Method and device for evaluating comprehensive performance of polymer pellets and computer equipment

Abstract

The invention discloses a method and a device for evaluating comprehensive performance of polymer granules and computer equipment, and relates to the technical field of power capacitors. The method comprises the following steps: screening a plurality of key indicators of polymer pellets; quantifying the relative importance between every two key indexes, and constructing a judgment matrix; solving an index weight vector; scoring the construction effect of each key index through multiple experts, and constructing a fuzzy membership normalization matrix; performing fuzzy operation on the index weight vector and the fuzzy membership normalization matrix to obtain a fuzzy weight vector; and performing normalization processing on the parameter data of the polymer pellets, and performing operation on the normalized parameter data and the fuzzy weight vector to obtain a comprehensive performance score of the polymer pellets. According to the technical scheme, decision support is provided for performance evaluation and screening of the polymer pellets for the capacitor.

Description

Technical Field

[0001] This invention relates to the field of power capacitor technology, and in particular to a method, apparatus, and computer equipment for comprehensive performance evaluation of polymer granules. Background Technology

[0002] In modern power grid systems, polymer film capacitors are key devices for ensuring power quality and optimizing power transmission. Their performance directly affects the stability and transmission efficiency of the power grid. In scenarios such as reactive power compensation of high-voltage transmission lines and the consumption of new energy in smart grids, polymer film capacitors, with their high rated voltage, strong surge resistance and good self-healing properties, play an important role in improving the power factor and stabilizing voltage. Polymer granules (such as polyester, polyimide, polytetrafluoroethylene, polycarbonate, etc.) are the basic raw materials for film preparation. Their purity, isotacticity, microstructure and other performance parameters directly determine the core indicators of capacitor dielectric films such as dielectric loss and breakdown strength, thereby affecting the operating efficiency of the power grid.

[0003] Currently, my country relies heavily on imports for polymer granules (especially polypropylene granules) used in power grid capacitors. Their manufacturing process is complex, requiring specific parameters for different granules, such as melt extrusion and biaxial stretching. Against this backdrop, a scientific and comprehensive evaluation of polymer granule performance is crucial. Current performance evaluation methods for capacitor polymer granules suffer from incompleteness. Traditional methods only isolate and test a few macroscopic indicators such as melt flow index and ash content, failing to incorporate microstructural features like the number of crystal rings and spherulite size into the evaluation system. This makes it difficult to establish a correlation model between microscopic parameters and dielectric and mechanical properties. Furthermore, independent evaluations of individual indicators do not adequately reflect the interrelationships between them, failing to reveal the intrinsic relationships between indicator parameters. In the weighting stage, existing methods rely solely on expert experience or simple entropy weighting, failing to objectively quantify the contribution of each indicator to overall performance. Moreover, existing methods lack in-depth data mining during processing, making it difficult to extract key information from massive amounts of test data, resulting in evaluation results that do not accurately reflect the true performance of polymer granules. Summary of the Invention

[0004] This invention provides a method, apparatus, and computer equipment for comprehensive performance evaluation of polymer granules, aiming to achieve accurate and objective evaluation of the performance of polymer granules for capacitors.

[0005] The method for evaluating the comprehensive performance of polymer granules provided by this invention includes the following steps:

[0006] S1, screen multiple key indicators of polymer granules, and construct a multi-dimensional comprehensive performance evaluation index system for polymer granules;

[0007] S2, quantify the relative importance of each pair of the key indicators and construct a judgment matrix;

[0008] S3. Solve for the weight of each key indicator based on the judgment matrix, and normalize the weight of each key indicator to obtain the indicator weight vector.

[0009] S4. Multiple experts score the construction effect of each key indicator, construct a fuzzy membership degree matrix, and normalize the fuzzy membership degree matrix to obtain a fuzzy membership degree normalized matrix.

[0010] S5, perform fuzzy operations on the index weight vector and the fuzzy membership normalization matrix to obtain the fuzzy weight vector;

[0011] S6, normalize the parameter data of the polymer granules, and calculate the comprehensive performance score of the polymer granules by combining the normalized parameter data with the fuzzy weight vector.

[0012] Optionally, the polymer granules are polypropylene granules.

[0013] Optionally, the polymer granules are one of polyester, polyimide, polytetrafluoroethylene, and polycarbonate.

[0014] Optionally, the key indicators include the degree index, ash content, hot melt peak temperature, crystallization temperature, crystallinity, oxidation induction period, melt mass flow rate, melt flow ratio, tensile yield stress, nominal strain at tensile fracture, and tensile modulus of elasticity.

[0015] Optionally, step S3 further includes: performing a consistency check on the indicator weight vector, wherein the consistency check is calculated using the following formula:

[0016] , ;

[0017] In the formula, CI is the consistency index. Let be the largest eigenvalue, n be the number of indicators, RI be the random consistency index, and CR be the consistency test ratio; when the consistency test ratio CR is less than 0.1, the indicator weight vector is considered reliable.

[0018] Optionally, in step S4, the fuzzy membership matrix is:

[0019] ;

[0020] in, Let n be the number of experts and n be the number of indicators. This represents the score given by the l-th expert for the construction effect of the n-th indicator; the formula for normalizing the fuzzy membership matrix is:

[0021] ;

[0022] in, The fuzzy membership matrix represents the th element in the fuzzy membership matrix. The expert commented on the first The scoring of the construction effectiveness of each indicator. The fuzzy membership normalization matrix represents the first... The expert commented on the first The scoring of the construction effect of each indicator; the fuzzy membership normalization matrix is ​​represented as:

[0023] .

[0024] Optionally, in step S5, fuzzy operations are performed on the index weight vector and the fuzzy membership normalization matrix using the following formula:

[0025] =W'·R';

[0026] In the formula, Let W' represent the fuzzy weight vector, W' represent the index weight vector, and R' represent the fuzzy membership normalization matrix.

[0027] Optionally, in step S6, before normalizing the parameter data of the polymer granules, the following step is further included:

[0028] The key indicators are divided into extremely large indicators, extremely small indicators, and intermediate indicators. The extremely large indicators are those with larger values, which indicate better polymer granule performance. The extremely small indicators are those with smaller values, which indicate better polymer granule performance. The intermediate indicators are those other than the extremely large and extremely small indicators, and the closer their values ​​are to the optimal values, the better the polymer granule performance.

[0029] Optionally, the parameter data corresponding to the extremely large index is normalized using the following formula:

[0030] ;

[0031] In the formula, x is the original value of the parameter data, x' is the transformed value of the parameter data, and C maxmax This represents the maximum value in the extremely large indicator parameter data.

[0032] The parameter data corresponding to the extremely small index is normalized using the following formula:

[0033] ;

[0034] In the formula, x is the original value of the parameter data, x' is the transformed value of the parameter data, and C minmax This represents the maximum value in the extremely small indicator parameter data;

[0035] The parameter data corresponding to the intermediate index is normalized using the following formula:

[0036] ;

[0037] In the formula, x is the original value of the parameter data, x' is the transformed value of the parameter data, and C opt C represents the optimal value among the intermediate index parameter data. max This represents the maximum value among the intermediate indicator parameters.

[0038] Optionally, in step S6, the normalized parameter data and the fuzzy weight vector are calculated using the following formula:

[0039] ;

[0040] In the formula, S represents the overall performance score. Let T be the transpose of the fuzzy weight vector, and X' be the normalized parameter data of the polymer granules. X' is represented as:

[0041] ;

[0042] In the formula, This represents the parameter data after normalization of the nth indicator.

[0043] This invention provides a comprehensive performance evaluation device for polymer granules, comprising:

[0044] The indicator system establishment module is used to screen key indicators for polymer granules;

[0045] The indicator weight calculation module is used to quantify the relative importance of multiple key indicators pairwise, construct a judgment matrix, solve the weight of each key indicator based on the judgment matrix, and normalize the weight of each key indicator to obtain the indicator weight vector.

[0046] The performance evaluation module uses multiple experts to evaluate the construction effect of each key indicator, constructs a fuzzy membership matrix, and normalizes the fuzzy membership matrix to obtain a fuzzy membership normalized matrix.

[0047] The expert fuzzy comprehensive evaluation module is used to perform fuzzy operations on the index weight vector and the fuzzy membership normalization matrix to obtain the fuzzy weight vector.

[0048] The data input and processing module is used to input parameter data of various key indicators of polymer granules and to normalize the parameter data of polymer granules.

[0049] The comprehensive performance scoring module is used to calculate the comprehensive performance score of the polymer granules by performing normalized parameter data and the fuzzy weight vector.

[0050] The present invention provides a computer device including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method for comprehensive performance evaluation of polymer granules.

[0051] The present invention has the following beneficial effects:

[0052] The comprehensive performance evaluation method for polymer granules provided by this invention first establishes a multi-dimensional evaluation index system, then constructs a judgment matrix by comparing the relative importance of each index pairwise, and calculates the weight vector of each index. This invention further constructs an expert fuzzy comprehensive evaluation model, which uses multiple experts to score the construction effect of each key index, and performs fuzzy operations on the index weight vector and the fuzzy membership normalization matrix to obtain a fuzzy weight vector, making the index evaluation more objective. After normalizing the parameter data corresponding to each index of the polymer granules, the comprehensive performance score of the polymer granules can be obtained by operating on the fuzzy weight vector. This invention, through the close coupling of parameter data and index weights, can accurately capture subtle differences in the performance of polymer granules, objectively and comprehensively reflecting their performance advantages and disadvantages. This provides a reliable basis for quality control, R&D optimization, and market access of polymer granules for capacitors, and provides decision support for manufacturers to evaluate and select the performance of polymer granules for capacitors. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a flowchart of a method for evaluating the comprehensive performance of polymer granules in some embodiments of the present invention.

[0055] Figure 2 This is a schematic diagram of the comprehensive performance evaluation index system for polypropylene granules in some embodiments of the present invention. Detailed Implementation

[0056] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0057] The terms “comprising” and “having” and any variations thereof in this invention are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product or device.

[0058] The technical solution provided by this invention is mainly applied to the raw material quality control of the large-scale production of capacitor films in power systems, covering the entire process from sampling inspection of polymer granules before procurement, incoming inspection, film production process control to final product performance traceability.

[0059] Specifically, for the testing and incoming inspection stages, this invention can replace the inefficient traditional "sampling and testing + offline laboratory analysis" model, achieving comprehensive quality control across different granules and batches, preventing substandard raw materials from entering the production line, and establishing a granule performance scoring database to provide granule screening decision support for subsequent production. For the premixing, extrusion, stretching, and shaping of film raw materials, by monitoring key performance parameters, processing parameters, and processing phenomena of granules online, dynamic feedback is provided to the production line control system, achieving precise matching of granule performance data and process parameters, ensuring the quality stability of film production. For the outgoing performance traceability scenario, by combining granule batches, manufacturers, and the comprehensive granule performance score constructed through this invention, a correlation database of granule performance, film processing technology, film performance, and capacitor performance can be established, providing raw material quality traceability basis for upstream granule manufacturers and downstream capacitor manufacturers in the industry chain.

[0060] This invention achieves comprehensive data processing of granular material parameters through a technical path of constructing an evaluation index system, establishing an evaluation calculation model, and outputting scoring data. Specifically, it can acquire multi-source index data through intelligent testing equipment (such as infrared spectrometers, X-ray analyzers, scanning electron microscopes, optical microscopes, tensile testing machines, etc.), construct an evaluation calculation model that includes functions such as data input, data quantification, matrix calculation, and scoring output through computer equipment and related program application environments, build a database or connect to machine learning models through cloud or local data storage, and generate correlation evaluation reports on granular material performance, process, and film performance or control production command output through industrial control interfaces (such as triggering qualified / unqualified warehouse management systems or production feeding systems). Therefore, this invention not only objectively and comprehensively reflects the performance of polymer granules, but also plays a positive role in improving production efficiency, quality control, and the iteration of new materials and processes throughout the entire polymer film capacitor industry chain, and can strongly support the modern production of power grid equipment and the efficient operation of power systems.

[0061] See Figure 1 In some embodiments, the method for evaluating the comprehensive performance of polymer granules provided by the present invention includes steps S1 to S6:

[0062] S1, screening multiple key indicators of polymer granules to construct a multi-dimensional comprehensive performance evaluation index system for polymer granules.

[0063] This invention first constructs a comprehensive performance evaluation index system based on the characteristics of capacitor polymer granules. The key indicators used include macroscopic indicators (such as melt index, ash content, etc.) and microscopic indicators (such as the number of crystal rings, the size of crystal spheres, and other microstructural features) to comprehensively and holistically reflect the various performance characteristics of polymer granules and provide a scientific and complete measurement standard for the performance evaluation of granules.

[0064] In this embodiment of the invention, the polymer granules can be polypropylene granules, which are most widely used in power capacitors; the technical solutions provided in this embodiment of the invention are also applicable to other polymer granules for capacitors, such as polyester, polyimide, polytetrafluoroethylene, polycarbonate, etc.; when conducting a comprehensive performance evaluation of different polymer granules, the key indicators suitable for the comprehensive performance evaluation of the polymer granules can be selected according to the actual situation.

[0065] S2 quantifies the relative importance of multiple key indicators pairwise and constructs a judgment matrix.

[0066] In this step, different indicators are compared pairwise by combining expert experience and experimental data analysis to determine their relative importance to the performance of capacitor polymer granules, and then a judgment matrix is ​​constructed.

[0067] Specifically, pairwise comparisons of different indicators refer to numerically assigning importance to any two indicators. For example, importance can be scaled to 1, 3, and 5 based on the levels of equal importance, slightly important, and significantly important. If indicator A and indicator B are equally important, then the relative importance of indicator A to indicator B is 1. If indicator C is slightly more important than indicator A, then the relative importance of indicator C to indicator A is 3, and the relative importance of indicator A to indicator C is 1 / 3. If indicator D is significantly more important than indicator A, then the relative importance of indicator D to indicator A is 5, and the relative importance of indicator A to indicator D is 1 / 5. The scale of relative importance is not limited to this example. Specific scales can be set according to the calculation precision, the number of indicators, and the complexity of the relationship between indicators. For example, scales with more levels such as 1, 2, 3, 4, and 5 can be set (corresponding to standards such as equal importance, slightly important, moderately important, relatively important, and very important), or scales with a larger span such as 1, 5, and 10.

[0068] The judgment matrix constructed based on importance can be represented by the following formula:

[0069] ;

[0070] In the formula, X represents the judgment matrix, and n is the number of indicators; c i，j This represents the importance of the i-th indicator relative to the j-th indicator (0 < i ≤ n, 0 < j ≤ n, and i and j are both integers), for example, c 1,1 This indicates the importance of the first indicator relative to the indicator itself (value is 1), c 1,2 This indicates the relative importance of the first indicator to the second indicator, c. 2,1 This indicates the relative importance of the second indicator compared to the first indicator.

[0071] S3. Solve for the weight of each key indicator based on the judgment matrix, and normalize the weight of each key indicator to obtain the indicator weight vector.

[0072] Based on the judgment matrix X, the largest eigenvalue can be solved using the following formula. and the largest eigenvalue The corresponding weight vector W, where each value in the weight vector W represents the weight of each indicator:

[0073] ;

[0074] ;

[0075] Wherein, W' is the normalized weight vector of the weight vector W, that is, the index weight vector.

[0076] For example, consider the judgment matrix (taking the ideal consistency matrix as an example) formed by the relative importance of the following three indicators A, B, and C:

[0077] ;

[0078] Its largest eigenvalue =3, the corresponding feature vector W (i.e., weight vector) can be derived by proportionally scaling the elements in each row, for example W=[6, 3, 1] (or other scaling forms, such as [3, 1.5, 0.5], because the feature vector is scalable).

[0079] The essence of normalizing the weights is to convert the weight values ​​of the feature vector (weight vector) W into relative importance proportions, so that the normalized weight vector W' obtained after processing satisfies the conventional constraint that the sum of the weight values ​​is 1, which makes it easier to intuitively quantify the importance of each indicator.

[0080] For the feature vector W=[6, 3, 1], its corresponding normalized weight vector W'=[0.6, 0.3, 0.1] indicates that the normalized weights of indicators A, B, and C are 0.6, 0.3, and 0.1, respectively. "Weight" means the degree of contribution of the indicators to the comprehensive score when calculating the comprehensive score in the subsequent process.

[0081] This step also includes: performing a consistency check on the indicator weight vector, which is calculated using the following formula:

[0082] , ;

[0083] In the formula, CI is the consistency index. Let RI be the largest eigenvalue, n be the number of indicators, RI be the random consistency index, and CR be the consistency test ratio. When the consistency test ratio CR is less than 0.1, the indicator weight vector is considered reliable.

[0084] Taking the aforementioned indicator weight vector W'=[0.6, 0.3, 0.1] as an example, its largest eigenvalue... =3, and the order of the judgment matrix n=3, then its consistency test ratio CR=0 can be calculated, which satisfies the condition <0.1, and the weight vector W' of the index is judged to be reliable.

[0085] In other embodiments, the decision matrix is ​​not an ideally consistent matrix (i.e., a matrix that does not satisfy transitivity), such as the following decision matrix:

[0086] ;

[0087] Its largest eigenvalue can be obtained by solving the characteristic equation (solving for the roots of the nth-degree polynomial equation in λ according to XW=λW). ≈3.038. For a third-order matrix, n=3, and the random consistency index RI=0.52, the consistency test ratio CR≈0.037<0.1 can be obtained. This indicates that although the judgment matrix is ​​not an ideal consistency matrix, the degree of inconsistency is acceptable, and the corresponding index weight vector W' obtained by solving is reliable.

[0088] S4 involves multiple experts scoring the construction effectiveness of each key indicator, constructing a fuzzy membership matrix, and then normalizing the fuzzy membership matrix to obtain a fuzzy membership normalized matrix.

[0089] The fuzzy membership matrix is ​​represented as:

[0090] ;

[0091] in, Let n be the number of experts and n be the number of indicators. This represents the score given by the l-th expert for the construction effectiveness of the n-th indicator.

[0092] The formula for normalizing the fuzzy membership matrix is:

[0093] ;

[0094] in, Represents the fuzzy membership matrix of the th The expert commented on the first The scoring of the construction effectiveness of each indicator. The fuzzy membership normalization matrix represents the th The expert commented on the first The scoring of the construction effectiveness of each indicator.

[0095] The fuzzy membership normalization matrix can be expressed as:

[0096] .

[0097] For example, in some implementations, the scores of three experts on the construction effectiveness of indicators A, B, and C are obtained (the construction effectiveness is evaluated based on the relative importance of each indicator and whether the final weights conform to expert experience), such as the following fuzzy membership matrix:

[0098] ;

[0099] In the formula, the first expert scored the construction effect of indicators A, B, and C as 100, 50, and 100 respectively; the second expert scored the construction effect of indicators A, B, and C as 75, 75, and 75 respectively; and the third expert scored the construction effect of indicators A, B, and C as 100, 50, and 75 respectively.

[0100] Normalizing the fuzzy membership matrix involves normalizing each row of elements within the matrix, ensuring that each row satisfies the conventional constraint that the sum of the elements in each row is 1.

[0101] The fuzzy membership normalization matrix corresponding to the above 3rd order matrix is:

[0102] R'= ;

[0103] S5. Perform fuzzy operations on the index weight vector and the fuzzy membership normalization matrix to obtain the fuzzy weight vector.

[0104] Specifically, fuzzy operations are performed on the index weight vector and the fuzzy membership normalization matrix using the following formula:

[0105] =W'·R';

[0106] In the formula, Let W' represent the fuzzy weight vector, W' represent the index weight vector, and R' represent the fuzzy membership normalization matrix.

[0107] For example, if the index weight vector W' is [0.6, 0.3, 0.1], the fuzzy membership normalization matrix is:

[0108] R'= ;

[0109] The fuzzy weight vector can be calculated. ≈[0.384,0.242,0.373].

[0110] S6. Normalize the parameter data of the polymer granules, and calculate the comprehensive performance score of the polymer granules by combining the normalized parameter data with the fuzzy weight vector.

[0111] Before normalizing the parameter data of polymer granules, the following steps are also included:

[0112] Key performance indicators are categorized into three types: extremely large, extremely small, and intermediate. Extremely large indicators are those with higher values, indicating better polymer granule performance. Extremely small indicators are those with lower values, indicating better polymer granule performance. Intermediate indicators are those other than extremely large and extremely small indicators; the closer their values ​​are to the optimal value, the better the polymer granule performance. The optimal value refers to the target value determined based on experimental research, practical experience, or industry standards for polymer granule performance. The polymer granules corresponding to this target value exhibit optimal performance under the evaluation criteria for that indicator.

[0113] The parameter data corresponding to extremely large indicators are normalized using the following formula:

[0114] ;

[0115] In the formula, x is the original value of the parameter data, x' is the transformed value of the parameter data, and C maxmax This represents the maximum value in the extremely large indicator parameter data.

[0116] The parameter data corresponding to the extremely small indicators are normalized using the following formula:

[0117] ;

[0118] In the formula, x is the original value of the parameter data, x' is the transformed value of the parameter data, and C minmax This represents the maximum value in the extremely small indicator parameter data;

[0119] The parameter data corresponding to intermediate indicators are normalized using the following formula:

[0120] ;

[0121] In the formula, x is the original value of the parameter data, x' is the transformed value of the parameter data, and C opt C represents the optimal value among the intermediate index parameter data. max This represents the maximum value among the intermediate indicator parameters.

[0122] The normalized parameter data and the fuzzy weight vector are calculated using the following formula:

[0123] ;

[0124] In the formula, S represents the overall performance score. Let T be the transpose of the fuzzy weight vector, and X' be the normalized parameter data of the polymer granules. X' is represented as:

[0125] ;

[0126] In the formula, This represents the parameter data after normalization of the nth indicator.

[0127] For example, for a certain batch of polymer granules, there are three key indicators A, B, and C; indicator A is an extremely large indicator, with an original value of 50 and a maximum value of 100, so its parameter conversion value is 50 / 100=0.5; indicator B is an extremely small indicator, with an original value of 2 and a maximum value of 10, so its parameter conversion value is (10-2) / 10=0.8; indicator C is an intermediate indicator, with an original value of 0.4, a maximum value of 1, and an optimal value of 0.5, so its parameter conversion value is [1-(0.5-0.4) / 1]=0.9; the normalized parameter data of the polymer granules is X'=[0.5, 0.8, 0.9].

[0128] If the fuzzy weight vector of this batch of polymer granules is calculated through the aforementioned steps... ≈[0.384, 0.242, 0.373], then the overall score S of the polymer granules can be calculated:

[0129] =0.5×0.384+0.8×0.242+0.9×0.373≈0.7213;

[0130] Furthermore, the calculated comprehensive score can also be converted into a score that is more in line with daily judgment. For example, S×100=72.13 means that the comprehensive score of this batch of polymer granules is 72.13.

[0131] The comprehensive performance evaluation method for polymer granules provided in this invention first establishes a multi-dimensional evaluation index system. Then, it constructs a judgment matrix by comparing the relative importance of each pair of indicators and calculates the weight vector of each indicator. Furthermore, it utilizes multiple experts to score the construction effect of each key indicator, and performs fuzzy operations on the indicator weight vector and the fuzzy membership normalization matrix to obtain a fuzzy weight vector, making the indicator evaluation more objective. After normalizing the parameter data corresponding to each indicator of the polymer granules, it performs calculations with the fuzzy weight vector to obtain the comprehensive performance score of the polymer granules.

[0132] By tightly coupling parameter data with index weights, this invention can accurately capture subtle differences in the performance of polymer granules, objectively and comprehensively reflecting their performance quality. This provides a reliable basis for quality control, R&D optimization, and market access of polymer granules for capacitors, and provides decision support for manufacturers to evaluate and screen the performance of polymer granules.

[0133] To better illustrate the implementation of the technical solution of the present invention, the present invention also proposes the following specific embodiments. It should be noted that the following specific embodiments are merely exemplary and are not intended to limit the scope of protection of the present invention in any way.

[0134] Example 1:

[0135] Step 1, refer to Figure 2 Taking polypropylene granules as an example, the following 11 key indicators are selected to construct a comprehensive performance evaluation index system for polypropylene granules used in capacitors, including: density index C1, ash content C2, hot melt peak temperature C3, crystallization temperature C4, crystallinity C5, oxidation induction period C6, melt mass flow rate C7, melt flow ratio C8, tensile yield stress C9, nominal strain at tensile fracture C10, and tensile modulus of elasticity C11.

[0136] The regularity index refers to the mass percentage of isotactic (i-PP) in polypropylene resin. The higher the regularity index, the higher the crystallinity and regularity of the polypropylene granules, and the better their mechanical properties and heat resistance are also improved accordingly. This index is a very large index.

[0137] Ash content is the residue remaining after resin is completely burned off at high temperature. It is used to determine the content of trace metals and other inorganic impurities in organic resin. If the ash content is too high, it will reduce the purity of polypropylene granules, thereby affecting their mechanical properties, processing performance and appearance quality. This indicator is a very small indicator.

[0138] The hot melt peak temperature reflects the melting characteristics of polypropylene granules. Its value affects the processing and molding temperature window, crystallinity, and performance of polypropylene granules. A higher hot melt peak temperature means that the polypropylene granules have higher crystallinity and better heat resistance. This index is an intermediate index, and the closer the parameter is to the optimal value, the better.

[0139] Crystallization temperature affects the crystallization rate, crystal structure, and crystallinity of polypropylene granules, which in turn affects the mechanical properties, thermal stability, and transparency of the products. A suitable crystallization temperature can enable polypropylene granules to obtain good comprehensive properties. This indicator is considered an intermediate indicator.

[0140] Increased crystallinity significantly improves the rigidity, heat distortion temperature, and dimensional stability of polypropylene, but at the same time reduces the material's transparency, impact resistance, and molding shrinkage. Nucleating agents are needed to regulate the crystal structure to balance the overall performance. This indicator is considered an intermediate-level indicator.

[0141] The oxidation induction period indicates the antioxidant properties of polypropylene granules. A longer oxidation induction period means that polypropylene granules are less prone to oxidative degradation during processing and use, and can better maintain their performance stability and service life. This indicator is a very large indicator.

[0142] Melt flow rate reflects the fluidity of polypropylene granules in the molten state. The higher the melt flow rate, the better the fluidity of the polypropylene granules and the easier they are to process and mold. However, it may have a certain impact on the mechanical properties of the product. This indicator is an intermediate indicator.

[0143] Melt flow ratio is the ratio of the melt flow rate of polypropylene granules at different shear rates. It reflects the shear sensitivity of polypropylene granules; the higher the melt flow ratio, the more sensitive the polypropylene granules are to shear. During processing, its flowability is controlled by adjusting the shear rate to meet the requirements of different molding processes. This indicator is considered an intermediate-level indicator.

[0144] Tensile yield stress is used to measure the ability of polypropylene granules to resist tensile failure. It affects the performance of polypropylene products when subjected to tensile loads. The larger the value, the greater the tensile force that the product can withstand. This index is a very large index.

[0145] The nominal strain at tensile break indicates the deformation capacity of polypropylene granules when stretched to the point of fracture. A larger nominal strain at tensile break indicates that the polypropylene granules have better toughness and ductility, and the products made from them can withstand a greater degree of stretching without breaking. This index is considered an extremely large index.

[0146] The tensile modulus of elasticity characterizes the stiffness of polypropylene granules during the elastic deformation stage. The higher the value, the less likely the polypropylene granules are to undergo elastic deformation, and the products made from them can better maintain their shape and dimensional stability when subjected to external forces. This index is considered an intermediate index.

[0147] Step 2: Through expert judgment and research and analysis of experimental parameters, compare different indicators pairwise to determine their relative importance to capacitor performance and construct a judgment matrix.

[0148] The comparison results are shown in Table 1, where the values ​​1, 3, and 5 represent the importance of the two elements, as detailed in Table 2. The values ​​in Table 1 can be optimized and modified as our understanding of polypropylene granules and manufacturing technology improve.

[0149] Table 1. Comparison of Comprehensive Performance Evaluation Indicators for Polypropylene Granules for Capacitors

[0150]

[0151] Table 2 Explanation of the Quantitative Scale for Relative Importance

[0152]

[0153] Based on the data in Table 1, construct the judgment matrix X as shown below:

[0154] .

[0155] Step 3: Calculation of the indicator weight vector.

[0156] Based on the judgment matrix X, the largest eigenvalue can be solved using the following formula. and the largest eigenvalue The corresponding weight vector W:

[0157] ;

[0158] ;

[0159] Wherein, W' is the normalized weight vector of the weight vector W, that is, the index weight vector.

[0160] In this embodiment, the maximum eigenvalue is obtained. =12, and its corresponding indicator weight vector is:

[0161] W'=[0.2198, 0.2198, 0.1265, 0.0786, 0.1090, 0.0667, 0.0544, 0.0448, 0.0348, 0.0228, 0.0228].

[0162] The consistency of the weight vector of this indicator is judged. The consistency judgment is mainly based on the consistency index CI, the random consistency index RI, and the consistency test CR to check whether the judgment matrix has satisfactory consistency. The formula for calculating CI is as follows:

[0163] ;

[0164] Set n=11, Substituting 12 into the equation, we get CI = 0.1.

[0165] The random consistency index RI can be obtained by looking up a table, and the RI values ​​are shown in Table 3.

[0166] Table 3 Random Consistency Index (RI)

[0167]

[0168] The CR value for consistency testing is calculated as follows:

[0169] ;

[0170] A CR value less than 0.1 indicates that the judgment matrix X passes the consistency test, and the calculated index weight vector W' is reliable.

[0171] Step 4: Evaluate the effectiveness of the comprehensive performance evaluation index system for polypropylene granules for capacitors by l different experts, and assign four levels to each index: “Excellent”, “Good”, “Medium” and “Poor”, i.e., evaluation V = {Excellent, Good, Medium, Poor}, and the specific scores and meanings are shown in Table 4.

[0172] Table 4. Evaluation Index System for Comprehensive Performance of Polypropylene Granules for Capacitors

[0173]

[0174] For example, for the indicator "Isokinetic Index", in step 2, its relative importance with other indicators is quantified by scoring 1, 3, 5 or 1 / 3, 1 / 5. If, according to the experience assessment of an invited expert, each score for the relative importance of "Isokinetic Index" does not deviate from the normal range, that is, "all indicators do not deviate from the normal range" in the evaluation of the relative importance of the isokinetic index, then the expert can evaluate the construction effect of "Isokinetic Index" as "excellent" and select a specific score within the scoring range [76, 100].

[0175] Based on the data evaluated by l experts, the fuzzy membership matrix can be obtained:

[0176] ;

[0177] in, This represents the score given by the lth expert for the construction effectiveness of the indicator "Isometric Index". This represents the score given by the lth expert regarding the construction effectiveness of the indicator "grey score". This represents the score given by the lth expert for the construction effectiveness of the indicator "tensile elastic modulus".

[0178] The fuzzy membership matrix R is normalized using the following formula:

[0179] ;

[0180] in, Represents the fuzzy membership matrix of the th The expert commented on the first The scoring of the construction effectiveness of each indicator. The fuzzy membership normalization matrix represents the th The expert commented on the first The scoring of the construction effectiveness of each indicator.

[0181] This leads to the fuzzy membership normalization matrix R':

[0182] .

[0183] The index weight vector W' is subjected to fuzzy operation with the fuzzy membership normalization matrix R' to obtain the fuzzy weight vector. :

[0184] .

[0185] Step 5: Process the parameter data of polypropylene granules for capacitors; specifically, in order to maintain the uniformity of parameter types, the extremely large, extremely small and intermediate parameters are processed according to the parameter data normalization method provided above.

[0186] Step 6: The overall performance of polypropylene granules is scored based on the index parameter data, and then polypropylene granules that meet the requirements for capacitors are selected.

[0187] Suppose the parameter data of a certain brand and model of polypropylene granules is X=[x1, x2, ..., x 11 ], where x1, x2, ..., x 11 The parameter data corresponding to the 11 indicators are processed by normalization in step 5 to obtain the transformed value X'=[ , ..., If the overall performance score S of the polypropylene granules of this brand and model is:

[0188] ;

[0189] In the formula, The index weight vector is calculated in step 4, and T is the transpose symbol.

[0190] Furthermore, comprehensive performance scores can be calculated for different brands and models of polypropylene granules, and the scores can be sorted from high to low, thus providing decision support for manufacturers to evaluate and select polypropylene granules based on their performance.

[0191] Based on the above method embodiments, the present invention also provides a comprehensive performance evaluation device for polymer granules.

[0192] In some embodiments, the polymer pellet comprehensive performance evaluation device includes an index system establishment module, an index weight calculation module, an effect scoring module, an expert fuzzy comprehensive evaluation module, a data input and processing module, and a comprehensive performance scoring module.

[0193] The indicator system establishment module is used to screen key indicators for polymer granules.

[0194] Key indicators include macroscopic indicators (such as melt index, ash content, etc.) and microscopic indicators (such as the number of crystal rings, crystal ball size, and other microstructural characteristics).

[0195] The indicator weight calculation module is used to quantify the relative importance of multiple key indicators pairwise, construct a judgment matrix, solve the weight of each key indicator based on the judgment matrix, and normalize the weight of each key indicator to obtain the indicator weight vector.

[0196] The judgment matrix constructed based on importance can be represented by the following formula:

[0197] ;

[0198] In the formula, X represents the judgment matrix, and n is the number of indicators; c i，j This represents the importance of the i-th indicator relative to the j-th indicator (0 < i ≤ n, 0 < j ≤ n, and i and j are both integers), for example, c 1,1 This indicates the importance of the first indicator relative to the indicator itself (value is 1), c 1,2 This indicates the relative importance of the first indicator to the second indicator, c. 2,1 This indicates the relative importance of the second indicator compared to the first indicator.

[0199] Based on the judgment matrix X, the largest eigenvalue can be solved using the following formula. and the largest eigenvalue The corresponding weight vector W, where each value in the weight vector W represents the weight of each indicator:

[0200] ;

[0201] ;

[0202] Wherein, W' is the normalized weight vector of the weight vector W, that is, the index weight vector.

[0203] The performance evaluation module uses multiple experts to score the construction effect of each key indicator, constructs a fuzzy membership matrix, and normalizes the fuzzy membership matrix to obtain a fuzzy membership normalized matrix.

[0204] The fuzzy membership matrix is ​​represented as:

[0205] ;

[0206] in, Let n be the number of experts and n be the number of indicators. This represents the score given by the l-th expert for the construction effectiveness of the n-th indicator.

[0207] The formula for normalizing the fuzzy membership matrix is:

[0208] ;

[0209] in, Represents the fuzzy membership matrix of the th The expert commented on the first The scoring of the construction effectiveness of each indicator. The fuzzy membership normalization matrix represents the th The expert commented on the first The scoring of the construction effectiveness of each indicator.

[0210] The fuzzy membership normalization matrix can be expressed as:

[0211] .

[0212] The expert fuzzy comprehensive evaluation module is used to perform fuzzy operations on the index weight vector and the fuzzy membership normalization matrix to obtain the fuzzy weight vector.

[0213] Specifically, fuzzy operations are performed on the index weight vector and the fuzzy membership normalization matrix using the following formula:

[0214] =W'·R';

[0215] In the formula, Let W' represent the fuzzy weight vector, W' represent the index weight vector, and R' represent the fuzzy membership normalization matrix.

[0216] The data input and processing module is used to input parameter data of various key indicators of polymer granules and to normalize the parameter data of polymer granules.

[0217] Before normalization, key indicators need to be categorized into extremely large, extremely small, and intermediate indicators. The parameter data corresponding to extremely large indicators are normalized using the following formula:

[0218] ;

[0219] In the formula, x is the original value, x' is the transformed value, and C maxmax This represents the maximum value in the extremely large indicator parameter data.

[0220] The parameter data corresponding to the extremely small indicators are normalized using the following formula:

[0221] ;

[0222] In the formula, x is the original value, x' is the transformed value, and C minmax This represents the maximum value in the extremely small indicator parameter data;

[0223] The parameter data corresponding to intermediate indicators are normalized using the following formula:

[0224] ;

[0225] In the formula, x is the original value, x' is the transformed value, and C opt C represents the optimal value among the intermediate index parameter data. max This represents the maximum value among the intermediate indicator parameters.

[0226] The comprehensive performance scoring module is used to calculate the comprehensive performance score of polymer granules by performing normalized parameter data and fuzzy weight vector.

[0227] The normalized parameter data and the fuzzy weight vector are calculated using the following formula:

[0228] ;

[0229] In the formula, S represents the overall performance score. Let T be the transpose of the fuzzy weight vector, and X' be the normalized parameter data of the polymer granules. X' is represented as:

[0230] ;

[0231] In the formula, This represents the parameter data after normalization of the nth indicator.

[0232] The apparatus provided in this embodiment of the invention is used to implement the comprehensive performance evaluation method for polymer granules provided in the above embodiments. The specific parameter settings of each module can be referred to the aforementioned method embodiments. The beneficial effects of the aforementioned method embodiments can all be achieved by this apparatus, and will not be elaborated here.

[0233] According to the foregoing method embodiments, the present invention also provides a computer device; the computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the polymer granule comprehensive performance evaluation method provided in any of the foregoing embodiments; therefore, the computer device can achieve all the technical effects that the polymer granule comprehensive performance evaluation method provided in the foregoing embodiments can achieve, and will not be repeated here.

[0234] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can implement the processes of the above method embodiments. Any references to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory.

[0235] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for evaluating the comprehensive performance of polymer granules, characterized in that, Including the following steps: S1 represents several key indicators for screening polymer granules; S2, quantify the relative importance of each pair of the key indicators and construct a judgment matrix; S3. Solve for the weight of each key indicator based on the judgment matrix, and normalize the weight of each key indicator to obtain the indicator weight vector. S4. Obtain the scores of multiple experts on the construction effect of each key indicator, construct a fuzzy membership degree matrix, and normalize the fuzzy membership degree matrix to obtain a fuzzy membership degree normalized matrix. S5, perform fuzzy operations on the index weight vector and the fuzzy membership normalization matrix to obtain the fuzzy weight vector; S6. Normalize the parameter data of the polymer granules, and then perform calculations on the normalized parameter data and the fuzzy weight vector to obtain the comprehensive performance score of the polymer granules.

2. The method for comprehensive performance evaluation of polymer granules according to claim 1, characterized in that, The key indicators include the degree index, ash content, hot melt peak temperature, crystallization temperature, crystallinity, oxidation induction period, melt mass flow rate, melt flow ratio, tensile yield stress, nominal strain at tensile fracture, and tensile modulus of elasticity.

3. The method for comprehensive performance evaluation of polymer granules according to claim 1, characterized in that, Step S3 further includes: performing a consistency check on the indicator weight vector, wherein the consistency check is calculated using the following formula: ， ； In the formula, CI is the consistency index. Let be the largest eigenvalue, n be the number of indicators, RI be the random consistency index, and CR be the consistency test ratio; when the consistency test ratio CR is less than 0.1, the indicator weight vector is considered reliable.

4. The method for comprehensive performance evaluation of polymer granules according to claim 1, characterized in that, The fuzzy membership matrix is: ； in, Let n be the number of experts and n be the number of indicators. This represents the score given by the l-th expert for the construction effectiveness of the n-th indicator; The formula for normalizing the fuzzy membership matrix is ​​as follows: ； in, The fuzzy membership matrix represents the th element in the fuzzy membership matrix. The expert commented on the first The scoring of the construction effectiveness of each indicator. The fuzzy membership normalization matrix represents the first... The expert commented on the first The scoring of the construction effect of each indicator; the fuzzy membership normalization matrix is ​​represented as: 。 5. The method for evaluating the comprehensive performance of polymer granules according to claim 1, characterized in that, In step S5, fuzzy operations are performed on the index weight vector and the fuzzy membership normalization matrix using the following formula: =W'·R'; In the formula, Let W' represent the fuzzy weight vector, W' represent the index weight vector, and R' represent the fuzzy membership normalization matrix.

6. The method for comprehensive performance evaluation of polymer granules according to claim 1, characterized in that, Before normalizing the parameter data of polymer granules, the following steps are also included: The key indicators are divided into extremely large indicators, extremely small indicators, and intermediate indicators; the extremely large indicators are those whose larger values ​​indicate better polymer granule performance; the extremely small indicators are those whose smaller values ​​indicate better polymer granule performance; and the intermediate indicators are those other than the extremely large and extremely small indicators.

7. The method for evaluating the comprehensive performance of polymer granules according to claim 6, characterized in that, The parameter data corresponding to the extremely large indicators are normalized using the following formula: ； In the formula, x is the original value of the parameter data, x' is the transformed value of the parameter data, and C maxmax This represents the maximum value in the parameter data corresponding to the extremely large indicator. The parameter data corresponding to the extremely small index is normalized using the following formula: ； In the formula, x is the original value of the parameter data, x' is the transformed value of the parameter data, and C minmax This represents the maximum value in the parameter data corresponding to the extremely small indicator. The parameter data corresponding to the intermediate index is normalized using the following formula: ； In the formula, x is the original value of the parameter data, x' is the transformed value of the parameter data, and C opt C represents the optimal value among the parameter data corresponding to the intermediate indicator. max This represents the maximum value among the parameter data corresponding to the intermediate indicator.

8. The method for comprehensive performance evaluation of polymer granules according to claim 1, characterized in that, In step S6, the normalized parameter data and the fuzzy weight vector are calculated using the following formula: ； In the formula, S represents the overall performance score. Let T be the transpose of the fuzzy weight vector, and X' be the normalized parameter data of the polymer granules. X' is represented as: ； In the formula, This represents the parameter data after normalization of the nth indicator.

9. A comprehensive performance evaluation device for polymer granules, characterized in that, include: The indicator system establishment module is used to screen key indicators for polymer granules; The indicator weight calculation module is used to quantify the relative importance of multiple key indicators pairwise, construct a judgment matrix, solve the weight of each key indicator based on the judgment matrix, and normalize the weight of each key indicator to obtain the indicator weight vector. The performance evaluation module uses multiple experts to evaluate the construction effect of each key indicator, constructs a fuzzy membership matrix, and normalizes the fuzzy membership matrix to obtain a fuzzy membership normalized matrix. The expert fuzzy comprehensive evaluation module is used to perform fuzzy operations on the index weight vector and the fuzzy membership normalization matrix to obtain the fuzzy weight vector. The data input and processing module is used to input parameter data of various key indicators of polymer granules and to normalize the parameter data of polymer granules. The comprehensive performance scoring module is used to calculate the comprehensive performance score of the polymer granules by performing normalized parameter data and the fuzzy weight vector.

10. A computer device, characterized in that, The device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the comprehensive performance evaluation method for polymer granules as described in any one of claims 1-8.

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