Weld joint track real-time positioning method

Abstract

The invention discloses a real-time positioning method for a weld track, and belongs to the technical field of laser positioning, and the method comprises the steps: obtaining an environment state coefficient based on the environment light intensity and the temperature of a structured light emitter; a welding state coefficient is obtained based on the welding smoke particle concentration and the arc light intensity; acquiring an environment stability coefficient based on the welding state coefficient and the environment state coefficient under the vibration amplitude of the current structured light emitter; obtaining a structured light stability coefficient based on the laser wavelength, the line width and the power of the structured light emitter; obtaining a distance-reflection matching degree based on the laser working distance and the workpiece surface reflectance under the environment stability coefficient and the structured light stability coefficient; constructing a divergence angle optimization model based on the distance-reflection matching degree and the current laser divergence angle to output a target laser divergence angle; according to the method, high-precision real-time positioning of the welding seam track under the strong interference environment is achieved through multi-model cooperation, and the robustness and adaptability of a welding automation system are remarkably improved.

Description

Technical Field

[0001] The invention belongs to the technical field of laser positioning, and in particular relates to a real-time positioning method for a weld trajectory. Background Art

[0002] In the field of welding automation, traditional structured light vision positioning technology faces the challenge of multi-physics field coupling interference. Factors such as strong arc light, smoke particles, equipment vibration, and ambient light fluctuations generated during welding can cause positioning offsets and light spot distortion in traditional single-parameter control methods. Existing technologies typically use fixed parameter models and are unable to dynamically adapt to complex working conditions. Summary of the Invention

[0003] In view of the deficiencies in the prior art, the present invention provides a real-time positioning method for a weld trajectory, which solves the above problems.

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions: A real-time positioning method for a weld trajectory, comprising the following steps: An environmental state model is constructed based on the ambient light intensity and the temperature of the structured light emitter to output the environmental state coefficient; Based on the welding fume particle concentration and arc intensity, a welding state model is constructed to output the welding state coefficient; Based on the welding state coefficient and environmental state coefficient under the current structured light emitter vibration amplitude, an environmental stability model is constructed to output the environmental stability coefficient; Based on the laser wavelength, line width and structured light emitter power, a structured light stability model is constructed to output the structured light stability coefficient; Based on the laser working distance under the environmental stability coefficient and the structured light stability coefficient and the reflectivity of the workpiece surface, a distance-reflectivity matching model is constructed to output the distance-reflectivity matching; Based on the current distance-reflection matching degree and the current laser divergence angle, a divergence angle optimization model is constructed to output the target laser divergence angle.

[0005] On the basis of the above technical solutions, the present invention also provides the following optional technical solutions: Further technical solution: The steps of constructing an environmental state model and outputting environmental state coefficients based on the ambient light intensity and the temperature of the structured light emitter are as follows: Perform maximum-minimum normalization on the current ambient light intensity to obtain the ambient light intensity index; The temperature index is obtained by performing a ratio processing on the difference between the current structured light emitter temperature and the optimal operating temperature of the structured light emitter and the difference between the maximum allowable temperature and the optimal operating temperature of the structured light emitter; An environmental state model is constructed based on the ambient light intensity index and the temperature index. The environmental state model is expressed as:

[0006] in, represents the environmental state coefficient, Represents the ambient light intensity index, represents the temperature index, represents the weight coefficient and .

[0007] Further technical solution: The steps of constructing a welding state model and outputting welding state coefficients based on welding fume particle concentration and arc intensity are as follows: Perform maximum-minimum normalization processing on the current welding fume particle concentration to obtain a welding fume particle concentration index; Import the current arc intensity into the formula Get the arc intensity index, Indicates the reference arc intensity, Indicates the current arc intensity; A welding state model is constructed based on the welding fume particle concentration index and the arc intensity index. The welding state model is expressed as:

[0008] in, Indicates the welding state coefficient, represents the smoke attenuation coefficient, Indicates the welding fume particle concentration index, represents the arc intensity index, represents the weight coefficient and ; The welding fume particle concentration index and the current arc intensity index are imported into the welding state model to output the welding state coefficient.

[0009] Further technical solution: The steps of constructing an environmental stability model and outputting the environmental stability coefficient based on the welding state coefficient and the environmental state coefficient under the current structured light emitter vibration amplitude are as follows: The current vibration amplitude of the structured light emitter is compared with the maximum allowable vibration amplitude to obtain a vibration amplitude index; An environmental stability model is constructed based on the vibration amplitude index, the environmental state coefficient, and the welding state coefficient. The environmental stability model is expressed as:

[0010] in, represents the environmental stability coefficient, represents the environmental state coefficient, Indicates the welding state coefficient, represents the vibration amplitude index, represents the weight coefficient and ; The vibration amplitude index, environmental state coefficient and welding state coefficient are imported into the environmental stability model to output the environmental stability coefficient.

[0011] Further technical solution: The steps of constructing a structured light stability model and outputting a structured light stability coefficient based on the laser wavelength, line width and structured light emitter power are as follows: Perform maximum-minimum normalization on the current line width to obtain the line width index; The wavelength index is obtained by performing a ratio process on the absolute value of the difference between the current laser wavelength and the nominal wavelength and the wavelength tolerance (indicating the sensitivity of the wavelength deviation); A structured light stability model is constructed based on the line width index, wavelength index, and current structured light emitter power. The structured light stability model is expressed as:

[0012] in, represents the structural light stability coefficient, represents the wavelength index, represents the line width index, Indicates the current structured light transmitter power, represents the optimal structured light transmitter power, represents the standard deviation of the structured light emitter power, represents the weight coefficient and ; The linewidth index, wavelength index and current structured light emitter power are imported into the structured light stability model to output the structured light stability coefficient.

[0013] Further technical solution: The distance-reflection matching model is expressed as:

[0014] in, Indicates the distance-reflectance matching, Indicates the current laser working distance. Indicates the optimal laser working distance, represents the standard deviation of the working distance, represents the environmental stability coefficient, represents the welding stability coefficient, Indicates the reflectivity of the workpiece surface.

[0015] Further technical solution: The divergence angle optimization model is expressed as:

[0016] in, represents the target divergence angle, Indicates the current divergence angle, Indicates the distance-reflectance matching, Indicates the target distance-reflectivity matching degree, Indicates adjustment gain coefficient.

[0017] Further technical solution: The temperature of the structured light emitter is between the minimum allowable temperature and the maximum allowable temperature.

[0018] The present invention provides a real-time positioning method for weld seam trajectories, which has the following advantages compared with the prior art: 1. This invention uses a dual coupling of the environmental state model (light intensity / temperature) and the welding state model (smoke / arc light), combined with the exponential attenuation mechanism of vibration effects, to enable the system to improve its comprehensive anti-interference capability under conditions of strong arc light, high smoke and equipment vibration, effectively suppressing positioning deviation. 2. Real-time adjustment of the laser divergence angle based on distance-reflection matching allows the spot energy distribution to adapt to different working distances and surface reflectivity (e.g., stainless steel is highly reflective, cast iron is low-reflective), improving the fringe signal-to-noise ratio and resolving the adaptability defects of traditional fixed parameter modes. 3. The structural light stability model (wavelength / linewidth / power) is linked with the environmental stability model to compensate for the wavelength shift caused by laser temperature drift and the optical plane distortion caused by mechanical vibration. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0021] The specific implementation of the present invention is described in detail below with reference to specific embodiments.

[0022] In existing technologies, the field of welding automation has long faced the challenge of multi-physics field coupling interference. Traditional structured light vision positioning technology uses a fixed parameter model, which makes it difficult to cope with the dynamic changes in strong arc light, smoke particles, equipment vibration, and ambient light fluctuations during the welding process. For example, in a welding workshop, high temperatures cause the temperature of the structured light emitter to rise, and the concentration of welding smoke particles changes continuously with the welding process. The coupling of these factors causes light spot distortion and positioning offset, affecting the accuracy of weld trajectory recognition. Existing methods only compensate for single interference factors and cannot achieve the coordinated suppression of multiple sources of interference.

[0023] See also Figure 1, provided in one embodiment of the present invention, is a real-time positioning method for a weld trajectory, comprising the following steps: An environmental state model is constructed based on the ambient light intensity and the temperature of the structured light emitter to output the environmental state coefficient; Based on the welding fume particle concentration and arc intensity, a welding state model is constructed to output the welding state coefficient; Based on the welding state coefficient and environmental state coefficient under the current structured light emitter vibration amplitude, an environmental stability model is constructed to output the environmental stability coefficient; Based on the laser wavelength, line width and power of the structured light emitter, a structured light stability model is constructed to output the structured light stability coefficient; Based on the laser working distance under the environmental stability coefficient and the structured light stability coefficient and the reflectivity of the workpiece surface, a distance-reflectivity matching model is constructed to output the distance-reflectivity matching; Based on the distance-reflection matching degree and the current laser divergence angle, a divergence angle optimization model is constructed to output the target laser divergence angle.

[0024] Among them, the environmental state model refers to the combined impact of ambient light intensity and equipment temperature on light source stability by quantifying it, and is used to eliminate the superimposed interference of ambient light fluctuations and equipment temperature rise on the spot quality. The welding state model refers to the dynamic correlation analysis between welding fume particle concentration and arc intensity, and is used to suppress the coupling effect of fume obstruction and strong light interference during welding. The environmental stability model refers to the synergistic effect of comprehensive vibration amplitude, environmental state, and welding state, and is used to evaluate the combined impact of mechanical vibration and multi-source interference. The structured light stability model is used to evaluate the beam quality based on laser physical parameters to ensure the stability of the light source output characteristics. The distance-reflection matching model combines environmental stability, light source stability, and workpiece surface characteristics to optimize the matching relationship between laser working distance and surface reflection. The divergence angle optimization model dynamically adjusts the beam divergence angle based on the matching degree. Specifically, this can be achieved by calculating the gain coefficient and the difference product, and is used to correct the spot shape in real time.

[0025] Specifically, this method quantifies the combined influence of ambient light intensity and emitter temperature through the environmental state model, eliminating the interference of external lighting changes and equipment temperature rise on the light source; the welding state model calculates the dynamic attenuation effect of welding fume particle concentration and arc intensity in real time, reducing the optical interference generated by the welding process; the environmental stability model integrates the synergistic effect of vibration amplitude with the environment and welding state, and evaluates the combined influence of mechanical vibration and multi-source interference; the structural light stability model analyzes the physical property deviation of laser wavelength, line width and power to ensure the stability of the light source output parameters; the distance-reflection matching model comprehensively considers the environmental stability, light source stability and workpiece surface characteristics, and calculates the matching degree of the optimal working distance and reflection parameters; the divergence angle optimization model adjusts the laser divergence angle according to the real-time matching degree, forming a closed-loop control mechanism, and ultimately realizing the dynamic optimization of the spot shape.

[0026] Compared with existing technologies, traditional methods only compensate for single interference factors through fixed thresholds, such as adjusting laser power to address arc interference or optimizing working distance to compensate for smoke. This solution establishes a dynamic correlation model of multiple physical fields, integrating ambient light, temperature, smoke, vibration, light source parameters, and workpiece characteristics into a unified calculation framework, achieving coordinated suppression of multiple interference sources.

[0027] Through the above technical solution, this application effectively solves the problem of spot distortion and positioning offset caused by multi-physical field coupling interference during welding. Through the collaborative calculation of multi-dimensional models, the changes in environmental conditions, welding interference and light source parameters are perceived in real time, the laser divergence angle is dynamically adjusted, and the quality of structured light imaging is improved. In welding scenarios with strong arc light and high welding smoke particle concentrations, the system can automatically suppress light intensity attenuation and scattering effects to ensure the accuracy of weld trajectory recognition; under conditions of equipment vibration and temperature fluctuations, the reliability of light source parameters is maintained through comprehensive evaluation of the stability model, avoiding positioning inaccuracies caused by insufficient single interference compensation in traditional methods. Preferably, the steps of constructing an environmental state model and outputting an environmental state coefficient based on the ambient light intensity and the temperature of the structured light emitter are: Perform maximum-minimum normalization on the current ambient light intensity to obtain the ambient light intensity index; performing ratio processing on the difference between the current structured light emitter temperature and the optimal operating temperature of the structured light emitter and the difference between the maximum allowable temperature and the optimal operating temperature of the structured light emitter to obtain a temperature index, wherein the current structured light emitter temperature is between the minimum allowable temperature and the maximum allowable temperature; An environmental state model is constructed based on the ambient light intensity index and the temperature index. The environmental state model is expressed as:

[0028] in, represents the environmental state coefficient and , Represents the ambient light intensity index, represents the temperature index, represents the weight coefficient and , The larger the value, the more stable the environment.

[0029] Among them, the ambient light intensity index refers to a standardized parameter that maps ambient light intensities of different dimensions to the [0,1] range through maximum-minimum normalization processing. Specifically, it can be achieved by collecting ambient light intensity data in real time and then calculating its relative ratio to the preset maximum and minimum light intensity values, which is used to eliminate the impact of differences in lighting conditions on the evaluation results.

[0030] Among them, the temperature index refers to a dimensionless parameter that characterizes the degree to which the temperature of the structured light emitter deviates from the optimal working state. It can be calculated by dividing the difference between the current temperature and the optimal temperature by the maximum allowable temperature difference. By limiting the temperature range, it ensures that the device is in a safe operating range, while amplifying the sensitivity of temperature fluctuations to stability.

[0031] Among them, the weight coefficient refers to the allocation parameter used to adjust the contribution ratio of ambient light and temperature factors to the comprehensive evaluation results. Specifically, it can be dynamically adjusted using empirical values ​​or adaptive algorithms, and the priority configuration under different working conditions can be achieved by satisfying the constraint that the sum of the coefficients is 1.

[0032] Specifically, the ambient light intensity index establishes a unified evaluation benchmark by eliminating dimensional differences, allowing the degree of interference in strong and weak light environments to be compared horizontally. The temperature index converts the absolute temperature difference into a relative deviation rate through ratio calculation, which not only reflects the temperature rise trend of the equipment but also avoids the risk of extreme over-limit. In model construction, the temperature index is processed using a square term, so that the impact of high temperature fluctuations on the stability coefficient presents a nonlinear amplification characteristic, which is more in line with the law of accelerated degradation of equipment performance with increasing temperature under actual working conditions. The dynamic allocation mechanism of weight coefficients allows the importance weight of environmental factors to be adjusted according to the welding scenario. For example, the weight of the temperature term can be appropriately increased in a high-temperature workshop environment.

[0033] Compared with existing technologies, traditional methods typically use a single environmental parameter threshold or fixed-weight linear superposition, which makes it difficult to accurately reflect the dynamic environmental state under the influence of multiple factors. This solution, by introducing a nonlinear combination model and normalized exponential transformation, achieves a coordinated assessment of ambient light interference and equipment temperature rise, overcoming the one-sidedness of single-parameter assessment. It also enhances the assessment sensitivity of high-temperature conditions through the design of a temperature squared term.

[0034] Through the above technical solution, this application can dynamically quantify the combined impact of changes in light intensity and equipment temperature fluctuations in the welding environment, providing a real-time basis for environmental stability assessment for the structured light vision system. This effectively suppresses positioning offsets and light spot distortion caused by environmental interference, and improves weld trajectory recognition accuracy. The continuous output characteristics of the environmental state coefficient enable subsequent control modules to pre-adjust based on stability change trends, avoiding the system response lag caused by sudden environmental disturbances in traditional methods.

[0035] Preferably, the steps of constructing a welding state model and outputting a welding state coefficient based on welding fume particle concentration and arc intensity are as follows: Perform maximum-minimum normalization processing on the current welding fume particle concentration to obtain a welding fume particle concentration index; Import the current arc intensity into the formula Get the arc intensity index, Indicates the reference arc intensity, Indicates the current arc intensity; A welding state model is constructed based on the welding fume particle concentration index and the arc intensity index. The welding state model is expressed as:

[0036] in, represents the welding state coefficient and , represents the smoke attenuation coefficient, Indicates the welding fume particle concentration index, represents the arc intensity index, represents the weight coefficient and , The larger the value, the smaller the interference; The welding fume particle concentration index and arc intensity index are imported into the welding state model to output the welding state coefficient.

[0037] Among them, the maximum-minimum normalization processing of the current welding fume particle concentration refers to the standardized operation of mapping the original concentration data to the range of 0 to 1. Specifically, the concentration data can be collected by a sensor (laser scattering particle sensor or resistive dust sensor). This processing eliminates dimensional differences and quantifies the degree of interference of fume on light path scattering.

[0038] The arc light intensity index calculation formula refers to suppressing strong arc light interference through the ratio of reference intensity to actual intensity. Specifically, the arc light intensity can be measured in real time by a photoelectric sensor and then subtracted into a nonlinear function for calculation. This formula reduces the flooding effect of high-intensity arc light on structured light imaging.

[0039] The exponential decay function refers to the nonlinear mapping of the welding fume particle concentration index in the model, which can be specifically implemented using a natural exponential function, which enhances the aggravation of the interference degree caused by high-concentration fume.

[0040] Among them, the weight coefficient refers to the contribution ratio parameter of smoke and arc factors in the model. It can be dynamically adjusted using empirical values ​​or adaptive algorithms. This coefficient realizes autonomous identification and compensation of major interference factors under different working conditions.

[0041] Specifically, welding fume particle concentration is collected by a sensor and normalized to a welding fume particle concentration index ranging from 0 to 1. This index directly reflects the level of scattering interference caused by fume particles on structured light. Arc intensity is measured in real time by a photoelectric sensor and substituted into a nonlinear formula to calculate the arc intensity index. This index decreases with increasing actual arc intensity, effectively suppressing the negative impact of high-intensity arc on imaging quality. During the model construction phase, the welding fume particle concentration index is input into an exponential decay function, resulting in an exponential increase in interference when the welding fume particle concentration exceeds a threshold. The arc intensity index is then linearly superimposed to reflect its persistent interference with imaging quality. The weighting coefficient is dynamically adjusted based on the operating conditions. The exponential decay term is weighted higher when the welding fume particle concentration is too high, while the arc term is weighted higher when the arc intensity suddenly increases, enabling a prioritized and comprehensive assessment of interference factors.

[0042] Compared with existing technologies, traditional methods use fixed thresholds to determine smoke or arc interference, which cannot quantify the coupled effects of multiple interference factors. This solution, by establishing a dynamic model incorporating nonlinear functions, not only achieves a coordinated assessment of welding smoke particle concentration and arc intensity, but also automatically identifies the main interference source through weight assignment. Existing linear compensation methods for arc interference are prone to failure under strong arc light. This solution uses an adaptive arc intensity index calculation formula that automatically reduces its influence when the arc intensity exceeds a reference value.

[0043] Through the above technical solution, this application can quantify in real time the comprehensive interference level of smoke and arc light on the visual positioning system during welding, accurately characterize the scattering effect of high-concentration smoke and the flooding effect of strong arc light through a nonlinear model, and use a dynamic weight distribution mechanism to achieve the primary and secondary distinction of interference factors, providing accurate interference degree parameters for subsequent stability evaluation, thereby effectively improving the anti-interference ability of the structured light vision system in complex welding environments.

[0044] Preferably, the steps of constructing an environmental stability model and outputting an environmental stability coefficient based on the welding state coefficient and the environmental state coefficient under the current vibration amplitude of the structured light emitter are: The current vibration amplitude of the structured light emitter is compared with the maximum allowable vibration amplitude to obtain a vibration amplitude index; An environmental stability model is constructed based on the vibration amplitude index, the environmental state coefficient, and the welding state coefficient. The environmental stability model is expressed as:

[0045] in, represents the environmental stability coefficient and , represents the environmental state coefficient, Indicates the welding state coefficient, represents the vibration amplitude index, represents the weight coefficient and , The larger the value, the stronger the anti-interference ability; The vibration amplitude index, environmental state coefficient and welding state coefficient are imported into the environmental stability model to output the environmental stability coefficient.

[0046] Among them, the vibration amplitude index refers to the ratio of the current structured light emitter vibration amplitude to the maximum allowable vibration amplitude. Specifically, it can be achieved by using a sensor to measure the vibration amplitude and then performing proportional calculations. It is used to quantify the degree of influence of vibration on the stability of the light source. The environmental state coefficient refers to the stability index calculated by the ambient light intensity and the temperature of the structured light emitter. Specifically, it can be achieved by combining normalization processing and weighted square terms. It is used to characterize the comprehensive interference of ambient light changes and temperature fluctuations on the system. The welding state coefficient refers to the interference degree index calculated by the welding smoke particle concentration and arc intensity. Specifically, it can be achieved by exponential attenuation and normalization processing. It is used to reflect the dynamic changes of smoke shielding and arc interference during welding. The environmental stability model refers to a mathematical relationship that integrates the vibration amplitude index, environmental state coefficient and welding state coefficient. Specifically, it can be achieved by combining linear weighting with an exponential function. It is used to dynamically evaluate the system's anti-interference ability under complex interference. Specifically, the vibration amplitude index converts the mechanical vibration amplitude into a standardized parameter through ratio processing. For example, when the vibration amplitude reaches 80% of the maximum allowable value, the vibration amplitude index is 0.8. The environmental state coefficient and the welding state coefficient quantify the interference factors from the two dimensions of environmental conditions and welding process respectively. For example, the environmental state coefficient can enhance the influence of temperature offset through the square of temperature difference. In the environmental stability model, the first term The positive superposition effect when the environment and welding state are stable at the same time is enhanced by the product form. For example, when both are close to 1, this term contributes to the maximum stability. The second term The negative impact of increasing vibration amplitude is quickly suppressed through the exponential decay function. For example, when the vibration amplitude exponent increases by 0.1, the exponential term decays by about 10%. The priority of different interference factors can be adjusted according to actual working conditions. For example, in a high temperature environment, the priority of Weighted to enhance sensitivity to vibration. Compared to existing technologies, which typically compensate for only a single interference source (such as vibration or arc light) independently, this solution, by constructing a multi-factor coupling model, can simultaneously address the synergistic interference of mechanical vibration, ambient light fluctuations, welding fume, and arc light. For example, the existing fixed-threshold vibration suppression strategy cannot adapt to the dynamically changing welding fume particle concentration. However, this solution dynamically adjusts the anti-interference strategy through the environmental stability coefficient, automatically increasing the vibration suppression weight when the welding fume particle concentration suddenly increases. Through the above technical solution, the present application can quantify the comprehensive impact of composite interference on the stability of structured light in real time, and provide a dynamic evaluation basis for subsequent laser parameter optimization. For example, when coupled interference occurs during welding due to increased equipment vibration and increased welding smoke particle concentration, the environmental stability coefficient automatically decreases, triggering the divergence angle adjustment module to increase the laser focusing accuracy, thereby maintaining the stability of weld trajectory positioning. Furthermore, through the model structure of exponential function and linear combination, the basic correlation between the environment and the welding state is retained, and the rapid response capability to sudden changes in vibration amplitude is enhanced, effectively solving the positioning offset problem caused by multi-physical field coupling.

[0047] Preferably, the steps of constructing a structured light stability model and outputting a structured light stability coefficient based on the laser wavelength, line width, and structured light emitter power are as follows: Perform maximum-minimum normalization on the current line width to obtain the line width index; The wavelength index is obtained by performing a ratio process on the absolute value of the difference between the current laser wavelength and the nominal wavelength and the wavelength tolerance (indicating the sensitivity of the wavelength deviation); A structured light stability model is constructed based on the line width index, wavelength index, and current structured light emitter power. The structured light stability model is expressed as:

[0048] in, represents the structural light stability coefficient and , represents the wavelength index, represents the line width index, Indicates the current structured light transmitter power, represents the optimal structured light transmitter power, represents the standard deviation of the structured light emitter power, represents the weight coefficient and , The higher the value, the more reliable the light source; The linewidth index, wavelength index and current structured light emitter power are imported into the structured light stability model to output the structured light stability coefficient.

[0049] Among them, line width normalization processing refers to the calculation process of mapping the actual line width value to the standardized range. Specifically, it can be implemented by using the maximum-minimum normalization algorithm to eliminate the dimensional differences of line width under different working conditions and convert the line width fluctuation into a comparable exponential form. The wavelength index refers to the quantitative ratio of the wavelength deviation degree to the system sensitivity. It can be implemented by dividing the absolute value difference by the tolerance threshold, and is used to reflect the nonlinear impact of the wavelength deviation from the nominal value on the light source quality. Among them, the structured light stability model refers to an evaluation function that integrates wavelength, linewidth and power parameters. It can be implemented by weighted summation of exponential attenuation terms and Gaussian distribution terms. The exponential function is used to characterize the sensitivity of wavelength and linewidth deviations, and the Gaussian function is used to describe the stability attenuation law when the power deviates from the optimal state. Specifically, linewidth normalization is used to convert linewidth data of different dimensions into standardized indices. For example, when the linewidth fluctuates in the range of 0.1nm to 0.5nm, a linewidth index in the range of 0 to 1 is obtained after normalization. For the wavelength offset, the ratio of the wavelength offset to the system tolerance is calculated. For example, when the nominal wavelength is 980nm and the tolerance is 5nm, the wavelength index corresponding to the actual wavelength of 982nm is 0.4. In the power parameter processing, the Gaussian function is used to evaluate the degree of deviation between the current power and the optimal power. For example, when the power standard deviation is set to 10W, the index corresponding to the current power deviation of 5W from the optimal value is 0.882. By assigning weight coefficients to the three indices and performing linear combinations, the final output structured light stability coefficient can dynamically reflect the comprehensive state of the light source in the three dimensions of wavelength stability, linewidth consistency and power control accuracy. Compared to existing technologies, traditional methods typically monitor only a single parameter or use fixed thresholds to determine light source status, such as issuing an alarm based solely on the power fluctuation range. This solution, however, constructs a multi-parameter coupling model that simultaneously considers the sensitivity to wavelength shift, normalization of linewidth fluctuations, and the Gaussian distribution of power deviations. It employs a combination of nonlinear functions to more accurately characterize the changing patterns of light source stability, overcoming the problem of misjudgment caused by single-parameter evaluation. Through the above technical solution, this application can quantify the operating status of the laser light source in three key parameters: wavelength, linewidth, and power, in real time, and dynamically evaluate the comprehensive stability of the structured light emitter. When a sudden change in welding fume particle concentration or equipment vibration causes wavelength shift, the model can quickly identify the coupled impact of linewidth broadening and power fluctuations on light source quality, providing accurate light source status data for subsequent adjustment of the laser divergence angle, thereby reducing weld trajectory positioning errors and spot imaging distortion caused by light source instability.

[0050] Preferably, the distance-reflectance matching model is expressed as:

[0051] in, Indicates the distance-reflectance matching and , Indicates the current laser working distance. Indicates the optimal laser working distance, represents the standard deviation of the working distance, represents the environmental stability coefficient, represents the welding stability coefficient, Indicates the reflectivity of the workpiece surface. The higher the value, the better the image quality.

[0052] Among them, the environmental stability coefficient refers to the comprehensive anti-interference capability parameter output by the environmental state model and the welding state model, which is used to quantify the degree of interference of the environment on structured light imaging. The structured light stability coefficient refers to the reliability index calculated by the laser wavelength, line width and emitter power parameters, which is used to characterize the stability of the light source itself. The current laser working distance refers to the real-time distance between the structured light emitter and the workpiece surface, which can be measured by a laser ranging sensor or a binocular vision system. The optimal laser working distance refers to the pre-calibrated reference distance when the imaging quality is optimal. The working distance standard deviation refers to the tolerance range parameter that allows the laser working distance to deviate from the optimal value. The reflectivity of the workpiece surface refers to the reflectivity parameter of the workpiece surface to the laser, which can be achieved by using an optical sensor to measure the reflected light intensity and normalize it, and is used to reflect the impact of different materials or rough surfaces on imaging. Specifically, the product of the environmental stability coefficient and the structured light stability coefficient is used as the basic weight, and the synergistic effect of the two is used to suppress the impact of external interference and light source fluctuations on imaging. The exponential term penalizes the deviation between the current working distance and the optimal distance in the form of a Gaussian function. The greater the distance deviation, the more significant the matching degree attenuation, thereby constraining the laser working distance to be within the allowable range. The surface reflectivity of the workpiece is directly related to the surface reflection characteristics as a multiplicative factor, and the reflective differences of different materials or roughness are incorporated into the model. Through dynamic fusion of multiple parameters, the model unifies environmental stability, light source reliability, distance deviation and surface reflective characteristics into a matching index, providing a quantitative basis for subsequent divergence angle optimization. Compared to existing technologies, traditional methods typically adjust laser parameters based solely on fixed parameters or a single variable, such as compensation based solely on working distance or reflectivity, without comprehensively considering the effects of environmental interference, light source fluctuations, and the coupling of surface properties. This solution, by constructing a multi-factor fusion matching model, can dynamically adapt to multi-physics interference in complex working conditions, avoiding the degradation of imaging quality caused by single parameter adjustments. Through the above technical solution, this application solves the problem of image quality degradation during welding caused by insufficient environmental stability, poor light source reliability, working distance deviation, and surface reflection differences. By dynamically quantifying the matching index and optimizing the laser divergence angle parameters, precise positioning of the weld trajectory under complex working conditions can be achieved, improving the adaptability and reliability of the automated welding system.

[0053] Preferably, the divergence angle optimization model is expressed as:

[0054] in, represents the target divergence angle, Indicates the current divergence angle, Indicates the distance-reflectance matching, Indicates the target distance-reflectivity matching degree, Indicates adjustment gain coefficient.

[0055] The target divergence angle refers to the laser beam diffusion angle that is dynamically adjusted according to the real-time working conditions. Specifically, it can be achieved by using a tunable optical element or an electrically controlled focusing lens group to compensate for the light spot deformation caused by environmental interference.

[0056] The current divergence angle refers to the reference value of the divergence angle of the light beam currently output by the laser transmitter, which can be measured and obtained through the laser's built-in sensor or an external spot analyzer as the initial reference for adjustment.

[0057] Among them, the current distance-reflection matching degree refers to the optical adaptation index calculated by comprehensively considering the environmental stability and light source stability. It can be calculated in real time through a multi-sensor fusion algorithm to characterize the degree of deviation between the current imaging quality and the ideal state.

[0058] The target distance-reflection matching degree refers to the preset optimal threshold of imaging quality, which can be determined through calibration experiments or historical data statistics and serves as the target reference value for divergence angle adjustment.

[0059] The adjustment gain coefficient refers to a proportional parameter that controls the rate of change of the divergence angle, which can be specifically used to balance the adjustment sensitivity and the risk of system oscillation through system stability testing or dynamic optimization of an adaptive algorithm.

[0060] Specifically, the model achieves dynamic optimization of the divergence angle through a closed-loop feedback mechanism. When the distance-reflection matching degree decreases due to the concentration of welding dust particles or equipment vibration during welding, the model uses the deviation between the current matching degree and the target value as an input parameter and calculates the divergence angle adjustment amount through a linear proportional relationship. The adjustment gain coefficient is set as a configurable parameter. For example, a fuzzy control algorithm is used to automatically adjust according to the rate of change of the working conditions, thereby achieving a balance between fast response and system stability. When the matching degree is higher than the target value, the divergence angle is proportionally reduced to enhance the energy concentration of the spot; when the matching degree is lower than the target value, the divergence angle is proportionally expanded to compensate for the attenuation of light intensity. This forms a real-time adjustment loop based on the optical adaptation index, which effectively suppresses positioning deviations caused by environmental interference.

[0061] Through the above-mentioned technical solution, this application solves the problem of spot distortion caused by dynamic interference during the welding process and achieves real-time optimization of structured light imaging quality. Specifically, in scenarios where the concentration of welding fume particles fluctuates, the effective spot area is maintained by dynamically expanding the divergence angle. When the working distance changes due to equipment vibration, the divergence angle is automatically corrected through matching feedback to reduce imaging blur caused by focal length offset. Furthermore, the introduction of an adjustment gain coefficient avoids frequent parameter oscillations caused by sensor noise or transient interference, ensuring the stability of the control system.

[0062] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0063] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A real-time positioning method for weld seam trajectory, characterized in that: The following steps are involved: An environmental state model is constructed based on the ambient light intensity and the temperature of the structured light emitter to output the environmental state coefficient; Based on the welding fume particle concentration and arc intensity, a welding state model is constructed to output the welding state coefficient; Based on the welding state coefficient and environmental state coefficient under the current structured light emitter vibration amplitude, an environmental stability model is constructed to output the environmental stability coefficient; Based on the laser wavelength, line width and structured light emitter power, a structured light stability model is constructed to output the structured light stability coefficient; Based on the laser working distance under the environmental stability coefficient and the structured light stability coefficient and the reflectivity of the workpiece surface, a distance-reflectivity matching model is constructed to output the distance-reflectivity matching; Based on the distance-reflection matching degree and the current laser divergence angle, a divergence angle optimization model is constructed to output the target laser divergence angle.

2. The real-time positioning method for weld seam trajectory according to claim 1, characterized in that: The steps to construct an environmental state model and output the environmental state coefficient based on the ambient light intensity and the temperature of the structured light emitter are as follows: Perform maximum-minimum normalization on the current ambient light intensity to obtain the ambient light intensity index; performing ratio processing on the difference between the current structured light emitter temperature and the optimal operating temperature of the structured light emitter and the difference between the maximum allowable temperature and the optimal operating temperature of the structured light emitter to obtain a temperature index, wherein the current structured light emitter temperature is between the minimum allowable temperature and the maximum allowable temperature; An environmental state model is constructed based on the ambient light intensity index and the temperature index. The environmental state model is expressed as: in, represents the environmental state coefficient and , Represents the ambient light intensity index, represents the temperature index, represents the weight coefficient and , The larger the value, the more stable the environment.

3. The real-time positioning method for weld seam trajectory according to claim 2, characterized in that: The steps to construct a welding state model and output welding state coefficients based on welding fume particle concentration and arc intensity are as follows: Perform maximum-minimum normalization processing on the current welding fume particle concentration to obtain a welding fume particle concentration index; Import the current arc intensity into the formula Get the arc intensity index, Indicates the reference arc intensity, Indicates the current arc intensity; A welding state model is constructed based on the welding fume particle concentration index and the arc intensity index. The welding state model is expressed as: in, represents the welding state coefficient and , represents the smoke attenuation coefficient, Indicates the welding fume particle concentration index, represents the arc intensity index, represents the weight coefficient and , The larger the value, the smaller the interference; The current welding fume particle concentration index and the current arc intensity index are imported into the welding state model to output the welding state coefficient.

4. The real-time positioning method for weld seam trajectory according to claim 3, characterized in that: The steps for constructing an environmental stability model and outputting the environmental stability coefficient based on the welding state coefficient and the environmental state coefficient under the current structured light transmitter vibration amplitude are as follows: The current vibration amplitude of the structured light emitter is compared with the maximum allowable vibration amplitude to obtain a vibration amplitude index; An environmental stability model is constructed based on the vibration amplitude index, the environmental state coefficient, and the welding state coefficient. The environmental stability model is expressed as: in, represents the environmental stability coefficient and , represents the environmental state coefficient, Indicates the welding state coefficient, represents the vibration amplitude index, represents the weight coefficient and , The larger the value, the stronger the anti-interference ability; The vibration amplitude index, environmental state coefficient and welding state coefficient are imported into the environmental stability model to output the environmental stability coefficient.

5. The real-time positioning method for weld seam trajectory according to claim 4, characterized in that: The steps to construct a structured light stability model and output the structured light stability coefficient based on the laser wavelength, linewidth and structured light emitter power are as follows: Perform maximum-minimum normalization on the current line width to obtain the line width index; The wavelength index is obtained by performing a ratio process on the absolute value of the difference between the current laser wavelength and the nominal wavelength and the wavelength tolerance; A structured light stability model is constructed based on the line width index, wavelength index, and current structured light emitter power. The structured light stability model is expressed as: in, represents the structural light stability coefficient and , represents the wavelength index, represents the line width index, Indicates the current structured light transmitter power, represents the optimal structured light transmitter power, represents the standard deviation of the structured light emitter power, represents the weight coefficient and , The higher the value, the more reliable the light source; The linewidth index, wavelength index and current structured light emitter power are imported into the structured light stability model to output the structured light stability coefficient.

6. The real-time positioning method for weld seam trajectory according to any one of claim 5, characterized in that: The distance-reflectance matching model is expressed as: in, Indicates the distance-reflectance matching and , Indicates the current laser working distance. Indicates the optimal laser working distance, represents the standard deviation of the working distance, represents the environmental stability coefficient, represents the welding stability coefficient, Indicates the reflectivity of the workpiece surface. The higher the value, the better the image quality.

7. The real-time positioning method for weld seam trajectory according to claim 6, characterized in that: The divergence angle optimization model is expressed as: in, represents the target divergence angle, Indicates the current divergence angle, Indicates the distance-reflectance matching degree, Indicates the target distance-reflectivity matching degree, Indicates adjustment gain coefficient.

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