A Method and Platform for Reinforcing Polypropylene Materials Based on Basalt Long Fibers

Abstract

This invention discloses a method and platform for reinforcing polypropylene materials based on basalt long fibers, relating to the field of polypropylene material technology. The method includes: analyzing existing preparation processes, extracting process parameter sets and dividing them into interface treatment and blending / granulation parameter sets; performing sample fitting on the two parameter sets to construct univariate parameter intervals, and defining interface treatment parameter spaces and blending / granulation parameter spaces; applying a multi-objective optimization algorithm in the above two parameter spaces to obtain first and second treatment scheme sets respectively; randomly combining the two scheme sets to generate M comprehensive treatment schemes, evaluating the relative treatment costs based on preset indicators; and finally selecting the cost-optimized scheme for preparing polypropylene reinforced materials. This achieves the technical effects of data-driven accurate determination of parameter spaces, comprehensive balance of performance and cost to generate high-application-value schemes, and improved material reinforcement effects.

Description

Technical Field

[0001] This invention relates to the field of polypropylene materials technology, and in particular to a method and platform for reinforcing polypropylene materials based on basalt long fibers. Background Technology

[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 blending granulation processes, and the process parameters have a significant impact on material properties and production costs. Common process parameter control often relies on experience-based adjustments, lacking systematic and precise methods for classification and optimization. Especially when used in valve materials for agricultural applications, parameter optimization is not targeted enough, leading to poor interfacial bonding between basalt long fibers and the polypropylene matrix, and low blending granulation efficiency. Furthermore, past optimizations have often focused on improving a single performance characteristic, resulting in a cost-benefit imbalance in practical applications. Additionally, previous parameter determinations were largely based on theoretical models and subjective assumptions, which deviate significantly from actual production, easily introducing errors and affecting the reliability of process optimization. Summary of the Invention

[0004] This invention provides a method and platform for reinforcing polypropylene materials based on basalt long fibers, which solves the technical problems in the prior art such as inaccurate process parameter division and optimization, difficulty in balancing material performance and production costs, and errors caused by subjective experience in parameter determination. It achieves the technical effects of data-driven accurate determination of parameter space, comprehensive balance of performance and cost to generate high application value solutions, and improved material reinforcement effect.

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

[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 granulation parameter set.

[0007] The interface processing parameter set and the blending granulation parameter set are respectively fitted and analyzed based on sample data to construct a univariate parameter interval set, and the interface processing 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 scheme set, and a second optimization solution is performed in the blending granulation parameter space to obtain a second processing scheme set.

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

[0010] Based on the performance-relative treatment cost evaluation results, the comprehensive treatment scheme with the optimal relative treatment cost was determined for the preparation of polypropylene reinforced materials.

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

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

[0013] Analyze the current preparation process information, extract process node parameters, auxiliary agent addition parameters, and process setting index category parameters, and output them as the current process parameter set.

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

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

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

[0017] In one feasible implementation, a fitting analysis based on sample data is performed on the interface processing parameter set and the blending granulation parameter set to construct a univariate parameter interval set, including:

[0018] Define the parameters included in the interface processing parameter set and the blending granulation parameter set as analysis targets, and obtain the analysis target set.

[0019] Based on the set of analytical targets, a first analytical target is determined. Using the current preparation process information as the sample data, a univariate fitting analysis is performed 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 feature set is taken to generate the first univariate parameter interval, and the univariate fitting analysis is performed by traversing the analysis target set. The first univariate parameter interval and a plurality of univariate parameter intervals are output as the univariate parameter interval set.

[0021] In one feasible implementation, the interface processing parameter space and the blending granulation parameter space are defined separately by combining parameter mapping relationships, including:

[0022] Establish a multi-dimensional space.

[0023] Based on the parameter mapping relationship, the single-variable parameter interval set is divided into an interface processing parameter interval set and a blending granulation parameter interval set.

[0024] The interface processing parameter space is obtained by marking the parameter space according to the set of interface processing parameter intervals in the multidimensional space.

[0025] The blending granulation parameter space is obtained by marking the parameter space according to the set of blending granulation parameter intervals in the multidimensional space.

[0026] In one 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 set of processing schemes, and a second optimization solution is performed in the blending granulation parameter space to obtain a second set of processing schemes, including:

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

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

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

[0030] For each of the M integrated processing schemes, the unit cost of raw materials, energy consumption, and equipment compatibility are statistically analyzed, and the corresponding integrated processing cost is calculated based on the preset cost model.

[0031] Based on the satisfaction of material performance indicators and the optimality of cost, a performance-relative treatment cost evaluation is conducted to obtain the performance-relative treatment cost evaluation results.

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

[0033] Configure interval clipping constraints, wherein the interval clipping constraints are clipping ratio values.

[0034] Apply the interval clipping constraint to each single-variable parameter interval, and remove the interval ranges of the preceding and following interval clipping constraints for each single-variable parameter interval to generate the clipping result.

[0035] Update each single-variable parameter range according to the cropping result.

[0036] Secondly, the present invention also 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 treatment parameter set and a blending granulation parameter set.

[0038] The parameter space construction module is used to perform fitting analysis based on sample data on the interface processing parameter set and the blending granulation parameter set respectively, construct a univariate parameter interval set, and define the interface processing 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 scheme set, and to perform a second optimization solution in the blending granulation parameter space to obtain a second processing scheme set.

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

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

[0042] This invention discloses a method and platform for reinforcing polypropylene materials based on basalt long fibers, comprising: analyzing current preparation process information, obtaining a set of current process parameters, 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 based on sample data on the interface treatment parameter set and the blending granulation parameter set respectively, constructing corresponding univariate parameter interval sets, and defining the interface treatment parameter space and the blending granulation parameter space according to the parameter mapping relationship; applying a multi-objective optimization algorithm to perform a first optimization solution in the interface treatment parameter space to obtain a first treatment scheme set; performing a second optimization solution in the blending granulation parameter space to obtain a second treatment scheme set; and performing a combination of the first treatment scheme set and the second treatment scheme set. The method and platform for reinforcing polypropylene materials based on basalt long fibers disclosed in this invention solves the technical problems of inaccurate process parameter division and optimization, difficulty in balancing material performance and production cost, and error caused by subjective experience in parameter determination. It achieves the technical effects of data-driven accurate determination of parameter space, comprehensive balance of performance and cost to generate high-application-value solutions, and improved material reinforcement effect. The method and platform for reinforcing polypropylene materials based on basalt long fibers disclosed in this invention solves the technical problems of inaccurate process parameter division and optimization, difficulty in balancing material performance and production cost, and susceptibility of parameter determination to errors caused by subjective experience. Attached Figure Description

[0043] Figure 1 This is a schematic flowchart of the polypropylene material reinforcement method based on basalt long fibers according to the present invention.

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

[0045] Figure labeling: 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 Implementation

[0046] The above technical solutions will now be described in detail with reference to the accompanying drawings and specific embodiments to provide a better understanding of them. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. It should be understood that the present invention is not limited to the exemplary embodiments used only to explain the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. Furthermore, it should be noted that, for ease of description, only the parts related to the present invention are shown in the drawings, not all of them.

[0047] Example 1, as Figure 1 This is a schematic flowchart of the 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 includes:

[0048] S100: 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.

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

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

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

[0052] In some embodiments, the existing preparation process information is parsed to obtain the existing process parameter set, and the process parameter set is divided into an interface treatment parameter set and a blending granulation parameter set, including:

[0053] The preparation process, additive usage, and process settings 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 to extract process node parameters, additive addition parameters, and process setting index category parameters, and output as the current process parameter set. According to the action nodes, the current process parameter set is divided into interface treatment stage and blending granulation stage, and the interface treatment parameter set and the blending granulation parameter set are generated accordingly.

[0054] Specifically, the interface treatment parameter set mainly involves process parameters that improve the bonding force between fibers and the matrix, such as the temperature, time, pH value, and type and amount of coupling agent used in fiber surface treatment. The blending and granulation parameter set includes parameters that affect the uniformity of polymer blending and the quality of particle formation, such as the melting temperature, shear rate, residence time, and the amount of toughening agent and filler used during blending.

[0055] Specifically, firstly, through literature review, factory testing, or experimental records, information on the preparation process 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 agents, antioxidants, lubricants, dispersants, etc.; process settings: temperature (180℃), screw speed (80rpm), fiber content (20wt%), coupling agent concentration (1wt%), etc. Then, the above information was structured and extracted to form a set of current process parameters, exemplarily including:

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

[0057] Furthermore, based on the roles of the aforementioned parameters in the process flow, they are divided into two subsets: the interface treatment parameter set (used to enhance the 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 stages of the process variables but also provides structured support for subsequent staged control strategies.

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

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

[0060] Specifically, the interface treatment auxiliary 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 force between basalt fiber and polypropylene matrix and enhance interfacial compatibility. The interface treatment process parameter subset includes the operating conditions required during the treatment process, such as temperature, reaction pH, treatment time, and stirring rate, which directly affect the reaction efficiency and interfacial bonding quality between the auxiliary agents and the fiber.

[0061] Specifically, in the interface treatment stage, basalt long fibers typically require surface modification to improve their compatibility with polypropylene. The parameters for this stage can be further refined as follows:

[0062] Subset of interface treatment additive parameters, such as: modifier addition parameters: such as the addition amount of silane coupling agent KH550 2.5wt%; coupling agent addition method: such as spraying, impregnation, 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: e.g., 110℃; pH control: e.g., adjust to a weakly acidic pH of 4.5 to 5.5; reaction time: e.g., 30 minutes; stirring method and rate: e.g., magnetic stirring at 300 rpm.

[0064] Example: In the development of valve materials for agricultural use, basalt fibers are impregnated with KH550 at a temperature of 105℃, 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 subset of phase bulk additive parameters and a subset of blending process parameters. The subset of phase bulk additive parameters includes at least toughening agent addition parameters, filler addition parameters, reinforcing agent addition parameters, and comonomer or blend material addition parameters. The subset of blending process parameters includes at least melting temperature, shear rate, and fiber length retention rate.

[0066] Specifically, the subset of phase bulk additive parameters refers to various functional additives introduced into the polypropylene matrix, such as toughening agents, fillers, reinforcing agents, comonomers, or other blended polymers. Their function is to adjust the mechanical properties, thermal stability, and processing rheology of the material. The subset of blending process parameters refers to process control variables such as temperature, shear rate, and fiber length retention rate involved in twin-screw extrusion or melt blending. These are crucial to fiber dispersion, orientation, and the performance of the final composite material.

[0067] Specifically, in 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] Subset of phase bulk additive parameters, such as: toughening agent addition parameters: such as POE (polyolefin elastomer) addition of 10wt%; filler addition parameters: such as talc and CaCO3 addition of 5wt%; reinforcing agent addition parameters: such as chopped glass fiber of 3wt%; comonomers or blends: such as adding EVA or SEBS modifier of 8wt%.

[0069] Subset of blending process parameters, such as: melt temperature: such as 210~230℃; shear rate (screw speed): such as 100~150rpm; 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 cooling or water cooling granulation.

[0070] Example: In a scenario involving agricultural drip irrigation valve materials, a combination of POE toughening and talc filling, along with a melting temperature of 220℃ and low shear blending at 120rpm, was used to 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 granulation parameter set into "auxiliary parameter subset" and "process parameter subset", the dimensional expansion and hierarchical modeling of material preparation process parameters are realized, making process control more refined from "overall adjustment" to "auxiliary-process dual-channel optimization", which helps to improve the accuracy and controllability of process control.

[0072] S200: Perform fitting analysis based on sample data on the interface processing parameter set and the blending granulation parameter set respectively, construct a univariate parameter interval set, and define the interface processing parameter space and the blending granulation parameter space respectively in combination with the parameter mapping relationship.

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

[0074] Specifically, the univariate parameter range set is determined by performing univariate analysis on each parameter to identify its effective range that significantly affects material properties. The parameter mapping relationship describes the correlation between different parameters and process nodes, enabling the integration of the univariate parameter range set 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, provides an effective operating platform for multi-objective optimization algorithms, improves the efficiency and effectiveness of optimization solutions, helps to find better combinations of process parameters, and enhances the overall performance of materials.

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

[0077] Define the parameters included in the interface processing parameter set and the blending granulation parameter set as analysis targets, and obtain an analysis target set; determine a first analysis target based on the analysis target set, and perform univariate fitting analysis using the current preparation process information as the sample data 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; 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 a plurality of univariate parameter intervals as the univariate parameter interval set.

[0078] Specifically, sample data refers to the data set containing process parameters and corresponding material performance indicators collected during actual experiments or production processes. For example, the tensile strength, impact strength, and other performance test results of 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 processing temperature, coupling agent concentration, melting temperature, and shear rate.

[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 controlled within the statistical tolerance range. Methods include linear fitting, polynomial fitting, and locally weighted regression (LOWESS). A univariate characteristic curve is the fitting function curve between a parameter and a performance index; a univariate parameter interval refers to the range of parameter values ​​corresponding to the curve; the set of intervals for multiple parameter terms constitutes the univariate parameter interval set.

[0080] Specifically, firstly, an analytical target set is extracted from the structured parameter set, namely: analytical targets from the interface treatment parameter set (e.g., KH550 concentration, treatment temperature, and treatment time); and analytical targets from the blending and granulation parameter set (e.g., melt temperature, shear rate, and fiber length retention rate), forming the analytical target set. Then, using a specific parameter from the analytical target set (e.g., KH550 concentration) as the primary analytical target, a univariate fitting analysis is performed based on 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 to each performance dimension to obtain a set of univariate characteristic curves, which constitute the first univariate characteristic set:

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

[0085] C KH550 - Impact strength: The fitted curve is a parabola, with the optimal range being 2.0–2.8 wt%.

[0086] C KH550 - Flexural modulus: The fitted curve rises slightly and then tends to plateau.

[0087] Optionally, from the aforementioned characteristic curves, extract the parameter range corresponding to each performance index meeting the design requirements, and take the intersection to obtain the first univariate parameter range. Further, repeat the above process to perform univariate fitting analysis on each of the other parameter items in the analysis target set, ultimately forming a complete set of univariate parameter ranges.

[0088] Through the univariate fitting analysis based on sample data described above, a set of univariate parameter intervals covering multiple key process parameters was constructed, realizing the transformation from empirical parameter setting to data-driven parameter determination. The beneficial technical effects include: more targeted parameter optimization, directly identifying the optimal or most stable parameter intervals through characteristic curves, avoiding blind trial and error; more interpretable performance prediction, with the univariate relationship between each process parameter and performance visualized, helping process engineers understand the parameter's mechanism of action; providing a foundation for multivariate collaborative optimization, as univariate intervals can serve as initial boundary conditions for multivariate optimization (such as response surface analysis, genetic algorithms, and Bayesian optimization); and improved product consistency and reliability, as setting process parameters within the optimal univariate interval helps control product performance fluctuations.

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

[0090] In some implementations, before outputting the first univariate parameter interval and a plurality of univariate parameter intervals as the set of univariate parameter intervals, the following steps are also included:

[0091] Configure interval clipping constraints, wherein the interval clipping constraints are clipping ratio values; apply the interval clipping constraints to each single variable parameter interval, respectively removing the interval ranges of the preceding interval clipping constraint portion and the following interval clipping constraint portion of each single variable parameter interval, and generate clipping results; update each single variable parameter interval accordingly with the clipping results.

[0092] Specifically, interval clipping constraints refer to the operational constraints that remove the boundaries of the fitted univariate parameter intervals. They are usually expressed in the form of clipping ratios (such as 10% or 15%) and are used to remove the edge values ​​at both ends of the interval. Although these edge values ​​meet the performance requirements, they may cause problems such as decreased marginal returns, large performance fluctuations, high difficulty in process control, or insufficient reliability in actual processes.

[0093] Specifically, the first interval trimming refers to the portion of the parameter interval that is proportionally removed from the beginning (smaller value); the second interval trimming refers to the portion of the parameter interval that is proportionally removed from the end (larger value). The trimming result is a narrower but more stable parameter interval obtained after applying the trimming ratio, which serves as the final parameter range used for process settings.

[0094] Optionally, a uniform or parameter-specific cutting ratio value can be set as follows: Uniform cutting ratio: For example, setting all parameter ranges to cut 10% at both ends, i.e., cutting 5% at both ends; Parameter-specific cutting ratio: For example, setting the cutting ratio to 15% for KH550 concentration and 5% for melting temperature, to reflect the sensitivity of different parameters to performance.

[0095] Specifically, the above trimming operation is performed on all univariate parameter intervals to obtain a trimming result set, which is then output as the formal univariate parameter interval set for subsequent process window setting, multivariate optimization, or intelligent control input.

[0096] By introducing and applying interval trimming constraints, the resulting single-variable parameter interval set has the following significant technical advantages: it eliminates edge fluctuation regions, retains core intervals with higher performance stability, and reduces the risk of performance anomalies caused by process disturbances; the reduced parameter intervals are closer to actual control precision, which helps to adjust equipment precisely and automate execution; setting process parameters within the trimmed stable intervals helps to reduce performance fluctuations between batches; and the narrower parameter intervals make it easier for the control system to achieve rapid convergence and error suppression in closed-loop regulation.

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

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

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

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

[0101] Specifically, firstly, a complete multi-dimensional 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 granulation parameter interval set; next, for each parameter subset, all points or regions that satisfy the interval constraints are marked in the subspace formed by its corresponding dimension, and the interface treatment parameter space and the blending granulation parameter space are obtained accordingly.

[0102] Optionally, the labeling method can be: low-difference sampling methods such as grid sampling, Latin hypercube sampling (LHS), Sobol sequence, or directly constructing a bounding box to represent the feasible region.

[0103] Through the above process, the set of univariate parameter intervals is mapped to a multidimensional space and divided into two subspaces—interface processing and blending granulation—based on process logic. This achieves a systematic expression of parameter combinations: compared to univariate intervals, parameter space can express the synergistic combination relationships between multiple parameters, helping to capture "combination optimality" rather than "single-point optimality"; it improves optimization efficiency and accuracy: searching or optimizing in a labeled parameter space (such as response surface analysis or genetic algorithms) can significantly narrow the search range and improve computational efficiency; and it enhances visualization and decision support capabilities: the multidimensional parameter space can be visualized through dimensionality reduction, assisting process engineers in understanding parameter relationships.

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

[0105] Specifically, multi-objective optimization algorithms are mathematical algorithms capable of simultaneously optimizing multiple objective functions, often used to seek balances and optimal solutions among multiple performance indicators. Based on multi-objective optimization algorithms, optimization solutions can be obtained in the interface treatment parameter space and the blending granulation parameter space, with material interfacial bonding force, dispersibility, tensile properties, corrosion resistance, and thermal stability as objective functions, respectively.

[0106] The first and second processing scheme sets each contain a series of different combinations of process parameters 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, several optimal combinations of process parameters can be obtained in the two key stages of interface treatment and blending granulation. These solutions not only consider the improvement of material properties but also take into account the balance between different performance indicators, providing diverse options for the generation of subsequent comprehensive treatment schemes.

[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 scheme set, and a second optimization solution is performed in the blending granulation parameter space to obtain a second processing scheme set, including:

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

[0110] Specifically, interfacial bonding strength refers to the bond strength between basalt fibers and polypropylene matrix, which determines the efficiency of the composite material in the stress transfer process; dispersion measures the degree of 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 material's mechanical load-bearing capacity; corrosion resistance measures the material's ability to maintain its properties in acid, alkali, or salt spray environments; thermal stability refers to the material's ability to maintain its structure and properties at high temperatures, and is commonly characterized by thermogravimetric temperature (TGA) or heat distortion temperature (HDT).

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

[0113] Input space: Interface processing parameter space, dimensions such as KH550 concentration, processing temperature, and processing time; Objective function: Maximize interface binding force (unit: MPa) and maximize fiber dispersion (quantitative indicators, such as image uniformity score or particle size variation coefficient CV); Optimization method: NSGA-II algorithm is adopted, with a population size of 100 and an evolutionary generation of 200; Output result: A Pareto front solution set is obtained, called the first processing scheme set, and each solution corresponds to a set of interface processing parameter combinations.

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

[0115] Table 2. Exemplary Set of First Processing Schemes

[0116]

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

[0118] Input space: Blending and granulation parameter space, with dimensions such as melt temperature, screw speed, and blending time; Objective function: Maximize tensile strength (MPa), maximize corrosion resistance (e.g., mass retention rate %) after salt spray test, and maximize thermal stability (e.g., thermogravimetric initiation temperature T5%, unit: °C); Optimization method: Use the MOEA / D algorithm combined with a weighted decomposition strategy to handle the three-objective problem; Output result: Obtain a second set of processing schemes, where each solution is a set of blending and granulation parameter combinations.

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

[0120] Table 3. Exemplary Set of First Processing Schemes

[0121]

[0122]

[0123] Through the above process, multi-objective optimization algorithms are applied in the parameter spaces of interface treatment and blending granulation, respectively. This decomposes the complex composite material preparation process into two key stages, which are then optimized separately. This helps to reduce dimensionality and improve optimization efficiency. By using multi-objective algorithms, the performance sacrifice caused by a single performance orientation is avoided, and a comprehensive performance improvement is achieved. This provides a high-quality solution set for subsequent process combination recommendations.

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

[0125] Specifically, random combination refers to randomly selecting and combining schemes from the first and second treatment scheme sets to form a comprehensive treatment scheme. Pre-defined evaluation indicators are pre-set standards used to assess the merits of each comprehensive treatment scheme, including material performance index satisfaction and treatment cost. Performance-relative treatment cost evaluation involves conducting a cost-benefit analysis of each comprehensive treatment scheme based on the pre-defined evaluation indicators, comparing different schemes to determine which scheme has the lowest relative cost and highest efficiency while meeting performance requirements and achieving optimal performance.

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

[0127] Through the above process, multiple comprehensive treatment options can be generated, and the processing cost of each option can be evaluated while meeting the material performance requirements. This helps to select the optimal option from among many possible solutions that meets the performance requirements and has a lower cost.

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

[0129] For each of the M integrated processing schemes, the unit cost of raw materials, energy consumption, and equipment compatibility are statistically analyzed, and the corresponding integrated processing cost is calculated according to the preset cost model. Based on the material performance index satisfaction and cost optimization, a performance-relative processing cost evaluation is performed to obtain the performance-relative processing cost evaluation results.

[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 energy costs (such as electricity costs) caused by processing temperature, time, equipment power, etc.; equipment compatibility refers to whether the process parameters are compatible with the existing production line. If modification or replacement of equipment is required, additional costs will be included.

[0131] Specifically, the preset cost model is a function model used to calculate the overall processing cost. It is usually a linear or weighted summation model, where the weights are adjustable to reflect the importance of different cost factors.

[0132] Specifically, material performance index satisfaction refers to the degree to which a solution achieves target values ​​in 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 constitutes the two-dimensional basis for performance-relative processing cost evaluation.

[0133] Specifically, from the aforementioned optimization results, a set of performance indicators corresponding to each scheme is obtained. Then, target values ​​are set, and the performance satisfaction score of each scheme is calculated, such as by weighted summation after normalization. Next, the performance satisfaction and overall cost of all M schemes are sorted in two dimensions to construct a performance-cost two-dimensional plane. Based on the evaluation strategy (such as performance satisfaction ≥ threshold, cost below average), a set of relatively advantageous schemes is selected as the final recommended process configuration scheme.

[0134] Optionally, a weighted approach can be used to integrate material performance index satisfaction and cost optimization, and the optimal performance and cost-effective solution can be selected as the final recommended process configuration based on this integration. Here, material performance index satisfaction represents the requirement for performance optimization, while cost optimization incorporates considerations of cost rationality.

[0135] By introducing a pre-set cost model, cost assessment and performance matching of the optimized comprehensive processing scheme were achieved, enabling intuitive comparison and ranking of multiple candidate schemes. In addition to optimizing material performance, actual production costs were also considered, improving the engineering feasibility of the scheme. This provides important data support for subsequent deployment, cost control, and intelligent recommendation.

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

[0137] Specifically, the optimal relative processing cost refers to the solution with the lowest overall processing cost (e.g., the solution with the highest cost-effectiveness ratio) while ensuring that the material performance meets application requirements. In other words, this solution is not necessarily the one with the strongest absolute performance, but rather the option with the lowest cost while providing sufficient performance, reflecting the concept of "optimal cost-effectiveness" in industrial practice. This solution will be practically applied in the production process of basalt long fiber reinforced polypropylene composites, completed 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 this invention has the following technical effects:

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

[0140] Example 2, as Figure 2 This is a schematic diagram of the polypropylene material reinforcement platform based on basalt long fibers according to the present invention. For example, Figure 1 The flowchart of the polypropylene material reinforcement method based on basalt long fibers of the present invention can be seen 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 embodiments described above, the present invention also provides a polypropylene material reinforcement platform based on basalt long fibers, comprising:

[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 treatment parameter set and a blending granulation parameter set.

[0143] The parameter space construction module 12 is used to perform fitting analysis based on sample data on the interface processing parameter set and the blending granulation parameter set respectively, construct a univariate parameter interval set, and define the interface processing 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 scheme set, and to perform a second optimization solution in the blending granulation parameter space to obtain a second processing scheme set.

[0145] The combination and cost evaluation module 14 is used to randomly combine the first processing scheme set and the second processing scheme set to form M comprehensive processing schemes, and to 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 scheme with the optimal relative treatment cost based on the performance-relative treatment cost evaluation results, for the preparation of polypropylene reinforced materials.

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

[0148] The current preparation process information acquisition 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, and 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, additive addition parameters, and process setting index category parameters, and output the current process parameter set.

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

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

[0152] In some implementations, the blending granulation parameter set includes a subset of phase bulk additive parameters and a subset of blending process parameters. The subset of phase bulk additive parameters includes at least toughening agent addition parameters, filler addition parameters, reinforcing agent addition parameters, and comonomer or blend material addition parameters. The subset of blending process parameters 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 granulation parameter set as analysis targets, and to acquire the analysis target set.

[0155] The univariate fitting analysis and characteristic set acquisition unit is used to determine the first analysis target based on the analysis target set, perform univariate fitting analysis on the current preparation process information as the sample data, and acquire the 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] The 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, and to traverse the analysis target set to perform univariate fitting analysis, and output the first univariate parameter interval and a plurality of univariate parameter intervals as the univariate parameter interval set.

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

[0158] Multidimensional space creation unit, used to create multidimensional space.

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

[0160] The interface processing parameter space acquisition unit is used to acquire the interface processing parameter space by marking the parameter space according to the set of interface processing parameter intervals in the multi-dimensional space.

[0161] The blending granulation parameter space acquisition unit is used to obtain the blending granulation parameter space by marking the parameter space according to the set of blending granulation parameter intervals in the multi-dimensional 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, with the material interface bonding force and dispersion as the objective function, to obtain the first set of optimization schemes.

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

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

[0166] The integrated processing scheme cost calculation unit is used to calculate the unit cost of raw materials, energy consumption and equipment adaptability of each of the M integrated processing schemes, and calculate the corresponding integrated processing cost according to the preset cost model.

[0167] The relative processing cost evaluation unit is used to evaluate the performance-relative processing cost based on the satisfaction of material performance indicators and the optimality of cost, and to obtain the performance-relative processing cost evaluation results.

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

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

[0170] The univariate parameter interval trimming subunit is used to apply the interval trimming constraint to each univariate parameter interval, and remove the interval range of the previous interval trimming constraint part and the subsequent interval trimming constraint part of each univariate parameter interval respectively to generate the trimming result.

[0171] The univariate parameter interval update subunit is used to update each univariate parameter interval according to the clipping result.

[0172] It should be understood that the focus of the embodiments mentioned in this specification is their difference from other embodiments. The specific embodiments in the aforementioned Embodiment 1 are also applicable to the polypropylene material reinforcement platform based on basalt long fibers described in Embodiment 2. For the sake of brevity, they will not be elaborated further here.

[0173] It should be understood that the embodiments disclosed in this invention and the above description enable those skilled in the art to implement this invention. However, this 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 foregoing embodiments or make equivalent substitutions for 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 this invention, and should all be included within the protection scope of this invention.

Claims

1. A method for reinforcing a polypropylene material based on basalt continuous fibers, characterized in that, The method comprises the following steps: Resolving the current preparation process information, obtaining the current process parameter set, and dividing the process parameter set into interface treatment parameter set and blending granulation parameter set; Respectively performing fitting analysis based on sample data on the interface treatment parameter set and the blending granulation parameter set, constructing a single variable parameter interval set, and defining an interface treatment parameter space and a blending granulation parameter space respectively in combination with a parameter mapping relationship; Applying a multi-objective optimization algorithm, performing first optimization solving in the interface treatment parameter space to obtain a first processing scheme set, and performing second optimization solving in the blending granulation parameter space to obtain a second processing scheme set; Randomly combining the first processing scheme set and the second processing scheme set to form M comprehensive processing schemes, and performing performance-relative processing cost evaluation based on a preset evaluation index; According to the performance-relative processing cost evaluation result, determining the comprehensive processing scheme with the optimal relative processing cost for the preparation of polypropylene reinforced material; The interface treatment parameter set comprises an interface treatment auxiliary parameter subset and an interface treatment process parameter subset; wherein the interface treatment auxiliary parameter subset at least comprises modifier addition parameter, coupling agent addition parameter; the interface treatment process parameter subset at least comprises temperature, pH, reaction time; The blending granulation parameter set comprises a phase body auxiliary parameter subset and a blending process parameter subset, wherein the phase body auxiliary parameter subset at least comprises toughening agent addition parameter, filler addition parameter, reinforcing agent addition parameter, and copolymer monomer or blending material addition parameter; the blending process parameter subset at least comprises melting temperature, shear rate, and fiber length retention rate.

2. The basalt continuous fiber-based polypropylene material reinforcement method according to claim 1, wherein, Resolving the current preparation process information, obtaining the current process parameter set, and dividing the process parameter set into interface treatment parameter set and blending granulation parameter set, comprising: Collecting the preparation process, auxiliary agent use and process setting value of basalt fiber reinforced polypropylene material under agricultural application conditions to form the current preparation process information; Analyzing the current preparation process information, extracting process node parameters, auxiliary agent addition parameters and process setting index category parameters, and outputting as the current process parameter set; According to the action node, the current process parameter set is divided into interface treatment stage and blending granulation stage, and the interface treatment parameter set and the blending granulation parameter set are correspondingly generated.

3. The basalt continuous fiber-based polypropylene material reinforcement method according to claim 2, wherein, Respectively performing fitting analysis based on sample data on the interface treatment parameter set and the blending granulation parameter set, constructing a single variable parameter interval set, comprising: Defining the parameter items included in the interface treatment parameter set and the blending granulation parameter set as analysis targets, and obtaining an analysis target set; Based on the analysis target set, a first analysis target is determined, and single variable fitting analysis is performed on the current preparation process information as the sample data to obtain a first single variable characteristic set, wherein the single variable characteristic set comprises a plurality of single variable characteristic curves corresponding to a plurality of material performance dimensions; The first single variable parameter interval and a plurality of single variable parameter intervals are taken as the single variable parameter interval set.

4. The basalt continuous fiber-based polypropylene material reinforcement method according to claim 3, wherein, The interface processing parameter space and the blending and granulation parameter space are respectively defined by combining the parameter mapping relationship, including: A multi-dimensional space is established; The single variable parameter interval set is divided into the interface processing parameter interval set and the blending and granulation parameter interval set according to the parameter mapping relationship; The interface processing parameter space is obtained by performing parameter space marking on the interface processing parameter interval set in the multi-dimensional space; The blending and granulation parameter space is obtained by performing parameter space marking on the blending and granulation parameter interval set in the multi-dimensional space.

5. The basalt continuous fiber-based polypropylene material reinforcement method according to claim 4, wherein, A multi-objective optimization algorithm is applied to perform first optimization solving in the interface processing parameter space to obtain a first processing scheme set, and second optimization solving in the blending and granulation parameter space to obtain a second processing scheme set, including: A multi-objective optimization algorithm is applied to the interface processing parameter space to obtain a first optimization scheme set, taking material interface bonding force and dispersibility as objective functions; A multi-objective optimization algorithm is applied to the blending and granulation parameter space to obtain a second optimization scheme set, taking tensile property, corrosion resistance and thermal stability as objective functions.

6. The basalt continuous fiber-based polypropylene material reinforcement method according to claim 4, wherein, Performance-relative processing cost evaluation is performed based on a preset evaluation index, including: The unit cost of raw materials, energy consumption and equipment adaptability involved in the M comprehensive processing schemes are respectively counted, and the corresponding comprehensive processing cost is calculated according to a preset cost model; Performance-relative processing cost evaluation is performed based on material performance index satisfaction and cost optimality to obtain the performance-relative processing cost evaluation result.

7. The basalt continuous fiber-based polypropylene material reinforcement method according to claim 3, wherein, Before the first single variable parameter interval and a plurality of single variable parameter intervals are taken as the single variable parameter interval set, there is further including: An interval cutting constraint is configured, wherein the interval cutting constraint is a cutting proportion value; The interval cutting constraint is applied to each single variable parameter interval to respectively remove the interval range of the front interval cutting constraint part and the rear interval cutting constraint part of each single variable parameter interval to generate a cutting result; Each single variable parameter interval is updated according to the cutting result.

8. Basalt long fiber based polypropylene material reinforced platform characterized in that, The basalt long fiber-based polypropylene material reinforcing method of any one of claims 1-7, including: A current process analysis module is configured to analyze current preparation process information, obtain a current process parameter set, and divide the process parameter set into an interface processing parameter set and a blending and granulation parameter set; A parameter space construction module is configured to respectively perform fitting analysis on the interface processing parameter set and the blending and granulation parameter set based on sample data, construct a single variable parameter interval set, and respectively define an interface processing parameter space and a blending and granulation parameter space by combining a parameter mapping relationship; An optimization solving module is configured to apply a multi-objective optimization algorithm to perform first optimization solving in the interface processing parameter space to obtain a first processing scheme set, and perform second optimization solving in the blending and granulation parameter space to obtain a second processing scheme set; An optimization solving module is configured to apply a multi-objective optimization algorithm to perform first optimization solving in the interface processing parameter space to obtain a first processing scheme set, and perform second optimization solving in the blending and granulation parameter space to obtain a second processing scheme set; The combination and cost evaluation module is configured to randomly combine the first processing scheme set and the second processing scheme set to form M comprehensive processing schemes, and perform performance-relative processing cost evaluation based on preset evaluation indexes. The optimal scheme selection module is configured to determine a comprehensive processing scheme with optimal relative processing cost according to the performance-relative processing cost evaluation result, and the comprehensive processing scheme is used for preparation of the polypropylene reinforced material.

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