Control Strategy Optimization Method for Laser Cutting Machines

Abstract

The present invention discloses a method for optimizing the control strategy of a laser cutting machine, which relates to the technical field of laser cutting. The method includes: constructing an operating data space of the laser cutting machine to obtain the cutting design information of the target laser cutting machine and the workpiece; performing matching retrieval on the operating data to establish an operating data memory bank; dividing the workpiece into equivalent regions based on the cutting design information to obtain an equivalent cutting region set; analyzing the control strategy to determine a control strategy parameter set; using the control strategy parameter set to perform cutting feedback detection on the laser cutting machine to obtain a sensed feedback data stream; performing optimization analysis on the control strategy parameter set to determine an optimized control strategy parameter set, and implementing strategy optimization control on the laser cutting machine. The technical problem that the cutting quality of the existing laser cutting machine is unstable under different workpieces and cutting conditions is solved, and the technical effects of improving the optimization accuracy of the control strategy and maintaining the stability of the cutting quality are achieved.

Description

Technical Field

[0001] This application relates to the field of laser cutting technology, and particularly to a method for optimizing the control strategy for a laser cutting machine. Background Art

[0002] As an efficient and precise cutting method in modern manufacturing, laser cutting technology is widely used in many fields such as metal processing, electronic manufacturing, automotive manufacturing, aerospace, and art design. However, with the continuous improvement of industrial production requirements, traditional laser cutting machines face many technical challenges in terms of control strategies. Existing control methods usually rely on preset parameters and lack the ability to dynamically adapt to the characteristics of different workpieces and real-time cutting conditions, resulting in low cutting efficiency and unstable cutting quality. This limits the application effect and production efficiency of laser cutting technology in complex and diverse cutting tasks.

[0003] In the related technologies at the present stage, there is a technical problem that the cutting quality of laser cutting machines is unstable under different workpieces and cutting conditions. Summary of the Invention

[0004] This application solves the technical problem that the cutting quality of existing laser cutting machines is unstable under different workpieces and cutting conditions by providing a method for optimizing the control strategy for a laser cutting machine.

[0005] This application provides a method for optimizing the control strategy for a laser cutting machine, including:

[0006] Constructing a laser cutting machine operation data space through data mining technology and obtaining the cutting design information of the target laser cutting machine and the target workpiece; performing matching retrieval on the laser cutting machine operation data space according to the attribute information of the target laser cutting machine to obtain a target laser cutting machine operation data memory bank; performing equivalent region segmentation on the target workpiece based on the cutting design information to obtain a set of workpiece equivalent cutting regions; using the target laser cutting machine operation data memory bank to perform control strategy analysis on the set of workpiece equivalent cutting regions in sequence to determine a set of control strategy parameters for equivalent cutting regions; controlling the target laser cutting machine to perform cutting feedback detection on the target workpiece based on the set of control strategy parameters for equivalent cutting regions to obtain a cutting region perception feedback data stream; performing integrated optimization analysis on the set of control strategy parameters for equivalent cutting regions based on the cutting region perception feedback data stream to determine a set of optimized control strategy parameters for the cutting machine, and performing strategy optimization control on the target laser cutting machine through the set of optimized control strategy parameters for the cutting machine.

[0007] The control strategy optimization method for a laser cutting machine proposed in this application first constructs an operating data space of the laser cutting machine through data mining technology to obtain the cutting design information of the target laser cutting machine and the workpiece. The operating data is matched and retrieved according to the attribute information of the laser cutting machine, and an operating data memory library is established. The workpiece is divided into equivalent regions based on the cutting design information to obtain an equivalent cutting region set. The operating data memory library is used to analyze the control strategy for the equivalent cutting region to determine a control strategy parameter set. The control strategy parameter set is used to perform cutting feedback detection on the laser cutting machine to obtain a sensed feedback data stream. Based on the feedback data stream, an optimization analysis is performed on the control strategy parameter set to determine an optimized control strategy parameter set, and strategy optimization control is implemented on the laser cutting machine, achieving the technical effect of improving the accuracy of control strategy optimization and maintaining stable cutting quality when facing different types of workpieces and diverse cutting conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings of the embodiments of the present invention will be briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the systems according to the embodiments of this application. It should be understood that the operations described above or below do not necessarily need to be executed precisely in sequence. On the contrary, according to the need, they can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several steps can be removed from these processes.

[0009] Figure 1 It is a schematic flowchart of the control strategy optimization method for a laser cutting machine provided by an embodiment of this application;

[0010] Figure 2 It is a schematic flowchart of the process of obtaining an operating data memory library for the control strategy optimization method for a laser cutting machine provided by an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0011] The above description is only an overview of the technical solutions of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the description. And in order to make the above and other purposes, features, and advantages of this application more obvious and understandable, the specific embodiments of this application are specifically described below.

[0012] In order to make the purpose, technical solutions, and advantages of this application clearer, the present application will be further described in detail below with reference to the drawings. The described embodiments should not be regarded as limitations of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this application.

[0013] In the following description, "some embodiments" are involved, which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict. The terms "first / second" involved are only used to distinguish similar objects and do not represent a specific order for the objects. The terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that comprises a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application.

[0014] Embodiments of this application provide a method for optimizing the control strategy for a laser cutting machine, as Figure 1 shown, the method includes:

[0015] Step S100, constructing a laser cutting machine operation data space through data mining technology and obtaining the cutting design information of the target laser cutting machine and the target workpiece. Specifically, by using data mining technology, multi-dimensional data such as laser power output, cutting speed, focus position, and cutting gas flow are collected from the operation records of laser cutting machines of different models, manufacturers, and cutting task scenarios. After sorting, classifying, and structuring, a laser cutting machine operation data space is constructed; the attribute information of the target laser cutting machine, such as its type, structural specifications, and power level, and the cutting design information of the target workpiece, such as its material, shape, size, and expected cutting quality standard, are obtained. The former helps to accurately match and screen relevant data in the data space subsequently, and the latter serves as the key target basis for optimizing the control strategy, laying a foundation for the entire laser cutting control strategy optimization process.

[0016] Step S200: Match and retrieve the laser cutting machine operation data space according to the attribute information of the target laser cutting machine to obtain the target laser cutting machine operation data memory bank. Specifically, standardize and convert the attribute information such as the type, structural specifications, and power level of the target laser cutting machine into quantifiable numerical values or range descriptions, and thus construct a multi-dimensional index system. In the laser cutting machine operation data space, starting from the top layer according to the index system, first screen the data set by type, then further narrow down the range using the structural specifications index, and finally accurately match according to the power level index, so as to accurately screen out the data subset that highly matches the attributes of the target laser cutting machine. After integrating, sorting, classifying, correlating, and quality evaluating and screening it, generate the target laser cutting machine operation data memory bank. This memory bank, as a tailor-made treasure trove of experience, can provide important basis and reference for subsequent cutting control strategy analysis.

[0017] In a possible implementation manner, as Figure 2 shown, when matching and retrieving the laser cutting machine operation data space according to the attribute information of the target laser cutting machine to obtain the target laser cutting machine operation data memory bank, step S200 further includes step S210 of obtaining the laser cutting machine attribute factor information, where the laser cutting machine attribute factor information includes the cutting machine type, structural specifications, power level, and cutting material. Specifically, collect the attribute factor information related to the laser cutting machine. For the cutting machine type, clarify whether it belongs to a carbon dioxide laser cutting machine, a fiber laser cutting machine, a disk laser cutting machine, or other specific types, because the working principles of different types of laser generators are different and have a significant impact on the cutting process. For example, fiber laser cutting machines have higher energy conversion efficiency and beam quality when cutting metal materials, while carbon dioxide laser cutting machines have unique advantages in cutting some non-metal materials. In terms of structural specifications, accurately measure the size of the workbench, including the specific values of length, width, and height, which determines the maximum size range of workpieces that can be processed; determine the stroke range of the cutting head, which directly affects the cutting ability for workpieces of different heights or depths; understand the accuracy of the mechanical transmission system. A high-precision transmission system can achieve more precise cutting positioning. For example, when cutting micro-parts or workpieces with extremely high precision requirements for the cutting edge, the transmission accuracy is crucial. The power level information is also indispensable. Determine whether low power (such as a few hundred watts) is suitable for fine cutting of thin materials, medium power (several kilowatts) can handle the cutting of general thickness materials, or high power (tens of thousands of watts) is used for cutting thick and hard materials. Record the type of materials that the laser cutting machine is good at cutting, such as whether it is mainly for metal materials (such as steel, aluminum, copper, etc.), or focuses on non-metal materials (such as plastics, wood, ceramics, etc.), or has a certain cutting ability for multiple materials. These information will provide a basic basis for subsequent data processing.

[0018] Step S220: Classify and code the attributes of the laser cutting machine operation data space according to the laser cutting machine attribute factor information, and obtain a set of cutting machine attribute codes. Specifically, based on the obtained laser cutting machine attribute factor information, classify and code the attributes of the laser cutting machine operation data space. For the cutting machine type, set specific coding rules. For example, a carbon dioxide laser cutting machine is coded as "C02", and a fiber laser cutting machine is coded as "Fiber", etc., so that different types of cutting machines can be clearly distinguished in data classification. For the structural specifications, convert information such as the workbench size, cutting head travel, and mechanical transmission accuracy into digital codes. For example, divide different intervals according to the length and width dimensions of the workbench, and assign a specific coding number to each interval; code the cutting head travel according to different length ranges; and set corresponding coding categories for the mechanical transmission accuracy according to the size of its error value. The power level is also coded as "L", "M", "H", etc. for low, medium, and high powers respectively. The cutting material is coded according to the material type. For example, metal materials are coded as "M", non-metal materials are coded as "N", and multiple materials are coded as "B", etc. Through the coding method, the multi-dimensional attribute information of each laser cutting machine is converted into a unified coding format, thereby obtaining a set of cutting machine attribute codes, enabling each piece of laser cutting machine operation data in the data space to be quickly classified and retrieved according to its corresponding code.

[0019] Step S230: Based on the set of cutting machine attribute codes, perform clustering division on the laser cutting machine operation data space to obtain a laser cutting machine operation clustering data set. Specifically, based on the generated set of cutting machine attribute codes, perform clustering division on the laser cutting machine operation data space. Use the clustering algorithm and, based on the attribute codes, gather the laser cutting machine operation data with similar attribute codes together. For example, group the laser cutting machine operation data with a type code of "Fiber", a power level code of "M", a cutting material code of "M", and a structural specification code within a certain similar interval into one category. A laser cutting machine operation clustering data set is obtained, and the data in each cluster has high similarity, reflecting the operation laws and characteristics of the laser cutting machine under similar attribute conditions. The clustering division helps reduce the scope and complexity of data search, improve the efficiency and accuracy of data retrieval, and lay a foundation for subsequent precise data matching for the target laser cutting machine.

[0020] Step S240: Retrieve the operating data memory bank of the target laser cutting machine by performing attribute matching retrieval based on the attribute information of the target laser cutting machine and the laser cutting machine operation clustering dataset. Specifically, perform attribute matching retrieval based on the attribute information of the target laser cutting machine and the laser cutting machine operation clustering dataset. After converting the attribute information such as the type, structural specifications, power level, and cutting material of the target laser cutting machine into corresponding codes, perform matching searches in the laser cutting machine operation clustering dataset. Starting from each cluster in the clustering dataset, sequentially compare the codes of the target laser cutting machine with the coded attributes of the data in the cluster. For example, if the target laser cutting machine is a fiber laser cutting machine, medium power, cutting metal materials, and the structural specifications are within a specific range, first search in the cluster with the type "Fiber", power level "M", and cutting material "M", and further compare whether the structural specification codes match. Through precise attribute matching retrieval, filter out the data in the clustering dataset that is most matched with the attributes of the target laser cutting machine, and integrate the data to obtain the operating data memory bank of the target laser cutting machine. The memory bank contains the operating data of other laser cutting machines with similar attributes to the target laser cutting machine, such as parameter information such as the optimal power setting, cutting speed, and focal position when cutting specific materials, which can provide valuable reference and inspiration for the target laser cutting machine when processing different cutting tasks, help optimize its cutting control strategy, and improve cutting quality and efficiency.

[0021] Step S300: Perform equivalent region segmentation on the target workpiece based on the cutting design information to obtain a set of workpiece equivalent cutting regions. Specifically, analyze the cutting design information of the target workpiece, covering geometric shape, dimensional requirements, and material properties, etc., accurately extract segmentation indicators such as geometric shape category, dimensional range, and material type, use computer-aided design or specialized modeling tools to construct a three-dimensional model of the target workpiece, perform spatial segmentation marking on the three-dimensional model according to the segmentation indicators, divide the parts with the same or similar geometric shape, dimensional range, and material type into regions respectively and assign corresponding marks to form a workpiece index segmentation region set and a region segmentation index label set, and finally perform clustering integration on the workpiece index segmentation region set according to the region segmentation index label set, gather the regions with the same or similar labels, thereby determining the set of workpiece equivalent cutting regions. Each region in this region set has similar cutting characteristic requirements, which is conducive to the formulation of subsequent cutting control strategies.

[0022] In one possible implementation, the target workpiece is segmented into equivalent regions based on the cutting design information to obtain a set of equivalent cutting regions. Step S300 further includes step S310, where three-dimensional modeling is performed based on the cutting design information to create a solid model of the target workpiece. Specifically, three-dimensional modeling is performed based on the obtained cutting design information of the target workpiece. The cutting design information contains detailed dimensional data of the workpiece, such as length, width, height, and the specific shape and size descriptions of each component. This data forms the basic framework for constructing the three-dimensional model. Regarding shape, if the workpiece is a complex mechanical part, its contour consists of multiple curved surfaces, flat surfaces, and various transitional shapes. Modeling requires accurately constructing the corresponding geometric shape entity based on this shape information using professional three-dimensional modeling software (such as CAD software). For example, if the workpiece has a curved surface, the modeling process requires accurately drawing the curved surface by defining parameters such as the center point, radius, starting angle, and ending angle. Simultaneously, the workpiece's internal structural information is analyzed. If internal features such as holes and slots exist, they must be accurately represented in the three-dimensional model, with their position, shape, and dimensions determined. By fully integrating and accurately drawing the cutting design information, a three-dimensional model of the target workpiece is established. The model will present the overall appearance of the workpiece in an intuitive three-dimensional form, providing a visual basis for subsequent segmentation operations.

[0023] In step S320, segmentation metrics are extracted from the workpiece cutting application information to obtain a set of workpiece segmentation metrics. The set includes geometric shape, cutting dimensions, and material properties. Specifically, after establishing a 3D model of the target workpiece, the workpiece cutting application information is analyzed to extract segmentation metrics. From a geometric perspective, the shape characteristics of the workpiece as a whole and its components are identified to determine whether it is a simple geometric shape (such as a circle, square, triangle, etc.) or a complex composite shape or a special-shaped structure. For example, for a workpiece composed of multiple cylinders and rectangular parallelepipeds, the shape category of each component must be clearly defined. Regarding cutting dimensions, in addition to the overall length, width, and height, the relative size relationships between components and the thickness information of different locations must also be considered. For example, for a plate-shaped workpiece with different thicknesses, the thickness values of the thin and thick areas must be recorded, as well as their location distribution within the entire workpiece. Material properties are a key indicator. It is necessary to determine whether the workpiece material is metal (such as steel, aluminum, copper, etc.) or non-metal (such as plastic, wood, ceramic, etc.). If it is a metal, further analysis is performed on performance parameters such as hardness, melting point, and thermal conductivity. For example, alloy steel workpieces with higher hardness are more difficult to cut and require higher-energy cutting methods. Meanwhile, aluminum, with its lower melting point, requires precise laser energy control to prevent over-melting. Through analysis, a set of workpiece segmentation metrics, including geometry, cutting dimensions, and material properties, is obtained. These metrics serve as an important basis for dividing workpiece regions.

[0024] Step S330: Based on the workpiece segmentation index set, perform spatial segmentation and labeling on the target workpiece solid model in sequence to obtain a workpiece index segmentation region set and a region segmentation index label set. Specifically, based on the extracted workpiece segmentation index set, perform spatial segmentation and labeling operations on the target workpiece solid model. Starting from the geometric shape index, label regions with the same or similar geometric shapes. For example, label all circular parts as one category and square parts as another category. In terms of cutting dimensions, unify the labeling of regions with the same thickness or thicknesses close within a certain error range, and indicate their dimension ranges. For example, label regions with a thickness between 3 and 5 millimeters as a group and record their thickness intervals. For the material property index, group regions with the same material and similar properties under the same label. For example, label together all regions made of steel with a hardness within a certain range. Perform spatial segmentation and labeling on the target workpiece solid model in sequence to obtain multiple labeled regions. These regions form the workpiece index segmentation region set, and at the same time, each region corresponds to a specific region segmentation index label, which records information such as the geometric shape, cutting dimensions, and material properties of this region, forming the region segmentation index label set.

[0025] Step S340: Cluster and integrate the workpiece index segmentation region set according to the region segmentation index label set to obtain the workpiece equivalent cutting region set. Specifically, cluster and integrate the workpiece index segmentation region set according to the region segmentation index label set. Cluster regions with the same or similar region segmentation index labels together to form the workpiece equivalent cutting region set. For example, all regions labeled as circular, with a thickness between 2 and 3 millimeters and made of aluminum will be integrated into an equivalent cutting region. During the clustering and integration process, the handling of some boundary regions will be involved. For regions with partially similar but not completely identical indicators, determine whether to merge them into a certain equivalent cutting region according to the requirements of the actual cutting process and the degree of influence on the cutting effect. Through the clustering and integration operation, each region in the obtained workpiece equivalent cutting region set has relatively similar cutting characteristic requirements. When formulating the subsequent cutting control strategy, each equivalent cutting region can be considered uniformly, reducing the complexity of the control strategy, improving the efficiency and accuracy of strategy formulation, and thus laying a foundation for realizing efficient and precise laser cutting operations.

[0026] Step S400: Analyze the control strategies for the workpiece equivalent cutting area set in sequence using the target laser cutting machine operation data memory bank, and determine the equivalent cutting area control strategy parameter set. Specifically, extract data from the target laser cutting machine operation data memory bank. For the first area in the workpiece equivalent cutting area set, according to its geometric shape, cutting size, material properties and other characteristics, search for the operation data of similar areas in the memory bank and compare for matching. Then disassemble the workpiece cutting control target, construct a workpiece cutting effect fitness function with indicators such as cutting accuracy and speed. Next, analyze the area cutting characteristics and construct a list of strategy analysis algorithms, select a suitable algorithm according to the characteristic information to determine the equivalent area control strategy algorithm set. Then, determine the equivalent area cutting strategy parameter threshold according to the data memory bank, and use the fitness function to conduct a preliminary search and evaluation to obtain the preliminary strategy parameter fitness. Finally, use the algorithm set and the preliminary fitness to iteratively optimize the threshold until the preset conditions are met to determine the equivalent cutting area control strategy parameter set for the current area, and repeat the operation for the remaining areas until the parameter sets for all areas are determined.

[0027] In a possible implementation, analyze the control strategies for the workpiece equivalent cutting area set in sequence using the target laser cutting machine operation data memory bank, and determine the equivalent cutting area control strategy parameter set. Step S400 further includes step S410: disassemble and analyze the workpiece cutting control target to obtain a workpiece cutting evaluation index set, and based on the workpiece cutting evaluation index set, fit and construct a workpiece cutting effect fitness function. Specifically, disassemble and analyze the workpiece cutting control target. The cutting control target is multi-dimensional, covering aspects such as cutting accuracy, cutting speed, cutting surface roughness, and the size of the heat affected zone during cutting. For example, in the cutting of precision parts in the aerospace field, the cutting accuracy requirement is extremely high, accurate to the micron level to ensure the assembly accuracy and performance of the parts; while in the cutting of some building decoration materials, the cutting speed may be more critical because a large amount of materials need to be processed quickly. Through a detailed analysis of different aspects, obtain the workpiece cutting evaluation index set. Based on the index set, use the method of mathematical modeling to fit and construct a workpiece cutting effect fitness function. The function takes indicators such as cutting accuracy, speed, roughness, and heat affected zone as variables, and reflects the relative importance of each indicator in the overall cutting effect by setting different weights. For example, for the cutting of parts with high precision requirements, the weight of cutting accuracy may be set higher, while for rough machining scenarios with relatively low surface quality requirements, the weight of cutting speed can be appropriately increased. The constructed fitness function can quantitatively evaluate the cutting effect under different control strategies, providing a clear evaluation criterion for subsequent strategy parameter optimization.

[0028] Step S420: Analyze the cutting characteristics of the workpiece equivalent cutting area set to obtain the equivalent area cutting characteristic information set. Specifically, analyze the cutting characteristics of the workpiece equivalent cutting area set. For each equivalent cutting area, study its material properties, including the type of material (metal or non-metal), physical properties of the material (such as hardness, melting point, thermal conductivity, etc.). For example, for high-hardness metal materials such as tungsten alloy, the cutting is more difficult and requires higher laser energy and slower cutting speed; while for materials with low melting points such as tin, precise control of laser energy is required during cutting to prevent over-melting. At the same time, analyze the influence of the geometric shape of the area on cutting. For example, a circular area requires the laser head to perform circular motion during cutting, which poses special requirements for the planning of the cutting trajectory and the dynamic adjustment of the cutting speed; for complex polygon areas, the cutting strategy at the corners needs to be considered to avoid incomplete cutting or overburning. Analyze the size of the area. Larger-sized areas may require adjustment of the laser spot size and power distribution to ensure uniform cutting quality throughout the area. Through analysis, obtain the equivalent area cutting characteristic information set, providing a basis for selecting appropriate control strategy algorithms.

[0029] Step S430: Construct a list of strategy analysis algorithms, and based on the list of strategy analysis algorithms, perform matching analysis and selection on the equivalent area cutting characteristic information set respectively to determine the equivalent area control strategy algorithm set. Specifically, construct a list of strategy analysis algorithms. The list includes various algorithms applicable to laser cutting control strategy analysis, such as genetic algorithms, particle swarm optimization algorithms, simulated annealing algorithms, etc. These algorithms have their own characteristics. Genetic algorithms have strong global search capabilities and can find the optimal solution in a large parameter space, but the computational complexity is relatively high; particle swarm optimization algorithms have a fast convergence speed and are suitable for situations where the range of parameter changes is somewhat understood; simulated annealing algorithms perform well in dealing with problems with multiple local optimal solutions. Based on the list of strategy analysis algorithms, perform matching analysis and selection on the equivalent area cutting characteristic information set respectively. For example, if the equivalent area is a region with complex shape, variable material properties, and extremely high requirements for cutting accuracy, then the genetic algorithm may be a more appropriate choice because it can comprehensively search for the optimal cutting strategy parameters in the complex parameter space; while for a region with relatively simple shape, stable material properties, and high requirements for cutting speed, the particle swarm optimization algorithm can quickly determine the appropriate parameter combination. Through matching analysis, determine the equivalent area control strategy algorithm set to ensure that the most suitable algorithm can be used to optimize the strategy parameters for different equivalent cutting areas.

[0030] Step S440: Using the equivalent region control strategy algorithm set and the workpiece cutting effect fitness function, perform strategy parameter optimization on the workpiece equivalent cutting region set respectively based on the target laser cutting machine operation data memory bank, and determine the equivalent cutting region control strategy parameter set. Specifically, use the equivalent region control strategy algorithm set and the workpiece cutting effect fitness function to perform strategy parameter optimization on the workpiece equivalent cutting region set respectively based on the target laser cutting machine operation data memory bank. Extract historical cutting data similar to the current equivalent cutting region from the target laser cutting machine operation data memory bank, and determine the equivalent region cutting strategy parameter threshold according to the data, that is, determine the approximate value range of parameters such as laser power, cutting speed, focus position, and auxiliary gas flow rate. For example, for an equivalent cutting region of a certain specific material and shape, the data in the memory bank shows that when the laser power is between 800 and 1200 watts and the cutting speed is between 1 and 3 meters per minute, there may be a better cutting effect, then these ranges are used as the initial strategy parameter search range. Then, use the workpiece cutting effect fitness function to perform a preliminary search and evaluation within the equivalent region cutting strategy parameter threshold, select several groups of parameter combinations and substitute them into the fitness function to calculate its function value, and obtain the equivalent region preliminary strategy parameter fitness, so as to understand the advantages and disadvantages of these initial parameter combinations. Finally, use the equivalent region control strategy algorithm set and the equivalent region preliminary strategy parameter fitness to perform iterative evaluation and optimization on the equivalent region cutting strategy parameter threshold. During the iteration process, the algorithm continuously adjusts the parameter combination according to the value of the fitness function, gradually narrows the search range until the preset iteration conditions are met, such as reaching a predetermined number of iterations, the fitness function value converges to a certain accuracy, or finding a parameter combination that meets specific cutting effect requirements, etc., determine the equivalent cutting region control strategy parameter set, and obtain the best control strategy parameter combination for each equivalent cutting region to achieve efficient and accurate laser cutting.

[0031] In a possible implementation, the equivalent region control strategy algorithm set and the workpiece cutting effect fitness function are adopted to optimize the strategy parameters for the workpiece equivalent cutting region set respectively based on the target laser cutting machine operation data memory bank, and an equivalent cutting region control strategy parameter set is determined. Step S440 further includes step S441 of parsing the strategy parameters for the workpiece equivalent cutting region set respectively based on the target laser cutting machine operation data memory bank to obtain equivalent region cutting strategy parameter thresholds. Specifically, analyze the target laser cutting machine operation data memory bank. For each region in the workpiece equivalent cutting region set, sort out the historical cutting task data similar to it in the memory bank. The data covers various control strategy parameters used in the past when cutting workpieces with similar geometric shapes, the same or similar materials, and similar cutting sizes, such as the setting range of laser power, the value range of cutting speed, the adjustment range of focus position, and the magnitude of auxiliary gas flow. Through the analysis and induction of the data, determine the relatively reasonable equivalent region cutting strategy parameter thresholds for the current equivalent cutting region. For example, if the current workpiece equivalent cutting region is a stainless steel circular region with a thickness of 6 mm, extract the task data of cutting similar stainless steel circular regions with a thickness between 5 and 7 mm from the memory bank. After statistical analysis, it is concluded that the laser power may be more appropriate between 1200 and 1500 watts, and the cutting speed is probably in the range of 1.5 to 2.5 m / min, initially determining the cutting strategy parameter thresholds for this region and providing a parameter range limit based on evidence for subsequent search and evaluation.

[0032] Step S442: Conduct a preliminary search and evaluation within the equivalent region cutting strategy parameter threshold using the workpiece cutting effect fitness function to obtain the fitness of the preliminary strategy parameters for the equivalent region. Specifically, after determining the equivalent region cutting strategy parameter threshold, perform a preliminary search and evaluation within the threshold range using the pre-constructed workpiece cutting effect fitness function. Substitute different parameter combinations within the threshold range into the fitness function for calculation in sequence. For example, within the threshold ranges of the above laser power and cutting speed, select multiple different combinations of power and speed, such as (1200 watts, 1.5 meters per minute), (1300 watts, 2 meters per minute), etc., and substitute the combinations into the fitness function with variables such as cutting accuracy, cutting surface roughness, cutting speed, and heat affected zone. Through the calculation of the fitness function, obtain the fitness of the preliminary strategy parameters for the equivalent region corresponding to each group of parameter combinations. The fitness value intuitively reflects the pros and cons of this group of parameter combinations in achieving the cutting goal. For instance, a higher fitness value obtained after substituting a group of parameter combinations indicates that under this parameter setting, the cutting accuracy is higher, the cutting surface roughness is lower, the cutting speed is faster, and the heat affected zone is smaller. Conversely, it indicates that there are deficiencies in certain cutting effect indicators for this group of parameter combinations, thus providing initial evaluation information and screening basis for further iterative optimization.

[0033] Step S443: Using the equivalent region control strategy algorithm set and the fitness of the preliminary equivalent region strategy parameters, iteratively evaluate and optimize the threshold of the equivalent region cutting strategy parameters until the preset iteration conditions are met, and determine the equivalent cutting region control strategy parameter set. Specifically, use the previously selected equivalent region control strategy algorithm set and the obtained fitness of the preliminary equivalent region strategy parameters to iteratively evaluate and optimize the threshold of the equivalent region cutting strategy parameters. Taking the genetic algorithm as an example, multiple parameter combinations randomly generated within the threshold range are used as the initial population. Each parameter combination is regarded as an individual, and its fitness value is used as the fitness evaluation index of the individual. According to the selection, crossover, and mutation operation rules of the genetic algorithm, individuals with higher fitness are selected in each generation of the population for crossover and mutation operations to generate a new population. The individuals in the new population are substituted into the fitness function again to calculate the fitness value, and so on, iterating in a loop. During the iteration process, continuously compare the best fitness value in each generation of the population with the preset iteration conditions. The preset iteration conditions include reaching a predetermined number of iterations, such as 100 iterations; or the fitness function value converges to a certain accuracy, for example, the change in the best fitness value of the population for 10 consecutive generations is less than 0.01, etc. When the preset iteration conditions are met, the best parameter combination in the population at this time is the determined equivalent cutting region control strategy parameter set. The finally determined parameter set can optimize the cutting effect in the cutting task of the current equivalent cutting region. On the premise of ensuring the cutting accuracy, it can improve the cutting speed as much as possible, reduce the surface roughness of the cut and minimize the heat affected zone, thereby providing reliable control strategy parameter support for efficient and precise laser cutting operations.

[0034] Step S500: Based on the equivalent cutting region control strategy parameter set, control the target laser cutting machine to perform cutting feedback detection on the target workpiece to obtain a cutting region perception feedback data stream. Specifically, according to the equivalent cutting region control strategy parameter set, accurately set parameters such as the laser power, cutting head movement speed, focus position, and auxiliary gas flow rate of the target laser cutting machine, and then start cutting. During cutting, feedback detection is carried out by means of a power sensor installed on the laser generator, a speed and position sensor on the cutting head, a temperature and image sensor near the cutting region, etc. The power sensor at the laser generator monitors the actual output value and deviation of the power, the speed sensor on the cutting head captures the speed fluctuation, the position sensor tracks the position deviation, the temperature sensor monitors the change in cutting temperature, and the image sensor takes a real-time image of the cutting surface. Integrate the information in multiple aspects such as laser power, cutting head state, and cutting region environment in a specific time series and data format to obtain a cutting region perception feedback data stream.

[0035] Step S600: Based on the cutting area perception feedback data stream, perform integrated optimization analysis on the equivalent cutting area control strategy parameter set to determine the optimized control strategy parameter set for the cutting machine, and perform strategy optimization control on the target laser cutting machine through the optimized control strategy parameter set for the cutting machine. Specifically, preprocess the cutting area perception feedback data stream, use filtering algorithms, image enhancement, etc. to process data noise, missing values, and outliers and standardize them, and extract key features such as temperature features and image features; determine the equivalent area strategy parameter optimization target set based on feature analysis, and analyze multiple target constraints such as cutting surface roughness, heat affected zone, and cutting efficiency; then parse the equivalent cutting area control strategy parameter set according to the optimization target, and generate multiple equivalent area optimization strategy parameter sets using methods such as random mutation according to the optimization direction; finally, using the idea of ensemble learning, evaluate the performance of each parameter set with indicators such as cutting surface roughness and determine the optimal parameter set through ensemble methods such as weighted average as the optimized control strategy parameter set for the cutting machine, apply it to the target laser cutting machine to achieve strategy optimization control and continuously cycle and optimize.

[0036] In a possible implementation, based on the cutting area perception feedback data stream, an integrated optimization analysis is performed on the equivalent cutting area control strategy parameter set to determine the optimized control strategy parameter set for the cutting machine, and the target laser cutting machine is subjected to strategy optimization control through the optimized control strategy parameter set for the cutting machine. Step S600 further includes step S610, which preprocesses the cutting area perception feedback data stream and analyzes the optimization target to determine the equivalent area strategy parameter optimization target set. Specifically, the cutting area perception feedback data stream is preprocessed. Since there may be many problems with the data collected by the sensors, for example, the data of the laser power sensor may generate noise due to electromagnetic interference, the cutting surface image captured by the image sensor may be blurred or have uneven illumination, and the data of the position sensor may have a small drift, etc. Therefore, multi-source data processing technology is used for preprocessing. For the noise data, a filtering algorithm is used, such as low-pass filtering to remove high-frequency noise and make the data curve smoother, so as to accurately obtain the true change trend of the laser power. For the image data, image enhancement processing is performed. By adjusting parameters such as contrast and brightness, key features such as the texture and edges of the cutting surface are highlighted, so as to more accurately analyze information such as the roughness of the cutting surface. After the data cleaning and sorting are completed, the optimization target analysis is carried out. Analyze the data characteristics in the cutting area perception feedback data stream. If it is found that the cutting surface roughness data shows that the current cutting surface is relatively rough and exceeds the expected quality standard, then reducing the cutting surface roughness is set as an important optimization target; if the temperature data indicates that the temperature of the heat-affected zone is too high during the cutting process, which may affect the material properties and dimensional accuracy of the workpiece, then reducing the heat-affected zone becomes one of the key optimization directions. At the same time, considering the importance of cutting efficiency to production, it is not possible to simply pursue the optimization of a certain index while ignoring the overall efficiency. For example, it is set that while ensuring a certain percentage reduction in the cutting surface roughness, the reduction range of the cutting speed needs to be controlled within a reasonable range, so as to determine the equivalent area strategy parameter optimization target set and provide a clear direction guidance for subsequent parameter optimization.

[0037] Step S620: Optimize and analyze the equivalent cutting region control strategy parameter set based on the equivalent region strategy parameter optimization target set to obtain an equivalent region strategy parameter optimization rule set. Specifically, based on the determined equivalent region strategy parameter optimization target set, optimize and analyze the equivalent cutting region control strategy parameter set. Analyze the internal relationship between the current control strategy parameters and the optimization targets. For example, if we want to reduce the cutting surface roughness, we need to study the synergistic relationship between the laser power and the cutting speed. Usually, appropriately reducing the cutting speed and slightly adjusting the laser power can make the laser energy act more evenly on the cutting surface, thus improving the roughness. At the same time, the focus position also has an important impact on the cutting effect. If the focus position is inaccurate, it will lead to uneven energy distribution on the cutting surface, thereby affecting the roughness and cutting accuracy. By analyzing the correlation between the parameters and the optimization targets, determine the adjustment direction and approximate adjustment range of each parameter, so as to obtain the equivalent region strategy parameter optimization rule set. For example, if the optimization targets are to reduce the cutting surface roughness and minimize the heat affected zone, the rule set may stipulate specific adjustment rules such as reducing the laser power by 5% - 10% based on the original value, reducing the cutting speed by 10% - 15%, and slightly adjusting the focus position upward by 0.5 - 1 mm, providing a basis for subsequent mutation optimization.

[0038] Step S630: Mutate and optimize the equivalent cutting region control strategy parameter set according to the equivalent region strategy parameter optimization rule set to obtain multiple equivalent region optimized strategy parameter sets. Specifically, mutate and optimize the equivalent cutting region control strategy parameter set according to the equivalent region strategy parameter optimization rule set. Use multiple mutation methods to generate multiple equivalent region optimized strategy parameter sets. For example, using the random mutation method, for the laser power parameter, centered on the original parameter value, within the range set by the optimization rule set, randomly generate several mutation values. For example, if the original laser power is 1500 W, according to the rule set, randomly select a mutation value between 1350 - 1425 W; for the cutting speed parameter, also randomly mutate within the specified range. At the same time, a gradient-based mutation method can also be used, mutating according to the gradient direction of the optimization objective function, making the mutated parameters more likely to move towards the optimization target. Through these mutation operations, diversify the various parameters in the original equivalent cutting region control strategy parameter set and combine them to form multiple equivalent region optimized strategy parameter sets. For example, an equivalent region optimized strategy parameter set may be a laser power of 1380 W, a cutting speed of 1.8 m / min, and a focus position of 5.2 mm, and another may be a laser power of 1400 W, a cutting speed of 1.7 m / min, and a focus position of 5.3 mm, etc., providing a rich set of candidate parameters for subsequent integrated optimization analysis.

[0039] Step S640: Conduct an integrated optimization analysis on the multiple equivalent region optimization strategy parameter sets to determine the cutting machine optimization control strategy parameter set. Specifically, conduct an integrated optimization analysis on the multiple equivalent region optimization strategy parameter sets. Using the method of integrated learning, regard the parameter sets as multiple different models or decision-making schemes. First, for each equivalent region optimization strategy parameter set, use the data in the cutting area perception feedback data stream to conduct a performance evaluation. Use indicators such as cutting surface roughness, heat affected zone size, and cutting speed as evaluation criteria, and calculate the scores of each parameter set on these indicators. For example, for a parameter set, by simulating its effect during the cutting process, it is calculated that the cutting surface roughness is 5 microns, the heat affected zone area is 2 square millimeters, and the cutting speed is 2 meters per minute. According to the pre-set weights and evaluation formulas, obtain its comprehensive score. Then, adopt an integration method, such as the weighted average method, and assign different weights according to the performance scores of each parameter set, and comprehensively integrate the multiple parameter sets. Or adopt the voting method. When there are differences in the decisions of multiple parameter sets on certain indicators, determine the final parameter values by a majority vote. Through the integrated optimization analysis method, determine the optimal parameter set, that is, the cutting machine optimization control strategy parameter set. The finally determined parameter set can achieve precise control of the target laser cutting machine on the basis of meeting the multi-objective optimization requirements, improve cutting quality, efficiency and stability, and provide a better control strategy for laser cutting operations.

[0040] In a possible implementation manner, when conducting an integrated optimization analysis on the multiple equivalent region optimization strategy parameter sets to determine the cutting machine optimization control strategy parameter set, step S640 further includes step S641: Use the workpiece cutting effect fitness function to conduct a fitness evaluation on the multiple equivalent region optimization strategy parameter sets to obtain multiple equivalent region strategy parameter fitnesses. Specifically, use the pre-constructed workpiece cutting effect fitness function to conduct a fitness evaluation on the multiple equivalent region optimization strategy parameter sets. The fitness function uses key indicators such as cutting accuracy, cutting speed, cutting surface roughness, and heat affected zone size during the cutting process as variables. For each equivalent region optimization strategy parameter set, substitute the parameters therein, such as laser power, cutting speed, focal position, etc. into the fitness function for calculation. For example, if an equivalent region optimization strategy parameter set sets the laser power to 1200 watts, the cutting speed to 2 meters per minute, and the focal position to 3 millimeters, after substituting the parameters into the fitness function, the function will analyze the cutting accuracy, cutting surface roughness, and heat affected zone size, etc. under this parameter combination, and calculate a fitness value according to the set weights. By performing such calculations on the multiple equivalent region optimization strategy parameter sets in sequence, obtain multiple equivalent region strategy parameter fitnesses. The fitness value intuitively reflects the pros and cons of each parameter set in achieving an ideal cutting effect. The higher the fitness value, the closer the parameter set is to the expected target in terms of the comprehensive cutting effect.

[0041] Step S642: Based on the fitness of the multiple equivalent region strategy parameters, optimize and select the multiple equivalent region optimization strategy parameter sets to determine the equivalent region optimization strategy parameter set. Specifically, according to the obtained fitness of the multiple equivalent region strategy parameters, optimize and select the multiple equivalent region optimization strategy parameter sets. Sort the fitness values of each parameter set and select some parameter sets with higher fitness values. For example, if there are 10 equivalent region optimization strategy parameter sets, after fitness evaluation, it is found that the fitness values of 3 of them are significantly higher than those of the others. During the optimization and selection process, not only the high or low of the fitness value is considered, but also the characteristics of each parameter set need to be comprehensively analyzed. For instance, although a certain parameter set has a high fitness value, the laser power it requires may be very close to the maximum power limit of the laser cutting machine, which may cause great pressure on the equipment in actual application. In this case, it is necessary to carefully consider whether to select this parameter set. Through comprehensive analysis and comparison, determine one or several relatively optimal equivalent region optimization strategy parameter sets, which can ensure good cutting effects while also having a certain degree of feasibility and stability.

[0042] Step S643: Conduct an integrated impact analysis on the equivalent region optimization strategy parameter set to obtain the cutting machine optimization control strategy parameter set. Specifically, conduct an integrated impact analysis on the selected equivalent region optimization strategy parameter set. Considering that in the actual laser cutting process, the various equivalent cutting regions are not completely independent and there are mutual influence relationships among them. For example, the cutting sequence of adjacent regions and heat conduction during the cutting process will affect the overall cutting effect. Analyze their integrated impact by simulating the interaction of different equivalent region optimization strategy parameter sets during the cutting process of the entire workpiece. For example, when a region is cut quickly with a high power, it may have a thermal impact on the material properties of the adjacent region, thereby affecting the cutting quality of the adjacent region. According to the integrated impact analysis, further adjust and optimize the selected parameter set. For example, appropriately reduce the cutting power of a certain region to reduce the thermal impact on the adjacent region, and at the same time adjust the cutting speed of other regions to make up for the loss of the overall cutting efficiency. After the integrated impact analysis and optimization, finally determine the cutting machine optimization control strategy parameter set, which can achieve the optimal control of the entire target laser cutting machine based on the analysis of the mutual influence of each equivalent cutting region, and achieve the best cutting effect and the highest production efficiency.

[0043] In a possible implementation, an integrated impact analysis is performed on the equivalent region optimization strategy parameter set to obtain a cutting machine optimization control strategy parameter set. Step S643 further includes step S6431 of performing a cutting impact analysis on each cutting region in the workpiece equivalent cutting region set to obtain cutting region impact level information. Specifically, a cutting impact analysis is performed on each cutting region in the workpiece equivalent cutting region set. Since each region of the workpiece does not exist in isolation during the cutting process and there are various forms of mutual influence between them. For example, in terms of cutting depth, when the cutting depth of a region is relatively large, it may change the stress distribution inside the workpiece, thereby affecting the cutting stability of adjacent regions. If a region with a relatively deep depth is cut first, it may cause deformation of the material in the surrounding regions, resulting in difficulty in accurately controlling the cutting path when cutting subsequent regions. From the perspective of the cutting path, if there are intersections or connection parts in the cutting paths of adjacent regions, the cutting accuracy and edge quality of the previously cut region will affect the cutting starting point and the connection effect of the cutting edge of the subsequent region. By comprehensively analyzing factors such as cutting depth and cutting path, the degree of influence of each region on other regions is determined, thereby obtaining the cutting region impact level information. For example, a region located in the center of the workpiece and with a complex structure, any minor change during its cutting process may have a chain reaction on multiple surrounding regions, so the impact level of this region is relatively high; while a region located at the edge and with a simple shape has a relatively small influence range and a lower impact level.

[0044] Step S6432, based on the cutting region impact level information, perform a cutting correlation impact analysis to determine a set of cutting region correlation impact factors. Specifically, based on the cutting region impact level information, perform a cutting correlation impact analysis. The mutual correlations between regions with different impact levels are sorted out in detail to determine a set of cutting region correlation impact factors. For example, for a region with a high impact level, the heat, vibration, and material deformation generated during its cutting are the main correlation impact factors. When a region with a high impact level is cut, a large amount of heat released may be conducted to adjacent regions with a low impact level, changing the physical properties of the material in the low-level regions, such as reducing the hardness of the material, thereby affecting its cutting difficulty and cutting effect. Vibration may also be transmitted through the structure of the workpiece, interfering with the cutting accuracy of adjacent regions. For a region with a low impact level, the adjustment of its own cutting parameters may have a relatively small impact on a region with a high impact level, but the cumulative effect of multiple regions with a low impact level cannot be ignored. For example, a change in the cutting order of multiple regions with a low impact level may change the stress distribution of the entire workpiece, thereby indirectly affecting the cutting quality of a region with a high impact level. By analyzing and summarizing the correlation impact factors, a set of cutting region correlation impact factors is constructed, providing a basis for subsequent weighted integration analysis.

[0045] Step S6433: Based on the set of influence factors associated with the cutting area, perform influence-weighted integration analysis on the set of equivalent area optimization strategy parameters to obtain the set of optimized control strategy parameters for the cutting machine. Specifically, perform influence-weighted integration analysis on the set of equivalent area optimization strategy parameters according to the set of influence factors associated with the cutting area to obtain the set of optimized control strategy parameters for the cutting machine. For each set of equivalent area optimization strategy parameters, analyze the importance weights of each parameter under the influence of different cutting area associations. For example, for the laser power parameter, if a certain area is greatly affected by the heat conduction of adjacent high-influence level areas, then in the parameter set of this area, the weight of the laser power needs to be adjusted according to the degree of heat influence. If the heat influence may cause excessive melting of the material, it is necessary to appropriately reduce the weight of the laser power to avoid deterioration of the cutting quality. Similarly, for the cutting speed parameter, if a certain area is greatly affected by vibration and the cutting speed needs to be reduced to improve the cutting accuracy, then the weight of the cutting speed parameter will also change accordingly. Through the weight adjustment and integration analysis of each parameter under different associated influences, comprehensively analyze the overall cutting effect of the entire workpiece equivalent cutting area set, and finally determine the set of optimized control strategy parameters applicable to the entire laser cutting machine. The parameter set can achieve precise control of the target laser cutting machine on the basis of fully considering the mutual influence between regions, improve the cutting quality and efficiency, and ensure that the cutting process of the entire workpiece reaches the optimal state.

[0046] In the embodiment of the present application, a running data space of a laser cutting machine is constructed through data mining technology to obtain the cutting design information of the target laser cutting machine and the workpiece. The running data is matched and retrieved according to the attribute information of the laser cutting machine, and a running data memory library is established. The workpiece is divided into equivalent areas based on the cutting design information to obtain a set of equivalent cutting areas. The control strategy analysis of the equivalent cutting areas is carried out using the running data memory library to determine the set of control strategy parameters. The laser cutting machine is subjected to cutting feedback detection using the set of control strategy parameters to obtain a sensed feedback data stream. Based on the feedback data stream, the set of control strategy parameters is optimized and analyzed to determine the set of optimized control strategy parameters, and the strategy optimization control is implemented on the laser cutting machine, achieving the technical effect of improving the accuracy of control strategy optimization and maintaining the stability of cutting quality in the face of different types of workpieces and diverse cutting conditions.

[0047] The above specific embodiments do not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present application shall be included within the protection scope of the present application. In some cases, the actions or steps described in the present application can be executed in a different order from that in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or consecutive order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A control strategy optimization method for a laser cutting machine, characterized in that, The method includes: Constructing a laser cutting machine operation data space through data mining technology and obtaining cutting design information of a target laser cutting machine and a target workpiece; Performing matching retrieval on the laser cutting machine operation data space according to the attribute information of the target laser cutting machine to obtain a target laser cutting machine operation data memory bank, including: Obtaining laser cutting machine attribute factor information, where the laser cutting machine attribute factor information includes cutting machine type, structural specifications, power level, and cutting material; Performing attribute classification coding on the laser cutting machine operation data space according to the laser cutting machine attribute factor information to obtain a cutting machine attribute coding set; Performing clustering division on the laser cutting machine operation data space based on the cutting machine attribute coding set to obtain a laser cutting machine operation clustering data set; Performing attribute matching retrieval on the laser cutting machine operation clustering data set according to the attribute information of the target laser cutting machine to obtain a target laser cutting machine operation data memory bank; Performing equivalent region segmentation on the target workpiece based on the cutting design information to obtain a workpiece equivalent cutting region set; Using the target laser cutting machine operation data memory bank to perform control strategy analysis on the workpiece equivalent cutting region set in sequence to determine an equivalent cutting region control strategy parameter set; Controlling the target laser cutting machine to perform cutting feedback detection on the target workpiece based on the equivalent cutting region control strategy parameter set to obtain a cutting region perception feedback data stream; Performing integrated optimization analysis on the equivalent cutting region control strategy parameter set based on the cutting region perception feedback data stream to determine a cutting machine optimization control strategy parameter set, and performing strategy optimization control on the target laser cutting machine through the cutting machine optimization control strategy parameter set.

2. The control strategy optimization method for a laser cutting machine according to claim 1, characterized in that The obtaining of the workpiece equivalent cutting region set includes: Performing three-dimensional modeling based on the cutting design information to establish a target workpiece solid model; Extracting segmentation indexes of workpiece cutting application information to obtain a workpiece segmentation index set, where the workpiece segmentation index set includes geometric shape, cutting size, and material performance; Performing spatial segmentation marking on the target workpiece solid model in sequence based on the workpiece segmentation index set to obtain a workpiece index segmentation region set and a region segmentation index label set; Clustering and integrating the workpiece index segmentation region set according to the region segmentation index label set to obtain the workpiece equivalent cutting region set.

3. The control strategy optimization method for a laser cutting machine according to claim 1, characterized in that The determination of the equivalent cutting region control strategy parameter set includes: Decomposing and analyzing the workpiece cutting control target to obtain a workpiece cutting evaluation index set, and fitting and constructing a workpiece cutting effect fitness function based on the workpiece cutting evaluation index set; Performing cutting characteristic analysis on the workpiece equivalent cutting region set to obtain an equivalent region cutting characteristic information set; Constructing a list of strategy analysis algorithms, and respectively performing matching analysis and selection on the equivalent region cutting characteristic information set based on the list of strategy analysis algorithms to determine an equivalent region control strategy algorithm set; Using the equivalent region control strategy algorithm set and the workpiece cutting effect fitness function, perform strategy parameter optimization on the workpiece equivalent cutting region set respectively based on the target laser cutting machine operation data memory bank to determine the equivalent cutting region control strategy parameter set.

4. The control strategy optimization method for a laser cutting machine according to claim 3, characterized in that, The determination of the equivalent cutting region control strategy parameter set includes: Perform strategy parameter analysis on the workpiece equivalent cutting region set respectively based on the target laser cutting machine operation data memory bank to obtain the equivalent region cutting strategy parameter threshold; Use the workpiece cutting effect fitness function to perform preliminary search and evaluation within the equivalent region cutting strategy parameter threshold to obtain the equivalent region preliminary strategy parameter fitness; Use the equivalent region control strategy algorithm set and the equivalent region preliminary strategy parameter fitness to perform iterative evaluation and optimization on the equivalent region cutting strategy parameter threshold until the preset iteration condition is met, and determine the equivalent cutting region control strategy parameter set.

5. The control strategy optimization method for a laser cutting machine according to claim 4, characterized in that The determination of the cutting machine optimization control strategy parameter set includes: Perform preprocessing and optimization target analysis on the cutting region perception feedback data stream to determine the equivalent region strategy parameter optimization target set; Perform optimization analysis on the equivalent cutting region control strategy parameter set based on the equivalent region strategy parameter optimization target set to obtain the equivalent region strategy parameter optimization rule set; Perform mutation optimization on the equivalent cutting region control strategy parameter set according to the equivalent region strategy parameter optimization rule set to obtain multiple equivalent region optimization strategy parameter sets; Perform integrated optimization analysis on the multiple equivalent region optimization strategy parameter sets to determine the cutting machine optimization control strategy parameter set.

6. The control strategy optimization method for a laser cutting machine according to claim 5, characterized in that The determination of the cutting machine optimization control strategy parameter set includes: Use the workpiece cutting effect fitness function to perform fitness evaluation on the multiple equivalent region optimization strategy parameter sets to obtain multiple equivalent region strategy parameter fitnesses; Perform optimization selection on the multiple equivalent region optimization strategy parameter sets based on the multiple equivalent region strategy parameter fitnesses to determine the equivalent region optimization strategy parameter set; Perform integrated influence analysis on the equivalent region optimization strategy parameter set to obtain the cutting machine optimization control strategy parameter set.

7. The control strategy optimization method for a laser cutting machine according to claim 6, wherein, The obtaining of the cutting machine optimization control strategy parameter set includes: Perform cutting influence analysis on each cutting region in the workpiece equivalent cutting region set respectively to obtain the cutting region influence level information; Perform cutting correlation influence analysis based on the cutting region influence level information to determine the cutting region correlation influence factor set; Perform influence weighted integration analysis on the equivalent region optimization strategy parameter set based on the cutting region correlation influence factor set to obtain the cutting machine optimization control strategy parameter set.

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