Preparation method and system of antibacterial flame-retardant toilet lid polypropylene material

Abstract

The invention relates to the technical field of toilet lid preparation, in particular to a preparation method and system of an antibacterial flame-retardant toilet lid polypropylene material, and the method comprises the steps: obtaining a target material data set based on a material text library, identifying an optimal material group in the target material data set, setting an initial material ratio set based on an optimal antibacterial material and an optimal flame-retardant material, and obtaining an optimal material group in the initial material ratio set; preparing a test toilet lid set according to the initial material ratio set, performing performance test on the test toilet lid set to obtain a comprehensive performance value set, obtaining an optimal material ratio set according to the comprehensive performance value set and the initial material ratio set, and repeating the steps to obtain a target material ratio in the last return step. According to the preparation method, the matching accuracy in the preparation process of the antibacterial flame-retardant toilet lid can be improved, and the preparation cost of the toilet lid is reduced.

Description

Technical Field

[0001] This invention relates to the field of toilet seat manufacturing technology, and in particular to a method and system for preparing an antibacterial and flame-retardant polypropylene material for toilet seats. Background Technology

[0002] In modern life, the toilet seat is an important component of the bathroom, and its hygiene and safety performance are of paramount importance. Due to the humid environment and high frequency of use in the bathroom, bacteria can easily grow on the surface of the toilet seat, posing a health risk to users. At the same time, considering the potential fire sources in public places or homes, the flame-retardant performance of the toilet seat cannot be ignored.

[0003] Traditionally, the preparation of antibacterial and flame-retardant polypropylene toilet seat materials usually involves directly adding a fixed proportion of antibacterial agents and flame retardants. Although this method can achieve antibacterial and flame-retardant functions to a certain extent, it has obvious problems of unstable performance and difficulty in cost control. Due to the lack of a systematic material screening and optimization process, this method is difficult to ensure the consistency of material performance under different environmental conditions, and often requires excessive addition of expensive antibacterial and flame retardants to achieve the expected effect, resulting in high costs. Summary of the Invention

[0004] This invention provides a method and system for preparing antibacterial and flame-retardant polypropylene material for toilet seats. Its main purpose is to improve the accuracy of the proportioning during the preparation of antibacterial and flame-retardant toilet seats and reduce the cost of toilet seat preparation.

[0005] To achieve the above objectives, the present invention provides a method for preparing an antibacterial and flame-retardant polypropylene toilet seat material, comprising:

[0006] Receive material preparation instructions, determine a material text library based on the material preparation instructions, wherein the material text library includes multiple material articles, and each material article includes one or more candidate materials;

[0007] Based on the material text library, antibacterial and flame-retardant materials are searched to obtain a target material dataset. The target material dataset includes multiple target material data, and the target material data includes: target material and target performance value. The target performance value includes: target antibacterial performance value and target flame-retardant performance value.

[0008] Identify the optimal material group in the target material dataset, where the optimal material group includes: the optimal antibacterial material and the optimal flame retardant material;

[0009] Based on the optimal antibacterial material, the optimal flame retardant material, and the preset polypropylene material, an initial material ratio set is set. According to the initial material ratio set, a set of test toilet seats is prepared. The test toilet seats in the set of test toilet seats correspond one-to-one with the initial material ratios in the initial material ratio set.

[0010] Performance tests were conducted on all toilet seats in the test set to obtain a comprehensive performance value set, which includes antibacterial performance value and flame retardant performance value.

[0011] Based on the comprehensive performance value set and the initial material ratio set, the preset genetic population is iterated to obtain the optimal material ratio set;

[0012] The optimal material ratio set is used as the initial material ratio set, and the step of preparing the test toilet seat set based on the initial material ratio set is returned until the number of times the return step is not less than a preset number threshold.

[0013] The optimal material ratio set in the last return step is recorded as the candidate material ratio set, and the target material ratio is obtained based on the candidate material ratio set.

[0014] Optionally, the search for antibacterial and flame-retardant materials based on a materials text library to obtain a target materials dataset includes:

[0015] Material articles are extracted sequentially from the material text library, the candidate material groups contained in the material articles are identified, and based on the material articles, the performance of each candidate material in the candidate material group is analyzed to obtain a material performance value group.

[0016] The material property value group is matched with the candidate material group to obtain the candidate material data group;

[0017] Summarize the candidate material data groups corresponding to each material text in the material text library to obtain the candidate material data group set;

[0018] Data filtering is performed on the dataset of materials to be selected to obtain the target material dataset.

[0019] Optionally, based on the materials article, performance analysis is performed on each candidate material in the candidate material group to obtain a set of material performance values, including:

[0020] Selectable materials are extracted sequentially from the candidate material group, and performance experiments and comparative experiments of the candidate materials are identified in the material article. The candidate materials include: candidate flame retardant materials and candidate antibacterial materials, and the performance tests include the candidate materials.

[0021] Based on performance and comparative experiments, the material performance values ​​of the candidate materials are calculated. The material performance values ​​include flame retardant performance values ​​and antibacterial performance values, and the flame retardant performance values ​​and antibacterial performance values ​​correspond to the candidate flame retardant material and the candidate antibacterial material, respectively.

[0022] Summarize the material property values ​​to obtain a set of material property values.

[0023] Optionally, the step of filtering the dataset of materials to be selected to obtain the target material dataset includes:

[0024] Determine the original material set in the candidate material data set, and perform the following operation on each original material in the original material set:

[0025] Extract the original performance value group corresponding to the original material from the candidate material data group set, wherein the original performance values ​​in the original performance value group come from different candidate material data groups in the candidate material data group set;

[0026] Calculate the performance standard deviation based on the original performance value set;

[0027] If the performance standard deviation is not greater than the preset standard deviation threshold, the original material is recorded as the target material, and the mean value is calculated based on the original performance value group to obtain the target performance value.

[0028] The target material and target performance values ​​are matched to obtain the target material data;

[0029] The target material dataset is obtained by summarizing the target material data corresponding to the target material in the original material set.

[0030] Optionally, identifying the optimal material set in the target material dataset includes:

[0031] The target material dataset is divided into data groups of similar materials, including: similar antibacterial data group and similar flame retardant data group.

[0032] Extract similar material data groups sequentially from the similar material data group set. The similar material data in the similar material data group includes: similar material and similar performance values.

[0033] Perform the following operation on each data point of the same material in the same data group:

[0034] Determine the unit material cost of similar materials in the data of similar materials. If the unit material cost is not greater than the preset controllable cost threshold, calculate the material cost-effectiveness based on the similar performance value and the unit material cost. The material cost-effectiveness is the ratio of the similar performance value to the unit material cost.

[0035] Summarize the cost-effectiveness of materials to obtain a material cost-effectiveness group, identify the optimal cost-effectiveness in the material cost-effectiveness group, and determine the optimal material corresponding to the optimal cost-effectiveness.

[0036] The optimal material group is obtained by summarizing the data of similar material groups and finding the best material group.

[0037] Optionally, the step of iterating the preset genetic population to obtain the optimal material ratio set based on the comprehensive performance value set and the initial material ratio set includes:

[0038] The genetic population is initialized based on the initial material ratio set and the comprehensive performance value set to obtain an initial genetic population, wherein the initial genetic population includes multiple initial chromosomes, and the gene sequences contained in the initial chromosomes correspond to the initial material ratios.

[0039] Genetic iteration is performed on the initial genetic population to obtain the optimal genetic population. The genetic iteration includes gene selection, gene crossover, and gene mutation.

[0040] Determine the optimal set of chromosomes in the optimal genetic population, and identify the optimal gene sequence contained in each optimal chromosome in the optimal chromosome set to obtain the optimal gene sequence set;

[0041] The optimal material ratio set was determined based on the optimal gene sequence set.

[0042] Optionally, the step of performing genetic iteration on the initial genetic population to obtain the optimal genetic population includes:

[0043] Determine the initial fitness set corresponding to multiple initial chromosomes in the initial genetic population;

[0044] Based on the initial fitness set, gene selection, gene crossover, and gene mutation are sequentially performed on the initial genetic population to obtain an updated genetic population, which includes multiple updated chromosomes.

[0045] The updated chromosomes were extracted sequentially from multiple updated chromosomes in the updated genetic population to determine the updated gene sequences in the updated chromosomes.

[0046] Construct updated gene vectors based on updated gene sequences;

[0047] Based on the comprehensive performance value set and the initial material ratio set, the update fitness of the updated gene vector is calculated;

[0048] The update fitness set is obtained by summing the update fitness of each update chromosome in multiple update chromosomes;

[0049] The updated fitness set and the updated genetic population are used as the initial fitness set and the initial genetic population, respectively. The steps of gene selection, gene crossover and gene mutation are performed on the initial genetic population according to the initial fitness set in sequence until the number of the return steps is not less than the preset standard number of iterations.

[0050] The updated genetic population from the last return step is taken as the optimal genetic population.

[0051] Optionally, calculating the update fitness of the updated gene vector based on the comprehensive performance value set and the initial material ratio set includes:

[0052] The initial material proportions are extracted sequentially from the initial material proportion set, and the initial performance values ​​corresponding to the initial material proportions are identified in the comprehensive performance value set.

[0053] Construct an initial proportion vector based on the initial material proportions;

[0054] Based on the initial matching vector and the updated gene vector, the matching weight value is calculated, wherein the matching weight value is expressed as:

[0055]

[0056] Where w represents the weighting ratio, exp(*) represents an exponential function with the natural constant as the base, and Q c G represents the initial matching vector. x This indicates updating the gene vector, where d represents the vector dimension of the initial matching vector or the vector dimension of the updated gene vector.

[0057] Summarize the proportion weight value and initial performance value corresponding to each initial material proportion in the initial material proportion set to obtain the proportion weight value set and the initial performance value set;

[0058] Based on the set of matching weight values ​​and the set of initial performance values, a cost-effectiveness analysis is performed on the updated gene vector to obtain the updated fitness.

[0059] Optionally, the step of performing a cost-effectiveness analysis on the updated gene vector based on the matching weight value set and the initial performance value set to obtain the updated fitness includes:

[0060] Determine the cost of the updated materials based on the updated gene vector;

[0061] The update fitness is calculated based on the updated material cost, the set of proportion weight values, and the set of initial performance values, where the update fitness is expressed as:

[0062]

[0063] Where P represents the update fitness, α1 represents the preset cost coefficient, F represents the update material cost, α2 represents the preset performance coefficient, n represents the number of initial performance values ​​in the initial performance value set, and w i H represents the i-th weight value in the weight set. i This represents the i-th initial performance value in the initial performance value set.

[0064] To achieve the above objectives, the present invention also provides a preparation system for an antibacterial and flame-retardant polypropylene toilet seat material, comprising:

[0065] The target material selection module is used to receive material preparation instructions, determine a material text library based on the material preparation instructions, wherein the material text library includes multiple material articles, and each material article includes one or more candidate materials, and searches for antibacterial and flame retardant materials based on the material text library to obtain a target material dataset, wherein the target material dataset includes multiple target material data, and the target material data includes: target material and target performance value, the target performance value includes: target antibacterial performance value and target flame retardant performance value;

[0066] The initial ratio setting module is used to identify the optimal material group in the target material dataset. The optimal material group includes the optimal antibacterial material and the optimal flame retardant material. Based on the optimal antibacterial material, the optimal flame retardant material and the preset polypropylene material, an initial material ratio set is set. According to the initial material ratio set, a test toilet seat set is prepared. The test toilet seats in the test toilet seat set correspond one-to-one with the initial material ratios in the initial material ratio set.

[0067] The optimal material ratio optimization module is used to perform performance tests on all test toilet seats in the test toilet seat set to obtain a comprehensive performance value set, which includes antibacterial performance value and flame retardant performance value. Based on the comprehensive performance value set and the initial material ratio set, the preset genetic population is iterated to obtain the optimal material ratio set.

[0068] The target material ratio determination module is used to take the optimal material ratio set as the initial material ratio set and return to the step of preparing the test toilet seat set according to the initial material ratio set until the number of return steps is not less than a preset number threshold. The optimal material ratio set in the last return step is recorded as the candidate material ratio set, and the target material ratio is obtained based on the candidate material ratio set.

[0069] To address the above problems, the present invention also provides an electronic device, the electronic device comprising:

[0070] Memory, storing at least one instruction; and

[0071] The processor executes the instructions stored in the memory to implement the above-described method for preparing antibacterial and flame-retardant polypropylene toilet seat material.

[0072] To address the aforementioned problems, the present invention also provides a computer-readable storage medium storing at least one instruction, which is executed by a processor in an electronic device to implement the above-described method for preparing antibacterial and flame-retardant polypropylene toilet seat material.

[0073] To address the problems described in the background section, this invention first searches for antibacterial and flame-retardant materials based on a materials text database to obtain a target materials dataset. This step transforms a large amount of literature data into a target materials dataset with practical application value, providing data support for subsequent material screening and optimization. Then, the optimal material group is identified within the target materials dataset. This step comprehensively considers material performance and cost, effectively identifying the optimal combination of antibacterial and flame-retardant materials. This not only ensures that the antibacterial and flame-retardant performance of the materials is optimal but also takes cost-effectiveness into account. Furthermore, based on the comprehensive performance value set and the initial material ratio set, the genetic population is iteratively processed to obtain the optimal material ratio set. This step introduces a genetic algorithm to optimize the material ratio, comprehensively considering material performance and cost, and simulating the process of natural selection. This process involves progressively selecting the optimal material ratio scheme, thus avoiding the bias of manual judgment based on experience and minimizing trial and error, thereby improving the accuracy and efficiency of material ratio formulation. Finally, the optimal material ratio set is used as the initial material ratio set, and the above genetic optimization steps are repeated. Through multiple iterations, the material ratio is continuously adjusted towards a better direction, gradually approaching the theoretically optimal ratio, improving the accuracy and reliability of the final material ratio, and verifying the effectiveness of the genetic algorithm optimization results. Finally, this scheme obtains the target material ratio after multiple iterations of optimization. This target material ratio can effectively control costs while ensuring antibacterial and flame-retardant properties, achieving a balance between performance and cost, and ultimately producing antibacterial and flame-retardant polypropylene toilet seat material that meets practical application requirements. Therefore, this invention can improve the accuracy of the formulation process in the preparation of antibacterial and flame-retardant toilet seats and reduce the cost of toilet seat production. Attached Figure Description

[0074] Figure 1 This is a schematic flowchart illustrating a method for preparing an antibacterial and flame-retardant polypropylene toilet seat material according to an embodiment of the present invention.

[0075] Figure 2 This is a functional block diagram of a preparation system for an antibacterial and flame-retardant toilet seat polypropylene material provided in an embodiment of the present invention.

[0076] Figure 3 This is a schematic diagram of the structure of an electronic device that implements the preparation method of the antibacterial and flame-retardant polypropylene toilet seat material according to an embodiment of the present invention.

[0077] Explanation of reference numerals in the attached figures:

[0078] 10. Electronic device; 11. Processor; 12. Memory; 13. Bus.

[0079] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0080] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0081] This application provides a method for preparing an antibacterial and flame-retardant polypropylene toilet seat material. The execution subject of this method includes, but is not limited to, at least one electronic device configured to execute the method provided in this application, such as a server or a terminal. In other words, the method for preparing the antibacterial and flame-retardant polypropylene toilet seat material can be executed by software or hardware installed on a terminal device or a server device, and the software may be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0082] Reference Figure 1 The diagram shown is a flowchart illustrating a method for preparing an antibacterial and flame-retardant toilet seat polypropylene material according to an embodiment of the present invention. In this embodiment, the method for preparing the antibacterial and flame-retardant toilet seat polypropylene material includes:

[0083] S1. Receive material preparation instructions and determine a material text library based on the material preparation instructions. The material text library includes multiple material articles, and each material article includes one or more candidate materials.

[0084] It is clear that the material preparation instruction refers to a human-initiated instruction to prepare polypropylene material. The material text library refers to a database containing multiple material articles, where material articles refer to research papers, patent documents, or experimental reports, etc. These material articles include research on antibacterial or flame-retardant materials, such as: "Study on the Antibacterial Properties of Chitosan Composite Materials," which discusses in detail the inhibition rate of chitosan combined with silver nanoparticles against Escherichia coli and Staphylococcus aureus, and verifies the variation of its antibacterial properties with the addition ratio through experimental data.

[0085] S2. Based on the material text library, search for antibacterial and flame-retardant materials to obtain the target material dataset. The target material dataset includes multiple target material data, and the target material data includes: target material and target performance value. The target performance value includes: target antibacterial performance value and target flame-retardant performance value.

[0086] Understandably, the target material data refers to a combination of data including the material name and the corresponding material properties. If the material name is related to antibacterial materials, the corresponding material property is the target antibacterial performance value, which represents the antibacterial ability of the target material. If the material name is related to flame-retardant materials, the corresponding material property is the target flame-retardant performance value, which represents the flame-retardant ability of the target material.

[0087] Specifically, the search for antibacterial and flame-retardant materials based on a materials text library to obtain a target materials dataset includes:

[0088] Material articles are extracted sequentially from the material text library, the candidate material groups contained in the material articles are identified, and based on the material articles, the performance of each candidate material in the candidate material group is analyzed to obtain a material performance value group.

[0089] The material property value group is matched with the candidate material group to obtain the candidate material data group;

[0090] Summarize the candidate material data groups corresponding to each material text in the material text library to obtain the candidate material data group set;

[0091] Data filtering is performed on the dataset of materials to be selected to obtain the target material dataset.

[0092] Understandably, the candidate material data set includes multiple candidate material data sets, and each candidate material data set includes a material performance value and a candidate material. The material performance value refers to a numerical value that quantifies the performance of the candidate material. Matching the material performance value set with the candidate material set involves: sequentially extracting material performance values ​​from the material performance value set, identifying the candidate material corresponding to each material performance value in the candidate material set, pairing the candidate material and the material performance value using key-value pairs to obtain candidate material data, and summarizing the candidate material data corresponding to each material performance value to obtain the candidate material data set.

[0093] Furthermore, since the candidate material data sets obtained from different material articles may contain candidate material data with the same material name, it is necessary to select these candidate material data with the same material name and keep the candidate material data with a smaller standard deviation of performance value (less than the standard deviation threshold) as the target material data.

[0094] Importantly, the aforementioned flame-retardant materials include: inorganic aluminum hypophosphite, melamine hydrobromide, and polymethyl methacrylate (PMMA). Inorganic aluminum hypophosphite is an inorganic compound whose main flame-retardant principle is through thermal decomposition to produce phosphoric acid and other phosphorus-containing compounds. These phosphorus-containing compounds form a protective insulating film on the material surface. This film effectively isolates oxygen and heat, preventing further combustion. Melamine hydrobromide is a compound formed by the reaction of melamine and hydrobromic acid. When the material is heated and burning, melamine hydrobromide decomposes to produce hydrogen bromide. Hydrogen bromide can capture free radicals in the combustion reaction, interrupting the chain reaction and thus achieving flame retardancy. PMMA is a polymeric flame retardant. Under high-temperature conditions, PMMA can form a carbonaceous layer on the material surface. This carbonaceous layer prevents the volatilization and diffusion of combustible substances inside the material, effectively isolating oxygen and heat.

[0095] In detail, based on the materials article, performance analysis is performed on each candidate material in the selected materials group to obtain a set of material performance values, including:

[0096] Selectable materials are extracted sequentially from the candidate material group, and performance experiments and comparative experiments of the candidate materials are identified in the material article. The candidate materials include: candidate flame retardant materials and candidate antibacterial materials, and the performance tests include the candidate materials.

[0097] Based on performance and comparative experiments, the material performance values ​​of the candidate materials are calculated. The material performance values ​​include flame retardant performance values ​​and antibacterial performance values, and the flame retardant performance values ​​and antibacterial performance values ​​correspond to the candidate flame retardant material and the candidate antibacterial material, respectively.

[0098] Summarize the material property values ​​to obtain a set of material property values.

[0099] It is clear that the performance test refers to the test conducted after the candidate material is added, and the comparative test refers to the test conducted without the candidate material being added. For example, the performance test involves adding 5% chitosan to a polypropylene substrate and testing its antibacterial rate against Escherichia coli. The comparative test involves conducting the same test using pure polypropylene material without added chitosan and recording the difference in antibacterial rate.

[0100] Furthermore, the candidate flame-retardant material refers to a candidate material with flame-retardant properties, such as magnesium hydroxide, aluminum hydroxide, etc. The candidate antibacterial material refers to a candidate material with antibacterial properties, such as natural antibacterial agents such as chitosan, chitosan, propolis, and allicin, and organic antibacterial agents composed of compounds such as quaternary ammonium salts, quaternary phosphonium salts, and organic guanidines.

[0101] Understandably, the flame retardant performance value and the antibacterial performance value refer to the numerical values ​​that quantify the flame retardant ability of the selected flame retardant material and the numerical values ​​that quantify the antibacterial ability of the selected antibacterial material, respectively.

[0102] Importantly, the calculation of the material performance value of the candidate material based on performance experiments and comparative experiments includes: obtaining the material addition mass of the candidate material in the performance experiment, determining the performance result value of the performance experiment and the comparative result value of the comparative experiment, where the performance result value and the comparative result value refer to the experimental values ​​obtained after conducting the performance experiment and the comparative experiment, respectively. For example, in a certain literature, an experiment was conducted on the antibacterial ability of a certain material, and the final experimental value obtained is the antibacterial rate of the material. The material performance value is calculated based on the material addition mass, the performance result value, and the comparative result value, where the material performance value is expressed as: |A1-A2| / (A1×K), where A1 represents the comparative result value, A2 represents the performance result value, K represents the material addition mass, and |*| represents taking the absolute value. For example, if the antibacterial rate of a certain comparative experiment is 20%, the antibacterial rate of the performance experiment is 80%, and the material addition mass is 0.05kg, then the material performance value is: |20%-80%| / (20%×0.05)=60.

[0103] Specifically, the process of filtering the dataset of materials to be selected to obtain the target material dataset includes:

[0104] Determine the original material set in the candidate material data set, and perform the following operation on each original material in the original material set:

[0105] Extract the original performance value group corresponding to the original material from the candidate material data group set, wherein the original performance values ​​in the original performance value group come from different candidate material data groups in the candidate material data group set;

[0106] Calculate the performance standard deviation based on the original performance value set;

[0107] If the performance standard deviation is not greater than the preset standard deviation threshold, the original material is recorded as the target material, and the mean value is calculated based on the original performance value group to obtain the target performance value.

[0108] The target material and target performance values ​​are matched to obtain the target material data;

[0109] The target material dataset is obtained by summarizing the target material data corresponding to the target material in the original material set.

[0110] It is clear that the original material set refers to the combination of non-repeating target materials contained in the candidate material data set. For example, if a candidate material data set includes: data set 1 (material A, performance value A), data set 2 (material B, performance value B), and data set 3 (material A, performance value C), then the original material set contained in this candidate material data set is (material A, material B). The original performance value set refers to the combination of material performance values ​​corresponding to the original materials.

[0111] Furthermore, the performance standard deviation refers to the standard deviation of all raw performance values ​​in the raw performance value group. The standard deviation threshold is a human-set constant about the standard deviation; optionally, the standard deviation threshold is set to 0.15. The target performance value refers to the average of all raw performance values ​​in the raw performance value group.

[0112] It should be explained that if the performance standard deviation is greater than the standard deviation threshold, it means that the performance of the original material corresponding to the performance standard deviation varies greatly under different usage environments. If the performance standard deviation is not greater than the standard deviation threshold, it means that the performance of the original material tends to be stable under different usage environments. That is, when the original material is used to manufacture toilet seats, its performance is highly consistent with the performance value obtained in advance.

[0113] S3. Identify the optimal material group in the target material dataset, where the optimal material group includes: the optimal antibacterial material and the optimal flame retardant material.

[0114] It is clear that the optimal material group includes a combination of the optimal antibacterial material and the optimal flame retardant material. The optimal antibacterial material and the optimal flame retardant material refer to the antibacterial material and flame retardant material that can be used to make toilet seats after combining cost and performance.

[0115] Specifically, identifying the optimal material set in the target material dataset includes:

[0116] The target material dataset is divided into data groups of similar materials, including: similar antibacterial data group and similar flame retardant data group.

[0117] Extract similar material data groups sequentially from the similar material data group set. The similar material data in the similar material data group includes: similar material and similar performance values.

[0118] Perform the following operation on each data point of the same material in the same data group:

[0119] Determine the unit material cost of similar materials in the data of similar materials. If the unit material cost is not greater than the preset controllable cost threshold, calculate the material cost-effectiveness based on the similar performance value and the unit material cost. The material cost-effectiveness is the ratio of the similar performance value to the unit material cost.

[0120] Summarize the cost-effectiveness of materials to obtain a material cost-effectiveness group, identify the optimal cost-effectiveness in the material cost-effectiveness group, and determine the optimal material corresponding to the optimal cost-effectiveness.

[0121] The optimal material group is obtained by summarizing the data of similar material groups and finding the best material group.

[0122] It is clear that the aforementioned "similar material data set" refers to a collection of multiple similar material data sets obtained after division. Since there are two types of materials in this scheme: antibacterial materials and flame-retardant data, the similar material data set here includes two similar material data sets: a similar antibacterial data set and a similar flame-retardant data set. "Similar materials" and "similar performance values" refer to the target material and its performance value within the similar material data, respectively. "Unit material cost" refers to the purchase price per kilogram of material; for example, the unit cost of chitosan is 200 yuan / kg, and the unit cost of magnesium hydroxide flame retardant is 150 yuan / kg. "Material cost-effectiveness" refers to the ratio of similar performance value to unit material cost. "Controllable cost threshold" refers to a manually set cost constant, which is related to the actual project budget and is set manually. "Optimal cost-effectiveness" refers to the material with the highest cost-effectiveness value among the material cost-effectiveness values; further, "optimal material" refers to the similar material corresponding to the optimal cost-effectiveness.

[0123] S4. Based on the optimal antibacterial material, the optimal flame retardant material, and the preset polypropylene material, set an initial material ratio set. According to the initial material ratio set, prepare a set of test toilet seats. The test toilet seats in the set of test toilet seats correspond one-to-one with the initial material ratios in the initial material ratio set.

[0124] It should be explained that the polypropylene material refers to a thermoplastic polymer substrate with high chemical resistance and mechanical strength, such as polypropylene PP-HM022, which has a melt index of 22 g / 10 min and is suitable for injection molding. The initial material ratio set includes multiple initial material ratios, and the initial material ratio refers to the material ratio of the optimal antibacterial material, the optimal flame retardant material, the polypropylene material, and other related materials in the actual preparation of the test toilet seat. In the actual preparation process, in addition to the optimal antibacterial material, the optimal flame retardant material, and the polypropylene material, other materials are added, such as plasticizers (e.g., phthalates) to improve the material's flexibility and processability, antioxidants (e.g., BHT) to delay material aging, and color masterbatches to provide customized appearance. This solution only focuses on the flame retardant and antibacterial properties of the toilet seat, and therefore does not impose specific restrictions on other materials.

[0125] For example, the initial material ratio includes three flame retardant materials: inorganic aluminum hypophosphite, melamine hydrobromide, and polymethyl methacrylate. These three flame retardant materials are mixed in a certain ratio to form a flame retardant, and the initial material ratios of these three flame retardant materials in the initial material ratio set are 5%, 12%, and 8%, respectively.

[0126] Furthermore, setting an initial material ratio set refers to manually setting different proportions based on experience to obtain an initial material ratio set. Since this initial material ratio set needs to be optimized later, it only sets initial values ​​for the subsequent optimization process. The experimental toilet seat refers to a toilet seat prepared under the guidance of a certain initial material ratio, and the preparation process is not specifically limited here. Preparing a specific physical object based on the material ratio is also a routine operation.

[0127] S5. The performance of the test toilet seats in the test toilet seat set is tested to obtain a comprehensive performance value set, which includes antibacterial performance value and flame retardant performance value.

[0128] It should be explained that the performance test refers to measuring the antibacterial performance value and flame retardant performance value of the test toilet seat, and the comprehensive performance value refers to the weighted sum of the antibacterial performance value and the flame retardant performance value. For example: comprehensive performance value = antibacterial coefficient × antibacterial performance value + flame retardant coefficient × flame retardant performance value. The antibacterial coefficient and flame retardant coefficient can be set to 0.8 and 0.2 respectively (because in the environment where the toilet seat is used, antibacterial ability is more important than flame retardant ability).

[0129] Furthermore, the antibacterial performance value and flame retardant performance value refer to the numerical values ​​of the antibacterial ability and flame retardant ability of the toilet seat, respectively, as determined by quantitative testing. The performance tests include, for example: antibacterial performance test: using the ISO 22196 standard, E. coli is inoculated onto the surface of the toilet seat, and the antibacterial rate is measured after 24 hours; flame retardant performance test: according to the UL94 standard, the self-extinguishing time of the material and whether the dripping material ignites the absorbent cotton are recorded in a vertical burning test. Both the ISO 22196 and UL94 standards have detailed operating procedures, which will not be elaborated upon here.

[0130] Understandably, when obtaining antibacterial and flame retardant performance values, it is necessary to normalize these values ​​to eliminate the influence of dimensions on subsequent calculations of overall performance and fitness.

[0131] S6. Based on the comprehensive performance value set and the initial material ratio set, iterate the preset genetic population to obtain the optimal material ratio set.

[0132] It should be explained that the genetic population refers to a set of candidate solutions that encode different material ratios, with each individual (chromosome) representing a ratio scheme (e.g., 10% antibacterial material, 15% flame retardant material, and 75% polypropylene). The optimal material ratio set refers to the set of solutions corresponding to each chromosome in the last iteration of the genetic population.

[0133] In detail, the step of iterating the preset genetic population based on the comprehensive performance value set and the initial material ratio set to obtain the optimal material ratio set includes:

[0134] The genetic population is initialized based on the initial material ratio set and the comprehensive performance value set to obtain an initial genetic population, wherein the initial genetic population includes multiple initial chromosomes, and the gene sequences contained in the initial chromosomes correspond to the initial material ratios.

[0135] Genetic iteration is performed on the initial genetic population to obtain the optimal genetic population. The genetic iteration includes gene selection, gene crossover, and gene mutation.

[0136] Determine the optimal set of chromosomes in the optimal genetic population, and identify the optimal gene sequence contained in each optimal chromosome in the optimal chromosome set to obtain the optimal gene sequence set;

[0137] The optimal material ratio set was determined based on the optimal gene sequence set.

[0138] It is clear that the initial genetic population refers to the genetic population after initialization. Initialization refers to randomly generating an initial material ratio that meets constraints (e.g., a total ratio of 100%). This initialization step is a common technique in genetic optimization and will not be elaborated further. During the initialization process, the fitness calculation process for each chromosome in the genetic population is the same as the subsequent fitness update calculation process. The gene sequence refers to the numerical sequence in the chromosome that encodes the proportion of each material. For example, the gene sequence [0.1, 0.15, 0.75] corresponds to 10% antibacterial material, 15% flame retardant material, and 75% polypropylene. The optimal genetic population refers to the initial genetic population after completing the iteration. Gene selection, gene crossover, and gene mutation refer to selecting excellent individuals based on fitness, exchanging partial gene fragments, and randomly adjusting gene values, respectively. These are all conventional techniques for updating the genetic population and will not be elaborated further.

[0139] Furthermore, the genetic population comprises multiple chromosomes, each containing multiple genes, and each gene corresponds to a material ratio in the material allocation. The optimal chromosome set refers to the set of chromosomes contained in the optimal genetic population. The optimal gene sequence refers to the gene sequence corresponding to the highest fitness of the optimal chromosome in all genetic iterations; the data contained in this gene sequence constitutes the optimal material allocation.

[0140] In detail, the genetic iteration of the initial genetic population to obtain the optimal genetic population includes:

[0141] Determine the initial fitness set corresponding to multiple initial chromosomes in the initial genetic population;

[0142] Based on the initial fitness set, gene selection, gene crossover, and gene mutation are sequentially performed on the initial genetic population to obtain an updated genetic population, which includes multiple updated chromosomes.

[0143] The updated chromosomes were extracted sequentially from multiple updated chromosomes in the updated genetic population to determine the updated gene sequences in the updated chromosomes.

[0144] Construct updated gene vectors based on updated gene sequences;

[0145] Based on the comprehensive performance value set and the initial material ratio set, the update fitness of the updated gene vector is calculated;

[0146] The update fitness set is obtained by summing the update fitness of each update chromosome in multiple update chromosomes;

[0147] The updated fitness set and the updated genetic population are used as the initial fitness set and the initial genetic population, respectively. The steps of gene selection, gene crossover and gene mutation are performed on the initial genetic population according to the initial fitness set in sequence until the number of the return steps is not less than the preset standard number of iterations.

[0148] The updated genetic population from the last return step is taken as the optimal genetic population.

[0149] Understandably, the initial fitness set refers to the set of multiple fitness values ​​corresponding to multiple initial chromosomes. The updated genetic population refers to the initial genetic population after gene selection, gene crossover, and gene mutation. The updated chromosome refers to the chromosomes contained in the updated genetic population. The updated gene sequence refers to the gene sequence in the updated chromosome. The updated gene sequence includes multiple updated material ratios, and these multiple updated material ratios constitute the updated material ratio. The updated material ratio refers to the solution value corresponding to each gene in the updated gene sequence. For example, if the solution values ​​corresponding to each gene in the gene sequence of a certain chromosome are 0.1, 0.2, 0.3, and 0.4, then the proportion of materials corresponding to these genes is 0.1, 0.2, 0.3, and 0.4, and the updated material ratio is 0.1:0.2:0.3:0.4. The updated gene vector refers to the vector composed of the values ​​in the updated gene sequence. The updated fitness refers to the fitness of the updated chromosome in the next iteration.

[0150] In detail, the calculation of the update fitness of the updated gene vector based on the comprehensive performance value set and the initial material ratio set includes:

[0151] The initial material proportions are extracted sequentially from the initial material proportion set, and the initial performance values ​​corresponding to the initial material proportions are identified in the comprehensive performance value set.

[0152] Construct an initial proportion vector based on the initial material proportions;

[0153] Based on the initial matching vector and the updated gene vector, the matching weight value is calculated, wherein the matching weight value is expressed as:

[0154]

[0155] Where w represents the weighting ratio, exp(*) represents an exponential function with the natural constant as the base, and Q c G represents the initial matching vector. x This indicates updating the gene vector, where d represents the vector dimension of the initial matching vector or the vector dimension of the updated gene vector.

[0156] Summarize the proportion weight value and initial performance value corresponding to each initial material proportion in the initial material proportion set to obtain the proportion weight value set and the initial performance value set;

[0157] Based on the set of matching weight values ​​and the set of initial performance values, a cost-effectiveness analysis is performed on the updated gene vector to obtain the updated fitness.

[0158] Understandably, the initial performance value refers to the comprehensive performance value corresponding to the initial material ratio. The initial ratio vector refers to a vector composed of the proportions in the initial material ratio. For example, if the initial material ratio is 0.1:0.2:0.3:0.4, then the initial ratio vector is (0.1 0.2 0.3 0.4). The ratio weight value refers to a numerical value that quantifies the importance of the initial performance value in subsequent calculations of fitness updates. The larger the ratio weight value, the higher the importance of the initial performance value in subsequent calculations of fitness updates.

[0159] In detail, the cost-effectiveness analysis of the updated gene vector based on the matching weight value set and the initial performance value set to obtain the updated fitness includes:

[0160] Determine the cost of the updated materials based on the updated gene vector;

[0161] The update fitness is calculated based on the updated material cost, the set of proportion weight values, and the set of initial performance values, where the update fitness is expressed as:

[0162]

[0163] Where P represents the update fitness, α1 represents the preset cost coefficient, F represents the update material cost, α2 represents the preset performance coefficient, n represents the number of initial performance values ​​in the initial performance value set, and w i H represents the i-th weight value in the weight set. i This represents the i-th initial performance value in the initial performance value set.

[0164] It is clear that the updated material cost refers to the cost of the material ratio corresponding to the updated gene vector. The cost coefficient refers to an artificially set value representing the importance of material cost in the toilet seat manufacturing process, and the performance coefficient refers to an artificially set value representing the importance of material performance in the toilet seat manufacturing process.

[0165] Importantly, determining the update material cost based on the updated gene vector includes: first, identifying the material corresponding to each vector element in the updated gene vector and obtaining the unit cost (price per gram or kilogram) of these materials to obtain a unit cost group; then, weighting and summing the unit cost groups according to the proportions corresponding to the updated gene vectors to obtain the total material cost, as shown in the specific formula below: Among them, F z Let m represent the total material cost, m represent the quantity of unit cost in the unit cost group (i.e., the vector dimension for updating the gene vector), and f represent the total material cost. k B represents the k-th unit cost in the unit cost group. k This indicates updating the k-th vector element in the gene vector. After obtaining the total material cost, since there is a difference in dimensions between the total material cost and the initial performance value, it is necessary to normalize the total material cost. The Sigmoid function can be introduced to normalize the total material cost, with the specific formula: F = Sigmoid(F z ).

[0166] S7. Take the optimal material ratio set as the initial material ratio set, and return to the step of preparing the test toilet seat set according to the initial material ratio set, until the number of times the return step is not less than the preset number threshold.

[0167] It should be explained that the number of times the return step is executed refers to the number of times the step of preparing the test toilet seat set based on the initial material ratio set is executed. The threshold number is a human-set constant, for example, 10 times. Since the results of the genetic optimization algorithm may differ from the actual results, after each genetic optimization, it is necessary to perform actual calibration based on the optimal material ratio set obtained after this genetic optimization. By repeating the above steps, the final candidate material ratio set can be made to have high accuracy.

[0168] S8. Record the optimal material ratio set in the last return step as the candidate material ratio set, and obtain the target material ratio based on the candidate material ratio set.

[0169] It is clear that the candidate material ratio set obtained after multiple genetic optimizations and practical calibrations can balance antibacterial properties, flame retardant properties, and cost. This ensures that the toilet seat material prepared based on this candidate material ratio set meets both antibacterial and flame retardant requirements while maintaining a controllable unit cost. The candidate material ratio corresponding to the candidate material ratio with the highest fitness value of the target material ratio index is defined as follows.

[0170] To address the problems described in the background section, this invention first searches for antibacterial and flame-retardant materials based on a materials text database to obtain a target materials dataset. This step transforms a large amount of literature data into a target materials dataset with practical application value, providing data support for subsequent material screening and optimization. Then, the optimal material group is identified within the target materials dataset. This step comprehensively considers material performance and cost, effectively identifying the optimal combination of antibacterial and flame-retardant materials. This not only ensures that the antibacterial and flame-retardant performance of the materials is optimal but also takes cost-effectiveness into account. Furthermore, based on the comprehensive performance value set and the initial material ratio set, the genetic population is iteratively processed to obtain the optimal material ratio set. This step introduces a genetic algorithm to optimize the material ratio, comprehensively considering material performance and cost, and simulating the process of natural selection. This process involves progressively selecting the optimal material ratio scheme, thus avoiding the bias of manual judgment based on experience and minimizing trial and error, thereby improving the accuracy and efficiency of material ratio formulation. Finally, the optimal material ratio set is used as the initial material ratio set, and the above genetic optimization steps are repeated. Through multiple iterations, the material ratio is continuously adjusted towards a better direction, gradually approaching the theoretically optimal ratio, improving the accuracy and reliability of the final material ratio, and verifying the effectiveness of the genetic algorithm optimization results. Finally, this scheme obtains the target material ratio after multiple iterations of optimization. This target material ratio can effectively control costs while ensuring antibacterial and flame-retardant properties, achieving a balance between performance and cost, and ultimately producing antibacterial and flame-retardant polypropylene toilet seat material that meets practical application requirements. Therefore, this invention can improve the accuracy of the formulation process in the preparation of antibacterial and flame-retardant toilet seats and reduce the cost of toilet seat production.

[0171] like Figure 2 The diagram shown is a functional block diagram of a preparation system for antibacterial and flame-retardant polypropylene toilet seat material provided in an embodiment of the present invention.

[0172] The antibacterial and flame-retardant toilet seat polypropylene material preparation system 100 of the present invention can be installed in an electronic device. Depending on the functions implemented, the antibacterial and flame-retardant toilet seat polypropylene material preparation system 100 may include a target material selection module 101, an initial ratio setting module 102, an optimal ratio optimization module 103, and a target ratio determination module 104. The module described in this invention can also be called a unit, referring to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0173] The target material selection module 101 is used to receive material preparation instructions, determine a material text library based on the material preparation instructions, wherein the material text library includes multiple material articles, and each material article includes one or more candidate materials, and search for antibacterial and flame retardant materials based on the material text library to obtain a target material dataset, wherein the target material dataset includes multiple target material data, and the target material data includes: target material and target performance value, the target performance value includes: target antibacterial performance value and target flame retardant performance value;

[0174] The initial ratio setting module 102 is used to identify the optimal material group in the target material dataset, wherein the optimal material group includes: the optimal antibacterial material and the optimal flame retardant material. Based on the optimal antibacterial material, the optimal flame retardant material and the preset polypropylene material, an initial material ratio set is set. According to the initial material ratio set, a test toilet seat set is prepared, wherein the test toilet seats in the test toilet seat set correspond one-to-one with the initial material ratios in the initial material ratio set.

[0175] The optimal ratio optimization module 103 is used to perform performance tests on all test toilet seats in the test toilet seat set to obtain a comprehensive performance value set, wherein the comprehensive performance value includes antibacterial performance value and flame retardant performance value. Based on the comprehensive performance value set and the initial material ratio set, the preset genetic population is iterated to obtain the optimal material ratio set.

[0176] The target ratio determination module 104 is used to take the optimal material ratio set as the initial material ratio set and return to the step of preparing the test toilet seat set according to the initial material ratio set until the number of return steps is not less than a preset number threshold. The optimal material ratio set in the last return step is recorded as the candidate material ratio set, and the target material ratio is obtained based on the candidate material ratio set.

[0177] In detail, the modules in the antibacterial and flame-retardant toilet seat polypropylene material preparation system 100 described in this embodiment of the invention employ the same methods as described above during use. Figure 1 The preparation method of the antibacterial and flame-retardant polypropylene toilet seat material described herein is the same as that used in this paper, and it can produce the same technical effect, so it will not be repeated here.

[0178] like Figure 3 The diagram shown is a schematic representation of an electronic device for implementing a method for preparing antibacterial and flame-retardant polypropylene toilet seat material according to an embodiment of the present invention.

[0179] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a method program for preparing antibacterial and flame-retardant polypropylene toilet seat material.

[0180] The memory 11 includes at least one type of readable storage medium, including flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as the portable hard drive of the electronic device 1. In other embodiments, the memory 11 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device 1. Furthermore, the memory 11 includes both internal storage units and external storage devices of the electronic device 1. The memory 11 can be used not only to store application software and various types of data installed on the electronic device 1, such as the code of the preparation method program for antibacterial and flame-retardant polypropylene toilet seat material, but also to temporarily store data that has been output or will be output.

[0181] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., a method for preparing antibacterial and flame-retardant polypropylene toilet seat material) and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0182] The bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize the connection and communication between the memory 11 and at least one processor 10, etc.

[0183] Figure 3 Only electronic devices with components are shown; those skilled in the art will understand that... Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0184] For example, although not shown, the electronic device 1 may also include a power supply (such as a battery) to power various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management system, thereby enabling functions such as charging management, discharging management, and power consumption management through the power management system. The power supply may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0185] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish communication connections between the electronic device 1 and other electronic devices.

[0186] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired or wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device 1 and to display a visual user interface.

[0187] The preparation method program for the antibacterial and flame-retardant polypropylene toilet seat material stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When run in the processor 10, it can achieve the following:

[0188] Receive material preparation instructions, determine a material text library based on the material preparation instructions, wherein the material text library includes multiple material articles, and each material article includes one or more candidate materials;

[0189] Based on the material text library, antibacterial and flame-retardant materials are searched to obtain a target material dataset. The target material dataset includes multiple target material data, and the target material data includes: target material and target performance value. The target performance value includes: target antibacterial performance value and target flame-retardant performance value.

[0190] Identify the optimal material group in the target material dataset, where the optimal material group includes: the optimal antibacterial material and the optimal flame retardant material;

[0191] Based on the optimal antibacterial material, the optimal flame retardant material, and the preset polypropylene material, an initial material ratio set is set. According to the initial material ratio set, a set of test toilet seats is prepared. The test toilet seats in the set of test toilet seats correspond one-to-one with the initial material ratios in the initial material ratio set.

[0192] Performance tests were conducted on all toilet seats in the test set to obtain a comprehensive performance value set, which includes antibacterial performance value and flame retardant performance value.

[0193] Based on the comprehensive performance value set and the initial material ratio set, the preset genetic population is iterated to obtain the optimal material ratio set;

[0194] The optimal material ratio set is used as the initial material ratio set, and the step of preparing the test toilet seat set based on the initial material ratio set is returned until the number of times the return step is not less than a preset number threshold.

[0195] The optimal material ratio set in the last return step is recorded as the candidate material ratio set, and the target material ratio is obtained based on the candidate material ratio set.

[0196] Specifically, the processor 10's implementation method for the above instructions can be found in [reference needed]. Figures 1 to 3 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0197] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or system capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0198] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following:

[0199] Receive material preparation instructions, determine a material text library based on the material preparation instructions, wherein the material text library includes multiple material articles, and each material article includes one or more candidate materials;

[0200] Based on the material text library, antibacterial and flame-retardant materials are searched to obtain a target material dataset. The target material dataset includes multiple target material data, and the target material data includes: target material and target performance value. The target performance value includes: target antibacterial performance value and target flame-retardant performance value.

[0201] Identify the optimal material group in the target material dataset, where the optimal material group includes: the optimal antibacterial material and the optimal flame retardant material;

[0202] Based on the optimal antibacterial material, the optimal flame retardant material, and the preset polypropylene material, an initial material ratio set is set. According to the initial material ratio set, a set of test toilet seats is prepared. The test toilet seats in the set of test toilet seats correspond one-to-one with the initial material ratios in the initial material ratio set.

[0203] Performance tests were conducted on all toilet seats in the test set to obtain a comprehensive performance value set, which includes antibacterial performance value and flame retardant performance value.

[0204] Based on the comprehensive performance value set and the initial material ratio set, the preset genetic population is iterated to obtain the optimal material ratio set;

[0205] The optimal material ratio set is used as the initial material ratio set, and the step of preparing the test toilet seat set based on the initial material ratio set is returned until the number of times the return step is not less than a preset number threshold.

[0206] The optimal material ratio set in the last return step is recorded as the candidate material ratio set, and the target material ratio is obtained based on the candidate material ratio set.

[0207] In the embodiments provided by this invention, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and actual implementations may have other classification methods.

[0208] The modules described as separate components may or may not be physically separate. The components shown as modules 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.

[0209] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0210] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0211] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for preparing an antibacterial and flame-retardant polypropylene material for toilet seats, characterized in that, The method includes: Receive material preparation instructions, determine a material text library based on the material preparation instructions, wherein the material text library includes multiple material articles, and each material article includes one or more candidate materials; Based on the material text library, antibacterial and flame-retardant materials are searched to obtain a target material dataset. The target material dataset includes multiple target material data, and the target material data includes: target material and target performance value. The target performance value includes: target antibacterial performance value and target flame-retardant performance value. Identify the optimal material group in the target material dataset, where the optimal material group includes: the optimal antibacterial material and the optimal flame retardant material; Based on the optimal antibacterial material, the optimal flame retardant material, and the preset polypropylene material, an initial material ratio set is set. According to the initial material ratio set, a set of test toilet seats is prepared. The test toilet seats in the set of test toilet seats correspond one-to-one with the initial material ratios in the initial material ratio set. Performance tests were conducted on all toilet seats in the test set to obtain a comprehensive performance value set, which includes antibacterial performance value and flame retardant performance value. Based on the comprehensive performance value set and the initial material ratio set, the preset genetic population is iterated to obtain the optimal material ratio set; The optimal material ratio set is used as the initial material ratio set, and the step of preparing the test toilet seat set based on the initial material ratio set is returned until the number of times the return step is not less than a preset number threshold. The optimal material ratio set in the last return step is recorded as the candidate material ratio set, and the target material ratio is obtained based on the candidate material ratio set.

2. The preparation method of the antibacterial and flame-retardant polypropylene toilet seat material as described in claim 1, characterized in that, The search for antibacterial and flame-retardant materials based on a materials text library yields a target materials dataset, including: Material articles are extracted sequentially from the material text library, the candidate material groups contained in the material articles are identified, and based on the material articles, the performance of each candidate material in the candidate material group is analyzed to obtain a material performance value group. The material property value group is matched with the candidate material group to obtain the candidate material data group; Summarize the candidate material data groups corresponding to each material text in the material text library to obtain the candidate material data group set; Data filtering is performed on the dataset of materials to be selected to obtain the target material dataset.

3. The preparation method of the antibacterial and flame-retardant polypropylene toilet seat material as described in claim 2, characterized in that, Based on the materials article, performance analysis is performed on each candidate material in the selected materials group to obtain a set of material performance values, including: Selectable materials are extracted sequentially from the candidate material group, and performance experiments and comparative experiments of the candidate materials are identified in the material article. The candidate materials include: candidate flame retardant materials and candidate antibacterial materials, and the performance tests include the candidate materials. Based on performance and comparative experiments, the material performance values ​​of the candidate materials are calculated. The material performance values ​​include flame retardant performance values ​​and antibacterial performance values, and the flame retardant performance values ​​and antibacterial performance values ​​correspond to the candidate flame retardant material and the candidate antibacterial material, respectively. Summarize the material property values ​​to obtain a set of material property values.

4. The preparation method of the antibacterial and flame-retardant polypropylene toilet seat material as described in claim 3, characterized in that, The data filtering of the candidate material dataset to obtain the target material dataset includes: Determine the original material set in the candidate material data set, and perform the following operation on each original material in the original material set: Extract the original performance value group corresponding to the original material from the candidate material data group set, wherein the original performance values ​​in the original performance value group come from different candidate material data groups in the candidate material data group set; Calculate the performance standard deviation based on the original performance value set; If the performance standard deviation is not greater than the preset standard deviation threshold, the original material is recorded as the target material, and the mean value is calculated based on the original performance value group to obtain the target performance value. The target material and target performance values ​​are matched to obtain the target material data; The target material dataset is obtained by summarizing the target material data corresponding to the target material in the original material set.

5. The preparation method of the antibacterial and flame-retardant polypropylene toilet seat material as described in claim 4, characterized in that, The process of identifying the optimal material set in the target material dataset includes: The target material dataset is divided into data groups of similar materials, including: similar antibacterial data group and similar flame retardant data group. Extract similar material data groups sequentially from the similar material data group set. The similar material data in the similar material data group includes: similar material and similar performance values. Perform the following operation on each data point of the same material in the same data group: Determine the unit material cost of similar materials in the data of similar materials. If the unit material cost is not greater than the preset controllable cost threshold, calculate the material cost-effectiveness based on the similar performance value and the unit material cost. The material cost-effectiveness is the ratio of the similar performance value to the unit material cost. Summarize the cost-effectiveness of materials to obtain a material cost-effectiveness group, identify the optimal cost-effectiveness in the material cost-effectiveness group, and determine the optimal material corresponding to the optimal cost-effectiveness. The optimal material group is obtained by summarizing the data of similar material groups and finding the best material group.

6. The method for preparing the antibacterial and flame-retardant polypropylene toilet seat material as described in claim 5, characterized in that, The step of iterating through a preset genetic population based on a comprehensive performance value set and an initial material ratio set to obtain an optimal material ratio set includes: The genetic population is initialized based on the initial material ratio set and the comprehensive performance value set to obtain an initial genetic population, wherein the initial genetic population includes multiple initial chromosomes, and the gene sequences contained in the initial chromosomes correspond to the initial material ratios. Genetic iteration is performed on the initial genetic population to obtain the optimal genetic population. The genetic iteration includes gene selection, gene crossover, and gene mutation. Determine the optimal set of chromosomes in the optimal genetic population, and identify the optimal gene sequence contained in each optimal chromosome in the optimal chromosome set to obtain the optimal gene sequence set; The optimal material ratio set was determined based on the optimal gene sequence set.

7. The preparation method of the antibacterial and flame-retardant polypropylene toilet seat material as described in claim 6, characterized in that, The process of performing genetic iteration on the initial genetic population to obtain the optimal genetic population includes: Determine the initial fitness set corresponding to multiple initial chromosomes in the initial genetic population; Based on the initial fitness set, gene selection, gene crossover, and gene mutation are sequentially performed on the initial genetic population to obtain an updated genetic population, which includes multiple updated chromosomes. The updated chromosomes were extracted sequentially from multiple updated chromosomes in the updated genetic population to determine the updated gene sequences in the updated chromosomes. Construct updated gene vectors based on updated gene sequences; Based on the comprehensive performance value set and the initial material ratio set, the update fitness of the updated gene vector is calculated; The update fitness set is obtained by summing the update fitness of each update chromosome in multiple update chromosomes; The updated fitness set and the updated genetic population are used as the initial fitness set and the initial genetic population, respectively. The steps of gene selection, gene crossover and gene mutation are performed on the initial genetic population according to the initial fitness set in sequence until the number of the return steps is not less than the preset standard number of iterations. The updated genetic population from the last return step is taken as the optimal genetic population.

8. The method for preparing the antibacterial and flame-retardant polypropylene toilet seat material as described in claim 7, characterized in that, The calculation of the update fitness of the updated gene vector based on the comprehensive performance value set and the initial material ratio set includes: The initial material proportions are extracted sequentially from the initial material proportion set, and the initial performance values ​​corresponding to the initial material proportions are identified in the comprehensive performance value set. Construct an initial proportion vector based on the initial material proportions; Based on the initial matching vector and the updated gene vector, the matching weight value is calculated, wherein the matching weight value is expressed as: Where w represents the weighting ratio, exp(*) represents an exponential function with the natural constant as the base, and Q c G represents the initial matching vector. x This indicates updating the gene vector, where d represents the vector dimension of the initial matching vector or the vector dimension of the updated gene vector. Summarize the proportion weight value and initial performance value corresponding to each initial material proportion in the initial material proportion set to obtain the proportion weight value set and the initial performance value set; Based on the set of matching weight values ​​and the set of initial performance values, a cost-effectiveness analysis is performed on the updated gene vector to obtain the updated fitness.

9. The method for preparing the antibacterial and flame-retardant polypropylene toilet seat material as described in claim 8, characterized in that, The method of performing a cost-effectiveness analysis on the updated gene vector based on the matching weight value set and the initial performance value set to obtain the updated fitness includes: Determine the cost of the updated materials based on the updated gene vector; The update fitness is calculated based on the updated material cost, the set of proportion weight values, and the set of initial performance values, where the update fitness is expressed as: Where P represents the update fitness, α1 represents the preset cost coefficient, F represents the update material cost, α2 represents the preset performance coefficient, n represents the number of initial performance values ​​in the initial performance value set, and w i H represents the i-th weight value in the weight set. i This represents the i-th initial performance value in the initial performance value set.

10. A preparation system for an antibacterial and flame-retardant polypropylene toilet seat material, characterized in that, The system includes: The target material selection module is used to receive material preparation instructions, determine a material text library based on the material preparation instructions, wherein the material text library includes multiple material articles, and each material article includes one or more candidate materials, and searches for antibacterial and flame retardant materials based on the material text library to obtain a target material dataset, wherein the target material dataset includes multiple target material data, and the target material data includes: target material and target performance value, the target performance value includes: target antibacterial performance value and target flame retardant performance value; The initial ratio setting module is used to identify the optimal material group in the target material dataset. The optimal material group includes the optimal antibacterial material and the optimal flame retardant material. Based on the optimal antibacterial material, the optimal flame retardant material and the preset polypropylene material, an initial material ratio set is set. According to the initial material ratio set, a test toilet seat set is prepared. The test toilet seats in the test toilet seat set correspond one-to-one with the initial material ratios in the initial material ratio set. The optimal material ratio optimization module is used to perform performance tests on all test toilet seats in the test toilet seat set to obtain a comprehensive performance value set, which includes antibacterial performance value and flame retardant performance value. Based on the comprehensive performance value set and the initial material ratio set, the preset genetic population is iterated to obtain the optimal material ratio set. The target material ratio determination module is used to take the optimal material ratio set as the initial material ratio set and return to the step of preparing the test toilet seat set according to the initial material ratio set until the number of return steps is not less than a preset number threshold. The optimal material ratio set in the last return step is recorded as the candidate material ratio set, and the target material ratio is obtained based on the candidate material ratio set.

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