A door and window processing parameter optimization method and system

Abstract

The application provides a door and window processing parameter optimization method and system, relates to the door and window processing field, and obtains door and window parameters and external environment information, establishes a door and window simulation model according to the door and window parameters, converts the external environment information into physical load parameters, then performs accelerated simulation operation on the door and window simulation model based on the physical load parameters, obtains door and window performance evaluation results, and finally generates door and window design optimization information according to the evaluation results, and simulates the long-term performance of the door and window within a preset time period by applying multiple frequency load cycles. Through the technical scheme, the application can effectively solve the problem that the existing technology lacks accurate prediction and evaluation capability for the small local deformation of the super-large size and slender profile door and window under the long-term complex environment and the cumulative influence of the small local deformation on the long-term reliability and optical quality of the door and window, and avoids the performance attenuation and quality problems that may occur in the actual application of the traditional method.

Description

Technical Field

[0001] This application relates to the field of door and window processing technology, and more specifically, to a method and system for optimizing door and window processing parameters. Background Technology

[0002] In modern architectural design, with the continuous improvement of people's requirements for the quality of their living and working environments, and the ongoing innovation of architectural aesthetics, the demand for customized doors and windows is showing unprecedented growth. Especially in high-end building projects, there is an increasing demand for doors and windows with extra-large sizes, irregular curved surfaces, and designs that offer ultimate transparency and ultra-narrow frame widths. To meet these complex and stringent design requirements, door and window manufacturers have generally introduced advanced processing parameter optimization systems. These systems aim to balance material costs and production efficiency while meeting various indicators such as structural strength and thermal performance through automated calculations.

[0003] However, existing optimization systems have revealed some deep-seated limitations when dealing with such extremely customized doors and windows. Traditional calculation methods and optimization logic often focus on meeting instantaneous or static performance indicators, lacking the ability to accurately predict and assess the minute local deformations that may occur in ultra-large, slender profiles under long-term complex environmental conditions (such as diurnal temperature variations and seasonal wind pressure changes), and the cumulative impact of these micro-deformations on the overall long-term reliability of doors and windows (such as sealing integrity and thermal insulation performance) and optical quality (such as glass surface flatness). This leads to situations where the solutions generated by the optimization system, while theoretically perfect, may experience unexpected performance degradation and quality problems after a period of time in practical applications, such as sealing failure, condensation in the insulating layer, or even optical distortion of the glass, resulting in high rework costs for enterprises. Summary of the Invention

[0004] This application discloses a method and system for optimizing door and window processing parameters. It aims to solve the problem that existing door and window processing parameter optimization systems lack the ability to accurately predict and evaluate the small local deformations that may occur under long-term complex environmental conditions and their cumulative impact on the overall long-term reliability and optical quality of doors and windows when dealing with extremely customized doors and windows. This leads to performance degradation and quality problems in practical applications, such as sealing failure, condensation in the hollow layer, and optical distortion of the glass.

[0005] The technical solution of this application is as follows: In a first aspect, this application discloses a method for optimizing door and window processing parameters, including: Obtain door and window parameters and external environment information; Establish a simulation model of doors and windows based on their parameters; Transform external environmental information into physical load parameters; Accelerated simulation is performed on the door and window simulation model based on physical load parameters to obtain door and window performance evaluation results. Accelerated simulation refers to the operation of applying multiple load cycles to simulate the long-term performance of doors and windows within a preset time period. Based on the performance evaluation results of doors and windows, information for optimizing door and window design is generated.

[0006] Secondly, this application also discloses a door and window processing parameter optimization system, which includes: The external environment information acquisition module is used to acquire door and window parameters and external environment information; The door and window simulation model module is used to create door and window simulation models based on door and window parameters. The physical load parameter conversion module is used to convert external environment information into physical load parameters; The door and window performance evaluation result module is used to perform accelerated simulation operation on the door and window simulation model based on physical load parameters to obtain the door and window performance evaluation results; among them, accelerated simulation operation refers to the operation of applying multiple load cycles to simulate the long-term performance of doors and windows within a preset time period. The window and door design optimization information module is used to generate window and door design optimization information based on the window and door performance evaluation results.

[0007] Beneficial Effects: The door and window processing parameter optimization method disclosed in this application acquires door and window parameters and external environmental information, establishes a door and window simulation model based on the parameters, transforms the external environmental information into physical load parameters, and then performs accelerated simulation on the door and window simulation model based on the physical load parameters to obtain door and window performance evaluation results. Finally, it generates door and window design optimization information based on the evaluation results. The core of this method lies in the introduction of an "accelerated simulation operation" mechanism, which simulates the long-term performance of doors and windows within a preset time period by applying multiple load cycles. Through this technical solution, this application can effectively solve the problem in the prior art of lacking the ability to accurately predict and evaluate the small local deformations that may occur in ultra-large, thin-profile doors and windows under long-term complex environmental conditions and their cumulative impact on the overall long-term reliability and optical quality of doors and windows. Specifically, this method no longer only focuses on instantaneous or static performance indicators, but through accelerated simulation operation, it can predict the dynamic response and cumulative damage of doors and windows under long-term complex environments such as diurnal temperature differences and seasonal wind pressure changes, such as the fatigue life of sealing strips, the risk of condensation in the insulating layer, and glass optical distortion. This allows the solutions generated by the optimization system to more accurately reflect the actual long-term performance of doors and windows, avoiding performance degradation and quality problems that may occur in practical applications using traditional methods. Attached Figure Description

[0008] Figure 1 This is a schematic diagram illustrating a method for optimizing door and window processing parameters provided in this application.

[0009] Figure 2 This is a schematic diagram of a door and window processing parameter optimization system provided in this application. Detailed Implementation

[0010] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0011] Reference Figure 1 The diagram illustrates a method for optimizing door and window processing parameters according to an embodiment of the present invention, which may specifically include the following steps: S101, obtain door and window parameters and external environment information; S102, Establish a door and window simulation model based on the door and window parameters; S103 converts external environmental information into physical load parameters; S104, Accelerated simulation operation is performed on the door and window simulation model based on physical load parameters to obtain the door and window performance evaluation results; wherein, accelerated simulation operation refers to the operation of applying multiple load cycles to simulate the long-term performance of doors and windows within a preset time period. S105: Generate window and door design optimization information based on the window and door performance evaluation results.

[0012] In this embodiment of the invention, door and window parameters and external environmental information are acquired, and a door and window simulation model is established based on the door and window parameters. Subsequently, the external environmental information is converted into physical load parameters, and accelerated simulation is performed on the door and window simulation model based on these physical load parameters to obtain door and window performance evaluation results. Accelerated simulation refers to applying multiple load cycles to simulate the long-term performance of doors and windows within a preset time period. Finally, door and window design optimization information is generated based on the door and window performance evaluation results. This application, by introducing an accelerated simulation mechanism, can more accurately predict the performance of doors and windows in long-term complex environments, thereby effectively solving the shortcomings of traditional optimization methods in predicting long-term reliability and providing a more reliable optimization solution for customized door and window design.

[0013] To facilitate a clearer understanding of the technical solutions in this application, the following explanations are provided for some key terms involved. Door and window parameters refer to specific data describing the geometric dimensions, material properties, and structural composition of doors and windows, such as the cross-sectional shape of the profile, the thickness and type of glass, and the elastic modulus of the sealing material. External environmental information refers to data on external conditions faced by doors and windows during use, such as temperature, humidity, wind pressure, and solar radiation intensity. A door and window simulation model is a digital model built based on door and window parameters, used to simulate the mechanical, thermal, and optical performance responses of doors and windows under various loads. Physical load parameters are physical quantities that can be directly applied to the simulation model after converting external environmental information, such as periodic shear force, vibration frequency, vibration amplitude, and temperature fluctuations. Accelerated simulation is a testing method that simulates the long-term performance of doors and windows within a short, preset time period by applying multiple load cycles, aiming to quickly evaluate the durability and reliability of doors and windows. The door and window performance evaluation results are data on various performance indicators of doors and windows obtained after accelerated simulation, such as sealing integrity, structural stiffness, thermal performance, and optical quality. Window and door design optimization information provides specific suggestions or adjustments to improve window and door designs based on performance evaluation results.

[0014] The core of the door and window processing parameter optimization method in this application lies in achieving accurate prediction and optimization of the long-term performance of doors and windows through a series of steps.

[0015] First, it is necessary to obtain the parameters of the doors and windows, as well as information about the external environment. This can be achieved in several ways. For example, it can be done manually by designers or engineers who input data such as geometric dimensions, material types, and connection methods of the doors and windows based on the design drawings and material lists. Alternatively, structured door and window parameter data can be directly exported from design software through an integrated CAD / CAM system. Obtaining external environmental information also has multiple approaches. For instance, historical meteorological data can be consulted to obtain macro-environmental data such as the annual average temperature, extreme temperatures, wind speed, and rainfall at a specific geographical location. Alternatively, an environmental sensor network can be deployed to monitor micro-environmental data such as temperature, humidity, wind pressure, and solar radiation intensity at the door and window installation site in real time.

[0016] Secondly, a simulation model of the doors and windows is established based on the acquired parameters. The establishment of this simulation model is fundamental for subsequent accelerated simulation operations. One approach is to use finite element analysis (FEA) software to discretize the various components of the doors and windows (such as profiles, glass, seals, and hardware) into a finite number of elements, and define the material properties and connection relationships of each element, thereby constructing a simulation model that reflects the overall mechanical behavior of the doors and windows. Another approach is to use multibody dynamics (MBD) software, treating the doors and windows as a system composed of multiple rigid or flexible bodies connected by hinges, sliders, etc., to simulate the kinematic and dynamic responses of the doors and windows under dynamic loads.

[0017] Next, the external environmental information is transformed into physical load parameters. This step is crucial for connecting the real environment with the simulation model. For example, wind speed and direction information from historical meteorological data can be converted into distributed pressure loads acting on the surfaces of doors and windows through fluid dynamics calculations. Temperature fluctuation data can be converted into stress loads caused by the thermal expansion or contraction of materials. Seismic wave data can be converted into inertial force loads borne by the door and window structure. These transformed physical load parameters can then be input into the door and window simulation model in numerical form.

[0018] Then, accelerated simulation is performed on the door and window simulation model based on physical load parameters to obtain the door and window performance evaluation results. Accelerated simulation is one of the core innovations of this application. For example, by applying periodic loads with frequencies far exceeding those of the actual environment, fatigue damage that doors and windows may experience over years or even decades of use can be simulated in a short time. Specifically, if doors and windows experience 1000 temperature cycles per year in the actual environment, 10,000 temperature cycles can be applied in a single day in the accelerated simulation, thereby rapidly evaluating the long-term performance of doors and windows within a preset time period. During the simulation, data such as stress, strain, deformation, and temperature distribution in key parts of the door and window simulation model can be monitored, and performance indicators such as the sealing integrity, structural stiffness, thermal performance, and optical quality of the doors and windows can be calculated based on this data to form the door and window performance evaluation results.

[0019] Finally, based on the performance evaluation results of doors and windows, optimization information for door and window design is generated. For example, if the performance evaluation results show a significant decrease in the sealing integrity of doors and windows in the later stages of accelerated simulation, optimization information can be generated, suggesting adjustments to the type of sealing material, increasing the cross-sectional dimensions of the sealing strip, or improving the sealing structure design. If insufficient structural stiffness leads to glass deformation, suggestions can be made to increase the wall thickness of the profiles, optimize the cross-sectional geometry of the profiles, or add support points. This optimization information can be output in the form of reports, parameter adjustment recommendations, or new design schemes to guide further design and manufacturing of doors and windows.

[0020] This application introduces an accelerated simulation mechanism to more accurately predict the performance of doors and windows under long-term complex environments. Specifically, by applying multiple load cycles over a preset time period, it simulates the long-term performance of doors and windows, overcoming the limitations of traditional methods that only focus on static or instantaneous performance. For example, traditional methods may only assess the structural strength of doors and windows through static load tests, ignoring the cumulative impact of dynamic loads such as long-term temperature cycling and wind pressure fluctuations on the fatigue life of sealing materials. The accelerated simulation of this application can simulate these dynamic and periodic loads, thereby revealing problems such as sealing failure and condensation in the insulating layer that may occur in doors and windows during long-term use. In this way, this application can provide more comprehensive and accurate performance evaluation results for doors and windows, and generate more targeted and effective optimization information for door and window design based on these results. Compared with existing technologies, the advantage of this application is that it can significantly improve the long-term reliability and durability of door and window design schemes, reduce performance degradation and quality problems in actual applications, and thus reduce rework costs.

[0021] In a preferred embodiment of this application, a method for optimizing door and window processing parameters is further proposed. The external environmental information includes periodic shear force data from deformation simulation, vibration frequency and amplitude data from micro-vibration sensors, and temperature fluctuation data from temperature sensors. The physical load parameters include multi-dimensional damage composite loads and energy dissipation accumulation rates. The accelerated simulation of the door and window simulation model based on the physical load parameters yields door and window performance evaluation results, including: Based on the periodic shear force data simulated by the above deformation using a weighted function, the vibration frequency data and vibration amplitude data of the micro-vibration sensor, and the temperature fluctuation data of the temperature sensor, a multi-dimensional damage composite load is established; wherein, the above multi-dimensional damage composite load refers to multi-source, multi-dimensional composite load information. Based on the above multi-dimensional damage composite load, the energy dissipation accumulation rate of the door and window sealing material is calculated; the energy dissipation accumulation rate refers to the speed at which damage accumulates. The aforementioned multidimensional damage composite load and energy dissipation accumulation rate were applied to the door and window simulation model for accelerated simulation to obtain the door and window performance evaluation results.

[0022] Specifically, the aforementioned external environmental information is refined into various specific data types, aiming to more comprehensively capture the complex environmental impacts that doors and windows experience during actual use. Deformation simulation data of periodic shear forces, for example, can be derived from simulating or actually measuring the periodic deformation of doors and windows under external factors such as wind pressure, earthquakes, or structural settlement, reflecting the response of the door and window structure under repeated shear stress. Vibration frequency and amplitude data from micro-vibration sensors can be collected in real time by micro-vibration sensors installed on the door and window structure, characterizing the microscopic vibration characteristics of doors and windows under daily use, wind vibration, or traffic vibration, which may lead to material fatigue. Temperature fluctuation data from temperature sensors is used to record temperature changes in the environment in which the doors and windows are located, including diurnal temperature differences and seasonal changes; temperature fluctuations have a significant impact on the thermal expansion and contraction and aging rate of materials.

[0023] Specifically, the aforementioned physical load parameters are defined as multi-dimensional damage composite load and energy dissipation accumulation rate. Multi-dimensional damage composite load can be understood as a comprehensive load representation formed by integrating various external environmental information (such as shear force, vibration frequency, vibration amplitude, and temperature fluctuations) through a specific weighting function. It can simultaneously reflect the combined damage effects of different environmental factors on window and door materials and structures. For example, this weighting function can be set according to the sensitivity of different environmental factors to the performance of windows and doors to ensure that the composite load accurately reflects the actual damage mechanism. The energy dissipation accumulation rate specifically refers to the rate at which internal damage accumulates in window and door materials (especially sealing materials) under the aforementioned multi-dimensional damage composite load. This rate can serve as a key indicator for assessing the fatigue, aging, and failure risk of materials, and its calculation typically involves the material's mechanical properties, damage evolution models, and load history.

[0024] In practical applications, the process of establishing a multi-dimensional damage composite load first requires preprocessing and standardizing the acquired data on periodic shear forces from deformation simulation, vibration frequency and amplitude data from micro-vibration sensors, and temperature fluctuation data from temperature sensors. Subsequently, a predefined weighting function is applied to fuse these data from different sources and with different dimensions. For example, weighting coefficients based on expert experience, historical data analysis, or machine learning model training can be used to transform shear stress, vibration energy, and temperature stress into a unified damage equivalent, thereby constructing a multi-dimensional damage composite load that can comprehensively characterize the combined damage suffered by doors and windows.

[0025] Furthermore, based on the aforementioned multi-dimensional damage composite load, the energy dissipation accumulation rate of the window and door sealing materials is calculated. This involves establishing a damage mechanics model for the window and door sealing materials, which can describe the energy dissipation mechanism and damage evolution process of the material under composite loads. For example, a model based on continuous damage mechanics or fatigue accumulation theory can be used, and the accumulation rate of internal damage in the material under a specific load history can be quantified through integration or iterative calculation. The calculation result of this rate directly reflects the aging and failure trend of the window and door sealing materials during accelerated simulation operation.

[0026] Finally, the established multi-dimensional damage composite load and the calculated energy dissipation accumulation rate are used as inputs to accelerate the simulation of the door and window model. This means that the simulation model is no longer subjected to a single or simplified load, but rather to a composite load that comprehensively reflects the influence of the actual complex environment, and its internal damage accumulation process is driven by the energy dissipation accumulation rate. In this way, the simulation model can more realistically simulate the performance degradation and damage accumulation of doors and windows under the coupled effects of multiple environmental factors during long-term use, thereby obtaining more accurate and reliable performance evaluation results for doors and windows. This provides a more solid data foundation for door and window design optimization, effectively avoiding evaluation biases caused by simplified load parameters or incomplete consideration of damage mechanisms, and thus significantly improving the long-term performance and reliability of door and window products.

[0027] In some preferred embodiments, a specific example is given below. Suppose a long-term evaluation and optimization of the sealing performance of a novel energy-saving door and window is required. First, deformation sensors, micro-vibration sensors, and temperature sensors are deployed at the door and window installation locations to collect data on periodic shear forces, vibration frequencies, vibration amplitudes, and temperature fluctuations under typical wind pressure cycles, traffic vibrations, and seasonal temperature changes. For example, the deformation sensor records a periodic deformation of 0.1 mm per hour in the door and window frame under wind pressure, the micro-vibration sensor records continuous vibration at a frequency of 50 Hz and an amplitude of 0.01 mm, and the temperature sensor records a daily temperature difference cycle of 10 degrees Celsius.

[0028] Next, using this raw data, a multi-dimensional composite damage load is established through a preset weighting function. For example, different weighting coefficients can be set for shear stress caused by wind pressure, fatigue stress caused by vibration, and thermal stress caused by temperature changes, and these can be combined into a unified damage equivalent. For example, shear stress contributes 40%, vibration fatigue contributes 30%, and thermal stress contributes 30%. This yields a load spectrum that comprehensively reflects the composite damage effects experienced by door and window sealing materials in actual environments.

[0029] Subsequently, based on this multidimensional damage composite load, and combined with a damage mechanics model of door and window sealing materials (such as EPDM rubber), the energy dissipation accumulation rate is calculated. This model can be a nonlinear model based on strain energy density or fatigue life curves. By inputting the composite load spectrum, it predicts the energy dissipation of the sealing material in each load cycle and accumulates to calculate its damage level. For example, the calculation results show that, under simulated external conditions, the energy dissipation accumulation rate of the sealing material is 0.001 joules per cubic centimeter per hour, indicating that its damage is accumulating at a specific rate.

[0030] Finally, the multi-dimensional damage composite load and energy dissipation accumulation rate were used as inputs to accelerate the simulation of the door and window model. The simulation model was configured to update the mechanical property parameters (such as elastic modulus and strength) of the sealing material according to the energy dissipation accumulation rate while applying the composite load, simulating its performance degradation under long-term service. For example, by simulating 10,000 load cycles, equivalent to simulating a 10-year actual service life, the simulation results showed that the sealing performance of the sealing material decreased by 15%, and microcracks appeared. Based on this assessment result, door and window design optimization information can be generated, such as suggesting the replacement of sealing materials with more fatigue-resistant ones, or adjusting the cross-sectional geometry of the profiles to reduce shear stress, thereby improving the long-term reliability of doors and windows.

[0031] In some embodiments described above in this application, accelerated simulation of door and window models is performed based on physical load parameters to simulate the long-term performance of doors and windows within a preset time period. However, in practical applications, the long-term performance degradation of door and window materials often involves complex and nonlinear damage accumulation and degradation processes. Traditional accelerated simulation methods may struggle to accurately capture these microscopic, time-dependent material behaviors, thus affecting the accuracy of performance evaluation results and potentially leading to inaccurate information for door and window design optimization. If these problems are not addressed, the prediction of long-term reliability and durability of doors and windows will have significant deviations, potentially preventing design solutions from effectively addressing the complex environmental challenges of actual use. Therefore, this application further proposes a more refined accelerated simulation method. By introducing material degradation parameters and acceleration factors, the physical load parameters during the simulation process are dynamically adjusted, and a microscopic damage monitoring and feedback mechanism is incorporated to more accurately assess the long-term performance of doors and windows.

[0032] Specifically, the above-mentioned accelerated simulation of the door and window simulation model based on physical load parameters yields the following performance evaluation results: Obtain material degradation parameters under different environmental conditions; An acceleration factor corresponding to the material degradation parameters is generated by the nonlinear relationship between the accumulation of internal damage and the degradation process of the material; the acceleration factor is a parameter characterizing the nonlinear relationship between the accumulation of internal damage and the degradation process of the material. Based on the acceleration factor, the cycle frequency, amplitude, and duration of the physical load parameters applied to the door and window simulation model are adjusted to obtain the adjusted door and window simulation model. Monitor the cumulative microscopic damage data of the adjusted door and window simulation model; The deviation between the accumulated microscopic damage data and the preset nonlinear parameters is calculated. The acceleration factor or physical load parameters are adjusted according to the deviation. The adjusted door and window simulation model is then subjected to accelerated simulation to obtain the door and window performance evaluation results. The preset nonlinear parameters refer to the damage accumulation rate predicted by the nonlinear acceleration model.

[0033] The material degradation parameters refer to parameters describing the changes in the performance of door and window materials over time under different environmental conditions (e.g., temperature, humidity, ultraviolet radiation, mechanical stress, etc.). Examples include the elastic modulus decay rate of polymer sealing materials, the light transmittance change rate of glass coatings, and the surface hardness loss rate of profiles. These parameters are typically obtained through experimental testing, historical data analysis, or theoretical model prediction. The nonlinear relationship refers to the fact that the relationship between the accumulation of internal damage (e.g., microcrack propagation, molecular chain breakage) and the macroscopic degradation process (e.g., strength reduction, stiffness reduction) is not a simple linear proportional relationship, but rather exhibits a complex, accelerating or decelerating nonlinear evolution law. For example, degradation may be slow in the early stages of damage, but the degradation rate will increase sharply after reaching a certain critical point. The acceleration factor is a dimensionless parameter used to quantify the multiple relationship between the material degradation rate under accelerated simulation conditions and the degradation rate under actual use conditions. This factor is generated through the aforementioned nonlinear relationship and can reflect the accelerating effect of specific material degradation parameters under accelerated environments. For example, when the temperature increases by 10 degrees Celsius, the degradation rate of a certain material may accelerate by 2 times, then the acceleration factor is 2.

[0034] Furthermore, adjusting the cycle frequency, amplitude, and duration of the physical load parameters applied to the door and window simulation model means dynamically modifying the characteristics of the physical loads (such as periodic shear forces, vibration loads, temperature fluctuations, etc.) applied to the simulation model based on the calculated acceleration factor. For example, to simulate 10 years of aging, if the acceleration factor is 10, the cycle frequency of the load can be increased tenfold, or the number of load cycles can be increased tenfold in the same time period. Simultaneously, the amplitude of the load may be adjusted to match the stress level under accelerated conditions, and the simulation duration can be shortened accordingly. The microscopic damage accumulation data refers to the quantitative data of the minute damage generated at the material level inside the door and window simulation model during the accelerated simulation process. For example, this could include stress concentration areas within the material, the initiation and propagation of microcracks, the degree of fatigue damage accumulation, changes in molecular structure, etc. This data can be obtained through finite element analysis, molecular dynamics simulation, or other multi-scale simulation techniques. The preset nonlinear parameters refer to the material damage accumulation rate predicted based on a nonlinear acceleration model (such as the Arrhenius model, the Eyring model, or a more complex damage mechanics model). These parameters are determined in advance through theoretical analysis, experimental verification, or empirical data, and serve as a reference benchmark for the micro-damage accumulation data during the simulation process. The deviation refers to the difference between the actual micro-damage accumulation data monitored during accelerated simulation and the preset nonlinear parameters (i.e., the damage accumulation rate predicted by the nonlinear accelerated model). This deviation reflects the accuracy of the current simulation model in simulating the actual degradation behavior of the material.

[0035] The proposed solution introduces material degradation parameters and acceleration factors, which can more accurately characterize the nonlinear damage accumulation and degradation behavior of door and window materials during long-term use, making the predicted damage accumulation rate consistent with the actual or theoretically predicted nonlinear degradation behavior, thus greatly improving the accuracy and reliability of accelerated simulation.

[0036] As a specific implementation method, a concrete example is given below. Suppose we need to evaluate the sealing performance degradation of a novel sealing strip over a ten-year service life. First, we experimentally obtain the changes in material degradation parameters such as tensile strength and elongation at break of the sealing strip over time under different temperatures, humidity levels, and UV irradiation intensities. Analyzing these curves reveals an accelerating nonlinear relationship between internal damage accumulation (e.g., molecular chain breakage) and macroscopic performance degradation (e.g., decrease in tensile strength). That is, under specific environmental stress, after damage accumulation reaches a certain level, the degradation rate significantly accelerates. Based on this nonlinear relationship, an acceleration factor model can be generated. For example, when the ambient temperature increases from 25°C to 60°C, the degradation rate of the sealing strip may accelerate by 5 times, then the acceleration factor is 5.

[0037] In the door and window simulation model, the material properties of the sealing strip are modeled. Based on the aforementioned acceleration factor, the physical load parameters applied to the simulation model are adjusted. For example, if the sealing strip is subjected to 1000 temperature cycles and 500 shear stress cycles per year in actual use, in order to simulate ten years of performance in one year, the temperature cycle frequency can be increased to 5000 cycles / year, and the shear stress cycle frequency can be increased to 2500 cycles / year. The amplitude and duration of each cycle are adjusted accordingly to match the accelerated degradation effect reflected by the acceleration factor.

[0038] During accelerated simulation, the cumulative microscopic damage data of the sealing strip in the simulation model is continuously monitored. For example, finite element analysis is used to track stress concentration areas and the initiation and propagation of microcracks within the sealing strip. Simultaneously, a damage accumulation rate predicted by a nonlinear accelerated model (e.g., the Arrhenius-Eyring model) is preset as a reference. At a certain stage of the simulation, the deviation between the monitored microscopic damage accumulation data and the preset nonlinear parameters is calculated. If the damage accumulation rate predicted by the simulation model is found to be lower than the preset value, the acceleration factor is adjusted according to the deviation, for example, by slightly increasing it, or by directly adjusting the cycle frequency or amplitude of the physical load parameters to make it closer to the preset nonlinear degradation trend. Through this iterative adjustment, the simulation model is ensured to more accurately simulate the nonlinear degradation behavior of the sealing strip during long-term use, ultimately yielding more reliable evaluation results for door and window sealing performance.

[0039] In some embodiments of this application, when generating window and door design optimization information based on window and door performance evaluation results, traditional methods often employ single-objective optimization or simple weighted summation to handle multiple design objectives. This can lead to difficulties in obtaining a globally optimal or balanced solution when conflicts exist between different performance indicators. For example, suppose that when optimizing window and door design, multiple aspects such as visible profile width, structural stiffness, thermal performance, optical quality, and long-term lifespan of sealing materials need to be considered simultaneously. If only one performance is improved, other performance aspects may be sacrificed or costs increased, and vice versa. If the above problems are not addressed, the generated window and door design optimization information may not fully meet the comprehensive requirements for multiple performance aspects in practical applications, and may even lead to poor performance of the design scheme in certain key performance aspects. To address this, this application proposes a more comprehensive and efficient method for generating window and door design optimization information. By introducing a multi-objective optimization strategy, it aims to balance and coordinate multiple conflicting optimization objectives in window and door design, thereby obtaining a more robust and practical design scheme.

[0040] In a preferred embodiment of the present invention, the method further includes the following steps: obtaining optimization targets for door and window design information; wherein, the optimization targets include visible width of profiles, structural stiffness, thermal performance, optical quality, and long-term lifespan of sealing materials; Construct a multi-objective decision space by optimizing objectives; An optimization strategy based on Pareto front search is executed to generate a set of non-dominated solutions in the multi-objective decision space. Design parameters that meet a preset threshold are selected from the set of non-dominated solutions and the design parameters are determined as optimization information for door and window design.

[0041] Specifically, the optimization goals for obtaining door and window design information refer to identifying the various performance indicators that need to be improved or achieved during the door and window design process. These optimization goals can be understood as the superior characteristics that designers hope door and window products will exhibit in different aspects. For example, the visible width of the profile refers to the visually exposed size of the door and window profile, which is usually minimized while ensuring structural strength to increase the lighting area and aesthetics; structural stiffness refers to the ability of doors and windows to resist deformation, which is a key indicator for ensuring the stability and safety of doors and windows; thermal performance refers to the performance of doors and windows in terms of heat insulation, which directly affects building energy consumption; optical quality involves the light transmittance, clarity, and anti-glare characteristics of doors and windows; and the long-term lifespan of sealing materials focuses on the degradation of the sealing performance of doors and windows during long-term use. These goals are often interrelated and may conflict. For example, improving structural stiffness may require increasing the profile width, thus affecting the visible width of the profile.

[0042] The construction of a multi-objective decision space through optimization objectives refers to defining all design variables to be optimized, their value ranges, and the corresponding objective functions together as a multi-dimensional mathematical space. In this space, each point represents a possible door / window design scheme, and the coordinates of this point in different objective dimensions reflect the performance of that design scheme across various optimization objectives. This decision space provides the foundation for subsequent optimization algorithms.

[0043] In practical applications, implementing an optimization strategy based on Pareto front search refers to employing an algorithm that can simultaneously consider multiple optimization objectives and find a set of optimal solutions rather than a single optimal solution. The Pareto front is the set of all non-dominated solutions in a multi-objective optimization problem. A solution is called non-dominated if and only if no other solution is superior to it on at least one objective and not inferior to it on all other objectives. Through Pareto front search, a set of solutions that achieve a balance among the various optimization objectives can be obtained, and none of these solutions can be further improved without sacrificing the performance of other objectives.

[0044] Furthermore, generating a set of non-dominated solutions within the multi-objective decision space refers to using the Pareto front search algorithm to traverse or explore the decision space, identify, and collect all door and window design schemes that satisfy the non-dominated conditions. These non-dominated solutions constitute the Pareto front, representing the optimal trade-off point that can be achieved among the various optimization objectives under the current design constraints.

[0045] Therefore, selecting design parameters that meet preset thresholds from the set of non-dominated solutions means, after obtaining the set of non-dominated solutions, setting a series of performance thresholds based on actual needs or design specifications. For example, the structural stiffness can be set to be greater than a certain value, the thermal performance to be better than a certain standard, or the visible width of the profile to be less than a certain upper limit. Then, specific combinations of design parameters that simultaneously meet all preset thresholds are selected from the set of non-dominated solutions. These selected design parameters are the preferred solutions that satisfy both multi-objective balance and actual engineering requirements.

[0046] Ultimately, defining the design parameters as optimization information for door and window design means using the filtered and confirmed set of design parameters as the final optimized solution to guide door and window manufacturing and processing. This information can be directly used to guide production or as input for further refined design.

[0047] In some preferred embodiments, a specific example is given below. Suppose a window and door manufacturer wants to optimize the design of its new energy-efficient windows and doors, mainly considering the following optimization objectives: 1. Visible width of the profile: It is desirable to keep it as small as possible to maximize the light-receiving area and improve aesthetics (goal 1: minimization).

[0048] 2. Structural stiffness: It is desirable to have the greatest possible stiffness to ensure resistance to wind pressure and deformation (Objective 2: Maximize).

[0049] 3. Thermal performance: We want the U-value (heat transfer coefficient) to be as small as possible to improve the thermal insulation effect (objective 3: minimization).

[0050] 4. Cost: We want to keep manufacturing costs as low as possible (Objective 4: Minimize).

[0051] First, obtain these optimization objectives and define relevant design variables, such as the cross-sectional shape parameters of the profile, the number and type of glass layers, and the material and structure of the sealing strip. Then, construct a multi-objective decision space using these design variables and optimization objectives.

[0052] Next, an optimization strategy based on Pareto front search is executed, such as a multi-objective evolutionary algorithm like NSGA-II (Non-dominated sorting genetic algorithm II). This algorithm iteratively generates a series of door and window design schemes in the decision space and evaluates their performance on four optimization objectives. After several generations of evolution, the algorithm converges and generates a non-dominated solution set. This non-dominated solution set may contain hundreds of design schemes, each offering a unique trade-off between profile visible width, structural stiffness, thermal performance, and cost. For example, one scheme may have excellent thermal performance but higher cost, while another may have lower cost but slightly lower structural stiffness.

[0053] Then, design parameters that meet preset thresholds are selected from this set of non-dominated solutions. For example, the manufacturer might set the following thresholds: 1. The visible width of the profile must be less than 50mm.

[0054] 2. The structural stiffness must be greater than a certain specific value (for example, to meet the requirements of resisting a category 12 typhoon).

[0055] 3. The U value must be less than 1.5 W / (m²·K).

[0056] 4. Costs must be below a certain budget limit.

[0057] These thresholds filter out design schemes that do not meet actual engineering requirements from the set of non-dominated solutions, ultimately resulting in a smaller, more refined set of optimal design parameters. These filtered design parameters, such as specific profile cross-sectional dimensions, glass configurations, and sealing material combinations, are identified as optimization information for door and window design, directly guiding the development and production of new products. In this way, manufacturers can ensure that their new door and window products achieve an optimal balance across multiple key performance indicators and meet stringent market and regulatory requirements.

[0058] In some embodiments of this application, the door and window design information includes the cross-sectional geometry of the profiles, the curved shape of the glass, and the material combination of the connection interfaces; the optimization objective for obtaining the door and window design information includes: The cross-sectional geometry of the profile, the curved shape of the glass, and the material combination of the connection interface are converted into adjustable numerical parameters. Multiphysics simulations are performed on adjustable numerical parameters to obtain multiphysics simulation results; Based on the multiphysics simulation results, the performance index of the optimization target is calculated; A multidimensional correlation matrix is ​​constructed based on the performance indicators of the optimization objectives. The elements of the multidimensional correlation matrix reflect the degree of mutual influence between the optimization objectives under different combinations of design parameters through nonlinear influence coefficients. Identify elements in the multidimensional correlation matrix that exceed a preset threshold to determine the coupling relationship of the elements, and obtain the adjusted performance threshold and priority based on the coupling relationship of the elements; Based on the adjusted performance thresholds and priorities, optimization targets for door and window design information are generated.

[0059] Specifically, window and door design information can be understood as the various design elements that constitute window and door products, including the cross-sectional geometry of the profiles, the curved shape of the glass, and the material combination of the connection interfaces. The cross-sectional geometry of the profiles refers to the cross-sectional shape and dimensions of components such as the window and door frames and sashes, including the number of cavities, wall thickness, and width of the profiles. The curved shape of the glass refers to the geometric form of the glass panels, such as flat glass, curved glass, or irregularly shaped glass. The material combination of the connection interfaces refers to the types and combinations of materials used to connect different window and door components, such as sealants, gaskets, and fasteners. This design information forms the basis of window and door performance, and its purpose is to provide adjustable input for subsequent optimization.

[0060] Furthermore, converting the cross-sectional geometry of the profile, the curved shape of the glass, and the material composition of the connection interfaces into adjustable numerical parameters refers to parametric processing of the aforementioned non-numerical design information. For example, the wall thickness of the profile can be converted into a specific millimeter value, the radius of curvature of the glass can be converted into a numerical value, and the material composition can be quantified through coding or material property parameters. The purpose is to convert complex design information into a computer-processable numerical form for simulation and optimization calculations.

[0061] Among these methods, multiphysics simulations are performed on adjustable numerical parameters to obtain multiphysics simulation results. This can be understood as a simulation analysis that simultaneously considers the interactions of multiple physical fields such as structural mechanics, heat transfer, fluid mechanics, and acoustics. For example, it can simulate the structural deformation of doors and windows under wind pressure, heat transfer under temperature difference, and sealing performance under rainwater. The purpose is to comprehensively evaluate the overall performance of doors and windows under different combinations of design parameters.

[0062] In practical applications, the performance indicators of the optimization target are calculated based on the multiphysics simulation results. These are quantitative evaluation values ​​extracted from the multiphysics simulation results, such as structural stiffness, thermal performance (e.g., heat transfer coefficient U-value), optical quality (e.g., light transmittance, shading coefficient), sealing performance (e.g., air leakage), and the long-term lifespan of materials. The purpose is to transform complex simulation results into quantitative indicators that can be directly used for evaluation and comparison.

[0063] Based on this, a multidimensional correlation matrix is ​​constructed using the performance indicators of the optimization objectives. The elements of this matrix, through nonlinear influence coefficients, reflect the degree of mutual influence between the optimization objectives under different combinations of design parameters. The multidimensional correlation matrix is ​​a mathematical tool used to characterize the mutual influence relationships between different optimization objectives. Each element in the matrix, i.e., the nonlinear influence coefficient, quantifies the degree of influence on other optimization objectives when one objective changes; this influence may be nonlinear. For example, increasing the structural stiffness of doors and windows may increase the weight of the profiles, thereby affecting thermal performance or cost. Its purpose is to reveal the inherent connections and potential conflicts between optimization objectives, providing a basis for subsequent optimization decisions.

[0064] This process involves identifying elements in the multidimensional correlation matrix that exceed a preset threshold to determine their coupling relationships. Based on these relationships, adjusted performance thresholds and priorities are derived. The preset threshold is used to filter out significantly influential correlations. Elements exceeding the threshold indicate a strong coupling relationship between the corresponding optimization objectives, meaning their changes significantly affect each other. By analyzing these coupling relationships, the original performance requirements can be adjusted. For example, if a strong negative correlation is found between structural stiffness and thermal performance, it may be necessary to adjust their respective performance thresholds and determine their priorities in the optimization process to achieve overall optimization. The aim is to balance conflicting objectives in multi-objective optimization, ensuring the effectiveness of the optimization process.

[0065] Therefore, based on the adjusted performance thresholds and priorities, optimization objectives for door and window design information are generated. The final generated optimization objectives for door and window design information are no longer isolated, but rather a comprehensive set of objectives after interrelationship analysis and priority adjustment. These objectives are more instructive, guiding the design optimization process and enabling it to more effectively converge to the optimal design solution that meets multiple performance requirements.

[0066] This application's solution parameterizes window and door design information and comprehensively evaluates its performance using multiphysics simulation, thereby obtaining quantified performance indicators. Based on this, by constructing a multidimensional correlation matrix and analyzing the nonlinear influence coefficients between optimization objectives, a deep understanding of the coupling relationships between these objectives can be achieved. By identifying coupling elements exceeding preset thresholds, it is possible to accurately determine which optimization objectives have significant interactions, and then adjust performance thresholds and priorities based on these coupling relationships. This systematic approach effectively solves the problems of ambiguous optimization objective definitions and difficulty in coordinating conflicts between objectives in traditional optimization methods. It ensures that the complex correlation between design parameters and performance objectives is fully considered when generating window and door design optimization information, thereby avoiding local optima and achieving global optimization.

[0067] In some preferred embodiments, a specific example is given below. Suppose we need to optimize the design of a high-performance energy-saving window. First, the window design information is parameterized; for example, the wall thickness of the profile, the number of cavities, the number of glass layers, the thickness of the insulating layer, and the elastic modulus of the sealant are converted into adjustable numerical parameters. Next, for these parameter combinations, multiphysics simulations are performed to simulate the window's performance in heat conduction, structural deformation, and acoustic insulation under different environmental conditions. For example, the simulation yields performance indicators such as the heat transfer coefficient (U-value), wind pressure deformation, and sound insulation under different designs. Then, a multidimensional correlation matrix is ​​constructed based on these performance indicators. The elements of this matrix reflect the mutual influence between optimization objectives such as the U-value, wind pressure deformation, and sound insulation through nonlinear influence coefficients. For example, increasing the profile wall thickness may improve structural stiffness (reduce deformation), but may increase material costs and slightly affect the U-value. By identifying elements in the matrix that exceed a preset threshold, for example, a strong negative correlation is found between the U-value and wind pressure deformation, meaning that improving wind pressure resistance may lead to a deterioration of the U-value. Based on this coupling relationship, the performance thresholds for the U-value and wind pressure deformation can be adjusted, and their priorities in the optimization process can be determined. For example, the U-value can be optimized first, provided that the minimum structural stiffness requirement is met. Finally, based on the adjusted performance thresholds and priorities, a clear set of door and window design optimization objectives is generated to guide subsequent design iterations and parameter selection.

[0068] This application further proposes the following steps for constructing a multidimensional correlation matrix based on the performance indicators of the optimization objective: The performance of the optimization target is standardized to obtain standardized parameters. The standardized parameters are subjected to parameter dimensionality reduction processing based on high-dimensional space mapping in order to identify and extract key parameter combinations that meet the preset conditions for sensitivity analysis; For the combination of key parameters, a nonlinear response surface fitting is performed to obtain the nonlinear response surface fitting result; Based on the nonlinear response surface fitting results, the multidimensional correlation matrix is ​​constructed.

[0069] Specifically, standardizing the performance of the optimization target means unifying performance indicators with different dimensions and numerical ranges to the same scale. For example, Z-score standardization or Min-Max standardization can be used. The purpose is to eliminate the influence of dimensions, make different indicators comparable, and prevent certain indicators with larger values ​​from dominating the subsequent analysis.

[0070] The parameter dimensionality reduction process based on high-dimensional space mapping for standardized parameters can be understood as projecting the original high-dimensional data into a low-dimensional feature space through mathematical transformations, while preserving as much of the main information and structure of the data as possible. The aim is to simplify model complexity, reduce computational load, and identify the key parameter combinations that have the most significant impact on the optimization objective. This presupposition refers to the key parameter combinations that have the greatest impact on the optimization objective according to sensitivity analysis, thus focusing on the core influencing factors. High-dimensional space mapping can employ nonlinear dimensionality reduction algorithms such as principal component analysis, t-SNE, and UMAP.

[0071] In practical applications, nonlinear response surface fitting is performed for the key parameter combination. Specifically, this means constructing a mathematical model to describe the nonlinear relationship between the key parameter combination and the performance of the optimization target. For example, nonlinear fitting methods such as multinomial regression, radial basis function network, and support vector regression can be used. The purpose is to accurately capture the complex nonlinear interaction between parameters and performance, and to provide a quantitative relationship model for the subsequent construction of the correlation matrix.

[0072] Furthermore, constructing the multidimensional correlation matrix based on the nonlinear response surface fitting results refers to using the fitting model to quantify the degree of mutual influence between different optimization objectives under different combinations of design parameters, and filling these quantified relationships into the corresponding positions of the matrix. For example, each element in the matrix can represent the influence coefficient of one optimization objective on another optimization objective, which can be obtained through the partial derivatives of the fitting model or the results of sensitivity analysis.

[0073] In some preferred embodiments, a specific example is given below. Suppose we need to optimize three objectives: structural stiffness, thermal performance, and long-term lifespan of sealing materials for doors and windows. First, the structural stiffness values ​​(e.g., deformation), thermal performance values ​​(e.g., heat transfer coefficient), and sealing material lifespan (e.g., failure time) obtained through multiphysics simulations are standardized to a range of 0 to 1, resulting in standardized parameters. Next, these standardized parameters undergo dimensionality reduction based on high-dimensional space mapping, for example, using principal component analysis to identify the key design parameter combinations that have the greatest impact on these three optimization objectives, such as profile wall thickness, number of glass layers, and sealing strip material type. Then, for these identified key parameter combinations, nonlinear response surface fitting is performed to construct a mathematical model describing how these key parameter combinations nonlinearly affect structural stiffness, thermal performance, and sealing material lifespan. For example, a quadratic polynomial model can be fitted to represent the relationship between wall thickness, number of glass layers, and sealing material type and each performance index. Finally, based on these nonlinear response surface fitting results, the nonlinear influence coefficients between different optimization objectives are calculated and filled into a multidimensional correlation matrix. For example, one element in the matrix can represent the degree to which changes in profile wall thickness affect structural stiffness, while another element can represent the degree to which changes in the number of glass layers affect thermal performance, as well as the potential coupling effects between them. Thus, this multidimensional correlation matrix can clearly reveal the complex interactions between various optimization objectives, providing precise guidance for subsequent optimization decisions.

[0074] This application further proposes a more refined parameter dimensionality reduction method, which aims to ensure the accurate identification and extraction of key parameter combinations that significantly affect the optimization objective through a series of systematic steps.

[0075] Specifically, the standardized parameters mentioned above include door and window design parameters. The steps involved in performing dimensionality reduction processing on the standardized parameters based on high-dimensional space mapping to identify and extract key parameter combinations that significantly impact the optimization objective include: The design parameters of doors and windows are preprocessed. The preprocessing includes unifying the dimensions of the parameters and analyzing the distribution characteristics of the parameters to obtain the distribution characteristics of the parameters. Based on the distribution characteristics of the parameters, a nonlinear mapping function is selected to map the original high-dimensional parameter space to a low-dimensional feature space, thus obtaining the dimensionality-reduced parameters. In the low-dimensional feature space, a graph-based clustering method is used to group the dimensionality-reduced parameters to obtain key parameter combinations. Sensitivity analysis is performed on the identified key parameter combinations to obtain the sensitivity analysis results; the sensitivity analysis refers to the analytical method for evaluating the degree of influence of changes in input parameters on the output results; Based on the sensitivity analysis results, key parameter combinations that significantly affect the optimization objective are selected and used as the final output of the dimensionality reduction process to identify and extract key parameter combinations that meet the preset conditions of the sensitivity analysis.

[0076] In this context, window and door design parameters can be understood as various adjustable variables affecting window and door performance, such as the geometric dimensions of the profiles, material properties, glass type, and sealing specifications. Preprocessing these parameters aims to eliminate the influence of different dimensions and understand their inherent distribution patterns, laying the foundation for subsequent dimensionality reduction and analysis. For example, dimensional unification can be achieved through normalization or standardization methods, while distribution characteristic analysis can employ methods such as histograms and kernel density estimation.

[0077] Furthermore, nonlinear mapping functions refer to mathematical functions that can capture the nonlinear structure and relationships in high-dimensional data, such as kernel principal component analysis (KPCA), t-SNE (t-distributed stochastic neighbor embedding), or autoencoders. By selecting an appropriate nonlinear mapping function, the complex relationships in the original high-dimensional parameter space can be effectively projected to a low-dimensional feature space, while preserving as much important structural information of the data as possible.

[0078] Furthermore, graph-based clustering methods treat the dimensionality-reduced parameters as nodes in a graph, constructing the graph by defining similarities or distances between nodes, and then using the graph's structural properties for clustering. For example, algorithms such as spectral clustering, hierarchical clustering, or DBSCAN can be used to group parameters with similar characteristics or interrelationships, thereby identifying potential key parameter combinations. The aim is to discover the inherent structure and correlations within the dimensionality-reduced data, grouping related parameters together for subsequent analysis.

[0079] Sensitivity analysis is a systematic method used to quantify the impact of changes in input parameters on model output. Specifically, it assesses how changes in a single parameter or combination of parameters lead to changes in the performance metrics of the optimization objective. Its purpose is to identify the parameters that have the greatest impact on the optimization objective, thereby guiding subsequent optimization directions. For example, sensitivity analysis can be performed using methods such as analysis of variance, the Sobol index, or the Morris method.

[0080] Through the above technical solution, this application overcomes the limitation of existing technologies in accurately identifying key parameters when processing complex, high-dimensional door and window design parameters. Specifically, by preprocessing the door and window design parameters, the consistency of data quality and analytical foundation is ensured; by selecting a nonlinear mapping function for dimensionality reduction, the nonlinear relationships between parameters are effectively captured, avoiding information loss; by using a graph theory-based clustering method, deep correlations between parameters can be discovered, identifying more representative combinations of key parameters; and the introduction of sensitivity analysis further quantifies the impact of these parameters on the optimization objective, ensuring that the selected key parameters are truly influential. Therefore, this application can significantly improve the efficiency and accuracy of door and window processing parameter optimization, making the optimization process more focused on core influencing factors, thereby achieving better door and window performance and a longer service life.

[0081] In some preferred embodiments, a specific example is given below. Suppose that the design of a new type of energy-saving door and window needs to be optimized, and its design parameters include dozens or even hundreds of parameters such as profile wall thickness, number of cavities, type of sealing strip material, number of glass layers, glass spacing, number of Low-E film layers, and type of hardware.

[0082] First, these door and window design parameters are preprocessed. For example, all length units are standardized to millimeters, and material properties (such as thermal conductivity and elastic modulus) are normalized to a range of 0 to 1. Simultaneously, the distribution characteristics of each parameter are analyzed; for instance, it is found that the profile wall thickness parameter may follow a normal distribution, while the sealing strip material type is a discrete categorical variable.

[0083] Secondly, based on the distribution characteristics of these parameters, a suitable nonlinear mapping function is selected, such as kernel principal component analysis (KPCA). KPCA maps the original high-dimensional parameter space (e.g., 100 parameters) to a low-dimensional feature space (e.g., 10 feature dimensions). This process can capture complex nonlinear relationships between parameters, such as the combined effect of profile wall thickness and the number of cavities on structural stiffness.

[0084] Next, in the dimensionality-reduced 10-dimensional feature space, graph-based clustering methods, such as spectral clustering, are used. The parameter points after dimensionality reduction are treated as nodes in a graph, and a graph is constructed based on their distance or similarity in the low-dimensional space, followed by clustering. Through clustering, it can be discovered that some parameter combinations frequently appear together or have similar influence patterns. For example, "profile wall thickness - number of cavities - structural stiffness" may form a key parameter combination, while "number of glass layers - glass spacing - thermal performance" may form another key parameter combination.

[0085] Then, sensitivity analysis is performed on these key parameter combinations identified through clustering. For example, for the combination of "profile wall thickness - number of cavities," its value is changed and input into a door and window simulation model to observe changes in optimization objectives such as structural stiffness and thermal performance. The results of the sensitivity analysis may show that, within a certain range, the profile wall thickness has a much greater impact on structural stiffness than the number of cavities.

[0086] Finally, based on the sensitivity analysis results, the key parameter combinations that have the most significant impact on the optimization objective are selected. For example, if the sensitivity analysis shows that the profile wall thickness, the number of glass layers, and the type of sealing strip material are the three most critical parameters affecting the overall performance of doors and windows, then in subsequent optimization iterations, these three parameters can be prioritized for adjustment and optimization, thereby efficiently finding the optimal door and window design scheme.

[0087] This application further proposes the following steps for performing sensitivity analysis on the identified key parameter combinations to obtain the sensitivity analysis results: The perturbation range of the key parameter combination is segmented, and the segmentation is based on the gradient change characteristics of the target performance. Within each segment, perturbation samples of key parameter combinations are generated, and the density of the perturbation samples is adjusted according to the correlation of the target performance within that segment. The disturbance samples are input into the door and window simulation model to simulate the performance parameters of the doors and windows; Identify and optimize local sensitive areas of the target response based on performance parameters; For the local sensitive region, a local fine-grained perturbation is performed. The local fine-grained perturbation is obtained by reducing the perturbation step size and increasing the number of perturbation samples. Based on the results of the local fine-tuning perturbation, the degree of influence of the combination of key parameters on the optimization objective is calculated, and the sensitivity analysis results are obtained.

[0088] Specifically, segmenting the perturbation range of key parameter combinations involves dividing the entire range where key parameters may change into several sub-intervals. These sub-intervals are not arbitrary but based on the gradient change characteristics of the target performance. For example, in regions where performance changes drastically with larger gradient values, this region will be divided into one or more smaller segments to more precisely capture its sensitivity; conversely, in regions where performance is relatively stable with smaller gradient values, larger segments can be used. This gradient-based segmentation strategy aims to ensure more intensive analysis in critical regions while avoiding unnecessary computation in non-critical regions.

[0089] Within each segment, perturbation samples of key parameter combinations are generated, and the density of these perturbation samples is adjusted based on their correlation with the target performance within that segment. This means that the number and distribution of perturbation samples dynamically change across different segments. For example, in segments where there is a strong correlation between the target performance and the key parameters, the density of perturbation samples is increased to more accurately characterize this strong correlation; while in segments with weaker correlation, the density of perturbation samples can be appropriately reduced to save computational resources. This adaptive sample density adjustment strategy ensures sufficient data support in critical regions while optimizing overall computational efficiency.

[0090] The disturbance samples are input into the door and window simulation model, and the performance parameters of the doors and windows are simulated. This means that for each generated disturbance sample, a simulation run will be performed in the door and window simulation model to obtain the performance data of the doors and windows under that specific combination of parameters. These performance parameters can be structural stiffness, thermal performance, optical quality, or the long-term lifespan of sealing materials, etc.

[0091] Identifying locally sensitive regions of the target response based on performance parameters. After obtaining performance parameters corresponding to a large number of perturbation samples, analyzing this data can identify local regions where the target performance is particularly sensitive to changes in key parameters. These regions may manifest as sharp changes in the performance curve, inflection points, or intervals with significant nonlinear responses.

[0092] For the aforementioned locally sensitive regions, a refined local perturbation is performed. Once these regions are identified, to gain a deeper understanding and quantify their sensitivity, the perturbation step size is further reduced and the number of perturbation samples is increased, resulting in denser sampling and simulation. This refined local perturbation aims to provide higher-resolution sensitivity information, ensuring sufficiently accurate analysis results in critical regions.

[0093] Based on the results of the localized refined perturbation, the impact of key parameter combinations on the optimization objective is calculated, yielding sensitivity analysis results. Finally, by comprehensively analyzing the perturbation sample data across all segments and the results of the localized refined perturbation, the specific impact of each key parameter combination on the performance of the optimization objective can be accurately calculated, resulting in comprehensive sensitivity analysis results. This allows for a more effective focus on parameter regions that significantly influence the optimization objective, avoiding resource waste in non-critical areas. Furthermore, the identification and refined perturbation of localized sensitive areas ensure that even in highly nonlinear and complex systems, subtle parameter influences can be accurately captured. This provides more accurate and reliable sensitivity analysis results for generating window and door design optimization information, thereby supporting more efficient and optimized window and door design decisions.

[0094] In some preferred embodiments, it is assumed that a sensitivity analysis is needed to optimize the structural stiffness of doors and windows in order to determine the degree of influence of key parameters in the profile cross-sectional geometry (such as profile wall thickness, number of cavities, etc.).

[0095] First, the perturbation range of the key parameter, profile wall thickness, is segmented. Preliminary simulations or empirical data reveal that when the profile wall thickness is within a specific range (e.g., 2.0mm to 2.5mm), the structural stiffness of the doors and windows exhibits a significant gradient change, meaning the stiffness changes rapidly with the wall thickness. Therefore, this range is divided into one or more smaller segments. In other ranges (e.g., 1.5mm to 2.0mm or 2.5mm to 3.0mm), the change in structural stiffness is relatively gradual, and these can be divided into larger segments.

[0096] Secondly, perturbation samples are generated within each segment. For example, in the "high gradient" segment from 2.0 mm to 2.5 mm, due to the high correlation between structural stiffness and profile wall thickness, the generation density of perturbation samples is increased, for example, one sample is generated every 0.05 mm. In the "low gradient" segment from 1.5 mm to 2.0 mm, due to the lower correlation, the sample density can be appropriately reduced, for example, one sample is generated every 0.1 mm.

[0097] Subsequently, these disturbance samples (i.e., different wall thickness values) are input into the door and window simulation model to simulate and obtain the structural stiffness performance parameters corresponding to each wall thickness value.

[0098] By analyzing these performance parameters, localized sensitive areas in the structural stiffness response to profile wall thickness can be identified. For example, a significant inflection point or nonlinear enhancement region in structural stiffness may be found around 2.2 mm.

[0099] For this locally sensitive region (e.g., 2.15 mm to 2.25 mm), a localized fine-grained perturbation is performed. Specifically, the perturbation step size is further reduced (e.g., from 0.05 mm to 0.01 mm), and the number of perturbation samples is increased to perform a more intensive simulation within this region.

[0100] Finally, based on the perturbation results of all segments and the results of local refined perturbations, the influence of profile wall thickness on the structural stiffness of doors and windows is calculated, thus obtaining accurate sensitivity analysis results. In this way, the key influence range and degree of profile wall thickness on structural stiffness can be identified efficiently and accurately, providing data support for subsequent profile design optimization.

[0101] In some embodiments of the above method, the step of generating perturbation samples of key parameter combinations within each segment, wherein the density of the perturbation samples is adjusted according to the degree of nonlinearity of the target performance being optimized within that segment, includes: For the performance of the optimization target within the segment, calculate the corresponding gradient information and curvature information; Based on the gradient information and the curvature information, identify the micro-nonlinear fluctuation region within the segment; the micro-nonlinear fluctuation region refers to the region where the gradient or curvature exceeds a preset threshold. Within the microscopic nonlinear fluctuation region, the generation density of perturbation samples is increased; Outside the micro-nonlinear fluctuation region, maintain or reduce the generation density of perturbation samples; Perturbation samples of key parameter combinations are generated, and the density of the perturbation samples is adjusted according to the degree of nonlinearity of the target performance within the segment.

[0102] Specifically, gradient information refers to the direction and rate of change of the target performance with the combination of key parameters, reflecting the steepness of the performance change; curvature information describes the curvature of the performance surface, revealing the change in the rate of performance change. By calculating this information, the nonlinear characteristics of the performance can be quantified. The micro-nonlinear fluctuation region can be understood as a local region in the parameter space where the target performance exhibits significant nonlinear characteristics. Specifically, when the absolute value of the gradient or curvature exceeds a preset threshold, it indicates that the performance response to parameter changes within that region is nonlinear, and there may be inflection points, abrupt changes, or rapid shifts. The aim is to accurately pinpoint complex regions requiring focused attention. In practical applications, within the identified micro-nonlinear fluctuation regions, increasing the generation density of perturbation samples—for example, using a smaller perturbation step size or a denser sampling grid—ensures that the performance response in these complex regions is fully explored and accurately characterized. The goal is to capture all important details within the nonlinear region. Correspondingly, outside the region of micro-nonlinear fluctuations, i.e., in the region where performance is relatively stable or linear, the generation density of perturbation samples can be maintained or appropriately reduced. For example, a larger perturbation step size or a sparse sampling grid can be used. The aim is to avoid unnecessary waste of computational resources and improve overall analysis efficiency.

[0103] The above technical solutions can more effectively guide subsequent parameter optimization, avoid local optima or incorrect judgments caused by insufficient sampling, thereby significantly improving the accuracy and effectiveness of door and window design optimization information, and providing more reliable protection for the long-term performance and lifespan of doors and windows.

[0104] Secondly, referring to Figure 2 This application further proposes a door and window processing parameter optimization system, which includes: External environment information acquisition module 201 is used to acquire door and window parameters and external environment information; The door and window simulation model module 202 is used to establish a door and window simulation model based on the door and window parameters. The physical load parameter conversion module 203 is used to convert the external environment information into physical load parameters; The door and window performance evaluation result module 204 is used to perform accelerated simulation operation on the door and window simulation model based on the physical load parameters to obtain the door and window performance evaluation result; wherein, the accelerated simulation operation refers to the operation of applying multiple load cycles to simulate the long-term performance of doors and windows within a preset time period. The door and window design optimization information module 205 is used to generate door and window design optimization information based on the door and window performance evaluation results.

[0105] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for optimizing door and window processing parameters, characterized in that, include: Obtain door and window parameters and external environment information; Establish a door and window simulation model based on the stated door and window parameters; The external environment information is converted into physical load parameters; Based on the physical load parameters, an accelerated simulation operation is performed on the door and window simulation model to obtain the door and window performance evaluation results; wherein, the accelerated simulation operation refers to the operation of applying multiple load cycles to simulate the long-term performance of doors and windows within a preset time period. Based on the performance evaluation results of the doors and windows, optimization information for door and window design is generated.

2. The method for optimizing door and window processing parameters according to claim 1, characterized in that, The external environmental information includes periodic shear force data from deformation simulation, vibration frequency and amplitude data from micro-vibration sensors, and temperature fluctuation data from temperature sensors; the physical load parameters include multi-dimensional damage composite loads and energy dissipation accumulation rates; the accelerated simulation operation of the door and window simulation model based on the physical load parameters yields door and window performance evaluation results, including: Based on the periodic shear force data simulated by the deformation through the weighted function, the vibration frequency data and vibration amplitude data of the micro-vibration sensor, and the temperature fluctuation data of the temperature sensor, a multi-dimensional damage composite load is established; wherein, the multi-dimensional damage composite load refers to multi-source, multi-dimensional composite load information. Based on the aforementioned multidimensional damage composite load, the energy dissipation accumulation rate of the door and window sealing material is calculated; the energy dissipation accumulation rate refers to the speed at which damage accumulates. The multidimensional damage composite load and energy dissipation accumulation rate are applied to the door and window simulation model for accelerated simulation to obtain the door and window performance evaluation results.

3. The method for optimizing door and window processing parameters according to claim 1, characterized in that, The accelerated simulation of the door and window simulation model based on the physical load parameters yields the door and window performance evaluation results, including: Obtain material degradation parameters under different environmental conditions; An acceleration factor corresponding to the material degradation parameters is generated by the nonlinear relationship between the accumulation of internal damage and the degradation process of the material; the acceleration factor is a parameter characterizing the nonlinear relationship between the accumulation of internal damage and the degradation process of the material. Based on the acceleration factor, the cycle frequency, amplitude, and duration of the physical load parameters applied to the door and window simulation model are adjusted to obtain the adjusted door and window simulation model. Monitor the cumulative microscopic damage data of the adjusted door and window simulation model; The deviation between the accumulated microscopic damage data and the preset nonlinear parameters is calculated. The acceleration factor or physical load parameters are adjusted according to the deviation. The adjusted door and window simulation model is then subjected to accelerated simulation to obtain the door and window performance evaluation results. The preset nonlinear parameters refer to the damage accumulation rate predicted by the nonlinear acceleration model.

4. The method for optimizing door and window processing parameters according to claim 1, characterized in that, The method includes: The optimization objectives for obtaining door and window design information include: visible width of profiles, structural stiffness, thermal performance, optical quality, and long-term lifespan of sealing materials. Construct a multi-objective decision space by optimizing objectives; An optimization strategy based on Pareto front search is executed to generate a set of non-dominated solutions in the multi-objective decision space. Design parameters that meet a preset threshold are selected from the set of non-dominated solutions and the design parameters are determined as optimization information for door and window design.

5. The method for optimizing door and window processing parameters according to claim 4, characterized in that, The door and window design information includes the cross-sectional geometry of the profiles, the curved shape of the glass, and the material combination of the connection interfaces; the optimization objectives for obtaining the door and window design information include: The cross-sectional geometry of the profile, the curved shape of the glass, and the material combination of the connection interface are converted into adjustable numerical parameters. Multiphysics simulations are performed on adjustable numerical parameters to obtain multiphysics simulation results; Based on the multiphysics simulation results, the performance index of the optimization target is calculated; A multidimensional correlation matrix is ​​constructed based on the performance indicators of the optimization objectives. The elements of the multidimensional correlation matrix reflect the degree of mutual influence between the optimization objectives under different combinations of design parameters through nonlinear influence coefficients. Identify elements in the multidimensional correlation matrix that exceed a preset threshold to determine the coupling relationship of the elements, and obtain the adjusted performance threshold and priority based on the coupling relationship of the elements; Based on the adjusted performance thresholds and priorities, optimization targets for door and window design information are generated.

6. The method for optimizing door and window processing parameters according to claim 5, characterized in that, The construction of a multidimensional correlation matrix based on the performance metrics of the optimization objective includes: The performance of the optimization target is standardized to obtain standardized parameters. The standardized parameters are subjected to parameter dimensionality reduction processing based on high-dimensional space mapping in order to identify and extract key parameter combinations that meet the preset conditions for sensitivity analysis; For the combination of key parameters, a nonlinear response surface fitting is performed to obtain the nonlinear response surface fitting result; Based on the nonlinear response surface fitting results, the multidimensional correlation matrix is ​​constructed.

7. The method for optimizing door and window processing parameters according to claim 6, characterized in that, The standardized parameters include door and window design parameters; the step of performing parameter dimensionality reduction processing based on high-dimensional space mapping on the standardized parameters to identify and extract key parameter combinations that meet the preset conditions for sensitivity analysis includes: The design parameters of doors and windows are preprocessed. The preprocessing includes unifying the dimensions of the parameters and analyzing the distribution characteristics of the parameters to obtain the distribution characteristics of the parameters. Based on the distribution characteristics of the parameters, a nonlinear mapping function is selected to map the original high-dimensional parameter space to a low-dimensional feature space, thus obtaining the dimensionality-reduced parameters. In the low-dimensional feature space, a graph-based clustering method is used to group the dimensionality-reduced parameters to obtain key parameter combinations. Sensitivity analysis is performed on the identified key parameter combinations to obtain the sensitivity analysis results; the sensitivity analysis refers to the analytical method for evaluating the degree of influence of changes in input parameters on the output results; Based on the sensitivity analysis results, key parameter combinations that significantly affect the optimization objective are selected and used as the final output of the dimensionality reduction process to identify and extract key parameter combinations that meet the preset conditions of the sensitivity analysis.

8. The method for optimizing door and window processing parameters according to claim 7, characterized in that, The sensitivity analysis is performed on the identified key parameter combinations to obtain the sensitivity analysis results, including: The perturbation range of the key parameter combination is segmented, and the segmentation is based on the gradient change characteristics of the target performance. Within each segment, perturbation samples of key parameter combinations are generated, and the density of the perturbation samples is adjusted according to the correlation of the target performance within that segment. The disturbance samples are input into the door and window simulation model to simulate the performance parameters of the doors and windows; Identify and optimize local sensitive areas of the target response based on performance parameters; For the local sensitive region, a local fine-grained perturbation is performed. The local fine-grained perturbation is obtained by reducing the perturbation step size and increasing the number of perturbation samples. Based on the results of the local fine-tuning perturbation, the degree of influence of the combination of key parameters on the optimization objective is calculated, and the sensitivity analysis results are obtained.

9. The method for optimizing door and window processing parameters according to claim 8, characterized in that, The step of generating perturbation samples of key parameter combinations within each segment, wherein the density of the perturbation samples is adjusted according to the correlation of the target performance within that segment, includes: For the performance of the optimization target within the segment, calculate the corresponding gradient information and curvature information; Based on the gradient information and the curvature information, identify the micro-nonlinear fluctuation region within the segment; the micro-nonlinear fluctuation region refers to the region where the gradient or curvature exceeds a preset threshold. Within the microscopic nonlinear fluctuation region, the generation density of perturbation samples is increased; Outside the micro-nonlinear fluctuation region, maintain or reduce the generation density of perturbation samples; Perturbation samples of key parameter combinations are generated, and the density of the perturbation samples is adjusted according to the degree of nonlinearity of the target performance within the segment.

10. A door and window processing parameter optimization system, characterized in that, The system includes: The external environment information acquisition module is used to acquire door and window parameters and external environment information; The door and window simulation model module is used to create a door and window simulation model based on the door and window parameters. The physical load parameter conversion module is used to convert the external environment information into physical load parameters; The door and window performance evaluation result module is used to perform accelerated simulation operation on the door and window simulation model based on the physical load parameters to obtain the door and window performance evaluation result; wherein, the accelerated simulation operation refers to the operation of applying multiple load cycles to simulate the long-term performance of doors and windows within a preset time period. The door and window design optimization information module is used to generate door and window design optimization information based on the door and window performance evaluation results.

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