Unsaturated polyester structure adjustment method combined with application demand analysis

Abstract

The present invention provides an unsaturated polyester structure adjustment method combined with application demand analysis, which relates to the technical field of unsaturated polyesters, including: obtaining application scenario information of target unsaturated polyesters, obtaining a set of application demand factors; obtaining basic component information of basic unsaturated polyesters, including multiple component types; obtaining component ratio intervals of multiple component types; within the component ratio interval, taking performance demand factors as optimization targets, optimizing the component ratios of multiple component types to obtain the optimal component ratio; obtaining the addition amount interval of functional additives; taking functional demand factors as targets, matching the addition amount of functional additives within the addition amount interval to obtain the optimal addition amount; and preparing the target unsaturated polyester. The present invention solves the technical problem that it is difficult for the prior art to accurately convert specific application requirements into specific performance requirements of materials, resulting in poor performance of materials in specific application scenarios.

Description

Technical Field

[0001] The invention relates to the technical field of unsaturated polyesters, and in particular to a method for adjusting the structure of unsaturated polyesters in combination with application demand analysis. Background Art

[0002] Unsaturated polyester has wide applicability in multiple application fields, such as the development of unsaturated polyester materials with excellent conductivity, flame retardancy and thermal stability, which are suitable for packaging and insulation materials of electronic components. However, it is difficult for existing technologies to accurately convert specific application requirements into specific performance requirements of materials, resulting in poor performance or lack of functions of materials in specific application scenarios; in addition, the preparation process of unsaturated polyester is complex and costly, and the existing technology lacks effective evaluation and optimization methods, making it difficult to balance performance, function, preparation difficulty and cost. Summary of the invention

[0003] This application provides a method for adjusting the structure of unsaturated polyesters in combination with application demand analysis, aiming to solve the technical problem that the existing technology is difficult to accurately convert specific application requirements into specific performance requirements of the material, resulting in poor performance of the material in specific application scenarios.

[0004] The present application discloses a method for adjusting the structure of unsaturated polyester in combination with application demand analysis, the method comprising: obtaining application scenario information of a target unsaturated polyester, performing a multi-dimensional application demand analysis on the application scenario information, and obtaining a set of application demand factors, wherein the set of application demand factors comprises performance demand factors and function demand factors; connecting to an unsaturated polyester production management system, and obtaining basic component information of a basic unsaturated polyester, wherein the basic component information comprises a plurality of component types; obtaining a component ratio range of the plurality of component types; within the component ratio range, taking the performance demand factors as optimization targets, optimizing the component ratios of the plurality of component types, and obtaining an optimal component ratio; obtaining an addition amount range of a functional additive; taking the function demand factors as targets, matching the addition amount of the functional additive within the addition amount range, and obtaining an optimal addition amount; and preparing the target unsaturated polyester according to the optimal component ratio and the optimal addition amount.

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

[0006] By obtaining application scenario information and multi-dimensional application demand analysis, a set of demand factors including performance requirements and functional requirements is formed to achieve precise matching of requirements with material performance; by obtaining the component ratio range and the addition amount range of functional additives, and taking the demand factors as the optimization target, the optimization algorithm is used to systematically optimize the component ratio and additive amount to obtain the optimal component ratio and addition amount, ensuring the best performance and function of the material in the specific application scenario; in the preparation process, small-batch preparation experiments are conducted to obtain the actual preparation difficulty and cost data, and scientific evaluation is carried out through evaluation functions, and feedback optimization is carried out based on the evaluation results to ultimately ensure the feasibility and economy of the preparation process.

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

[0008] Figure 1 A schematic flow chart of a method for adjusting the structure of unsaturated polyester combined with application demand analysis is provided for the embodiment of the present application;

[0009] Figure 2 A flow chart of obtaining a set of application demand factors in a method for adjusting the structure of unsaturated polyester combined with application demand analysis is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0010] The embodiments of the present application provide a method for adjusting the structure of unsaturated polyester in combination with application demand analysis, thereby solving the technical problem that the prior art is difficult to accurately convert specific application requirements into specific performance requirements of the material, resulting in poor performance of the material in specific application scenarios.

[0011] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically described below in conjunction with the drawings in the specification.

[0012] like Figure 1 As shown, the embodiment of the present application provides a method for adjusting the structure of unsaturated polyester in combination with application demand analysis, the method comprising:

[0013] Acquiring application scenario information of the target unsaturated polyester, performing multi-dimensional application demand analysis on the application scenario information, and obtaining a set of application demand factors, wherein the set of application demand factors includes performance demand factors and function demand factors;

[0014] By connecting to the client, we can obtain the specific requirements and use environment of unsaturated polyester materials, including use environment, such as indoor, outdoor, high temperature, high humidity, etc.; use methods, such as structural materials, coatings, adhesives, etc.; service life requirements, such as short-term, long-term, permanent, etc.; and other specific application requirements, such as transparency, color, surface treatment, etc. This information is analyzed in multiple dimensions to obtain requirements in different dimensions, including performance demand factors and functional demand factors. Among them, performance requirements involve the physical and chemical properties of the material itself, such as strength, toughness, corrosion resistance, etc.; functional requirements involve additional functions of the material, such as flame retardancy, conductivity, antibacterial properties, etc. Through multi-dimensional application demand analysis, specific application demand factors are extracted and organized into a structured set of demand factors.

[0015] Furthermore, if Figure 2 As shown, the method includes:

[0016] Obtain the performance requirement factors, wherein the performance requirement factors include but are not limited to one or more of tensile strength, flexural strength, impact toughness, chemical corrosion resistance, solvent resistance, water resistance, thermal stability, thermal expansion coefficient, and high temperature resistance; obtain the functional requirement factors, wherein the functional requirement factors include but are not limited to one or more of flame retardancy, conductivity, antibacterial property, optical transparency, fluidity, curing time, low VOC emission, and non-toxicity.

[0017] According to the specific application scenario, the importance of each performance requirement factor is weighted. For example, for structural materials, tensile strength and bending strength may be priority factors, where tensile strength is the ability of the material to resist fracture under tension, bending strength is the ability of the material to resist fracture under bending, and impact toughness is the ability of the material to absorb energy under impact load. Other performance requirement factors are similar to them. For the sake of brevity of the specification, other performance requirement factors will not be described one by one.

[0018] According to the specific application scenario, the importance of each functional requirement factor is weighted. For example, for medical use, antibacterial property and non-toxicity may be the priority factors, where antibacterial property refers to the ability of the material to resist the growth of microorganisms, and non-toxicity refers to the safety of the material to the human body and the environment. Other functional requirement factors are similar to them. For the sake of brevity of the specification, other functional requirement factors will not be described one by one.

[0019] Connecting to the unsaturated polyester production management system, obtaining basic component information of basic unsaturated polyester, wherein the basic component information includes multiple component types; obtaining component ratio intervals of the multiple component types;

[0020] Connect to the unsaturated polyester production management system, extract the basic component information of unsaturated polyester from the production management system, including but not limited to various monomers, initiators, accelerators, plasticizers, fillers, etc., format the extracted component information to ensure data consistency. Classify the component information according to chemical properties, physical properties and functional properties to obtain multiple component types.

[0021] Collect historical production data, including ingredient ratios and corresponding performance data of different batches and formulas, conduct statistical analysis on historical data, calculate common ratios, averages, standard deviations and other statistics for each ingredient type, and determine the ingredient ratio range for each ingredient type based on the statistical results.

[0022] By obtaining the basic component information of unsaturated polyester and its component ratio range, data support and scientific basis are provided for the subsequent optimization of component ratios and matching of functional additive addition amounts.

[0023] Within the component ratio range, taking the performance requirement factor as the optimization target, optimizing the component ratios of the multiple component types to obtain the optimal component ratio;

[0024] The specific requirements and importance of various performance demand factors, such as tensile strength, flexural strength, chemical corrosion resistance, etc., are clarified. According to the performance demand factors, a component ratio optimization function is established. An initial component ratio combination is randomly generated within a determined component ratio range. The fitness of the initial component ratio combination is calculated through the component ratio optimization function. Within the component ratio range, the component ratio combination is continuously optimized through iterative calculation to improve the fitness value. After multiple iterations, the component ratio combination with the largest fitness value is found, which is the optimal component ratio.

[0025] Furthermore, the method of optimizing the ratio of the components of the multiple component types to obtain the optimal ratio of the components includes:

[0026] A component ratio optimization function for optimizing the component ratio is constructed as follows:

[0027]

[0028] Among them, F is the component fitness, x i is the component ratio variable, x i including x1,x2,...,x m , m is the number of component types, P j (x i ) is x i The jth performance impact indicator, ω j is the weight of the jth performance impact indicator, and n is the total number of performance impact indicators;

[0029] Within the component ratio interval, an initial component ratio is randomly generated, and a plurality of performance influencing indicators of the initial component ratio are obtained by analysis; according to the component ratio optimization function, the plurality of performance influencing indicators are calculated to obtain the initial component fitness of the initial component ratio; based on the initial component fitness, the component ratio is continuously optimized to obtain the optimal component ratio with the maximum fitness.

[0030] Specifically, the component ratio optimization function is as follows:

[0031]

[0032] ω j is the weight of the jth performance influencing index, where different weights are assigned to each performance factor according to its importance in a specific application. The purpose of the weight is to reflect the importance of each factor in the overall optimization process. For example, if tensile strength is more important than bending strength, then the weight of tensile strength will be greater. Set the ratio of each component in unsaturated polyester as a variable and obtain the component ratio variable x i For example, suppose there are m components, and the proportions of each component are recorded as x1, x2, ..., x m For any component ratio variable x i , the weighted sum of all performance-affecting indicators is used as the optimization objective function. This objective function comprehensively considers all performance requirement factors and expresses the optimization direction of the overall performance in the form of its weighted sum.

[0033] According to the previously determined component ratio range, the minimum and maximum ratios of each component are defined, and within the determined range, an initial component ratio combination is generated using a random number generation algorithm, such as uniform distribution or normal distribution, and multiple performance impact indicators of the initial component ratio combination are obtained based on a pre-trained performance impact analysis network.

[0034] The performance impact index corresponding to the initial component ratio combination is substituted into the optimization function to calculate the initial component fitness of the combination.

[0035] Select an optimization algorithm, such as a genetic algorithm, to simulate the natural selection process, and continuously optimize the component ratio through selection, crossover and mutation operations, or a particle swarm optimization algorithm to simulate group behavior and optimize through the experience of individuals and groups. Set the maximum number of iterations or termination conditions, such as when the fitness value change is less than a certain threshold, use the generated initial component ratio combination as the initial population of the optimization algorithm, calculate the fitness value of each component ratio combination in the current population in each iteration, generate a new component ratio combination through the selection, crossover, mutation or individual and group update operations of the algorithm, and calculate its fitness value, save the component ratio combination with the highest fitness value after each iteration, and select the component ratio combination with the highest fitness value as the optimal component ratio after the number of iterations or termination conditions are reached.

[0036] Furthermore, the analysis obtains a plurality of performance influencing indicators of the initial component ratio, and the method includes:

[0037] According to the preparation data record of unsaturated polyester, a sample component ratio set and multiple sample performance influencing indicators of the sample unsaturated polyester are obtained; the sample component ratio set and multiple sample performance influencing indicators are used to train a performance impact analysis network; based on the performance impact analysis network, a performance impact analysis is performed on the initial component ratio to obtain the multiple performance impact indicators.

[0038] Collect historical preparation data from the unsaturated polyester production management system, including various component ratios and corresponding performance test results. If historical data is insufficient, prepare unsaturated polyester samples with different component ratios through experiments, measure their performance indicators, supplement the data set, and form a sample component ratio set. Organize the performance test results of each sample, such as tensile strength, flexural strength, etc., into a structured form to form multiple sample performance influencing indicator sets.

[0039] Select machine learning models, such as random forests and support vector machines, to perform performance impact analysis. Divide sample data into training sets and test sets, for example, set the ratio to 80% training set and 20% test set. Select component ratios as input features and performance impact indicators as output targets. Use training set data to train the selected model and adjust model parameters so that the model can accurately predict performance indicators. Optimize model performance through cross-validation, hyperparameter tuning and other methods to prevent overfitting or underfitting. Use test set data to evaluate the predictive performance of the model. Based on the evaluation results, select the optimal performance impact analysis model to form the final performance impact analysis network.

[0040] The generated initial component ratio is input into the performance impact analysis network, and the performance impact of the initial component ratio is predicted using the trained performance impact analysis network, and the predicted value of each performance indicator is output to obtain the multiple performance impact indicators.

[0041] Obtaining an addition amount range of the functional additive; taking the functional requirement factor as a target, matching the addition amount of the functional additive within the addition amount range to obtain an optimal addition amount; preparing the target unsaturated polyester according to the optimal component ratio and the optimal addition amount.

[0042] Collect historical production data in the production management system, including the usage and addition amounts of functional additives in different batches and formulas, or consult relevant technical literature to obtain the recommended addition amount range of different functional additives and form an addition amount range.

[0043] Determine the functional demand factors, including but not limited to flame retardancy, conductivity, antibacterial property, optical transparency, fluidity, curing time, low VOC emission, non-toxicity, etc. According to the application scenario and specific needs, assign weights to each functional demand factor, take the functional demand factor as the optimization target, and establish the addition amount optimization function. The addition amount optimization function is similar to the component ratio optimization function, and for the sake of brevity of the specification, it will not be repeated here.

[0044] In a determined addition amount interval, a plurality of initial addition amount combinations are randomly generated. Based on the generated initial addition amount combinations, the functional indexes of each addition amount combination are calculated using experimental data or simulation tools. The calculated functional indexes are substituted into the optimization function, and the fitness value of each initial addition amount combination is calculated. A suitable optimization algorithm, such as a genetic algorithm, a particle swarm optimization algorithm, a simulated annealing algorithm, etc., is selected for addition amount optimization. In the addition amount interval, the addition amount combination is continuously optimized through iterative calculation to improve the fitness value. After each iteration, the functional indexes of the current addition amount combination are evaluated, and the addition amount is adjusted according to the rules of the optimization algorithm. After multiple iterations, the addition amount combination with the largest fitness value is found, which is the optimal addition amount.

[0045] The basic ingredients are added to the reactor in proportion and mixed initially to ensure that the ingredients are evenly distributed. After the initial mixing, the functional additives are gradually added in the optimal amount and stirred continuously to ensure that the additives are fully dispersed, and the reaction temperature and time are controlled to ensure that the reaction is complete while avoiding overreaction that leads to performance degradation. Furthermore, according to specific application requirements, the prepared unsaturated polyester is molded, such as casting, compression molding, stretching and other processes, to prepare products of target shape and specifications.

[0046] Furthermore, before preparing the target unsaturated polyester according to the optimal component ratio and the optimal addition amount, the method further comprises:

[0047] The set of application demand factors also includes process demand factors, wherein the process demand factors include a preset preparation difficulty and a preset preparation cost; the target unsaturated polyester is prepared in small batches according to the optimal component ratio and the optimal addition amount, and the actual preparation difficulty and the actual preparation cost are obtained by analysis; based on a preparation process evaluation function, the actual preparation difficulty and the actual preparation cost are evaluated in combination with the preset preparation difficulty and the preset preparation cost, and a preparation process evaluation coefficient is generated; based on the preparation process evaluation coefficient, the optimal component ratio and the optimal addition amount are evaluated and optimized.

[0048] The set of application demand factors also includes process demand factors, which include preset preparation difficulty and preset preparation cost. The preset preparation difficulty includes the difficulty that may be encountered during the preparation process, such as equipment complexity, number of process steps, operator skill requirements, etc. The preset preparation cost includes raw material cost, energy consumption, labor cost, equipment depreciation, etc. Construct a process demand matrix to quantify the preparation difficulty and cost for subsequent analysis.

[0049] According to the optimal ingredient ratio and optimal addition amount, small batches of unsaturated polyester are prepared, and the various parameters in the preparation process and the actual difficulties encountered, such as the complexity of the process steps, the difficulty of operation, equipment failure, etc., are recorded. According to the actual recorded situation, the preparation difficulty is scored and quantified to form the actual preparation difficulty data; the various costs of small batch preparation are recorded, including raw material costs, energy consumption, labor costs, equipment depreciation, etc., to form the actual preparation cost data.

[0050] The actual preparation difficulty, cost and the preset preparation difficulty and cost data are input into the evaluation function to calculate the preparation process evaluation coefficient, and evaluate the deviation and adaptability of the actual preparation situation relative to the expected situation. According to the preparation process evaluation coefficient, it is determined whether the optimal component ratio and optimal addition amount need to be adjusted.

[0051] Furthermore, the method comprises:

[0052] The preparation process evaluation function is constructed, and the formula is as follows:

[0053]

[0054] Among them, E is the preparation process evaluation coefficient, D act is the actual preparation difficulty, D pre is the preset preparation difficulty, C act is the actual preparation cost, C pre is the preset preparation cost, ω d To prepare the difficulty weight, ω c is the preparation cost weight, and ω d +ωc =1, α is the exponential adjustment parameter of the preparation difficulty, and β is the exponential adjustment parameter of the preparation cost.

[0055] Specifically, the preparation process evaluation function is as follows:

[0056]

[0057] Among them, E is the preparation process evaluation coefficient, which is used to comprehensively evaluate the gap between the actual preparation process and the preset target, ω d To prepare the difficulty weight, ω c is the preparation cost weight, which is used to balance the relative importance of the two in the evaluation. The sum of the two is 1, so as to achieve a comprehensive balance of various evaluation factors. α is the exponential adjustment parameter of the preparation difficulty, and β is the exponential adjustment parameter of the preparation cost, which is used to adjust its nonlinear influence in the evaluation function.

[0058] By using this preparation process evaluation function, it can be ensured that while meeting performance requirements, the material preparation process has a reasonable difficulty and cost, thereby achieving the best balance between material performance and preparation feasibility. This method can systematically optimize the preparation process of unsaturated polyester based on weighing various factors, so as to better meet application requirements.

[0059] Furthermore, the method for evaluating and optimizing the optimal component ratio and the optimal addition amount based on the preparation process evaluation coefficient includes:

[0060] Obtain a preset preparation process evaluation coefficient; determine whether the preparation process evaluation coefficient is less than or equal to the preset preparation process evaluation coefficient; if so, generate evaluation pass information, and based on the evaluation pass information, use the optimal component ratio and the optimal addition amount to prepare the target unsaturated polyester.

[0061] Determine the baseline of the process standard, set the expected range of preparation difficulty and cost based on historical data and industry standards, and determine a target preparation process evaluation coefficient, which represents the ideal situation of the process. For example, the value is between 0 and 1, and the closer to 1, the more ideal the process.

[0062] The actual preparation process evaluation coefficient is compared with the preset preparation process evaluation coefficient to determine whether it is less than or equal to the preset preparation process evaluation coefficient. If so, it is clearly stated in the evaluation report that the evaluation is passed, and the final optimal component ratio and optimal addition amount are recorded, so as to prepare the unsaturated polyester.

[0063] Furthermore, the method further comprises:

[0064] If not, generate evaluation failure information; based on the evaluation failure information, calculate the difference between the preparation process evaluation coefficient and the preset preparation process evaluation coefficient to obtain the preparation process evaluation coefficient difference; based on the preparation process evaluation coefficient difference, perform feedback optimization of the optimal component ratio and the optimal addition amount.

[0065] If not, a report indicating that the assessment failed will be generated, and the problems and deficiencies encountered in the preparation process will be described in detail, such as excessive process difficulty, costs exceeding expectations, etc.

[0066] Calculate the difference between the actual preparation process evaluation coefficient and the preset preparation process evaluation coefficient, record the calculated evaluation coefficient difference in the evaluation report, and clarify the specific degree of deviation. Analyze the difference and determine the main factors causing the deviation, such as unreasonable component ratio, inappropriate addition amount, improper process parameter setting, etc.

[0067] Analyze the specific reasons for the difference in evaluation coefficients, determine which factors need to be adjusted and optimized, determine the priority of optimization based on the difference size and cause analysis, and give priority to solving the problems with greater impact. According to the difference feedback, adjust the component ratio, recalculate its impact on various performance indicators, find a new optimal component ratio, and after multiple iterations of optimization, select the component ratio and addition amount combination with the best evaluation coefficient.

[0068] In summary, the unsaturated polyester structure adjustment method combined with application demand analysis provided in the embodiments of the present application has the following technical effects:

[0069] By obtaining application scenario information and multi-dimensional application demand analysis, a set of demand factors including performance requirements and functional requirements is formed to achieve precise matching of requirements with material performance; by obtaining the component ratio range and the addition amount range of functional additives, and taking the demand factors as the optimization target, the optimization algorithm is used to systematically optimize the component ratio and additive amount to obtain the optimal component ratio and addition amount, ensuring the best performance and function of the material in the specific application scenario; in the preparation process, small-batch preparation experiments are conducted to obtain the actual preparation difficulty and cost data, and scientific evaluation is carried out through evaluation functions, and feedback optimization is carried out based on the evaluation results to ultimately ensure the feasibility and economy of the preparation process.

[0070] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for adjusting the structure of unsaturated polyester in combination with application demand analysis, characterized in that: The method comprises: Acquiring application scenario information of the target unsaturated polyester, performing multi-dimensional application demand analysis on the application scenario information, and obtaining an application demand factor set, wherein the application demand factor set includes performance demand factors and function demand factors; Connecting to the unsaturated polyester production management system to obtain basic component information of the basic unsaturated polyester, wherein the basic component information includes multiple component types; Obtaining component ratio intervals of the multiple component types; Within the component ratio range, taking the performance requirement factor as the optimization target, optimizing the component ratios of the multiple component types to obtain the optimal component ratio; Obtain the addition amount range of functional additives; Taking the functional requirement factor as the target, matching the addition amount of the functional additive within the addition amount range to obtain the optimal addition amount; Preparing the target unsaturated polyester according to the optimal component ratio and the optimal addition amount; The method comprises: Obtaining the performance requirement factors, wherein the performance requirement factors include one or more of tensile strength, flexural strength, impact toughness, chemical corrosion resistance, solvent resistance, water resistance, thermal stability, thermal expansion coefficient, and high temperature resistance; Obtaining the functional requirement factors, wherein the functional requirement factors include one or more of flame retardancy, electrical conductivity, antibacterial property, optical transparency, fluidity, curing time, low VOC emission, and non-toxicity; The method of optimizing the component ratio of the multiple component types to obtain the optimal component ratio includes: A component ratio optimization function for optimizing the component ratio is constructed as follows: ; Among them, F is the component fitness, is the component ratio variable, include , m is the number of component types, for The jth performance impact indicator of is the weight of the jth performance impact indicator, and n is the total number of performance impact indicators; Randomly generating an initial component ratio within the component ratio interval, and analyzing and obtaining a plurality of performance influencing indicators of the initial component ratio; Calculating the multiple performance influencing indicators according to the component ratio optimization function to obtain the initial component fitness of the initial component ratio; The component ratio is continuously optimized based on the initial component fitness to obtain the optimal component ratio with the maximum fitness.

2. The unsaturated polyester structure adjustment method combined with application demand analysis according to claim 1, characterized in that: The analysis obtains a plurality of performance influencing indicators of the initial component ratio, and the method includes: According to the preparation data record of unsaturated polyester, a sample component ratio set and multiple sample performance influencing indicators of the sample unsaturated polyester are obtained; Using the sample component ratio set and multiple sample performance impact indicators, training a performance impact analysis network; A performance impact analysis is performed on the initial component ratio based on the performance impact analysis network to obtain the multiple performance impact indicators.

3. The unsaturated polyester structure adjustment method combined with application demand analysis according to claim 1, characterized in that: Before preparing the target unsaturated polyester according to the optimal component ratio and the optimal addition amount, the method further comprises: The application requirement factor set also includes process requirement factors, wherein the process requirement factors include a preset preparation difficulty and a preset preparation cost; Carrying out small batch preparation of the target unsaturated polyester according to the optimal component ratio and the optimal addition amount, and analyzing and obtaining the actual preparation difficulty and actual preparation cost; Based on the preparation process evaluation function, the actual preparation difficulty and the actual preparation cost are combined to perform preparation process evaluation on the actual preparation difficulty and the actual preparation cost to generate a preparation process evaluation coefficient; The optimal component ratio and the optimal addition amount are evaluated and optimized based on the preparation process evaluation coefficient.

4. The method for adjusting the structure of unsaturated polyester in combination with application demand analysis as claimed in claim 3, characterized in that: The method for evaluating and optimizing the optimal component ratio and the optimal addition amount based on the preparation process evaluation coefficient comprises: Obtaining preset preparation process evaluation coefficients; Determining whether the manufacturing process evaluation coefficient is less than or equal to the preset manufacturing process evaluation coefficient; If so, generate evaluation pass information, and based on the evaluation pass information, use the optimal component ratio and the optimal addition amount to prepare the target unsaturated polyester.

5. The unsaturated polyester structure adjustment method combined with application demand analysis as claimed in claim 4, characterized in that: The method also includes: If not, an assessment failure message is generated; Based on the evaluation failure information, calculating the difference between the preparation process evaluation coefficient and the preset preparation process evaluation coefficient to obtain the preparation process evaluation coefficient difference; Feedback optimization of the optimal component ratio and the optimal addition amount is performed based on the difference in the preparation process evaluation coefficient.

6. The method for adjusting the structure of unsaturated polyester in combination with application demand analysis as claimed in claim 3, characterized in that: The method comprises: The preparation process evaluation function is constructed, and the formula is as follows: ; Where E is the preparation process evaluation coefficient, Due to the actual difficulty of preparation, To set the difficulty of preparation, is the actual preparation cost, To preset the preparation cost, To prepare the difficulty weight, is the preparation cost weight, and , is the index adjustment parameter for the difficulty of preparation, is the exponential adjustment parameter for the preparation cost.

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