Zipper and windproof curtain fabric connection optimization method and device

Abstract

The invention belongs to the technical field of material connection, and discloses a zipper and windproof curtain fabric connection optimization method and equipment, and the method comprises the steps: obtaining the characteristic environment temperature and the characteristic ultraviolet intensity of a high-altitude environment; the model is dynamically adjusted through the ultrasonic welding parameters, and optimized ultrasonic welding process parameters are obtained through calculation according to the characteristic environment temperature and the characteristic ultraviolet intensity; the ultrasonic welding process parameters comprise welding frequency, welding pressure, welding time and welding amplitude; ultrasonic welding equipment is controlled, the zipper base cloth and the windproof curtain fabric are welded according to the optimized ultrasonic welding process parameters, and edge joints of the zipper base cloth and the windproof curtain fabric are aligned and jointly welded to a connecting cloth belt; welding process parameters are monitored in real time to adjust ultrasonic welding process parameters; the method has the advantage that the durability and reliability of the connection of the zipper and the windproof curtain fabric in the high-altitude environment of the high-rise building are improved.

Description

Technical Field

[0001] The present application relates to the technical field of material connection, and in particular to a method and device for optimizing the connection between a zipper and windproof curtain fabric. Background Art

[0002] Large-format wind curtains are required around glass exterior walls, corridors, halls, etc. of commercial buildings for shielding, shading, or blocking wind. Wind curtains are usually placed in a roller shutter box in a retracted state. When in use, they are released by a motor. In order to maintain the stability of the wind curtain, inner and outer tracks are usually set at the two lateral ends of the wind curtain. The outer track is vertically fixed to the building, and the inner track slides inside the outer track. The inner track is usually made of a flexible and rollable material and is fixed to the wind curtain by a zipper. In order to ensure that the inner track and the wind curtain can be installed firmly and reliably, the zipper connection technology is particularly important.

[0003] Especially in the high-altitude environments of high-rise buildings, windproof curtains can effectively resist strong winds, isolate the external low-temperature environment, and protect against high-intensity ultraviolet radiation. First, the drastic temperature difference between day and night in high-altitude environments will cause the windproof curtains and zipper connections to be in a long-term cycle of repeated thermal expansion and contraction, accelerating material fatigue and degradation of connection performance. Secondly, the intensity of ultraviolet radiation in high-rise buildings is extremely high. Long-term strong ultraviolet radiation significantly accelerates the aging process of windproof curtains and zipper materials, reducing the mechanical properties of the materials. In addition, the daily rolling and unrolling operations of windproof curtains will further increase the mechanical stress impact of the connection parts, making them more susceptible to damage during daily use.

[0004] These multiple unfavorable factors, coupled and compounded in the actual application of wind curtains in high-rise buildings, make the traditional wind curtain-to-zipper connection method susceptible to significant reductions in connection strength and even serious problems such as cracking and failure under the combined effects of factors such as long-term high-intensity UV exposure, frequent temperature fluctuations, and continuous structural deformation during daily winding and unwinding operations. Therefore, effectively improving the durability and reliability of the zipper-to-wind curtain connection in the unique application scenario of wind curtains in high-rise buildings has become a key technical challenge that needs to be solved urgently.

[0005] In view of the above problems, the existing technology is in urgent need of improvement. Summary of the Invention

[0006] The purpose of this application is to provide a method and device for optimizing the connection between a zipper and a windproof curtain fabric, which has the advantage of improving the durability and reliability of the connection between the zipper and the windproof curtain fabric in a high-altitude environment of a high-rise building.

[0007] In a first aspect, the present application provides a method for optimizing the connection between a zipper and a windproof curtain fabric, which is used to optimize the connection process between the windproof curtain fabric and the zipper on a high-rise building. The method comprises the following steps: A1. Obtain characteristic ambient temperature and characteristic ultraviolet intensity of the high-altitude environment; A2. Utilizing a dynamic ultrasonic welding parameter adjustment model, the optimized ultrasonic welding process parameters are calculated based on the characteristic ambient temperature and characteristic UV intensity. Ultrasonic welding process parameters include welding frequency, welding pressure, welding time, and welding amplitude. A3. Control the ultrasonic welding equipment to weld the zipper base fabric to the windproof curtain fabric according to optimized ultrasonic welding process parameters. Align the edge seams of the zipper base fabric and the windproof curtain fabric and weld them together to the connecting tape. A4. Real-time monitoring of welding process parameters to adjust ultrasonic welding process parameters.

[0008] This method optimizes the welding process by obtaining high-altitude environmental parameters and dynamically adjusting ultrasonic welding parameters based on the environmental parameters. It has the advantage of improving the durability and reliability of the connection between the zipper and the windproof curtain fabric in a high-altitude environment.

[0009] Preferably, step A1 includes: A101. Based on the geographic characteristics of the target tent deployment location, retrieve historical ambient temperature data and historical UV intensity data for several locations matching the target deployment location from the high-altitude building environment database. The high-altitude environment database contains historical ambient temperature data and historical UV intensity data for multiple typical high-altitude building locations, as well as corresponding geographic characteristics. A102. Based on the retrieved historical ambient temperature data and historical UV intensity data for several locations, calculate statistical characteristic parameters of the ambient temperature and UV intensity, respectively; the statistical characteristic parameters include average, maximum, and minimum values; A103. Perform a weighted average calculation on each statistical characteristic parameter of the ambient temperature corresponding to the retrieved locations to obtain a corresponding first weighted average statistical characteristic parameter item; perform a weighted average calculation on each statistical characteristic parameter of the ultraviolet intensity corresponding to the retrieved locations to obtain a corresponding second weighted average statistical characteristic parameter item; A104. Calculate the characteristic ambient temperature by comprehensively analyzing the first weighted average statistical characteristic parameter items; calculate the characteristic ultraviolet intensity by comprehensively analyzing the second weighted average statistical characteristic parameter items.

[0010] In this way, the characteristic ambient temperature and characteristic ultraviolet intensity can be obtained more accurately, laying the foundation for the subsequent optimization of ultrasonic welding parameters, thereby improving the welding quality and the reliability of the connection structure.

[0011] Preferably, the ultrasonic welding parameter dynamic adjustment model includes a material property sub-model, a welding thermodynamic sub-model and a welding quality assessment sub-model; The material property sub-model is used to calculate the elastic modulus, yield strength and thermal expansion coefficient of the materials of the connecting tape, zipper base fabric and windproof curtain fabric under different ambient temperatures and UV intensities; The welding thermodynamic sub-model is used to calculate the temperature distribution and stress distribution in the welding area during ultrasonic welding; The welding quality assessment sub-model is used to assess the welding strength and welding uniformity of the welding area.

[0012] Step A2 includes: A201. Based on the characteristic ambient temperature and the characteristic UV intensity, use the material property submodel to calculate the elastic modulus, yield strength, and thermal expansion coefficient of the connecting tape, zipper base fabric, and windproof curtain fabric. A202. Using the elastic modulus, yield strength, and thermal expansion coefficient of the connecting tape, zipper base fabric, and wind curtain fabric as inputs to the welding thermodynamics sub-model, the temperature and stress distributions in the weld area are calculated for different combinations of welding frequency, welding pressure, welding time, and welding amplitude. A203. Use the temperature and stress distributions in the weld area as inputs to the weld quality assessment sub-model to calculate weld strength and weld uniformity for different combinations of welding frequency, welding pressure, welding time, and welding amplitude. A204. Based on the welding strength and welding uniformity under different combinations of welding frequency, welding pressure, welding time and welding amplitude, with the goal of maximizing welding strength and optimizing welding uniformity, a multi-objective optimization algorithm is used to solve the optimized welding frequency, optimized welding pressure, optimized welding time and optimized welding amplitude.

[0013] As a result, the ultrasonic welding process parameters are optimized based on environmental factors and welding quality targets, making the welding parameter optimization process more specific, operational and effective, ensuring the reliability and durability of the connection between the zipper and the windproof curtain fabric in the extreme high-altitude environment of high-rise buildings.

[0014] Preferably, step A204 includes: B1. The NSGA-II algorithm is used to solve the welding strength and uniformity under different combinations of welding frequency, welding pressure, welding time, and welding amplitude, and a Pareto optimal solution set is obtained. Each solution in the Pareto optimal solution set corresponds to a combination of welding frequency, welding pressure, welding time, and welding amplitude. B2. Obtain the welding strength and welding uniformity corresponding to each solution in the Pareto optimal solution set to calculate the corresponding welding strength margin and welding uniformity margin; the welding strength margin is the ratio of the welding strength to a preset welding strength threshold, and the welding uniformity margin is the ratio of the welding uniformity to a preset welding uniformity threshold; B3. For each solution in the Pareto optimal solution set, perform a weighted sum calculation on the corresponding welding strength margin and welding uniformity margin to obtain the corresponding comprehensive evaluation index; B4. Select the solution with the largest comprehensive evaluation index in the Pareto optimal solution set as the optimized welding frequency, optimized welding pressure, optimized welding time and optimized welding amplitude.

[0015] Preferably, step B1 includes: B101. For different combinations of welding frequency, welding pressure, welding time, and welding amplitude, initialize the NSGA-II algorithm population as the initial parent population; set the population size to N, where N is a preset positive integer; B102. For each welding parameter combination, use the ultrasonic welding parameter dynamic adjustment model to calculate the corresponding weld strength and weld uniformity, and then calculate the corresponding fitness value based on the weld strength and weld uniformity; B103. Based on the fitness value of each welding parameter combination, perform selection, crossover, and mutation operations to generate a new population as the offspring population. The selection operation uses the binary tournament selection method, the crossover operation uses the simulated binary crossover method, and the mutation operation uses the polynomial mutation method. During the mutation operation, the mutation probability is adjusted based on the ambient temperature and pressure. The lower the temperature and the lower the pressure, the higher the mutation probability. B104. Merge the parent population and the child population, perform non-dominated sorting, calculate the crowding distance of each solution, and select the top N solutions as the new parent population based on the non-dominated rank and crowding distance. B105. Determine whether the maximum number of iterations has been reached. If so, output the latest parent population as the Pareto optimal solution set; otherwise, return to step B102.

[0016] Preferably, step B104 includes: The merged population is stratified according to the non-dominant rank, and multiple non-dominant rank levels are obtained; For solutions in the same non-dominated hierarchy, the target difference between each solution and its adjacent solutions is calculated, and the sum of the target differences corresponding to the same solution is used as the crowding distance of the corresponding solution; the target differences include welding strength difference and welding uniformity difference; According to the non-dominated hierarchy level and crowding distance, the elite retention strategy is adopted to select the top N solutions as the new parent population.

[0017] Preferably, after step B2 and before step B3, the method further comprises the following steps: B5. Obtain the temperature and stress distribution of the welding area corresponding to each solution in the Pareto optimal solution set; B6. Based on the weld strength, weld uniformity, and temperature and stress distribution in the weld area corresponding to each solution in the Pareto optimal solution set, combined with Miner's linear cumulative damage theory, calculate the fatigue life assessment parameters of the welded structure under specific cyclic loading; B7. Correct the welding strength margin and welding uniformity margin according to the fatigue life assessment parameters.

[0018] Preferably, step B6 includes: B601. Based on the temperature and stress distributions in the weld region corresponding to each solution in the Pareto optimal solution set, extract multiple characteristic points within the weld region and obtain the stress amplitude and average stress of each characteristic point under a specific cyclic load. B602. For each characteristic point, calculate the equivalent stress amplitude based on its stress amplitude and mean stress using the modified Morrow mean stress correction algorithm; B603. Based on the SN curves of the connecting tape, zipper base fabric, and wind curtain fabric, query the fatigue life corresponding to the equivalent stress amplitude. B604. Based on the fatigue life obtained, calculate the damage index for each characteristic point according to Miner's linear cumulative damage theory. The damage index is the ratio of the number of cycles under a specific cyclic load to the fatigue life. B605. Perform weighted averaging of the damage indices of all characteristic points to obtain fatigue life assessment parameters for the welded structure; the weight coefficient of the weighted averaging calculation is inversely proportional to the distance from each characteristic point to the center of the weld area.

[0019] Preferably, step A4 includes: A401 real-time acquisition of ultrasonic welding equipment welding current, welding voltage, welding power and welding head vibration amplitude, and the collected data is stored; A402. Calculate the energy input curve during welding based on the stored welding current, welding voltage, welding power, and vibration amplitude data of the welding head; A403. Compare the energy input curve with the preset energy input curve threshold range to determine whether the welding process is stable; A404. If the welding process is unstable, the PID control algorithm is used to adjust the welding frequency, welding pressure, welding time and welding amplitude of the ultrasonic welding equipment according to the degree of deviation of the energy input curve relative to the energy input curve threshold range, so that the energy input curve is restored to the preset energy input curve threshold range.

[0020] In a second aspect, the present application provides a device for optimizing the connection between a zipper and a windproof curtain fabric, for optimizing the connection process between a windproof curtain fabric and a zipper on a high-rise building, the device comprising: Environmental parameter acquisition module, used to obtain characteristic ambient temperature and characteristic ultraviolet intensity of high-altitude environment; The welding parameter calculation module is used to calculate the optimized ultrasonic welding process parameters based on the characteristic ambient temperature and characteristic ultraviolet intensity using the ultrasonic welding parameter dynamic adjustment model; the ultrasonic welding process parameters include welding frequency, welding pressure, welding time and welding amplitude; The welding control module is used to control the ultrasonic welding equipment to weld the zipper base fabric and the windproof curtain fabric according to the optimized ultrasonic welding process parameters, wherein the edge seams of the zipper base fabric and the windproof curtain fabric are aligned and welded together on the connecting fabric tape; the welding quality monitoring module is used to monitor the welding process parameters in real time to adjust the ultrasonic welding process parameters.

[0021] Beneficial effects: The present application provides a method and device for optimizing the connection between a zipper and a windproof curtain fabric. By obtaining the high-altitude environmental parameters of a high-rise building and dynamically adjusting the ultrasonic welding parameters based on the environmental parameters, the welding process is optimized, which has the advantage of improving the durability and reliability of the connection between the zipper and the windproof curtain fabric in a high-altitude environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flow chart of the method for optimizing the connection between a zipper and windproof curtain fabric provided in an embodiment of the present application.

[0023] Figure 2 Schematic diagram of the connection optimization device between the zipper and the windproof curtain fabric provided in an embodiment of the present application.

[0024] Figure 3 This is a diagram of the connection structure between the zipper and the windproof curtain fabric provided in an embodiment of the present application.

[0025] Explanation of the reference numbers: 1. Environmental parameter acquisition module; 2. Welding parameter calculation module; 3. Welding control module; 4. Welding quality monitoring module; 5. Zipper; 6. Connecting tape; 7. Windproof curtain fabric. DETAILED DESCRIPTION

[0026] The technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. The components of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for which protection is claimed, but merely represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of this application.

[0027] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0028] refer to Figure 1 This application proposes a method for optimizing the connection between a zipper and a windproof curtain fabric, which is used to optimize the connection process between the windproof curtain fabric and the zipper on a high-rise building. The method comprises the following steps: A1. Obtain the characteristic ambient temperature and characteristic UV intensity of the high-altitude environment of high-rise buildings; A2. Utilizing a dynamic ultrasonic welding parameter adjustment model, the optimized ultrasonic welding process parameters are calculated based on the characteristic ambient temperature and characteristic UV intensity. Ultrasonic welding process parameters include welding frequency, welding pressure, welding time, and welding amplitude. A3. Control the ultrasonic welding equipment and weld the zipper base fabric 5 and the windproof curtain fabric 7 according to the optimized ultrasonic welding process parameters, wherein the zipper base fabric 5 and the windproof curtain fabric 7 are aligned with the edge seams and welded together to the connecting tape 6; A4. Real-time monitoring of welding process parameters to adjust ultrasonic welding process parameters.

[0029] Among them, in step A1, the characteristic ambient temperature and characteristic ultraviolet intensity are representative parameters of the high-altitude environment. They directly affect material properties and welding quality. Obtaining these parameters is the basis for subsequent optimization of the welding process.

[0030] The core of step A2 is the dynamic ultrasonic welding parameter adjustment model, which calculates the optimal welding parameter combination for the current environment based on the environmental parameters obtained in step A1. The model can include sub-models for material properties, welding thermodynamics, and welding quality assessment, respectively, to analyze material properties, thermodynamic behavior during welding, and weld quality.

[0031] In step A3 , the ultrasonic welding equipment is controlled to perform welding according to the optimized parameters calculated in step A2 , and the zipper base fabric 5 and the windproof curtain fabric 7 are connected through the connecting tape 6 .

[0032] Among them, in step A4, real-time monitoring of process parameters such as welding current, voltage, power or welding head vibration amplitude can timely detect abnormalities in the welding process and adjust the welding parameters according to the monitoring results to ensure the stability of the welding process.

[0033] Specifically, this application proposes an optimization method to address the technical issue of insufficient connection strength between the windproof curtain fabric and the zipper in the high-altitude environment of high-rise buildings. First, the characteristic ambient temperature and characteristic UV intensity of the target tent deployment location are obtained. These environmental parameters serve as inputs to an ultrasonic welding parameter dynamic adjustment model. The ultrasonic welding parameter dynamic adjustment model then calculates optimized ultrasonic welding process parameters, including welding frequency, welding pressure, welding time, and welding amplitude, based on the input environmental parameters. The ultrasonic welding equipment is then controlled to weld the connecting tape 6, the zipper base fabric 5, and the windproof curtain fabric 7 using the calculated optimized parameters. During the welding process, parameters such as welding current and voltage are monitored in real time, and the actual parameters are compared with preset ranges. If deviations occur during the welding process, such as unstable energy input, parameters such as welding frequency and welding pressure are adjusted using methods such as a PID control algorithm to restore stability and ensure welding quality. This method can thus achieve a reliable connection between the zipper and the windproof curtain fabric in the high-altitude environment of a high-rise building, improving the overall performance of the tent.

[0034] In some embodiments, step A1 comprises: A101. Based on the geographic characteristics of the target tent deployment location, retrieve historical ambient temperature data and UV intensity data for several locations matching the target deployment location from the high-altitude environmental database. The high-altitude environmental database contains historical ambient temperature data and UV intensity data for multiple typical high-rise building locations, as well as corresponding geographic characteristics. A102. Based on the retrieved historical ambient temperature data and historical UV intensity data for several locations, calculate statistical characteristic parameters of the ambient temperature and UV intensity, respectively; the statistical characteristic parameters include average, maximum, and minimum values; A103. Perform a weighted average calculation on each statistical characteristic parameter of the ambient temperature corresponding to the retrieved locations to obtain a corresponding first weighted average statistical characteristic parameter item; perform a weighted average calculation on each statistical characteristic parameter of the ultraviolet intensity corresponding to the retrieved locations to obtain a corresponding second weighted average statistical characteristic parameter item; A104. Calculate the characteristic ambient temperature by comprehensively calculating the first weighted average statistical characteristic parameter items; and calculate the characteristic ultraviolet intensity by comprehensively calculating the second weighted average statistical characteristic parameter items.

[0035] In step A101, the geographic features may include some or all of longitude, latitude, altitude, climate type, and so on. A high-altitude environment database for high-rise buildings is pre-established to store and manage environmental data for high-rise buildings. The data in the database can be derived from long-term meteorological observation records, satellite remote sensing data, or field measurements. The retrieval and matching process first determines the geographic features of the target deployment location, then compares these features with the geographic features of various locations in the database to identify several locations whose geographic feature similarity meets a preset threshold.

[0036] In step A102, statistical characteristic parameters are calculated by performing statistical analysis on the historical ambient temperature data and historical UV intensity data for each retrieved location. For example, for ambient temperature, the average value of the historical data is calculated to represent the typical temperature level at that location, and the maximum and minimum values ​​are calculated to reflect the temperature range.

[0037] In step A103, the weighted average calculation is performed to comprehensively consider environmental data from multiple similar locations, enhancing the representativeness of characteristic environmental parameters. The weight coefficient can be determined based on the geographic similarity between the search location and the target deployment location; the higher the similarity, the greater the weight coefficient. For example, a Gaussian weighting function or an inverse distance weighting method can be used to determine the weight coefficient.

[0038] In step A104, the characteristic ambient temperature is obtained by combining the various first weighted average statistical characteristic parameter items. One implementation method is to calculate the difference between the maximum and minimum values ​​of the first weighted average statistical characteristic parameter to obtain the maximum temperature difference, and then calculate the weighted sum of the average value of the first weighted average statistical characteristic parameter and the maximum temperature difference as the characteristic ambient temperature. Similarly, the difference between the maximum and minimum values ​​of the second weighted average statistical characteristic parameter is calculated to obtain the maximum intensity difference, and then calculate the weighted sum of the average value of the second weighted average statistical characteristic parameter and the maximum intensity difference as the characteristic ultraviolet intensity.

[0039] Specifically, through step A101, historical environmental data for multiple locations that match the geographical characteristics of the target tent deployment location can be effectively retrieved from the high-rise building high-altitude environmental database, ensuring the targeted and relevant selection of environmental data. Through step A102, the historical environmental data of the retrieved multiple locations is statistically analyzed to calculate statistical characteristic parameters of ambient temperature and ultraviolet intensity, allowing subsequent calculations to be based on more comprehensive environmental information and improving the accuracy of environmental parameter characterization. Through the weighted average calculation in step A103, different weights can be assigned based on the relevance of the retrieved locations, making the final characteristic environmental parameters more closely aligned with the actual conditions of the target deployment location. Finally, through the comprehensive calculation in step A104, characteristic ambient temperature and characteristic ultraviolet intensity that can represent the environmental characteristics of the target deployment location can be obtained, providing more accurate input for the subsequent optimization of ultrasonic welding parameters, thereby improving the welding quality and the reliability of the connection structure. As a result, the characteristic ambient temperature and characteristic ultraviolet intensity can be more accurately obtained, laying the foundation for the subsequent optimization of ultrasonic welding parameters, thereby improving the welding quality and the reliability of the connection structure.

[0040] In some embodiments, the ultrasonic welding parameter dynamic adjustment model includes a material property sub-model, a welding thermodynamic sub-model, and a welding quality assessment sub-model; the material property sub-model is used to calculate the elastic modulus, yield strength, and thermal expansion coefficient of the materials of the connecting tape 6, the zipper base fabric 5, and the windproof curtain fabric 7 under different ambient temperatures and ultraviolet intensities; the welding thermodynamic sub-model is used to calculate the temperature distribution and stress distribution in the welding area during ultrasonic welding; and the welding quality assessment sub-model is used to assess the welding strength and uniformity of the welding area. Step A2 includes: A201. Based on the characteristic ambient temperature and characteristic ultraviolet intensity, the material property sub-model is used to calculate the elastic modulus, yield strength, and thermal expansion coefficient of the connecting tape 6, the zipper base fabric 5, and the windproof curtain fabric 7; A202. Using the elastic modulus, yield strength, and thermal expansion coefficient of the connecting tape 6, zipper base fabric 5, and windproof curtain fabric 7 as inputs to the welding thermodynamics sub-model, the temperature and stress distributions in the weld area are calculated for different combinations of welding frequency, welding pressure, welding time, and welding amplitude. A203. Use the temperature and stress distributions in the weld area as inputs to the weld quality assessment sub-model to calculate weld strength and weld uniformity for different combinations of welding frequency, welding pressure, welding time, and welding amplitude. A204. Based on the welding strength and welding uniformity under different combinations of welding frequency, welding pressure, welding time and welding amplitude, with the goal of maximizing welding strength and optimizing welding uniformity, a multi-objective optimization algorithm is used to solve the optimized welding frequency, optimized welding pressure, optimized welding time and optimized welding amplitude.

[0041] Among them, the material property sub-model, welding thermodynamics sub-model and welding quality assessment sub-model are integrated into the ultrasonic welding parameter dynamic adjustment model to achieve dynamic optimization of welding parameters. Specifically, the material property sub-model can quantify the specific impact of high-altitude environmental factors of high-rise buildings on material properties, providing an accurate material parameter basis for the subsequent optimization of welding process parameters. The welding thermodynamics sub-model can simulate the thermodynamic behavior of the ultrasonic welding process and predict the temperature and stress state of the welding area under different welding parameter combinations. The welding quality assessment sub-model can quantitatively evaluate the welding quality under different welding parameter combinations and realize the correlation between welding parameters and welding quality. Therefore, through the collaborative work of the three sub-models, the ultrasonic welding parameter dynamic adjustment model can dynamically adjust the welding parameters according to the high-altitude environmental characteristics of high-rise buildings, and achieve the optimization of welding strength and welding uniformity.

[0042] Specifically, the dynamic ultrasonic welding parameter adjustment model first utilizes a material property sub-model to consider the effects of the characteristic ambient temperature and UV intensity of the high-altitude environment of a high-rise building on the material properties of the connecting tape 6, zipper base fabric 5, and windproof curtain fabric 7. This model calculates parameters such as the elastic modulus, yield strength, and thermal expansion coefficient of the materials under specific conditions. These material parameters are then input into the welding thermodynamics sub-model to simulate the ultrasonic welding process and calculate the temperature and stress distributions in the weld area for different welding parameter combinations. Next, the welding quality assessment sub-model uses the output of the welding thermodynamics sub-model as input to evaluate weld strength and uniformity for different welding parameter combinations. Finally, based on the output of the welding quality assessment sub-model, a multi-objective optimization algorithm is employed to determine the optimal welding frequency, welding pressure, welding time, and welding amplitude, with the goals of maximizing weld strength and optimizing weld uniformity. This allows for the optimization of ultrasonic welding process parameters based on environmental factors and welding quality objectives, making the welding parameter optimization process more specific, operational, and effective, ensuring the reliability and durability of the zipper-to-windproof curtain connection in extreme high-altitude environments.

[0043] In some specific embodiments, the material property sub-model can be constructed using a combination of experimental testing and finite element simulation. For example, the mechanical properties of the connecting tape 6, zipper base fabric 5, and windproof curtain fabric 7 under different environmental conditions can be tested through environmental simulation experiments such as high temperature, low temperature, and ultraviolet radiation, thereby establishing a material property database. Simultaneously, a material property prediction model can be established using finite element software to enable rapid calculation of material property parameters. The welding thermodynamic sub-model can be constructed using finite element analysis methods. For example, a thermal-mechanical coupling analysis model of the ultrasonic welding process can be established using finite element software to simulate physical processes such as vibration, frictional heating, heat conduction, and material deformation of the welding head, and calculate the temperature and stress field distribution in the weld area. The welding quality assessment sub-model can be constructed using a welding strength prediction model and a welding uniformity evaluation model. For example, the welding strength prediction model can predict the strength of the weld joint based on the temperature and stress field distribution in the weld area, combined with material strength theory. The welding uniformity evaluation model can be evaluated by calculating the uniformity coefficient of the temperature and stress field distribution in the weld area.

[0044] In some preferred embodiments, step A204 includes: B1. The NSGA-II algorithm is used to solve the welding strength and uniformity under different combinations of welding frequency, welding pressure, welding time, and welding amplitude, and a Pareto optimal solution set is obtained. Each solution in the Pareto optimal solution set corresponds to a combination of welding frequency, welding pressure, welding time, and welding amplitude. B2. Obtain the welding strength and welding uniformity corresponding to each solution in the Pareto optimal solution set to calculate the corresponding welding strength margin and welding uniformity margin; the welding strength margin is the ratio of the welding strength to a preset welding strength threshold, and the welding uniformity margin is the ratio of the welding uniformity to a preset welding uniformity threshold; B3. For each solution in the Pareto optimal solution set, perform a weighted sum calculation on the corresponding welding strength margin and welding uniformity margin to obtain the corresponding comprehensive evaluation index; B4. Select the solution with the largest comprehensive evaluation index in the Pareto optimal solution set as the optimized welding frequency, optimized welding pressure, optimized welding time and optimized welding amplitude.

[0045] Specifically, in step B1, the NSGA-II algorithm is used to solve the Pareto optimal solution set, which represents the optimal solution set for the trade-off between welding strength and welding uniformity. Each solution is a non-dominated solution, meaning that both welding strength and welding uniformity cannot be improved simultaneously. In step B2, the welding strength margin and welding uniformity margin are calculated to assess the extent to which welding strength and welding uniformity exceed preset thresholds, reflecting the safety level of the solution. The larger the margin, the more reliable the welding quality. In step B3, the welding strength margin and welding uniformity margin are combined into a comprehensive evaluation index through weighted sum calculation. The weight coefficient can be adjusted according to the degree of emphasis on welding strength and welding uniformity in actual applications. In step B4, the solution with the largest comprehensive evaluation index is selected as the optimized welding parameter combination.

[0046] Specifically, for optimal welding parameter selection, the NSGA-II algorithm is first used to perform a multi-objective optimization of weld strength and weld uniformity under different welding parameter combinations, thereby obtaining a Pareto optimal solution set. Then, for each solution in the Pareto optimal solution set, the weld strength margin and weld uniformity margin are calculated. The weld strength margin is obtained by comparing the weld strength with a preset weld strength threshold, and the weld uniformity margin is obtained by comparing the weld uniformity with a preset weld uniformity threshold. Next, for each solution, the weld strength margin and weld uniformity margin are weighted and summed to obtain a comprehensive evaluation index. The weight coefficient reflects the relative importance attached to weld strength and weld uniformity. Finally, the solution with the highest comprehensive evaluation index in the Pareto optimal solution set is selected, and the corresponding welding frequency, welding pressure, welding time, and welding amplitude are determined as the optimized welding parameters. This method can achieve an optimal balance between weld strength and weld uniformity.

[0047] In some possible implementations, step B1 includes: B101. For different combinations of welding frequency, welding pressure, welding time, and welding amplitude, initialize the NSGA-II algorithm population as the initial parent population; set the population size to N, where N is a preset positive integer; B102. For each welding parameter combination, use the ultrasonic welding parameter dynamic adjustment model to calculate the corresponding weld strength and weld uniformity, and then calculate the corresponding fitness value based on the weld strength and weld uniformity; B103. Based on the fitness value of each welding parameter combination, perform selection, crossover, and mutation operations to generate a new population as the offspring population. The selection operation uses the binary tournament selection method, the crossover operation uses the simulated binary crossover method, and the mutation operation uses the polynomial mutation method. During the mutation operation, the mutation probability is adjusted based on the ambient temperature and pressure. The lower the temperature and the lower the pressure, the higher the mutation probability. B104. Merge the parent population and the child population, perform non-dominated sorting, calculate the crowding distance of each solution, and select the top N solutions as the new parent population based on the non-dominated rank and crowding distance. B105. Determine whether the maximum number of iterations has been reached. If so, output the latest parent population as the Pareto optimal solution set; otherwise, return to step B102.

[0048] In step B101, in order to start the NSGA-II algorithm, it is necessary to first set the initial parent population. Specifically, a random generation method can be used to randomly generate multiple sets of welding parameter combinations within the respective value ranges of welding frequency, welding pressure, welding time, and welding amplitude. These welding parameter combinations are used as individuals in the initial population. The population size N can be set according to the actual computing resources and optimization accuracy requirements. For example, N can be set to 100 or 200.

[0049] Among them, in step B102, for each welding parameter combination in the initial population, it is necessary to calculate through the ultrasonic welding parameter dynamic adjustment model to obtain the predicted welding strength and welding uniformity under this parameter combination. Welding strength and welding uniformity are key indicators for evaluating welding quality. After being calculated by the dynamic adjustment model, they can be used as the basis for evaluating the quality of this group of welding parameters. The fitness value is the key parameter used to drive the optimization of the NSGA-II algorithm. It is calculated based on welding strength and welding uniformity. The calculation method of the fitness value can be set according to specific needs. For example, the welding strength and welding uniformity can be weighted and summed to obtain a comprehensive fitness value, or the welding strength and welding uniformity can be optimized as two objective functions respectively.

[0050] Among them, in step B103, after obtaining the fitness value of each welding parameter combination, it is necessary to perform selection, crossover, and mutation operations based on the fitness value to generate a new offspring population. The selection operation aims to select excellent individuals from the parent population and pass them on to the offspring population. The binary tournament selection method is a commonly used selection method. Its specific implementation method is as follows: two individuals are randomly selected from the parent population each time, their fitness values ​​are compared, and the individual with the better fitness value is selected and passed on to the offspring population. The crossover operation aims to generate new individuals by combining the characteristics of different individuals in the parent population. The simulated binary crossover method is a commonly used crossover method. It realizes information exchange between individuals in the population by simulating the gene recombination process in biology. The mutation operation aims to introduce new genetic information by randomly changing certain gene values ​​in individuals, maintain the diversity of the population, and prevent the algorithm from falling into local optimality. Polynomial mutation is a commonly used mutation method, which randomly perturbs the genes in individuals through polynomial distribution. It is worth noting that in the mutation operation, the mutation probability is dynamically adjusted according to the ambient temperature and air pressure (the ambient temperature and air pressure here refer to the temperature and air pressure of the processing site, which can be obtained through actual measurement). When the ambient temperature is lower and the air pressure is smaller, the mutation probability is higher. This is because, in a low temperature and low pressure environment, the uncertainty of the welding process increases. By increasing the mutation probability, the global search capability of the algorithm can be enhanced to better adapt to the particularity of the high-altitude environment of high-rise buildings.

[0051] Among them, in step B104, after the offspring population is generated, the parent population and the offspring population need to be merged, and the better individuals are selected from them as the new parent population for the next round of iteration. Non-dominated sorting is a commonly used sorting method in multi-objective optimization. It stratifies the population according to the dominance relationship between individuals, and individuals in the front layer are better than those in the back layer. The crowding distance is used to measure the distribution density of individuals in the population, which can maintain the diversity of the population and avoid premature convergence of the algorithm. Specifically, for individuals in the same non-dominated hierarchy level, the distance between each individual and the adjacent individuals in the target space is calculated. The larger the distance, the greater the crowding distance. When selecting a new parent population, individuals with a lower non-dominated hierarchy are preferred. For individuals in the same non-dominated hierarchy level, individuals with a larger crowding distance are selected. By combining non-dominated sorting and crowding distance, it can be ensured that the selected parent population continues to evolve in the direction of the Pareto optimal solution set.

[0052] Among them, in step B105, the maximum number of iterations can be set according to the actual optimization requirements and computing resources. For example, it can be set to 100 times or 200 times. If the maximum number of iterations is reached, it is considered that the algorithm has converged. At this time, the latest parent population is output as the Pareto optimal solution set. The Pareto optimal solution set contains multiple groups of optimized welding parameter combinations. Each group of parameter combinations represents a relatively good compromise solution for both welding strength and welding uniformity. If the maximum number of iterations is not reached, return to step B102 and continue iterative optimization until the stop condition is met.

[0053] Through the above technical solution, the present application optimizes the ultrasonic welding parameters through the NSGA-II algorithm defined in step B1 under the characteristic ambient temperature and characteristic ultraviolet intensity of the high-altitude environment of high-rise buildings, and can more effectively find a welding parameter combination with better welding strength and welding uniformity under the complex environmental parameters of the high-altitude environment, thereby improving the welding quality of the windproof curtain and the zipper base fabric in the high-altitude environment.

[0054] In some possible implementations, step B104 includes: The merged population is stratified according to the non-dominant rank, and multiple non-dominant rank levels are obtained; For solutions within the same non-dominated hierarchy, the target difference between each solution and its adjacent solutions is calculated, and the sum of the target differences corresponding to the same solution is taken as the crowding distance of the corresponding solution; the target difference includes the welding strength difference and the welding uniformity difference; according to the non-dominated hierarchy and the crowding distance, the elite retention strategy is adopted to select the top N solutions as the new parent population.

[0055] Stratifying the merged population based on non-dominated ranks to obtain multiple non-dominated rank levels is a stratification process to distinguish the quality of solutions within the population. Specifically, this stratification can be achieved using the fast non-dominated sorting method. This method compares the dominance relationships between different solutions within the population and divides the population into different non-dominated rank levels. This ensures that solutions with higher non-dominated ranks are preferentially selected, thereby improving the quality of the population.

[0056] Among them, for solutions within the same non-dominated hierarchy, the target difference between each solution and the adjacent solutions is calculated separately, and the sum of the target differences corresponding to the same solution is used as the crowding distance of the corresponding solution; the target difference includes the welding strength difference and the welding uniformity difference, which means that after the non-dominated hierarchy is completed, the crowding distance of each solution needs to be further calculated for the solutions within the same non-dominated hierarchy. The crowding distance is used to measure the sparseness of the distribution of solutions in the target space. Specifically, the crowding distance is obtained by calculating the sum of the differences between each solution and the adjacent solutions in the same hierarchy in various target dimensions, including welding strength and welding uniformity. The larger the crowding distance, the sparser the solutions around the solution. By calculating the crowding distance, the diversity of the population can be maintained and the algorithm can be prevented from converging to the local optimal solution too early.

[0057] An elite retention strategy is employed to select the top N solutions as the new parent population based on the non-dominated hierarchy and crowding distance. This means that after the non-dominated hierarchy is stratified and the crowding distance is calculated, the best solutions are selected from the merged population, combining the non-dominated hierarchy and crowding distance. These solutions serve as the new parent population for the next iteration. Specifically, the elite retention strategy prioritizes solutions with higher non-dominated hierarchies. For solutions within the same non-dominated hierarchy, the solution with the larger crowding distance is selected. This elite retention strategy ensures that excellent solutions are retained and passed on to the next generation of the population, while also taking into account population diversity, thereby improving the performance of the optimization algorithm and its global search capabilities.

[0058] Through the above technical solution, the present application can perform non-dominated sorting more effectively and reasonably calculate the congestion distance, so as to select a better solution as the new parent population, so that the multi-objective optimization algorithm can more effectively find the global optimal solution or an approximate global optimal solution, and obtain a better combination of welding frequency, welding pressure, welding time and welding amplitude, thereby improving welding strength and welding uniformity.

[0059] Preferably, after step B2 and before step B3, the method further comprises the following steps: B5. Obtain the temperature and stress distribution of the welding area corresponding to each solution in the Pareto optimal solution set; B6. Based on the weld strength and weld uniformity corresponding to each solution in the Pareto optimal solution set, as well as the temperature and stress distributions obtained in step B5, and combined with Miner's linear cumulative damage theory, calculate the fatigue life assessment parameters of the welded structure under specific cyclic loading; B7. Correct the welding strength margin and welding uniformity margin according to the fatigue life assessment parameters.

[0060] In step B5, the temperature distribution and stress distribution of the corresponding welding area can be calculated using the welding thermodynamics sub-model for each welding parameter combination.

[0061] Among them, in step B6, Miner's linear cumulative damage theory is a method for evaluating the fatigue life of a material under variable amplitude cyclic loads. The theory assumes that the damage caused by each cyclic load is linearly accumulated. When the cumulative damage reaches a certain level, the material will suffer fatigue failure. In this application, it is first necessary to determine the specific cyclic load spectrum that the welded structure may withstand in actual applications. For example, the zipper connection part of the wind curtain of a high-rise building may be subjected to cyclic tensile loads caused by wind, human operation, etc. Then, according to the stress distribution in the welding area, the stress amplitude and average stress at different positions in the welding area under the cyclic load can be analyzed. Combined with the SN curve (stress-life curve) of the material, the fatigue life of the material under different stress levels can be evaluated. Finally, based on Miner's linear cumulative damage theory, the damage under different stress levels is accumulated to obtain the fatigue life evaluation parameters of the welded structure. The fatigue life evaluation parameters can take a variety of forms, such as the number of fatigue life cycles, fatigue damage index, etc., which are used to characterize the ability of the welded structure to resist fatigue failure.

[0062] Among them, in step B7, the fatigue life assessment parameters calculated in step B6 are used to correct the welding strength margin and the welding uniformity margin. The specific correction method can be that if the fatigue life assessment parameters indicate that the welded structure has a longer fatigue life, the values ​​of the welding strength margin and the welding uniformity margin can be appropriately increased, otherwise they can be reduced. By correcting the fatigue life assessment parameters, the calculation of the comprehensive evaluation index can comprehensively consider the static strength, welding uniformity and fatigue life of the welded joint, thereby more comprehensively evaluating the welding quality. For example, the welding strength margin and the welding uniformity margin are recorded as M_1 and M_2 respectively. The corrected welding strength margin and welding uniformity margin are M'_1=M_1*(1+k*D) and M'_2=M_2*(1+k*D_2), respectively, where M'_1 is the corrected welding strength margin, M'_2 is the corrected welding uniformity margin, and k is the correction coefficient, which can be set according to actual needs, for example, 0.2. Therefore, the larger the fatigue life evaluation parameter is, the larger the corrected welding strength margin and welding uniformity margin are, and the influence of fatigue performance on the comprehensive evaluation index is enhanced.

[0063] Specifically, this solution further considers the fatigue performance of welded structures under cyclic loading, avoiding the problem of only considering static strength and uniformity while ignoring fatigue life. In high-altitude windbreak applications, the welded connection between the zipper and the windbreak fabric is subjected to long-term cyclic loading, and fatigue failure is a potential failure mode. Therefore, by adding steps B5 to B7, fatigue life assessment is incorporated into the optimization of welding process parameters. This optimized welding process parameters not only ensure the initial weld strength and uniformity of the welded joint, but also improve the fatigue life of the welded joint, ensuring the long-term reliability of the welded connection. Step B5 obtains the temperature and stress distribution in the weld area, providing basic data for fatigue life assessment. Step B6 utilizes Miner's linear cumulative damage theory and the SN curve to assess the fatigue life of the welded structure and quantify the fatigue life as a fatigue life assessment parameter. Step B7 uses the fatigue life assessment parameters to correct the weld strength margin and weld uniformity margin, enabling the subsequent calculation of comprehensive evaluation indicators to comprehensively balance static strength, weld uniformity, and fatigue life. The resulting optimized welding process parameters achieve the optimal balance between static strength, weld uniformity, and fatigue life for the welded joint. Therefore, the welding process parameters obtained by the optimization method of claim 7 can improve the durability and reliability of the connection between the zipper and the windproof curtain fabric in a high-altitude environment, and reduce the safety risks caused by the failure of the welding connection in the extreme environment of high altitude of high-rise buildings.

[0064] In this embodiment, step B6 includes: B601. Based on the temperature and stress distributions in the weld region corresponding to each solution in the Pareto optimal solution set, extract multiple characteristic points within the weld region and obtain the stress amplitude and average stress of each characteristic point under a specific cyclic load. B602. For each characteristic point, calculate the equivalent stress amplitude based on the stress amplitude and mean stress using the modified Morrow mean stress correction algorithm; B603. According to the SN curve of the connecting tape 6, the zipper base fabric 5 and the wind curtain fabric 7 material, query the fatigue life corresponding to the equivalent stress amplitude; B604. Based on the fatigue life obtained, calculate the damage index for each characteristic point according to Miner's linear cumulative damage theory. The damage index is the ratio of the number of cycles under a specific cyclic load to the fatigue life. B605. Perform weighted averaging of the damage indices of all characteristic points to obtain fatigue life assessment parameters for the welded structure; the weight coefficient of the weighted averaging calculation is inversely proportional to the distance from each characteristic point to the center of the weld area.

[0065] Step B6 defines the calculation method for fatigue life assessment parameters. This approach aims to more precisely and accurately assess the fatigue life of welded structures. This solution, taking into account the uneven temperature and stress distribution within the weld region, innovatively proposes extracting multiple characteristic points within the weld region for fatigue life assessment.

[0066] Wherein, in step B601, according to the temperature distribution and stress distribution data of the welding area obtained in the previous step, multiple characteristic points are extracted, and the stress amplitude and average stress of these characteristic points under cyclic load are obtained. Specifically, finite element analysis software such as ANSYS or Abaqus can be used to post-process the temperature field and stress field data of the welding area, and the positions where the temperature gradient is large and the stress is concentrated in the welding area are identified as characteristic points (for example, the welding area can be meshed, and then the comprehensive evaluation index of each grid node is obtained according to the temperature gradient and stress weighted calculation of each grid node, and the grid nodes with the largest comprehensive evaluation index are selected as characteristic points). The number of characteristic points can be adjusted according to the size and shape of the welding area. For example, for a smaller welding area, 3-5 characteristic points can be extracted, and for a larger welding area, 5-10 characteristic points can be extracted. The stress amplitude and average stress can be determined according to the cyclic load spectrum under actual working conditions. For example, the cyclic load spectrum can be processed using the rain flow counting method to obtain the number of cycles under different stress levels, and then the damage index under each stress level is calculated according to the Miner linear cumulative damage theory. By extracting characteristic points, the differences in stress states at different locations within the welding area can be captured, providing basic data for subsequent fatigue life assessment.

[0067] Among them, in step B602, for each characteristic point, the equivalent stress amplitude is calculated using the modified Morrow mean stress correction algorithm. Specifically, the calculation can be performed using the following formula: Δσeq = Δσ*(1-σm / σf')^b, where Δσeq represents the equivalent stress amplitude, Δσ represents the stress amplitude, σm represents the mean stress, σf' represents the fatigue strength coefficient, and b represents the fatigue strength index. The modified Morrow model takes into account the effect of mean stress on fatigue life. Compared with the traditional equivalent stress calculation method, it can reflect the fatigue characteristics of the material under cyclic loads.

[0068] In step B603, the fatigue life corresponding to the equivalent stress amplitude is queried based on the material's SN curve. The SN curve represents a material's fatigue performance, describing the material's fatigue life at different stress levels. Specifically, SN curve data can be obtained by consulting relevant material manuals or fatigue databases based on the materials of the connecting tape 6, zipper base fabric 5, and windproof curtain fabric 7. The SN curve is typically represented on a logarithmic scale, with the stress amplitude on the horizontal axis and the fatigue life on the vertical axis. By querying the SN curve, the fatigue life of each characteristic point at a specific stress level can be determined.

[0069] Among them, in step B604, based on the queried fatigue life, the damage index of each characteristic point is calculated according to Miner's linear cumulative damage theory. Miner theory is a theory of fatigue damage accumulation, which can evaluate the fatigue life of a structure under variable amplitude load. The damage index is the ratio of the number of cycles under a specific cyclic load to the fatigue life. The calculation formula is: D = n / M, where D represents the damage index, n represents the number of cycles under a specific cyclic load, and M represents the fatigue life. By calculating the damage index, the degree of fatigue damage of each characteristic point can be quantified.

[0070] Among them, in step B605, the damage index of all feature points is weighted averaged to obtain the fatigue life assessment parameters of the welded structure. The weighted average method comprehensively considers the fatigue damage of multiple feature points in the welding area, making the fatigue life assessment parameters more representative. The weight coefficient is inversely proportional to the distance from the feature point to the center of the welding area, which means that the feature points closer to the center of the welding area have a greater weight in the fatigue life assessment, which is in line with the actual stress characteristics of the welded structure, because the center of the welding area is usually a location of stress concentration and is prone to fatigue failure. The weight coefficient can be determined according to an inverse proportional function. For example, the weight coefficient can be set to w_i=C / d_i, where w_i represents the weight coefficient of the i-th feature point, d_i represents the distance from the i-th feature point to the center of the welding area, and C represents a constant. The constant C can be adjusted according to actual conditions. For example, it can be set to the average radius of the welding area.

[0071] Through the above technical solution, the present application can evaluate the fatigue life of welded structures more finely and accurately. The solution takes into account the uneven temperature distribution and stress distribution inside the welding area. By extracting multiple feature points in the welding area and combining the modified Morrow average stress correction algorithm, SN curve and Miner linear cumulative damage theory, a refined evaluation of the fatigue life of the welded structure is achieved, overcoming the general defects of traditional evaluation methods, making the fatigue life evaluation results more accurate and reliable, and thus providing a reference for the structural design and optimization of windproof curtains in high-altitude environments of high-rise buildings.

[0072] In some embodiments, step A4 comprises: A401 real-time acquisition of ultrasonic welding equipment welding current, welding voltage, welding power and welding head vibration amplitude, and the collected data is stored; A402. Calculate the energy input curve during welding based on the stored welding current, welding voltage, welding power, and vibration amplitude data of the welding head; A403. Compare the energy input curve with the preset energy input curve threshold range to determine whether the welding process is stable; A404. If the welding process is unstable, the PID control algorithm is used to adjust the welding frequency, welding pressure, welding time and welding amplitude of the ultrasonic welding equipment according to the degree of deviation of the energy input curve relative to the energy input curve threshold range, so that the energy input curve is restored to the preset energy input curve threshold range.

[0073] In step A401, the welding current, welding voltage, welding power, and welding head vibration amplitude are collected in real time to provide a data basis for the subsequent calculation of the energy input curve. This collection can be accomplished by sensors, such as current sensors, voltage sensors, power sensors, and displacement sensors, which are used to detect the welding current, welding voltage, welding power, and welding head vibration amplitude, respectively. Data can be stored in a data logger or computer system in digital or analog form.

[0074] In step A402, the energy input curve is calculated based on electrical and mechanical principles. Energy input can be understood as the total energy applied to the weld area during welding, which can be calculated using parameters such as welding current, welding voltage, welding time, and welding head vibration amplitude. For example, the energy input can be approximately equal to the product of welding voltage and welding current multiplied by welding time (hereinafter referred to as the energy input calculated in this manner as the first energy input). Alternatively, more precisely, the effect of welding head vibration amplitude on the energy input can be considered (for example, a correction coefficient can be obtained by querying a correction coefficient mapping table based on the vibration amplitude, and then the correction coefficient can be multiplied by the first energy input to obtain the final energy input. The correction coefficient mapping table can be established in advance based on experiments).

[0075] In step A403, the preset energy input curve threshold range is determined based on empirical or experimental data and represents the normal fluctuation range of energy input during a stable welding process. By comparing the real-time calculated energy input curve with this threshold range, it is possible to determine whether the welding process has deviated from normal conditions. For example, if the energy input curve continuously exceeds the upper limit of the threshold range for a period exceeding a preset time threshold, it indicates excessive energy input, posing a risk of over-welding, and the welding process is deemed unstable. If the energy input curve continuously falls below the lower limit of the threshold range for a period exceeding a preset time threshold, it indicates insufficient energy input, posing a risk of under-welding, and the welding process is similarly deemed unstable. Otherwise, the welding process is deemed stable.

[0076] In step A404, the PID control algorithm is a commonly used closed-loop control algorithm that adjusts the control deviation through three steps: proportional, integral, and differential. In this solution, the control deviation is the degree of deviation between the actual energy input curve and the threshold range of the energy input curve. Based on the degree of deviation, the PID control algorithm calculates the required adjustments for welding frequency, welding pressure, welding time, and welding amplitude, and controls the ultrasonic welding equipment to perform the adjustments, thereby restoring the energy input curve to within the preset threshold range and ensuring the stability of the welding process. For example, if the energy input is too high, the PID controller can reduce the welding amplitude or shorten the welding time; if the energy input is too low, the PID controller can increase the welding pressure or increase the welding frequency. Through real-time adjustment of the PID control algorithm, the energy input curve is controlled within the preset threshold range, ensuring the stability of the welding process and, in turn, the reliability of the welding quality.

[0077] refer to Figure 2 The present application further proposes a device for optimizing the connection between a zipper and a windproof curtain fabric, which is used to optimize the connection process between the windproof curtain fabric and the zipper on a high-rise building. The device comprises: Environmental parameter acquisition module 1 is used to obtain the characteristic ambient temperature and characteristic ultraviolet intensity of the high-altitude environment of the high-rise building (for the specific process, please refer to step A1 above); Welding parameter calculation module 2 is used to calculate the optimized ultrasonic welding process parameters based on the characteristic ambient temperature and characteristic ultraviolet intensity using the ultrasonic welding parameter dynamic adjustment model; the ultrasonic welding process parameters include welding frequency, welding pressure, welding time, and welding amplitude (for the specific process, refer to step A2 above); Welding control module 3, used to control the ultrasonic welding equipment to weld the connecting tape 6, the zipper base fabric 5 and the windproof curtain fabric 7 according to the optimized ultrasonic welding process parameters (for the specific process, refer to step A3 above); The welding quality monitoring module 4 is used to monitor the welding process parameters in real time to adjust the ultrasonic welding process parameters (for the specific process, refer to step A4 above).

[0078] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0079] In addition, the units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected based on actual needs to achieve the purpose of the solution of this embodiment.

[0080] Furthermore, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0081] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any actual relationship or order between these entities or operations.

[0082] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for optimizing the connection between a zipper and a windproof curtain fabric, used to optimize the connection process between the windproof curtain fabric and the zipper on a high-rise building, characterized in that: The steps of the method include: A1. Obtain characteristic ambient temperature and characteristic ultraviolet intensity of the high-altitude environment; A2. Utilizing a dynamic ultrasonic welding parameter adjustment model, the optimized ultrasonic welding process parameters are calculated based on the characteristic ambient temperature and characteristic UV intensity. Ultrasonic welding process parameters include welding frequency, welding pressure, welding time, and welding amplitude. A3. Control the ultrasonic welding equipment to weld the zipper base fabric to the windproof curtain fabric according to optimized ultrasonic welding process parameters. Align the edge seams of the zipper base fabric and the windproof curtain fabric and weld them together to the connecting tape. A4. Real-time monitoring of welding process parameters to adjust ultrasonic welding process parameters.

2. The method for optimizing the connection between a zipper and a windproof curtain fabric according to claim 1, characterized in that: Step A1 includes: A101. Based on the geographic characteristics of the target tent deployment location, retrieve historical ambient temperature data and historical UV intensity data for several locations matching the target deployment location from the high-altitude building environment database. The high-altitude environment database contains historical ambient temperature data and historical UV intensity data for multiple typical high-altitude building locations, as well as corresponding geographic characteristics. A102. Based on the retrieved historical ambient temperature data and historical UV intensity data for several locations, calculate statistical characteristic parameters of the ambient temperature and UV intensity, respectively; the statistical characteristic parameters include average, maximum, and minimum values; A103. Perform a weighted average calculation on each statistical characteristic parameter of the ambient temperature corresponding to the retrieved locations to obtain a corresponding first weighted average statistical characteristic parameter item; perform a weighted average calculation on each statistical characteristic parameter of the ultraviolet intensity corresponding to the retrieved locations to obtain a corresponding second weighted average statistical characteristic parameter item; A104. Calculate the characteristic ambient temperature by comprehensively analyzing the first weighted average statistical characteristic parameter items; calculate the characteristic ultraviolet intensity by comprehensively analyzing the second weighted average statistical characteristic parameter items.

3. The method for optimizing the connection between a zipper and a windproof curtain fabric according to claim 1, characterized in that: The ultrasonic welding parameter dynamic adjustment model includes a material property sub-model, a welding thermodynamic sub-model and a welding quality assessment sub-model; The material property sub-model is used to calculate the elastic modulus, yield strength and thermal expansion coefficient of the materials of the connecting tape, zipper base fabric and windproof curtain fabric under different ambient temperatures and UV intensities; The welding thermodynamic sub-model is used to calculate the temperature distribution and stress distribution in the welding area during ultrasonic welding; The welding quality assessment sub-model is used to assess the welding strength and welding uniformity of the welding area.

4. The method for optimizing the connection between a zipper and a windproof curtain fabric according to claim 3, characterized in that: The step A2 comprises: A201. Based on the characteristic ambient temperature and the characteristic UV intensity, use the material property submodel to calculate the elastic modulus, yield strength, and thermal expansion coefficient of the connecting tape, zipper base fabric, and windproof curtain fabric. A202. Using the elastic modulus, yield strength, and thermal expansion coefficient of the connecting tape, zipper base fabric, and wind curtain fabric as inputs to the welding thermodynamics sub-model, the temperature and stress distributions in the weld area are calculated for different combinations of welding frequency, welding pressure, welding time, and welding amplitude. A203. Use the temperature and stress distributions in the weld area as inputs to the weld quality assessment sub-model to calculate weld strength and weld uniformity for different combinations of welding frequency, welding pressure, welding time, and welding amplitude. A204. Based on the welding strength and welding uniformity under different combinations of welding frequency, welding pressure, welding time and welding amplitude, with the goal of maximizing welding strength and optimizing welding uniformity, a multi-objective optimization algorithm is used to solve the optimized welding frequency, optimized welding pressure, optimized welding time and optimized welding amplitude.

5. The method for optimizing the connection between a zipper and a windproof curtain fabric according to claim 3, characterized in that: Step A204 includes: B1. The NSGA-II algorithm is used to solve the welding strength and uniformity under different combinations of welding frequency, welding pressure, welding time, and welding amplitude, and a Pareto optimal solution set is obtained. Each solution in the Pareto optimal solution set corresponds to a combination of welding frequency, welding pressure, welding time, and welding amplitude. B2. Obtain the welding strength and welding uniformity corresponding to each solution in the Pareto optimal solution set to calculate the corresponding welding strength margin and welding uniformity margin; the welding strength margin is the ratio of the welding strength to a preset welding strength threshold, and the welding uniformity margin is the ratio of the welding uniformity to a preset welding uniformity threshold; B3. For each solution in the Pareto optimal solution set, perform a weighted sum calculation on the corresponding welding strength margin and welding uniformity margin to obtain the corresponding comprehensive evaluation index; B4. Select the solution with the largest comprehensive evaluation index in the Pareto optimal solution set as the optimized welding frequency, optimized welding pressure, optimized welding time and optimized welding amplitude.

6. The method for optimizing the connection between a zipper and a windproof curtain fabric according to claim 5, characterized in that: Step B1 includes: B101. For different combinations of welding frequency, welding pressure, welding time, and welding amplitude, initialize the NSGA-II algorithm population as the initial parent population; set the population size to N, where N is a preset positive integer; B102. For each welding parameter combination, use the ultrasonic welding parameter dynamic adjustment model to calculate the corresponding weld strength and weld uniformity, and then calculate the corresponding fitness value based on the weld strength and weld uniformity; B103. Based on the fitness value of each welding parameter combination, perform selection, crossover, and mutation operations to generate a new population as the offspring population. The selection operation uses the binary tournament selection method, the crossover operation uses the simulated binary crossover method, and the mutation operation uses the polynomial mutation method. During the mutation operation, the mutation probability is adjusted based on the ambient temperature and pressure. The lower the temperature and the lower the pressure, the higher the mutation probability. B104. Merge the parent population and the child population, perform non-dominated sorting, calculate the crowding distance of each solution, and select the top N solutions as the new parent population based on the non-dominated rank and crowding distance. B105. Determine whether the maximum number of iterations has been reached. If so, output the latest parent population as the Pareto optimal solution set; otherwise, return to step B102.

7. The method for optimizing the connection between a zipper and a windproof curtain fabric according to claim 6, characterized in that: Step B104 includes: stratifying the merged population according to the non-dominated level to obtain a plurality of non-dominated level levels; For solutions in the same non-dominated hierarchy, the target difference between each solution and its adjacent solutions is calculated, and the sum of the target differences corresponding to the same solution is used as the crowding distance of the corresponding solution; the target differences include welding strength difference and welding uniformity difference; According to the non-dominated hierarchy level and crowding distance, the elite retention strategy is adopted to select the top N solutions as the new parent population.

8. The method for optimizing the connection between a zipper and a windproof curtain fabric according to claim 5, characterized in that: After step B2 and before step B3, the method further includes the following steps: B5. Obtain the temperature and stress distribution of the welding area corresponding to each solution in the Pareto optimal solution set; B6. Based on the weld strength, weld uniformity, and temperature and stress distribution in the weld area corresponding to each solution in the Pareto optimal solution set, combined with Miner's linear cumulative damage theory, calculate the fatigue life assessment parameters of the welded structure under specific cyclic loading; B7. Correct the welding strength margin and welding uniformity margin according to the fatigue life assessment parameters.

9. The method for optimizing the connection between a zipper and a windproof curtain fabric according to claim 8, characterized in that: Step B6 includes: B601. Based on the temperature and stress distributions in the weld region corresponding to each solution in the Pareto optimal solution set, extract multiple characteristic points within the weld region and obtain the stress amplitude and average stress of each characteristic point under a specific cyclic load. B602. For each characteristic point, calculate the equivalent stress amplitude based on its stress amplitude and mean stress using the modified Morrow mean stress correction algorithm; B603. Based on the SN curves of the connecting tape, zipper base fabric, and wind curtain fabric, query the fatigue life corresponding to the equivalent stress amplitude. B604. Based on the fatigue life obtained, calculate the damage index for each characteristic point according to Miner's linear cumulative damage theory. The damage index is the ratio of the number of cycles under a specific cyclic load to the fatigue life. B605. Perform weighted averaging of the damage indices of all characteristic points to obtain fatigue life assessment parameters for the welded structure; the weight coefficient of the weighted averaging calculation is inversely proportional to the distance from each characteristic point to the center of the weld area.

10. The method for optimizing the connection between a zipper and a windproof curtain fabric according to claim 1, characterized in that: Step A4 includes: A401 real-time acquisition of ultrasonic welding equipment welding current, welding voltage, welding power and welding head vibration amplitude, and the collected data is stored; A402. Calculate the energy input curve during welding based on the stored welding current, welding voltage, welding power, and vibration amplitude data of the welding head; A403. Compare the energy input curve with the preset energy input curve threshold range to determine whether the welding process is stable; A404. If the welding process is unstable, the PID control algorithm is used to adjust the welding frequency, welding pressure, welding time and welding amplitude of the ultrasonic welding equipment according to the degree of deviation of the energy input curve relative to the energy input curve threshold range, so that the energy input curve is restored to the preset energy input curve threshold range.

11. A device for optimizing the connection between a zipper and a windproof curtain fabric, used to optimize the connection process between the windproof curtain fabric and the zipper on a high-rise building, characterized in that: The device includes: Environmental parameter acquisition module, used to obtain characteristic ambient temperature and characteristic ultraviolet intensity of high-altitude environment; The welding parameter calculation module is used to calculate the optimized ultrasonic welding process parameters based on the characteristic ambient temperature and characteristic ultraviolet intensity using the ultrasonic welding parameter dynamic adjustment model; the ultrasonic welding process parameters include welding frequency, welding pressure, welding time and welding amplitude; The welding control module is used to control the ultrasonic welding equipment to weld the zipper base fabric and the windproof curtain fabric according to the optimized ultrasonic welding process parameters, wherein the edge seams of the zipper base fabric and the windproof curtain fabric are aligned and welded together on the connecting fabric tape; the welding quality monitoring module is used to monitor the welding process parameters in real time to adjust the ultrasonic welding process parameters.

Images