A method and system for processing spc non-slip floor

Abstract

The present application relates to the technical field of anti-slip floor processing, and particularly relates to a SPC anti-slip floor processing method and system, which comprises the following steps: analyzing the characteristics of the SPC anti-slip floor and obtaining target functional requirements, selecting raw material components according to the target functional requirements and generating a preliminary forming scheme; calculating forming processing parameters of each processing flow step in the forming manufacturing process according to the preliminary forming scheme; mapping and comparing the estimated target performance of the product predicted according to the forming processing parameters with the target functional requirements to generate a verification comparison result; modifying the forming processing parameters based on the verification comparison result to generate an SPC floor forming scheme based on the target functional requirements. Through the present application, the problems of inaccurate raw material selection, processing parameter optimization, performance prediction and inability to adapt to parameter adjustment changes under multiple requirements in the production process of the SPC anti-slip floor are effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of anti-skid floor processing, and in particular to a SPC anti-skid floor processing method and system. Background Art

[0002] SPC anti-slip flooring is made of stone-plastic composite materials. It offers advantages such as anti-slip, wear resistance, and strong water resistance. It is widely used in public places and high-traffic environments. In the traditional SPC flooring manufacturing process, the selection of raw materials, processing technology, and optimization of molding parameters all have a significant impact on the performance of the final product.

[0003] Existing technologies typically rely heavily on experimentation and experience to determine the ratio of processing parameters to raw materials. This can lead to unstable product performance, especially in complex, multi-layered structures where the intertwined parameters of the molding process are difficult to accurately predict and control. This approach cannot accurately adjust dynamically to meet different functional requirements, and therefore cannot guarantee optimal performance in different application scenarios. This makes product performance, especially in key performance areas such as anti-slip properties and durability, susceptible to processing errors.

[0004] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present disclosure and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0005] The present invention provides a method and system for processing an SPC anti-slip floor, which can effectively solve the problems in the background technology.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] A method for processing an SPC anti-slip floor, the method comprising:

[0008] Analyze the characteristics of SPC anti-slip flooring and obtain target functional requirements, select raw material components based on the target functional requirements and generate a preliminary molding plan;

[0009] Calculating the molding parameters of each processing step in the molding manufacturing process according to the preliminary molding plan;

[0010] Predicting the estimated target performance of the product in layers according to the molding process parameters, performing a mapping comparison with the target functional requirements, and generating a verification comparison result;

[0011] The forming processing parameters are modified based on the verification and comparison results to generate an SPC floor forming plan based on the target functional requirements.

[0012] Furthermore, the raw material components are selected according to the target functional requirements and a preliminary molding plan is generated, including:

[0013] Establish material selection criteria that meet the stated target functional requirements;

[0014] Based on the material selection criteria, screening the adapted raw material components from the raw material component library and obtaining material component characteristics;

[0015] Establishing the layering requirements of the SPC anti-slip floor based on the material composition characteristics and the target functional requirements;

[0016] The compatibility of the original material composition with the layering requirements is verified through simulation analysis, and a preliminary molding solution that meets the target functional requirements is generated.

[0017] Furthermore, the compatibility of the original material composition with the stratification requirements is verified through simulation analysis, including:

[0018] Establishing a material adaptation analysis model based on the material composition characteristics and the stratification requirements, and mapping the original material composition to the stratification requirements;

[0019] Based on the material adaptation analysis model, simulating and analyzing the performance results of the original material components during the processing for each of the layered requirements;

[0020] Calculating the adaptation coefficient between the original material composition and each of the layering requirements based on the performance results;

[0021] The adaptation coefficient is integrated and analyzed to evaluate the adaptability between the original material composition and the stratification requirements.

[0022] Furthermore, the molding processing parameters of each step in the molding process are calculated according to the preliminary molding scheme, including:

[0023] Establishing a dynamic correlation model between the preliminary shaping scheme and the characteristics of the original material composition;

[0024] Analyzing the influence relationship between parameters in the molding process according to the design requirements of the preliminary molding scheme, and generating an initial processing parameter set adapted to the composition of the original material;

[0025] Analyze the initial processing parameter set step by step according to the dynamic correlation model to generate parameter analysis thresholds for each molding step;

[0026] The parameter analysis thresholds are integrated to determine the upper and lower limits of the parameters and generate the molding processing parameters.

[0027] Furthermore, the initial processing parameter set is analyzed step by step according to the dynamic correlation model, including:

[0028] Extracting SPC multi-dimensional parameters related to the processing steps in the initial processing parameter set;

[0029] Calculating the operating range of the SPC multi-dimensional parameters in each of the processing steps based on the dynamic correlation model;

[0030] Performing dynamic coupling analysis on the operating range in the order of the processing steps to generate the SPC multi-dimensional parameter set corresponding to each processing step;

[0031] The SPC multi-dimensional parameter set is mapped and compared with the target performance requirement, and the parameter analysis threshold that meets the design requirements is screened.

[0032] Furthermore, a dynamic coupling analysis is performed on the operating range in the order of the processing steps, including:

[0033] Establishing a parameter correlation matrix between the processing steps and determining the interaction and sensitivity of the SPC multi-dimensional parameters in each step;

[0034] Calculating the coupling weight of the SPC multi-dimensional parameters in each of the processing steps based on the parameter correlation matrix, and generating the operating range of each of the processing steps;

[0035] Performing a time series simulation for each of the operating ranges, evaluating the impact on subsequent processing steps and the target performance requirements, and generating a dynamic parameter set;

[0036] Inputting the dynamic parameter set into the next processing step to complete the dynamic coupling analysis of the processing step;

[0037] The results of the dynamic coupling analysis are integrated to generate a parameter set corresponding to each of the processing steps.

[0038] Furthermore, the estimated target performance of the product is predicted in layers according to the molding process parameters, including:

[0039] Performing layered performance mapping of the multi-layer structure of the SPC floor according to the molding processing parameters, and predicting the dynamic behavior change response of the raw material components during the processing layer by layer;

[0040] According to the target functional requirements and based on the time series, a nonlinear trend analysis is performed on the dynamic behavior change response, and the performance prediction weight of each layer is dynamically adjusted;

[0041] analyzing the coupling effect between the forming processing parameters on the target performance based on the performance prediction weight;

[0042] comprehensively predicting the estimated performance target of the product based on the performance prediction weight according to the result of the dynamic prediction.

[0043] Further, according to the target functional requirement and based on the time sequence, a nonlinear trend analysis is performed on the dynamic behavior change response, including:

[0044] obtaining dynamic change data of the forming processing parameters in the time sequence, and establishing a multi-dimensional time sequence matrix in the processing process according to the dynamic change data;

[0045] performing nonlinear regression analysis on the multi-dimensional time sequence matrix to identify key change points of each forming processing parameter in the forming process;

[0046] based on the weight distribution of the performance prediction weight, combining the key change points in the time sequence, and constructing a nonlinear performance influence function;

[0047] According to the nonlinear performance influence function, the dynamic interaction relationship between the forming processing parameters is analyzed in layers to generate a performance response curve of each layer to the target functional requirement.

[0048] An SPC anti-slip floor processing system, the system comprising:

[0049] An initial analysis module analyzes the characteristics of the SPC anti-slip floor and obtains the target functional requirement, selects the raw material composition according to the target functional requirement, and generates a preliminary forming scheme;

[0050] A parameter calculation module calculates the forming processing parameters of each processing flow step in the forming manufacturing process according to the preliminary forming scheme;

[0051] A mapping comparison module layer-by-layer predicts the estimated target performance of the product according to the forming processing parameters, and performs mapping comparison with the target functional requirement to generate a verification comparison result;

[0052] A scheme generation module corrects the forming processing parameters based on the verification comparison result to generate an SPC floor forming scheme based on the target functional requirement.

[0053] Further, the initial analysis module comprises:

[0054] A standard construction unit constructs a material selection standard meeting the target functional requirement;

[0055] A material selection unit selects an appropriate raw material composition from a raw material composition library based on the material selection standard and obtains the material composition characteristics;

[0056] Layered splitting units, establishing the layering requirements of SPC anti-slip flooring based on material composition characteristics and target functional requirements;

[0057] The solution generation unit verifies the compatibility of the original material composition with the layering requirements through simulation analysis, and generates a preliminary molding solution that meets the target functional requirements.

[0058] The technical solution of the present invention can achieve the following technical effects:

[0059] The performance stability of SPC anti-slip flooring is improved. Through dynamic optimization and precise simulation analysis, it ensures that the target functional requirements are met, improves production efficiency, reduces the time and cost of adjustment and experimentation, reduces uncertainty in the production process, and ensures the consistency of product quality.

[0060] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0062] Figure 1 The figure is a flow chart of a method for processing SPC anti-slip flooring;

[0063] Figure 2 A schematic diagram of the process for generating a preliminary molding solution;

[0064] Figure 3 Schematic diagram of the process for calculating molding processing parameters;

[0065] Figure 4 Schematic diagram of the structure for estimating target performance of predicted products. DETAILED DESCRIPTION

[0066] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0067] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0068] Embodiment 1;

[0069] like Figure 1 As shown, the present application provides a method for processing an SPC anti-slip floor, the method comprising:

[0070] S10: Analyze the characteristics of the SPC anti-slip floor and obtain the target functional requirements, select the raw material components according to the target functional requirements and generate a preliminary molding plan;

[0071] S20: Calculating molding parameters of each processing step in the molding manufacturing process according to the preliminary molding plan;

[0072] S30: Predict the estimated target performance of the product according to the molding processing parameters in a hierarchical manner, perform a mapping comparison with the target functional requirements, and generate a verification comparison result;

[0073] S40: Based on the verification and comparison results, the molding processing parameters are modified to generate an SPC floor molding plan based on the target functional requirements.

[0074] Specifically, first analyze the basic physical and chemical properties of SPC anti-slip flooring, such as wear resistance, pressure resistance, anti-slip, water resistance, etc., and obtain the target functional requirements based on the application scenario and usage requirements. For example, if the floor is used in a hospital environment, higher anti-slip and antibacterial properties are required; if it is used in a commercial place, more attention may be paid to wear resistance and aesthetics; according to the target functional requirements, select appropriate raw material components. Taking anti-slip flooring as an example, it may be necessary to select polyvinyl chloride (PVC), mineral powder and other components with good anti-slip properties. In the material selection process, consider the physical properties of different materials, such as hardness and friction coefficient, and generate a preliminary molding scheme, including preliminary parameters such as temperature, pressure, and time; according to the generated preliminary molding scheme, calculate the molding processing parameters of each processing step. For example, in the hot pressing process, it is necessary to determine the temperature (for example, 200°C), pressure (for example, 10 MPa) and time (e.g. 5 minutes). These parameters will be adjusted according to the characteristics of the raw material composition and the product design requirements. The calculated molding processing parameters will be used to predict the product in layers. The performance of each layer, such as anti-slip, wear resistance, thickness, etc., will be predicted through multi-level simulation analysis. For example, finite element analysis can be used to simulate the performance of the floor under different conditions. By predicting the performance of each layer, it is determined whether the target functional requirements are met. The prediction results will be mapped and compared with the target functional requirements to generate verification and comparison results. If it is found that some performance does not meet the requirements (e.g. low anti-slip performance), the molding processing parameters will be corrected. For example, the pressure can be increased or the temperature can be adjusted to optimize the anti-slip performance of the product. The layered prediction will be re-performed. The revised molding plan will be verified again with the target functional requirements to ensure that the final plan meets all expected functional requirements.

[0075] For example, when producing an SPC floor with high anti-slip performance, the target functional requirements are high anti-slip and good wear resistance. First, PVC and mineral powder are selected as raw materials, and a preliminary molding plan is generated. The molding temperature is set to 210°C, the pressure is 9MPa, and the time is 6 minutes. After simulation analysis, it is found that the anti-slip performance does not meet the expected standard. After correction, the pressure is adjusted to 10MPa and the temperature is maintained at 210°C. The simulation prediction is repeated. Finally, the adjusted processing parameters meet the target anti-slip requirements, thus completing the SPC floor molding plan based on the target functional requirements.

[0076] The technical solution of the present invention improves the performance stability of the SPC anti-slip floor, ensures that the target functional requirements are met through dynamic optimization and precise simulation analysis, improves production efficiency, reduces the time and cost of adjustment and experimentation, reduces the uncertainty in the production process, and ensures the consistency of product quality.

[0077] Furthermore, the raw material composition is selected according to the target functional requirements and a preliminary molding plan is generated, including:

[0078] Establish material selection criteria that meet target functional requirements;

[0079] Based on the material selection criteria, suitable raw material components are screened from the raw material component library and the material component characteristics are obtained;

[0080] Establish the layering requirements of SPC anti-slip flooring based on the material composition characteristics and target functional requirements;

[0081] Through simulation analysis, the compatibility of the original material composition with the layering requirements is verified, and a preliminary molding plan that meets the target functional requirements is generated.

[0082] As a preferred embodiment of the above, first, a material selection standard is constructed according to the target functional requirements (such as anti-slip, wear resistance, water resistance, etc.). The standard needs to take into account the physical properties of different materials. For example, for floors with high anti-slip requirements, the material selection standard will focus on raw materials with high friction coefficient, strong wear resistance and durability. For floors with high wear resistance requirements, it may focus on scratch-resistant and high-temperature resistant materials. In the raw material component library, based on the constructed material selection standard, suitable original material components are screened out. The original material component library contains a variety of raw materials with different proportions, such as PVC, mineral powder, fillers, etc., and the friction coefficient, hardness, wear resistance and other performance indicators of these raw materials are analyzed. Screen and select materials that meet functional requirements. For example, if the goal is to improve anti-slip properties, a formula containing a higher proportion of mineral powder can be selected because mineral powder can effectively increase surface friction. Conduct detailed property testing and analysis on the selected raw material components, including hardness, friction coefficient, compressive strength, water resistance, etc. These parameters will serve as the basic data for subsequent simulation analysis. By experimentally verifying the characteristics of different components, ensure that the selected materials meet the target functional requirements. Based on the material component characteristics and target functional requirements, establish the layering requirements of SPC anti-slip flooring. Different layers of materials should have different functions. For example, the surface layer may be required to have higher anti-slip properties, while the bottom layer should focus more on wear resistance and compression resistance. The specific requirements of each layer can be designed according to the characteristics of the components. For example, the surface layer may use a material with a higher friction coefficient, while the bottom layer uses a durable hard material. Computer simulation analysis or physical experiments are used to verify the compatibility of the selected raw materials with the layering requirements. During the simulation process, finite element analysis and other methods can be used to simulate the performance of different components during processing, and to check whether each layer can meet the expected anti-slip, wear-resistant and other properties. For example, simulation analysis is performed to determine whether the friction coefficient of the surface layer material meets the standard during use, and whether the bottom layer material can withstand the expected pressure and wear. According to the simulation analysis results, the material ratio or molding process parameters are adjusted; based on the simulation analysis results, a preliminary molding scheme that meets the target functional requirements is finally generated. The preliminary molding scheme includes the specific ratio of material components, molding temperature, pressure, time and other processing parameters. For example, the surface material may choose a specific ratio of PVC and mineral powder to ensure high anti-slip properties; the bottom layer uses a high-density mixture of PVC and mineral powder to ensure wear resistance and strength.

[0083] Furthermore, simulation analysis is used to verify the compatibility of the original material composition with the stratification requirements, including:

[0084] Establish a material adaptation analysis model based on material composition characteristics and stratification requirements, and map the original material composition to stratification requirements;

[0085] Based on the material adaptation analysis model, the performance results of the original material components during the processing are simulated and analyzed for each layer requirement;

[0086] Combined with the performance results, calculate the adaptation coefficient between the original material composition and the requirements of each layer;

[0087] Integration and analysis of the adaptation coefficient to evaluate the adaptability between the original material composition and the stratification requirements.

[0088] As a preferred embodiment of the above, based on the material composition characteristics and stratification requirements of the selected original material, a material adaptation analysis model is first established. The model should include the material performance data and the required characteristics of each layer. For example, the surface layer requires a higher friction coefficient and stronger compressive strength. The material adaptation analysis model maps the raw material composition with the specific stratification requirements and combines the material characteristics with the functional requirements of different levels. According to the established material adaptation analysis model, the requirements of each layer are simulated and analyzed through simulation software (such as finite element analysis, material simulation, etc.) to predict the performance of the raw material composition during the processing. For example, the simulation analysis can verify the friction coefficient of the surface material and the compressive strength of the bottom material. During the processing, the influence of possible factors such as temperature and pressure on the material performance should also be included in the analysis scope to ensure that the raw materials can meet the stratification requirements under different conditions. In the simulation analysis process, the adaptation coefficient is calculated based on the difference between the performance of the raw material and the stratification requirements. The adaptation coefficient is a measure of the degree of adaptation between the raw material composition and the stratification requirements. An indicator, for example, if the friction coefficient of a certain layer is lower than the expected requirement, the adaptation coefficient of this layer is low. By calculating the adaptation coefficient of each layer requirement, the adaptability between the material and the requirement can be quantified; the calculated adaptation coefficients of each layer are integrated and analyzed to evaluate the adaptability of the entire SPC anti-slip floor. If the overall adaptation coefficient is high, it indicates that the raw material selection is well adapted to the layering requirements; if the adaptation coefficient of a certain layer is low, it may be necessary to adjust the original material composition or molding process parameters to optimize performance. For example, if the wear resistance of the bottom layer does not meet the requirements, the proportion of mineral powder can be increased, or additives with higher wear resistance can be selected. The integrated adaptation coefficient can be used to guide subsequent molding solution adjustments; for example, the goal is to produce an SPC floor with high anti-slip and wear resistance. In the simulation analysis, PVC material with a high mineral powder content is used on the surface layer to improve anti-slip performance, and a PVC mineral powder mixture with stronger wear resistance is used on the bottom layer. Through simulation analysis, the friction coefficient of the surface layer meets the requirements and the adaptation coefficient is 0.95, while the wear resistance of the bottom layer is slightly lower and the adaptation coefficient is 0.85. After evaluation, it was found that the adaptation coefficient of the base layer was low. It was finally decided to increase the proportion of mineral powder and optimize the molding parameters to increase the adaptation coefficient of the base layer to 0.90, thus completing the SPC floor molding plan that meets the target functional requirements.

[0089] Furthermore, the molding parameters for each step in the molding process are calculated based on the preliminary molding plan, including:

[0090] establish a dynamic correlation model between the initial forming scheme and the characteristics of the raw material composition;

[0091] According to the design requirements of the initial forming scheme, analyze the influence relationship between the parameters in the forming process, and generate an initial processing parameter set suitable for the raw material composition;

[0092] According to the dynamic correlation model, the initial processing parameter set is analyzed step by step to generate parameter analysis thresholds for each forming step;

[0093] Integrate the parameter analysis thresholds to determine the upper and lower limits of the parameters and generate the forming processing parameters.

[0094] As a preferred embodiment of the above, a dynamic correlation model is established to describe the relationship between the preliminary molding scheme and the characteristics of the original material composition. The dynamic correlation model will take into account the influence of the material composition on the various parameters in the molding process (such as temperature, pressure, time, etc.). Through experimental data, a mathematical relationship between the material characteristics and the molding process parameters can be established; according to the design requirements of the preliminary molding scheme, the mutual influence between the various processing parameters in the molding process is analyzed. For example, there may be a certain relationship between the molding temperature and pressure. When the temperature rises, it may be necessary to increase the pressure to ensure the fluidity of the material. According to the characteristics of the raw material composition, it is analyzed which parameters need to be adjusted first to ensure that the raw material performs best in the molding process. Based on the above analysis, an initial processing parameter set that is suitable for the selected raw material composition is generated; after the initial processing parameter set is established, the parameters of each processing step are analyzed step by step using the dynamic correlation model. For example, in the hot pressing molding process, there may be multiple steps, such as heating, pressing, cooling, etc. In each step, the temperature, pressure and other parameters are accurately adjusted according to the model to generate a parameter solution for each step. Parameter analysis threshold refers to the parameter range that ensures that material properties (such as anti-slip and wear resistance) reach the expected range during the molding process. For example, the temperature during the hot pressing process should be maintained between 200°C and 220°C to ensure the best anti-slip effect. The parameter analysis threshold of each step is integrated to obtain the final upper and lower limits of the parameters. By analyzing each step, the reasonable range of each parameter is determined to ensure that each step in the molding process can be accurately executed. These integrated upper and lower limits of the parameters will form the final molding processing parameter set. For example, after step-by-step analysis, the optimal heating temperature is determined to be 210°C, the pressure is 9.5MPa, and the time is 5 minutes. The resulting processing parameters will be used to guide the actual production process, ensuring that the various properties of SPC anti-slip flooring are effectively controlled. For example, in the production of SPC anti-slip flooring, the goal is to achieve excellent anti-slip and wear resistance. First, a dynamic correlation model was established using experimental data. It was found that when using materials with a high mineral powder ratio, higher molding pressure helps improve wear resistance, while lower temperature enhances anti-slip properties. Based on this analysis, a preliminary set of processing parameters was generated: temperature of 200°C, pressure of 8MPa, and molding time of 4 minutes. After step-by-step analysis, it was determined that the temperature during the heating phase should be between 190°C and 210°C, and the pressure should be controlled between 8MPa and 9MPa. Finally, by integrating these thresholds, the temperature was determined to be 205°C, the pressure was 8.5MPa, and the molding time was 5 minutes. These parameters were then generated to meet the desired functional requirements of the SPC anti-slip flooring.

[0095] Furthermore, the initial processing parameter set is analyzed step by step according to the dynamic correlation model, including:

[0096] extracting SPC multi-dimensional parameters related to each processing procedure step from the initial processing parameter set;

[0097] calculating the operating range of the SPC multi-dimensional parameters in each processing procedure step based on the dynamic correlation model;

[0098] performing dynamic coupling analysis on the operating range in the order of the processing procedure steps to generate the SPC multi-dimensional parameter set corresponding to each processing procedure step;

[0099] mapping and comparing the SPC multi-dimensional parameter set with the target performance requirement to screen the parameter analysis threshold meeting the design requirement.

[0100] As a preferred embodiment of the above, first, the SPC multi-dimensional parameters related to each processing procedure step are extracted from the initial processing parameter set, which include but are not limited to temperature, pressure, time, speed, density, etc., which have a direct impact on the material performance in the forming process (such as friction coefficient, wear resistance, etc.), for example, in the hot pressing forming step, the related multi-dimensional parameters may include temperature, pressure, forming time, etc.; in the cooling step, the related multi-dimensional parameters may include cooling speed and cooling time; based on the dynamic correlation model, the SPC multi-dimensional parameters in each processing procedure step are calculated to obtain their operating range, which refers to the range that each parameter can change in the processing process to ensure that the performance of the material meets the target requirement, for example, in the hot pressing process, the operating range of temperature may be 200°C to 220°C, and the operating range of pressure may be 8MPa to 10MPa, to ensure that the material can complete the optimal forming process within these parameter ranges; the operating parameters in each step are dynamically coupled and analyzed in the order of the processing procedure steps, considering the mutual influence between the processing steps, for example, the mutual relationship between temperature and pressure, through coupling analysis, the interaction and sensitivity of the parameters in each step can be calculated, thereby generating the SPC multi-dimensional parameter set of each step, for example, in the hot pressing stage, the combination of temperature and pressure may have an important influence on the slip resistance and wear resistance of the floor, therefore, a comprehensive analysis of these parameters is needed to ensure that their combination can achieve the best effect in all processing steps; the obtained SPC multi-dimensional parameter set is mapped and compared with the target performance requirement to screen the parameter analysis threshold meeting the design requirement, for example, if the target performance requirement is to improve the slip resistance and wear resistance, during the mapping and comparison process, the parameter set that can effectively enhance the friction coefficient and wear resistance is selected, through this screening process, it is determined which adjustment of the parameters can most effectively improve the performance of the final product, thereby providing the final parameter setting for the forming process.

[0101] Further, the dynamic coupling analysis on the operating range in the order of the processing procedure steps includes:

[0102] Establish a parameter correlation matrix between the processing steps and determine the interaction and sensitivity of the SPC multi-dimensional parameters in each step;

[0103] Based on the parameter correlation matrix, the coupling weights of the SPC multi-dimensional parameters in each processing step are calculated, and the operating range of each processing step is generated;

[0104] Perform timing simulation for each operating range to evaluate the impact on subsequent processing steps and target performance requirements, and generate a dynamic parameter set;

[0105] Input the dynamic parameter set into the next processing step to complete the dynamic coupling analysis of the processing step;

[0106] The results of dynamic coupling analysis are integrated to generate a set of parameters corresponding to each machining process step.

[0107] As a preferred embodiment of the above embodiment, a parameter association matrix is ​​established between the processing steps. The parameter association matrix is ​​used to determine the interaction and sensitivity between the SPC multidimensional parameters in each processing step. For example, in the hot pressing process, there may be an interaction between temperature, pressure and time. Temperature changes may require corresponding adjustments to pressure and time to achieve the best molding effect. By analyzing the relationship between the parameters in each step, it is determined which parameters have higher interaction sensitivity and which parameters have a greater impact on the performance of the final product. Based on the parameter association matrix, the coupling weight of the SPC multidimensional parameters in each processing step is calculated. The coupling weight refers to the degree of influence of a certain parameter on other parameters or the performance of the final product during the processing process. For example, in the hot pressing process, temperature may have a greater impact on wear resistance, while pressure has a more significant effect on anti-slip properties. By calculating the coupling weight, the relative importance of each parameter in each processing step can be clarified, providing a basis for subsequent parameter adjustment and optimization; a timing simulation is performed on the operating range of each step to evaluate the impact of each parameter on subsequent processing steps and target performance requirements. The timing simulation simulates the changes of parameters over time during the production process to evaluate their impact on subsequent steps (such as cooling, finishing, etc.) and final product performance. Through timing simulation, the optimal range of change for each parameter is determined and a dynamic parameter set is generated. For example, a gradual increase in temperature may affect the subsequent cooling process. Therefore, it is necessary to ensure that the temperature change rate is controlled during the heating stage to avoid negative impacts on the cooling stage; the generated dynamic parameter set is input into the next processing step to complete the dynamic coupling analysis. In each step, analyze how the output parameters of the previous step affect the molding process of the current step; integrate the dynamic coupling analysis results of all steps to generate a final parameter set corresponding to each processing step, which contains the optimal parameter combination in each step to ensure that each step in the molding process can optimize performance.

[0108] Further, according to the molding processing parameters, the estimated target performance of the product is predicted layer by layer, including:

[0109] According to the molding processing parameters, the multi-layer structure of the SPC floor is mapped layer by layer, and the dynamic behavior change response of the raw material composition in the processing process is predicted layer by layer;

[0110] According to the target function requirement and based on the time sequence, the dynamic behavior change response is analyzed for nonlinear trend, and the performance prediction weight of each layer is dynamically adjusted;

[0111] Based on the performance prediction weight, the coupling effect of the molding processing parameters on the target performance is dynamically predicted;

[0112] Based on the results of the dynamic prediction, the performance prediction weight is comprehensively predicted to predict the estimated performance target of the product.

[0113] As a preferred embodiment of the above, the multi-layer structure of the SPC floor is layered performance mapped according to the molding processing parameters. The functional requirements of each layer are different. The surface layer usually requires higher anti-slip properties, while the bottom layer requires stronger wear resistance. Through simulation and analysis, according to the raw material properties used in each layer (such as friction coefficient, hardness, compressive strength, etc.), the dynamic behavior change response of the original material components during the processing is predicted layer by layer. For example, the friction coefficient of the surface material may change with temperature during the hot pressing process, and the hardness of the bottom material may change with pressure. Through layered mapping, the performance changes of each layer under different processing conditions can be effectively evaluated; based on the target functional requirements (such as anti-slip properties, wear resistance, water resistance, etc.), and combined with time series data, a nonlinear trend analysis is performed on the dynamic behavior change response of each layer. Through time series analysis, the key points and trends of performance changes of each molding parameter during the processing process can be identified. For example, during the hot pressing process, temperature changes may affect the surface layer. The anti-slip performance has a greater impact, while the pressure change has a greater impact on the wear resistance of the bottom layer. Based on these analysis results, the performance prediction weight of each layer is dynamically adjusted. For example, for the surface layer requiring higher anti-slip performance, the weight of the anti-slip performance can be increased, and for the bottom layer requiring wear resistance, the weight of the wear resistance can be increased. After obtaining the performance prediction weights of each layer, the coupling effect between the molding processing parameters is further analyzed to dynamically predict the target performance. For example, temperature and pressure may affect the performance of the surface layer and the bottom layer at the same time, and their coupling effect may have a significant impact on the overall performance of the product. Through the dynamic prediction of these coupling effects, the performance change trend of the final product under different molding parameter conditions can be predicted, thereby adjusting the key parameters in the processing process. Based on the dynamic prediction results and the performance prediction weights, the prediction results of the performance of each layer are comprehensively considered to finally predict the estimated performance target of the product. For example, by comprehensively considering the prediction results of the surface layer and the bottom layer and combining the adjusted performance weights, the overall estimated performance of the product in terms of anti-slip and wear resistance is obtained.

[0114] For example, the production goal is to produce an SPC floor with high slip resistance and wear resistance. During the molding process, simulation analysis mapped the properties of the surface and base layers separately. The simulation results show that the slip resistance of the surface layer is primarily affected by temperature fluctuations, while the wear resistance of the base layer is more affected by pressure. Time series analysis revealed that a temperature of 200°C has the greatest impact on the slip resistance of the surface layer, so its weighting was increased to 60%. For the base layer, the weighting of pressure was increased based on the wear resistance requirements. Coupled analysis determined the optimal temperature (210°C) and pressure (9MPa). By combining the predicted performance weights of each layer, the final estimated performance targets, such as the slip resistance coefficient and wear resistance, met the design requirements. This series of predictions provided a basis for subsequent adjustments to processing parameters, ensuring that the SPC flooring met the requirements for high slip resistance and wear resistance during production.

[0115] Furthermore, nonlinear trend analysis of dynamic behavior change responses based on time series is performed according to target functional requirements, including:

[0116] Obtain the dynamic change data of the molding processing parameters in the time series, and establish a multi-dimensional time series matrix during the processing process based on the dynamic change data;

[0117] Perform nonlinear regression analysis on the multidimensional time series matrix to identify the key change points of each molding process parameter during the molding process;

[0118] Based on the weight distribution of performance prediction weights and combined with the key change points in the time series, a nonlinear performance impact function is constructed;

[0119] According to the nonlinear performance influence function, the dynamic interaction relationship between the forming processing parameters is analyzed in layers, and the performance response curves of each layer to the target functional requirements are generated.

[0120] As a preferred embodiment of the above, in the production process, the dynamic change data of each forming processing parameter in the time sequence is obtained, which can reflect the fluctuation trend of each parameter in the forming process with the change of time. Through the collection of these dynamic change data, a multi-dimensional time sequence matrix in the processing process is established, which contains multiple dimensional parameter data and reflects the change of each parameter with time through time sequence, providing a basis for subsequent nonlinear regression analysis and performance influence function construction; nonlinear regression analysis is performed on the established multi-dimensional time sequence matrix to identify the key change points of each forming processing parameter in the forming process. Nonlinear regression analysis can help identify which time points have the greatest impact on the final product performance of the forming processing parameter. For example, the change of temperature at a certain time point may have a significant impact on slip resistance, and the change of pressure may have a decisive effect on wear resistance in other time periods. Through regression analysis, the key change points are found to provide accurate time nodes for subsequent performance analysis; based on the key change points and the weight distribution of the performance prediction weight obtained in the previous step, the nonlinear performance influence function is constructed by combining the key change points in the time sequence, which is used to describe the influence of the change of the forming processing parameter in the time sequence on each performance (such as slip resistance, wear resistance, etc.). The nonlinear performance influence function can reveal how the change of parameters at different time points affects the final performance of the product. For example, the change of temperature may have a nonlinear effect on slip resistance, i.e. temperature has a greater impact on slip resistance within a certain range, while the change has a smaller impact in another range. The nonlinear performance influence function helps to understand this complex relationship; according to the nonlinear performance influence function, the dynamic interaction between the forming processing parameters is analyzed, and the performance response curve of each layer to the target functional requirement (such as slip resistance, wear resistance, etc.) is generated. The response curve of each layer indicates how the performance of that layer changes under different processing parameters. For example, in the hot pressing process, the slip resistance of the surface layer changes with the change of temperature, while the wear resistance of the bottom layer changes with the change of pressure. Through layered analysis, the degree of influence of different parameters on the performance of different layers can be determined, and the corresponding performance response curve is generated, providing an intuitive basis for optimizing the processing parameters.

[0121] For example, the production goal is to improve the slip resistance of the surface layer and the wear resistance of the bottom layer. First, collect the dynamic change data of temperature, pressure, forming time and other parameters in the processing process, and establish a multi-dimensional time sequence matrix. Through nonlinear regression analysis, it is found that the change of temperature has a greater impact on slip resistance, and the key change point appears at 200°C in the heating stage. Combined with the performance prediction weight, a nonlinear performance influence function is constructed, which shows that the temperature in the range of 200°C to 210°C has the most significant impact on slip resistance. Through layered analysis, the performance response curve of slip resistance and wear resistance under different processing conditions is generated, providing a basis for the final optimization of processing parameters.

[0122] Embodiment 2:

[0123] Based on the same inventive concept as the SPC anti-slip floor processing method in the aforementioned embodiment, the present invention further provides an SPC anti-slip floor processing system, the system comprising:

[0124] Initial analysis module: analyzes the characteristics of SPC anti-slip flooring and obtains target functional requirements, selects raw material components based on the target functional requirements, and generates a preliminary molding plan;

[0125] Parameter calculation module, which calculates the molding parameters of each processing step in the molding manufacturing process according to the preliminary molding plan;

[0126] The mapping and comparison module predicts the estimated target performance of the product based on the molding processing parameters, compares it with the target functional requirements, and generates verification and comparison results;

[0127] The solution generation module modifies the molding processing parameters based on the verification and comparison results, and generates an SPC floor molding solution based on the target functional requirements.

[0128] The above-mentioned adjustment system in the present invention can effectively realize a SPC anti-slip floor processing method, and the technical effects that can be achieved are as described in the above-mentioned embodiments and will not be repeated here.

[0129] More specifically, the initial analysis module includes:

[0130] Standard building blocks, building material selection criteria that meet target functional requirements;

[0131] A material selection unit, based on material selection criteria, screens suitable raw material components from the raw material component library and obtains material component characteristics;

[0132] Layered splitting units, establishing the layering requirements of SPC anti-slip flooring based on material composition characteristics and target functional requirements;

[0133] The solution generation unit verifies the compatibility of the original material composition with the layering requirements through simulation analysis, and generates a preliminary molding solution that meets the target functional requirements.

[0134] Similarly, the above-mentioned optimization schemes for the system can also respectively achieve the corresponding optimization effects of the method in Example 1, which will not be repeated here.

[0135] Although the present application has been described with reference to specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and drawings are merely illustrative of the present application as defined herein and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, the present application is intended to include such modifications and variations as fall within the scope of the present application and its equivalents.

Claims

1. A method for processing SPC anti-slip floor, characterized in that: The method comprises: Analyze the characteristics of SPC anti-slip flooring and obtain target functional requirements, select raw material components based on the target functional requirements and generate a preliminary molding plan; Calculate the molding parameters for each process step in the molding manufacturing process according to the preliminary molding plan, including: Establishing a dynamic correlation model between the preliminary shaping scheme and the characteristics of the original material composition; Analyzing the influence relationship between parameters in the molding process according to the design requirements of the preliminary molding scheme, and generating an initial processing parameter set adapted to the composition of the original material; The initial processing parameter set is analyzed step by step according to the dynamic association model to generate parameter analysis thresholds for each molding step, including: Extracting SPC multi-dimensional parameters related to the processing steps in the initial processing parameter set; Calculating the operating range of the SPC multi-dimensional parameters in each of the processing steps based on the dynamic correlation model; Performing a dynamic coupling analysis on the operating range in the order of the processing steps to generate the SPC multi-dimensional parameter set corresponding to each processing step, including: Establishing a parameter correlation matrix between the processing steps and determining the interaction and sensitivity of the SPC multi-dimensional parameters in each step; Calculating the coupling weight of the SPC multi-dimensional parameters in each of the processing steps based on the parameter correlation matrix, and generating the operating range of each of the processing steps; Performing a time series simulation for each of the operating ranges, evaluating the impact on subsequent processing steps and target performance requirements, and generating a dynamic parameter set; Inputting the dynamic parameter set into the next processing step to complete the dynamic coupling analysis of the processing step; Integrating the results of the dynamic coupling analysis to generate a parameter set corresponding to each of the processing steps; Mapping and comparing the SPC multi-dimensional parameter set with the target performance requirements, and screening the parameter analysis thresholds that meet the design requirements; Integrating the parameter analysis thresholds to determine upper and lower limits of parameters and generating the molding processing parameters; The estimated target performance of the product is predicted layer by layer based on the molding process parameters, and mapped and compared with the target functional requirements to generate a verification comparison result, including: Performing layered performance mapping of the multi-layer structure of the SPC floor according to the molding processing parameters, and predicting the dynamic behavior change response of the raw material components during the processing layer by layer; According to the target functional requirements and based on the time series, a nonlinear trend analysis is performed on the dynamic behavior change response, and the performance prediction weight of each layer is dynamically adjusted, including: Acquiring dynamic change data of the molding process parameters in the time series, and establishing a multi-dimensional time series matrix during the processing according to the dynamic change data; Performing nonlinear regression analysis on the multidimensional time series matrix to identify key change points of each molding process parameter during the molding process; Constructing a nonlinear performance impact function based on the weight distribution of the performance prediction weights and in combination with the key change points in the time series; Performing a hierarchical analysis on the dynamic interaction relationship between the molding process parameters according to the nonlinear performance influence function to generate a performance response curve of each level to the target function requirement; Dynamically predicting target performance by analyzing the coupling effects between the molding process parameters based on the performance prediction weights; Based on the results of the dynamic prediction, the estimated performance target of the product is predicted by combining the performance prediction weights; The forming processing parameters are modified based on the verification and comparison results to generate an SPC floor forming plan based on the target functional requirements.

2. The SPC anti-slip floor processing method according to claim 1, characterized in that: Select raw material components based on the target functional requirements and generate a preliminary molding plan, including: Establish material selection criteria that meet the stated target functional requirements; Based on the material selection criteria, screening the adapted raw material components from the raw material component library and obtaining material component characteristics; Establishing the layering requirements of the SPC anti-slip floor based on the material composition characteristics and the target functional requirements; The compatibility of the original material composition with the layering requirements is verified through simulation analysis, and a preliminary molding solution that meets the target functional requirements is generated.

3. The SPC anti-slip floor processing method according to claim 2, characterized in that: Verify the compatibility of the original material composition with the stratification requirements through simulation analysis, including: Establishing a material adaptation analysis model based on the material composition characteristics and the stratification requirements, and mapping the original material composition to the stratification requirements; Based on the material adaptation analysis model, simulating and analyzing the performance results of the original material components during the processing for each of the layered requirements; Calculating the adaptation coefficient between the original material composition and each of the layering requirements based on the performance results; The adaptation coefficient is integrated and analyzed to evaluate the adaptability between the original material composition and the stratification requirements.

4. A SPC anti-slip floor processing system, characterized in that: The SPC anti-slip floor processing method according to claim 1 is used, wherein the system comprises: Initial analysis module: analyzes the characteristics of SPC anti-slip flooring and obtains target functional requirements, selects raw material components based on the target functional requirements, and generates a preliminary molding plan; Parameter calculation module, which calculates the molding parameters of each processing step in the molding manufacturing process according to the preliminary molding plan; The mapping and comparison module predicts the estimated target performance of the product based on the molding processing parameters, compares it with the target functional requirements, and generates verification and comparison results; The solution generation module modifies the molding processing parameters based on the verification and comparison results, and generates an SPC floor molding solution based on the target functional requirements.

5. The SPC anti-slip floor processing system according to claim 4, characterized in that: The initial analysis module includes: Standard building blocks, building material selection criteria that meet target functional requirements; A material selection unit, based on material selection criteria, screens suitable raw material components from the raw material component library and obtains material component characteristics; Layered splitting units, establishing the layering requirements of SPC anti-slip flooring based on material composition characteristics and target functional requirements; The solution generation unit verifies the compatibility of the original material composition with the layering requirements through simulation analysis, and generates a preliminary molding solution that meets the target functional requirements.

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