An intelligent control system for laser cutting based on the Internet of Things

Abstract

The present invention belongs to the technical field of laser cutting data control, and specifically discloses a laser cutting intelligent control system based on the Internet of Things, including: obtaining control instructions of a laser cutting head at each cutting position, identifying the cross-sectional morphology characteristics of the previous cutting position, analyzing the control instruction compensation index of the next transition position, and optimizing and evaluating the control instruction of the next transition position based on this; by identifying the transition skill level of each section belonging to the cutting sub-path of each transition position, analyzing the cross-sectional distortion level of each transition position of the laser cutting target, and determining the cutting effect index of the laser cutting target based on this; by analyzing the cutting energy consumption of each transition position of the laser cutting target and the stability index of the laser cutting operation, calculating the cutting energy consumption ratio of each transition position, and at the same time, predicting the cutting energy consumption ratio of the laser cutting head under the control instruction control of each transition position, and comparing to determine the threshold value of the corresponding optimization amplitude of the instruction change.

Description

Technical Field

[0001] The present invention belongs to the technical field of laser cutting data regulation and control, and relates to a laser cutting intelligent control system based on the Internet of Things. Background Art

[0002] The birth and development of laser cutting technology is an important milestone in the field of modern manufacturing. This technology has the characteristics of high beam focusing, high energy density and strong penetration, which is very suitable for high-precision cutting of materials. With the continuous maturity and advancement of laser technology, laser cutting has gradually moved from laboratories to industrial applications and has become an indispensable part of modern manufacturing. However, with the continuous development of industrial production and the continuous advancement of materials science, traditional laser cutting equipment has gradually revealed its limitations in automation and intelligence, and it is difficult to meet the urgent needs of modern manufacturing for efficient, precise and flexible cutting. Against this background, the research and development of laser cutting intelligent control systems came into being.

[0003] In the prior art, there are also some related solutions involving flexible regulation of laser cutting data. For example, the Chinese patent publication number CN115041828A discloses a laser cutting control method, system and cutting controller, which obtains updated cutting parameters according to the current cutting speed and target cutting parameters through the cutting device, uses the updated cutting parameters as the updated target cutting parameters, repeatedly executes the cutting according to the target cutting parameters, and detects the current cutting speed in real time, so that the cutting device outputs the updated target laser energy corresponding to the current cutting speed according to the current cutting speed at the starting point, end point or corner of the target cutting trajectory to perform laser cutting on the object to be cut, avoids uneven cutting at the cutting edge of the object to be cut, and finally achieves consistency of laser cutting.

[0004] Although the above scheme proposes some solutions for flexible control of laser cutting data, it still has the following limitations. Specifically, the existing technology is more focused on fixed-focus cutting of cutting materials of uniform thickness, without considering over-cutting or under-cutting caused by specified focused cutting at different thickness positions of the same plate, and without considering the smoothness of data conversion between different bevel point positions on different cutting surfaces.

[0005] In addition, the existing technology is limited to regulating the cutting parameters with deviations, does not consider the energy consumption of the actual cutting state, and does not determine the threshold value of the deviation parameter regulation requirement based on the energy consumption. Summary of the invention

[0006] In view of this, in order to solve the problems raised in the above background technology, a laser cutting intelligent control system based on the Internet of Things is proposed.

[0007] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides a laser cutting intelligent control system based on the Internet of Things, the system comprising: a cutting data acquisition module, used to obtain the cutting generation contour of the laser cutting target, and obtain the current size contour of the laser cutting target, and compare and generate control instructions for each cutting position and the laser cutting head at the corresponding position.

[0008] The cutting state detection module is used to determine the cutting trajectory of the laser cutting target, extract the starting cutting position and each transition position therefrom, perform the cutting operation on the starting cutting position, and analyze the control instruction compensation index of the corresponding transition position of the starting cutting position.

[0009] The control instruction optimization module is used to obtain the contour information of the transition position corresponding to the starting cutting position, optimize and evaluate the control instructions of the transition position corresponding to the starting cutting position, and then optimize and evaluate the control instructions of each transition position in the same way. Each transition position is numbered .

[0010] The overall profile evaluation module is used to detect the cutting surface level at each transition position and analyze the cutting effect indicators of the laser cutting target.

[0011] The cutting energy consumption analysis module is used to obtain the optimization instructions for each transition position of the laser cutting target, calculate the cutting energy consumption ratio of each transition position of the laser cutting target, and determine the threshold value of the corresponding optimization amplitude of the instruction change.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention obtains the control instructions of the laser cutting head at each cutting position to determine the cutting trajectory of the laser cutting target, and then analyzes the control instruction compensation index of the next transition position by identifying the cross-sectional morphology characteristics of the previous cutting position. Based on this, the control instruction of the next transition position is optimized and evaluated, various variables in the cutting process are monitored in real time, and system parameters are dynamically adjusted according to real-time data to ensure the stability and reliability of the cutting process.

[0013] (2) The present invention analyzes the cross-sectional distortion level of each transition position of the laser cutting target by identifying the transition skill level of each segment belonging to the cutting sub-path at each transition position of the laser cutting target, and determines the cutting effect index of the laser cutting target accordingly, which helps to optimize the laser cutting strategy in a targeted manner, such as adjusting parameters such as cutting speed, to reduce distortion and improve cutting quality.

[0014] (3) The present invention calculates the cutting energy consumption ratio of each transition position by analyzing the cutting energy consumption of each transition position of the laser cutting target and the stability index of the laser cutting operation, which can identify the area or operation stage with high energy consumption, thereby helping to optimize the control instructions, reduce unnecessary energy consumption, and achieve the goal of energy conservation and emission reduction, which is of great significance for reducing production costs and improving resource utilization efficiency. At the same time, by predicting the cutting energy consumption ratio of the laser cutting head under the control of the control instructions at each transition position, and comparing and determining the threshold value of the corresponding optimization amplitude of the instruction change, a more scientific and reasonable instruction change strategy can be formulated. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0016] Figure 1 This is a schematic diagram of the connection of the system modules of the present invention.

[0017] Figure 2 This is a diagram showing the cutting angle of the present invention.

[0018] Figure 3 This is a structural display diagram of the present invention in which the cutting sub-path is divided into uniform sections.

[0019] Reference numerals: 1. Cutting surface corresponding to the transition position between the laser cutting head and the laser cutting target, 2. Cutting sub-path. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] See also Figure 1 As shown, the present invention provides a laser cutting intelligent control system based on the Internet of Things, which includes: a cutting data acquisition module, a cutting state detection module, a control instruction optimization module, an overall contour evaluation module, and a cutting energy consumption analysis module.

[0022] The cutting data acquisition module is connected to the cutting state detection module, the cutting state detection module is connected to the control instruction optimization module, and the control instruction optimization module is respectively connected to the overall contour evaluation module and the cutting energy consumption analysis module.

[0023] The cutting data acquisition module is used to obtain the cutting generation contour of the laser cutting target and the current size contour of the laser cutting target, and compare and generate control instructions for each cutting position and the laser cutting head at the corresponding position.

[0024] In a preferred embodiment, the control instructions include fixed parameter instructions and dynamic parameter instructions.

[0025] The fixed parameter instructions include cutting angles and cutting sub-paths.

[0026] The dynamic parameter instructions include focus parameters and cutting speed.

[0027] The focus parameters refer to the focus position, focal length, and power density.

[0028] The cutting state detection module is used to determine the cutting trajectory of the laser cutting target according to the control instructions of the laser cutting head at each cutting position, extract the starting cutting position and each transition position therefrom, and after performing the cutting operation on the starting cutting position, analyze the control instruction compensation index of the starting cutting position corresponding to the transition position.

[0029] The starting cutting position refers to the first cutting position corresponding to the cutting trajectory of the laser cutting target, and the transition positions refer to all cutting positions except the first cutting position.

[0030] In a preferred embodiment, the steps of determining the cutting trajectory of the laser cutting target include: extracting the control instructions of the laser cutting head at each cutting position, constructing different transposition paths for all cutting positions, calculating the complexity of the operation changes when all cutting positions are cut according to different transposition paths, and then screening out the transposition path with the minimum complexity of the operation instructions, and determining it as the cutting trajectory of the laser cutting target.

[0031] The complexity of the operation change is determined by the comprehensive change degree of the control instructions when all cutting positions are transposed. The specific method of obtaining the comprehensive change degree of the control instructions when the cutting positions are transposed is: extract the adjacent cutting positions of the cutting position from the transposition path of all cutting positions, compare the cutting angle, focus parameters, and cutting speed of the cutting position with the corresponding cutting angle, focus parameters, and cutting speed of the adjacent cutting position, take the difference and compare it with the corresponding preset reference difference, and then sum it to obtain the comprehensive change degree of the control instructions of the cutting position and its adjacent cutting positions.

[0032] In a further preferred embodiment, the analysis of the control instruction compensation index of the transition position corresponding to the starting cutting position includes: obtaining a preset cross-sectional feature group corresponding to excessive cross-sectional burning and insufficient cross-sectional cutting, detecting the corresponding cross-sectional morphological features of the starting cutting position of the laser cutting target through a CNN algorithm, matching the preset cross-sectional feature group corresponding to excessive cross-sectional burning and insufficient cross-sectional cutting with the corresponding cross-sectional morphological features of the starting cutting position of the laser cutting target respectively, assigning weights to the features of the preset cross-sectional feature group corresponding to excessive cross-sectional burning and insufficient cross-sectional cutting matching the corresponding cross-sectional morphological features of the starting cutting position of the laser cutting target, such as assigning a value of 1, summing up to obtain a comprehensive assignment between excessive cross-sectional burning and insufficient cross-sectional cutting and the starting cutting position of the laser cutting target, comparing and screening out the cross-sectional cutting attribute to which the maximum assignment value belongs, and recording it as the cross-sectional cutting attribute corresponding to the starting cutting position of the laser cutting target, and the cross-sectional cutting attribute is excessive cross-sectional burning and insufficient cross-sectional cutting.

[0033] The preset cross-section feature group corresponding to the excessive cross-section burning is as follows: rough cross-section and accumulation of molten metal.

[0034] The cross-section cutting is insufficient for the corresponding preset cross-section feature group, such as: dross phenomenon, residues formed on the edge of the cross section, and blurred cutting lines.

[0035] When the cross-sectional cutting attribute corresponding to the starting cutting position of the laser cutting target is excessive cross-sectional burn, the preset change direction of the control instruction corresponding to the excessive cross-sectional burn is obtained, and the cross-sectional morphological features corresponding to the starting cutting position of the laser cutting target are compared with the preset standard cross-sectional morphological features of the laser cutting target to obtain the cross-sectional morphological feature difference value corresponding to the starting cutting position of the laser cutting target, and then obtain the ratio of the cross-sectional morphological feature difference value to the preset reference feature difference value to obtain the cross-sectional difference coefficient corresponding to the starting cutting position of the laser cutting target as the control instruction compensation index for the transition position corresponding to the starting cutting position.

[0036] The change direction is parameter increase or parameter decrease.

[0037] The preset change direction of the operation instruction corresponding to the excessive section burn is as follows: focus parameter down-adjustment and cutting speed up-adjustment.

[0038] When the section cutting attribute corresponding to the starting cutting position of the laser cutting target is insufficient section cutting, the preset change direction of the corresponding control instruction of insufficient section cutting is obtained, and similarly, the control instruction compensation index of the corresponding transition position of the starting cutting position is identified. .

[0039] The cross-section cutting is not in accordance with the preset change direction of the corresponding control instruction, such as: focus parameter increase, cutting speed decrease.

[0040] The control instruction optimization module is used to obtain the contour information of the transition position corresponding to the starting cutting position, optimize and evaluate the control instructions of the transition position corresponding to the starting cutting position, and then optimize and evaluate the control instructions of each transition position in the same way. Each transition position is numbered .

[0041] The optimizing and evaluating the control instructions for the transition positions corresponding to the starting cutting position specifically includes optimizing and evaluating the dynamic parameter instructions belonging to the control instructions for the transition positions corresponding to the starting cutting position.

[0042] See also Figure 2 As shown, in a preferred embodiment, the control instructions for the transition position corresponding to the starting cutting position are optimized and evaluated, including: obtaining the material thickness of the transition position corresponding to the starting cutting position from the current size contour of the laser cutting target, and obtaining the cutting angle in the fixed parameter instruction.

[0043] The cutting angle refers to: the angle between the cutting surface corresponding to the transition position between the laser cutting head and the laser cutting target and the vertical surface.

[0044] The material thickness of the transition position corresponding to the starting cutting position is matched with the preset focus parameters corresponding to each material thickness to obtain the focus parameters corresponding to the transition position. , and then determine the corresponding optimization evaluation value of the focus parameter of the transition position corresponding to the starting cutting position , where Indicates that the preset change direction of the control instruction of the corresponding section cutting attribute of the laser cutting target starting cutting position is parameter downward adjustment. Indicates that the preset change direction of the control instruction of the section cutting attribute corresponding to the starting cutting position of the laser cutting target is parameter increase.

[0045] See also Figure 3 As shown in FIG. 1 , the cutting sub-path at the transition position corresponding to the starting cutting position is divided into uniform segments with a fixed distance as a node, the contour curvature of each segment on the cutting sub-path is identified, and the cutting angle of each segment on the cutting sub-path is extracted. , evaluate the transition skill level of each segment of the cutting subpath corresponding to the transition position of the starting cutting position , and then determine the corresponding optimization evaluation value of the cutting speed at the transition position corresponding to the starting cutting position , where Indicates the preset unit transition skill level corresponding to the expected cutting speed, Indicates the preset convenient cutting angle. Indicates the number of the segment on the cutting subpath, , Indicates the number of segments on the cutting subpath.

[0046] The present invention obtains the control instructions of the laser cutting head at each cutting position to determine the cutting trajectory of the laser cutting target, and then analyzes the control instruction compensation index of the next transition position by identifying the cross-sectional morphology characteristics of the previous cutting position. Based on this, the control instruction of the next transition position is optimized and evaluated, various variables in the cutting process are monitored in real time, and system parameters are dynamically adjusted according to real-time data to ensure the stability and reliability of the cutting process.

[0047] The overall profile evaluation module is used to detect the cutting surface level of each transition position, analyze the cutting effect index of the laser cutting target, and provide effect feedback.

[0048] In a preferred embodiment, the analysis of the cutting effect index of the laser cutting target includes: detecting the corresponding cross-sectional morphology characteristics of each transition position of the laser cutting target through the CNN algorithm, based on the acquisition method of the corresponding cross-sectional differential coefficient of the starting cutting position of the laser cutting target, similarly acquiring the corresponding cross-sectional differential coefficient of each transition position of the laser cutting target .

[0049] Based on the method of obtaining the transition skill level of each segment of the cutting sub-path corresponding to the transition position of the starting cutting position, the transition skill level of each segment of the cutting sub-path corresponding to each transition position of the laser cutting target is obtained in the same way. .

[0050] Analyze the cross-sectional distortion level of each transition position of the laser cutting target , based on which the cutting effect index of the laser cutting target is determined ,in Indicates the preset cross-section reference distortion level.

[0051] The present invention analyzes the cross-sectional distortion level of each transition position of the laser cutting target by identifying the transition skill level of each segment belonging to the cutting sub-path of each transition position of the laser cutting target, and determines the cutting effect index of the laser cutting target accordingly, which helps to optimize the laser cutting strategy in a targeted manner, such as adjusting parameters such as cutting speed, to reduce distortion and improve cutting quality.

[0052] The cutting energy consumption analysis module is used to obtain the optimization instructions for each transition position of the laser cutting target, calculate the cutting energy consumption ratio of each transition position of the laser cutting target, and determine the threshold value of the corresponding optimization amplitude of the instruction change, which is used as the instruction optimization change standard. Therefore, in the subsequent laser cutting control process, there is no need to optimize and change the transition position where the optimization instruction is lower than the threshold value of the corresponding optimization amplitude of the instruction change.

[0053] In a preferred embodiment, the calculation of the cutting energy consumption ratio of each transition position of the laser cutting target includes: taking the focus parameter of the control instruction of the transition position corresponding to the starting cutting position for optimizing the evaluation and the corresponding optimized evaluation value of the cutting speed as the optimization instruction of the transition position, thereby obtaining the optimization instruction of each transition position, and analyzing the cutting energy consumption of each transition position of the laser cutting target according to the corresponding optimized evaluation value of the optimization instruction of each transition position of the laser cutting target. and stability indicators of laser cutting operations .

[0054] Convert the optimization evaluation value of the optimization instruction at each transition position into a normalized metric , calculate the cutting energy consumption ratio of each transition position .

[0055] The method of converting the optimization evaluation value corresponding to the optimization instruction of each transition position into a normalized metric is specifically as follows: setting the basic operating values ​​of the focus parameters and the cutting speed, comparing the optimization evaluation values ​​corresponding to the focus parameters and the cutting speed of each transition position with them, obtaining the corresponding control gain of the focus parameters and the cutting speed of each transition position, and further summing them to obtain the normalized metric corresponding to the optimization instruction of each transition position.

[0056] In a further preferred embodiment, the detection of the cutting energy consumption at each transition position includes: extracting the actual control value of the focus parameter at each transition position from the laser cutting control platform, screening out the corresponding power density of the optimization instruction of the laser cutting target at each transition position, multiplying it with the preset power consumption conversion factor, and obtaining the power consumption at each transition position. .

[0057] Get the length of the corresponding cutting sub-path at each transition position and cutting depth , evaluate the cutting energy consumption at each transition position , where They represent the preset path reference length and cutting reference depth respectively, and e is a natural constant.

[0058] In a further preferred embodiment, the specific detection method of the stability index of the laser cutting operation is: obtaining the corresponding actual control value of the optimization instruction of each transition position from the laser cutting control platform, comparing it with the corresponding optimization evaluation value of the optimization instruction of the corresponding transition position, and obtaining the optimization instruction difference value of each transition position .

[0059] Compare the actual control values ​​corresponding to the optimization instructions of each transition position with each other and take the mean square error , determine the stability index of laser cutting operation .

[0060] In a further preferred embodiment, the determination instruction changes the threshold of the corresponding optimization amplitude, including: according to the optimization instruction corresponding to each transition position, the power energy consumption acquisition method is used to control the optimization evaluation value, and similarly, the cutting energy consumption of the laser cutting head is obtained under the control instruction of each transition position. .

[0061] According to the optimization instruction of each transition position, the corresponding optimization evaluation value is converted into a normalized measurement acquisition method. Similarly, the normalized measurement of the laser cutting head under the control instruction of each transition position is obtained. .

[0062] Calculate the cutting energy consumption ratio of the laser cutting head under the control command control at each transition position , where The angle of the laser cutting target is 180 degrees, and the cutting energy consumption ratio of the corresponding transition position is compared with that of the laser cutting target to obtain the cutting efficiency enhancement ratio of each transition position.

[0063] The difference between the corresponding optimization evaluation value of the optimization instruction of each transition position and its control instruction is obtained and recorded as the optimization amplitude of each transition position.

[0064] The optimized amplitudes of each transition position are sorted in ascending order, and then the optimized amplitudes of the corresponding transition positions after sorting are used as the horizontal coordinate, and the cutting efficiency enhancement ratio of each transition position is used as the vertical coordinate to construct an optimized efficiency relationship curve, and identify the contour characteristics of the optimized efficiency relationship curve, including progressive characteristics and fluctuation characteristics.

[0065] The method for identifying the contour characteristics of the optimization performance relationship curve is: identifying the number of peak points and the number of valley points in the optimization performance relationship curve. If the number of peak points or the number of valley points is greater than 1, the contour characteristics of the optimization performance relationship curve are recorded as fluctuation characteristics; if the number of peak points and the number of valley points are both 1, the contour characteristics of the optimization performance relationship curve are recorded as progressive characteristics.

[0066] When the contour characteristic of the optimization performance relationship curve is a progressive characteristic, the progressive point in the curve is extracted, and the horizontal coordinate of the progressive point is obtained as the threshold of the corresponding optimization amplitude of the instruction change; when the contour characteristic of the optimization performance relationship curve is a fluctuating characteristic, the valley points in the curve are extracted, and the horizontal coordinate of each valley point is obtained, and the average is calculated as the threshold of the corresponding optimization amplitude of the instruction change.

[0067] The method for extracting the asymptotic point in the curve is as follows: obtaining the curvature of each coordinate point in the optimization performance relationship curve, selecting the first coordinate point whose curvature is within a specified curvature range, and recording it as the asymptotic point in the curve, wherein the specified curvature range refers to the asymptotic point in the curve. The range of values ​​close to 0 but not 0.

[0068] The present invention calculates the cutting energy consumption ratio of each transition position by analyzing the cutting energy consumption of each transition position of the laser cutting target and the stability index of the laser cutting operation, which can identify the area or operation stage with higher energy consumption, thereby helping to optimize the control instructions, reduce unnecessary energy consumption, and achieve the goal of energy conservation and emission reduction, which is of great significance for reducing production costs and improving resource utilization efficiency. At the same time, by predicting the cutting energy consumption ratio of the laser cutting head under the control of the control instructions of each transition position, and comparing and determining the threshold of the corresponding optimization amplitude of the instruction change, a more scientific and reasonable instruction change strategy can be formulated. When it is monitored that the corresponding optimization amplitude of the instruction change does not exceed the threshold, the system does not trigger the instruction change, thereby avoiding the ineffective increase of energy consumption.

[0069] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A laser cutting intelligent control system based on the Internet of Things, characterized in that: The system includes: The cutting data acquisition module is used to obtain the cutting generation contour of the laser cutting target and the current size contour of the laser cutting target, and compare and generate the control instructions of each cutting position and the laser cutting head at the corresponding position; The cutting state detection module is used to determine the cutting trajectory of the laser cutting target, extract the starting cutting position and each transition position, perform the cutting operation on the starting cutting position, and analyze the control instruction compensation index of the corresponding transition position of the starting cutting position; The starting cutting position refers to the first cutting position corresponding to the cutting trajectory of the laser cutting target, and the transition positions refer to all cutting positions except the first cutting position; The control instruction optimization module is used to obtain the contour information of the transition position corresponding to the starting cutting position, optimize and evaluate the control instructions of the transition position corresponding to the starting cutting position, and then optimize and evaluate the control instructions of each transition position in the same way. Each transition position is numbered ; The overall profile evaluation module is used to detect the cutting surface level at each transition position and analyze the cutting effect index of the laser cutting target; A cutting energy consumption analysis module is used to obtain the optimization instructions for each transition position of the laser cutting target, calculate the cutting energy consumption ratio of each transition position of the laser cutting target, and determine the threshold value of the corresponding optimization amplitude of the instruction change; The control instruction compensation index of the transition position corresponding to the starting cutting position is analyzed, and the content includes: obtaining a preset section feature group corresponding to excessive section burn and insufficient section cut, detecting the section morphology characteristics corresponding to the starting cutting position of the laser cutting target by a CNN algorithm, and comparing and identifying the section cutting attributes corresponding to the starting cutting position of the laser cutting target, where the section cutting attributes are excessive section burn and insufficient section cut; When the cutting attribute of the section corresponding to the starting cutting position of the laser cutting target is excessive section burn, the preset change direction of the control instruction corresponding to the excessive section burn is obtained, and the cross-sectional morphological characteristics corresponding to the starting cutting position of the laser cutting target are compared with the preset standard cross-sectional morphological characteristics of the laser cutting target to obtain the cross-sectional morphological characteristic difference value corresponding to the starting cutting position of the laser cutting target, and then the ratio of the cross-sectional morphological characteristic difference value to the preset reference characteristic difference value is obtained to obtain the cross-sectional difference coefficient corresponding to the starting cutting position of the laser cutting target as the control instruction compensation index of the corresponding transition position of the starting cutting position. ; When the section cutting attribute corresponding to the starting cutting position of the laser cutting target is insufficient section cutting, the preset change direction of the corresponding control instruction of insufficient section cutting is obtained, and similarly, the control instruction compensation index of the corresponding transition position of the starting cutting position is identified. ; The optimization evaluation of the control instruction of the transition position corresponding to the starting cutting position includes: obtaining the material thickness of the transition position corresponding to the starting cutting position, and obtaining the cutting angle in the fixed parameter instruction; The material thickness of the transition position corresponding to the starting cutting position is matched with the preset focus parameters corresponding to each material thickness to obtain the focus parameters corresponding to the transition position. , and then determine the corresponding optimization evaluation value of the focus parameter of the transition position corresponding to the starting cutting position , where Indicates that the preset change direction of the control instruction of the corresponding section cutting attribute of the laser cutting target starting cutting position is parameter downward adjustment. Indicates that the preset change direction of the control instruction of the section cutting attribute corresponding to the starting cutting position of the laser cutting target is parameter increase; Taking the fixed distance as the node, the cutting sub-path corresponding to the transition position of the starting cutting position is divided into uniform segments, the contour curvature of each segment on the cutting sub-path is identified, and the cutting angle of each segment on the cutting sub-path is extracted. , evaluate the transition skill level of each segment of the cutting subpath corresponding to the transition position of the starting cutting position , and then determine the corresponding optimization evaluation value of the cutting speed at the transition position corresponding to the starting cutting position , where Indicates the preset unit transition skill level corresponding to the expected cutting speed, Indicates the preset convenient cutting angle. Indicates the number of the segment on the cutting subpath, , Indicates the number of segments on the cutting subpath.

2. According to the Internet of Things-based laser cutting intelligent control system of claim 1, it is characterized in that: The control instructions include fixed parameter instructions and dynamic parameter instructions; The fixed parameter instructions include cutting angle and cutting sub-path; The dynamic parameter instructions include focus parameters and cutting speed.

3. According to the Internet of Things-based laser cutting intelligent control system of claim 1, it is characterized in that: The steps of determining the cutting trajectory of the laser cutting target include: extracting the control instructions of the laser cutting head at each cutting position, constructing different transposition paths for all cutting positions, calculating the complexity of operation changes when cutting is controlled at all cutting positions according to different transposition paths, and then screening out the transposition path with the minimum complexity of the operation instructions, and determining it as the cutting trajectory of the laser cutting target.

4. According to the Internet of Things-based laser cutting intelligent control system of claim 1, it is characterized in that: The analysis of the cutting effect index of the laser cutting target includes: detecting the corresponding cross-sectional morphological features of each transition position of the laser cutting target through the CNN algorithm, and obtaining the corresponding cross-sectional differential coefficients of each transition position of the laser cutting target ; Obtain the transition skill level of each segment of the cutting subpath at each transition position of the laser cutting target ; Analyze the cross-sectional distortion level of each transition position of the laser cutting target , based on which the cutting effect index of the laser cutting target is determined ,in Indicates the preset cross-section reference distortion level.

5. According to the Internet of Things-based laser cutting intelligent control system of claim 1, it is characterized in that: The calculation of the cutting energy consumption ratio of each transition position of the laser cutting target includes: obtaining the optimization instructions of each transition position, analyzing the cutting energy consumption of each transition position of the laser cutting target and stability indicators of laser cutting operations ; Convert the optimization evaluation value of the optimization instruction at each transition position into a normalized metric , calculate the cutting energy consumption ratio of each transition position .

6. The intelligent laser cutting control system based on Internet of Things according to claim 5 is characterized in that: The detection of the cutting energy consumption at each transition position includes: extracting the actual control value of the focus parameter at each transition position, screening out the corresponding power density of the optimization instruction of the laser cutting target at each transition position, multiplying it with the preset power consumption conversion factor, and obtaining the power consumption at each transition position. ; Get the length of the corresponding cutting sub-path at each transition position and cutting depth , evaluate the cutting energy consumption at each transition position , where They represent the preset path reference length and cutting reference depth respectively, and e is a natural constant.

7. The intelligent laser cutting control system based on Internet of Things according to claim 6 is characterized in that: The specific detection method of the stability index of the laser cutting operation is: obtain the corresponding actual control value of the optimization instruction of each transition position, compare it with the corresponding optimization evaluation value of the optimization instruction of the corresponding transition position, and obtain the optimization instruction difference value of each transition position ; Compare the actual control values ​​corresponding to the optimization instructions of each transition position with each other and take the mean square error , determine the stability index of laser cutting operation .

8. The intelligent laser cutting control system based on the Internet of Things according to claim 1 is characterized in that: The determination of the threshold of the corresponding optimization amplitude of the instruction change includes: calculating the cutting energy consumption ratio of the laser cutting head under the control of the control instruction at each transition position, comparing it with the cutting energy consumption ratio of the transition position corresponding to the laser cutting target, and obtaining the cutting efficiency enhancement ratio of each transition position; Obtain the difference between the corresponding optimization evaluation value of the optimization instruction of each transition position and its control instruction, and record it as the optimization amplitude of each transition position; The optimized amplitudes of each transition position are sorted in ascending order, and then the optimized amplitudes of each transition position after sorting are used as the horizontal coordinates, and the cutting efficiency enhancement ratios of each transition position are used as the vertical coordinates to construct an optimized efficiency relationship curve, and the contour characteristics of the optimized efficiency relationship curve are identified, including progressive characteristics and fluctuation characteristics; When the contour characteristic of the optimization performance relationship curve is a progressive characteristic, the progressive point in the curve is extracted, and the horizontal coordinate of the progressive point is obtained as the threshold of the corresponding optimization amplitude of the instruction change; when the contour characteristic of the optimization performance relationship curve is a fluctuating characteristic, the valley points in the curve are extracted, and the horizontal coordinate of each valley point is obtained, and the average is calculated as the threshold of the corresponding optimization amplitude of the instruction change.

Images