A method for optimizing the composition ratio of a polyurethane pressure-sensitive adhesive composition

Abstract

The present invention provides a method for optimizing the composition ratio of a polyurethane pressure-sensitive adhesive composition, relating to the technical field of data processing. The method can iteratively process a list of composition ratios to be optimized, an initial performance value list, an initial composition adjustment priority list, and a target performance value list according to a composition prediction model, a performance prediction model, and a priority prediction model to obtain a target composition ratio list, thereby optimizing the composition ratio of the polyurethane pressure-sensitive adhesive composition. The method gradually reduces errors through a closed-loop feedback mechanism, reduces human intervention, and fully considers the influence of the composition adjustment sequence, thereby facilitating improved optimization efficiency and accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, in particular to a method for optimizing the component ratio of a polyurethane pressure-sensitive adhesive composition. Background Art

[0002] Polyurethane pressure-sensitive adhesive compositions are pressure-sensitive adhesives widely used in electronics, packaging, and industrial applications. They combine the flexibility of polyurethane with the instant adhesion of pressure-sensitive adhesives, resulting in excellent durability, weather resistance, and bonding properties. However, polyurethane pressure-sensitive adhesive compositions prepared based on different component ratios exhibit varying performance, cost, and application effectiveness. Therefore, optimizing the component ratio of the polyurethane pressure-sensitive adhesive composition is necessary to achieve the desired performance.

[0003] In the prior art, the methods for optimizing the composition ratio of polyurethane pressure-sensitive adhesive compositions are mainly based on manual optimization or optimization based on mathematical models. Manual optimization adjusts the composition ratio through manual experience and target performance to achieve optimization; optimization based on mathematical models is achieved by establishing a mathematical model for describing the relationship between the input composition ratio and the output performance, and determining the optimal composition ratio through an optimization algorithm to achieve optimization.

[0004] However, the above method also has the following technical problems:

[0005] Manual optimization mainly relies on the experience of the operator, which is highly subjective and unstable. Especially when multiple components are involved, repeated experiments and adjustments are required, which is time-consuming and labor-intensive and prone to missing the optimal solution. When there are complex nonlinear relationships or multivariate interactions, it is more difficult to establish a mathematical model that accurately describes the relationship between input (ingredient ratio) and output (performance). Insufficient experimental data or noise interference may also lead to a large deviation between the model prediction results and the actual situation. It can be seen that relying solely on mathematical models may lead to large prediction errors. In addition, the above methods do not take into account the impact of the order of component adjustment on performance. The order of adding different components may significantly affect performance. Therefore, the optimization efficiency and accuracy of the component ratio of the polyurethane pressure-sensitive adhesive composition based on the above method are low. Summary of the Invention

[0006] In view of the above technical problems, the technical solution adopted by the present invention is:

[0007] A method for optimizing the composition ratio of a polyurethane pressure-sensitive adhesive composition comprises the following steps:

[0008] S1, the list of components to be optimized is the distribution ratio list B, the initial performance value list C corresponding to B, the initial component adjustment priority list D corresponding to C, and the target performance value list V = {V1, V2, ..., Vj ,…,V n} is input into the component prediction model to obtain the intermediate component ratio list E, B includes the component ratio to be optimized corresponding to each component identifier in the component identifier list A corresponding to the polyurethane pressure-sensitive adhesive composition, and C includes R1, R2, ..., R j ,…,R n The corresponding initial performance value, R j is the performance identifier of the jth performance of the polyurethane pressure-sensitive adhesive composition, j ranges from 1 to n, n is the number of performances of the polyurethane pressure-sensitive adhesive composition, D includes the initial component adjustment priority corresponding to each component identifier in A, V j R j The corresponding target performance value, E includes the intermediate component ratio corresponding to each component identification in A.

[0009] S2. Input E into the performance prediction model to obtain the intermediate performance value list F corresponding to E = {F1, F2, ..., F j ,…,F n}, F j R in F j The corresponding intermediate performance value.

[0010] S3, if ∑ n j＝1 (K j ×|V j -F j |)≤G, then E is used as the target component ratio list, otherwise, E, F and V are input into the priority prediction model to obtain the intermediate component adjustment priority list H corresponding to F and enter step S4, K j R j The corresponding preset importance weight, G is the preset performance difference threshold, and H includes the intermediate component adjustment priority corresponding to each component identifier in A.

[0011] S4. Set E as B, F as C, H as D and go to step S1.

[0012] The present invention has at least the following beneficial effects:

[0013] The present invention provides a method for optimizing the composition ratio of a polyurethane pressure-sensitive adhesive composition. The method comprises inputting a composition ratio list to be optimized, an initial performance value list corresponding to the composition ratio list to be optimized, an initial component adjustment priority list corresponding to the initial performance value list, and a target performance value list into a composition prediction model to obtain an intermediate composition ratio list; inputting the intermediate composition ratio list into a performance prediction model to obtain an intermediate performance value list corresponding to the intermediate composition ratio list; if the intermediate performance values ​​in the intermediate performance value list meet the conditions, the intermediate composition ratio list is used as the target composition ratio list; otherwise, the intermediate composition ratio list, the intermediate performance value list, and the target performance value list are input into the priority prediction model to obtain the intermediate composition ratio list corresponding to the intermediate performance value list. The target component ratio list is obtained by iteratively processing the component ratio list to be optimized, the initial performance value list, the initial component adjustment priority list and the target performance value list according to the component prediction model, the performance prediction model and the priority prediction model, thereby achieving the optimization of the component ratio of the polyurethane pressure-sensitive adhesive composition, gradually reducing the error through the closed-loop feedback mechanism, reducing human intervention, and fully considering the influence of the component adjustment order, which is conducive to improving the optimization efficiency and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0015] Figure 1 The present invention provides a flowchart of a method for optimizing the composition ratio of a polyurethane pressure-sensitive adhesive composition. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0017] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar tasks and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0018] The embodiment of the present invention provides a method for optimizing the composition ratio of a polyurethane pressure-sensitive adhesive composition, such as Figure 1 As shown, the method includes the following steps:

[0019] S1, the list of components to be optimized is the distribution ratio list B, the initial performance value list C corresponding to B, the initial component adjustment priority list D corresponding to C, and the target performance value list V = {V1, V2, ..., V j ,…,V n} is input into the component prediction model to obtain an intermediate component ratio list E, wherein B includes the component ratio to be optimized corresponding to each component identifier in the component identifier list A corresponding to the polyurethane pressure-sensitive adhesive composition, and C includes R1, R2, ..., R j ,…,R n The corresponding initial performance value, R j is the performance identifier of the jth performance of the polyurethane pressure-sensitive adhesive composition, j ranges from 1 to n, n is the number of performances of the polyurethane pressure-sensitive adhesive composition, D includes the initial component adjustment priority corresponding to each component identifier in A, V j R j The corresponding target performance value, E includes the intermediate component ratio corresponding to each component identification in A.

[0020] Specifically, A includes several component identifiers of components that can be used to constitute the polyurethane pressure-sensitive adhesive composition.

[0021] In a specific embodiment, the ratio of the components to be optimized corresponding to the component identification is the ratio of the components corresponding to the component identification in the polyurethane pressure-sensitive adhesive composition to be optimized. The polyurethane pressure-sensitive adhesive composition to be optimized is a polyurethane pressure-sensitive adhesive composition determined by a person skilled in the art from several existing polyurethane pressure-sensitive adhesive compositions based on actual needs, wherein the ratios of the components corresponding to the component identifications in two different polyurethane pressure-sensitive adhesive compositions are not exactly the same.

[0022] Specifically, the ratio of ingredients is used to express the proportion of ingredients.

[0023] Optionally, the proportions of the ingredients can be expressed as percentages; for example, in a composition XX consisting of ingredient 1, ingredient 2, ingredient 3, and ingredient 4, the proportion of ingredient 1 is 70%, the proportion of ingredient 2 is 10%, the proportion of ingredient 3 is 4%, and the proportion of ingredient 4 is 6%.

[0024] Specifically, the initial performance value is the performance value of the polyurethane pressure-sensitive adhesive composition prepared based on each component ratio to be optimized in the component ratio to be optimized list; it can be understood as: the performance value of the polyurethane pressure-sensitive adhesive composition to be optimized.

[0025] Specifically, the properties of the polyurethane pressure-sensitive adhesive composition include bonding performance, flexibility, cohesive strength, heat resistance, low temperature resistance, aging resistance and light transmittance.

[0026] Specifically, the initial component adjustment priority is used to indicate the order of adjusting the proportions of the components corresponding to the component identifiers in the process of adjusting the performance values ​​corresponding to various properties of the polyurethane pressure-sensitive adhesive composition from initial performance values ​​to target performance values.

[0027] Furthermore, priority is given to adjusting the ratio of the component corresponding to the component identifier corresponding to the initial component adjustment priority; it can be understood that: the smaller the initial component adjustment priority, the earlier the adjustment order of the ratio of the component corresponding to the component identifier corresponding to the initial component adjustment priority, for example: the initial component adjustment priority corresponding to component identifier 3 is 1, and the initial component adjustment priorities corresponding to other component identifiers are all greater than 1, which means that in the process of adjusting the performance values ​​corresponding to various properties of the polyurethane pressure-sensitive adhesive composition from the initial performance values ​​to the target performance values, the ratio of the component corresponding to component identifier 3 is adjusted first.

[0028] In a specific embodiment, the initial component adjustment priority is determined by a person skilled in the art based on the cost required to adjust each component (including material cost, process complexity and process difficulty); the lower the comprehensive cost required to adjust the component corresponding to the component identifier corresponding to the initial component adjustment priority, the lower the initial component adjustment priority. The comprehensive cost can be calculated through the quantified material cost, process complexity and process difficulty. Giving priority to adjusting components with low comprehensive costs is conducive to cost saving.

[0029] Specifically, the intermediate component ratio is the ratio of the components output by the component prediction model; it can be understood as: the ratio of each component in the polyurethane pressure-sensitive adhesive composition corresponding to the predicted target performance value list.

[0030] Optionally, the component prediction model is a model obtained by training a multi-layer perceptron (MLP) for the component ratio prediction task by those skilled in the art, wherein MLP is a feedforward artificial neural network model that can map multiple input data sets to a single output data set and can be used to process complex nonlinear relationships. Through a large amount of training data, the MLP model can learn the existing component ratios, performance values, and the relationship between component adjustment priorities and target performance values, thereby achieving accurate prediction and output of the ratio of each component.

[0031] S2. Input E into the performance prediction model to obtain the intermediate performance value list F corresponding to E = {F1, F2, ..., F j ,…,F n}, F j R in F j The corresponding intermediate performance value.

[0032] Specifically, the intermediate performance value is the performance value output by the performance prediction model; it can be understood as: the predicted performance value of the polyurethane pressure-sensitive adhesive composition prepared based on each intermediate component ratio in the intermediate component ratio list.

[0033] Optionally, the performance prediction model is a model obtained by training a support vector regression (SVR) algorithm specifically for the performance prediction task. SVR is a regression method based on a support vector machine (SVM) with good generalization capabilities and the ability to effectively handle small sample data, high-dimensional data, and nonlinear relationships. SVR can accurately predict the performance value corresponding to each property based on the ratio of all components output by the component prediction model by constructing an optimal hyperplane to fit the data points while allowing a certain error range.

[0034] S3, if ∑ n j＝1 (K j ×|V j -F j |)≤G, then E is used as the target component ratio list, otherwise, E, F and V are input into the priority prediction model to obtain the intermediate component adjustment priority list H corresponding to F and enter step S4, K j R j The corresponding preset importance weight, G is the preset performance difference threshold, where H includes the intermediate component adjustment priority corresponding to each component identifier in A.

[0035] Specifically, the greater the preset importance weight is, the higher the importance of the performance corresponding to the performance identifier corresponding to the preset importance weight is.

[0036] In a specific embodiment, the preset importance weight is determined by those skilled in the art according to the importance of the performance of the polyurethane pressure-sensitive adhesive composition, which will not be described in detail here.

[0037] In a specific embodiment, the usage scenario information corresponding to the polyurethane pressure-sensitive adhesive composition is input into a weight acquisition model to obtain the preset importance weights corresponding to each performance identifier of the polyurethane pressure-sensitive adhesive composition. The weight acquisition model is a neural network model trained by technical personnel in this field for the weight acquisition task, which will not be repeated here.

[0038] Specifically, the usage scenario information corresponding to the polyurethane pressure-sensitive adhesive composition is information related to the scenario in which the polyurethane pressure-sensitive adhesive composition is used; for example, the temperature, humidity, area, etc. of the scenario in which the polyurethane pressure-sensitive adhesive composition is used.

[0039] Specifically, the preset performance difference threshold is determined by those skilled in the art based on the importance of the performance of the polyurethane pressure-sensitive adhesive composition and the allowable error corresponding to the performance value of the target performance of the polyurethane pressure-sensitive adhesive composition, and will not be elaborated here.

[0040] Through the above steps, it is determined whether to use the intermediate component ratio list as the target component ratio list based on the preset importance weight, intermediate performance value, target performance value and preset performance difference threshold corresponding to the performance identifier. The larger the preset importance weight, the higher the importance of the performance corresponding to the performance identifier corresponding to the preset importance weight. The importance of performance is fully taken into consideration. The importance of performance is different in different usage scenarios. Flexible adjustment of the preset importance weight according to different usage scenarios can meet the needs of different usage scenarios and is conducive to improving the accuracy of the obtained target component ratio list.

[0041] Specifically, the target ingredient ratio list includes the target ingredient ratio corresponding to each ingredient identifier in A. The target ingredient ratio corresponding to the ingredient identifier can be understood as: the intermediate ingredient ratio corresponding to the ingredient identifier in the intermediate ingredient ratio list corresponding to the target ingredient ratio list.

[0042] Specifically, the intermediate component adjustment priority is the component adjustment priority output by the priority prediction model; it can be understood as: in the process of predicting that the performance values ​​corresponding to each performance of the polyurethane pressure-sensitive adhesive composition are adjusted from the intermediate performance value to the target performance value, the adjustment order of the ratio of the components corresponding to the component identification.

[0043] Furthermore, priority is given to adjusting the ratio of the component corresponding to the component identifier corresponding to the intermediate component adjustment priority; it can be understood that: the smaller the intermediate component adjustment priority, the earlier the adjustment order of the ratio of the component corresponding to the component identifier corresponding to the intermediate component adjustment priority; for example: the intermediate component adjustment priority corresponding to component identifier 3 is 1, and the intermediate component adjustment priorities corresponding to other component identifiers are all greater than 1, which means that in the process of adjusting the performance values ​​corresponding to various properties of the polyurethane pressure-sensitive adhesive composition from the intermediate performance values ​​to the target performance values, the ratio of the component corresponding to component identifier 3 is adjusted first.

[0044] Optionally, the priority prediction model is a model obtained by training a decision tree by technical personnel in this field for the task of obtaining the priority of component adjustment. The decision tree is a rule-based learning algorithm that can perform rule learning and decision-making based on various input conditions (such as the predicted ratio of each component, the predicted performance value of each performance, and the performance value of each performance ultimately required), thereby outputting a reasonable component adjustment priority.

[0045] In a specific embodiment, B, C, and V are input into a priority prediction model to obtain D, and the priority prediction model is used to obtain an initial component adjustment priority list, which reduces human intervention and improves optimization efficiency.

[0046] S4. Set E as B, F as C, H as D and go to step S1.

[0047] Specifically, after obtaining the target component ratio list, the polyurethane pressure-sensitive adhesive composition is prepared based on each target component ratio in the target component ratio list to optimize the component ratio of the polyurethane pressure-sensitive adhesive composition.

[0048] Through the above steps, the list of component ratios to be optimized, the list of initial performance values, the list of initial component adjustment priorities, and the list of target performance values ​​are iteratively processed according to the component prediction model, the performance prediction model, and the priority prediction model to obtain a target component ratio list, thereby achieving optimization of the component ratio of the polyurethane pressure-sensitive adhesive composition. The error is gradually reduced through a closed-loop feedback mechanism, and human intervention is reduced. At the same time, the influence of the component adjustment order is fully considered, which is conducive to improving optimization efficiency and accuracy.

[0049] In a specific embodiment, the following step S01 is further included before step S1:

[0050] S01. Obtain a count value count, where count is initially 0.

[0051] After step S2, the following steps S21-S25 are included:

[0052] S21, if count < L, then insert F into the intermediate performance value list set and enter step S24, where L is the preset cycle number threshold; if count ≥ L, then insert M in the current intermediate performance value list set M p-L+1 , M p-L+2 ,…,M p as a candidate performance value list to obtain a candidate performance value list set N={N1, N2, . . . , N y ,…,N q}, N y ={N y1 , N y2 ,…,N yj ,…,N yn}, where M = {M1, M2, ..., M x ,…,M p}, M x is the xth intermediate performance value list in M, x ranges from 1 to p, p is the number of intermediate performance value lists in M, M p-L+1 is the list of the p-L+1th intermediate performance values ​​in M, M p-L+2 is the list of the p-L+2th intermediate performance values ​​in M, N y is the yth candidate performance value list, y ranges from 1 to q, q is the number of candidate performance value lists, N yj N y Middle R j Corresponding candidate performance values, among which, those skilled in the art know that the preset cycle number threshold is a value pre-set by those skilled in the art according to actual needs, for example: 3, 4, 5, which will not be repeated here.

[0053] Specifically, the intermediate performance value list set is initially NULL.

[0054] Specifically, q=L.

[0055] Specifically, N y Middle R j The corresponding candidate performance value can be understood as y The corresponding intermediate performance value list R j The corresponding intermediate performance value.

[0056] S22. Get N y The corresponding target performance difference T y , T y Meet the following conditions:

[0057] T y =∑ n j＝1 (K j ×|V j -N yj |).

[0058] S23. If T1≤G, T2≤G, ..., T y ≤G,…,T p ≤G, then min(T1, T2, ..., T y ,…,T p ) The intermediate component ratio list corresponding to the intermediate performance value list is used as the target component ratio list, otherwise, enter step S24, min() is the minimum value acquisition function.

[0059] S24. Input E, F, and V into the priority prediction model to obtain H.

[0060] S25. Set count=count+1, set E as B, F as C, H as D and go to step S2.

[0061] Through the above steps, a count value is set, and a set of intermediate performance value lists is obtained based on the continuously updated count value and a preset loop count threshold. When the count value is not less than the preset loop count threshold, the most recent L intermediate performance value lists are extracted from the intermediate performance value list set as candidate performance value lists, ensuring that the optimization process only considers the most recently obtained intermediate performance value lists, which is conducive to improving optimization efficiency and accuracy. The target performance difference corresponding to each candidate performance value list is obtained. If there is a target performance difference greater than the preset performance difference threshold, it means that there is an intermediate component ratio list corresponding to the intermediate performance value lists obtained L times that cannot be used as a target component ratio list. At this time, the count value is updated so that the intermediate performance value list set is updated, and the target component ratio list is re-obtained. If all target performance differences are not greater than the preset performance difference threshold, it means that the intermediate component ratio lists corresponding to the intermediate performance value lists obtained L times can all be used as target component ratio lists, which can ensure that the final result reaches the optimal or near-optimal state in multiple performance indicators. At this time, the intermediate component ratio list corresponding to the intermediate performance value list with the smallest target performance difference is used as the target component ratio list, further improving the accuracy of the optimization.

[0062] In a specific embodiment, before step S1, the following steps are further included to obtain B:

[0063] S001. Obtain the original component ratio list set Q and the original performance value list set U corresponding to Q, where Q = {Q1, Q2, ..., Q e ,…,Q f}, Q e is the e-th original ingredient ratio list, the value of e ranges from 1 to f, f is the number of original ingredient ratio lists, U={U1,U2,…,U e ,…,U f}, Ue Q e A list of corresponding raw performance values.

[0064] Specifically, the original composition ratio list is a composition ratio list corresponding to one of several existing polyurethane pressure-sensitive adhesive compositions, including the original composition ratio corresponding to each component identification in A.

[0065] Specifically, the original ingredient ratio corresponding to the ingredient identification is the ratio of the ingredient corresponding to the ingredient identification in the polyurethane pressure-sensitive adhesive composition corresponding to the corresponding original ingredient ratio list.

[0066] Specifically, different lists of original component ratios correspond to two different polyurethane pressure-sensitive adhesive compositions.

[0067] Specifically, the original performance value list includes R1, R2, ..., R j ,…,R n The corresponding raw performance values.

[0068] Specifically, the original performance values ​​in the original performance value list are the performance values ​​of the polyurethane pressure-sensitive adhesive composition prepared based on the original component ratios in the original component ratio list corresponding to the original performance value list, which can be understood as: the performance values ​​of the polyurethane pressure-sensitive adhesive composition corresponding to the original component ratio list corresponding to the original performance value list.

[0069] S002. Obtain sample data and construct a sample data set X, where (Q e , U e , U r ) as the input feature in the sample data, and Z er As the input feature (Q e , U e , U r ) corresponding output value, U r Q r The corresponding raw performance value list, Q r is the rth original component ratio list, r ranges from 1 to f and r≠e, Z er For Q e The original composition ratio in is adjusted so that Q e With Q r Same, which makes U e with U r The same cost value required.

[0070] Specifically, the cost value is calculated by quantifying the material cost, process complexity and process difficulty involved in adjusting the ratio of each component.

[0071] Specifically, if Z er If it is a positive number, it represents the relationship between Q e The original composition ratio in is adjusted so that Q e With Q r Same, which makes U e with U r Same, need to pay extra with Z er The corresponding cost (including material cost, process complexity and process difficulty); if Z er is 0, indicating that Q e The original composition ratio in is adjusted so that Q e With Q r Same, which makes U e with U r Same, no additional cost required; if Z er If it is a negative number, it indicates that Q e The original composition ratio in is adjusted so that Q e With Q r Same, which makes U e with U r Same, need to reduce with Z er The corresponding costs.

[0072] Specifically, X includes f×(f-1) sample data.

[0073] Specifically, a sample data includes an input feature and an output value corresponding to the input feature.

[0074] S003. Train the neural network model according to X to obtain a cost value prediction model, where the cost value prediction model can predict the target cost value according to the input features.

[0075] S004、Q e , U e , V) as Q e The corresponding target input features are input into the cost value prediction model to obtain Q e The corresponding target cost DJ e .

[0076] S005, min(Q1, Q2, ..., Q e ,…,Q f ) is listed as B.

[0077] Through the above steps, a sample data set is constructed based on the original component ratio list set, the original performance value list set, and the cost value calculated by quantifying the material cost, process complexity, and process difficulty involved in adjusting the ratio of each component. The material cost, process complexity, and process difficulty of the components are fully taken into account, and the cost can also be objectively measured. The neural network model is trained based on the sample data set to obtain a cost value prediction model, so that the neural network model can capture complex input-output relationships, thereby improving the accuracy of the target cost value prediction. Based on the original component ratio list, the original performance value list corresponding to the original component ratio list, the target performance value list, and the cost value prediction model, the target cost value corresponding to the original component ratio list is obtained. The target cost value can represent the cost required to adjust the original performance value list corresponding to the original component ratio list to the target performance value list. Therefore, the original component ratio list corresponding to the minimum target cost value is used as the component ratio list to be optimized. The original component ratio list with the minimum cost is selected as the component ratio list to be optimized, which can save costs and avoid unnecessary resource consumption.

[0078] In step S005, it also includes: min(Q1, Q2, ..., Q e ,…,Q f ) The polyurethane pressure-sensitive adhesive composition corresponding to the original component ratio list is used as the polyurethane pressure-sensitive adhesive composition to be optimized.

[0079] The present invention provides a method for optimizing the composition ratio of a polyurethane pressure-sensitive adhesive composition. The method comprises inputting a composition ratio list to be optimized, an initial performance value list corresponding to the composition ratio list to be optimized, an initial component adjustment priority list corresponding to the initial performance value list, and a target performance value list into a composition prediction model to obtain an intermediate composition ratio list; inputting the intermediate composition ratio list into a performance prediction model to obtain an intermediate performance value list corresponding to the intermediate composition ratio list; if the intermediate performance values ​​in the intermediate performance value list meet the conditions, the intermediate composition ratio list is used as the target composition ratio list; otherwise, the intermediate composition ratio list, the intermediate performance value list, and the target performance value list are input into the priority prediction model to obtain the intermediate composition ratio list corresponding to the intermediate performance value list. The target component ratio list is obtained by iteratively processing the component ratio list to be optimized, the initial performance value list, the initial component adjustment priority list and the target performance value list according to the component prediction model, the performance prediction model and the priority prediction model, thereby achieving the optimization of the component ratio of the polyurethane pressure-sensitive adhesive composition, gradually reducing the error through the closed-loop feedback mechanism, reducing human intervention, and fully considering the influence of the component adjustment order, which is conducive to improving the optimization efficiency and accuracy.

[0080] An embodiment of the present invention also provides a non-transitory computer-readable storage medium, which can be set in an electronic device to store a computer program related to a method in the method embodiment. The computer program is loaded and executed by the processor to implement the method provided in the above embodiment.

[0081] An embodiment of the present invention further provides an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method provided in the above embodiment when executing the computer program.

[0082] An embodiment of the present invention further provides a computer program product comprising program code. When the program product is run on an electronic device, the program code is used to enable the electronic device to execute the steps of the method according to various exemplary embodiments of the present invention described above in this specification.

[0083] Although some specific embodiments of the present invention have been described in detail by way of examples, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present invention. It should also be understood by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the present invention.

Claims

1. A method for optimizing the composition ratio of a polyurethane pressure-sensitive adhesive composition, characterized in that: The method comprises the following steps: S1, the list of components to be optimized is the distribution ratio list B, the initial performance value list C corresponding to B, the initial component adjustment priority list D corresponding to C, and the target performance value list V = {V1, V2, ..., V j ,…,V n } is input into the component prediction model to obtain the intermediate component ratio list E, B includes the component ratio to be optimized corresponding to each component identifier in the component identifier list A corresponding to the polyurethane pressure-sensitive adhesive composition, and C includes R1, R2, ..., R j ,…,R n The corresponding initial performance value, R j is the performance identifier of the jth performance of the polyurethane pressure-sensitive adhesive composition, j ranges from 1 to n, n is the number of performances of the polyurethane pressure-sensitive adhesive composition, D includes the initial component adjustment priority corresponding to each component identifier in A, V j R j The corresponding target performance value, E includes the intermediate component ratio corresponding to each component identification in A; S2. Input E into the performance prediction model to obtain the intermediate performance value list F corresponding to E = {F1, F2, ..., F j ,…,F n }, F j R in F j The corresponding intermediate performance value; S3, if ∑ n j＝1 (K j ×|V j -F j |)≤G, then E is used as the target component ratio list, otherwise, E, F and V are input into the priority prediction model to obtain the intermediate component adjustment priority list H corresponding to F and enter step S4, K j R j The corresponding preset importance weight, G is the preset performance difference threshold, and H includes the intermediate component adjustment priority corresponding to each component identifier in A; S4. Set E as B, F as C, H as D and go to step S1.

2. The method for optimizing the composition ratio of the polyurethane pressure-sensitive adhesive composition according to claim 1, wherein: A includes several component identifiers of components that can be used to constitute the polyurethane pressure-sensitive adhesive composition.

3. The method for optimizing the composition ratio of the polyurethane pressure-sensitive adhesive composition according to claim 2, wherein: The ratio of the components to be optimized corresponding to the component identification is the ratio of the components corresponding to the component identification in the polyurethane pressure-sensitive adhesive composition to be optimized.

4. The method for optimizing the composition ratio of the polyurethane pressure-sensitive adhesive composition according to claim 1, wherein: The initial performance values ​​are performance values ​​of the polyurethane pressure-sensitive adhesive compositions prepared based on the various component ratios to be optimized in the list of component ratios to be optimized.

5. The method for optimizing the composition ratio of the polyurethane pressure-sensitive adhesive composition according to claim 2, wherein: The initial component adjustment priority is used to indicate the order of adjusting the proportions of the components corresponding to the component identifiers in the process of adjusting the performance values ​​corresponding to various properties of the polyurethane pressure-sensitive adhesive composition from initial performance values ​​to target performance values.

6. The method for optimizing the composition ratio of the polyurethane pressure-sensitive adhesive composition according to claim 5, characterized in that: The smaller the initial component adjustment priority is, the earlier the order of adjusting the proportion of the component corresponding to the component identifier corresponding to the initial component adjustment priority is.

7. The method for optimizing the composition ratio of the polyurethane pressure-sensitive adhesive composition according to claim 1, wherein: The larger the preset importance weight is, the higher the importance of the performance corresponding to the performance identifier corresponding to the preset importance weight is.

8. The method for optimizing the composition ratio of the polyurethane pressure-sensitive adhesive composition according to claim 1, wherein: Before step S1, the method further includes the following step S01: S01. Obtain a count value count, where count is initially 0.

9. The method for optimizing the composition ratio of the polyurethane pressure-sensitive adhesive composition according to claim 8, characterized in that: S2 then includes the following steps S21-S25: S21, if count < L, then insert F into the intermediate performance value list set and enter step S24, where L is the preset cycle number threshold; if count ≥ L, then insert M in the current intermediate performance value list set M p-L+1 , M p-L+2 ,…,M p as a candidate performance value list to obtain a candidate performance value list set N={N1, N2, . . . , N y ,…,N q }, N y ={N y1 , N y2 ,…,N yj ,…,N yn }, where M = {M1, M2, ..., M x ,…,M p }, M x is the xth intermediate performance value list in M, x ranges from 1 to p, p is the number of intermediate performance value lists in M, M p-L+1 is the list of the p-L+1th intermediate performance values ​​in M, M p-L+2 is the list of the p-L+2th intermediate performance values ​​in M, N y is the yth candidate performance value list, y ranges from 1 to q, q is the number of candidate performance value lists, N yj N y Middle R j the corresponding candidate performance values; S22. Get N y The corresponding target performance difference T y , T y Meet the following conditions: T y =∑ n j＝1 (K j ×|V j -N yj |); S23. If T1≤G, T2≤G, ..., T y ≤G,…,T p ≤G, then min(T1, T2, ..., T y ,…,T p ) The intermediate component ratio list corresponding to the intermediate performance value list is used as the target component ratio list, otherwise, proceed to step S24, min() is the minimum value acquisition function; S24, input E, F, and V into the priority prediction model to obtain H; S25. Set count=count+1, set E as B, F as C, H as D and go to step S2.

10. The method for optimizing the composition ratio of the polyurethane pressure-sensitive adhesive composition according to claim 9, characterized in that: The intermediate performance value list set is initially NULL.

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