An automated analysis and optimization system for tire component construction dimensions

Abstract

This application relates to the field of tire manufacturing technology, specifically to an automatic analysis and optimization system for tire component construction dimensions. The system includes a data acquisition module, a pressure analysis module, an asynchronous analysis module, and a parameter optimization module. The data acquisition module acquires production data during tire rolling based on the current construction dimensions. The pressure analysis module determines the risk level of thickness deviation in each pressure zone. The asynchronous analysis module determines the overall pressure-thickness asynchronousity. The parameter optimization module adjusts the parameter variation range used in the next iteration of the genetic algorithm. This system can dynamically adjust the parameter variation range based on the actual process effect of each iteration, effectively shortening the construction dimension optimization cycle.

Description

Technical Field

[0001] This application relates to the field of tire manufacturing technology, and specifically to an automatic analysis and optimization system for tire component construction dimensions. Background Technology

[0002] In tire manufacturing, the prototyping process is a core step in tire forming. Key construction dimensions such as rolling pressure and ply thickness directly determine the consistency of prototyping dimensions, thus affecting the tire's structural strength and mass production pass rate. With the development of industrial intelligence, automatic optimization methods based on genetic algorithms are widely introduced into the parameter optimization process of construction dimensions. By simulating the natural evolution mechanism, the optimal solution is searched in the parameter space to replace traditional manual trial and error adjustments, thereby improving production efficiency and product quality.

[0003] In existing technologies, genetic algorithms typically use a fixed parameter mutation range for iterative search, meaning that the range of parameter variation remains constant during each generation of evolution.

[0004] However, this fixed strategy has obvious defects in practical applications: when the parameter variation range is set large, it often leads to a large number of invalid iterations, slow convergence speed, and long optimization cycle; conversely, when the variation range is set small, the algorithm is prone to getting trapped in local optima, and the output construction size parameters are difficult to adapt to the fluctuating requirements under different production conditions. Therefore, this fixed strategy cannot take into account both iteration efficiency and generalization ability, resulting in poor optimization effect of construction size. Summary of the Invention

[0005] To address the technical problem that optimizing construction dimensions based on fixed parameter variation ranges cannot simultaneously balance iterative efficiency and generalization ability, resulting in poor optimization performance, this application aims to provide an automatic analysis and optimization system for tire component construction dimensions. The specific technical solution adopted is as follows: This application provides an automatic analysis and optimization system for tire component construction dimensions, including a data acquisition module, a pressure analysis module, an asynchronicity analysis module, and a parameter optimization module. The data acquisition module acquires production data during tire rolling based on the current construction dimensions. This production data includes the entry time of the molding component, pressure acquisition sequences and thickness acquisition sequences for each of the multiple pressure zones, and the current construction dimensions are generated based on the variation amplitude of current parameters in a genetic algorithm. The pressure analysis module determines the thickness deviation risk level for each pressure zone based on the differences between adjacent pressure values ​​in the pressure acquisition sequence of each pressure zone and the difference between the acquisition time of the first pressure value and the entry time of the molding component. This thickness deviation risk level characterizes the degree of risk of deviation in the thickness data of the pressure zone. The asynchronicity analysis module determines the overall pressure-thickness asynchronicity based on the difference between the pressure change trend of the pressure acquisition sequence and the thickness change trend of the thickness acquisition sequence for each pressure zone, combined with the thickness deviation risk level for each pressure zone. The parameter optimization module adjusts the parameter variation amplitude used in the next iteration of the genetic algorithm based on the overall pressure-thickness asynchronicity and a preset asynchronicity threshold.

[0006] Optionally, the pressure analysis module is specifically used to: determine the pressure instability of each pressure-bearing region based on the difference between adjacent pressure values ​​in the pressure acquisition sequence of each pressure-bearing region, wherein the pressure instability is used to characterize the fluctuation range of pressure values ​​within the pressure-bearing region; determine the pressure lag time of each pressure-bearing region based on the difference between the acquisition time of the first pressure value of each pressure-bearing region and the entry time of the molding component; determine the pressure coefficient of each pressure-bearing region based on the pressure lag time of each pressure-bearing region, wherein the pressure coefficient is used to characterize the duration of pressure time in the pressure-bearing region; and determine the thickness deviation risk level of each pressure-bearing region based on the pressure instability of each pressure-bearing region and the pressure coefficient.

[0007] Optionally, when the pressure analysis module is used to determine the pressure instability of each pressure zone based on the difference between adjacent pressure values ​​in the pressure acquisition sequence of each pressure zone, it is specifically used to: determine the absolute value of the difference between each pair of adjacent pressure values ​​in the pressure acquisition sequence of the first pressure zone, where the first pressure zone is any one of multiple pressure zones; and determine the average of all the absolute values ​​of the differences as the pressure instability of the first pressure zone.

[0008] Optionally, the asynchronicity analysis module is specifically used to: determine the local asynchronicity of each pressure-receiving region based on the difference between the pressure change trend of the pressure acquisition sequence and the thickness change trend of the thickness acquisition sequence; determine the weight coefficient of each pressure-receiving region based on the thickness deviation risk level of each pressure-receiving region, the weight coefficient being used to characterize the data reliability of the pressure-receiving region; and perform a weighted average of the local asynchronicity of all pressure-receiving regions based on the weight coefficient of each pressure-receiving region to obtain the overall pressure-thickness asynchronicity.

[0009] Optionally, when the asynchronicity analysis module determines the local asynchronicity of each pressure-receiving region based on the difference between the pressure change trend of the pressure acquisition sequence and the thickness change trend of the thickness acquisition sequence, the module specifically performs the following: performs smooth fitting within a sliding time window on the pressure acquisition sequence and thickness acquisition sequence of the first pressure-receiving region to obtain the pressure change rate and thickness change rate of each sliding time window; determines the window asynchronicity of the first pressure-receiving region in each pair of adjacent sliding time windows based on the pressure change rate and thickness change rate within adjacent sliding time windows; and determines the average of the window asynchronicity of the first pressure-receiving region in all adjacent sliding time windows as the local asynchronicity of the first pressure-receiving region.

[0010] Optionally, when the asynchronous analysis module is used to perform smooth fitting within a sliding time window on the pressure acquisition sequence of the first pressure-bearing region to obtain the pressure change rate of each sliding time window, it specifically performs the following: segments the pressure acquisition sequence according to a preset time length to obtain multiple sliding time windows; performs linear fitting on the pressure data within each sliding time window to obtain the slope of the pressure curve within each sliding time window; and determines the slope of the pressure curve within each sliding time window as the pressure change rate of the corresponding sliding time window.

[0011] Optionally, when the asynchronicity analysis module is used to determine the window asynchronicity of the first pressure-bearing region in each pair of adjacent sliding time windows based on the pressure change rate and thickness change rate within adjacent sliding time windows, it is specifically used to: determine the instantaneous matching degree within each sliding time window based on the ratio of the thickness change rate to the pressure change rate within each sliding time window; and determine the absolute value of the difference between the instantaneous matching degrees of adjacent sliding time windows as the window asynchronicity of the first pressure-bearing region in the corresponding adjacent sliding time window.

[0012] Optionally, the parameter optimization module is specifically used to: determine the relative deviation rate based on the difference between the overall pressure thickness asynchrony and the preset asynchrony threshold, the relative deviation rate being used to characterize the degree of deviation between the current iteration effect and the target stability; and adjust the current parameter variation amplitude based on the relative deviation rate to obtain the parameter variation amplitude for the next iteration.

[0013] Optionally, the parameter optimization module is also specifically used to: obtain the maximum upward adjustment ratio and the maximum downward adjustment ratio of the variation amplitude; determine a reasonable range of variation amplitude based on the maximum upward adjustment ratio, the maximum downward adjustment ratio of the variation amplitude and the current parameter variation amplitude; and limit the parameter variation amplitude of the next iteration to within the reasonable range of variation amplitude.

[0014] Optionally, the parameter optimization module is further configured to: terminate the iteration and output the optimal construction size parameters when the absolute value of the difference between the overall pressure thickness asynchrony and the preset asynchrony threshold satisfies the preset convergence condition, wherein the preset convergence condition is that the absolute value of the difference between the overall pressure thickness asynchrony obtained in three consecutive iterations and the preset asynchrony threshold is less than the preset difference threshold.

[0015] This application has the following beneficial effects: The automatic analysis and optimization system for tire component construction dimensions provided in this application acquires production data from the rolling process, then assesses the risk of thickness deviation in each pressure zone and the overall pressure-thickness asynchrony, and finally dynamically adjusts the search strategy based on stability feedback. This achieves adaptive adjustment of the genetic algorithm parameter variation amplitude based on actual production data from the rolling process, breaking through the limitation of fixed variation amplitude in existing technologies. It can dynamically adjust the parameter variation amplitude according to the actual process effect of each iteration, solving the core problem of difficulty in balancing iteration efficiency and generalization ability. This can effectively shorten the construction dimension optimization cycle, reduce computing power consumption, and improve the adaptability of output parameters under different production conditions. Attached Figure Description

[0016] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A structural diagram of an automatic analysis and optimization system for tire component construction dimensions provided in one embodiment of this application; Figure 2 This is a flowchart illustrating an automatic analysis and optimization method for the construction dimensions of tire components, provided as an embodiment of this application. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an automatic analysis and optimization system for tire component construction dimensions proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0020] The following description, in conjunction with the accompanying drawings, details the specific scheme of the automatic analysis and optimization system for tire component construction dimensions provided in this application.

[0021] Please see Figure 1 The diagram shows a structural diagram of an automatic analysis and optimization system for tire component construction dimensions provided in one embodiment of this application.

[0022] like Figure 1 As shown, the automatic analysis and optimization system 10 for tire component construction dimensions includes a data acquisition module 101, a pressure analysis module 102, an asynchronous analysis module 103, and a parameter optimization module 104.

[0023] The data acquisition module 101 is used to acquire production data during the tire rolling process based on the current construction dimensions.

[0024] The production data includes the entry time of the molding component, the pressure acquisition sequence and thickness acquisition sequence of each of the multiple pressure zones, and the current construction dimension is the construction dimension generated based on the variation amplitude of the current parameters in the genetic algorithm.

[0025] It should be understood that the current parameter variation range is the parameter variation range used by the genetic algorithm in this iteration. After the genetic algorithm generates the current construction size based on the current parameter variation range, it can use the current construction size as the setting parameter of the roller pressing equipment to perform the roller pressing production of tire-type rubber parts, and collect production data synchronously during the production process.

[0026] Optionally, the current construction dimensions include parameters such as roller pressure and layer thickness.

[0027] Optionally, before data acquisition, a laser profile sensor can be installed at the discharge end of the rolling process. Its scanning line is perpendicular to the molded material conveying direction and parallel to the roller axis, that is, the scanning line extends along the entire width of the molded material. It scans frame by frame at a fixed frequency (e.g., 50Hz). Each frame of data includes the profile height of multiple discrete points along the width of the molded material. The profile height refers to the vertical distance from the laser profile sensor to the surface of the object being measured (molded material surface or reference surface), and the unit is usually millimeters. At the same time, multiple pressure sensors are installed at equal intervals (e.g., 30 cm) along the full width of the roller to ensure that the layout of all pressure sensors covers the pressure distribution along the full width of the roller. Then, a width range (e.g., extending 15 cm on each side) near the location of each pressure sensor is defined as a pressure area. One pressure sensor corresponds to one pressure area and is used to independently collect the real-time pressure value of the pressure area.

[0028] Optionally, the entry time of the molding component is obtained as follows: when the molding component has not passed through, the contour height of multiple discrete position points on the surface of the roller or the support platform scanned by the sensor is collected, and the contour height at this time is determined as the reference surface height of each discrete position point; then, the number of effective reflection points (position points where the height difference between the contour height and the reference surface is greater than a preset jump threshold (e.g., 0.5 mm)) in each frame is identified, and it is determined whether the number of effective reflection points exceeds a preset point threshold (e.g., 80% of the number of discrete position points). If the number of effective reflection points exceeds the preset point threshold in multiple consecutive frames (e.g., 3 frames), the acquisition time of the first frame in which the number of effective reflection points exceeds the preset point threshold is recorded as the entry time of the molding component into the roller pressing working area.

[0029] Optionally, the thickness acquisition sequence is obtained as follows: starting from the moment the molding component enters, the contour height acquired by the laser contour sensor is recorded as the molding height. Since the molding has a certain thickness, its upper surface is closer to the sensor than the platform surface. Therefore, the molding height measured by the sensor is less than the reference surface height. The molding height is subtracted from the reference surface height to obtain the molding thickness value at each discrete location point. The thickness values ​​of discrete locations within the same pressure area are averaged to obtain the thickness value of each pressure area at the acquisition moment. This thickness value is acquired based on a fixed frequency to obtain the thickness acquisition sequence of each pressure area.

[0030] Optionally, the pressure acquisition sequence is obtained as follows: starting from the moment the molding component enters, the pressure sensor continuously acquires pressure values ​​at a sampling frequency synchronized with the laser profile sensor, forming a pressure acquisition sequence for each pressure area.

[0031] It should be noted that the pressure acquisition sequence (or thickness acquisition sequence) includes at least three pressure values ​​(or thickness values).

[0032] Optionally, the data acquisition module 101 can also record the acquisition time of each pressure value.

[0033] The pressure analysis module 102 is used to determine the thickness deviation risk of each pressure zone based on the difference between adjacent pressure values ​​in the pressure acquisition sequence of each pressure zone and the difference between the acquisition time of the first pressure value and the entry time of the molding component.

[0034] The thickness deviation risk level is used to characterize the degree of risk of deviation in the thickness data of the pressure area, that is, the comprehensive impact of pressure instability on thickness accuracy after being amplified by the duration of pressure. The longer the duration of pressure, the more significant the amplification effect of pressure fluctuation on thickness deviation.

[0035] It should be understood that the greater the difference between the time of the first pressure value acquisition and the time of entry of the molding component, and the greater the difference between adjacent pressure values, the greater the risk of deviation in thickness data.

[0036] In one implementation of this application, the pressure analysis module 102 is specifically used to: determine the pressure instability of each pressure-bearing region based on the difference between adjacent pressure values ​​in the pressure acquisition sequence of each pressure-bearing region; determine the pressure lag time of each pressure-bearing region based on the difference between the acquisition time of the first pressure value of each pressure-bearing region and the entry time of the molding component; determine the pressure coefficient of each pressure-bearing region based on the pressure lag time of each pressure-bearing region; and determine the thickness deviation risk of each pressure-bearing region based on the pressure instability of each pressure-bearing region and the pressure coefficient.

[0037] Among them, the pressure instability degree is used to characterize the fluctuation range of the pressure value in the pressure area. The greater the pressure fluctuation, the more unstable the rolling process is, and the more likely the thickness data is to deviate. The pressure coefficient is used to characterize the duration of the pressure time in the pressure area.

[0038] In one alternative implementation, taking the first pressure zone as an example, which can be any one of multiple pressure zones, the absolute value of the difference between each pair of adjacent pressure values ​​in the pressure acquisition sequence of the first pressure zone can be determined, and then the mean of all the absolute values ​​of the differences can be determined as the pressure instability of the first pressure zone.

[0039] For example, assuming the pressure acquisition sequence includes pressure 1, pressure 2, and pressure 3, then adjacent pressure values ​​include pressure 1 and pressure 2, and pressure 2 and pressure 3.

[0040] Optionally, the degree of pressure instability satisfies the following formula: ; in, Indicates the area under pressure The degree of pressure instability Indicates the area under pressure The number of pressure values ​​in the pressure acquisition sequence. Indicates the number of adjacent pressure values. Indicates the area under pressure The pressure acquisition sequence of the first One pressure value, Indicates the area under pressure The pressure acquisition sequence of the first One pressure value, express and The absolute value of the difference between them.

[0041] It should be understood that the mean of the absolute values ​​of all differences reflects the average level of pressure fluctuation within a pressure zone throughout the rolling process.

[0042] Optionally, the moment when the pressure sensor first detects the effective pressure value (i.e., the pressure value exceeds the preset lower limit threshold of the process, such as 0.05MPa) in each pressure zone can be recorded as the moment when the first pressure value of the pressure zone is collected, which can eliminate the environmental noise interference of the pressure sensor.

[0043] Understandably, the pressure lag time is used to characterize the degree of delay in pressure establishment relative to the entry of the molding compound into the roller pressing working area. The longer the pressure lag time, the shorter the duration of the molding compound being pressed, the lower the duration of the pressure in the pressed area, and the smaller the pressure coefficient should be.

[0044] Optionally, the difference between the time when the first pressure value of a pressure zone is collected and the time when the molding component enters can be determined as the pressure hysteresis time of the pressure zone.

[0045] Optionally, the pressure lag time of all pressure-bearing regions can be normalized, mapping the pressure lag time to the interval between 0 and 1, and the difference between 1 and the normalized pressure lag time can be determined as the pressure coefficient of the corresponding pressure-bearing region.

[0046] Optionally, the compressive coefficient satisfies the following formula: ; in, Indicates the area under pressure The compressive coefficient, Indicates the area under pressure The time lag under pressure, This represents a normalization function, such as max-min normalization.

[0047] Based on this formula, it should be understood that The smaller the value, the longer the pressure duration, the longer the continuous impact of pressure fluctuations on the rubber compound, the larger the pressure coefficient, and the greater the amplification of the risk of thickness deviation.

[0048] Optionally, the thickness deviation risk level satisfies the following formula: ; in, Indicates the area under pressure The thickness deviates from the risk level. Indicates the area under pressure The compressive coefficient, Indicates the area under pressure The degree of pressure instability This represents an exponential function with the natural constant e as its base, used to amplify the degree of pressure instability along the time dimension of the pressure duration; the more fully the pressure is applied (…). The larger ( The larger the pressure, the more significant the continuous impact of pressure fluctuations on the rubber compound, and the greater the amplification of the risk of thickness deviation.

[0049] The above-mentioned method for determining the thickness deviation risk decomposes the assessment of the thickness deviation risk into two dimensions: pressure fluctuation and pressure persistence. By introducing a pressure coefficient to amplify the pressure instability over time, it can more accurately reflect the combined impact of pressure lag and fluctuation on thickness accuracy. This provides reliable data credibility weights for subsequent calculations of overall asynchronicity and improves the accuracy of the system's assessment of the stability of the rolling process.

[0050] The asynchronous analysis module 103 is used to determine the overall pressure-thickness asynchronousity based on the difference between the pressure change trend of the pressure acquisition sequence and the thickness change trend of the thickness acquisition sequence for each pressure region, combined with the thickness deviation risk of each pressure region.

[0051] It should be understood that the overall pressure-thickness asynchrony is used to characterize the instability of the dynamic matching relationship between pressure and thickness during the rolling process. The greater the overall pressure-thickness asynchrony, the worse the synchronization between the pressure change trend and the thickness change trend.

[0052] Understandably, by comparing the degree of matching between pressure change trends and thickness change trends, the dynamic synchronicity of pressure and thickness can be assessed; at the same time, by combining the risk of thickness deviation, the reliability of the difference can be quantified, and the overall pressure-thickness asynchronicity that can represent the stability of the entire molding component rolling process can be obtained.

[0053] The parameter optimization module 104 is used to adjust the parameter variation range used in the next iteration of the genetic algorithm based on the overall pressure thickness asynchrony and the preset asynchrony threshold.

[0054] It should be understood that the preset asynchronicity threshold is used as a benchmark value to determine the stability of the pressure-thickness matching relationship in the current iteration. This value is greater than zero and has the same dimension as the overall pressure-thickness asynchronicity. By comparing the overall pressure-thickness asynchronicity with this preset asynchronicity threshold, a dynamic decision is made on whether to expand or shrink the parameter variation amplitude in the next iteration. This allows for a finer search to improve efficiency when stability is high and a wider search to enhance generalization ability when stability is poor, thus achieving a balance between iteration efficiency and quality search.

[0055] It is understandable that when the overall pressure and thickness are highly asynchronous, i.e., when the stability is poor, it indicates that the parameter configuration of the current iteration (i.e., the current construction dimensions) has failed to establish a stable correspondence between pressure and thickness. It may be in a non-optimal region of the parameter space or have large process fluctuations. In this case, the parameter variation range should be increased, the hybridization range should be expanded, more parameter combinations should be introduced, the generalization ability and global search ability of the algorithm should be enhanced, and the algorithm should avoid getting stuck in local optima.

[0056] In one alternative implementation, the relative deviation rate can be determined based on the difference between the overall pressure thickness asynchrony and the preset asynchrony threshold. Then, the current parameter variation amplitude can be adjusted based on the relative deviation rate to obtain the parameter variation amplitude for the next iteration.

[0057] The relative deviation rate is used to characterize the degree of deviation between the current iteration effect and the target stability, reflecting the deviation of the pressure-thickness matching stability of the current rolling process from the expected level.

[0058] Alternatively, the relative deviation rate satisfies the following formula: ; in, This indicates the relative deviation rate. This indicates the asynchronous nature of the overall pressure thickness. This represents a preset asynchronicity threshold; an example value could be 0.05.

[0059] In this formula, The denominator represents the deviation of the overall pressure thickness asynchrony from a preset asynchrony threshold. This is used to convert the deviation into a relative deviation rate, using the preset asynchronous threshold itself as the denominator, so that... It can adapt to different threshold settings. This indicates the proportion of the deviation relative to the preset asynchronicity threshold.

[0060] Understandably, when the relative deviation rate is greater than 0, it indicates that the current process stability does not meet the requirements, and the parameter variation range should be increased. When the relative deviation rate is equal to 0, it indicates that the current process is in a critical state, and the parameter variation range does not need to be adjusted. When the relative deviation rate is less than 0, it indicates that the current process stability is better than the requirements, and at this time, the parameter variation range can be further reduced.

[0061] Optionally, the parameter variation magnitude in the next iteration satisfies the following formula: ; in, This indicates the magnitude of parameter variation in the next iteration. This indicates the magnitude of the current parameter variation; an example value could be 0.1. This indicates the relative deviation rate.

[0062] In this formula, when the relative deviation rate is negative, the parameter variation amplitude in the next iteration will be smaller than the current parameter variation amplitude, and the system will optimize the parameters within a smaller range. When the relative deviation rate is positive, the parameter variation amplitude in the next iteration will be greater than the current parameter variation amplitude, and the system will expand the search range. This negative feedback adjustment mechanism ensures that the iterative process can adaptively adjust the search strategy according to the actual stability.

[0063] In one alternative implementation, to prevent excessive adjustment of parameter variation range from causing algorithm instability, the parameter optimization module 104 can also obtain the maximum upward adjustment ratio and the maximum downward adjustment ratio of variation range; based on the maximum upward adjustment ratio, the maximum downward adjustment ratio of variation range and the current parameter variation range, determine a reasonable range of variation range; and limit the parameter variation range of the next iteration to within the reasonable range of variation range.

[0064] Optionally, the upper limit of the reasonable range of variation can be determined based on the maximum upward adjustment ratio of the variation amplitude, and the lower limit of the reasonable range of variation amplitude can be determined based on the maximum downward adjustment ratio of the variation amplitude.

[0065] Optionally, the upper limit of the reasonable range of variation satisfies the following formula: ; in, This represents the upper limit of the reasonable range of variation. This indicates the magnitude of the current parameter variation. This indicates the maximum upward adjustment percentage of the variation range; an example value could be 0.2 (i.e., 20%).

[0066] Optionally, the lower limit of the reasonable range of variation satisfies the following formula: ; in, This represents the lower limit of the reasonable range of variation. This indicates the magnitude of the current parameter variation. This indicates the maximum downward adjustment percentage of the variation range; an example value could be 0.1 (i.e., 10%).

[0067] It is understandable that if the parameter variation range of the next iteration calculated above exceeds the reasonable range, the corresponding boundary value is directly taken as the parameter variation range of the next iteration.

[0068] For example, assuming the current parameter variation is 0.1, the maximum upward adjustment of the variation is 0.2, and the maximum downward adjustment of the variation is 0.1, then the reasonable range for the variation is [0.09, 0.12]. If the parameter variation for the next iteration calculated based on the relative deviation rate is 0.15, then 0.12 is taken as the parameter variation for the next iteration. If the calculated parameter variation for the next iteration is 0.07, then 0.09 is taken as the parameter variation for the next iteration.

[0069] This embodiment establishes a quantitative mapping relationship between the overall pressure thickness asynchronicity and the search breadth by introducing a relative deviation rate as the adjustment basis. By setting a reasonable range for the variation amplitude, drastic fluctuations in the parameter variation amplitude are effectively prevented, ensuring that the genetic algorithm maintains a reasonable search range during the iteration process. This avoids both invalid iterations caused by excessive variation amplitude and premature convergence caused by excessively small variation amplitude, thereby improving iteration efficiency while ensuring the generalization ability of the optimization results.

[0070] In one implementation of this application, the parameter optimization module 104 is also used to determine the termination condition of the genetic algorithm iteration process.

[0071] Specifically, the parameter optimization module 104 is used to terminate the iteration and output the optimal construction size parameters when the absolute value of the difference between the overall pressure thickness asynchrony and the preset asynchrony threshold satisfies the preset convergence condition.

[0072] It should be understood that the preset convergence condition is that the absolute value of the difference between the overall pressure thickness asynchrony obtained from three consecutive iterations and the preset asynchrony threshold is less than the preset difference threshold, which is used to determine whether the iteration process has reached a stable state.

[0073] For example, the preset difference threshold can be a value of 0.001 or less, used to define the accuracy requirements for algorithm convergence.

[0074] In one optional implementation, the parameter optimization module 104 can maintain a convergence counter. After each iteration, it calculates the absolute value of the difference between the current overall pressure thickness asynchrony and a preset asynchrony threshold. If the absolute value of the difference is less than the preset difference threshold, the convergence counter is incremented by 1; otherwise, the convergence counter is reset to zero. When the convergence counter accumulates three times, it is determined that the preset convergence condition is met, the system terminates the iteration, and the construction dimensions generated by the genetic algorithm based on the parameter variation amplitude are determined as the optimal construction dimensions.

[0075] Optionally, if the preset convergence condition is not met, the parameter optimization module 104 can generate new construction dimensions based on the parameter variation amplitude after this adjustment and return to the data acquisition module 101 to continue the next iteration.

[0076] Optionally, if the convergence condition is not met even after the number of iterations exceeds the preset maximum number of iterations (e.g., 200 times), the construction dimension obtained in this iteration is determined as the current optimal construction dimension, and an alarm is triggered to indicate a process abnormality.

[0077] Understandably, by setting convergence conditions based on continuous stability, misjudgments caused by single-time accidental stability are avoided, ensuring that the output optimal construction dimension parameters have high reliability and stability. This termination mechanism, combined with the adaptive parameter adjustment mechanism, constitutes a complete closed-loop optimization process, enabling the system to converge as quickly as possible while ensuring optimization accuracy, reducing unnecessary computational power consumption, and improving the overall efficiency of tire component construction dimension optimization.

[0078] Based on the description of the above embodiments, the automatic analysis and optimization system 10 for tire component construction dimensions provided in this application acquires production data of the rolling process through the data acquisition module 101, then evaluates the thickness deviation risk of each pressure area through the pressure analysis module 102, evaluates the overall pressure thickness asynchrony through the asynchronous analysis module 103, and finally dynamically adjusts the search strategy based on stability feedback through the parameter optimization module 104. This achieves adaptive adjustment of the genetic algorithm parameter variation amplitude based on the actual production data of the rolling process, breaking through the limitation of fixed variation amplitude in the prior art. It can dynamically adjust the parameter variation amplitude according to the actual process effect of each iteration, solving the core problem that it is difficult to balance iteration efficiency and generalization ability. In this way, it can effectively shorten the construction dimension optimization cycle, reduce computing power consumption, and improve the adaptability of output parameters under different production conditions.

[0079] Combination Figure 1 ,like Figure 2As shown, when the asynchronous analysis module 103 determines the overall pressure-thickness asynchronousity based on the difference between the pressure change trend of the pressure acquisition sequence and the thickness change trend of the thickness acquisition sequence for each pressure area, and in combination with the thickness deviation risk of each pressure area, it can be achieved by executing the automatic analysis and optimization method for tire component construction dimensions provided by S201-S203.

[0080] S201. Based on the difference between the pressure change trend of the pressure acquisition sequence and the thickness change trend of the thickness acquisition sequence of each pressure region, determine the local asynchrony of each pressure region.

[0081] It should be understood that local asynchrony is used to characterize the degree of inconsistency between pressure changes and thickness changes within a single pressurized region.

[0082] In one optional implementation, the pressure acquisition sequence and thickness acquisition sequence of the first pressure-bearing region can be smoothly fitted within a sliding time window to obtain the pressure change rate and thickness change rate of each sliding time window in multiple sliding time windows; based on the pressure change rate and thickness change rate in adjacent sliding time windows, the window asynchrony of the first pressure-bearing region in each pair of adjacent sliding time windows is determined; the average of the window asynchrony of the first pressure-bearing region in all adjacent sliding time windows is determined as the local asynchrony of the first pressure-bearing region.

[0083] Optionally, the asynchronous analysis module 103 can segment the pressure acquisition sequence according to a preset time length to obtain multiple sliding time windows; perform linear fitting on the pressure data in each sliding time window to obtain the slope of the pressure curve in each sliding time window; and determine the slope of the pressure curve in each sliding time window as the pressure change rate of the corresponding sliding time window.

[0084] For example, the preset time length can be 0.5 seconds, corresponding to 25 consecutive sampling points at a sampling frequency of 50Hz, and the method for linear fitting can be least squares fitting.

[0085] Optionally, the same processing can be performed on the thickness acquisition sequence to obtain the thickness change rate. That is, the thickness acquisition sequence is segmented according to a preset time length, and the thickness data in each sliding time window is fitted with a straight line to obtain the slope of the thickness curve in each sliding time window. The slope of the thickness curve is then determined as the thickness change rate of the corresponding sliding time window.

[0086] It should be understood that if the pressure change trend and the thickness change trend in the rolling process maintain a relatively stable relationship, then as the pressure increases / decreases, the thickness will decrease / increase. This is reflected in the curve graph as the slope values ​​of the curves change similarly at each fixed acquisition interval (i.e., adjacent sliding time windows).

[0087] Optionally, after obtaining the pressure change rate and thickness change rate for each sliding time window, the instantaneous matching degree within each sliding time window can be determined based on the ratio of the thickness change rate to the pressure change rate for each sliding time window; the absolute value of the difference in instantaneous matching degree between adjacent sliding time windows is determined as the window asynchrony of the first pressure region in the corresponding adjacent sliding time window.

[0088] Alternatively, window asynchronicity satisfies the following formula: ; in, Represents a sliding time window With sliding time window Asynchronous windowing between windows Represents a sliding time window The rate of change of thickness, Represents a sliding time window The rate of pressure change Represents a sliding time window The rate of change of thickness, Represents a sliding time window The rate of pressure change For a very small value, such as 0.0001, its unit is the same as that in the denominator. Consistent units (i.e., MPa / s) are used to prevent calculations from being meaningless due to a denominator of zero. This indicates taking the absolute value.

[0089] In this formula, Represents a sliding time window Instantaneous matching degree, Represents a sliding time window The instantaneous matching degree is calculated as follows: Ideally, during the rolling process, the pressure change and thickness change within a sliding time window should satisfy an approximately linear inverse relationship; that is, the greater the rate of pressure change, the greater the rate of thickness change, and the ratio between the two remains constant. Therefore, the instantaneous matching degree is obtained by dividing the absolute value of the thickness change slope by the absolute value of the pressure change slope (with a small constant to prevent division by zero). The larger this instantaneous matching degree, the more drastic the thickness change caused by a unit pressure change; the smaller the ratio, the less responsive the pressure change is to the thickness change.

[0090] It should be understood that if the dynamic relationship between pressure and thickness remains stable throughout the rolling process, the instantaneous matching degree of adjacent sliding time windows should be very close, with the difference approaching zero. Conversely, if the instantaneous matching degree changes abruptly, it indicates that the response relationship between pressure and thickness has become asynchronous. For example, the pressure may suddenly increase but the thickness may not decrease in time, or the pressure may stabilize but the thickness may still fluctuate. Therefore, the asynchronicity of the windows is quantified by calculating the absolute value of the difference in the instantaneous matching degree of adjacent sliding time windows. Regardless of whether the instantaneous matching degree increases or decreases, as long as the magnitude of change is similar, it indicates low asynchronicity.

[0091] Subsequently, the asynchronicity analysis module 103 determines the mean of the window asynchronicity of the first stressed region across all adjacent sliding time windows as the local asynchronicity of the first stressed region.

[0092] S202. Determine the weighting coefficient of each pressure zone based on the thickness deviation risk of each pressure zone.

[0093] The weighting coefficient is used to characterize the data reliability of the stressed area.

[0094] It should be understood that the lower the deviation of the thickness of the pressure-bearing area from the risk level, the higher the process stability of the pressure-bearing area, the higher the reliability of its data, and the larger the weighting coefficient should be.

[0095] Optionally, the thickness deviation risk of all pressure zones can be normalized, for example, by using maximum-minimum inverse normalization. Specifically, first, the thickness deviation risk of all pressure zones is statistically analyzed, and the maximum and minimum values ​​are determined. For each pressure zone, its thickness deviation risk is substituted into the inverse normalization formula, i.e., the maximum value is subtracted from the thickness deviation risk of the pressure zone, and then divided by the difference between the maximum and minimum values. The calculated value is the weight coefficient of the pressure zone. Based on this processing, the pressure zone with a larger thickness deviation risk will have a smaller normalized weight coefficient.

[0096] S203. Based on the weighting coefficient of each pressure region, the local asynchrony of all pressure regions is weighted and averaged to obtain the overall pressure thickness asynchrony.

[0097] Optionally, the overall pressure thickness asynchrony satisfies the following formula: ; in, This indicates the asynchronous nature of the overall pressure thickness. Indicates the area under pressure The weighting coefficients, Indicates the area under pressure Local asynchronicity, Indicates the number of pressure zones.

[0098] The logic of this formula is to use the inverse normalized value of the thickness deviation risk level as a weighting coefficient to perform a weighted average of the local asynchrony of each pressure zone, where, This represents the weighted contribution of the local asynchrony of a single pressurized region to the overall pressure-thickness asynchrony. This represents the sum of the weighted contribution values ​​of all stressed regions. This represents the sum of the weighting coefficients for all pressure zones.

[0099] The asynchronicity analysis module 103 executes the above steps S201-S203 to divide the pressure acquisition sequence and thickness acquisition sequence into sliding time windows, and performs smooth fitting and rate of change analysis on each sliding time window. It quantifies the dynamic matching relationship between pressure and thickness into a calculable asynchronicity index. By evaluating the consistency of the rate of change of adjacent sliding time windows, it obtains the local asynchronicity of each pressure area. By introducing a weighted mechanism based on the risk of thickness deviation, it effectively filters out data interference in abnormal process areas, enabling the overall pressure and thickness asynchronicity to more accurately reflect the true stability of the rolling process, and providing a reliable decision basis for the adaptive adjustment of genetic algorithm parameters.

[0100] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0101] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. An automatic analysis and optimization system for the construction dimensions of tire components, characterized in that, It includes a data acquisition module, a stress analysis module, an asynchronous analysis module, and a parameter optimization module; The data acquisition module is used to acquire production data during the tire rolling process based on the current construction dimensions. The production data includes the entry time of the molding component, the pressure acquisition sequence and thickness acquisition sequence of each of the multiple pressure areas, and the current construction dimensions are the construction dimensions generated based on the variation amplitude of the current parameters in the genetic algorithm. The pressure analysis module is used to determine the thickness deviation risk level of each pressure area based on the difference between adjacent pressure values ​​in the pressure acquisition sequence of each pressure area and the difference between the acquisition time of the first pressure value and the entry time of the molding component. The thickness deviation risk level is used to characterize the degree of risk of deviation in the thickness data of the pressure area. The asynchronous analysis module is used to perform smooth fitting within sliding time windows on the pressure acquisition sequence and thickness acquisition sequence of the first pressure-bearing region, respectively, to obtain the pressure change rate and thickness change rate of each sliding time window. Based on the ratio of the thickness change rate to the pressure change rate of each sliding time window, the instantaneous matching degree within each sliding time window is determined. The absolute value of the difference in instantaneous matching degrees between adjacent sliding time windows is determined as the window asynchronousness of the first pressure-bearing region in the corresponding adjacent sliding time window. The mean of the window asynchronousness of the first pressure-bearing region in all adjacent sliding time windows is determined as the local asynchronousness of the first pressure-bearing region. Based on the thickness deviation risk of each pressure-bearing region, a weight coefficient is determined for each pressure-bearing region, and the weight coefficient is used to characterize the data reliability of the pressure-bearing region. Based on the weight coefficient of each pressure-bearing region, a weighted average of the local asynchronousness of all pressure-bearing regions is performed to obtain the overall pressure-thickness asynchronousness. The parameter optimization module is used to adjust the parameter variation range used in the next iteration of the genetic algorithm based on the overall pressure thickness asynchrony and the preset asynchrony threshold.

2. The automatic analysis and optimization system for tire component construction dimensions according to claim 1, characterized in that, The pressure analysis module is specifically used for: Based on the differences between adjacent pressure values ​​in the pressure acquisition sequence of each pressure zone, the pressure instability of each pressure zone is determined, and the pressure instability is used to characterize the fluctuation range of pressure values ​​within the pressure zone. The pressure lag time of each pressure zone is determined based on the difference between the acquisition time of the first pressure value of each pressure zone and the entry time of the molding component. Based on the pressure lag time of each pressure region, a pressure coefficient for each pressure region is determined, which is used to characterize the duration of pressure time in the pressure region; Based on the degree of pressure instability in each pressure zone and the pressure coefficient, the thickness deviation risk level of each pressure zone is determined.

3. The automatic analysis and optimization system for tire component construction dimensions according to claim 2, characterized in that, The pressure analysis module, when used to determine the degree of pressure instability in each pressure-affected area based on the differences between adjacent pressure values ​​in the pressure acquisition sequence of each pressure-affected area, is specifically used for: Determine the absolute value of the difference between each pair of adjacent pressure values ​​in the pressure acquisition sequence of the first pressure zone, where the first pressure zone is any one of multiple pressure zones; The mean of the absolute values ​​of all differences is used to determine the degree of pressure instability in the first pressure zone.

4. The automatic analysis and optimization system for tire component construction dimensions according to claim 1, characterized in that, The asynchronous analysis module, when used to perform smooth fitting within a sliding time window on the pressure acquisition sequence of the first pressure-bearing region to obtain the pressure change rate for each sliding time window, is specifically used for: The pressure acquisition sequence is segmented according to a preset time length to obtain multiple sliding time windows; The pressure data within each sliding time window are fitted with a straight line to obtain the slope of the pressure curve within each sliding time window. The slope of the pressure curve within each sliding time window is determined as the pressure change rate of the corresponding sliding time window.

5. The automatic analysis and optimization system for tire component construction dimensions according to claim 1, characterized in that, The parameter optimization module is specifically used for: Based on the difference between the overall pressure thickness asynchrony and the preset asynchrony threshold, the relative deviation rate is determined. The relative deviation rate is used to characterize the degree of deviation between the current iteration effect and the target stability. The parameter variation magnitude is adjusted based on the relative deviation rate to obtain the parameter variation magnitude for the next iteration.

6. The automatic analysis and optimization system for tire component construction dimensions according to claim 5, characterized in that, The parameter optimization module is also specifically used for: Obtain the maximum upward adjustment ratio and the maximum downward adjustment ratio of the mutation magnitude; Based on the maximum upward adjustment ratio of the variation amplitude, the maximum downward adjustment ratio of the variation amplitude, and the current parameter variation amplitude, a reasonable range of variation amplitude is determined. The parameter variation range for the next iteration is limited to a reasonable range.

7. The automatic analysis and optimization system for tire component construction dimensions according to claim 1, characterized in that, The parameter optimization module is also used for: If the absolute value of the difference between the overall pressure thickness asynchrony and the preset asynchrony threshold satisfies the preset convergence condition, the iteration is terminated and the optimal construction size parameters are output. The preset convergence condition is that the absolute value of the difference between the overall pressure thickness asynchrony obtained from three consecutive iterations and the preset asynchrony threshold is less than the preset difference threshold.

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