CN116551215A

CN116551215A - Laser scanning control method, device, equipment and storage medium of laser

Info

Publication number: CN116551215A
Application number: CN202310819413.1A
Authority: CN
Inventors: 牛星; 彭玉方; 金成立; 文海; 吴达; 李明
Original assignee: Beijing Sincoheren S&t Development Co ltd
Current assignee: Beijing Sincoheren S&t Development Co ltd
Priority date: 2023-07-06
Filing date: 2023-07-06
Publication date: 2023-08-08
Anticipated expiration: 2043-07-06
Also published as: CN116551215B

Abstract

The invention relates to the field of laser control, and discloses a laser scanning control method, device and equipment of a laser and a storage medium, which are used for improving the accuracy of laser scanning control of the laser. The method comprises the following steps: acquiring an initial scanning track of a target laser; performing track type analysis on the initial scanning track to determine the track type; extracting track parameters of the initial scanning track to generate a track parameter set; constructing a track equation of the initial scanning track, and determining a target track equation; calculating a thermal effect index to obtain a thermal effect index set; collecting laser beam parameters to obtain a laser beam parameter set, and generating optical distortion parameters of a target laser; and acquiring a real-time scanning track, analyzing track deviation data of the real-time scanning track through a target track equation based on the thermal effect index set and the laser beam parameter set, generating track deviation data, and controlling real-time parameters of the target laser through the track deviation data.

Description

Laser scanning control method, device, equipment and storage medium of laser

Technical Field

The present invention relates to the field of laser control, and in particular, to a laser scanning control method, apparatus, device, and storage medium for a laser.

Background

In the field of laser applications, laser scanning control is a critical technique. By controlling the scanning track of the laser, accurate laser irradiation and cutting can be realized, and the laser cutting device is widely applied to the fields of laser printing, laser engraving, laser radar and the like. Laser scanning control, however, faces several challenges, including thermal effects and the effects of optical distortion.

The conventional laser scanning control method often cannot fully consider the influence of thermal effects and optical distortion on the performance of the laser. Thermal effects can cause thermal expansion of the laser during operation, which in turn can cause deflection and shape changes in the scan trajectory. The optical distortion causes shape distortion of the laser beam and a change in focal position, thereby affecting the accuracy and stability of the laser.

Disclosure of Invention

The invention provides a laser scanning control method, a device, equipment and a storage medium of a laser, which are used for improving the accuracy of laser scanning control of the laser.

The first aspect of the present invention provides a laser scanning control method of a laser, the laser scanning control method of the laser comprising:

Acquiring an initial scanning track of a target laser, and performing laser operation control on the target laser based on the initial scanning track;

performing track type analysis on the initial scanning track to determine a track type corresponding to the initial scanning track;

extracting track parameters of the initial scanning track through the track type to generate a corresponding track parameter set, wherein the track parameter set comprises: curvature parameter set, inflection point coordinate set and direction parameter set;

constructing a track equation of the initial scanning track through the track parameter set, and determining a corresponding target track equation;

calculating the thermal effect index of the target laser in the laser operation control process to obtain a thermal effect index set;

collecting laser beam parameters of the target laser in a laser operation control process to obtain a laser beam parameter set, and generating optical distortion parameters corresponding to the target laser through the laser beam parameter set;

and acquiring a real-time scanning track of the target laser in a laser operation control process, analyzing track deviation data of the real-time scanning track through the target track equation based on the thermal effect index set and the laser beam parameter set, generating track deviation data, and controlling the real-time parameters of the target laser through the track deviation data.

With reference to the first aspect, in a first implementation manner of the first aspect of the present invention, the performing track type analysis on the initial scan track, and determining a track type corresponding to the initial scan track includes:

selecting control points of the initial scanning track to obtain a plurality of control point coordinates corresponding to the initial scanning track;

performing curve smoothness analysis on the initial scanning track through a plurality of control point coordinates to generate target curve smoothness;

and performing track type matching on the initial scanning track through the smoothness of the target curve, and determining the track type corresponding to the initial scanning track.

With reference to the first aspect, in a second implementation manner of the first aspect of the present invention, the extracting, by using the track type, a track parameter of the initial scan track, and generating a corresponding track parameter set, where the track parameter set includes: curvature parameter set, inflection point coordinate set and direction parameter set, include:

determining a feature point extraction algorithm through the track type to obtain a target feature point extraction algorithm;

traversing the characteristic points of the initial scanning track through the target characteristic point extraction algorithm to obtain a characteristic point set;

Performing curvature calculation on each feature point in the feature point set through a difference algorithm to obtain curvature parameters corresponding to each feature point, and taking the curvature parameters corresponding to each feature point as the curvature parameter set;

screening the characteristic points of the characteristic point set through the curvature parameter set to obtain an inflection point coordinate set;

and carrying out direction vector calculation on each feature point in the feature point set to obtain a direction parameter set, and combining the curvature parameter set, the inflection point coordinate set and the direction parameter set into the track parameter set.

With reference to the first aspect, in a third implementation manner of the first aspect of the present invention, the constructing a track equation of the initial scan track by using the track parameter set, and determining a corresponding target track equation includes:

carrying out parameter weight analysis on the track parameter set to generate a corresponding weight data set;

carrying out weight adjustment on the track parameter set through the weight data set to determine a target parameter set;

and constructing a track equation of the initial scanning track through the target parameter set, and determining a corresponding target track equation.

With reference to the first aspect, in a fourth implementation manner of the first aspect of the present invention, the calculating a thermal effect index of the target laser in a laser operation control process, to obtain a thermal effect index set, includes:

acquiring a temperature data set of the target laser in the operation control process through a temperature sensor;

calculating the temperature variation of the temperature data set to obtain a plurality of temperature variation data;

and based on the thermal expansion coefficient of the target laser, performing thermal effect index analysis on the target laser through the plurality of temperature variation data to obtain a thermal effect index set.

With reference to the first aspect, in a fifth implementation manner of the first aspect of the present invention, the collecting laser beam parameters of the target laser in a laser operation control process to obtain a laser beam parameter set, and generating optical distortion parameters corresponding to the target laser by using the laser beam parameter set includes:

performing shape analysis on a laser beam of the target laser in a laser operation control process to obtain laser beam shape data;

performing index analysis on a laser beam of the target laser in a laser operation control process through a beam analyzer to generate laser beam index data, wherein the laser beam index data comprises: spot diameter, beam waist position, and divergence angle;

Performing intensity analysis on a laser beam of the target laser in a laser operation control process through a laser beam calibrator, generating laser beam intensity data, and combining the laser beam shape data, the laser beam index data and the laser beam intensity data into the laser beam parameter set;

and generating optical distortion parameters corresponding to the target laser through the laser beam parameter set.

With reference to the fifth implementation manner of the first aspect, in a sixth implementation manner of the first aspect of the present invention, the generating, by the set of laser beam parameters, an optical distortion parameter corresponding to the target laser includes:

performing beam focusing index analysis through the laser beam shape data and the laser beam index data in the laser beam parameter set to obtain a target beam focusing index;

performing energy distribution analysis through the laser beam intensity data in the laser beam parameter set to generate a beam energy distribution result;

and performing optical distortion analysis through the target beam focusing index and the beam energy distribution result to generate the optical distortion parameters.

A second aspect of the present invention provides a laser scanning control apparatus of a laser, the laser scanning control apparatus of the laser including:

The acquisition module is used for acquiring an initial scanning track of the target laser and controlling the laser operation of the target laser based on the initial scanning track;

the analysis module is used for analyzing the track types of the initial scanning tracks and determining the track types corresponding to the initial scanning tracks;

the extraction module is used for extracting track parameters of the initial scanning track through the track type to generate a corresponding track parameter set, wherein the track parameter set comprises: curvature parameter set, inflection point coordinate set and direction parameter set;

the construction module is used for constructing a track equation of the initial scanning track through the track parameter set and determining a corresponding target track equation;

the calculation module is used for calculating the thermal effect index of the target laser in the laser operation control process to obtain a thermal effect index set;

the acquisition module is used for acquiring laser beam parameters of the target laser in the laser operation control process to obtain a laser beam parameter set, and generating optical distortion parameters corresponding to the target laser through the laser beam parameter set;

the generation module is used for collecting real-time scanning tracks of the target laser in the laser operation control process, analyzing track offset data of the real-time scanning tracks through the target track equation based on the thermal effect index set and the laser beam parameter set, generating track offset data, and controlling the real-time parameters of the target laser through the track offset data.

A third aspect of the present invention provides a laser scanning control apparatus of a laser, comprising: a memory and at least one processor, the memory having instructions stored therein; the at least one processor invokes the instructions in the memory to cause a laser scan control device of the laser to perform the laser scan control method of the laser described above.

A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored therein, which when run on a computer, cause the computer to perform the laser scanning control method of a laser as described above.

In the technical scheme provided by the invention, the initial scanning track of the target laser is obtained; performing track type analysis on the initial scanning track to determine the track type; extracting track parameters of the initial scanning track to generate a track parameter set; constructing a track equation of the initial scanning track, and determining a target track equation; calculating a thermal effect index to obtain a thermal effect index set; collecting laser beam parameters to obtain a laser beam parameter set, and generating optical distortion parameters of a target laser; and acquiring a real-time scanning track, analyzing track deviation data of the real-time scanning track through a target track equation based on the thermal effect index set and the laser beam parameter set, generating track deviation data, and controlling real-time parameters of the target laser through the track deviation data. By acquiring an initial scanning track of the target laser and controlling based on the track parameter set and the target track equation, accurate control of the laser can be achieved. This can improve the operation stability and accuracy of the laser, ensuring that the laser beam is scanned in accordance with a predetermined trajectory. The method can be used for analyzing the track type of the initial scanning track, extracting a related track parameter set, and acquiring laser beam parameters and combining calculation of optical distortion parameters to know and correct the optical distortion problem of the laser in the operation process. The quality and focusing effect of the laser beam can be improved, and the real-time parameter adjustment of the target laser can be realized by collecting real-time scanning track data and analyzing and controlling track offset data by combining a thermal effect index set and a laser beam parameter set. The method can improve and correct the track deviation problem caused by thermal effect and optical distortion, and maintain the stability and accuracy of the laser.

Drawings

FIG. 1 is a schematic diagram of an embodiment of a laser scanning control method of a laser according to an embodiment of the present invention;

FIG. 2 is a flow chart of extracting track parameters from an initial scan track by track type in an embodiment of the invention;

FIG. 3 is a flow chart of constructing a trajectory equation for an initial scan trajectory through a set of trajectory parameters in an embodiment of the invention;

FIG. 4 is a flow chart of calculating the thermal effect index of a target laser during laser operation control in accordance with an embodiment of the present invention;

FIG. 5 is a schematic diagram of an embodiment of a laser scanning control apparatus of a laser according to an embodiment of the present invention;

fig. 6 is a schematic diagram of an embodiment of a laser scanning control apparatus of a laser according to an embodiment of the present invention.

Detailed Description

The embodiment of the invention provides a laser scanning control method, device and equipment of a laser and a storage medium, which are used for improving the accuracy of laser scanning control of the laser.

The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.

For ease of understanding, a specific flow of an embodiment of the present invention is described below, referring to fig. 1, and an embodiment of a laser scanning control method of a laser in an embodiment of the present invention includes:

s101, acquiring an initial scanning track of a target laser, and performing laser operation control on the target laser based on the initial scanning track;

it is to be understood that the execution body of the present invention may be a laser scanning control device of a laser, and may also be a terminal or a server, which is not limited herein. The embodiment of the invention is described by taking a server as an execution main body as an example.

Specifically, it is a key step to acquire an initial scan trajectory of the target laser and perform laser operation control on the target laser based on the initial scan trajectory, and it may be implemented by various methods. One common method is to use sensors or measuring instruments to monitor and measure the scanning trajectory of the laser in real time. These sensors may include position sensors, accelerometers, gyroscopes, and the like. By mounting these sensors at critical locations of the laser, information about the position, angle, and speed of the laser in space can be obtained. For example, assume that there is a laser cutter that uses a laser beam to cut material. Before the initial run, the initial scan trajectory of the laser may first be recorded using a sensor system. These sensors may be mounted on the laser cutting head to measure the position and angle of the laser cutting head in space. By recording and saving this data, the server obtains the initial scan trajectory of the laser. Based on these initial scan trajectories, control algorithms can be developed to control the operation of the laser. The algorithm can calculate the required laser beam path and motion trajectory based on the laser position and angle information. These algorithms may be updated in real time or at predetermined time intervals to ensure that the laser is moving in the desired trajectory. For example, in a control system for a laser cutter, a PID control algorithm may be used to control the position and angle of the laser. The PID control algorithm compares the actual measured laser position to the desired trajectory and adjusts the operating parameters of the laser, such as speed, acceleration and position, to approximate the actual motion of the laser to the desired trajectory. In practice, the initial scan trajectory of the laser may be affected by a number of factors, such as mechanical errors, temperature variations, etc. Therefore, the control algorithm needs to be able to compensate and correct for these factors to ensure the accuracy and stability of the operation of the laser. In summary, the acquisition of an initial scan trajectory of a target laser and laser run control based on the trajectory is accomplished through the use of a sensor system and control algorithm. This approach ensures that the laser is moving in the desired trajectory and achieves accurate laser machining or scanning results.

S102, analyzing the track type of the initial scanning track, and determining the track type corresponding to the initial scanning track;

specifically, a plurality of control point coordinates are obtained from the initial scanning track through control point selection. These control points should be representative throughout the track, and effectively describe the shape and curve characteristics of the track. By performing curve smoothness analysis on these control point coordinates, the curve smoothness of the initial scan trajectory can be evaluated. This can be achieved by calculating the curvature of the curve, the change in tangential angle, etc. Smoothness analysis can help the server understand the curve characteristics of the initial scan trajectory and whether there are turning points that are too abrupt or discontinuous. The smoothness of the target curve obtained by analysis can be used as one of the characteristic parameters of the initial scan trajectory. And matching the characteristic parameters with the predefined track types to determine the track type corresponding to the initial scanning track. For example, assume that the server has one device for laser marking. Data of an initial scanning track is obtained through a sensor, and key control points are selected from the data. Through analysis of these control points, the server calculates the curve smoothness of the initial scan trajectory. The analysis results are assumed to show that the curve smoothness of the initial scan trajectory is high, and no curve change or turning point is obvious. Based on this feature, the server matches the initial scan trajectory to a smooth curved trajectory type. This means that the laser will follow a smoothly curved path. On the other hand, if the analysis shows that there is a significant curve change or turning point in the initial scan trajectory, the smoothness is low, then the server matches it as a curve trajectory type with an acute angle or complex shape. This will affect the way the laser moves to accommodate the change in curve. By analyzing and determining the track type of the initial scan track, the server better understands the characteristics of the track and provides guidance for subsequent laser run control. This helps ensure that the movement of the laser matches the type of track required, thus achieving accurate laser processing or scanning tasks.

S103, extracting track parameters of the initial scanning track through the track type to generate a corresponding track parameter set, wherein the track parameter set comprises: curvature parameter set, inflection point coordinate set and direction parameter set;

specifically, the server selects an appropriate feature point extraction algorithm according to the determined trajectory type. Different trajectory types may require different algorithms to extract key feature points. For example, for a straight-line trajectory, the server uses a straight-line fitting algorithm to extract feature points; for circular arc trajectories, a circular arc fitting algorithm may be used to extract feature points. And traversing the characteristic points of the initial scanning track by the server through the selected target characteristic point extraction algorithm, and obtaining a set of characteristic points. These feature points are the most representative and critical points in the initial trajectory. The server calculates curvature parameters of the feature points using a differential algorithm. The curvature represents the degree of curvature of the curve at a certain point. The server calculates the curvature value of the feature point through a differential algorithm. Each feature point corresponds to a curvature parameter, which is organized into a set of curvature parameters. The server then screens the feature point set with the curvature parameter set to determine coordinates of the inflection point. An inflection point is a point on a curve at which a significant change in position occurs, and generally represents a turn or point of the curve. Through screening, the server extracts inflection points in the feature point set and forms an inflection point coordinate set. Meanwhile, the server calculates direction parameters of the feature points. The direction parameter indicates the direction or trend of the curve at a certain point. The server obtains the direction parameters of each feature point by calculating the direction vector of the feature point. The direction parameters are organized into a set of direction parameters. The server combines the curvature parameter set, the inflection point coordinate set and the direction parameter set into a track parameter set. The track parameter set comprises key parameter information obtained by analyzing and extracting the initial scanning track. For example, assume that the server has a lidar system for the topography measurements. By laser scanning, the server acquires an initial scanning track of a section of topographic curve. According to the determination of the track type, the server selects a characteristic point extraction algorithm suitable for the topographic curve. By using the feature point extraction algorithm, the server traverses the initial scan trajectory and obtains a set of feature points. These feature points represent the most significant changes and inflection points in the topographical curve. The server uses a differential algorithm to perform curvature calculation on these feature points. The curvature parameter represents the degree of curvature of the topographical curve at each of the feature points. And calculating the difference, and obtaining the curvature parameter value corresponding to each characteristic point by the server. Meanwhile, the server screens the feature points to determine inflection point coordinates in the topographic curve. The inflection point represents a turn or corner in the topographical curve. The server extracts the coordinate information of the inflection point by analyzing and screening the feature point set. In addition, the server may also calculate a direction parameter for each feature point, representing the trend or orientation of the terrain curve at that point. And calculating the direction vector of the feature points, and obtaining the direction parameter corresponding to each feature point by the server. And combining the curvature parameter set, the inflection point coordinate set and the direction parameter set into a track parameter set. This set contains key parameter information of the topography curves, which can be used for further analysis and control.

S104, constructing a track equation of the initial scanning track through the track parameter set, and determining a corresponding target track equation;

in particular, the server performs a parameter weight analysis on the set of trajectory parameters, which aims to determine the importance of each parameter to the shape and characteristics of the trajectory. The set of weight data reflects the relative importance of each parameter in track construction. By analyzing the characteristics of the track and the application requirements, the server gives different weights to different parameters. For example, for a trajectory control system of an aircraft, the server may consider the curvature parameters of the trajectory to be critical to flight safety and stability, and thus the curvature parameters may be given higher weights. Whereas for inflection coordinates and direction parameters, lower weights may be given because they have relatively little effect on the overall trajectory shape. And carrying out weight adjustment on the track parameter set through the weight data set. This step aims at weighting the trajectory parameters according to their weight values to obtain an adjusted set of target parameters. By multiplying each parameter by a corresponding weight, the server adjusts the relative influence of the parameters so that they more accurately reflect the characteristics of the target trajectory. For example, if a certain curvature parameter has a higher weight value in the weight data set, the value of the curvature parameter will be amplified when the weight adjustment is made, thereby affecting the shape and the degree of curvature of the track more significantly. And constructing a track equation of the initial scanning track through the target parameter set, and determining a corresponding target track equation. This step involves applying parameters to a mathematical equation or model to generate a functional representation describing the target trajectory. For example, for trajectories on a two-dimensional plane, a mathematical model such as a polynomial equation or Bezier curve may be used to represent the target trajectory. By applying curvature parameters, inflection point coordinates, direction parameters, etc. in the target parameter set to a suitable mathematical model, the server constructs a trajectory equation that accurately describes the shape of the target trajectory. In this embodiment, the server uses the track parameter set to implement mathematical modeling of the initial scan track and determination of the target track equation through parameter weight analysis, parameter adjustment, and track equation construction. The trajectory equation has higher precision and accuracy, and can accurately describe the motion trajectory of the target laser.

S105, calculating a thermal effect index of the target laser in the laser operation control process to obtain a thermal effect index set;

specifically, the server needs to collect a temperature dataset of the target laser during the operation control process. For this purpose, a temperature sensor may be used to mount the sensor at a critical location of the laser to monitor the temperature change of the laser in real time. The sensor may record the temperature data of the laser and store it as a temperature dataset. The server calculates the temperature change amount of the temperature data set. By analyzing the temperature data of adjacent time points, the temperature variation of the laser in the operation control process can be calculated. These temperature change data represent the temperature change of the laser over different time periods. Based on the thermal expansion coefficient of the target laser, the server uses the temperature variation data to analyze the thermal effect index. The coefficient of thermal expansion is a material property that describes the degree of expansion or contraction of a material under a change in temperature. By multiplying the temperature variation data by the thermal expansion coefficient, the thermal effect index of the target laser during laser operation control can be calculated. A set of thermal effect indices is calculated from temperature change data for different time periods, each index representing the degree of thermal effect of the laser at a particular point in time. A higher thermal effect index means that the laser is more susceptible to temperature variations and more accurate compensation measures are needed. For example, consider that a high power laser may be affected by temperature variations during operation. The server is equipped with a temperature sensor and records the temperature data set of the laser during the operation control. By calculating the temperature change amounts at adjacent time points, the server obtains a series of temperature change amount data. The server knows the thermal expansion coefficient of the laser and uses the temperature variation data to calculate the thermal effect index. The server obtains the thermal effect index for each time point by multiplying the temperature change by the thermal expansion coefficient. For example, in high temperature environments, the laser may thermally expand, resulting in a shift or shape distortion of the laser beam. If at some point in time the temperature change is large and the thermal expansion coefficient of the laser is high, the corresponding thermal effect index will also be high, meaning that the performance of the laser may be greatly affected. Through analysis of the thermal effect index, the server evaluates the stability of the laser under temperature changes and determines whether compensation measures need to be taken to reduce the effect of the thermal effect. For example, a temperature compensation algorithm may be used to correct the position or shape of the laser output so that it remains stable and accurate. In summary, a temperature sensor is used for collecting a temperature data set, calculating a temperature variation, and a server is used for evaluating the thermal effect of a laser and obtaining a thermal effect index set by combining the thermal expansion coefficient of a target laser. Such analysis helps to understand the performance variation of the laser under different temperature conditions and provides basis for taking corresponding control and compensation measures to ensure stable operation and accurate output of the laser.

S106, collecting laser beam parameters of the target laser in the laser operation control process, obtaining a laser beam parameter set, and generating optical distortion parameters corresponding to the target laser through the laser beam parameter set;

specifically, laser beam shape analysis was performed. This may be achieved by using a suitable sensor or image processing technique to obtain shape data of the laser beam. The sensor may be a pixel sensor, a CCD camera, a laser beam detector, or the like. By placing the sensor in the appropriate position, the server captures the profile or edge information of the laser beam, thereby obtaining shape data of the laser beam. The server performs laser beam index analysis. This can use a beam analyzer to obtain index data of the laser beam. The beam analyzer can measure the indexes such as the diameter of the light spot, the position of the beam waist, the divergence angle and the like. By placing the beam analyzer in the path of the laser beam, the server measures the characteristics of the laser beam and obtains corresponding index data. Again, laser beam intensity analysis was performed. This may use a laser beam calibrator or a power meter to measure the intensity distribution of the laser beam. The laser beam calibrator may convert the laser beam into a measurable signal to obtain intensity data of the laser beam. By measuring the laser beam intensity at different positions or directions, the server knows the light intensity distribution of the laser beam. The laser beam shape data, the laser beam index data, and the laser beam intensity data are combined into a laser beam parameter set. These parameter sets will contain information about the laser beam shape, size, position, divergence and intensity. These parameters are key indicators describing the characteristics and performance of the laser beam. The optical distortion parameters of the target laser are generated from the set of laser beam parameters. This can be achieved by analysing the relation between the laser beam parameters and their effect on the laser performance. For example, data modeling, statistical analysis, or machine learning methods may be used to determine the correlation between laser beam parameters and optical distortion. By establishing a proper model or algorithm, the corresponding optical distortion parameters of the target laser can be generated for correcting the shape and characteristics of the laser beam so as to realize more accurate laser operation control. For example, assuming a laser cutter, the server would like to perform parameter acquisition and optical distortion analysis on its laser beam. The server uses appropriate sensors or image processing techniques to perform laser beam shape analysis. The server captures the profile information of the laser beam by placing a pixel sensor or CCD camera in the path of the laser beam. The sensor will record the light intensity distribution of the laser beam at different positions, thereby obtaining the shape data of the laser beam. The server uses a beam analyzer to perform laser beam index analysis. The beam analyzer will measure parameters such as spot diameter, beam waist position and divergence angle of the laser beam. The server obtains various index data of the laser beam by placing the beam analyzer on the path of the laser beam and scanning or spot-measuring the laser beam. The server uses a laser beam calibrator or power meter to perform laser beam intensity analysis. The laser beam calibrator will convert the laser beam into a measurable signal and the power meter will measure the power of the laser beam. The server obtains intensity data of the laser beam by measuring the intensity of the laser beam at different positions or directions. The laser beam shape data, the laser beam index data, and the laser beam intensity data are combined into a laser beam parameter set. These parameter sets will contain information about the laser beam shape, size, position, divergence and intensity. The server generates optical distortion parameters for the target laser by analyzing the relationship between the laser beam parameters and their effect on the laser performance. This may be accomplished by modeling mathematics, performing statistical analysis, or using machine learning algorithms. For example, the server uses regression analysis to determine the correlation between the laser beam parameters and the optical distortion, thereby generating parameters that correct the laser beam distortion. In this embodiment, the server acquires the optical distortion parameters of the target laser by acquiring the laser beam parameters and performing optical distortion analysis, and corrects the laser beam by using these parameters, so as to achieve more accurate laser operation control and optimize the laser processing effect. And from another aspect, the beam focus index analysis is performed using the laser beam shape data and the laser beam index data in the set of laser beam parameters. The beam focus index is a parameter describing the focusing performance of a laser beam and reflects the energy density of the laser beam at the focal point relative to the gain of the incident laser beam. By calculating the focus index, the server evaluates the focusing power and focus quality of the laser beam. The server performs energy distribution analysis using the laser beam intensity data in the laser beam parameter set. The laser beam intensity data reflects the energy distribution of the laser beam in space. By analyzing the energy distribution characteristics of the laser beam, such as the spot shape, spot size, and power density distribution, the server obtains important information about the energy distribution of the laser beam. The optical distortion analysis is performed by combining the target beam focusing index and the beam energy distribution result. The optical distortion parameters are parameters describing non-ideal characteristics of the laser beam during propagation, including aberrations, defocus, spherical aberration, etc. The server analyzes the degree of distortion of the laser beam by comparing the target beam focus index with the desired focus index and combining the beam energy distribution results, thereby determining the optical distortion parameters. For example, assume that the server has one laser for material processing. And calculating the focusing index of the target beam to be 0.85 by the server through the laser beam shape data and the laser beam index data in the laser beam parameter set. Meanwhile, by utilizing the laser beam intensity data in the laser beam parameter set, the server analyzes and obtains the energy distribution result of the laser beam, the size of the light spot is 2mm, and certain energy attenuation exists at the edge of the light spot. Based on these results, the server performs an optical distortion analysis. The server finds that the focus index of the target beam is lower than the desired value, indicating that the focusing performance of the beam is not ideal. Further, through analysis of the beam energy distribution results, the server finds that energy attenuation at the spot edge may lead to non-uniform processing effects. Therefore, the server further performs optical distortion analysis by taking the focusing index and the energy distribution result as the basis of the optical distortion parameters. In this example, the server may explore the distortion characteristics such as spherical aberration, etc. of the beam, and adjust parameters of the optical system, such as lens curvature, focusing distance, etc., according to the specific situation, so as to improve focusing performance and energy distribution uniformity of the beam. Through analysis of the optical distortion parameters, the server better knows the performance characteristics of the target laser, and further calibrates and optimizes the target laser. This helps to improve the machining accuracy and stability of the laser, making it better for various application fields.

S107, acquiring a real-time scanning track of the target laser in the laser operation control process, analyzing track deviation data of the real-time scanning track through a target track equation based on the thermal effect index set and the laser beam parameter set, generating track deviation data, and controlling real-time parameters of the target laser through the track deviation data.

Specifically, the real-time scan trajectory of the target laser is monitored and measured using appropriate sensors and measuring instruments. These sensors may include position sensors, angle sensors, accelerometers, etc. to obtain parameters such as the position, angle, and speed of the laser in space. By collecting and recording these data in real time, the server obtains the actual scan trajectory of the target laser. Meanwhile, the server also needs to establish a target trajectory equation according to the previously generated thermal effect index set and the laser beam parameter set. This equation will take into account the effect of thermal effects on the laser and the parametric properties of the laser beam to describe the law of motion of the laser more accurately. Based on the target track equation, the server analyzes track offset data of the real-time scanning track. By comparing the real-time scan trajectory with the target trajectory equation, the server calculates trajectory offset data, i.e., the difference between the actual trajectory and the target trajectory. These offset data may include positional offset, angular offset, time offset, and the like. For example, assume that there is a laser engraving machine that uses a laser beam to engrave a material. In the laser operation control process, a server collects real-time scanning tracks of the laser, including position and angle information. Meanwhile, the server has established a target trajectory equation, taking into account thermal effects and the parametric properties of the laser beam. The server calculates track offset data by comparing the real-time scan track with the target track equation. The offset data may tell the server the difference between the actual trajectory and the target trajectory and indicate whether the laser is moving as expected. If the track deviation is found to exceed the preset threshold value, the server performs real-time parameter control on the target laser according to the deviation data. For example, if the real-time scanned trajectory shows that the laser's position deviates from the target trajectory, the server corrects the offset by adjusting the laser's motion parameters (e.g., speed, acceleration). Similarly, if the angular offset is large, the server corrects the offset by adjusting the angle of the turntable or mirror of the laser. In addition, if the time offset is large, the server corrects by adjusting the scanning speed of the laser or the timing of the trigger signal. Through real-time track offset data analysis and corresponding parameter control, the server can correct the movement of the laser in time, so that the actual scanning track is consistent with the target track. The real-time parameter control can improve the motion precision and stability of the laser, and ensure that the laser is accurately positioned and irradiated to a target position in the working process.

In the embodiment of the invention, an initial scanning track of a target laser is acquired; performing track type analysis on the initial scanning track to determine the track type; extracting track parameters of the initial scanning track to generate a track parameter set; constructing a track equation of the initial scanning track, and determining a target track equation; calculating a thermal effect index to obtain a thermal effect index set; collecting laser beam parameters to obtain a laser beam parameter set, and generating optical distortion parameters of a target laser; and acquiring a real-time scanning track, analyzing track deviation data of the real-time scanning track through a target track equation based on the thermal effect index set and the laser beam parameter set, generating track deviation data, and controlling real-time parameters of the target laser through the track deviation data. By acquiring an initial scanning track of the target laser and controlling based on the track parameter set and the target track equation, accurate control of the laser can be achieved. This can improve the operation stability and accuracy of the laser, ensuring that the laser beam is scanned in accordance with a predetermined trajectory. The method can be used for analyzing the track type of the initial scanning track, extracting a related track parameter set, and acquiring laser beam parameters and combining calculation of optical distortion parameters to know and correct the optical distortion problem of the laser in the operation process. The quality and focusing effect of the laser beam can be improved, and the real-time parameter adjustment of the target laser can be realized by collecting real-time scanning track data and analyzing and controlling track offset data by combining a thermal effect index set and a laser beam parameter set. The method can improve and correct the track deviation problem caused by thermal effect and optical distortion, and maintain the stability and accuracy of the laser.

In a specific embodiment, the process of executing step S102 may specifically include the following steps:

(1) Selecting control points of the initial scanning track to obtain a plurality of control point coordinates corresponding to the initial scanning track;

(2) Performing curve smoothness analysis on the initial scanning track through a plurality of control point coordinates to generate target curve smoothness;

(3) And performing track type matching on the initial scanning track through the smoothness of the target curve, and determining the track type corresponding to the initial scanning track.

Specifically, the server selects some key positions as control points according to the data points of the initial scanning track. These control points should be well representative of the shape and characteristics of the entire trajectory. The selection of control points may be based on criteria such as local maxima, inflection points, or specific shape characteristics. The server uses the control points to perform curve smoothness analysis on the initial scan trajectory. One common method is to calculate the curvature or second derivative of the curve. By calculating the curvature value of the control point, the server evaluates the smoothness of the trajectory curve. Smaller curvature values represent smoother curves, while larger curvature values represent less smooth curves. Based on the smoothness analysis of the curve, the server further determines the track type of the initial scan track. Different track types have different smoothness characteristics. For example, a straight track typically has a lower curvature value, while a curved track has a higher curvature value. The server matches the initial scan trajectory into a predefined trajectory type by analyzing the curvature value of the control point and the smoothness characteristics of the trajectory. This may be determined by building a trajectory type classifier or based on a pre-defined trajectory model. Summarizing, control point selection and track type analysis of an initial scanning track are realized, the control points are required to be selected, the smoothness of the curve is calculated, and the track type is determined by comparing the smoothness characteristics. These methods can help the server understand and describe the shape and characteristics of the initial scan trajectory, providing a basis for subsequent trajectory analysis and control. Assume that a laser cutter is provided that generates an initial scan trajectory during operation. The server wishes to analyze and classify these trajectories. The server obtains data of the initial scan trajectory from a sensor of the laser cutter. The server processes the data and selects some key positions as control points. The selection of control points may be based on criteria such as light intensity variations, track direction variations, or specific profile characteristics. These control points are used for curve smoothness analysis. The server calculates a curvature value or second derivative between the control points to evaluate the smoothness of the track. Smaller curvature values indicate smoother curves, while larger curvature values indicate more curve changes or jaggies. Based on the smoothness analysis of the curve, the server determines the track type of the initial scan track. For example, for a laser cutter, the server classifies the trajectory into straight cut, arc cut, or free curve cut types, which have different smoothness characteristics. The server matches the initial scan trajectory into a predefined trajectory type by comparing the curvature value of the control point with the smoothness characteristics of the trajectory. Therefore, the server can know the track types generated by the laser cutting machine under different working conditions, and can adjust or optimize corresponding parameters according to the track types.

In a specific embodiment, as shown in fig. 2, the process of performing step S103 may specifically include the following steps:

s201, determining a feature point extraction algorithm through a track type to obtain a target feature point extraction algorithm;

s202, traversing characteristic points of an initial scanning track through a target characteristic point extraction algorithm to obtain a characteristic point set;

s203, performing curvature calculation on each feature point in the feature point set through a difference algorithm to obtain curvature parameters corresponding to each feature point, and taking the curvature parameters corresponding to each feature point as a curvature parameter set;

s204, screening the characteristic points of the characteristic point set through the curvature parameter set to obtain an inflection point coordinate set;

s205, carrying out direction vector calculation on each feature point in the feature point set to obtain a direction parameter set, and combining the curvature parameter set, the inflection point coordinate set and the direction parameter set into a track parameter set.

Specifically, according to the determination of the track type, the server selects a feature point extraction algorithm suitable for the track of the type. Different types of trajectories may correspond to different feature point extraction methods, for example, a straight-line trajectory may use the end points of straight-line segments as feature points, while a circular-arc trajectory may select some specific points on a circular arc as feature points. And selecting a corresponding characteristic point extraction algorithm according to the track type, and obtaining a target characteristic point extraction algorithm by the server. And applying a target characteristic point extraction algorithm on the initial scanning track to traverse the characteristic points. By traversing the points on the track and applying a feature point extraction algorithm, the server obtains a set of feature points that represent important shape information for the track. The server uses a differential algorithm to calculate a curvature parameter for each feature point in the set of feature points. Curvature is a measure that describes the degree of curvature of a curve. The difference algorithm may estimate a curvature value for each feature point by calculating curvature changes between feature points. And obtaining curvature parameters corresponding to each characteristic point by the server through a difference algorithm, and forming the curvature parameters into a curvature parameter set. And the server uses the curvature parameter set to screen the characteristic points of the characteristic point set. By analyzing the magnitude, trend or threshold of curvature parameters, the server identifies inflection points or significant points of change on the curve. These special points can be considered as inflection points of the trajectory, representing turns or bends of the curve. Through the screening process, the server obtains an inflection point coordinate set which contains important turning points on the track. Then, the server performs a direction vector calculation on each feature point in the feature point set to obtain a direction parameter set. The direction vector represents the direction of the curve at each feature point. By calculating the vectors of points around the feature points and the feature points, the server obtains the direction of the curve at the points. And combining the direction vectors of all the characteristic points, and obtaining a direction parameter set by the server. Finally, the server merges the curvature parameter set, the inflection point coordinate set and the direction parameter set into a track parameter set. The track parameter set contains curvature information, inflection point positions and direction information, which are of great significance for further analysis and description of the initial scan track. Through the track parameter set, the server further understands the features and properties of the initial scan track. For example, suppose there is one robot performing path planning on a map. The robot generates initial scan trajectories during motion, and the server wishes to analyze and classify these trajectories. The server determines the track type as a curve type according to the motion mode of the robot and the map environment. Based on this determination, the server selects a feature point extraction algorithm suitable for the curved track. In this case, the server selects the end points based on the curve segments as feature points. The server applies a target feature point extraction algorithm to the initial scan trajectory. By traversing the points on the trajectory and applying the feature point extraction algorithm, the server obtains a set of feature points representing important shape information of the curve. The curvature parameter of each feature point is calculated using a differential algorithm. The difference algorithm may estimate the curvature value by calculating the curvature change between the feature points. Assuming the server has a set of feature points A, B, C, the server calculates the curvature at point B as a curvature parameter. Through this process, the server obtains a set of curvature parameters for each feature point. The server uses the curvature parameter set to perform feature point screening on the feature point set. By analyzing the magnitude and the change trend of the curvature parameter, the server identifies the inflection point of the curve. The inflection point represents a turn or curve of the curve. Through the screening process, the server obtains a set of inflection coordinates. And carrying out direction vector calculation on each characteristic point in the characteristic point set to obtain a direction parameter set. The server calculates the vector of points around each feature point and the feature point, thereby obtaining the direction of the curve at the point. And combining the direction vectors of all the characteristic points, and obtaining a direction parameter set by the server. In this embodiment, the server obtains a track parameter set by performing control point selection, curve smoothness analysis and track type matching on the initial scan track, where the track parameter set includes a curvature parameter set, an inflection point coordinate set and a direction parameter set. These parameter sets may provide a detailed description and analysis of the initial scan trajectory, providing a basis and reference for subsequent trajectory processing and applications.

In a specific embodiment, as shown in fig. 3, the process of executing step S104 may specifically include the following steps:

s301, carrying out parameter weight analysis on the track parameter set to generate a corresponding weight data set;

s302, carrying out weight adjustment on the track parameter set through the weight data set to determine a target parameter set;

s303, constructing a track equation of the initial scanning track through the target parameter set, and determining a corresponding target track equation.

Specifically, when performing parameter weight analysis on a track parameter set, the server needs to consider the importance of each parameter to the track characteristics. This process of weight analysis may be based on domain knowledge, empirical rules, or data analysis. By comprehensively considering the effect and influence of each parameter, the server assigns a corresponding weight value to each parameter. For example, among the curvature parameters, inflection point coordinates, and direction parameters, the server may find the curvature parameters more important for the description of the trajectory shape, and thus may give higher weight. Whereas the inflection point coordinates and direction parameters may be more sensitive to the inflection and direction changes of the trajectory and thus may be given appropriate weight. Through such analysis, the server generates a corresponding set of weight data, including a weight value for each parameter. When the track parameter set is weighted by the weight data set, the server needs to apply a weight value to each parameter. This can be achieved by a simple multiplication operation. Multiplying each parameter by a corresponding weight can adjust its relative importance and degree of influence on the trajectory. For example, if the weight value of a parameter is high, the weight is multiplied Will increase its importance in the target parameter set. By weighting each parameter, the server determines a set of target parameters that reflect the relative contribution of the individual parameters in the trajectory modeling. In constructing the trajectory equation for the initial scan trajectory by the set of target parameters, the server will define the trajectory equation using the determined set of parameters. This may be done according to the selected trajectory model and equation form. The server uses appropriate mathematical models and algorithms to construct the trajectory equations based on the different meanings and roles of the parameters. For example, for a smooth curve fit, a polynomial function or B-spline curve may be used to represent the target trajectory. By combining the set of target parameters with the selected trajectory model, the server builds a target trajectory equation describing the initial scan trajectory. For example, assume that the server has an initial scan trajectory including curvature parameters, inflection coordinates, and direction parameters. The weight data set obtained after the server performs parameter weight analysis is as follows: curvature parameter weight: 0.7; inflection point coordinate weights: 0.3; directional parameter weight: 0.5. and according to the weight values, the server carries out weight adjustment on the track parameter set to determine the target parameter set. Assuming that the curvature parameter of the initial scan trajectory is 0.8, the inflection point coordinates are (2, 4), and the direction parameter is 45 degrees. The server performs weight adjustment by multiplying the corresponding weight value: value of curvature parameter after adjustment: 0.8 x 0.7=0.56; value after inflection point coordinate adjustment: (2, 4) ×0.3= (0.6, 1.2); value of direction parameter after adjustment: 45 x 0.5 = 22.5 degrees. A set of target parameters is obtained: curvature parameter: 0.56; coordinates of inflection points: (0.6, 1.2); direction parameters: 22.5 degrees. The server builds a track equation for the initial scan track using the set of target parameters to determine a corresponding target track equation. Assuming that the server chooses to describe the trajectory using a quadratic polynomial function, the target trajectory equation can be expressed as: y=ax ² +bx+c where a, b, c are coefficients that need to be determined. By correlating with the curvature parameters in the set of target parameters, the server determines the value of a. In association with the inflection point coordinates and the direction parameters, the server determines the values of b and c. For example, assume that the curvature parameter in the target parameter set is 0.56, the inflection point coordinates are (0.6, 1.2), the directionThe parameter was 22.5 degrees. The server constructs the target trajectory equation from these parameters. Let a=0.5, b=1, c=0 be chosen by the server as initial values, and then fine-tuned according to the parameters. Through further calculation and optimization processes, the server obtains a final target track equation: y=0.56 x ² +1x+0 this equation describes the target trajectory of the initial scan trajectory after parameter adjustment. Through the process, the server realizes the steps of parameter weight analysis, weight adjustment and target parameter set determination of the track parameter set, and track equation construction of the initial scanning track through the target parameter set. The method can help the server to accurately establish the target track equation according to the specific weight and parameter relation, thereby realizing accurate control and adjustment of the initial scanning track.

In a specific embodiment, as shown in fig. 4, the process of performing step S105 may specifically include the following steps:

s401, acquiring a temperature data set of a target laser in the operation control process through a temperature sensor;

s402, calculating temperature variation of the temperature data set to obtain a plurality of temperature variation data;

s403, based on the thermal expansion coefficient of the target laser, performing thermal effect index analysis on the target laser through a plurality of temperature change amount data to obtain a thermal effect index set.

Specifically, the server needs to install a temperature sensor to monitor the temperature of the target laser in real time. The temperature sensor can measure the surface temperature or the internal temperature of the laser and provide accurate temperature data. These temperature data may be communicated by sensors to the control system of the laser for real-time acquisition and recording. The server calculates the temperature variation of the temperature data set to obtain a plurality of temperature variation data. The temperature change amount means a change in temperature occurring over a period of time. By comparing the temperature values of the adjacent time points, the server calculates the temperature variation. For example, the server calculates a temperature difference between adjacent time points, or calculates a slope of the temperature to represent a rate of temperature change. In this way, the server obtains a plurality of temperature variation data, reflecting the temperature variation condition of the laser in the operation control process. Based on the thermal expansion coefficient of the target laser, the server performs thermal effect index analysis on the target laser by using a plurality of temperature variation data to obtain a thermal effect index set. The coefficient of thermal expansion is a property of a substance that describes the dimensional change of an object as the temperature changes. Different substances have different coefficients of thermal expansion, so the server needs to determine their coefficients of thermal expansion for the specific materials of the target laser. For example, assume that the target laser is made of an aluminum material, and the server is known to have a coefficient of thermal expansion of 0.000022/°c (degrees celsius). The temperature dataset acquired by the temperature sensor includes the following data points: 25 ℃, 30 ℃, 35 ℃ and 40 ℃. The server calculates the temperature variation between adjacent temperatures: 5 ℃, 5 ℃. The server multiplies the temperature variation amounts by the thermal expansion coefficient of the laser to obtain corresponding thermal effect indexes: 0.00011, 0.00011. As can be seen by this example, during temperature changes, the dimensions of the laser may change slightly, which may have an impact on the performance and accuracy of the laser. The server obtains a set of thermal effect indices by collecting temperature data, calculating the amount of temperature change, and combining the coefficients of thermal expansion. These thermal effect indices reflect the extent of dimensional change of the laser at different temperatures. By analyzing the thermal effect index, the server evaluates the effect of the thermal effect of the laser on its performance and stability and takes corresponding measures to compensate or adjust. In practical application, the temperature sensor continuously monitors the temperature change of the target laser, and transmits temperature data to the control system for processing. The control system may process the temperature data using suitable algorithms and methods, calculate the temperature change, and analyze the thermal effect index in combination with the thermal expansion coefficient of the laser. These analysis results may be provided to a system operator or an automated control system to monitor and adjust system parameters in real time during operation of the laser to minimize adverse effects of thermal effects on the laser performance. For example, in laser cutting systems, the stability of a high power laser is critical to the quality of the process. Variations in the temperature of the laser may cause deviations or distortions of the laser beam, thereby affecting the quality of the cut. The server obtains the thermal effect index set of the laser by collecting temperature data, calculating the temperature variation and combining the thermal expansion coefficient. Based on these indices, the server adjusts the laser control parameters, such as power, frequency, or scan speed, to achieve real-time temperature compensation. Thus, even under the condition of temperature change, the laser can still maintain stable cutting performance, and consistency of processing quality is ensured.

In a specific embodiment, the process of executing step S106 may specifically include the following steps:

(1) Performing shape analysis on a laser beam of the target laser in the laser operation control process to obtain laser beam shape data;

(2) Performing index analysis on a laser beam of a target laser in a laser operation control process by a beam analyzer to generate laser beam index data, wherein the laser beam index data comprises: spot diameter, beam waist position, and divergence angle;

(3) Performing intensity analysis on a laser beam of a target laser in a laser operation control process through a laser beam calibrator, generating laser beam intensity data, and combining the laser beam shape data, the laser beam index data and the laser beam intensity data into a laser beam parameter set;

(4) Generating optical distortion parameters corresponding to the target laser through the laser beam parameter set.

Specifically, laser beam shape analysis was performed. The shape analysis of the laser beam of the target laser is performed by using a suitable optical device, such as a camera or a laser beam analyzer. This may provide spatial distribution and profile information about the laser beam. For example, data of the spot shape, spot diameter, beam waist position, and the like of the laser beam in the lateral and longitudinal directions can be obtained. The server performs laser beam index analysis. By using a beam analyzer, a more detailed index analysis can be performed on the laser beam of the target laser. Common beam indexes include spot diameter, beam waist position, divergence angle and the like. The spot diameter refers to the transverse diameter of the laser beam at a certain position, the beam waist position refers to the position of the minimum diameter of the laser beam in the propagation process, and the divergence angle refers to the divergence degree of the laser beam. Again, laser beam intensity analysis was performed. The laser beam of the target laser is subjected to intensity analysis using a laser beam calibrator. This may provide data about the power distribution and energy density distribution of the laser beam. Laser beam intensity data is critical to precisely controlling the laser machining process and optimizing the output of the laser. Then, the laser beam shape data, the laser beam index data, and the laser beam intensity data are combined into a laser beam parameter set. The parameters in this set represent the key characteristics and properties of the laser beam. By combining these parameters, the laser beam characteristics can be better understood and accurate inputs can be provided for subsequent processing and analysis. The generation of optical distortion parameters for the target laser may be further performed based on the set of laser beam parameters. By analyzing the change rule and the characteristics of the laser beam parameters, the optical distortion condition of the laser output beam can be determined. The optical distortion parameters can be used to correct distortion caused by the laser beam during processing, thereby improving processing quality and accuracy. For example, assuming a target laser, the server would like to perform laser beam parameter analysis and optical distortion correction. The server captures an image of the laser beam using a camera and performs shape analysis. By analyzing the spot profile in the image, the server calculates the diameter and beam waist position of the laser beam. The server uses a beam analyzer to obtain more detailed index data. By measuring the diameter and position of the spot, and measuring the divergence angle of the laser beam, the server obtains more comprehensive index information about the laser beam. The server uses a laser beam calibrator to measure the intensity distribution of the laser beam. This can help the server to know the energy distribution of the laser beam in space, including the intensity and energy density of the spot. The laser beam shape data, the laser beam index data, and the laser beam intensity data are combined into a laser beam parameter set. This set contains detailed information about the laser beam shape, size, divergence and energy distribution. Based on the set of laser beam parameters, the server further analyzes and generates optical distortion parameters. For example, by comparing spot shapes and sizes under different laser beam parameters, the server determines the optical distortion of the laser beam. These distortion parameters can be used to correct shape distortion or offset during laser machining, thereby improving machining accuracy.

In a specific embodiment, the process of performing the step of generating the optical distortion parameters corresponding to the target laser by the set of laser beam parameters may specifically include the steps of:

(1) Performing beam focusing index analysis through the laser beam shape data and the laser beam index data in the laser beam parameter set to obtain a target beam focusing index;

(2) Performing energy distribution analysis through laser beam intensity data in a laser beam parameter set to generate a beam energy distribution result;

(3) And performing optical distortion analysis through the target beam focusing index and the beam energy distribution result to generate optical distortion parameters.

Specifically, the laser beam shape data and the laser beam index data are extracted from the laser beam parameter set. The laser beam shape data may describe a spatial distribution shape of the laser beam, such as an ellipse, a circle, a rectangle, or the like. The laser beam index data comprises parameters such as spot diameter, beam waist position, divergence angle and the like, and is used for quantitatively describing the characteristics of the laser beam. And performing beam focusing index analysis by using the laser beam shape data and the laser beam index data. The beam focusing index is one of important indexes for evaluating focusing ability of a laser beam, and can be measured by calculating a focal length or focusing effect of the laser beam. The target beam focusing index can be obtained by analyzing parameters such as the shape, divergence angle, spot diameter and the like of the laser beam. And performing energy distribution analysis by using the laser beam intensity data in the laser beam parameter set. The laser beam intensity data describes the energy distribution of the laser beam in space. By analyzing the distribution of the laser beam intensity in the lateral and longitudinal directions, an energy density profile or energy profile of the beam can be obtained. And performing optical distortion analysis through the target beam focusing index and the beam energy distribution result. Optical distortion refers to a phenomenon in which a laser beam undergoes a shape change or energy loss during transmission due to non-idealities of an optical system. By comparing the target beam focus index and the beam energy distribution result with the ideal beam, it is possible to analyze the distortion conditions that may exist in the optical system and further calculate the optical distortion parameters. For example, assume that the server performs an optical quality evaluation on one laser cutter. The server collects the laser beam shape data and the laser beam index data of the cutting machine in the laser operation control process, wherein the laser beam shape data and the laser beam index data comprise the circular shape, the spot diameter of the laser beam of 2mm and the divergence angle of 1.5 degrees. The server obtains the focus performance evaluation value of the target beam as good through the beam focus index analysis. The server uses a laser beam calibrator to analyze the intensity of the laser beam of the cutting machine, and an energy distribution diagram of the laser beam is obtained. After analysis of the energy distribution pattern, it was found that the energy distribution was relatively uniform across the cutting plane, with no significant concentration or non-uniform distribution of energy. By comparing the difference between the target beam focus index and the ideal beam focus index and observing the energy profile, the server concludes that the laser cutter performs well optically with less optical distortion. Based on the analysis, the server generates optical distortion parameters, such as degree and type of distortion, to further evaluate and control the performance of the laser cutter. In this embodiment, the optical distortion parameters of the target laser may be generated by collecting the laser beam shape data, the laser beam index data, and the laser beam intensity data, and analyzing and integrating them. These parameters are important for evaluating the performance of the laser, adjusting the optical system, and optimizing the laser machining process.

The method for controlling laser scanning of the laser in the embodiment of the present invention is described above, and the laser scanning control device of the laser in the embodiment of the present invention is described below, referring to fig. 5, one embodiment of the laser scanning control device of the laser in the embodiment of the present invention includes:

the acquisition module 501 is configured to acquire an initial scan trajectory of a target laser, and perform laser operation control on the target laser based on the initial scan trajectory;

the analysis module 502 is configured to perform a track type analysis on the initial scan track, and determine a track type corresponding to the initial scan track;

an extracting module 503, configured to extract a track parameter of the initial scan track according to the track type, and generate a corresponding track parameter set, where the track parameter set includes: curvature parameter set, inflection point coordinate set and direction parameter set;

a construction module 504, configured to construct a track equation for the initial scan track through the track parameter set, and determine a corresponding target track equation;

the calculating module 505 is configured to calculate a thermal effect index of the target laser in a laser operation control process, so as to obtain a thermal effect index set;

The acquisition module 506 is configured to acquire laser beam parameters of the target laser in a laser operation control process, obtain a laser beam parameter set, and generate optical distortion parameters corresponding to the target laser according to the laser beam parameter set;

the generating module 507 is configured to collect a real-time scanning track of the target laser in a laser operation control process, analyze track offset data of the real-time scanning track according to the target track equation based on the thermal effect index set and the laser beam parameter set, generate track offset data, and perform real-time parameter control on the target laser according to the track offset data.

Acquiring an initial scanning track of the target laser through the cooperative cooperation of the components; performing track type analysis on the initial scanning track to determine the track type; extracting track parameters of the initial scanning track to generate a track parameter set; constructing a track equation of the initial scanning track, and determining a target track equation; calculating a thermal effect index to obtain a thermal effect index set; collecting laser beam parameters to obtain a laser beam parameter set, and generating optical distortion parameters of a target laser; and acquiring a real-time scanning track, analyzing track deviation data of the real-time scanning track through a target track equation based on the thermal effect index set and the laser beam parameter set, generating track deviation data, and controlling real-time parameters of the target laser through the track deviation data. By acquiring an initial scanning track of the target laser and controlling based on the track parameter set and the target track equation, accurate control of the laser can be achieved. This can improve the operation stability and accuracy of the laser, ensuring that the laser beam is scanned in accordance with a predetermined trajectory. The method can be used for analyzing the track type of the initial scanning track, extracting a related track parameter set, and acquiring laser beam parameters and combining calculation of optical distortion parameters to know and correct the optical distortion problem of the laser in the operation process. The quality and focusing effect of the laser beam can be improved, and the real-time parameter adjustment of the target laser can be realized by collecting real-time scanning track data and analyzing and controlling track offset data by combining a thermal effect index set and a laser beam parameter set. The method can improve and correct the track deviation problem caused by thermal effect and optical distortion, and maintain the stability and accuracy of the laser.

The laser scanning control device of the laser in the embodiment of the present invention is described in detail from the point of view of the modularized functional entity in fig. 5 above, and the laser scanning control apparatus of the laser in the embodiment of the present invention is described in detail from the point of view of hardware processing below.

Fig. 6 is a schematic structural diagram of a laser scanning control device of a laser according to an embodiment of the present invention, where the laser scanning control device 600 may have a relatively large difference due to different configurations or performances, and may include one or more processors (central processing units, CPU) 610 (e.g., one or more processors) and a memory 620, and one or more storage media 630 (e.g., one or more mass storage devices) storing application programs 633 or data 632. Wherein the memory 620 and the storage medium 630 may be transitory or persistent storage. The program stored in the storage medium 630 may include one or more modules (not shown), each of which may include a series of instruction operations in the laser scanning control apparatus 600 of the laser. Still further, the processor 610 may be configured to communicate with the storage medium 630 and execute a series of instruction operations in the storage medium 630 on the laser scanning control device 600 of the laser.

The laser scan control device 600 of the laser may also include one or more power supplies 640, one or more wired or wireless network interfaces 650, one or more input/output interfaces 660, and/or one or more operating systems 631, such as Windows Serve, mac OS X, unix, linux, freeBSD, etc. It will be appreciated by those skilled in the art that the laser scanning control apparatus structure of the laser shown in fig. 6 does not constitute a limitation of the laser scanning control apparatus of the laser, and may include more or fewer components than shown, or may combine certain components, or may be arranged in different components.

The invention also provides a laser scanning control device of the laser, which comprises a memory and a processor, wherein the memory stores computer readable instructions, and the computer readable instructions, when executed by the processor, cause the processor to execute the steps of the laser scanning control method of the laser in the above embodiments.

The present invention also provides a computer readable storage medium, which may be a non-volatile computer readable storage medium, and may also be a volatile computer readable storage medium, where instructions are stored in the computer readable storage medium, when the instructions are executed on a computer, cause the computer to perform the steps of the laser scanning control method of the laser.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random acceS memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. The laser scanning control method of the laser is characterized by comprising the following steps of:

2. The method for controlling laser scanning according to claim 1, wherein the performing track type analysis on the initial scanning track to determine the track type corresponding to the initial scanning track includes:

3. The method according to claim 1, wherein the extracting track parameters from the initial scan track by the track type generates a corresponding track parameter set, and the track parameter set includes: curvature parameter set, inflection point coordinate set and direction parameter set, include:

4. The method according to claim 1, wherein the performing track equation construction on the initial scan track by the track parameter set, determining a corresponding target track equation, includes:

5. The method according to claim 1, wherein calculating the thermal effect index of the target laser during the laser operation control process, to obtain a thermal effect index set, comprises:

6. The method for controlling laser scanning of a laser according to claim 1, wherein the step of collecting the laser beam parameters of the target laser in the laser operation control process to obtain a laser beam parameter set, and generating the optical distortion parameters corresponding to the target laser by using the laser beam parameter set includes:

7. The method according to claim 6, wherein generating the optical distortion parameter corresponding to the target laser by the laser beam parameter set comprises:

8. A laser scanning control device of a laser, characterized in that the laser scanning control device of the laser comprises:

9. A laser scanning control apparatus of a laser, characterized in that the laser scanning control apparatus of the laser comprises: a memory and at least one processor, the memory having instructions stored therein;

the at least one processor invokes the instructions in the memory to cause a laser scan control device of the laser to perform the laser scan control method of the laser of any of claims 1-7.

10. A computer readable storage medium having instructions stored thereon, which when executed by a processor, implement a laser scanning control method of a laser according to any of claims 1-7.