A method, apparatus, terminal equipment, and storage medium for evaluating the comprehensive properties of biaxially oriented polypropylene film.

Abstract

This invention discloses a method, apparatus, terminal equipment, and storage medium for evaluating the comprehensive performance of biaxially oriented polypropylene (BOP) films, belonging to the field of materials evaluation technology. The method includes: constructing an initial data matrix based on the electrical, energy storage, mechanical, and structural performance indicators of the film to be evaluated; calculating the dispersion values ​​between each performance indicator based on the initial data matrix, and generating a weight matrix by calculating the weights of each performance indicator; constructing a trapezoidal cloud model corresponding to the evaluation level of each performance indicator based on a preset evaluation level table, and constructing a corresponding membership function based on the trapezoidal cloud model; constructing a membership matrix based on the membership function and each performance indicator; performing fuzzy synthesis operations on the weight matrix and the membership matrix to obtain a comprehensive evaluation vector; and determining the comprehensive performance level of the film to be evaluated based on the principle of maximum membership degree and the comprehensive evaluation vector. By implementing this invention, the problem of low evaluation accuracy in existing technologies can be solved.

Description

Technical Field

[0001] This invention relates to the field of material evaluation technology, and in particular to a method, apparatus, terminal equipment, and storage medium for evaluating the comprehensive properties of biaxially oriented polypropylene film. Background Technology

[0002] Biaxially oriented polypropylene (BOPP) film is a core energy storage and insulation material in fields such as film capacitors, pulse power devices, new energy vehicles, and smart grids. Among these, BOPP film has become the most widely used commercial polymer dielectric film due to its excellent insulation properties, low dielectric loss, and good processability. With the increasing demands for energy storage density, service temperature, and service life in high-end power electronic equipment, the optimization technology for BOPP film processes is rapidly developing. The industry urgently needs an objective and comprehensive method to evaluate the overall performance of polymer dielectric films with different formulations and processes.

[0003] Current performance evaluations of polymer dielectric films mostly rely on single core parameters such as DC breakdown field strength and discharge energy density, failing to comprehensively cover the film's insulation, energy storage, mechanical, and thermodynamic performance across all dimensions. Consequently, the evaluation results cannot accurately reflect the film's actual overall performance. Furthermore, existing comprehensive evaluation methods often employ subjective weighting methods like the Delphi method, with weights heavily dependent on expert experience. Significant differences in weights assigned by different evaluation bodies lead to a lack of objectivity, impartiality, and comparability among evaluation results. Therefore, the industry currently lacks a comprehensive and objective performance evaluation system for biaxially oriented polypropylene films, hindering the achievement of accurate evaluations. Summary of the Invention

[0004] This invention provides a method, apparatus, terminal equipment, and storage medium for evaluating the comprehensive performance of biaxially oriented polypropylene film, which can effectively solve the problems of low accuracy in comprehensive performance evaluation caused by single-parameter evaluation and reliance on subjective experience for evaluation weights in the prior art.

[0005] An embodiment of the present invention provides a method for evaluating the comprehensive properties of biaxially oriented polypropylene film, comprising: Obtain the electrical performance, energy storage performance, mechanical performance, and structural performance of the thin film to be evaluated; An initial data matrix is ​​constructed based on the electrical performance indicators, the energy storage performance indicators, the mechanical performance indicators, and the structural performance indicators. The dispersion values ​​between each performance index are calculated based on the initial data matrix, and the weights of each performance index are calculated based on the dispersion values ​​to generate a weight matrix. Based on the preset evaluation level table, a trapezoidal cloud model corresponding to the evaluation level of each performance indicator is constructed, and a membership function corresponding to each performance indicator is constructed based on the trapezoidal cloud model. Construct a membership matrix based on the membership function and various performance indicators; A fuzzy synthesis operation is performed based on the weight matrix and the membership matrix to obtain the comprehensive evaluation vector of the thin film to be evaluated. Based on the principle of maximum membership, the overall performance level of the thin film to be evaluated is determined according to the comprehensive evaluation vector.

[0006] Furthermore, the electrical performance indicators include DC breakdown field strength; the energy storage performance indicators include discharge energy density, charge-discharge efficiency, and charge-discharge cycle count; the mechanical performance indicators include tensile strength; and the structural performance indicators include melting temperature and crystallinity. Calculate the dispersion values ​​between each performance index based on the initial data matrix, calculate the weights of each performance index based on the dispersion values, and generate a weight matrix, including: The DC breakdown field strength, discharge energy density, charge-discharge efficiency, charge-discharge cycle number, tensile strength, melting temperature and crystallinity in the initial data matrix are normalized based on the specific gravity method to obtain the parameter values ​​of each parameter. The dispersion value of each parameter is calculated based on the weight of the parameter values. Calculate the parameter weights of each parameter based on the dispersion values, and calculate the weights of each performance index based on the parameter weights to generate a weight matrix.

[0007] Furthermore, based on a pre-defined evaluation level table, a trapezoidal cloud model is constructed for each performance indicator corresponding to its evaluation level, including: According to the preset evaluation level table, each parameter in each performance index is divided into several evaluation levels, and each evaluation level is sequentially numbered. Based on DC breakdown field strength, discharge energy density, charge-discharge efficiency, charge-discharge cycle count, tensile strength, melting temperature, and crystallinity, the expected range for each parameter, the entropy used to characterize the ambiguity of the transition zone at the evaluation level boundary, and the hyperentropy used to characterize the random fluctuation of entropy are calculated. Based on the sequential number, the expected intervals of each parameter, the entropy, and the hyperentropy, a trapezoidal cloud model is constructed for each performance index.

[0008] Furthermore, based on the trapezoidal cloud model, a membership function corresponding to each performance index is constructed, including: Based on the entropy and hyperentropy in the trapezoidal cloud model, generate randomized entropy corresponding to each evaluation level; Based on the positional relationship between DC breakdown field strength, discharge energy density, charge-discharge efficiency, charge-discharge cycle count, tensile strength, melting temperature, and crystallinity and the expected range of the corresponding evaluation level, a membership function corresponding to each parameter is generated.

[0009] Furthermore, based on the membership function and various performance indicators, a membership matrix is ​​constructed, including: The membership degree of each evaluation level is calculated based on the membership function, which corresponds to DC breakdown field strength, discharge energy density, charge-discharge efficiency, charge-discharge cycle count, tensile strength, melting temperature, and crystallinity. Construct a membership matrix based on the membership degree of each evaluation level, using each parameter as the row index and the evaluation level of each parameter as the column index.

[0010] Further, a fuzzy synthesis operation is performed based on the weight matrix and the membership matrix to obtain a comprehensive evaluation vector for the thin film to be evaluated, including: A weighted fuzzy synthesis operation is performed based on the weight matrix, the membership matrix, and a preset weighted synthesis operator to obtain a comprehensive evaluation vector for the thin film to be evaluated. The dimension of the comprehensive evaluation vector is consistent with the number of evaluation levels; the elements in the comprehensive evaluation vector are the comprehensive membership degrees of the film to be evaluated for each evaluation level.

[0011] Furthermore, based on the principle of maximum membership, the comprehensive performance level of the thin film to be evaluated is determined according to the comprehensive evaluation vector, including: Based on the comprehensive evaluation vector, determine the maximum comprehensive membership degree; The evaluation level corresponding to the highest comprehensive membership degree is taken as the comprehensive performance level of the film to be evaluated. If there are two or more equal and maximum values ​​in the comprehensive evaluation vector, the evaluation level with the highest performance index is taken as the comprehensive performance level of the film to be evaluated, according to the performance index of each evaluation level from high to low.

[0012] As an improvement to the above solution, another embodiment of the present invention provides an evaluation device for the comprehensive performance of biaxially oriented polypropylene film, comprising: The index data acquisition module is used to acquire the electrical performance index, energy storage performance index, mechanical performance index and structural performance index of the thin film to be evaluated. An initial data matrix construction module is used to construct an initial data matrix based on the electrical performance indicators, the energy storage performance indicators, the mechanical performance indicators, and the structural performance indicators. The weight matrix generation module is used to calculate the dispersion value between each performance index based on the initial data matrix, calculate the weight of each performance index based on the dispersion value, and generate a weight matrix. The membership function construction module is used to construct a trapezoidal cloud model corresponding to the evaluation level of each performance indicator based on a preset evaluation level table, and to construct the membership function corresponding to each performance indicator based on the trapezoidal cloud model. The membership weight construction module is used to construct a membership matrix based on the membership function and various performance indicators. The comprehensive evaluation vector operation module is used to perform fuzzy synthesis operation based on the weight matrix and the membership matrix to obtain the comprehensive evaluation vector of the thin film to be evaluated. The comprehensive performance evaluation module is used to determine the comprehensive performance level of the thin film to be evaluated based on the principle of maximum membership and the comprehensive evaluation vector.

[0013] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a method for evaluating the comprehensive performance of a biaxially oriented polypropylene film as described in the above embodiments.

[0014] Another embodiment of the present invention provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the method for evaluating the comprehensive performance of biaxially oriented polypropylene film described in the above embodiment.

[0015] By implementing this invention, at least the following beneficial effects are achieved: This invention provides a method, apparatus, terminal equipment, and storage medium for evaluating the comprehensive performance of biaxially oriented polypropylene (BOP) films. The method constructs a multi-index collaborative evaluation system from four dimensions: electrical, energy storage, mechanical, and structural, comprehensively covering all key performance requirements of BOP films during actual service. Electrical performance indicators correspond to the film's insulation reliability; energy storage performance indicators correspond to the film's energy storage and release capabilities; mechanical performance indicators correspond to the film's resistance to deformation and damage; and structural performance indicators correspond to the film's microstructure and thermal properties. By integrating performance information from these four dimensions, the system can comprehensively capture the film's performance effects in insulation, energy storage, processing, and thermal stability. Simultaneously, an initial data matrix is ​​constructed, and the information content of each performance index is quantified by calculating its dispersion value. Finally, weights are automatically assigned based on the amount of information, generating a weight matrix. The evaluation process of the weight matrix does not involve human judgment and can be dynamically adjusted according to the dispersion of the sample data, allowing films with different formulations, processes, and production batches to be evaluated uniformly. Furthermore, a trapezoidal cloud model is used to construct the membership function, simultaneously characterizing the fuzzy transition characteristics of the evaluation level boundary and the random fluctuation characteristics of the measured data. When parameter values ​​approach the evaluation grade boundary, the membership degree exhibits a continuous and smooth change rather than abrupt changes, demonstrating strong robustness to normal random errors. This effectively prevents evaluation grade jumps caused by minor deviations in individual test data, significantly improving the stability and accuracy of the evaluation results. Therefore, by constructing electrical, energy storage, mechanical, and structural performance indicators, weight matrices, and membership degree matrices, the accuracy of the comprehensive performance evaluation of thin films is improved. Attached Figure Description

[0016] Figure 1 This is a schematic flowchart of a method for evaluating the comprehensive performance of biaxially oriented polypropylene film according to an embodiment of the present invention. Figure 2 This is a weight distribution diagram of each parameter calculated based on the entropy weight method, provided by an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an evaluation device for the comprehensive performance of biaxially oriented polypropylene film provided in an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] See Figure 1To address the problem of low accuracy in comprehensive performance evaluation caused by single-parameter evaluation and reliance on subjective experience for evaluation weights in existing technologies, an embodiment of the present invention provides a flowchart illustrating a method for evaluating the comprehensive performance of biaxially oriented polypropylene films, comprising: S1. Obtain the electrical performance, energy storage performance, mechanical performance and structural performance of the thin film to be evaluated. Specifically, the film to be evaluated is a polypropylene film prepared by a biaxial stretching process. It possesses a structure characterized by oriented molecular chains, exhibiting excellent insulation strength, mechanical strength, and thermal stability, making it a core dielectric material for film capacitors. Electrical performance indicators characterize the insulation and conductivity of the material, directly determining the reliability of the film as an insulating medium. Energy storage performance indicators characterize the material's ability to store and release electrical energy, serving as the core basis for measuring the energy density and efficiency of capacitors. Mechanical performance indicators characterize the material's resistance to deformation and damage from external forces, affecting the film's processing performance and service life. Structural performance indicators characterize the material's microstructure and thermal properties, closely related to the film's fabrication process and long-term stability.

[0019] To illustrate, we first collect four core performance indicators of the thin film to be evaluated: electrical, energy storage, mechanical, and structural, to ensure that all key performance dimensions of the thin film to be evaluated are covered in actual service.

[0020] Indicatively, the films to be evaluated include, but are not limited to, pure polypropylene films, grafted modified polypropylene films, blended modified polypropylene films, and multilayer composite polypropylene films.

[0021] S2. Construct an initial data matrix based on the electrical performance indicators, the energy storage performance indicators, the mechanical performance indicators, and the structural performance indicators; Specifically, the initial data matrix is ​​a two-dimensional array formed by arranging the specific values ​​of multiple performance indicators of one or more thin films to be evaluated in rows and columns, which serves as the basis for subsequent data processing.

[0022] To illustrate, the collected performance parameters are organized into a standardized matrix form to provide a unified data format for subsequent mathematical calculations.

[0023] In a preferred embodiment of the present invention, measured data of the above 7 parameters of n thin films to be evaluated are obtained, and an initial data matrix of n rows and 7 columns is constructed as follows: Where i is the sample number of the thin film to be evaluated, i=1,2,…,n, and j is the parameter number, j=1,2,…,7; x ij This represents the measured value of the j-th parameter of the i-th thin film to be evaluated.

[0024] S3. Calculate the dispersion value between each performance index based on the initial data matrix, calculate the weight of each performance index based on the dispersion value, and generate a weight matrix. Specifically, the dispersion value reflects the degree of dispersion of performance indicator data; the larger the value, the greater the difference in performance indicators and the more information they contain. The weight matrix is ​​a one-dimensional array composed of the weights of each performance indicator, where each weight represents the degree of influence of the corresponding performance indicator on the overall performance.

[0025] To illustrate, based on the dispersion of measured data of multiple performance indicators, the objective weights of each performance indicator are calculated, completely avoiding interference from human factors.

[0026] Preferably, the electrical performance indicators include DC breakdown field strength; the energy storage performance indicators include discharge energy density, charge-discharge efficiency, and charge-discharge cycle count; the mechanical performance indicators include tensile strength; and the structural performance indicators include melting temperature and crystallinity. Calculate the dispersion values ​​between each performance index based on the initial data matrix, calculate the weights of each performance index based on the dispersion values, and generate a weight matrix, including: The DC breakdown field strength, discharge energy density, charge-discharge efficiency, charge-discharge cycle number, tensile strength, melting temperature and crystallinity in the initial data matrix are normalized based on the specific gravity method to obtain the parameter values ​​of each parameter. The dispersion value of each parameter is calculated based on the weight of the parameter values. Calculate the parameter weights of each parameter based on the dispersion values, and calculate the weights of each performance index based on the parameter weights to generate a weight matrix.

[0027] Specifically, DC breakdown field strength is the electric field strength of a material during breakdown under a DC voltage, measured in kV / mm, and is the most crucial indicator for evaluating the insulation performance of a material. Discharge energy density is the maximum electrical energy that a unit volume of material can release, measured in J / cm³. 3 The energy density of a capacitor is directly determined by its energy output. Charge / discharge efficiency, expressed as a percentage, is the ratio of discharge energy to charging energy, reflecting energy loss during storage. Charge / discharge cycle count is the number of charge / discharge cycles a material can withstand under specified conditions, reflecting its lifespan and aging resistance. Tensile strength, expressed in MPa, is the maximum stress a material can withstand before tensile fracture, reflecting its mechanical strength and processing performance. Melting temperature, expressed in °C, is the temperature at which a material transitions from a crystalline state to a molten state, reflecting its thermal stability and processing temperature range. Crystallinity is the percentage of the material's mass that is crystalline, and it is closely related to the material's mechanical, thermal, and dielectric properties.

[0028] Specifically, normalization is performed using the proportion method, which involves dividing the performance index value of each film to be evaluated by the sum of all film values ​​under that performance index to obtain the proportion of that sample under that performance index, with a value range of [0,1]. The degree of dispersion can be measured using information entropy, which measures the amount of information contained in the performance index. The smaller the information entropy, the greater the dispersion of the index value and the more information it contains.

[0029] Schematic representation: Electrical performance indicators include DC breakdown field strength, which is the most critical factor determining the reliability of thin-film insulation, directly affecting the rated voltage and breakdown probability of the capacitor. Energy storage performance indicators include discharge energy density, charge / discharge efficiency, and charge / discharge cycle count. These three parameters comprehensively reflect the energy storage performance of the thin film from the aspects of energy storage capacity, energy loss, and service life, respectively. Mechanical performance indicators include tensile strength, which directly affects the winding processing performance of the thin film and its mechanical stability inside the capacitor. Structural performance indicators include melt temperature and crystallinity. These two parameters reflect the microstructure characteristics of the polypropylene film and are closely related to the film's preparation process, thermal stability, and dielectric properties.

[0030] To illustrate, for each performance index, the proportion of the parameter value of each thin film sample to be evaluated to the sum of the index values ​​of all samples is calculated. When the parameter value of a sample is 0, a preset correction value is taken as its proportion to avoid the subsequent logarithmic calculation being meaningless.

[0031] Then, based on the weight of the parameter value for each thin film sample to be evaluated, the dispersion value of that parameter is calculated. The dispersion value ranges from [0,1]. When all the index values ​​of the thin film samples to be evaluated are exactly the same, the dispersion value is 1, indicating that the index does not contain any valid information; when the sample index values ​​differ more, the dispersion value is smaller, indicating that the index contains more information.

[0032] Then, the parameter weight for each parameter is calculated based on the dispersion value. The parameter weight is proportional to (1 - dispersion value), meaning the smaller the dispersion value, the larger the parameter weight. Finally, all parameter weights are normalized to ensure that the sum of all weights is 1, generating a weight matrix with the same dimensions as the number of performance indicators.

[0033] To illustrate, when calculating the weights, all seven parameters are positive benefit indicators, meaning that the larger the parameter value, the better the performance. Therefore, there is no need to convert them to negative indicators. If negative indicators such as cost or loss need to be introduced in practical applications, they can be converted into positive indicators first using the reciprocal method or the extreme value method before calculation.

[0034] In a preferred embodiment of the present invention, the proportion P of the parameter value of the i-th sample to be evaluated under the j-th parameter is calculated. ij : , where when x ij When =0, take the correction value P. ij =1×10 -10 To avoid the natural logarithm being meaningless. For example... Figure 2 As shown, the parameter weights are for DC breakdown field strength, discharge energy density, charge / discharge efficiency, charge / discharge cycle count, tensile strength, melting temperature, and crystallinity.

[0035] In a preferred embodiment of the present invention, the information entropy E of the j-th parameter is calculated. j : Where n is the total number of films to be evaluated, P ij Information entropy E represents the proportion of parameter values. j The value range is [0,1].

[0036] Calculate the parameter weight ω of the j-th parameter. j : Where m is the total number of parameters. All parameter weights satisfy the normalization requirement: .

[0037] The Delphi method, a current technology, requires multiple experts to score the importance of indicators and then take the average as the weight. This method is not only time-consuming and labor-intensive, but the professional backgrounds and personal preferences of different experts can lead to significant differences in weights. For example, an expert in insulation materials research might assign a higher weight to DC breakdown field strength, while an expert in energy storage technology research might assign a higher weight to discharge energy density, resulting in a lack of impartiality in the evaluation results. Subjective weighting methods yield fixed weights that cannot be adjusted based on the actual situation of the sample data. When the dispersion of certain indicators changes across different batches of samples, fixed weights cannot reflect this change, leading to distorted evaluation results. Furthermore, the determination of weights in subjective weighting methods lacks rigorous mathematical theoretical basis and relies more on empirical judgment; therefore, its scientific validity and reliability are widely questioned.

[0038] This embodiment employs the entropy weighting method for parameter weight calculation. The weight calculation using this method is entirely based on the statistical characteristics of the performance indicators of the thin film being evaluated, without involving any human judgment. This means that regardless of who performs the evaluation, using the same sample data will yield identical weights and evaluation results, completely resolving the bias problem caused by subjective weighting. Furthermore, it can automatically adjust the weights based on the actual dispersion of the sample data. When a performance indicator shows significant differences among thin film samples, it indicates that the indicator plays a crucial role in distinguishing the performance of different thin films and is therefore assigned a higher weight; conversely, when a performance indicator shows small differences among samples, it indicates that the indicator has a smaller impact on the overall performance and is assigned a lower weight. This adaptive capability allows the weights to truly reflect the actual importance of each indicator in a specific evaluation scenario.

[0039] Meanwhile, the dispersion value in this embodiment can accurately quantify the amount of information contained in each parameter under the performance index, and the proportional relationship between weight and information content is consistent with people's basic understanding of evaluation problems. Since the weight calculation process is completely reproducible, films from different laboratories, different companies, and different batches can be uniformly evaluated using this method, and the evaluation results are horizontally comparable. This is of great significance for establishing unified quality standards within the industry, promoting technical exchange, and upgrading products.

[0040] S4. Based on the preset evaluation level table, construct a trapezoidal cloud model corresponding to the evaluation level of each performance indicator, and construct the membership function corresponding to each performance indicator based on the trapezoidal cloud model. Specifically, the trapezoidal cloud model is an uncertainty transformation model based on fuzzy mathematics and probability theory, capable of simultaneously characterizing the fuzziness and randomness of performance indicators. The membership function is a function used to calculate the degree to which a specific value of a performance indicator belongs to a certain fuzzy concept, with a value range of [0,1].

[0041] To illustrate, the overall performance of the thin film to be evaluated is divided into multiple evaluation levels. A corresponding trapezoidal cloud model is established for each performance index as a membership function, while the fuzziness of the evaluation boundary and the randomness of the measured data are handled.

[0042] Preferably, based on a preset evaluation level table, a trapezoidal cloud model is constructed for each performance indicator corresponding to an evaluation level, including: According to the preset evaluation level table, each parameter in each performance index is divided into several evaluation levels, and each evaluation level is sequentially numbered. Based on DC breakdown field strength, discharge energy density, charge-discharge efficiency, charge-discharge cycle count, tensile strength, melting temperature, and crystallinity, the expected range for each parameter, the entropy used to characterize the ambiguity of the transition zone at the evaluation level boundary, and the hyperentropy used to characterize the random fluctuation of entropy are calculated. Based on the sequential number, the expected intervals of each parameter, the entropy, and the hyperentropy, a trapezoidal cloud model is constructed for each performance index.

[0043] Specifically, the evaluation level categorizes thin films into different grades based on their overall performance, facilitating quality grading and decision-making. The expected interval is the range of values ​​with a membership degree of 1 in the trapezoidal cloud model, representing the range of values ​​that completely belong to that evaluation level. Entropy (En) is one of the numerical characteristics of the trapezoidal cloud model, reflecting the degree of fuzziness of the evaluation level boundaries; the higher the entropy, the more fuzzy the boundaries. Hyperentropy (He) is another numerical characteristic of the trapezoidal cloud model, reflecting the degree of random fluctuation in entropy and embodying the randomness of the measured data.

[0044] Schematic, the overall performance of biaxially oriented polypropylene film is divided into four evaluation levels, ranked from highest to lowest: Excellent, Good, Average, and Poor, with each level assigned a sequential number. This four-level classification method satisfies the quality grading requirements in engineering applications without making the evaluation results overly detailed and meaningless due to too many levels.

[0045] Then, for each performance indicator, based on industry standards in the field, the range of actual test data for commercial products, and the performance requirements of high-end applications, the expected range corresponding to each evaluation level is determined. The division of the expected range should follow the principle of stricter standards for high-end and more lenient standards for low-end, ensuring that the excellent level can truly represent the industry-leading level, while the poor level can clearly distinguish unqualified products.

[0046] Based on the expected range for each evaluation level, the corresponding entropy and hyperentropy are calculated. The coefficients used for hyperentropy calculation are determined based on the statistical characteristics of a large amount of measured data from BOPP films, accurately reflecting the degree of random fluctuation in the measured data. Finally, the sequential number, expected range, entropy, and hyperentropy of each evaluation level are combined to construct a trapezoidal cloud model corresponding to each performance index.

[0047] In a preferred embodiment of the present invention, the number of evaluation levels can be adjusted according to actual application requirements. For example, for the more demanding aerospace field, it can be divided into 5 or 6 levels; for the general civilian field, it can be divided into 3 levels. The specific values ​​of the expected range should also be adjusted accordingly based on the performance requirements of different application scenarios. For example, the expected range for films used in new energy vehicles should be higher than that for films used in general consumer electronics.

[0048] In a preferred embodiment of the present invention, taking DC breakdown field strength as an example, the construction process of the trapezoidal cloud model is explained as follows: First, the DC breakdown field strength is divided into four levels: Excellent (Level 3), Good (Level 2), Average (Level 1), and Poor (Level 0). Then, the expected ranges for each level are determined as follows: Excellent [650, 750] kV / mm, Good [600, 650] kV / mm, Average [500, 600] kV / mm, and Poor [400, 500] kV / mm. Combining the excellent level number 3, the expected range [650, 750], the entropy 16.67, and the super-entropy 2.50, the trapezoidal cloud model for the excellent level of DC breakdown field strength is obtained. Following the same method, trapezoidal cloud models for the other six parameters corresponding to the four levels can be constructed.

[0049] Traditional fuzzy comprehensive evaluation methods use fixed triangular or trapezoidal membership functions, where the membership degree is a fixed value and cannot reflect the random fluctuations of the measured data itself. However, core parameters of BOPP films, such as DC breakdown field strength, have significant probabilistic characteristics. The results of multiple tests on the same sample under the same conditions may vary considerably, and fixed membership functions cannot handle this uncertainty. Traditional membership functions exhibit abrupt changes in membership degree at the boundaries of evaluation levels. For example, when the breakdown field strength decreases from 650 kV / mm to 649 kV / mm, the membership degree suddenly drops from 1 to 0, which is clearly unrealistic. In reality, there is almost no difference in performance between breakdown field strengths of 649 kV / mm and 650 kV / mm, and they should not be classified into different levels.

[0050] This embodiment employs a trapezoidal cloud model to construct the evaluation system. By introducing the digital feature of hyperentropy, the trapezoidal cloud model simultaneously addresses two types of uncertainty in the evaluation process. Entropy handles the fuzziness of the evaluation level boundaries, while hyperentropy handles the randomness of the measured data. This design fully conforms to the inherent characteristics of BOPP film performance parameters, making the evaluation results more closely aligned with engineering realities. The membership function of the trapezoidal cloud model is continuous, without abrupt changes at the evaluation level boundaries. As parameter values ​​approach the boundaries, the membership gradually changes, rather than suddenly dropping from 1 to 0. This smooth transition characteristic avoids jumps in evaluation levels due to small changes in parameter values, improving the stability and reliability of the evaluation results. The magnitude of hyperentropy reflects the credibility of the evaluation results. Smaller hyperentropy indicates smaller random fluctuations in the measured data and more reliable evaluation results; larger hyperentropy indicates greater random fluctuations in the measured data and lower credibility of the evaluation results. By analyzing the magnitude of hyperentropy, the credibility of the evaluation results can be quantitatively assessed, providing more comprehensive information for decision-making.

[0051] Preferably, the membership function corresponding to each performance index is constructed based on the trapezoidal cloud model, including: Based on the entropy and hyperentropy in the trapezoidal cloud model, generate randomized entropy corresponding to each evaluation level; Based on the positional relationship between DC breakdown field strength, discharge energy density, charge-discharge efficiency, charge-discharge cycle count, tensile strength, melting temperature, and crystallinity and the expected range of the corresponding evaluation level, a membership function corresponding to each parameter is generated.

[0052] Specifically, randomized entropy is a normally distributed random variable generated based on entropy and hyperentropy, used to simulate the random fluctuations of measured data for various performance indicators.

[0053] Schematic, for each evaluation level of the trapezoidal cloud model, a randomized entropy En' following a normal distribution N(En,He²) is generated based on its entropy En and hyperentropy He. A new randomized entropy is generated each time the membership degree is calculated to simulate the random fluctuations of the measured data. The measured parameter values ​​x of the thin film sample to be evaluated are compared with the expected interval [Ex] of the corresponding evaluation level. min ,Ex max The positional relationship of x is used to calculate the membership degree in three cases: when x falls within the expected interval, i.e., Ex... min ≤x≤Ex max A membership degree μ=1 indicates that the parameter value completely belongs to this evaluation level. When x is less than the minimum value of the expected interval, i.e., x... <Ex min Membership degree μ = exp[-(x - Ex] min ) 2 / (2En' 2 This indicates that the degree to which the parameter value belongs to this evaluation level decreases exponentially as the distance from the interval increases. When x is greater than the maximum value of the expected interval, i.e., x > Ex... max Membership degree μ = exp[-(x - Ex] max ) 2 / (2En' 2 The expression ] indicates that the degree to which a parameter value belongs to that evaluation level decreases exponentially as the distance from the interval increases. Combining the above calculation rule with the randomized entropy generation process, a membership function corresponding to each parameter and each evaluation level is formed.

[0054] In a preferred embodiment of the present invention, the calculation formulas for the digital feature entropy En and hyperentropy He of the trapezoidal cloud model are as follows: , , where Ex min The minimum value of the expected interval for the trapezoidal cloud model; Ex max This represents the maximum value within the expected interval of the trapezoidal cloud model. The formula for calculating the randomized entropy En' is: , where En is entropy, reflecting the fuzziness of the transition zone of the grade boundary; He is hyperentropy, reflecting the random fluctuation degree of entropy. Then, according to the specific numerical values of the parameters, the membership degree μ is calculated. When x < Exmin, the calculation formula is: When x > Exmax, the calculation formula is: When x is in the interval of [Ex min , Ex max , the membership degree μ is 1.

[0055] In a preferred embodiment of the present invention, in order to improve the calculation efficiency and the stability of the results, the method of taking the average value by multiple random simulations can be used to calculate the membership degree. That is, for the same parameter value, multiple randomized entropies are generated, multiple membership degrees are calculated, and then the average value is taken as the final membership degree.

[0056] The membership degree calculated by the traditional membership function is a definite numerical value, without considering the random fluctuation of the measured data. This means that for the same parameter of the same sample, no matter how many times it is tested, the same membership degree will be obtained. Since the membership function is discontinuous at the boundary, a small test error may cause a huge change in the membership degree, thereby affecting the final evaluation result. For example, when the parameter value is exactly near the boundary of two grades, a single test error may cause a jump in the evaluation grade. The traditional method can only give a definite evaluation grade and cannot quantify the uncertainty degree of the evaluation result. In fact, when the parameter value is close to the boundary, the uncertainty of the evaluation result is very large.

[0057] By introducing randomized entropy in the construction of the membership function in this embodiment, the obtained membership degree is no longer a definite numerical value but a random variable. This more conforms to the probabilistic characteristics of the BOPP film performance parameters and can truly reflect the random fluctuation of the measured data. Since the membership function is a continuous exponential function, a small change in the parameter value will only cause a small change in the membership degree and will not mutate. This makes the evaluation result highly robust to the normal random errors in the test process and avoids the jump of the evaluation grade caused by the deviation of individual test data.

[0058] S5. Construct a membership matrix according to the membership function and each performance index; Specifically, the membership matrix represents a two-dimensional array formed by arranging the membership degrees of each performance index corresponding to each evaluation grade in rows and columns.

[0059] Schematically, substitute each parameter in the performance index of the film to be evaluated into the membership function, calculate the membership degree of each parameter corresponding to each evaluation grade, and construct a membership matrix.

[0060] Preferably, a membership matrix is ​​constructed based on the membership function and various performance indicators, including: The membership degree of each evaluation level is calculated based on the membership function, which corresponds to DC breakdown field strength, discharge energy density, charge-discharge efficiency, charge-discharge cycle count, tensile strength, melting temperature, and crystallinity. Construct a membership matrix based on the membership degree of each evaluation level, using each parameter as the row index and the evaluation level of each parameter as the column index.

[0061] Specifically, the row index represents the performance parameter corresponding to each row in the membership matrix. The column index represents the evaluation level corresponding to each column in the membership matrix.

[0062] Schematic, for each parameter under each performance index of the thin film to be evaluated, the measured value is substituted into the membership function corresponding to the four evaluation levels to calculate the membership degree of that parameter for each evaluation level. Using the seven performance parameters as row indices and the four evaluation levels as column indices, all calculated membership degrees are arranged sequentially to construct a 7x4 membership degree matrix R. The elements r in the matrix... jk This represents the membership degree of the j-th parameter to the k-th evaluation level, where j=1,2,...,7 correspond to DC breakdown field strength, discharge energy density, charge-discharge efficiency, charge-discharge cycle count, tensile strength, melting temperature, and crystallinity, respectively; k=0,1,2,3 correspond to four evaluation levels: poor, average, good, and excellent, respectively.

[0063] In a preferred embodiment of the present invention, the expression for the 7-row, 4-column membership matrix R is: , where r jk Let be the membership degree of the j-th parameter corresponding to the k-th evaluation level, where k = 0, 1, 2, 3 correspond to the four evaluation levels of poor, average, good, and excellent, respectively.

[0064] In a preferred embodiment of the present invention, when constructing the membership matrix, it should be ensured that the sum of the membership degrees of each row does not need to be equal to 1, because the membership degree of the trapezoidal cloud model is calculated based on the degree of fuzzy concepts, and there are overlapping areas between different levels.

[0065] Because existing technologies mostly use a single parameter or a few parameters for evaluation, the membership matrix has very few rows and contains very limited information, failing to comprehensively reflect the overall performance of the thin film. As mentioned earlier, the membership degrees calculated using a fixed membership function in existing technologies are inaccurate, leading to biases in the data within the membership matrix and consequently affecting the final evaluation results.

[0066] The membership matrix in this embodiment contains all membership information for the seven core performance parameters corresponding to four evaluation levels, comprehensively reflecting the performance of the sample under evaluation across various performance dimensions. By analyzing the membership matrix, not only can the overall performance level of the sample be obtained, but it is also possible to clearly see which parameters the sample excels in and which parameters have weaknesses, providing a clear direction for subsequent formulation optimization and process improvement. Since the membership is calculated based on a trapezoidal cloud model with hyperentropy, it can simultaneously handle fuzziness and randomness, making the data in the membership matrix more accurate and reliable, truly reflecting the actual performance level of the sample. Furthermore, the membership matrix can be easily converted into visualizations such as heatmaps, intuitively displaying the membership distribution of the sample across various parameters and levels, facilitating rapid understanding and analysis of the evaluation results by evaluators.

[0067] S6. Perform fuzzy synthesis operation based on the weight matrix and the membership matrix to obtain the comprehensive evaluation vector of the thin film to be evaluated; Specifically, the comprehensive evaluation vector represents a one-dimensional array consisting of the comprehensive membership degrees of the thin film to be evaluated for each evaluation level.

[0068] To illustrate, the weight matrix and membership matrix are fuzzy synthesized to obtain the comprehensive membership degree of the thin film to be evaluated for each evaluation level.

[0069] Preferably, a fuzzy synthesis operation is performed based on the weight matrix and the membership matrix to obtain a comprehensive evaluation vector for the thin film to be evaluated, including: A weighted fuzzy synthesis operation is performed based on the weight matrix, the membership matrix, and a preset weighted synthesis operator to obtain a comprehensive evaluation vector for the thin film to be evaluated. The dimension of the comprehensive evaluation vector is consistent with the number of evaluation levels; the elements in the comprehensive evaluation vector are the comprehensive membership degrees of the film to be evaluated for each evaluation level.

[0070] Specifically, the comprehensive membership degree represents the degree of comprehensiveness of the thin film to be evaluated corresponding to a certain evaluation level, and is the weighted sum of the membership degrees of each parameter corresponding to the level.

[0071] Schematably, a weighted average type M(・,+) fuzzy weighted composite operator is used. This is the most suitable operator for comprehensive evaluation problems because it can fully consider the influence of all indicators without losing any information. A weighted average operation is performed on the 1×7 weight matrix W and the 7×4 membership matrix R to obtain a 1×4 fuzzy comprehensive evaluation vector B. The four calculated comprehensive membership degrees are arranged in order to generate the comprehensive evaluation vector. Where b0, b1, b2, and b3 correspond to the comprehensive membership degree of the four evaluation levels: poor, average, good, and excellent, respectively.

[0072] Existing technologies use a "maximum or minimum" operator for fuzzy synthesis. This operator only considers the indicator with the highest weight and the level with the highest membership degree, completely ignoring the influence of other indicators, resulting in the loss of a large amount of information and biased evaluation results. Some simple synthesis methods use equal-weighted summation, failing to consider the different degrees of influence of different indicators on the overall performance, leading to unreasonable evaluation results.

[0073] This embodiment employs a weighted average fuzzy synthesis operator. This operator, by multiplying the weight of each indicator by its membership degree and then summing the results, fully utilizes the information from all seven parameters without losing any relevant information. This means the evaluation result is the combined effect of all indicators, accurately reflecting the overall performance level of the sample. Through the weighting, indicators with higher importance have a greater impact on the overall membership degree, while those with lower importance have a smaller impact. For example, discharge energy density has the highest weight, thus having the greatest impact on the overall evaluation result, which perfectly aligns with the core requirements of BOPP film as an energy storage medium. The overall membership degree is a weighted sum of the membership degrees of each parameter; therefore, by analyzing the contribution of each parameter to the overall membership degree, it is clear which parameters have the greatest impact on the final evaluation result and which parameters are the main factors affecting overall performance. This provides a clear direction for subsequent formulation optimization and process improvement.

[0074] S7. Based on the principle of maximum membership, determine the comprehensive performance level of the thin film to be evaluated according to the comprehensive evaluation vector.

[0075] Schematic, based on the principle of maximum membership, the final comprehensive performance level of the film to be evaluated is determined from the comprehensive evaluation vector. The final comprehensive performance level can be directly used in scenarios such as material formulation optimization, modification effect quantification, production process improvement, product quality grading, and selection decisions.

[0076] Preferably, based on the principle of maximum membership, the comprehensive performance level of the thin film to be evaluated is determined according to the comprehensive evaluation vector, including: Based on the comprehensive evaluation vector, determine the maximum comprehensive membership degree; The evaluation level corresponding to the highest comprehensive membership degree is taken as the comprehensive performance level of the film to be evaluated. If there are two or more equal and maximum values ​​in the comprehensive evaluation vector, the evaluation level with the highest performance index is taken as the comprehensive performance level of the film to be evaluated, according to the performance index of each evaluation level from high to low.

[0077] Specifically, the principle of maximum membership means that the film to be evaluated belongs to the evaluation level with the highest overall membership.

[0078] Schematic diagram: Extract all comprehensive membership values ​​from the comprehensive evaluation vector and find the maximum value. If there is only one maximum value in the comprehensive evaluation vector, the evaluation level corresponding to that maximum value is taken as the final comprehensive performance level of the thin film to be evaluated. If there are two or more equal comprehensive membership values ​​in the comprehensive evaluation vector, both of which are maximum values, the evaluation level with the highest performance is selected as the final level, in descending order of performance. This is because, when performance is comparable, the better level should be prioritized to avoid unnecessary downgrading.

[0079] In a preferred embodiment of the present invention, in order to more comprehensively reflect the overall performance of the sample, in addition to providing the final grade determination, a comprehensive score can also be provided. The comprehensive score can be obtained by multiplying the evaluation grade number by the corresponding comprehensive membership degree and then summing the results; the higher the comprehensive score, the better the overall performance.

[0080] In a preferred embodiment of the present invention, nine biaxially oriented polypropylene (BOPP) film samples with different formulations and processes were selected for comprehensive evaluation, including two commercially available pure BOPP films, two modified BOPP films, and five laboratory-prepared BOPP films. First, seven core performance parameters for each sample were collected to construct a 9×7 initial data matrix. Then, the objective weights of each indicator were calculated using the entropy weight method. Next, a trapezoidal cloud model with four evaluation levels was established, and the membership degree of each parameter corresponding to each level was calculated to construct a membership degree matrix. Fuzzy synthesis was then performed to obtain the comprehensive evaluation vector. Finally, the comprehensive performance level of each sample was determined based on the principle of maximum membership degree. The evaluation results showed that the maleic anhydride-grafted modified BOPP film achieved an excellent comprehensive performance level, while the polyetherimide blended modified BOPP film achieved a good comprehensive performance level, which is completely consistent with its performance in actual engineering applications.

[0081] Most existing methods use only one or two core parameters for evaluation, failing to reflect the comprehensive performance of thin films. For example, evaluating thin film performance solely based on DC breakdown field strength ignores key indicators such as energy storage efficiency, mechanical strength, and thermal stability, leading to significant discrepancies between evaluation results and actual service performance. A few comprehensive evaluation methods employ subjective weighting methods such as the Delphi method and the analytic hierarchy process (AHP), where weights heavily rely on the personal experience and professional background of experts. Different experts may have significantly different perceptions of the importance of the same indicator, resulting in completely different evaluation results for the same sample under different evaluation subjects, lacking objectivity and cross-comparability. Traditional fuzzy comprehensive evaluation methods use fixed membership functions, which can only handle the fuzziness of evaluation level boundaries and cannot consider the random fluctuations of the measured data itself. Since core parameters of BOPP thin films, such as DC breakdown field strength and charge-discharge cycle life, have significant probabilistic characteristics, fixed membership functions make the evaluation results highly sensitive to experimental errors, prone to level jumps, and unable to accurately distinguish subtle differences between different modification schemes.

[0082] This embodiment constructs a seven-core indicator evaluation system covering four dimensions: electrical, energy storage, mechanical, and structural, comprehensively reflecting the overall performance of BOPP films in terms of insulation, energy storage, efficiency, lifespan, processing, and thermal stability. This means that the evaluation results are no longer a one-sided reflection of a single dimension, but can truly reflect the overall performance of the film in actual service. For example, a modified film with a slightly lower breakdown field strength but significantly improved energy storage efficiency and cycle life can be objectively and fairly evaluated in this method, and will not be mistakenly judged as a substandard product by a single-parameter evaluation method.

[0083] The entropy weighting method calculates weights entirely based on the dispersion of measured data from multiple thin film samples, eliminating the interference of human factors. The greater the data dispersion, the more information the performance indicator contains, and the stronger its ability to distinguish overall performance; therefore, the greater the weight assigned. This objective weighting method ensures the uniqueness and reproducibility of the evaluation results. Different laboratories and different evaluators evaluating the same set of samples will obtain the exact same weights and evaluation results, providing a unified horizontal comparison standard for thin films with different formulations, processes, and batches.

[0084] A trapezoidal cloud model with hyperentropy is adopted as the membership function, and three numerical features—expectation, entropy, and hyperentropy—are introduced. Here, expectation represents the central value of the evaluation grade, entropy represents the fuzziness of the evaluation grade boundary, and hyperentropy represents the degree of random fluctuation in entropy. This design is specifically tailored to the probabilistic characteristics of the core parameters of BOPP films, effectively absorbing normal random errors during the testing process and avoiding jumps in evaluation grades due to small fluctuations in individual data. For example, the measured value of DC breakdown field strength typically has a normal fluctuation of ±5%. A traditional fixed membership function might downgrade the film from good to average if a single test result is slightly below the threshold. However, the trapezoidal cloud model of this invention can accommodate such normal fluctuations, maintaining the stability of the evaluation results.

[0085] In a preferred embodiment of the present invention, a comprehensive evaluation index system of seven core evaluation parameters is constructed to address the core service requirements of biaxially oriented polypropylene films. The specific parameters are shown in Table 1. Table 1 Evaluation Parameters and Units Nine biaxially oriented polypropylene (BOPP) film samples were selected for evaluation: Sample 1: Nordic Chemicals commercial BOPP film (hereinafter referred to as Nordic BOPP); Sample 2: Zhongyuan Petrochemical commercial BOPP film (hereinafter referred to as Zhongyuan BOPP); Sample 3: Maleic anhydride graft-modified Nordic BOPP film (hereinafter referred to as Nordic BOPP-M); Sample 4: Polyetherimide blend-modified Nordic BOPP film (hereinafter referred to as Nordic BOPP-E); Samples 5-9: Five other types of biaxially oriented polypropylene films. The measured data of seven evaluation parameters for the nine samples were obtained according to the standard testing methods described above, and a 9×7 initial data matrix was constructed. All parameters are positive indicators and do not require negative conversion; they are directly used for subsequent calculations.

[0086] Based on the formula: Calculate the weight of each sample under each evaluation parameter, normalize the weights, and then apply the formula. Calculate the information entropy of each evaluation parameter according to the formula. The objective weights of each evaluation parameter were calculated, and the measured values ​​and normalized weights of the sample data are shown in Tables 2 and 3 below: Table 2 Measured values ​​of each data point in the sample Table 3 Normalized values ​​of sample data The final weighted calculation results are as follows: DC breakdown field strength ω1 = 28.11%, discharge energy density ω2 = 35.54%, charge / discharge efficiency ω3 = 5.39%, charge / discharge cycle count ω4 = 0.60%, tensile strength ω5 = 20.66%, melting temperature ω6 = 0.24%, and crystallinity ω7 = 9.46%. The weight matrix W = [0.2811, 0.3554, 0.0539, 0.0060, 0.2066, 0.0024, 0.0946] has a total weight of 1, which satisfies the weight normalization requirement.

[0087] A membership function and evaluation level classification based on a trapezoidal cloud model were constructed, dividing the evaluation level into four levels: Excellent (level 3), Good (level 2), Average (level 1), and Poor (level 0). Based on the industry standards for BOPP film and the actual test data range of commercial products in this field, the numerical characteristics of the trapezoidal cloud model corresponding to the four levels for seven evaluation parameters were determined, as shown in Table 4 below: Table 4. Digital Feature Ranges of Trapezoidal Cloud Model A one-dimensional forward trapezoidal cloud generator is used to calculate the membership degrees of the measured parameters at each level, achieving simultaneous characterization of fuzziness and randomness. Taking the DC breakdown field strength of sample 3 as an example... The DC breakdown field strength of sample 3 is 719.74, according to the calculation formula: get hyperentropy Then, according to the formula for calculating randomized entropy: Generate randomized entropy .

[0088] The membership degree μ is calculated based on the specific parameter values. Since it falls within the 3rd level Ex interval [650, 750], its membership degree μ3 = 1 in the 3rd level interval. In 2nd, 1st, and 0th level Ex, x > Exmax, according to the calculation formula: The membership degree of sample 3 is μ2 = 0.000021 in the second-order interval; μ1 ≈ 0 in the first-order interval; and μ0 ≈ 0 in the second-order interval. Therefore, the membership degree of sample 3's DC breakdown field strength is: [0.000000, 0.000000, 0.000021, 1.000000].

[0089] For four core BOPP film samples, the measured parameters of each sample were substituted into a trapezoidal cloud model to calculate the membership degree of each parameter at four levels, constructing a 7×4 membership degree matrix. Taking sample 3, Nordic BOPP-M, as an example, its membership degree matrix R3 is: The weight matrix W = [0.2811, 0.3554, 0.0539, 0.0060, 0.2066, 0.0024, 0.0946] is combined with the membership matrix of each sample using a weighted average fuzzy synthesis operation to obtain the fuzzy comprehensive evaluation vector for each sample. Sample 1 Nordic BOPP: B1=[0.000,0.3806,0.7145,0.3483] In Sample 2, the original BOPP is: B2 = [0.000, 0.3873, 0.5829, 0.4357]. Sample 3 Nordic BOPP-M: B3=[0.0024,0.0951,0.4606,0.5768] Sample 4 Nordic BOPP-E: B4=[0.000,0.0472,0.6564,0.4379] Based on the principle of maximum membership, the final overall performance level of each sample is determined: Sample 1 Nordic BOPP: The element with the largest value in the fuzzy comprehensive evaluation vector B1 is 0.7145, which corresponds to a comprehensive membership degree of 2 and an evaluation level of good. In Sample 2, the largest element in the original BOPP fuzzy comprehensive evaluation vector B2 is 0.5829, which corresponds to a comprehensive membership degree of 2 and an evaluation level of "good". Sample 3 Nordic BOPP-M: The element with the largest value in the fuzzy comprehensive evaluation vector B3 is 0.5768, which corresponds to a comprehensive membership degree of 3 and an evaluation level of excellent. Sample 4 Nordic BOPP-E: The largest element in the fuzzy comprehensive evaluation vector B4 is 0.6564, which corresponds to a comprehensive membership degree of 2 and an evaluation level of "good".

[0090] Based on the final rating of the comprehensive performance evaluation, this invention proposes subsequent operational strategies, forming a complete closed loop from evaluation to decision-making.

[0091] When the overall performance of the biaxially oriented polypropylene film under test is rated as excellent, it indicates that it performs outstandingly in insulation, energy storage, efficiency, lifespan, mechanical properties, and thermodynamics, with no obvious weaknesses. It can be mass-produced and finalized as a film for high-end capacitors or a core insulating material for pulse power devices. It is recommended that this batch of film be included in a long-term reliability monitoring plan, with continued monitoring of all seven indicators for each batch to establish a performance baseline database for excellent ratings. The current formulation and preparation process should be frozen as the enterprise standard, and changes should be avoided unless absolutely necessary.

[0092] If the overall performance is rated as good, it indicates that the film generally meets the requirements of conventional applications, but one or two parameters are at a moderate level. In this case, targeted formulation optimization or process fine-tuning should be carried out to address the weaker parameters. For example, adjusting the grafting modification scheme for low discharge energy density or charge / discharge efficiency, optimizing the stretching ratio and heat treatment temperature for low tensile strength or crystallinity, and strengthening the purification process to reduce impurity content for low charge / discharge cycle count. Continuous monitoring of three to five batches of the film should be conducted to verify performance stability.

[0093] If the overall performance is rated as average, it indicates that the film has several parameters that are significantly low, exhibiting substantial performance defects. In this case, a root cause analysis should be conducted to check the purity of the raw materials, the film-forming process parameters, and systematic errors in the testing process. If necessary, the formulation or process route should be redesigned, such as by changing the resin grade, introducing a multilayer co-extrusion structure, or adjusting the modification scheme. After the improvements are completed, the film should be re-evaluated according to the steps of this invention. If it fails the evaluation, it is not suitable for use in energy storage capacitors and can be downgraded to ordinary packaging film or directly discarded.

[0094] If the overall performance is rated as poor, it indicates a serious defect in the film, such as excessively low breakdown field strength, insufficient discharge energy density, or significantly low cycle life. The use of this batch of film in any capacitor or energy storage product should be immediately stopped. A comprehensive investigation of raw materials, production equipment, and testing procedures should be conducted. The production line with persistent problems should be suspended and process parameters recalibrated. Simultaneously, the defective data should be archived as a reference case for subsequent quality training and process improvement.

[0095] This embodiment constructs a multi-index collaborative evaluation system from four dimensions: electrical, energy storage, mechanical, and structural, comprehensively covering all key performance requirements of biaxially oriented polypropylene (BOP) films during actual service. Electrical performance indicators correspond to the film's insulation reliability; energy storage performance indicators correspond to the film's energy storage and release capabilities; mechanical performance indicators correspond to the film's resistance to deformation and damage; and structural performance indicators correspond to the film's microstructure and thermal properties. By integrating performance information from these four dimensions, the system can comprehensively capture the film's performance effects in insulation, energy storage, processing, and thermal stability. Simultaneously, an initial data matrix is ​​constructed, and the information content of each performance indicator is quantified by calculating its dispersion value. Finally, weights are automatically assigned based on the amount of information, generating a weight matrix. The evaluation process of the weight matrix does not involve human judgment and can be dynamically adjusted according to the dispersion of the sample data, allowing films with different formulations, processes, and production batches to be evaluated uniformly. Furthermore, a trapezoidal cloud model is used to construct the membership function, simultaneously characterizing the fuzzy transition characteristics of the evaluation level boundary and the random fluctuation characteristics of the measured data. When parameter values ​​approach the evaluation grade boundary, the membership degree exhibits a continuous and smooth change rather than abrupt changes, demonstrating strong robustness to normal random errors. This effectively prevents evaluation grade jumps caused by minor deviations in individual test data, significantly improving the stability and accuracy of the evaluation results. Therefore, by constructing electrical, energy storage, mechanical, and structural performance indicators, weight matrices, and membership degree matrices, the accuracy of the comprehensive performance evaluation of thin films is improved.

[0096] See Figure 3 This is a schematic diagram of a device for evaluating the comprehensive performance of biaxially oriented polypropylene film according to an embodiment of the present invention, comprising: The index data acquisition module is used to acquire the electrical performance index, energy storage performance index, mechanical performance index and structural performance index of the thin film to be evaluated. An initial data matrix construction module is used to construct an initial data matrix based on the electrical performance indicators, the energy storage performance indicators, the mechanical performance indicators, and the structural performance indicators. The weight matrix generation module is used to calculate the dispersion value between each performance index based on the initial data matrix, calculate the weight of each performance index based on the dispersion value, and generate a weight matrix. The membership function construction module is used to construct a trapezoidal cloud model corresponding to the evaluation level of each performance indicator based on a preset evaluation level table, and to construct the membership function corresponding to each performance indicator based on the trapezoidal cloud model. The membership weight construction module is used to construct a membership matrix based on the membership function and various performance indicators. The comprehensive evaluation vector operation module is used to perform fuzzy synthesis operation based on the weight matrix and the membership matrix to obtain the comprehensive evaluation vector of the thin film to be evaluated. The comprehensive performance evaluation module is used to determine the comprehensive performance level of the thin film to be evaluated based on the principle of maximum membership and the comprehensive evaluation vector.

[0097] This invention provides an evaluation device for the comprehensive performance of biaxially oriented polypropylene (BOP) films. The device comprises: an index data acquisition module that acquires electrical, energy storage, mechanical, and structural performance indices of the film to be evaluated; an initial data matrix construction module that constructs an initial data matrix based on these indices; a weight matrix generation module that calculates the dispersion values ​​between performance indices based on the initial data matrix and assigns weights to each indices based on these dispersion values, generating a weight matrix; a membership function construction module that constructs a trapezoidal cloud model corresponding to each performance index based on a preset evaluation level table and constructs a membership function for each performance index based on the trapezoidal cloud model; a membership weight construction module that constructs a membership matrix based on the membership functions and performance indices; a comprehensive evaluation vector operation module that performs fuzzy synthesis operations on the weight matrix and membership matrix to obtain a comprehensive evaluation vector for the film; and finally, a comprehensive performance evaluation module that determines the comprehensive performance level of the film based on the comprehensive evaluation vector according to the maximum membership principle.

[0098] A multi-index collaborative evaluation system is constructed from four dimensions: electrical, energy storage, mechanical, and structural, comprehensively covering all key performance requirements of biaxially oriented polypropylene (BOP) films during actual service. Electrical performance indicators correspond to the film's insulation reliability; energy storage performance indicators correspond to the film's energy storage and release capabilities; mechanical performance indicators correspond to the film's resistance to deformation and damage; and structural performance indicators correspond to the film's microstructure and thermal properties. By integrating performance information from these four dimensions, the system can comprehensively capture the film's performance effects in insulation, energy storage, processing, and thermal stability. Simultaneously, an initial data matrix is ​​constructed, and the information content of each performance indicator is quantified by calculating its dispersion value. Finally, weights are automatically assigned based on the amount of information, generating a weight matrix. The evaluation process of the weight matrix does not involve human judgment and can be dynamically adjusted according to the dispersion of the sample data, allowing films with different formulations, processes, and production batches to be evaluated uniformly. Furthermore, a trapezoidal cloud model is used to construct the membership function, simultaneously characterizing the fuzzy transition characteristics of the evaluation level boundary and the random fluctuation characteristics of the measured data. When parameter values ​​approach the evaluation grade boundary, the membership degree exhibits a continuous and smooth change rather than abrupt changes, demonstrating strong robustness to normal random errors. This effectively prevents evaluation grade jumps caused by minor deviations in individual test data, significantly improving the stability and accuracy of the evaluation results. Therefore, by constructing electrical, energy storage, mechanical, and structural performance indicators, weight matrices, and membership degree matrices, the accuracy of the comprehensive performance evaluation of thin films is improved.

[0099] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0100] Those skilled in the art will understand that, for convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0101] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a method for evaluating the comprehensive performance of biaxially oriented polypropylene film as described in the above embodiments. The terminal device may be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The terminal device may include, but is not limited to, a processor and a memory.

[0102] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0103] The memory can be used to store the computer program. The processor implements various functions of the terminal device by running or executing the computer program stored in the memory and calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device or other volatile solid-state storage device.

[0104] Another embodiment of the present invention provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the method for evaluating the comprehensive performance of biaxially oriented polypropylene film described in the above embodiment.

[0105] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0106] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for evaluating the comprehensive properties of biaxially oriented polypropylene film, characterized in that, include: Obtain the electrical performance, energy storage performance, mechanical performance, and structural performance of the thin film to be evaluated; An initial data matrix is ​​constructed based on the electrical performance indicators, the energy storage performance indicators, the mechanical performance indicators, and the structural performance indicators. The dispersion values ​​between each performance index are calculated based on the initial data matrix, and the weights of each performance index are calculated based on the dispersion values ​​to generate a weight matrix. Based on the preset evaluation level table, a trapezoidal cloud model corresponding to the evaluation level of each performance indicator is constructed, and a membership function corresponding to each performance indicator is constructed based on the trapezoidal cloud model. Construct a membership matrix based on the membership function and various performance indicators; A fuzzy synthesis operation is performed based on the weight matrix and the membership matrix to obtain the comprehensive evaluation vector of the thin film to be evaluated. Based on the principle of maximum membership, the overall performance level of the thin film to be evaluated is determined according to the comprehensive evaluation vector.

2. The method for evaluating the comprehensive performance of biaxially oriented polypropylene film as described in claim 1, characterized in that, The electrical performance indicators include DC breakdown field strength; the energy storage performance indicators include discharge energy density, charge-discharge efficiency, and charge-discharge cycle count; the mechanical performance indicators include tensile strength; and the structural performance indicators include melting temperature and crystallinity. Calculate the dispersion values ​​between each performance index based on the initial data matrix, calculate the weights of each performance index based on the dispersion values, and generate a weight matrix, including: The DC breakdown field strength, discharge energy density, charge-discharge efficiency, charge-discharge cycle number, tensile strength, melting temperature and crystallinity in the initial data matrix are normalized based on the specific gravity method to obtain the parameter values ​​of each parameter. The dispersion value of each parameter is calculated based on the weight of the parameter values. Calculate the parameter weights of each parameter based on the dispersion values, and calculate the weights of each performance index based on the parameter weights to generate a weight matrix.

3. The method for evaluating the comprehensive performance of biaxially oriented polypropylene film as described in claim 2, characterized in that, Based on a pre-defined evaluation level table, a trapezoidal cloud model is constructed for each performance indicator corresponding to its evaluation level, including: According to the preset evaluation level table, each parameter in each performance index is divided into several evaluation levels, and each evaluation level is sequentially numbered. Based on DC breakdown field strength, discharge energy density, charge-discharge efficiency, charge-discharge cycle count, tensile strength, melting temperature, and crystallinity, the expected range for each parameter, the entropy used to characterize the ambiguity of the transition zone at the evaluation level boundary, and the hyperentropy used to characterize the random fluctuation of entropy are calculated. Based on the sequential number, the expected intervals of each parameter, the entropy, and the hyperentropy, a trapezoidal cloud model is constructed for each performance index.

4. The method for evaluating the comprehensive performance of biaxially oriented polypropylene film as described in claim 3, characterized in that, Based on the trapezoidal cloud model, a membership function corresponding to each performance index is constructed, including: Based on the entropy and hyperentropy in the trapezoidal cloud model, generate randomized entropy corresponding to each evaluation level; Based on the positional relationship between DC breakdown field strength, discharge energy density, charge-discharge efficiency, charge-discharge cycle count, tensile strength, melting temperature, and crystallinity and the expected range of the corresponding evaluation level, a membership function corresponding to each parameter is generated.

5. The method for evaluating the comprehensive performance of biaxially oriented polypropylene film as described in claim 2, characterized in that, Based on the membership function and various performance indicators, a membership matrix is ​​constructed, including: The membership degree of each evaluation level is calculated based on the membership function, which corresponds to DC breakdown field strength, discharge energy density, charge-discharge efficiency, charge-discharge cycle count, tensile strength, melting temperature, and crystallinity. Construct a membership matrix based on the membership degree of each evaluation level, using each parameter as the row index and the evaluation level of each parameter as the column index.

6. The method for evaluating the comprehensive performance of biaxially oriented polypropylene film as described in claim 2, characterized in that, Based on the weight matrix and the membership matrix, a fuzzy synthesis operation is performed to obtain the comprehensive evaluation vector of the thin film to be evaluated, including: A weighted fuzzy synthesis operation is performed based on the weight matrix, the membership matrix, and a preset weighted synthesis operator to obtain a comprehensive evaluation vector for the thin film to be evaluated. The dimension of the comprehensive evaluation vector is consistent with the number of evaluation levels; the elements in the comprehensive evaluation vector are the comprehensive membership degrees of the film to be evaluated for each evaluation level.

7. The method for evaluating the comprehensive performance of biaxially oriented polypropylene film as described in claim 6, characterized in that, Based on the principle of maximum membership, and according to the comprehensive evaluation vector, the comprehensive performance level of the thin film to be evaluated is determined, including: Based on the comprehensive evaluation vector, determine the maximum comprehensive membership degree; The evaluation level corresponding to the highest comprehensive membership degree is taken as the comprehensive performance level of the film to be evaluated. If there are two or more equal and maximum values ​​in the comprehensive evaluation vector, the evaluation level with the highest performance index is taken as the comprehensive performance level of the film to be evaluated, according to the performance index of each evaluation level from high to low.

8. An evaluation device for the comprehensive performance of biaxially oriented polypropylene film, characterized in that, include: The index data acquisition module is used to acquire the electrical performance index, energy storage performance index, mechanical performance index and structural performance index of the thin film to be evaluated. An initial data matrix construction module is used to construct an initial data matrix based on the electrical performance indicators, the energy storage performance indicators, the mechanical performance indicators, and the structural performance indicators. The weight matrix generation module is used to calculate the dispersion value between each performance index based on the initial data matrix, calculate the weight of each performance index based on the dispersion value, and generate a weight matrix. The membership function construction module is used to construct a trapezoidal cloud model corresponding to the evaluation level of each performance indicator based on a preset evaluation level table, and to construct the membership function corresponding to each performance indicator based on the trapezoidal cloud model. The membership weight construction module is used to construct a membership matrix based on the membership function and various performance indicators. The comprehensive evaluation vector operation module is used to perform fuzzy synthesis operation based on the weight matrix and the membership matrix to obtain the comprehensive evaluation vector of the thin film to be evaluated. The comprehensive performance evaluation module is used to determine the comprehensive performance level of the thin film to be evaluated based on the principle of maximum membership and the comprehensive evaluation vector.

9. A terminal device, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements a method for evaluating the overall performance of a biaxially oriented polypropylene film as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform a method for evaluating the overall performance of a biaxially oriented polypropylene film as described in any one of claims 1 to 7.

Images