Polypropylene material reinforcing method and platform based on basalt long fibers

Abstract

The invention discloses a basalt long fiber-based polypropylene material reinforcing method and platform, and relates to the technical field of polypropylene materials, the method comprises the following steps: analyzing an existing preparation process, extracting a process parameter set, and dividing the process parameter set into an interface treatment and blending granulation parameter set; performing sample fitting on the two types of parameter sets, constructing a single-variable parameter interval, and defining an interface processing parameter space and a blending granulation parameter space; applying a multi-objective optimization algorithm in the two parameter spaces to respectively obtain a first processing scheme set and a second processing scheme set; randomly combining the two scheme sets to generate M comprehensive processing schemes, and evaluating the relative processing cost according to a preset index; and finally, selecting a scheme with the optimal cost for preparing the polypropylene reinforced material. Therefore, the technical effects of accurately determining the parameter space through data driving, comprehensively balancing the performance and the cost to generate a high-application-value scheme and improving the material enhancement effect are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of polypropylene materials, and in particular to a polypropylene material reinforcement method and platform based on basalt long fibers. Background Art

[0002] In the current field of composite material preparation, reinforcing polypropylene materials with basalt fibers can significantly improve the performance indicators of polypropylene materials such as weather resistance, flexural modulus, tensile strength and notched impact strength.

[0003] Polypropylene materials based on basalt long fibers require interface treatment and co-blending and granulation processes, and the process parameters have a significant impact on material properties and production costs. Common process parameter control often relies on empirical adjustments and lacks systematic and precise division and optimization methods. Especially when it comes to valve materials used in agricultural scenarios, parameter optimization is not targeted enough, resulting in poor interface bonding between basalt long fibers and the polypropylene matrix, and low co-blending and granulation efficiency. In addition, past optimizations have mostly focused on improving a single performance, resulting in an imbalance in the cost-effectiveness of the solution in actual applications. At the same time, previous parameter determinations were mostly based on theoretical models and subjective experience assumptions, which deviated greatly from actual production and were prone to introducing errors, affecting the reliability of process optimization. Summary of the Invention

[0004] The present invention provides a polypropylene material reinforcement method and platform based on basalt long fibers to address the technical problems in the prior art, such as inaccurate process parameter division and optimization, difficulty in balancing material performance and production costs, and parameter determination susceptible to errors caused by subjective experience. The method achieves the technical effect of accurately determining parameter space through data-driven, comprehensively balancing performance and cost to generate solutions with high application value, and improving the material reinforcement effect.

[0005] In a first aspect, the present invention provides a method for reinforcing a polypropylene material based on basalt long fibers, wherein the method for reinforcing a polypropylene material based on basalt long fibers comprises:

[0006] The current preparation process information is analyzed to obtain the current process parameter set, and the process parameter set is divided into an interface treatment parameter set and a blending and granulation parameter set.

[0007] Fitting analysis based on sample data is performed on the interface treatment parameter set and the blending granulation parameter set respectively, a single variable parameter interval set is constructed, and the interface treatment parameter space and the blending granulation parameter space are defined respectively in combination with the parameter mapping relationship.

[0008] A multi-objective optimization algorithm is applied to perform a first optimization solution in the interface processing parameter space to obtain a first processing solution set, and a second optimization solution is performed in the blending and granulation parameter space to obtain a second processing solution set.

[0009] The first processing solution set and the second processing solution set are randomly combined to form M comprehensive processing solutions, and a performance-relative processing cost evaluation is performed based on preset evaluation indicators.

[0010] According to the performance-relative processing cost evaluation results, a comprehensive processing scheme with the best relative processing cost is determined for the preparation of polypropylene reinforced materials.

[0011] In a feasible implementation, the current preparation process information is parsed to obtain the current process parameter set, and the process parameter set is divided into an interface treatment parameter set and a blending and granulation parameter set, including:

[0012] The preparation process, auxiliary agent usage and process setting values ​​of basalt fiber reinforced polypropylene materials under agricultural application conditions are collected to form the current preparation process information.

[0013] The current preparation process information is analyzed, process node parameters, additive dosing parameters, and process setting index category parameters are extracted, and the output is the current process parameter set.

[0014] According to the action nodes, the current process parameter set is divided into an interface treatment stage and a blending and granulation stage, and the interface treatment parameter set and the blending and granulation parameter set are generated accordingly.

[0015] In one feasible implementation, the interface treatment parameter set includes an interface treatment agent parameter subset and an interface treatment process parameter subset. The interface treatment agent parameter subset includes at least modifier dosing parameters and coupling agent dosing parameters. The interface treatment process parameter subset includes at least temperature, pH, and reaction time.

[0016] In one feasible implementation, the blending and granulation parameter set includes a phase bulk additive parameter subset and a blending process parameter subset, wherein the phase bulk additive parameter subset includes at least toughening agent addition parameters, filler addition parameters, reinforcing agent addition parameters, and comonomer or blending material addition parameters. The blending process parameter subset includes at least melt temperature, shear rate, and fiber length retention rate.

[0017] In a feasible implementation, the interface treatment parameter set and the blending and granulation parameter set are respectively subjected to fitting analysis based on sample data to construct a single variable parameter interval set, including:

[0018] The parameter items included in the interface processing parameter set and the blending and granulation parameter set are defined as analysis targets, and the analysis target set is obtained.

[0019] A first analysis target is determined based on the analysis target set, and a univariate fitting analysis is performed on the sample data using the current preparation process information to obtain a first univariate characteristic set, wherein the univariate characteristic set includes a plurality of univariate characteristic curves corresponding to a plurality of material performance dimensions.

[0020] The intersection of the first univariate characteristic set is taken to generate the first univariate parameter interval, and the analysis target set is traversed to perform univariate fitting analysis, and the first univariate parameter interval and multiple univariate parameter intervals are output as the univariate parameter interval set.

[0021] In a feasible implementation, the interface treatment parameter space and the blending granulation parameter space are defined separately in combination with the parameter mapping relationship, including:

[0022] Create a multi-dimensional space.

[0023] The single variable parameter interval set is divided into an interface treatment parameter interval set and a blending and granulation parameter interval set according to the parameter mapping relationship.

[0024] Parameter space marking is performed in the multidimensional space according to the interface processing parameter interval set to obtain the interface processing parameter space.

[0025] Parameter space marking is performed in the multidimensional space according to the blending and granulation parameter interval set to obtain the blending and granulation parameter space.

[0026] In a feasible implementation, a multi-objective optimization algorithm is applied to perform a first optimization solution in the interface processing parameter space to obtain a first processing solution set, and a second optimization solution is performed in the blending and granulation parameter space to obtain a second processing solution set, including:

[0027] A multi-objective optimization algorithm is applied to the interface treatment parameter space, with the material interface bonding strength and dispersion as the objective functions, to obtain the first set of optimization solutions.

[0028] A multi-objective optimization algorithm was applied to the blending and granulation parameter space, and the second set of optimization solutions was obtained with tensile properties, corrosion resistance, and thermal stability as objective functions.

[0029] In one feasible implementation, a performance-relative processing cost evaluation is performed based on preset evaluation indicators, including:

[0030] For each of the M comprehensive treatment solutions, the unit cost of raw materials, energy consumption and equipment adaptability involved are statistically analyzed, and the corresponding comprehensive treatment cost is calculated based on a preset cost model.

[0031] Based on the material performance index satisfaction and cost optimality as evaluation basis, a performance-relative processing cost evaluation is performed to obtain the performance-relative processing cost evaluation result.

[0032] In a feasible implementation, before outputting the first univariate parameter interval and the plurality of univariate parameter intervals as the univariate parameter interval set, the method further includes:

[0033] Configure an interval cropping constraint, wherein the interval cropping constraint is a cropping ratio value.

[0034] The interval clipping constraint is applied to each single variable parameter interval, and the interval range of the front interval clipping constraint part and the back interval clipping constraint part of each single variable parameter interval are respectively eliminated to generate a clipping result.

[0035] Each univariate parameter interval is updated accordingly using the trimming result.

[0036] In a second aspect, the present invention further provides a polypropylene material reinforcement platform based on basalt long fibers, wherein the polypropylene material reinforcement platform based on basalt long fibers comprises:

[0037] The current process analysis module is used to analyze the current preparation process information, obtain the current process parameter set, and divide the process parameter set into an interface processing parameter set and a blending and granulation parameter set.

[0038] The parameter space construction module is used to perform fitting analysis on the interface treatment parameter set and the blending granulation parameter set based on sample data, construct a single variable parameter interval set, and define the interface treatment parameter space and the blending granulation parameter space respectively in combination with the parameter mapping relationship.

[0039] The optimization solution module is used to apply a multi-objective optimization algorithm to perform a first optimization solution in the interface processing parameter space to obtain a first processing solution set, and to perform a second optimization solution in the blending and granulation parameter space to obtain a second processing solution set.

[0040] The combination and cost evaluation module is used to randomly combine the first processing solution set and the second processing solution set to form M comprehensive processing solutions, and perform performance-relative processing cost evaluation based on preset evaluation indicators.

[0041] The optimal solution selection module is used to determine the comprehensive treatment solution with the best relative treatment cost based on the performance-relative treatment cost evaluation results, which is used for the preparation of polypropylene reinforced materials.

[0042] The present invention discloses a polypropylene material reinforcement method and platform based on basalt long fiber, comprising: analyzing current preparation process information, obtaining a current process parameter set, and dividing the process parameter set into an interface treatment parameter set and a blending granulation parameter set based on functional differences; performing fitting analysis on the interface treatment parameter set and the blending granulation parameter set based on sample data, constructing corresponding single variable parameter interval sets, and defining the interface treatment parameter space and the blending granulation parameter space respectively according to the parameter mapping relationship; in the interface treatment parameter space, applying a multi-objective optimization algorithm to perform a first optimization solution to obtain a first treatment solution set; in the blending granulation parameter space, performing a second optimization solution to obtain a second treatment solution set; and performing a fitting analysis on the first treatment solution set and the second treatment solution set. Random pairing and combination are performed to form M comprehensive treatment plans, and a performance-relative treatment cost evaluation is performed based on preset performance indicators and evaluation factors such as energy consumption and raw material utilization rate. According to the performance-relative treatment cost evaluation results, the comprehensive treatment plan with the best relative treatment cost is screened out and used in the preparation process of polypropylene reinforced materials to improve preparation efficiency and target performance. The polypropylene material reinforcement method and platform based on basalt long fibers disclosed in the present invention solve the technical problems of inaccurate process parameter division and optimization, difficulty in balancing material performance and production cost, and parameter determination being easily affected by subjective experience and leading to errors. It achieves the technical effect of accurately determining the parameter space driven by data, comprehensively balancing performance and cost to generate high application value plans, and improving the material reinforcement effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 The figure is a schematic flow chart of the polypropylene material reinforcement method based on basalt long fibers of the present invention.

[0044] Figure 2 This is a schematic structural diagram of the polypropylene material reinforcement platform based on basalt long fibers of the present invention.

[0045] Explanation of the accompanying symbols: current process analysis module 11, parameter space construction module 12, optimization solution module 13, combination and cost evaluation module 14, optimal solution selection module 15. DETAILED DESCRIPTION

[0046] The above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods of the specification to better understand the above technical solution. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments used only to explain the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, it should be noted that, for the convenience of description, only the parts related to the present invention, rather than all, are shown in the drawings.

[0047] Example 1, as Figure 1 The figure is a schematic flow chart of a polypropylene material reinforcement method based on basalt long fibers according to the present invention, wherein the polypropylene material reinforcement method based on basalt long fibers comprises:

[0048] S100: Analyze the current preparation process information, obtain the current process parameter set, and divide the process parameter set into an interface processing parameter set and a blending and granulation parameter set.

[0049] Specifically, current production process information refers to the original production process data currently used for basalt long fiber-reinforced polypropylene materials under actual industrial or experimental conditions, including process steps, equipment parameters, additive types and dosages, temperature / pressure settings, etc. The process parameter set refers to the set of quantifiable and controllable process variables extracted from this information, such as fiber pretreatment temperature, coupling agent concentration, melt temperature, screw speed, etc.

[0050] Among them, the interface treatment parameter set refers to a set of process variables specifically used to improve the interface compatibility between basalt fiber and polypropylene resin during the material preparation process; the blending and granulation parameter set refers to a set of process variables used in the actual mixing, melting, dispersion and granulation stages.

[0051] By conducting structured analysis and phased modeling of the existing preparation process, clear parameter boundaries and variable dimensions are provided for subsequent process optimization (such as intelligent regulation and parameter reconstruction).

[0052] In some embodiments, analyzing the current preparation process information to obtain the current process parameter set, and dividing the process parameter set into an interface treatment parameter set and a blending and granulation parameter set, includes:

[0053] The preparation process, additive usage and process setting values ​​of basalt fiber reinforced polypropylene materials under agricultural application conditions are collected to form the current preparation process information; the current preparation process information is analyzed, process node parameters, additive addition parameters, and process setting indicator category parameters are extracted, and output as the current process parameter set; according to the action nodes, the current process parameter set is divided into an interface treatment stage and a blending and granulation stage, and the interface treatment parameter set and the blending and granulation parameter set are generated accordingly.

[0054] Specifically, the interface treatment parameter set primarily involves process parameters that improve the bonding strength between the fiber and the matrix, such as the temperature, time, and pH value of the fiber surface treatment, as well as the type and amount of coupling agents. The blending and granulation parameter set includes parameters that affect the uniformity of polymer blending and the quality of pellet formation, such as the melt temperature, shear rate, residence time during blending, and the amount of toughening agents and fillers.

[0055] Specifically, first, through literature research, factory measurements or experimental records, the preparation process information of basalt fiber reinforced polypropylene materials under agricultural application conditions was collected, including: preparation process nodes, such as fiber pretreatment → surface modification → additive ratio → blending → melting → granulation; additive usage, such as silane coupling agent, antioxidant, lubricant, dispersant, etc.; process setting values: temperature (180°C), screw speed (80rpm), fiber content (20wt%), coupling agent concentration (1wt%), etc. Then, the above information was structured and extracted to form the current process parameter set, which includes:

[0056] Process node parameters: such as "surface treatment", "blending", "granulation", etc.; additive dosage parameters: such as "KH550: 2.5%", "antioxidant 1010: 0.5%"; process setting indicators: such as "temperature: 210℃", "screw speed: 120rpm", "fiber length: 6mm".

[0057] Furthermore, based on their role in the process flow, these parameters are divided into two subsets: the interface treatment parameter set (used to enhance fiber-matrix interface bonding) and the blending and granulation parameter set (used to achieve uniform dispersion and molding). This division not only clarifies the physical action stages of the process variables but also provides structural support for subsequent stage-by-stage control strategies.

[0058] Through the above process of collecting and structured analysis of current preparation process information, a set of controllable process parameters can be systematically extracted and divided into an "interface treatment parameter set" and a "blending and granulation parameter set" according to the physical action stage, achieving hierarchical modeling and parameter decoupling of complex process flows. This structured processing method provides clear variable boundaries for subsequent process optimization, helps to clarify the impact path of different parameters on material properties (such as interface strength, dispersion uniformity, and mechanical properties); supports parameter sensitivity analysis and optimization based on machine learning or process simulation; builds feedback control or intelligent adjustment systems based on stage parameters; and improves the reproducibility and standardization of preparation processes.

[0059] In some implementations, the interface treatment parameter set includes an interface treatment auxiliary agent parameter subset and an interface treatment process parameter subset; wherein, the interface treatment auxiliary agent parameter subset includes at least modifier dosing parameters and coupling agent dosing parameters; the interface treatment process parameter subset includes at least temperature, pH, and reaction time.

[0060] Specifically, the interface treatment additive parameter subset refers to the specific parameters such as the type, concentration, and addition method of various surfactants, coupling agents, and modifiers used in the interface modification stage. Its main purpose is to improve the interfacial bonding strength and enhance interfacial compatibility between basalt fiber and polypropylene matrix. The interface treatment process parameter subset includes operating conditions such as temperature, reaction pH, treatment time, and stirring rate required during the treatment process, which directly affect the reaction efficiency and interface bonding quality between the additive and fiber.

[0061] Specifically, during the interface treatment stage, basalt long fibers usually need to be surface modified to improve their compatibility with polypropylene. The parameters of this stage can be further refined as follows:

[0062] A subset of interface treatment agent parameters, such as: modifier addition parameters: such as the addition amount of silane coupling agent KH550 is 2.5wt%; coupling agent addition method: such as spraying, dipping, and ultrasonic assisted dispersion; additive type: such as maleic anhydride grafted polypropylene (MAH-g-PP) as an interface compatibilizer.

[0063] A subset of interface treatment process parameters, such as: treatment temperature: such as 110°C; pH value control: such as adjusting to weakly acidic pH = 4.5-5.5; reaction time: such as 30 minutes; stirring mode and rate: such as magnetic stirring 300 rpm.

[0064] Example: In the development of agricultural valve materials, KH550 is used to impregnate basalt fibers at a temperature of 105°C, pH 5.0, and a treatment time of 25 minutes, which can increase the interfacial shear strength between the fiber and the PP matrix by 20% to 35%.

[0065] In some implementations, the blending granulation parameter set includes a phase bulk additive parameter subset and a blending process parameter subset, wherein the phase bulk additive parameter subset includes at least toughening agent addition parameters, filler addition parameters, reinforcing agent addition parameters, and comonomer or blending material addition parameters; the blending process parameter subset includes at least melting temperature, shear rate, and fiber length retention rate.

[0066] Specifically, the bulk additive parameter subset refers to various functional additives introduced into the polypropylene matrix, such as tougheners, fillers, reinforcements, comonomers, or other blended polymers, which regulate the mechanical properties, thermal stability, processing rheology, etc. The blending process parameter subset refers to process control variables such as temperature, shear rate, and fiber length retention involved in twin-screw extrusion or melt blending, which are crucial to fiber dispersion, orientation, and the performance of the final composite material.

[0067] Specifically, during the blending and granulation stage, the stability and uniformity of material properties mainly depend on the additive system and process control, which can be divided into:

[0068] A subset of phase bulk additive parameters, such as: toughening agent addition parameters: such as POE (polyolefin elastomer) addition amount 10wt%; filler addition parameters: such as talcum powder, CaCO3 addition amount 5wt%; reinforcing agent addition parameters: such as short glass fiber 3wt%; comonomer or blending material: such as adding EVA or SEBS modifier 8wt%.

[0069] A subset of blending process parameters, such as: melt temperature: such as 210-230°C; shear rate (screw speed): such as 100-150 rpm; fiber length retention rate: target ≥80%, achieved through low shear / short-stroke screw design; blending time: such as 60-120 seconds; cooling method: such as air-cooled or water-cooled granulation.

[0070] For example, in a scenario for agricultural drip irrigation valve materials, a POE toughening + talc filling combination is used, combined with a melting temperature of 220°C and low shear blending at 120rpm, to ultimately obtain a high-performance composite material with an 18% increase in impact strength and a fiber retention rate of 85%.

[0071] By further refining the interface treatment parameter set and the blending and granulation parameter set into "additive parameter subset" and "process parameter subset", the dimensional expansion and hierarchical modeling of the material preparation process parameters are achieved, and the process control is refined from "overall adjustment" to "additive-process dual-channel optimization", which helps to improve the accuracy and controllability of process adjustment.

[0072] S200: performing fitting analysis based on sample data on the interface treatment parameter set and the blending and granulation parameter set, constructing a single variable parameter interval set, and defining the interface treatment parameter space and the blending and granulation parameter space respectively in combination with the parameter mapping relationship.

[0073] Specifically, fitting analysis based on sample data refers to using sample data collected in the existing preparation process to analyze the relationship between each process parameter and material properties through mathematical modeling and statistical analysis methods.

[0074] Specifically, the univariate parameter interval set is determined by performing univariate analysis on each parameter to determine its effective range that significantly affects material properties. The parameter mapping relationship describes the association between different parameters and process nodes, allowing the univariate parameter interval set to be integrated into a multidimensional space to form the interface treatment parameter space and the blending and granulation parameter space.

[0075] The single-variable parameter interval set constructed through the above process clarifies the effective range of each parameter, provides a clear search interval for subsequent optimization, and provides an effective operating platform for the multi-objective optimization algorithm, thereby improving the efficiency and effectiveness of the optimization solution, helping to find a better combination of process parameters and improve the comprehensive performance of the material.

[0076] In some embodiments, fitting analysis based on sample data is performed on the interface treatment parameter set and the blending and granulation parameter set to construct a univariate parameter interval set, including:

[0077] Define the parameter items included in the interface processing parameter set and the blending and granulation parameter set as analysis targets to obtain an analysis target set; determine a first analysis target based on the analysis target set, perform univariate fitting analysis on the current preparation process information as the sample data, and obtain a first univariate characteristic set, wherein the univariate characteristic set includes a plurality of univariate characteristic curves corresponding to a plurality of material performance dimensions; take the intersection of the first univariate characteristic set to generate the first univariate parameter interval, and traverse the analysis target set to perform univariate fitting analysis, and output the first univariate parameter interval and the plurality of univariate parameter intervals as the univariate parameter interval set.

[0078] Specifically, sample data refers to a data set collected during actual experiments or production that includes process parameters and corresponding material performance indicators, such as tensile strength and impact strength test results for a batch of materials at different KH550 concentrations. The analysis target set refers to the set of parameters selected from the interface treatment parameter set and the blending and granulation parameter set that require univariate analysis, such as treatment temperature, coupling agent concentration, melt temperature, shear rate, etc.

[0079] Specifically, univariate fitting analysis is used to analyze the functional relationship between a specific process parameter and material properties while keeping other variables constant or within statistical tolerances. Available methods include linear fitting, polynomial fitting, and locally weighted regression (LOWESS). A univariate characteristic curve is a fitted function curve between a parameter item and a performance indicator; a univariate parameter interval is the range of parameter values ​​corresponding to this curve; a collection of intervals for multiple parameter items constitutes a univariate parameter interval set.

[0080] Specifically, we first extracted the analysis target set from the structured parameter set. Specifically, we extracted the analysis targets from the interface treatment parameter set, such as KH550 concentration, treatment temperature, and treatment time; and the analysis targets from the blending and granulation parameter set, such as melt temperature, shear rate, and fiber length retention, to form the analysis target set. Then, we used a parameter from the analysis target set (such as KH550 concentration) as the first analysis target and performed a univariate fitting analysis based on the historical sample data. An example of the sample data is shown in Table 1 below:

[0081] Table 1 Exemplary sample data

[0082]

[0083] Then, based on the acquired sample data, the parameter is fitted with each performance dimension to obtain a set of univariate characteristic curves, forming the first univariate characteristic set:

[0084] C KH550 -Tensile strength: The fitting curve is a quadratic function with a peak at 2.5wt%.

[0085] C KH550 - Impact strength: The fitting curve is a parabola, and the optimal range is 2.0-2.8wt%.

[0086] C KH550 -Flexural modulus: The fitting curve rises slightly and then levels off.

[0087] Optionally, from the characteristic curves, the parameter intervals corresponding to each performance indicator meeting the design requirements are extracted, and the intersection is taken to obtain a first univariate parameter interval. Furthermore, the above process is repeated, performing univariate fitting analysis on each of the other parameter items in the analysis target set, ultimately forming a complete set of univariate parameter intervals.

[0088] Through the univariate fitting analysis based on sample data in the above process, a set of univariate parameter intervals covering multiple key process parameters was constructed, realizing the transition from empirical parameter setting to data-driven parameter determination. The beneficial technical effects brought about include: parameter optimization is more targeted, and the parameter interval with the best or most stable performance is directly identified through the characteristic curve, avoiding blind trial and error; performance prediction is more interpretable, and the univariate relationship between each process parameter and performance is visualized, which helps process personnel understand the parameter action mechanism; it provides a basis for multivariate collaborative optimization, and the univariate interval can be used as the initial boundary condition for multivariate optimization (such as response surface analysis, genetic algorithm, Bayesian optimization); it improves product consistency and reliability, and setting process parameters within the univariate optimal interval helps control product performance fluctuations.

[0089] In summary, the above steps provide a quantifiable, controllable, and predictable parameter basis for process optimization of basalt long fiber reinforced polypropylene materials through sample data-driven univariate characteristic modeling and parameter interval extraction. This is a key support link for realizing data-driven process design and intelligent manufacturing.

[0090] In some implementations, before outputting the first univariate parameter interval and the plurality of univariate parameter intervals as the univariate parameter interval set, the method further includes:

[0091] Configure an interval trimming constraint, wherein the interval trimming constraint is a trimming ratio value; apply the interval trimming constraint to each single variable parameter interval, and remove the interval range of the front interval trimming constraint part and the back interval trimming constraint part of each single variable parameter interval to generate a trimming result; update each single variable parameter interval accordingly with the trimming result.

[0092] Specifically, the interval clipping constraint refers to the operational constraint for eliminating the boundaries of the fitted single-variable parameter interval. It is usually expressed in the form of a clipping ratio value (such as 10%, 15%), and is used to remove the edge values ​​at both ends of the interval. Although these edge values ​​meet the performance requirements, they may have problems such as decreased marginal benefits, large performance fluctuations, high process control difficulty or insufficient reliability in the actual process.

[0093] Specifically, the front-end trimming refers to the portion of the parameter range (smaller values) that is proportionally removed at the beginning of the parameter range; the back-end trimming refers to the portion of the parameter range (larger values) that is proportionally removed at the end of the parameter range. The trimming result is a narrower but more stable parameter range after applying the trimming ratio, which serves as the final parameter range for process setting.

[0094] Optionally, set uniform or parameter-specific trimming ratio values ​​as follows: Uniform trimming ratio: For example, set all parameter intervals to trim 10% at both ends, that is, 5% at the front and 5% at the back; Parameter-specific trimming ratio: For example, set the trimming ratio for KH550 concentration to 15% and for melting temperature to 5% to reflect the sensitivity of different parameters to performance.

[0095] Specifically, the above-mentioned trimming operation is performed on all single-variable parameter intervals to obtain a set of trimming results, and finally the set is output as a formal single-variable parameter interval set for subsequent process window setting, multivariable optimization or intelligent control input.

[0096] By introducing and applying interval clipping constraints, the final set of single-variable parameter intervals has the following significant technical advantages: eliminating edge fluctuation areas, retaining core intervals with higher performance stability, and reducing the risk of performance anomalies caused by process disturbances; the reduced parameter interval is more in line with the actual control accuracy, which facilitates precise adjustment and automated execution of equipment; setting process parameters within the trimmed stable interval helps reduce performance fluctuations between batches; and the narrower parameter interval makes it easier for the control system to achieve rapid convergence and error suppression in closed-loop adjustment.

[0097] In some embodiments, the interface processing parameter space and the blending granulation parameter space are defined separately in combination with the parameter mapping relationship, including:

[0098] Establish a multidimensional space; divide the single variable parameter interval set into an interface treatment parameter interval set and a blending granulation parameter interval set according to the parameter mapping relationship; perform parameter space marking in the multidimensional space according to the interface treatment parameter interval set to obtain the interface treatment parameter space; perform parameter space marking in the multidimensional space according to the blending granulation parameter interval set to obtain the blending granulation parameter space.

[0099] Specifically, parameter mapping refers to the relationship between various parameters and process stages in the process flow, based on process logic or functional module division. For example, parameters such as KH550 concentration, treatment temperature, and treatment time are mapped to interface treatment process parameters, while melt temperature, screw speed, and blending time are mapped to blending and granulation process parameters.

[0100] Specifically, multidimensional space is a Cartesian space composed of multiple parameter dimensions. For example, if the interface treatment involves three parameters (concentration, time, and temperature), it corresponds to a three-dimensional space, with each dimension corresponding to a parameter value range. Parameter space labeling refers to the explicit marking or encoding of regions in multidimensional space that meet parameter interval constraints to facilitate subsequent search, optimization, or visualization. The result is the formation of an interface treatment parameter space and a blending and granulation parameter space, each representing a set of feasible process combinations.

[0101] Specifically, first, a complete multidimensional process space is constructed; then, according to the process flow structure, the parameter interval set is divided into two subsets, namely the interface treatment parameter interval set and the blending and granulation parameter interval set; then, for each parameter subset, all points or regions that meet the interval constraints are marked in the subspace formed by its corresponding dimension, and the interface treatment parameter space and the blending and granulation parameter space are obtained accordingly.

[0102] Optionally, the marking method may adopt: grid sampling, Latin hypercube sampling (LHS), Sobol sequence and other low-discrepancy sampling methods or directly construct a bounding box to represent the feasible area.

[0103] Through the above process, the single-variable parameter interval set is mapped to the multidimensional space and divided into two subspaces: interface treatment and blending granulation according to the process logic, thus realizing the systematic expression of parameter combinations: compared with the single-variable interval, the parameter space can express the synergistic combination relationship between multiple parameters, which helps to capture the "combination optimality" rather than the "single-point optimality"; it improves the optimization efficiency and accuracy: searching or optimizing in the marked parameter space (such as response surface analysis and genetic algorithm) can significantly narrow the search scope and improve computational efficiency; it enhances visualization and decision support capabilities: the multidimensional parameter space can be visualized through dimensionality reduction to assist process personnel in understanding the parameter relationship.

[0104] S300: Applying a multi-objective optimization algorithm, performing a first optimization solution in the interface processing parameter space to obtain a first processing solution set, and performing a second optimization solution in the blending and granulation parameter space to obtain a second processing solution set.

[0105] Specifically, a multi-objective optimization algorithm is a mathematical algorithm that can simultaneously optimize multiple objective functions and is often used to find a balance and optimal solution between multiple performance indicators. Based on this algorithm, optimization can be performed within the interface treatment parameter space and the blending and granulation parameter space, with the material interface bonding strength, dispersibility, tensile properties, corrosion resistance, and thermal stability as the objective functions.

[0106] Among them, the first processing solution set and the second processing solution set respectively contain a series of different process parameter combinations and their corresponding performance prediction results. These combinations are the optimal solutions obtained after algorithm screening and evaluation during the optimization process.

[0107] Through the above process, we can obtain multiple optimal process parameter combinations for the two key steps of interface treatment and blending granulation. These solutions not only consider the improvement of material properties but also balance different performance indicators, providing diverse options for the subsequent generation of comprehensive treatment solutions.

[0108] In some embodiments, a multi-objective optimization algorithm is applied to perform a first optimization solution in the interface processing parameter space to obtain a first processing solution set, and a second optimization solution is performed in the blending and granulation parameter space to obtain a second processing solution set, including:

[0109] A multi-objective optimization algorithm is applied to the interface treatment parameter space, with the material interface bonding strength and dispersion as the objective functions, to obtain the first optimization solution set; a multi-objective optimization algorithm is applied to the blending granulation parameter space, with tensile properties, corrosion resistance and thermal stability as the objective functions, to obtain the second optimization solution set.

[0110] Specifically, interfacial bonding strength refers to the bonding strength between basalt fiber and polypropylene matrix, which determines the efficiency of the composite material during stress transfer; dispersion measures the uniform distribution of fibers in the matrix, affecting the overall mechanical consistency and defect rate of the material.

[0111] Specifically, tensile properties are usually expressed as tensile strength or strain, reflecting the mechanical bearing capacity of the material; corrosion resistance measures the material's ability to maintain performance in acid, alkali or salt spray environments; thermal stability refers to the material's ability to maintain structure and performance at high temperatures, and is often characterized by thermal gravimetric analysis (TGA) or heat deformation temperature (HDT).

[0112] Specifically, the first optimization solution is performed in the interface processing parameter space, including:

[0113] Input space: interface treatment parameter space, dimensions such as KH550 concentration, treatment temperature, and treatment time; objective function: maximizing interfacial bonding strength (unit: MPa) and maximizing fiber dispersion (quantitative indicators such as image uniformity score or particle size variation coefficient CV); optimization method: using the NSGA-II algorithm, with the population size set to 100 and the evolutionary number of 200; output result: obtaining a Pareto frontier solution set, called the first treatment solution set, where each solution corresponds to a set of interface treatment parameter combinations.

[0114] For example, the first processing solution set is shown in Table 2 below:

[0115] Table 2: Example of a first treatment solution set

[0116]

[0117] Specifically, the second optimization solution is performed in the blending and granulation parameter space, including:

[0118] Input space: blending and granulation parameter space, dimensions such as melting temperature, screw speed, and blending time; objective function: maximizing tensile strength (MPa), maximizing corrosion resistance (such as mass retention rate after salt spray test), and maximizing thermal stability (such as thermal weight loss starting temperature T5%, unit: ℃); optimization method: using MOEA / D algorithm combined with weighted decomposition strategy to deal with the three-objective problem; output result: obtaining the second set of treatment solutions, each solution is a set of blending and granulation parameter combinations.

[0119] For example, the second processing solution set is shown in Table 3 below:

[0120] Table 3: Example of a first treatment solution set

[0121]

[0122]

[0123] Through the above process, the multi-objective optimization algorithm is applied in the two parameter spaces of interface treatment and blending granulation respectively, which can decompose the complex composite material preparation process into two key stages and optimize them separately, which helps to reduce the dimension and improve the optimization efficiency; the multi-objective algorithm avoids the performance sacrifice caused by a single performance orientation and achieves comprehensive performance improvement; and provides a high-quality solution set for subsequent process combination recommendations.

[0124] S400: Randomly combine the first processing solution set and the second processing solution set to form M comprehensive processing solutions, and perform performance-relative processing cost evaluation based on preset evaluation indicators.

[0125] Specifically, random combination refers to randomly selecting solutions from the first and second treatment solution sets and combining them to form a comprehensive treatment solution. Pre-set evaluation indicators are pre-set criteria for evaluating the pros and cons of each comprehensive treatment solution, including material performance indicator satisfaction and treatment cost. Performance-relative treatment cost evaluation is a cost-benefit analysis of each comprehensive treatment solution based on pre-set evaluation indicators. The evaluation compares different solutions that meet performance requirements and achieve optimal performance, determining which solution offers lower relative costs and higher benefits.

[0126] Specifically, the comprehensive treatment scheme refers to a set of complete process parameter configuration schemes obtained by combining the aforementioned two optimization stages (interface treatment + blending and granulation), that is, M candidate schemes.

[0127] Through the above process, multiple comprehensive treatment plans can be generated and the treatment cost of each plan can be evaluated while meeting the material performance requirements. This helps to screen out the optimal plan with the best performance and low cost among many possible plans.

[0128] In some embodiments, the performance-relative processing cost evaluation is performed based on a preset evaluation index, including:

[0129] For each of the M comprehensive treatment schemes, the unit cost of raw materials, energy consumption and equipment adaptability involved are statistically analyzed, and the corresponding comprehensive treatment cost is calculated based on a preset cost model; a performance-relative treatment cost evaluation is performed based on the satisfaction of material performance indicators and cost optimality to obtain the performance-relative treatment cost evaluation result.

[0130] Specifically, the unit cost of raw materials refers to the unit price (yuan / kg) of each raw material (such as KH550, polypropylene, basalt fiber, etc.) and its usage in the plan; energy consumption includes the energy consumption cost (such as electricity bill) caused by processing temperature, time, equipment power, etc.; equipment adaptability refers to whether the process parameters are suitable for the existing production line. If equipment needs to be modified or replaced, additional costs will be included.

[0131] Specifically, the preset cost model is a function model used to calculate the comprehensive processing cost, usually a linear or weighted sum model, in which the weights are adjustable weights used to reflect the importance of different cost factors.

[0132] Specifically, material performance index satisfaction refers to the extent to which a solution achieves target values ​​for key properties such as tensile strength and thermal stability; cost optimality refers to the relative cost level of the solution among all candidate solutions. The combination of these two factors forms the dual-dimensional basis for performance-relative processing cost evaluation.

[0133] Specifically, from the aforementioned optimization results, a set of performance indicators corresponding to each solution is obtained. A target value is then set, and a performance satisfaction score is calculated for each solution, such as through normalized weighted summation. Next, all M solutions are ranked by performance satisfaction and overall cost, constructing a two-dimensional performance-cost plane. Based on an evaluation strategy (e.g., performance satisfaction ≥ threshold, cost below mean), a set of relatively advantageous solutions is selected as the final recommended process configuration.

[0134] Optionally, material performance index satisfaction and cost optimality are combined in a weighted manner, and based on this combination, the solution with the best performance and appropriate cost is selected as the final recommended process configuration solution. Material performance index satisfaction represents the demand for optimal performance, while cost optimality introduces considerations for cost rationality.

[0135] By introducing a preset cost model, cost evaluation and performance matching of the optimized comprehensive treatment plan are achieved, and intuitive comparison and ranking of multiple candidate plans are realized. Among them, not only material performance is optimized, but also actual production costs are taken into account to improve the engineering feasibility of the plan; it provides important data support for subsequent deployment, cost control and intelligent recommendations.

[0136] S500: Based on the performance-relative processing cost evaluation results, determine the comprehensive processing plan with the best relative processing cost for the preparation of polypropylene reinforced materials.

[0137] Specifically, optimal relative processing cost refers to the solution with the lowest overall processing cost (i.e., the most cost-effective solution) while ensuring that material properties meet application requirements. In other words, this solution isn't necessarily the one with the highest absolute performance, but rather the lowest cost option while maintaining sufficient performance, reflecting the concept of "optimal cost-performance" in industrial practice. This solution will be applied in the production of basalt long fiber reinforced polypropylene composites through process paths such as interface treatment, twin-screw extrusion, injection molding, or compression molding.

[0138] In summary, the polypropylene material reinforcement method based on basalt long fibers provided by the present invention has the following technical effects:

[0139] By analyzing the current preparation process information, the current process parameter set is obtained, and the process parameter set is divided into an interface treatment parameter set and a blending and granulation parameter set based on functional differences. Fitting analysis is performed on the interface treatment parameter set and the blending and granulation parameter set based on sample data to construct corresponding univariate parameter interval sets, and the interface treatment parameter space and the blending and granulation parameter space are defined respectively based on the parameter mapping relationship. A multi-objective optimization algorithm is applied to perform a first optimization solution in the interface treatment parameter space to obtain a first treatment scheme set. A second optimization solution is performed in the blending and granulation parameter space to obtain a second treatment scheme set. The first treatment scheme set and the second treatment scheme set are randomly paired and combined to form M comprehensive treatment schemes, and a performance-relative treatment cost evaluation is performed based on preset performance indicators and evaluation factors such as energy consumption and raw material utilization. Based on the performance-relative treatment cost evaluation results, the comprehensive treatment scheme with the optimal relative treatment cost is screened and applied to the preparation process of polypropylene reinforced materials to improve preparation efficiency and target performance, thereby achieving the technical effect of accurately determining the parameter space through data-driven, comprehensively balancing performance and cost to generate high-application-value solutions, and improving the material reinforcement effect.

[0140] Example 2, as Figure 2 This is a schematic diagram of the structure of the polypropylene material reinforcement platform based on basalt long fibers of the present invention. For example, Figure 1 The schematic flow diagram of the polypropylene material reinforcement method based on basalt long fiber of the present invention can be shown as follows: Figure 2 The structure shown is implemented.

[0141] Based on the same concept as the polypropylene material reinforcement method based on basalt long fibers in the above embodiment, the present invention also provides a polypropylene material reinforcement platform based on basalt long fibers, including:

[0142] The current process analysis module 11 is used to analyze the current preparation process information, obtain the current process parameter set, and divide the process parameter set into an interface processing parameter set and a blending and granulation parameter set.

[0143] The parameter space construction module 12 is used to perform fitting analysis on the interface treatment parameter set and the blending granulation parameter set based on sample data, construct a single variable parameter interval set, and define the interface treatment parameter space and the blending granulation parameter space respectively in combination with the parameter mapping relationship.

[0144] The optimization solution module 13 is used to apply a multi-objective optimization algorithm to perform a first optimization solution in the interface processing parameter space to obtain a first processing solution set, and to perform a second optimization solution in the blending and granulation parameter space to obtain a second processing solution set.

[0145] The combination and cost evaluation module 14 is used to randomly combine the first processing solution set and the second processing solution set to form M comprehensive processing solutions, and perform performance-relative processing cost evaluation based on preset evaluation indicators.

[0146] The optimal solution selection module 15 is used to determine the comprehensive treatment solution with the optimal relative treatment cost based on the performance-relative treatment cost evaluation result, which is used for the preparation of polypropylene reinforced materials.

[0147] In some embodiments, the current process analysis module 11 includes:

[0148] The current preparation process information collection unit is used to collect the preparation process, auxiliary agent usage and process setting values ​​of basalt fiber reinforced polypropylene material under agricultural application conditions to form the current preparation process information.

[0149] The current process parameter set extraction unit is used to analyze the current preparation process information, extract process node parameters, auxiliary agent addition parameters, and process setting indicator category parameters, and output them as the current process parameter set.

[0150] The process parameter set division unit is used to divide the current process parameter set into an interface processing stage and a blending and granulation stage according to the action node, and generate the interface processing parameter set and the blending and granulation parameter set accordingly.

[0151] In some implementations, the interface treatment parameter set includes an interface treatment auxiliary agent parameter subset and an interface treatment process parameter subset; wherein, the interface treatment auxiliary agent parameter subset includes at least modifier dosing parameters and coupling agent dosing parameters; the interface treatment process parameter subset includes at least temperature, pH, and reaction time.

[0152] In some implementations, the blending granulation parameter set includes a phase bulk additive parameter subset and a blending process parameter subset, wherein the phase bulk additive parameter subset includes at least toughening agent addition parameters, filler addition parameters, reinforcing agent addition parameters, and comonomer or blending material addition parameters; the blending process parameter subset includes at least melting temperature, shear rate, and fiber length retention rate.

[0153] In some embodiments, the parameter space construction module 12 includes:

[0154] The analysis target definition and acquisition unit is used to define the parameter items included in the interface processing parameter set and the blending and granulation parameter set as analysis targets and acquire the analysis target set.

[0155] A univariate fitting analysis and characteristic set acquisition unit is used to determine a first analysis target based on the analysis target set, perform a univariate fitting analysis on the sample data using the current preparation process information, and obtain a first univariate characteristic set, wherein the univariate characteristic set includes a plurality of univariate characteristic curves corresponding to a plurality of material performance dimensions.

[0156] A univariate parameter interval set generation unit is used to take the intersection of the first univariate characteristic set to generate the first univariate parameter interval, traverse the analysis target set to perform univariate fitting analysis, and output the first univariate parameter interval and multiple univariate parameter intervals as the univariate parameter interval set.

[0157] In some embodiments, the parameter space construction module 12 further includes:

[0158] The multi-dimensional space establishment unit is used to establish a multi-dimensional space.

[0159] The parameter interval set division unit is used to divide the single variable parameter interval set into an interface treatment parameter interval set and a blending and granulation parameter interval set according to the parameter mapping relationship.

[0160] The interface processing parameter space acquisition unit is used to perform parameter space marking in the multidimensional space according to the interface processing parameter interval set to acquire the interface processing parameter space.

[0161] The blending and granulation parameter space acquisition unit is used to perform parameter space marking in the multidimensional space according to the blending and granulation parameter interval set to acquire the blending and granulation parameter space.

[0162] In some embodiments, the optimization solution module 13 includes:

[0163] The interface processing parameter space optimization unit is used to apply a multi-objective optimization algorithm to the interface processing parameter space, taking the material interface bonding strength and dispersion as the objective function, and obtain a first optimization solution set.

[0164] The blending and granulation parameter space optimization unit is used to apply a multi-objective optimization algorithm to the blending and granulation parameter space, taking tensile properties, corrosion resistance and thermal stability as objective functions, and obtain a second optimization solution set.

[0165] In some embodiments, the combination and cost evaluation module 14 includes:

[0166] The comprehensive treatment solution cost calculation unit is used to count the unit cost of raw materials, energy consumption and equipment adaptability involved in the M comprehensive treatment solutions respectively, and calculate the corresponding comprehensive treatment cost according to a preset cost model.

[0167] The relative processing cost evaluation unit is used to perform performance-relative processing cost evaluation based on the material performance index satisfaction and cost optimality, and obtain the performance-relative processing cost evaluation result.

[0168] In some implementations, the univariate parameter interval set generation unit in the parameter space construction module 12 includes:

[0169] The interval clipping constraint configuration subunit is used to configure the interval clipping constraint, wherein the interval clipping constraint is a clipping ratio value.

[0170] The single variable parameter interval cutting subunit is used to apply the interval cutting constraint to each single variable parameter interval, remove the interval range of the front interval cutting constraint part and the back interval cutting constraint part of each single variable parameter interval, and generate a cutting result.

[0171] The single variable parameter interval updating subunit is used to update each single variable parameter interval correspondingly according to the clipping result.

[0172] It should be understood that the embodiments mentioned in this specification focus on their differences from other embodiments. The specific embodiments in the aforementioned embodiment one are also applicable to the polypropylene material reinforced platform based on basalt long fibers described in embodiment two. For the sake of brevity of the specification, no further elaboration will be given here.

[0173] It should be understood that the embodiments disclosed in the present invention and the above description can enable those skilled in the art to use the present invention to implement the present invention. At the same time, the present invention is not limited to the embodiments mentioned above. It should be understood that those skilled in the art can still modify the technical solutions described in the above embodiments or replace some of the technical features therein with equivalents; and such modifications or replacements do not deviate from the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention and are all included in the scope of protection of the present invention.

Claims

1. A polypropylene material reinforcement method based on basalt long fibers, characterized in that: include: Analyzing the current preparation process information, obtaining the current process parameter set, and dividing the process parameter set into an interface treatment parameter set and a blending and granulation parameter set; Performing fitting analysis based on sample data on the interface treatment parameter set and the blending and granulation parameter set, respectively, constructing a single variable parameter interval set, and defining the interface treatment parameter space and the blending and granulation parameter space respectively in combination with the parameter mapping relationship; Applying a multi-objective optimization algorithm, performing a first optimization solution in the interface processing parameter space to obtain a first processing solution set, and performing a second optimization solution in the blending and granulation parameter space to obtain a second processing solution set; Randomly combining the first treatment solution set and the second treatment solution set to form M comprehensive treatment solutions, and performing performance-relative treatment cost evaluation based on preset evaluation indicators; According to the performance-relative processing cost evaluation results, a comprehensive processing scheme with the best relative processing cost is determined for the preparation of polypropylene reinforced materials.

2. The method for reinforcing polypropylene material based on basalt long fiber according to claim 1, characterized in that: Analyze the current preparation process information, obtain the current process parameter set, and divide the process parameter set into an interface treatment parameter set and a blending granulation parameter set, including: Collect the preparation process, adjuvant usage and process setting values ​​of basalt fiber reinforced polypropylene materials under agricultural application conditions to form the current preparation process information; Analyze the current preparation process information, extract process node parameters, additive dosage parameters, and process setting indicator category parameters, and output them as the current process parameter set; According to the action nodes, the current process parameter set is divided into an interface treatment stage and a blending and granulation stage, and the interface treatment parameter set and the blending and granulation parameter set are generated accordingly.

3. The method for reinforcing polypropylene material based on basalt long fiber according to claim 2, characterized in that: The interface treatment parameter set includes an interface treatment auxiliary agent parameter subset and an interface treatment process parameter subset; wherein, the interface treatment auxiliary agent parameter subset includes at least a modifier addition parameter and a coupling agent addition parameter; the interface treatment process parameter subset includes at least temperature, pH, and reaction time.

4. The method for reinforcing polypropylene material based on basalt long fiber according to claim 2, characterized in that: The blending granulation parameter set includes a phase bulk additive parameter subset and a blending process parameter subset, wherein the phase bulk additive parameter subset includes at least toughening agent addition parameters, filler addition parameters, reinforcing agent addition parameters, and comonomer or blending material addition parameters; the blending process parameter subset includes at least melting temperature, shear rate, and fiber length retention rate.

5. The method for reinforcing polypropylene material based on basalt long fiber according to claim 2, characterized in that: The interface treatment parameter set and the blending granulation parameter set are respectively subjected to fitting analysis based on sample data to construct a single variable parameter interval set, including: defining the parameter items included in the interface processing parameter set and the blending and granulation parameter set as analysis targets, and obtaining the analysis target set; Determining a first analysis target based on the analysis target set, performing a univariate fitting analysis on the sample data using the current preparation process information, and obtaining a first univariate characteristic set, wherein the univariate characteristic set includes a plurality of univariate characteristic curves corresponding to a plurality of material performance dimensions; The intersection of the first univariate characteristic set is taken to generate the first univariate parameter interval, and the analysis target set is traversed to perform univariate fitting analysis, and the first univariate parameter interval and multiple univariate parameter intervals are output as the univariate parameter interval set.

6. The method for reinforcing polypropylene material based on basalt long fiber according to claim 5, characterized in that: Combined with the parameter mapping relationship, the interface treatment parameter space and the blending granulation parameter space are defined respectively, including: Establish multi-dimensional space; Dividing the single variable parameter interval set into an interface treatment parameter interval set and a blending and granulation parameter interval set according to the parameter mapping relationship; Perform parameter space marking in the multidimensional space according to the interface processing parameter interval set to obtain the interface processing parameter space; Parameter space marking is performed in the multidimensional space according to the blending and granulation parameter interval set to obtain the blending and granulation parameter space.

7. The method for reinforcing polypropylene material based on basalt long fibers according to claim 6, characterized in that: Applying a multi-objective optimization algorithm, performing a first optimization solution in the interface processing parameter space to obtain a first processing solution set, and performing a second optimization solution in the blending and granulation parameter space to obtain a second processing solution set, including: Applying a multi-objective optimization algorithm to the interface treatment parameter space, taking the material interface bonding strength and dispersion as the objective functions, to obtain the first set of optimization solutions; A multi-objective optimization algorithm was applied to the blending and granulation parameter space, and the second set of optimization solutions was obtained with tensile properties, corrosion resistance, and thermal stability as objective functions.

8. The method for reinforcing polypropylene material based on basalt long fibers according to claim 6, characterized in that: Performance-relative processing cost evaluation is performed based on preset evaluation indicators, including: For each of the M comprehensive treatment solutions, statistics are collected on the unit cost of raw materials, energy consumption, and equipment adaptability involved, and the corresponding comprehensive treatment cost is calculated based on a preset cost model; Based on the material performance index satisfaction and cost optimality as evaluation basis, a performance-relative processing cost evaluation is performed to obtain the performance-relative processing cost evaluation result.

9. The method for reinforcing polypropylene material based on basalt long fibers according to claim 5, characterized in that: Before outputting the first univariate parameter interval and the plurality of univariate parameter intervals as the univariate parameter interval set, the method further includes: Configuring an interval clipping constraint, wherein the interval clipping constraint is a clipping ratio value; Applying the interval clipping constraint to each single variable parameter interval, respectively eliminating the interval range of the front interval clipping constraint part and the back interval clipping constraint part of each single variable parameter interval, to generate a clipping result; Each univariate parameter interval is updated accordingly using the trimming result.

10. Polypropylene material reinforced platform based on basalt long fiber, characterized in that: The method for reinforcing a polypropylene material based on basalt long fibers according to any one of claims 1 to 9 comprises: The current process analysis module is used to analyze the current preparation process information, obtain the current process parameter set, and divide the process parameter set into an interface treatment parameter set and a blending and granulation parameter set; a parameter space construction module for performing fitting analysis on the interface treatment parameter set and the blending and granulation parameter set based on sample data, constructing a single variable parameter interval set, and defining the interface treatment parameter space and the blending and granulation parameter space respectively in combination with the parameter mapping relationship; An optimization solution module is used to apply a multi-objective optimization algorithm to perform a first optimization solution in the interface processing parameter space to obtain a first processing solution set, and to perform a second optimization solution in the blending and granulation parameter space to obtain a second processing solution set; a combination and cost evaluation module, configured to randomly combine the first treatment solution set and the second treatment solution set to form M comprehensive treatment solutions, and perform performance-relative treatment cost evaluation based on preset evaluation indicators; The optimal solution selection module is used to determine the comprehensive treatment solution with the best relative treatment cost based on the performance-relative treatment cost evaluation results, which is used for the preparation of polypropylene reinforced materials.

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