients;

[0089] a transmittance coating strategy obtaining module 15, which is used for interactively obtaining a target transmittance constraint and combining the plurality of composite transmittance coefficients to perform transmittance optimization layer configuration to obtain a plurality of transmittance coating strategies;

[0090] a multi-layer composite coating module 16, which is used for performing coating fitness analysis on the plurality of composite coating strategies and the plurality of transmittance coating strategies to obtain a target coating optimization strategy, and performing multi-layer composite coating on a resin lens substrate of the lens user based on the target coating optimization strategy.

[0091] Further, the radiation protection requirement set obtaining module 11 in the system is also used for:

[0092] interactively obtaining a location parameter of the work environment and delimiting a radiation tracking space according to the location parameter;

[0093] pre-setting a radiation tracking time domain and a radiation tracking index set;

[0094] performing work environment radiation tracking collection on the lens user with the radiation tracking time domain and the radiation tracking space as spatiotemporal constraints and the radiation tracking index set as object constraints to obtain a radiation time-varying sequence set;

[0095] obtain a radiation protection limit set interactively with the radiation tracking index set as a constraint;

[0096] obtain the radiation protection requirement set by traversing the radiation time-varying sequence set with the radiation protection limit set.

[0097] Further, the plating scheme set obtaining module 12 in the system is further used for:

[0098] interactively obtaining a plurality of sample radiation types;

[0099] performing network data calling on a first sample radiation type to obtain a first sample protection scheme set, wherein the first sample protection scheme set includes a sample absorption plating scheme set, a sample reflection plating scheme set, and a sample filtering plating scheme set, and the first sample radiation type is any one of the plurality of sample radiation types;

[0100] By analogy, a plurality of sample protection scheme sets of the plurality of sample radiation types are obtained;

[0101] performing mapping storage of the plurality of sample radiation types and the plurality of sample protection scheme sets based on a knowledge graph to generate the plating scheme database;

[0102] synchronizing the radiation protection requirement set to the plating scheme database to perform plating scheme matching to generate the K plating scheme sets, wherein each plating scheme set includes a matching absorption plating scheme, a matching reflection plating scheme, and a matching filtering plating scheme.

[0103] Further, the composite plating film strategy obtaining module 13 in the system is further used for:

[0104] performing enumeration combination on the K plating scheme sets to obtain a plurality of plating scheme combinations;

[0105] performing plating sequence enumeration on the plurality of plating scheme combinations to obtain a plurality of plating scheme sequence groups;

[0106] interactively obtaining a standard plating sequence rule, performing depacketization processing on the plurality of plating scheme sequence groups, and performing abnormal plating sequence identification and screening based on the standard plating sequence rule to obtain the plurality of composite plating film strategies.

[0107] Further, the composite transmittance coefficient obtaining module 14 in the system is further used for:

[0108] synchronizing the plurality of composite plating film strategies to a pre-constructed plating prediction analysis network to perform plating optical performance prediction to obtain a plurality of plating performance sequences;

[0109] The pre-built coating stack prediction model is used to perform recursive analysis on the plurality of coating performance sequences, and the plurality of composite transmittance coefficients is obtained.

[0110] Further, the composite transmittance coefficient obtaining module 14 in the system is further configured to:

[0111] The plurality of sample absorption coating schemes and the plurality of sample first coating optical performances are obtained interactively, and the absorption coating prediction branch is constructed based on the plurality of sample absorption coating schemes and the plurality of sample first coating optical performances.

[0112] The plurality of sample reflection coating schemes and the plurality of sample first coating optical performances are obtained interactively, and the reflection coating prediction branch is constructed based on the plurality of sample reflection coating schemes and the plurality of sample first coating optical performances.

[0113] The plurality of sample filter coating schemes and the plurality of sample first coating optical performances are obtained interactively, and the filter coating prediction branch is constructed based on the plurality of sample filter coating schemes and the plurality of sample first coating optical performances.

[0114] The absorption coating prediction branch, the reflection coating prediction branch, and the filter coating prediction branch are connected in parallel to construct the coating prediction analysis network.

[0115] Further, the composite transmittance coefficient obtaining module 14 in the system is further configured to:

[0116] The plurality of sample front coating optical performances, the plurality of sample rear coating optical performances, and the plurality of sample composite coating optical performances are obtained interactively.

[0117] The coating stack prediction model is constructed based on a back propagation neural network, and the plurality of sample front coating optical performances, the plurality of sample rear coating optical performances, and the plurality of sample composite coating optical performances are used to train the coating stack prediction model until the output of the coating stack prediction model meets a preset requirement.

[0118] Based on the plurality of coating performance sequences, a first coating performance sequence is extracted and obtained, wherein the first coating performance sequence includes K coating optical performances, and the K coating optical performances have K sequence position identifiers.

[0119] Based on the K sequence position identifiers, a first coating optical performance and a second coating optical performance are called and obtained from the K coating optical performances, and coating stack performance prediction is performed based on the coating stack prediction model to obtain a first stack coating optical performance.

[0120] obtaining a third coating optical performance from the K coating optical performance calls based on the K sequence position identification, and performing coating accumulation performance prediction on the first accumulation coating optical performance and the third coating optical performance based on the coating accumulation prediction model to obtain a second accumulation coating optical performance;

[0121] By analogy, a K-1 accumulation coating optical performance is obtained, and the K-1 accumulation coating optical performance is taken as a first complex transmittance coefficient of the first coating performance sequence;

[0122] By analogy, the coating accumulation prediction model is used to perform recursive analysis on the plurality of coating performance sequences to obtain the plurality of complex transmittance coefficients.

[0123] Each of the embodiments in the specification is described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The coating optimization method for the new radiation-proof resin lens in the first embodiment and the specific example are also applicable to the coating optimization system for the new radiation-proof resin lens in the embodiment. The coating optimization system for the new radiation-proof resin lens in the embodiment can be clearly understood by the person skilled in the art through the detailed description of the coating optimization method for the new radiation-proof resin lens in the foregoing. Therefore, for the sake of brevity of the specification, the coating optimization system for the new radiation-proof resin lens in the embodiment is not described in detail. For the system disclosed in the embodiment, the description is relatively simple because it corresponds to the method disclosed in the embodiment. Therefore, refer to the method part for relevant description.

[0124] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0125] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application also intends to include these modifications and variations.

Claims

1. A coating optimization method for radiation-protective resin lenses, characterized in that, The method comprises: Performing work environment tracking analysis on the lens user to obtain a radiation protection requirement set, wherein the radiation protection requirement set comprises K radiation protection requirements of K radiation types; Obtain K coating scheme sets, wherein the K coating scheme sets are generated by synchronizing the radiation protection requirement set to a pre-constructed coating scheme database and performing coating scheme matching; Performing enumeration combination on the K coating scheme sets to obtain a plurality of composite coating strategies; Performing light transmittance fitting on the plurality of composite coating strategies to obtain a plurality of composite light transmittance coefficients, specifically comprising: Synchronize the plurality of composite coating strategies to the pre-constructed coating prediction analysis network to perform coating optical performance prediction to obtain a plurality of coating performance sequences; Pre-construct a coating accumulation prediction model, and perform recursive analysis on the plurality of coating performance sequences using the coating accumulation prediction model to obtain the plurality of composite light transmittance coefficients, specifically comprising: Interactively obtain a plurality of sample front coating optical performances, a plurality of sample rear coating optical performances, and a plurality of sample composite coating optical performances; Construct the coating accumulation prediction model based on a back propagation neural network, and train the coating accumulation prediction model using the plurality of sample front coating optical performances, the plurality of sample rear coating optical performances, and the plurality of sample composite coating optical performances until the output of the coating accumulation prediction model meets the preset requirements; Based on the plurality of coating performance sequences, a first coating performance sequence is obtained, wherein the first coating performance sequence comprises K coating optical performances, and the K coating optical performances have K sequence position identifiers; Based on the K sequence position identifiers, a first coating optical performance and a second coating optical performance are called from the K coating optical performances, and coating accumulation performance prediction is performed based on the coating accumulation prediction model to obtain a first accumulation coating optical performance; Based on the K sequence position identifiers, a third coating optical performance is called from the K coating optical performances, and coating accumulation performance prediction is performed based on the coating accumulation prediction model on the first accumulation coating optical performance and the third coating optical performance to obtain a second accumulation coating optical performance; By analogy, a K-1 accumulation coating optical performance is obtained, and the K-1 accumulation coating optical performance is taken as a first composite light transmittance coefficient of the first coating performance sequence; By analogy, recursive analysis is performed on the plurality of coating performance sequences using the coating accumulation prediction model to obtain the plurality of composite light transmittance coefficients; The method for constructing a coating prediction analysis network comprises: Interactively obtain a plurality of sample absorption coating schemes and a plurality of sample first coating optical performances, and construct an absorption coating prediction branch using the plurality of sample absorption coating schemes and the plurality of sample first coating optical performances; Interactively obtain a plurality of sample reflection coating schemes and a plurality of sample first coating optical performances, and construct a reflection coating prediction branch using the plurality of sample reflection coating schemes and the plurality of sample first coating optical performances; Interactively obtain a plurality of sample filter coating scheme and a plurality of sample first coating optical performance, and use the plurality of sample filter coating scheme and the plurality of sample first coating optical performance to construct a filter coating prediction branch; Parallel the absorption coating prediction branch, the reflection coating prediction branch and the filter coating prediction branch to construct the coating prediction analysis network; Interactively obtain a target light transmission constraint, and combine the plurality of composite light transmission coefficients to perform light transmission optimization layer configuration to obtain a plurality of light transmission coating strategies; Perform coating fitness analysis on the plurality of composite coating strategies and the plurality of light transmission coating strategies to obtain a target coating optimization strategy, and perform multi-layer composite coating on the resin lens substrate of the lens user based on the target coating optimization strategy.

2. The method of claim 1, wherein, Tracking analysis of the working environment of the lens user is performed to obtain a radiation protection requirement set, and the method further comprises: Interactively obtain the position parameters of the working environment, and delimit a radiation tracking space according to the position parameters; Predefine a radiation tracking time domain and a radiation tracking index set; Take the radiation tracking time domain and the radiation tracking space as spatiotemporal constraints, and take the radiation tracking index set as object constraints to perform working environment radiation tracking collection on the lens user to obtain a radiation time-varying sequence set; Interactively obtain a radiation protection limit set with the radiation tracking index set as constraints; Use the radiation protection limit set to traverse the radiation time-varying sequence set to obtain the radiation protection requirement set.

3. The method of claim 2, wherein, Obtain K coating scheme sets, wherein the K coating scheme sets are generated by synchronizing the radiation protection requirement set to a pre-constructed coating scheme database and performing coating scheme matching, and the method further comprises: Interactively obtain a plurality of sample radiation types; Perform network data calling on a first sample radiation type to obtain a first sample protection scheme set, wherein the first sample protection scheme set includes a sample absorption coating scheme set, a sample reflection coating scheme set and a sample filter coating scheme set, and the first sample radiation type is any one of the plurality of sample radiation types; Similarly, obtain a plurality of sample protection scheme sets of the plurality of sample radiation types; Based on the knowledge graph, perform mapping storage of the plurality of sample radiation types and the plurality of sample protection scheme sets to generate the coating scheme database; Synchronize the radiation protection requirement set to the coating scheme database to perform coating scheme matching to generate the K coating scheme sets, wherein each coating scheme set includes a matching absorption coating scheme, a matching reflection coating scheme and a matching filter coating scheme.

4. The method of claim 1, wherein, Perform enumeration combination on the K coating scheme sets to obtain a plurality of composite coating strategies, and the method further comprises: Perform enumeration combination on the K coating scheme sets to obtain a plurality of coating scheme combinations; Perform coating sequence enumeration on the plurality of coating scheme combinations to obtain a plurality of coating scheme sequences; Interactively obtain a standard coating sequence rule, perform depacketization processing on the plurality of coating scheme sequences, and perform abnormal coating sequence identification and screening based on the standard coating sequence rule to obtain the plurality of composite coating strategies.

5. A coating optimization system for radiation-protective resin lenses, characterized in that, The system for implementing the steps of the method of any one of claims 1 to 4 comprises: The radiation protection requirement set obtaining module is configured to perform work environment tracking analysis on the lens user, and obtain a radiation protection requirement set, wherein the radiation protection requirement set comprises K radiation protection requirements of K radiation types; The coating scheme set obtaining module is configured to obtain K coating scheme sets by synchronizing the radiation protection requirement set to a pre-constructed coating scheme database and performing coating scheme matching generation; The composite coating strategy obtaining module is configured to perform enumeration combination on the K coating scheme sets, and obtain a plurality of composite coating strategies; The composite light transmission coefficient obtaining module is configured to perform light transmission fitting on the plurality of composite coating strategies, and obtain a plurality of composite light transmission coefficients; The light transmission coating strategy obtaining module is configured to interactively obtain a target light transmission constraint, and perform light transmission optimization layer configuration in combination with the plurality of composite light transmission coefficients, and obtain a plurality of light transmission coating strategies; The multi-layer composite coating module is configured to perform coating fitness analysis on the plurality of composite coating strategies and the plurality of light transmission coating strategies, obtain a target coating optimization strategy, and perform multi-layer composite coating on the resin lens substrate of the lens user based on the target coating optimization strategy.

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