Intelligent recommendation method and system for production formula of light control film

Abstract

This invention discloses an intelligent recommendation method and system for dimming film production formulations, relating to the field of film production technology. The method includes the following steps: collecting historical production formulation data of the target dimming film; collecting formulation characteristic data related to the target dimming film; wherein the formulation characteristic data includes characteristic status data, raw material compatibility data, and performance data; processing and analyzing the formulation characteristic data and historical production formulation data to obtain a preliminary recommended formulation; obtaining a pre-treatment formulation for the target dimming film based on the formulation characteristic data; if the formulation difference characteristic is less than or equal to a formulation difference judgment threshold, then the preliminary recommended formulation is used as the actual predicted formulation; after determining whether the actual predicted formulation meets a preset formulation performance threshold, the formulation recommendation result is output, thereby improving product quality stability.

Description

Technical Field

[0001] This invention relates to the field of thin film production technology, and more specifically, to a method and system for intelligently recommending dimming film production formulations. Background Technology

[0002] Traditional formulations for dimming films rely heavily on the experience and repeated experiments of R&D personnel, resulting in long development cycles, high trial-and-error costs, and insufficient formulation stability. As the application scenarios for dimming films continue to expand, the requirements for product performance, raw material compatibility, and production efficiency are constantly increasing. Traditional formulation development models are no longer sufficient to meet the demands for high precision and high efficiency. Different target dimming films have varying characteristic constraints, performance indicators, and raw material compatibility requirements. Directly using generic formulations can easily lead to raw material incompatibility and substandard performance, thus affecting product quality and production stability. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the purpose of this invention is to provide an intelligent recommendation method and system for dimming film production formula.

[0004] To achieve the above objectives, the present invention provides the following technical solution: A smart recommendation method for dimming film production formulations, comprising the following steps: Collect historical production formula data of the target dimming film; collect formula characteristic data related to the target dimming film; wherein, the formula characteristic data includes characteristic condition data, raw material compatibility data and performance data; The formula feature data and historical production formula data are processed and analyzed to obtain a preliminary recommended formula; The pretreatment formulation for the target dimming film is obtained based on the formulation characteristic data; the formulation difference characteristics are obtained based on the pretreatment formulation and the preliminary recommended formulation. If the formula difference characteristics are greater than the preset formula difference judgment threshold, the raw material addition characteristics and raw material compatibility between the preliminary recommended formula and the pre-treated formula are used to perform raw material adaptation error compensation processing on the preliminary recommended formula to obtain the actual predicted formula. If the formula difference characteristics are less than or equal to the formula difference judgment threshold, the preliminary recommended formula will be used as the actual predicted formula. After determining whether the actual predicted formula meets the preset formula performance threshold, the formula recommendation result is output.

[0005] Preferably, the preliminary recommended formula is obtained by processing and analyzing the formula characteristic data and historical production formula data, specifically including the following steps: Historical production formula data are categorized into formula archives based on raw material type, proportion range, and production performance. The formulation feature data of the target dimming film is matched with the category features of the formulation archive, and the historical formulations that match the category features are used as the basic reference set. The characteristic data of the target dimming film, the basic reference set, and the target performance data are processed to obtain a combination scheme that meets the basic performance requirements; A preliminary formula group was obtained by integrating the raw material proportions of the combination scheme; A preliminary recommended formulation was obtained by performing consistency verification on the initial formulation group to adapt to the characteristics of the target dimming film.

[0006] Preferably, the characteristic data of the target dimming film, the basic reference set, and the target performance data are processed to obtain a combination scheme that meets the basic performance requirements, specifically including the following steps: A raw material combination correlation map was established based on the combination rules and ratio relationships among different raw materials in the basic reference collection formula. Based on the characteristic data of the target dimming film, a suitable raw material combination was selected from the raw material matching correlation spectrum; Based on the target performance data requirements, the raw material combinations are matched and verified to obtain a combination scheme that meets the basic performance requirements.

[0007] Preferably, the pretreatment formula for the target dimming film is obtained based on the formula characteristic data, specifically including the following steps: The target feature data is obtained by hierarchically screening the formula feature data; Determine the basic raw material composition of the target dimming film based on target feature data; The initial addition ratio of each basic raw material component is determined based on the performance data of the target characteristic data; The initial addition ratio is calibrated based on the compatibility range of each raw material in the raw material compatibility data to obtain the raw material calibration addition ratio; The pretreatment formula for the target dimming film is obtained by processing the raw material calibration addition ratio, basic raw material components, and compatibility parameters of raw material adaptation data.

[0008] Preferably, the pretreatment formula for the target dimming film is obtained by processing the raw material calibration addition ratio, basic raw material components, and compatibility parameters of the raw material adaptation data, specifically including the following steps: The raw material calibration addition ratio is combined with the basic raw material components to form the initial pretreatment formula; Determine whether there are any potential compatibility issues with the initial pretreatment formula based on the compatibility parameters of the raw material compatibility data; if there are potential compatibility issues, adjust the addition ratio of the corresponding raw material components in the initial pretreatment formula until the potential compatibility issues are eliminated. The pretreatment formula for the target dimming film is obtained by comparing and verifying the initial pretreatment formula with the target feature data.

[0009] Preferably, the method for obtaining the formula difference characteristics based on the pretreatment formula and the initially recommended formula specifically includes the following steps: Extract the target components of the pretreatment formula and the preliminary recommended formula; wherein, the target components include the raw material composition, the compatibility characteristics of each raw material, the raw material addition process, and the process requirements. The compatibility of the raw materials in the pretreatment formula with the initially recommended formula is determined based on the target components. Based on the compatibility, determine whether the raw materials of the initially recommended formula meet the raw material compatibility requirements of the pretreatment formula, and mark the raw materials that do not meet the raw material compatibility requirements as raw materials to be determined. Determine the differences in the addition process of the raw material to be determined in the pretreatment formula and the preliminary recommended formula. Determine the raw material compatibility difference based on whether the difference in the addition process affects the degree of raw material mismatch. Clarify the process differences between the pretreatment formula and the preliminary recommended formula according to the process requirements. Integrate raw material compatibility differences with process differences to form difference information; The influence of the difference information on formulation adaptation error compensation and film performance is used to obtain the formulation difference characteristics.

[0010] Preferably, based on the raw material addition characteristics and raw material compatibility between the preliminary recommended formula and the pretreatment formula, the raw material adaptation error compensation process is performed on the preliminary recommended formula to obtain the actual predicted formula, specifically including the following steps: The raw material addition status of the preliminary recommended formula and the pretreatment formula were obtained to obtain formula addition feature one and formula addition feature two respectively; The raw material addition range is processed based on the correspondence between formula addition feature one and formula addition feature two to obtain the current raw material range feature data; Determine the first formulation error influence factor associated with the raw material amplitude characteristic data, and obtain the first formulation error characteristic based on the first formulation error influence factor and the current raw material amplitude characteristic data; The pretreatment compatibility of the preliminary recommended formula and the pretreatment formula during raw material compounding is processed to obtain the second formula error characteristic; Based on the error characteristics of the first and second formulations, the raw material matching error is compensated for in the preliminary recommended formulation to obtain the actual predicted formulation.

[0011] Preferably, the pretreatment compatibility generated during the raw material compounding of the preliminary recommended formula and the pretreatment formula is processed to obtain the second formula error characteristic, specifically including the following steps: The pretreatment compatibility of the preliminary recommended formula and the pretreatment formula was collected during the raw material compounding process. The second formulation error influence factor associated with pretreatment compatibility is determined, and the second formulation error characteristic is obtained based on the second formulation error influence factor and the influence of the current formulation error increase or decrease on compatibility.

[0012] Preferably, after determining whether the actual predicted formula meets the preset formula performance threshold, the formula recommendation result is output, specifically including the following steps: The performance indicators of the actual predicted formula are compared with the preset formula performance thresholds one by one to obtain the performance deviation parameters that do not meet the standards. The actual predicted formulation is modified by performing performance correction processing on the actual predicted formulation based on the performance deviation parameters and the actual application scenario requirements of the target dimming film; Collect performance indicators of the modified formulation to confirm whether the modified formulation meets the preset formulation performance threshold; output the modified formulation that meets the formulation performance threshold as the formulation recommendation result.

[0013] A smart recommendation system for dimming film production formulations includes: Data Acquisition Module: Acquires historical production formula data of the target dimming film; acquires formula characteristic data related to the target dimming film; wherein, the formula characteristic data includes characteristic condition data, raw material compatibility data, and performance data; Analysis module: Processes and analyzes formula feature data with historical production formula data to obtain preliminary recommended formulas; Processing module: Obtains the pretreatment formula of the target dimming film based on the formula feature data; obtains the formula difference characteristics based on the pretreatment formula and the preliminary recommended formula; Compensation module: If the formula difference characteristics are greater than the preset formula difference judgment threshold, the raw material addition characteristics and raw material compatibility between the preliminary recommended formula and the pre-treated formula are used to perform raw material adaptation error compensation processing on the preliminary recommended formula to obtain the actual predicted formula. Determination module: If the formula difference characteristic is less than or equal to the formula difference judgment threshold, the preliminary recommended formula will be used as the actual predicted formula; Output module: After determining whether the actual predicted formula meets the preset formula performance threshold, output the formula recommendation result.

[0014] Compared with the prior art, the present invention has the following beneficial effects: This invention integrates historical production formula data with the formula characteristic data of the target dimming film to achieve intelligent analysis and precise recommendation of formulas. By constructing a raw material matching correlation map and combining it with feature constraints to screen suitable combinations, it ensures the rationality of raw material selection and the achievement of basic performance standards, avoiding the waste of resources caused by blind trial and error. The pre-processed formula generated based on the formula characteristic data undergoes graded screening, raw material calibration, and compatibility risk elimination to ensure formula stability and production adaptability. By comparing the differences between the pre-processed formula and the initially recommended formula and setting reasonable judgment thresholds for dynamic error compensation, the accuracy and practicality of the formula are improved. When the differences are within the threshold range, the initially recommended formula is adopted; when they exceed the threshold, precise adjustments are made through raw material adaptation error compensation to ensure the formula meets production requirements. Finally, performance threshold verification ensures that the output formula meets preset indicators, effectively reducing the production defect rate, improving product quality stability, and comprehensively enhancing the intelligence level and production efficiency of dimming film production formula development. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating an intelligent recommendation method for dimming film production formulations provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of a module for an intelligent recommendation system for dimming film production formulations provided in an embodiment of the present invention. Detailed Implementation

[0016] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0017] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0018] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0019] Reference Figures 1-2 As shown.

[0020] The embodiments further illustrate the intelligent recommendation method and system for dimming film production formulation proposed in this invention.

[0021] A smart recommendation method for dimming film production formulations, comprising the following steps: Collect historical production formula data for the target dimming film; collect formula characteristic data related to the target dimming film; among which, the formula characteristic data includes characteristic condition data, raw material compatibility data, and performance data; The formula feature data and historical production formula data are processed and analyzed to obtain a preliminary recommended formula; The pretreatment formulation for the target dimming film is obtained based on the formulation characteristic data; the formulation difference characteristics are obtained based on the pretreatment formulation and the preliminary recommended formulation. If the formula difference characteristics are greater than the preset formula difference judgment threshold, the raw material addition characteristics and raw material compatibility between the preliminary recommended formula and the pre-treated formula are used to perform raw material adaptation error compensation processing on the preliminary recommended formula to obtain the actual predicted formula. If the formula difference characteristics are less than or equal to the formula difference judgment threshold, the preliminary recommended formula will be used as the actual predicted formula. First, the pre-treated formula and the initially recommended formula are compared to obtain the formula difference characteristics. The formula difference characteristics quantify the degree of deviation between the pre-treated formula and the initially recommended formula in terms of raw material composition, compatibility characteristics, addition process and process requirements. At the same time, a formula difference judgment threshold is set. The formula difference judgment threshold is determined based on the performance tolerance of the dimming film and the actual production needs. The formula difference judgment threshold is used to determine whether the deviation is within an acceptable range.

[0022] The formula difference characteristics are compared numerically with a threshold. If the formula difference characteristics are less than or equal to the threshold, it indicates that the deviation between the two formulas is within the allowable range. The initially recommended formula already meets the performance and production requirements of the target dimming film, and no additional raw material adaptation error compensation is needed. It can be directly used as the final actual predicted formula in the subsequent performance verification stage. For example, if the formula difference judgment threshold is set to 6, and the formula difference characteristic is 4, since 4 is less than 6, the judgment condition is met. Therefore, the initially recommended formula is used as the actual predicted formula, and there is no need to adjust the raw material ratio and process parameters.

[0023] After determining whether the actual predicted formula meets the preset formula performance threshold, the formula recommendation result is output.

[0024] The preliminary recommended formula is obtained by processing and analyzing the formula feature data and historical production formula data, specifically including the following steps: Historical production formula data are categorized into formula archives based on raw material type, proportion range, and production performance. The formulation feature data of the target dimming film is matched with the category features of the formulation archive, and the historical formulations that match the category features are used as the basic reference set. Historical production formula data are divided into different categories according to raw material type, ratio range, and production performance indicators, resulting in several formula categories. Each formula category corresponds to a basic formula scheme. Formulas with the same or similar raw material type, ratio range, and production performance are grouped into one category to form a set of classified formula categories.

[0025] The formulation feature data of the target dimming film is matched with the features of each category in the formulation category set. The matching process is based on the indicators in the formulation feature data and compared with the features corresponding to each formulation category. The matching degree = 1 - |target feature value - category feature value| / category feature value. When the matching degree is greater than or equal to the set matching threshold, it is determined that the category feature matches the target formulation feature. The historical formulations that match the category feature are used as the basic reference set.

[0026] The characteristic data of the target dimming film, the basic reference set, and the target performance data are processed to obtain a combined solution that meets the basic performance requirements. The specific steps include: A raw material combination correlation map was established based on the combination rules and ratio relationships among different raw materials in the basic reference collection formula. Based on the characteristic data of the target dimming film, a suitable raw material combination was selected from the raw material matching correlation spectrum; Based on the target performance data requirements, the performance matching and verification of the raw material combination are carried out to obtain a combination scheme that meets the basic performance requirements. A preliminary formula group was obtained by integrating the raw material proportions of the combination scheme; A preliminary recommended formulation was obtained by performing consistency verification on the initial formulation group to adapt to the characteristics of the target dimming film.

[0027] A raw material matching correlation map is established based on the matching rules and correlations among different raw materials in the basic experimental data. This map records the compatibility, proportion range, and mutual influence between raw materials. Based on the characteristic constraint data of the target dimming film, suitable raw material combinations are screened from the raw material matching correlation map. The screening process matches the raw material combinations in the correlation map according to the characteristic constraint data, eliminating combinations that do not meet the characteristic constraints and retaining those that meet the basic compatibility requirements. Performance matching verification is performed on the raw material combinations according to the target performance data requirements, resulting in combinations that meet the basic performance needs. Performance matching verification involves calculating the matching degree between the performance indicators corresponding to the raw material combination and the target performance data. If the target performance requirements are met, the combination is retained; otherwise, adjustments are made. Performance matching degree = target performance value - actual performance value. When the performance matching degree is greater than or equal to 0, it is considered to meet the basic performance requirements. The raw material proportions of the combination schemes are integrated to obtain a preliminary formulation group. During the raw material proportion integration process, the addition ratio of each raw material is optimized and adjusted according to the proportion range and performance requirements of the raw material combinations to make the combination scheme more consistent with actual production. The initial formulation group undergoes a consistency check to obtain a recommended formulation that matches the characteristic constraints of the target dimming film. The consistency check will examine whether the proportions of each raw material in the initial formulation group meet the characteristic constraint requirements, and whether there are any process conflicts or performance issues. If the check passes, a recommended formulation is formed; if it fails, the process returns to the raw material proportioning integration step for readjustment until a recommended formulation that meets the requirements is obtained.

[0028] The pretreatment formula for the target dimming film is obtained based on the formula characteristic data, specifically including the following steps: The target feature data is obtained by hierarchically screening the formula feature data; Determine the basic raw material composition of the target dimming film based on target feature data; The initial addition ratio of each basic raw material component is determined based on the performance data of the target characteristic data; The initial addition ratio is calibrated based on the compatibility range of each raw material in the raw material compatibility data to obtain the raw material calibration addition ratio; The pretreatment formula for the target dimming film is obtained by processing the raw material calibration addition ratio, basic raw material components, and compatibility parameters of raw material adaptation data. The specific steps include: The raw material calibration addition ratio is combined with the basic raw material components to form the initial pretreatment formula; Determine whether there are any potential compatibility issues with the initial pretreatment formula based on the compatibility parameters of the raw material compatibility data; if there are potential compatibility issues, adjust the addition ratio of the corresponding raw material components in the initial pretreatment formula until the potential compatibility issues are eliminated. The pretreatment formula for the target dimming film is obtained by comparing and verifying the initial pretreatment formula with the target feature data.

[0029] First, the formulation feature data is graded and screened to extract target feature data that plays a key role in the performance of the target dimming film. The grading and screening process prioritizes features according to their impact on dimming performance, classifying features into core features, important features, and general features. Core feature data is retained first to form the target feature data set. Based on the target feature data, the basic raw material components of the target dimming film are determined. According to the raw material compatibility requirements and performance needs contained in the target feature data, the corresponding basic raw material types are matched to determine the types of basic raw materials that make up the dimming film, thus clarifying the composition of the basic raw material components.

[0030] The initial addition ratio of each basic raw material component is determined based on the performance data of the target characteristic data. The performance data includes requirements for transmittance and response time. The initial addition ratio of each basic raw material is calculated by the correspondence between the performance data and the raw material addition ratio: Initial addition ratio = Performance baseline value × Performance influence coefficient. Here, the performance baseline value is the basic ratio corresponding to the target performance data, and the performance influence coefficient is determined based on the degree of raw material requirement for different performance indicators. The initial addition ratio is calibrated according to the compatibility range of each raw material in the raw material compatibility data to obtain the calibrated addition ratio. The raw material compatibility range defines the reasonable range within which each raw material can be added. The initial addition ratio is compared with the compatibility range; if it exceeds the range, it is adjusted to within the range; if it is within the range, it remains unchanged. The calibration process ensures that the raw material addition ratio meets the production compatibility requirements. The calibrated addition ratio is combined with the basic raw material components to form the initial pretreatment formula. The combination of the basic raw material types and the calibrated addition ratio constitutes the initial raw material proportioning scheme. The compatibility parameters of the raw material compatibility data are used to determine whether there are any potential compatibility issues in the initial pretreatment formula. These compatibility parameters reflect the degree of mutual influence between raw materials. The compatibility degree is calculated as follows: Compatibility degree = 1 - |Raw material 1 compatibility parameter - Raw material 2 compatibility parameter| / Raw material 1 compatibility parameter. If the compatibility degree is less than the compatibility threshold, a potential compatibility issue is identified. If a compatibility issue exists, the addition ratio of the corresponding raw material component in the initial pretreatment formula is adjusted until the issue is eliminated. The adjustment process is based on the optimal ratio range for raw material compatibility to ensure compatibility between the raw materials. The initial pretreatment formula is compared and verified with the target characteristic data to obtain the pretreatment formula for the target dimming film. The comparison and verification checks whether the various indicators of the initial pretreatment formula meet the requirements of the target characteristic data, including raw material type, addition ratio, and performance indicators. If the verification passes, the final pretreatment formula is formed. If it fails, the process returns to the adjustment step for re-optimization until the constraints of the target characteristic data are met.

[0031] Based on the differences between the pretreatment formulation and the initially recommended formulation, the formulation difference characteristics were obtained, specifically including the following steps: Extract the target components of the pretreatment formulation and the preliminary recommended formulation; among which, the target components include the raw material composition, the compatibility characteristics of each raw material, the raw material addition process and the process requirements. The compatibility of the raw materials in the pretreatment formula with the initially recommended formula is determined based on the target components. Based on the compatibility, determine whether the raw materials of the initially recommended formula meet the raw material compatibility requirements of the pretreatment formula, and mark the raw materials that do not meet the raw material compatibility requirements as raw materials to be determined. Determine the differences in the addition process of the raw material to be determined in the pretreatment formula and the preliminary recommended formula. Determine the raw material compatibility difference based on whether the difference in the addition process affects the degree of raw material mismatch. Clarify the process differences between the pretreatment formula and the preliminary recommended formula according to the process requirements. Integrate raw material compatibility differences with process differences to form difference information; The influence of the difference information on formulation adaptation error compensation and film performance is used to obtain the formulation difference characteristics.

[0032] First, the target components of the pretreatment formula and the preliminary recommended formula are extracted. These components include raw material composition, compatibility characteristics of each raw material, raw material addition process, and process requirements. Raw material composition refers to the types and proportions of basic and auxiliary raw materials used in both formulas. Raw material compatibility characteristics include the compatibility and suitability range of the raw materials. The raw material addition process refers to the order and steps of adding each raw material. Process requirements involve the temperature, pressure, and reaction time of the production process. Based on these target components, the compatibility of the raw materials in the pretreatment formula and the preliminary recommended formula is determined. Compatibility = 1 - |Pretreatment Formula Raw Material Compatibility Value - Preliminary Recommended Formula Raw Material Compatibility Value| / Pretreatment Formula Raw Material Compatibility Value. A higher compatibility value indicates better raw material compatibility.

[0033] Based on compatibility, determine whether the raw materials in the initially recommended formulation meet the raw material compatibility requirements of the pretreatment formulation. Raw materials that do not meet the compatibility requirements are marked as raw materials to be determined. Assess the differences in the addition process between the raw materials to be determined and the initially recommended formulation. Determine the degree of impact of the addition process differences on the raw material mismatch to obtain the raw material compatibility difference. The degree of impact can be quantified by a difference weighting coefficient: Raw material compatibility difference = Addition process difference weighting coefficient × Addition process difference value. Clarify the process differences between the pretreatment formulation and the initially recommended formulation according to the process requirements. Process differences include numerical differences in process parameters and differences in process steps. Integrate the raw material compatibility differences and process differences to form difference information, which comprehensively reflects the deviations of the two formulations in terms of both raw materials and processes. Obtain the formulation difference characteristics based on the impact of the difference information on formulation compatibility error compensation and film performance.

[0034] The differences in raw material compatibility and process characteristics are integrated to form difference information. Raw material compatibility reflects the degree of deviation in the addition process of the raw material to be determined in two formulations, while process differences reflect the parameter deviations in the process requirements of the two formulations. The comprehensive difference information is obtained by adding the two together. Subsequently, the influence of this difference information on formulation compatibility error compensation and film performance is used to obtain formulation difference characteristics. The calculation of formulation difference characteristics requires the introduction of an influence coefficient, which is determined based on the degree of influence of the difference information on film performance. Formulation difference characteristic = difference information × performance influence coefficient. This coefficient is obtained through historical data statistics. For example, if the difference information increases by 1 unit and the film transmittance deviation increases by 0.5 percentage points, then the performance influence coefficient is 0.5. For instance, if the raw material compatibility difference is 3 and the process difference is 2, then the difference information = 3 + 2 = 5. If the performance influence coefficient is 0.4, then the formulation difference characteristic = 5 × 0.4 = 2. This value is used to measure the magnitude of the impact of the two formulation deviations on subsequent formulation adjustments and film performance, providing a quantitative basis for error compensation.

[0035] Based on the raw material addition characteristics and raw material compatibility between the preliminary recommended formula and the pretreatment formula, the raw material adaptation error compensation process is applied to the preliminary recommended formula to obtain the actual predicted formula. This process includes the following steps: The raw material addition status of the preliminary recommended formula and the pretreatment formula were obtained to obtain formula addition feature one and formula addition feature two respectively; The raw material addition range is processed based on the correspondence between formula addition feature one and formula addition feature two to obtain the current raw material range feature data; Determine the first formulation error influence factor associated with the raw material amplitude characteristic data, and obtain the first formulation error characteristic based on the first formulation error influence factor and the current raw material amplitude characteristic data; The second formulation error characteristic is obtained by processing the pretreatment compatibility of the preliminary recommended formulation and the pretreatment formulation during raw material compounding. The specific steps include: The pretreatment compatibility of the preliminary recommended formula and the pretreatment formula was collected during the raw material compounding process. Determine the second formulation error influence factor associated with pretreatment compatibility, and obtain the second formulation error characteristic based on the second formulation error influence factor and the influence of the current formulation error increase or decrease on compatibility; Based on the error characteristics of the first and second formulations, the raw material matching error is compensated for in the preliminary recommended formulation to obtain the actual predicted formulation.

[0036] First, the raw material addition status of the preliminary recommended formula and the pretreatment formula are obtained respectively, resulting in formula addition feature one and formula addition feature two. Formula addition feature one includes the addition ratio and addition order of each raw material in the preliminary recommended formula, while formula addition feature two corresponds to the raw material addition data of the pretreatment formula.

[0037] Based on the correspondence between formulation addition feature one and formulation addition feature two, the raw material addition range is processed to obtain the current raw material range feature data. The raw material addition range = |Raw material value of formulation addition feature one - Raw material value of formulation addition feature two|, which reflects the degree of deviation between the two formulations in terms of raw material addition amount.

[0038] Determine the first formula error influence factor associated with the raw material amplitude characteristic data. The first formula error influence factor is obtained from the statistics of historical production data and represents the degree of influence of the raw material addition amplitude on the formula error. Then, based on the first formula error influence factor and the current raw material amplitude characteristic data, the first formula error characteristic is obtained. The first formula error characteristic = raw material amplitude characteristic data × formula error influence factor 1.

[0039] The pretreatment compatibility of the preliminary recommended formula and the pretreatment formula during raw material compounding is processed to obtain the second formula error characteristic. First, the pretreatment compatibility of the two formulas during raw material compounding is collected. The compatibility reflects the stability of the raw materials after mixing. Then, the formula error influence factor II associated with the pretreatment compatibility is determined. This factor reflects the influence of compatibility changes on the formula error. Then, the second formula error characteristic is obtained based on the formula error influence factor II and the influence of the current formula error increase or decrease on the compatibility. The second formula error characteristic = pretreatment compatibility × formula error influence factor II.

[0040] The raw material matching error is compensated based on the first and second formula error characteristics to obtain the actual predicted formula. The compensation process combines the two error characteristics to adjust the raw material ratio and addition parameters of the preliminary recommended formula so that the final formula not only meets the basic framework of the recommended formula but also meets the requirements of the pretreatment formula. For example, when the first formula error characteristic is 2 and the second formula error characteristic is 1, the raw material ratio of the preliminary recommended formula is adjusted by 5% through error compensation to finally obtain the actual predicted formula that meets the requirements.

[0041] After determining whether the actual predicted formula meets the preset formula performance threshold, the formula recommendation result is output, which includes the following steps: The performance indicators of the actual predicted formula are compared with the preset formula performance thresholds one by one to obtain the performance deviation parameters that do not meet the standards. The actual predicted formulation is modified by performing performance correction processing on the actual predicted formulation based on the performance deviation parameters and the actual application scenario requirements of the target dimming film; Collect performance indicators of the modified formulation to confirm whether the modified formulation meets the preset formulation performance threshold; output the modified formulation that meets the formulation performance threshold as the formulation recommendation result.

[0042] First, the performance indicators of the actual predicted formula are compared with the preset formula performance thresholds one by one to obtain the performance deviation parameters that do not meet the standards. The performance deviation parameter = actual performance indicator - preset performance threshold. If the calculation result is greater than 0, it means that the performance indicator does not meet the standards.

[0043] Based on the performance deviation parameters and the actual application requirements of the target dimming film, the actual predicted formula is modified to obtain a corrected formula. The performance correction process optimizes performance by adjusting the raw material ratio or process parameters. For example, when the transmittance deviation parameter is 3, the proportion of transmittance-related raw materials is increased by 2 percentage points to correct it. Then, the performance indicators of the corrected formula are collected to confirm whether the corrected formula meets the preset formula performance threshold. If it does, the corrected formula is used as the recommended formula result and output. If it does not meet the threshold, the process returns to the performance correction step to continue adjustment until all performance indicators meet the preset threshold requirements, and finally, a qualified recommended formula result is output.

[0044] A smart recommendation system for dimming film production formulations includes: Data Acquisition Module: Acquires historical production formula data of the target dimming film; acquires formula characteristic data related to the target dimming film; among which, the formula characteristic data includes characteristic condition data, raw material compatibility data, and performance data; Analysis module: Processes and analyzes formula feature data with historical production formula data to obtain preliminary recommended formulas; Processing module: Obtains the pretreatment formula of the target dimming film based on the formula feature data; obtains the formula difference characteristics based on the pretreatment formula and the preliminary recommended formula; Compensation module: If the formula difference characteristics are greater than the preset formula difference judgment threshold, the raw material addition characteristics and raw material compatibility between the preliminary recommended formula and the pre-treated formula are used to perform raw material adaptation error compensation processing on the preliminary recommended formula to obtain the actual predicted formula. Determination module: If the formula difference characteristic is less than or equal to the formula difference judgment threshold, the preliminary recommended formula will be used as the actual predicted formula; Output module: After determining whether the actual predicted formula meets the preset formula performance threshold, output the formula recommendation result.

[0045] First, historical production formula data of the target dimming film is collected. This data includes the types of raw materials used in different batches of production, the ratio range, and the corresponding production performance indicators. At the same time, formula characteristic data related to the target dimming film is collected. Formula characteristic data includes characteristic status data, raw material compatibility data, and performance data. Characteristic status data reflects the application scenarios and specification requirements of the product. Raw material compatibility data clarifies the compatibility range and compatibility parameters of each raw material. Performance data sets the optical, electrical, and other performance thresholds that the product must achieve.

[0046] The analysis module processes and analyzes the formula feature data and historical production formula data. It establishes a formula archive by classifying historical data into formula categories according to raw material type, ratio range and production performance. Then, it matches the target formula features with the category features of the archive, selects suitable historical formulas as the basic reference set, establishes a raw material matching correlation graph based on the basic reference set, selects suitable raw material combinations according to the target feature data and performs performance matching verification, and integrates them to obtain a combination scheme that meets the basic performance. Finally, it generates a preliminary recommended formula through raw material ratio integration and consistency verification.

[0047] The processing module performs hierarchical screening of formula feature data to obtain target feature data. Based on this, the basic raw material components are determined and the initial addition ratio is set. Combined with the raw material compatibility range calibration, the raw material calibration addition ratio is obtained. This ratio is then combined with the basic raw material components to form an initial pretreatment formula. Based on the raw material compatibility parameters, compatibility risks are eliminated and verified with the target feature data to obtain the pretreatment formula. Then, the target components such as raw material composition, compatibility characteristics, addition process, and process requirements of the pretreatment formula and the preliminary recommended formula are extracted. The raw material compatibility is judged and the raw materials to be determined are marked. The differences in the addition process and process are analyzed, and the difference information is integrated and quantified to obtain the formula difference characteristics.

[0048] If the formula difference feature is greater than the preset formula difference judgment threshold, the raw material addition status of the preliminary recommended formula and the pre-processed formula are obtained respectively to obtain formula addition feature one and formula addition feature two. The raw material addition range is processed according to the correspondence between the two to obtain the current raw material range feature data. The first formula error feature is calculated by combining the associated formula error influence factor one. At the same time, the pre-processing compatibility of raw material compounding is collected. The second formula error feature is calculated by combining the associated formula error influence factor two. Then, the raw material adaptation error compensation is performed on the preliminary recommended formula according to the two error features to obtain the actual predicted formula. If the formula difference characteristics are less than or equal to the formula difference judgment threshold, the preliminary recommended formula will be directly used as the actual predicted formula.

[0049] The output module compares each performance indicator of the actual predicted formula with the preset formula performance threshold item by item to obtain the performance deviation parameters that do not meet the standards. It then performs performance corrections based on application scenario requirements to obtain a corrected formula. The performance indicators of the corrected formula are collected again to confirm whether they meet the thresholds. If they do, it is output as the recommended formula result. For example, when the target dimming film requires a transmittance between 30% and 50%, a formula with a transmittance of 35% from historical formulas is selected as the basic reference set. Suitable raw material combinations are screened using raw material matching correlation maps, and after performance verification, they are integrated to obtain a preliminary recommended formula. If, after calibration, the difference between the two formulas is 5 and the preset threshold is 4, the raw material addition ratio is adjusted through error compensation to make the transmittance of the actual predicted formula reach 42%, meeting the performance threshold requirements, and then the recommended result is output.

[0050] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0051] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0052] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention 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; and these 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 the present invention.

Claims

1. A method for intelligently recommending formulations for dimming films, characterized in that, The method includes the following steps: Collect historical production formula data of the target dimming film; collect formula characteristic data related to the target dimming film; wherein, the formula characteristic data includes characteristic condition data, raw material compatibility data and performance data; The formula feature data and historical production formula data are processed and analyzed to obtain a preliminary recommended formula; The pretreatment formulation for the target dimming film is obtained based on the formulation characteristic data; the formulation difference characteristics are obtained based on the pretreatment formulation and the preliminary recommended formulation. If the formula difference characteristics are greater than the preset formula difference judgment threshold, the raw material addition characteristics and raw material compatibility between the preliminary recommended formula and the pre-treated formula are used to perform raw material adaptation error compensation processing on the preliminary recommended formula to obtain the actual predicted formula. If the formula difference characteristics are less than or equal to the formula difference judgment threshold, the preliminary recommended formula will be used as the actual predicted formula. After determining whether the actual predicted formula meets the preset formula performance threshold, the formula recommendation result is output.

2. The intelligent recommendation method for dimming film production formulation according to claim 1, characterized in that, The preliminary recommended formula is obtained by processing and analyzing the formula feature data and historical production formula data, specifically including the following steps: Historical production formula data are categorized into formula archives based on raw material type, proportion range, and production performance. The formulation feature data of the target dimming film is matched with the category features of the formulation archive, and the historical formulations that match the category features are used as the basic reference set. The characteristic data of the target dimming film, the basic reference set, and the target performance data are processed to obtain a combination scheme that meets the basic performance requirements; A preliminary formula group was obtained by integrating the raw material proportions of the combination scheme; A preliminary recommended formulation was obtained by performing consistency verification on the initial formulation group to adapt to the characteristics of the target dimming film.

3. The intelligent recommendation method for dimming film production formulation according to claim 2, characterized in that, The characteristic data of the target dimming film, the basic reference set, and the target performance data are processed to obtain a combined solution that meets the basic performance requirements. The specific steps include: A raw material combination correlation map was established based on the combination rules and ratio relationships among different raw materials in the basic reference collection formula. Based on the characteristic data of the target dimming film, a suitable raw material combination was selected from the raw material matching correlation spectrum; Based on the target performance data requirements, the raw material combinations are matched and verified to obtain a combination scheme that meets the basic performance requirements.

4. The intelligent recommendation method for dimming film production formulation according to claim 1, characterized in that, The pretreatment formula for the target dimming film is obtained based on the formula characteristic data, specifically including the following steps: The target feature data is obtained by hierarchically screening the formula feature data; Determine the basic raw material composition of the target dimming film based on target feature data; The initial addition ratio of each basic raw material component is determined based on the performance data of the target characteristic data; The initial addition ratio is calibrated based on the compatibility range of each raw material in the raw material compatibility data to obtain the raw material calibration addition ratio; The pretreatment formula for the target dimming film is obtained by processing the raw material calibration addition ratio, basic raw material components, and compatibility parameters of raw material adaptation data.

5. The intelligent recommendation method for dimming film production formulation according to claim 4, characterized in that, The pretreatment formula for the target dimming film is obtained by processing the raw material calibration addition ratio, basic raw material components, and compatibility parameters of raw material adaptation data. The specific steps include: The raw material calibration addition ratio is combined with the basic raw material components to form the initial pretreatment formula; Determine whether there are any potential compatibility issues with the initial pretreatment formula based on the compatibility parameters of the raw material compatibility data; if there are potential compatibility issues, adjust the addition ratio of the corresponding raw material components in the initial pretreatment formula until the potential compatibility issues are eliminated. The pretreatment formula for the target dimming film is obtained by comparing and verifying the initial pretreatment formula with the target feature data.

6. The intelligent recommendation method for a dimming film production formula according to claim 1, characterized in that, Based on the differences between the pretreatment formulation and the initially recommended formulation, the formulation difference characteristics were obtained, specifically including the following steps: Extract the target components of the pretreatment formula and the preliminary recommended formula; wherein, the target components include the raw material composition, the compatibility characteristics of each raw material, the raw material addition process, and the process requirements. The compatibility of the raw materials in the pretreatment formula with the initially recommended formula is determined based on the target components. Based on the compatibility, determine whether the raw materials of the initially recommended formula meet the raw material compatibility requirements of the pretreatment formula, and mark the raw materials that do not meet the raw material compatibility requirements as raw materials to be determined. Determine the differences in the addition process of the raw material to be determined in the pretreatment formula and the preliminary recommended formula. Determine the raw material compatibility difference based on whether the difference in the addition process affects the degree of raw material mismatch. Clarify the process differences between the pretreatment formula and the preliminary recommended formula according to the process requirements. Integrate raw material compatibility differences with process differences to form difference information; The influence of the difference information on formulation adaptation error compensation and film performance is used to obtain the formulation difference characteristics.

7. The intelligent recommendation method for dimming film production formulation according to claim 1, characterized in that, Based on the raw material addition characteristics and raw material compatibility between the preliminary recommended formula and the pretreatment formula, the raw material adaptation error compensation process is applied to the preliminary recommended formula to obtain the actual predicted formula. This process includes the following steps: The raw material addition status of the preliminary recommended formula and the pretreatment formula were obtained to obtain formula addition feature one and formula addition feature two respectively; The raw material addition range is processed based on the correspondence between formula addition feature one and formula addition feature two to obtain the current raw material range feature data; Determine the first formulation error influence factor associated with the raw material amplitude characteristic data, and obtain the first formulation error characteristic based on the first formulation error influence factor and the current raw material amplitude characteristic data; The pretreatment compatibility of the preliminary recommended formula and the pretreatment formula during raw material compounding is processed to obtain the second formula error characteristic; Based on the error characteristics of the first and second formulations, the raw material matching error is compensated for in the preliminary recommended formulation to obtain the actual predicted formulation.

8. The intelligent recommendation method for dimming film production formulation according to claim 7, characterized in that, The second formulation error characteristic is obtained by processing the pretreatment compatibility of the preliminary recommended formulation and the pretreatment formulation during raw material compounding. The specific steps include: The pretreatment compatibility of the preliminary recommended formula and the pretreatment formula was collected during the raw material compounding process. The second formulation error influence factor associated with pretreatment compatibility is determined, and the second formulation error characteristic is obtained based on the second formulation error influence factor and the influence of the current formulation error increase or decrease on compatibility.

9. The intelligent recommendation method for dimming film production formulation according to claim 1, characterized in that, After determining whether the actual predicted formula meets the preset formula performance threshold, the formula recommendation result is output, which includes the following steps: The performance indicators of the actual predicted formula are compared with the preset formula performance thresholds one by one to obtain the performance deviation parameters that do not meet the standards. The actual predicted formulation is modified by performing performance correction processing on the actual predicted formulation based on the performance deviation parameters and the actual application scenario requirements of the target dimming film; Collect performance indicators of the modified formulation to confirm whether the modified formulation meets the preset formulation performance threshold; output the modified formulation that meets the formulation performance threshold as the formulation recommendation result.

10. An intelligent recommendation system for dimming film production formulations, applied to the intelligent recommendation method for dimming film production formulations according to any one of claims 1 to 9, characterized in that, include: Data Acquisition Module: Acquires historical production formula data for the target dimming film; Collect formulation characteristic data related to the target dimming film; wherein, the formulation characteristic data includes characteristic condition data, raw material compatibility data and performance data; Analysis module: Processes and analyzes formula feature data with historical production formula data to obtain preliminary recommended formulas; Processing module: Obtains the pretreatment formula of the target dimming film based on the formula feature data; obtains the formula difference characteristics based on the pretreatment formula and the preliminary recommended formula; Compensation module: If the formula difference characteristics are greater than the preset formula difference judgment threshold, the raw material addition characteristics and raw material compatibility between the preliminary recommended formula and the pre-treated formula are used to perform raw material adaptation error compensation processing on the preliminary recommended formula to obtain the actual predicted formula. Determination module: If the formula difference characteristic is less than or equal to the formula difference judgment threshold, the preliminary recommended formula will be used as the actual predicted formula; Output module: After determining whether the actual predicted formula meets the preset formula performance threshold, output the formula recommendation result.

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