Method and system for regulating and controlling laser energy and light spots of precise laser cutting die

Abstract

The invention relates to a laser energy and light spot regulation and control method and system for a precise laser cutting die, and the method comprises the following steps: carrying out the scanning collection and vector conversion of a laser cutting die, and obtaining cutting die cutting path vector data; material property determination is conducted on the laser cutting die through material detection equipment, and material property parameters are obtained; performing laser energy demand calculation and light spot form calculation on the laser cutting die based on the cutting die cutting path vector data and the material attribute parameters to obtain a regional energy parameter table and light spot form distribution data; performing laser parameter modulation on a laser based on the regional energy parameter table and the light spot form distribution data to obtain a real-time laser output control sequence; and based on the real-time laser output control sequence, a preset laser processing system is controlled to conduct cutting die cutting processing treatment. The technical problem that in the prior art, real-time sensing and responding capacity for material attributes and cutting path characteristics is lacked is solved.

Description

Technical Field

[0001] This invention relates to the field of die-cutting technology, and in particular to a method and system for controlling the laser energy and spot size of a precision laser die-cutting mold. Background Technology

[0002] Most current laser processing systems operate with fixed or preset laser parameters, lacking real-time perception and response capabilities to material properties and cutting path characteristics. This "one-cut" control method easily leads to problems during processing, such as excessive energy causing material ablation and expansion of the heat-affected zone, or insufficient energy resulting in incomplete cutting and edge burrs. Especially when processing heterogeneous composite materials or dies with complex vector paths, the laser energy requirements of different areas vary significantly. If precise regional and time-based control cannot be achieved, it will seriously affect processing quality and efficiency. In addition, the fixed spot shape also limits its adaptability to different cutting directions and curvature variation areas, making it difficult to balance cutting speed and edge quality. Summary of the Invention

[0003] The main objective of this invention is to provide a method for controlling the laser energy and spot size of a precision laser die-cutting mold, which solves the technical problem that the existing technology lacks the ability to perceive and respond to material properties and cutting path characteristics in real time.

[0004] To achieve the above objectives, the present invention provides a method for controlling the laser energy and spot size of a precision laser die-cutting mold, comprising the following steps: The laser cutting die is scanned, acquired, and converted into vector data to obtain the cutting path vector data of the die. Material properties of the laser die-cutting mold are determined using material testing equipment to obtain material property parameters; Based on the cutting path vector data of the die-cutting mold and the material property parameters, the laser energy demand and spot shape of the laser die-cutting mold are calculated to obtain a regional energy parameter table and spot shape distribution data. Based on the regional energy parameter table and the spot shape distribution data, the laser parameters are modulated to obtain a real-time laser output control sequence. The laser processing system is controlled by the real-time laser output control sequence to perform die cutting processing.

[0005] Furthermore, the scanning and vector conversion of the laser die-cutting mold to obtain the die-cutting path vector data includes: The laser die-cutting mold is scanned and acquired using an optical scanner to obtain the die-cutting mold outline image data, and the edge contour points of the die-cutting mold outline image data are extracted. The edge contour points are fitted with Bézier curves to obtain a continuous smooth contour curve, and the continuous smooth contour curve is sampled at equal intervals to obtain a sequence of die trajectory sampling points. Piecewise linear interpolation is performed on the sequence of sampling points of the die-cutting mold trajectory to obtain a vector path data structure. The vector path data structure is then optimized and sorted to obtain the die-cutting path vector data.

[0006] Furthermore, the material testing equipment includes a multi-wavelength spectrometer and an ultrasonic phased array probe. The material property determination of the laser die using the material testing equipment to obtain material property parameters includes: The reflectance of the metal surface of the laser-cut die was measured using a multi-wavelength spectrometer to obtain the material's spectral absorption characteristic curve. The laser die-cutting mold is ultrasonically scanned and detected using an ultrasonic phased array probe to obtain the internal acoustic characteristics of the material. Based on the internal acoustic characteristics of the material, the metal microstructure is analyzed to obtain the microscopic properties of the material. The material property parameters of the laser die-cutting mold are analyzed based on the spectral absorption characteristic curve and the microscopic characteristics of the material.

[0007] Furthermore, the step of calculating the laser energy requirement and spot shape of the laser die based on the cutting path vector data and material property parameters to obtain a regional energy parameter table and spot shape distribution data includes the following steps: The laser die is divided into cutting areas by meshing based on the cutting path vector data of the die, resulting in meshed cutting area data. The material properties of the meshed cutting area are then mapped based on the material property parameters to obtain a material property distribution map of the area. Based on the material property distribution map of the region, the energy demand gradient of the laser cutting mold in different regions is calculated to obtain the energy demand gradient field. Based on the energy demand gradient field, the gridded data of the cutting region is partitioned into energy demand areas to obtain the regional energy parameter table. Based on the regional energy parameter table, the laser cutting mold in different regions is analyzed for spot morphology to obtain an ideal spot intensity distribution. Based on the ideal spot intensity distribution, the spatial light modulator of the preset laser processing system is phase-coded to obtain a phase modulation function. Based on the phase modulation function, the parameters of the beam shaper of the laser processing system are controlled to obtain the beam spot morphology distribution data.

[0008] Furthermore, the step of calculating the energy demand gradient of the laser die-cutting mold in different regions based on the regional material property distribution map to obtain the energy demand gradient field includes: Thermophysical parameter decoupling analysis was performed on the material property distribution map of the region to obtain the material thermal response characteristics; Based on the thermal response characteristics of the material, the molten pool dynamics of the laser cutting die are calculated to obtain the molten pool evolution characteristics; The energy density of the cutting area of ​​the laser die is calibrated based on the molten pool evolution characteristics, and spatial energy distribution is performed based on the energy density of the cutting area to obtain an energy distribution profile. Based on the energy distribution profile, the multi-source thermal field distribution data of the laser cutting mold is calculated, and energy demand analysis is performed based on the multi-source thermal field distribution data to obtain the energy demand gradient field.

[0009] Furthermore, the step of partitioning the gridded data of the cut region based on the energy demand gradient field to obtain a regional energy parameter table includes: Spatial gradient analysis is performed on the energy demand gradient field to obtain an energy density contour map, and the energy density contour map is then divided into regions to obtain an energy clustering region map. Based on the energy clustering region map, energy matching calculations are performed on the gridded data of the cut region to obtain a grid energy mapping table; Based on the aforementioned grid energy mapping table, dynamic energy allocation is performed to obtain regional power control parameters. Based on the regional power control parameters, the energy modulation sequence of the cut region is optimized, and the energy modulation sequence is subjected to time-series correlation analysis to obtain a regional energy parameter table.

[0010] Furthermore, the step of modulating the laser parameters based on the regional energy parameter table and the spot morphology distribution data to obtain a real-time laser output control sequence includes the following steps: Based on the regional energy parameter table, the cutting area of ​​the laser die is serialized to obtain energy parameter serialization data. Then, based on the spot morphology distribution data, the energy parameter serialization data is mapped to spot morphology parameters to obtain spot morphology mapping parameters. The energy and spot parameters are decoupled and calculated for the spot morphology mapping parameters to obtain the parameter decoupling result. Based on the parameter decoupling result, the energy parameter serialization data is reconstructed to obtain the parameter reconstruction sequence. The laser control parameter mapping is performed on the reconstructed parameter sequence to obtain a laser control parameter mapping set; Based on the laser control parameter mapping set, the laser parameters are modulated in real time to obtain a real-time laser output control sequence.

[0011] This invention also provides a laser energy and spot control system for precision laser die-cutting, comprising: The acquisition module is used to scan and acquire data from the laser die-cutting mold and perform vector conversion to obtain vector data of the die-cutting path. The measurement module is used to measure the material properties of the laser die using material testing equipment to obtain material property parameters. The acquisition module is used to calculate the laser energy demand and spot shape of the laser die based on the cutting path vector data and the material property parameters, and to obtain regional energy parameter tables and spot shape distribution data. The acquisition module is used to modulate the laser parameters based on the regional energy parameter table and the spot shape distribution data to obtain a real-time laser output control sequence. The acquisition module is used to control a preset laser processing system to perform die cutting processing based on the real-time laser output control sequence.

[0012] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the methods described above.

[0013] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any of the methods described above.

[0014] This invention provides a method for controlling the laser energy and spot size of a precision laser die-cutting mold, comprising the following steps: scanning and acquiring data from the laser die-cutting mold and converting it into vector data to obtain cutting path vector data; measuring the material properties of the laser die-cutting mold using a material testing device to obtain material property parameters; calculating the laser energy requirement and spot shape of the laser die-cutting mold based on the cutting path vector data and the material property parameters to obtain a regional energy parameter table and spot shape distribution data; modulating the laser parameters based on the regional energy parameter table and spot shape distribution data to obtain a real-time laser output control sequence; and controlling a preset laser processing system to perform die-cutting processing based on the real-time laser output control sequence. This method solves the technical problem of the lack of real-time perception and response capabilities for material properties and cutting path characteristics in existing technologies, and achieves the technical effect of maintaining optimal spot size matching under different cutting directions and complex paths, effectively improving cut quality, and reducing edge burrs and heat-affected zones. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the steps of a method for controlling the laser energy and spot size of a precision laser die-cutting mold in one embodiment of the present invention; Figure 2This is a structural block diagram of the laser energy and spot control system for a precision laser die-cutting mold according to an embodiment of the present invention; Figure 3 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0016] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] like Figure 1 As shown, Figure 1 This invention provides a method for controlling the laser energy and spot size of a precision laser die-cutting mold, comprising the following steps: Step S1: Scan and collect the laser die-cutting mold data and perform vector conversion to obtain the die-cutting path vector data.

[0019] Specifically, the laser die-cutting mold is scanned and converted into vector data to obtain the cutting path vector data. This process first uses a high-precision optical scanning device to collect comprehensive data on the physical contour of the laser die-cutting mold, obtaining dense point cloud information of its surface geometry and edge features. Then, the collected point cloud data is input into the image processing module, where edge detection and contour tracking algorithms are used to extract the actual cutting boundary of the die-cutting mold, and noise and redundant information are further removed. Then, the discrete contour points are converted into continuous vector paths through curve fitting and Bezier interpolation techniques, thereby generating accurate cutting path vector data. This vector data not only contains the geometric coordinates of the path, but also records the direction, curvature changes, and key node information, ensuring that subsequent laser processing can accurately reproduce the original design intent. Moreover, this vector data format is compatible with mainstream laser control systems, facilitating path optimization and segmentation processing. For example, when processing complex irregular-shaped die-cutting molds for high-end electronic product packaging, this method can accurately identify the tiny snap-fit ​​slots and arc transition areas given by the die-cutting mold and convert them into high-fidelity vector paths. This allows the laser to accurately follow these fine structures for cutting in subsequent processing, avoiding dimensional deviations or structural breakage caused by path distortion, thereby ensuring the assembly accuracy and service life of the die-cutting mold.

[0020] Step S2: The material properties of the laser die are measured using a material testing device to obtain material property parameters.

[0021] Specifically, the material properties of the laser-cut die are measured using material testing equipment to obtain material property parameters. This process first utilizes non-contact material testing equipment integrated into the laser processing system, such as a near-infrared spectrometer, a laser-induced breakdown spectroscopy system, or a thermal conductivity sensing module, to perform real-time scanning and compositional analysis on the material surface provided by the laser-cut die. This obtains information on its physical and chemical properties, including but not limited to the material type, thickness, thermal absorptivity, reflectivity, thermal conductivity, and surface coating characteristics. Then, these raw signals are transformed into standardized material property parameters that can be used for laser parameter calculations through a preset algorithm model. These parameters not only reflect the overall uniformity of the material but also identify potential material differences or defects in local areas, providing a reliable basis for subsequent energy distribution and spot control. Moreover, this testing process is completed automatically before the die enters the processing area without manual intervention, ensuring the timeliness and accuracy of data acquisition. For example, when processing complex irregular-shaped die-cutting molds for high-end electronic product packaging, if the die-cutting mold is composed of multiple layers of composite materials (such as PET / aluminum foil / adhesive layer), the material testing equipment can accurately identify the material composition and thickness distribution of each area, and thus determine that a higher laser energy density is required in the aluminum foil layer, while the heat input needs to be controlled in the PET layer to prevent melting edges, thereby laying the foundation for achieving precise zoning processing.

[0022] Step S3: Based on the cutting path vector data of the die-cutting mold and the material property parameters, calculate the laser energy requirement and spot shape of the laser die-cutting mold to obtain a regional energy parameter table and spot shape distribution data.

[0023] Specifically, based on the cutting path vector data and material property parameters, the laser energy requirement and spot shape of the laser die are calculated, resulting in a regional energy parameter table and spot shape distribution data. This process first imports the cutting path vector data and material property parameters obtained in the previous steps into the laser process calculation module. Then, using the built-in physical simulation model and empirical database, the interaction process between the laser and the material is modeled and analyzed. The cutting path vector data provides geometric feature information of the path, such as line segment curvature, corner density, and contour closure, while the material property parameters provide the material's absorption characteristics of the laser. Key parameters such as thermal conductivity and damage threshold are combined to segment the cutting path using an algorithm. The required laser energy density is calculated for each segment to ensure effective cutting without overheating or incomplete melting, regardless of material type or geometric complexity. The algorithm dynamically adjusts the ideal spot shape based on path curvature and directional continuity. For example, a circular symmetrical spot is used in straight sections to increase cutting speed, while an elliptical or variable-focus spot is optimized at high-curvature corners to improve contour accuracy. This results in a regional energy parameter table and spot shape distribution data, including recommended energy values ​​and corresponding spot shape parameters for each region. For instance, when processing complex irregular-shaped die-cutting molds for high-end electronic product packaging, this method can identify areas with straight cutting lines, dense micropores, and sharp corners. Considering the multi-layer composite material properties, the system automatically calculates that higher energy and a small elliptical spot are needed in straight sections covered by aluminum foil to ensure penetration, while lower energy and a circular focused spot are used around the micropores in the PET film area to prevent thermal deformation, thus achieving optimal processing configuration across the entire region.

[0024] Step S4: Based on the regional energy parameter table and the spot shape distribution data, the laser parameters are modulated to obtain a real-time laser output control sequence.

[0025] Specifically, the laser parameters are modulated based on the regional energy parameter table and spot morphology distribution data to obtain a real-time laser output control sequence. This process first imports the regional energy parameter table and spot morphology distribution data generated in the previous step into the laser control system. The system sorts the processing requirements of each region according to the temporal sequence of the die-cutting path and, combined with the motion trajectory and speed information of the laser processing head, maps the spatial partition parameters into temporal control commands. This dynamically adjusts energy-related parameters such as the laser's output power, pulse frequency, and duty cycle. Simultaneously, it links the adjustable focus lens group or spatial light modulator in the optical path to adjust the focusing position, beam divergence angle, or mode distribution in real time according to the spot morphology distribution data. This achieves dynamic reconstruction of the spot shape and size, ensuring that the laser beam always has optimal energy density and geometric matching in different cutting segments. The entire modulation process is executed by a high-speed controller with a response time of less than milliseconds to adapt to the instantaneous changes required in high-speed processing. Finally, a real-time laser output control sequence completely synchronized with the cutting path is generated. This sequence accurately describes the laser energy state and spot morphology to be output at each moment throughout the entire processing. For example, when processing complex irregular-shaped die-cutting molds for high-end electronic product packaging, when the laser head travels to the PET material area containing dense micropores, the control system automatically reduces the output energy and switches to a small circular spot mode according to the real-time laser output control sequence to avoid material melting and sticking; while when entering the straight cutting section containing aluminum foil layer, the power is rapidly increased and adjusted to an elliptical long focal spot to improve cutting efficiency and edge quality, thereby achieving intelligent and adaptive high-precision processing throughout the entire process.

[0026] Step S5: Based on the real-time laser output control sequence, control the preset laser processing system to perform die cutting processing.

[0027] Specifically, the laser processing system is controlled by the real-time laser output control sequence to perform die cutting. This process first transmits the previously generated real-time laser output control sequence to the main control unit of the preset laser processing system. This system includes a laser, a galvanometer scanning device, a motion platform, and focusing optics. Upon receiving the control sequence, the main control unit synchronizes it with the spatiotemporal coordinates of the processing path and drives the laser to emit laser pulses according to the energy output parameters specified in the sequence. Simultaneously, the high-speed galvanometer system precisely deflects the beam direction based on the cutting path vector data, ensuring the laser spot accurately falls at the predetermined position given by the die. The motion platform, in coordination with the galvanometer, performs multi-axis linkage to achieve continuous scanning and cutting of complex trajectories. Throughout the processing, the control system continuously reads the instructions from the real-time laser output control sequence and dynamically adjusts the laser power intensity and spot shape, ensuring that cutting operations in different areas are executed according to the regional energy parameter table and spot shape distribution data, thereby guaranteeing the consistency and precision of the cutting quality. For example, when processing complex irregular-shaped die-cutting molds for high-end electronic product packaging, when the cutting path enters the micro-hole array area, the system automatically reduces the energy output and switches to a small-sized circular spot based on the real-time laser output control sequence to prevent the PET material from carbonizing or deforming at the edges due to heat accumulation. When the path turns into a straight section containing an aluminum foil layer, the laser energy is immediately increased and modulated into an elliptical long focal spot to enhance penetration and increase cutting speed. Ultimately, a high-quality cutting effect with no burrs, no melted edges, and precise dimensions is achieved on the entire die-cutting mold, fully demonstrating the precise control capability and process adaptability of this method in practical application scenarios.

[0028] In a specific embodiment, the step of scanning and converting the laser die-cutting mold to obtain the die-cutting path vector data includes: The laser die-cutting mold is scanned and acquired using an optical scanner to obtain the die-cutting mold outline image data, and the edge contour points of the die-cutting mold outline image data are extracted. The edge contour points are fitted with Bézier curves to obtain a continuous smooth contour curve, and the continuous smooth contour curve is sampled at equal intervals to obtain a sequence of die trajectory sampling points. Piecewise linear interpolation is performed on the sequence of sampling points of the die-cutting mold trajectory to obtain a vector path data structure. The vector path data structure is then optimized and sorted to obtain the die-cutting path vector data.

[0029] Specifically, the laser die-cutting mold is scanned and vectorized to obtain vector data of the cutting path. This process first involves high-resolution scanning of the laser die-cutting mold using an optical scanner to acquire complete two-dimensional or three-dimensional image information of its surface, forming the mold contour image data. This image data accurately reflects the actual shape and structure of the laser die-cutting mold, including its outer contour, internal holes, snap-fit ​​structure, and various fine geometric features. Subsequently, the system uses image processing algorithms to perform edge detection on the mold contour image data, employing operators such as Canny, Sobel, or Laplacian to extract clear edge contour points. These edge contour points accurately mark the positions of material boundaries, forming the original geometric skeleton. Next, Bézier curve fitting is performed on these discrete edge contour points. Through control point optimization algorithms, the point series is fitted into one or more continuous and smooth contour curves, thereby eliminating discontinuities caused by scanning noise or edge jaggedness, making the path smoother and conforming to the original design intent. This fitting method not only preserves key geometric features such as rounded corners, sharp corners, and curve transitions, but also effectively compresses the data volume and improves efficiency. To improve subsequent processing efficiency, the continuous smooth contour curve is then sampled at equal intervals according to a preset spatial step size. This ensures that the sampling points are evenly distributed along the path, avoiding the problem of excessively sparse point spacing in areas with large curvature and excessively dense redundancy in straight line segments. This results in an ordered sequence of die-cutting trajectory sampling points. This sequence not only maintains the geometric integrity of the path but also provides a stable data foundation for subsequent interpolation and path construction. Then, the die-cutting trajectory sampling point sequence is subjected to piecewise linear interpolation, that is, every two adjacent sampling points are connected by straight line segments to form a preliminary vector path composed of multiple line segments. This path is then encapsulated into a standard vector path data structure, which typically includes coordinate points, connection relationships, direction attributes, and topological information, making it easy for computer systems to recognize and call. Finally, to improve the efficiency and continuity of laser processing, the vector path data structure needs to be optimized and sorted. This involves rearranging the cutting sequence according to strategies such as the shortest path principle, minimum galvanometer deflection angle, or reducing idle travel, avoiding frequent laser head starts and stops or large jumps, thereby generating an efficient, coherent, and logically clear die-cutting path vector data.For example, when processing complex irregular-shaped die-cutting molds for high-end electronic product packaging, these molds typically contain multiple micro-holes, polygonal borders, and fine text markings. Through the aforementioned process, the optical scanner can completely capture its intricate structure, edge extraction accurately identifies the boundaries of each micron-level hole, Bezier fitting ensures a smooth transition between arcs and bevels, equidistant sampling ensures consistent path accuracy, piecewise linear interpolation constructs executable vector segments, and path optimization rationally plans the cutting sequence from the outer frame to the inner hole, avoiding material deformation due to uneven local heating. The final output die-cutting path vector data not only highly reproduces the original design but also has good processing feasibility, laying a solid foundation for subsequent laser energy control and precise cutting.

[0030] In a specific embodiment, the material testing equipment includes a multi-wavelength spectrometer and an ultrasonic phased array probe. The process of measuring the material properties of the laser die using the material testing equipment to obtain material property parameters includes: The reflectance of the metal surface of the laser-cut die was measured using a multi-wavelength spectrometer to obtain the material's spectral absorption characteristic curve. The laser die-cutting mold is ultrasonically scanned and detected using an ultrasonic phased array probe to obtain the internal acoustic characteristics of the material. Based on the internal acoustic characteristics of the material, the metal microstructure is analyzed to obtain the microscopic properties of the material. The material property parameters of the laser die-cutting mold are analyzed based on the spectral absorption characteristic curve and the microscopic characteristics of the material.

[0031] Specifically, the material properties of the laser-cut mold are determined using material testing equipment, including a multi-wavelength spectrometer and an ultrasonic phased array probe. First, the multi-wavelength spectrometer measures the broad-spectrum reflectivity of the metal surface of the laser-cut mold. By emitting multiple specific wavelengths of visible and near-infrared light beams and collecting their reflection responses on the material surface, data on the material's reflection characteristics at different laser wavelengths are constructed. Then, based on the principle of energy conservation, the absorption ratio of the material for each wavelength band is derived, ultimately forming a material spectral absorption characteristic curve. This curve accurately reflects the energy coupling efficiency when the laser interacts with the material, and is particularly crucial for selecting and matching the laser wavelength with the material's absorption peaks. Simultaneously, the system activates the ultrasonic phased array probe to perform non-destructive ultrasonic scanning of the laser-cut mold. This probe emits a controllable ultrasonic beam through an array transducer, penetrating the material's interior and receiving echo signals from different interfaces and grain boundaries, thus acquiring the internal structure of the material. The system analyzes the internal acoustic characteristics of the laser die, including acoustic impedance distribution, sound velocity attenuation, and scattering properties. These acoustic characteristics are closely related to the material's density, grain size, phase distribution, and defect state. An inversion algorithm is then used to deeply analyze these internal acoustic characteristics, identifying the material's uniformity, the presence of inclusions or layered structures, and generating microscopic properties characterizing the material's internal quality and structural state. This map, presented in a spatial mapping format, shows the microstructural differences in different regions of the die, providing a basis for assessing process risks such as heat-affected zone tendency and crack sensitivity. Subsequently, the system integrates the material's spectral absorption curve with its microscopic properties, combining a pre-set material database and physical model to comprehensively evaluate the overall material type, thickness distribution, thermal conductivity, melting point, and response characteristics to laser energy of the laser die. Finally, a set of structured material property parameters is output. These parameters not only cover macroscopic physical indicators but also include microscopic and optical properties affecting laser processing behavior, ensuring that subsequent energy calculations and spot control have a scientific basis. For example, when processing complex irregular-shaped die-cutting molds for high-end electronic product packaging, these molds are typically composed of multiple layers of composite metal materials, such as aluminum-plated PET-based composite boards or laminated structures of stainless steel and copper foil. A multi-wavelength spectrometer can identify that the aluminum layer has a low reflectivity and high absorptivity near a wavelength of 1064nm, making it suitable for processing with a fiber laser. Meanwhile, an ultrasonic phased array probe can detect the interlayer bonding strength and copper foil thickness fluctuations, discovering minor delamination or thickness unevenness in certain areas. Based on this, the system adjusts the material property parameters, indicating that the laser power density should be reduced in these areas to avoid interlayer peeling or perforation. This achieves accurate parameter modeling based on the actual material state, ensuring the stability and consistency of the entire die-cutting process.

[0032] In a specific embodiment, the step of calculating the laser energy demand and spot shape of the laser die based on the die cutting path vector data and the material property parameters to obtain a regional energy parameter table and spot shape distribution data includes the following steps: The laser die is divided into cutting areas by meshing based on the cutting path vector data of the die, resulting in meshed cutting area data. The material properties of the meshed cutting area are then mapped based on the material property parameters to obtain a material property distribution map of the area. Based on the material property distribution map of the region, the energy demand gradient of the laser cutting mold in different regions is calculated to obtain the energy demand gradient field. Based on the energy demand gradient field, the gridded data of the cutting region is partitioned into energy demand areas to obtain the regional energy parameter table. Based on the regional energy parameter table, the laser cutting mold in different regions is analyzed for spot morphology to obtain an ideal spot intensity distribution. Based on the ideal spot intensity distribution, the spatial light modulator of the preset laser processing system is phase-coded to obtain a phase modulation function. Based on the phase modulation function, the parameters of the beam shaper of the laser processing system are controlled to obtain the beam spot morphology distribution data.

[0033] Specifically, based on the cutting path vector data and material property parameters, the laser energy requirement and spot shape of the laser die are calculated to obtain a regional energy parameter table and spot shape distribution data. This process first uses the cutting path vector data as a basis to perform a refined cutting area gridding of the overall processing area of ​​the laser die. By dividing the spatial region enclosed by continuous vector paths into several regular or adaptive-sized grid units, cutting area gridding data with spatial indexing relationships is formed. Each grid unit corresponds to a local area on the die and can carry independent process parameter information. Subsequently, combined with the material property parameters obtained in the previous steps, including the material spectral absorption characteristic curve and material microstructure, the cutting area gridding data undergoes material property mapping processing. This involves spatially registering grid units at different locations with measured or derived material properties (such as thickness, thermal conductivity, reflectivity, and microstructure uniformity), thereby constructing a regional material property distribution map covering the entire die surface. This map not only reflects the macroscopic distribution of material types but also demonstrates the inconsistency in processing response caused by differences in microstructure. Based on this, the system further calculates the energy demand gradient of the laser cutting mold in different regions according to the material property distribution map of the region. By establishing a laser-material heat conduction model and combining the material's absorptivity, melting point, and thermal diffusivity, it simulates the energy density required for effective cutting in each grid region under standard process conditions. Dynamic correction factors are introduced based on factors such as path curvature and linear velocity changes, ultimately generating a continuously changing energy demand gradient field. This gradient field is presented in the form of a scalar field, intuitively showing the spatial transition trend from low-energy to high-energy regions. Subsequently, cluster analysis and threshold segmentation are performed on this energy demand gradient field to identify continuous regions with similar energy demands. Based on this, the gridded data of the cutting region is partitioned into energy demand areas, forming a regional energy parameter table containing each region number, coordinate range, and recommended laser power value, providing a basis for subsequent differentiated energy output.Next, the system analyzes the laser cutting pattern in different regions based on the regional energy parameter table. For each energy zone, combined with its material properties and path geometry (such as straight lines, arcs, acute angles, etc.), it calculates the ideal spot intensity distribution that achieves the best energy distribution and cutting quality. For example, a Gaussian circular spot is used in regions with high absorptivity and straight paths to improve efficiency, while a flat-topped or elliptical elongated spot is designed in regions with high curvature or material sensitivity to enhance contour control. Subsequently, the ideal spot intensity distribution is used as the objective function and input into the preset laser... In the phase inversion algorithm of the spatial light modulator of the optical processing system, the phase modulation function that can generate the spot shape is solved by iterative Fourier transform or Gerchberg-Saxton algorithm, and encoded as a driving signal. Finally, based on the phase modulation function, the beam shaper of the laser processing system is controlled in real time to adjust the phase delay characteristics of its internal liquid crystal array or micromirror array, thereby changing the phase distribution of the incident laser wavefront, realizing the spatial shaping of the beam, and finally outputting the dynamic spot shape that matches each region, forming complete spot shape distribution data. For example, when processing complex irregular-shaped die-cutting molds for high-end electronic product packaging, the die-cutting mold contains a composite structure of an aluminum-plated PET layer and stainless steel reinforcing ribs. The system divides the die-cutting mold into multiple grid regions through the above process, identifies that the aluminum layer region requires lower energy but has high requirements for beam uniformity, while the stainless steel rib region requires high-energy concentrated irradiation. By dividing the energy requirements into zones, the parameters of each segment are clearly defined, and at the corners, elliptical beams are calculated to avoid overheating. Circular Gaussian beams are used to increase the speed in straight sections. Then, a spatial light modulator generates the corresponding phase function, driving the beam shaper to adjust the beam shape in real time. This ensures that the entire die-cutting mold achieves high-quality synchronous cutting of multiple materials and features in one processing cycle, fully demonstrating the intelligent adaptability and process precision advantages of this method in complex application scenarios.

[0034] In a specific embodiment, the step of calculating the energy demand gradient of the laser die-cutting mold in different regions based on the regional material property distribution map to obtain the energy demand gradient field includes: Thermophysical parameter decoupling analysis was performed on the material property distribution map of the region to obtain the material thermal response characteristics; Based on the thermal response characteristics of the material, the molten pool dynamics of the laser cutting die are calculated to obtain the molten pool evolution characteristics; The energy density of the cutting area of ​​the laser die is calibrated based on the molten pool evolution characteristics, and spatial energy distribution is performed based on the energy density of the cutting area to obtain an energy distribution profile. Based on the energy distribution profile, the multi-source thermal field distribution data of the laser cutting mold is calculated, and energy demand analysis is performed based on the multi-source thermal field distribution data to obtain the energy demand gradient field.

[0035] Specifically, based on the material property distribution map of the region, the energy demand gradient of the laser cutting mold in different regions is calculated to obtain the energy demand gradient field. This process first performs thermophysical parameter decoupling analysis on the material property distribution map of the region. By separating and independently modeling the material property information obtained in the previous steps, such as thermal conductivity, specific heat capacity, density, melting point and surface absorptivity, in a spatial grid, the contribution weight of each parameter to the laser energy response is identified, and then an independent heat conduction equation is constructed in each grid region. This achieves a refined description of the thermal behavior in complex composite materials. Especially in the interface region of multilayer or heterogeneous materials, the decoupling analysis can accurately distinguish the thermal coupling effect and interface thermal resistance between different material layers, thereby obtaining a more realistic material thermal response characteristic. This characteristic not only includes the heating rate and thermal diffusion direction of the material under laser irradiation, but also reflects its phase transition trend and thermal damage sensitivity under the critical energy threshold. Based on this, the system further performs molten pool dynamics calculations on the laser cutting die based on the thermal response characteristics of the material. It uses the finite element method or finite difference method to simulate the melting process of the local area under the action of the laser beam, considering physical mechanisms such as surface tension, Marangoni convection, recoil pressure and vaporization evaporation, and dynamically tracks the formation, expansion and solidification process of the molten pool. It obtains key evolution parameters such as molten pool depth, width, morphological stability and edge wetting angle, forming molten pool evolution characteristics that vary with time and space. These characteristics provide a direct basis for judging whether complete penetration cutting is achieved or excessive melting is avoided. Subsequently, the system calibrates the energy density of the cutting area of ​​the laser die-cutting mold based on the molten pool evolution characteristics. Specifically, based on the required melt penetration depth or cutting width in different areas, it deduces the minimum laser energy density threshold required to maintain a stable molten pool. This threshold is then corrected using process parameters such as scanning speed and pulse duty cycle to ensure a dynamic balance between energy input and material removal. The recommended energy density values ​​for each grid cell are then spatially interpolated and smoothed to generate a continuous energy distribution profile covering the entire die surface. This profile displays the spatial gradient of energy demand in a three-dimensional curved surface, clearly identifying the distribution patterns of high-energy, transition, and low-energy regions. Next, based on the energy distribution profile, the system further calculates the multi-source thermal field distribution data that may be generated during the actual processing of the laser die-cutting mold. This includes the transient temperature field under the combined effects of the main laser beam heat source, adjacent path heat transfer, residual heat accumulation, and environmental heat dissipation. Through a thermal field superposition model, the system predicts the range of the heat-affected zone, the location of thermal stress concentration, and the material deformation trend, thereby identifying potential cutting defect risk areas due to thermal crosstalk.Finally, based on the multi-source thermal field distribution data, a comprehensive energy demand analysis is performed, and feedback compensation is applied to the initial energy distribution. For example, energy is appropriately reduced in areas with severe heat accumulation to prevent ablation, while energy is moderately increased in edge areas with rapid heat dissipation to ensure cutting penetration. Ultimately, an energy demand gradient field is generated that satisfies both physical cutting requirements and optimizes thermal management, providing a scientific basis for subsequent precise zonal energy supply. For instance, when processing complex irregular-shaped die-cutting molds for high-end electronic product packaging, the mold is composed of an aluminized PET film and local stainless steel reinforcing ribs. Through the above process analysis, the system found that the stainless steel area has fast thermal conductivity and a high melting point, requiring a higher energy density to form a stable molten pool. The PET layer, on the other hand, is highly thermally sensitive and prone to carbonization or blistering due to local overheating. The critical energy thresholds of the two materials are determined through molten pool dynamics simulation. Combined with the heat accumulation effect brought about by multi-path scanning, the energy output is actively reduced in dense corner areas. The resulting energy demand gradient field achieves differentiated and intelligent energy configuration for different materials and structures on the same die-cutting mold, significantly improving cutting quality and process stability.

[0036] In a specific embodiment, the step of partitioning the gridded data of the cut region based on the energy demand gradient field to obtain a regional energy parameter table includes: Spatial gradient analysis is performed on the energy demand gradient field to obtain an energy density contour map, and the energy density contour map is then divided into regions to obtain an energy clustering region map. Based on the energy clustering region map, energy matching calculations are performed on the gridded data of the cut region to obtain a grid energy mapping table; Based on the aforementioned grid energy mapping table, dynamic energy allocation is performed to obtain regional power control parameters. Based on the regional power control parameters, the energy modulation sequence of the cut region is optimized, and the energy modulation sequence is subjected to time-series correlation analysis to obtain a regional energy parameter table.

[0037] Specifically, based on the energy demand gradient field, the gridded data of the cut area is partitioned by energy demand to obtain a regional energy parameter table. This process first performs high-precision spatial gradient analysis on the energy demand gradient field. By calculating the partial derivative and rate of change of energy density in the spatial domain, boundary regions with drastic changes in energy demand and relatively stable plateau regions are identified. Based on this, an energy density contour map is generated. This contour map clearly depicts the spatial distribution of the same energy demand level in the form of closed curves, similar to contour lines in a topographic map. It can intuitively reflect the transition trend and gradient steepness from low-energy to high-energy regions. Subsequently, the system performs region segmentation processing on the energy density contour map. Image segmentation algorithms such as watershed, region growing, or cluster-based segmentation methods are used to divide the continuous contour lines into several independent closed regions with similar energy levels, forming an energy clustering region map. Each clustering region represents a specific energy demand level, such as a low-power region suitable for thin-layer materials, a high-power region suitable for thick metals, and a transitional modulation region between the two. Based on this, the system performs energy matching calculations on the gridded data of the cutting area according to the energy clustering region map. Each pre-divided grid cell is spatially superimposed and compared with its corresponding energy clustering region to determine its appropriate energy level. Combining the recommended energy density value and material response characteristics within that region, each grid cell is assigned specific laser power, pulse frequency, and action time parameters, thus constructing a grid energy mapping table containing each grid location and its corresponding process parameters. This mapping table achieves a refined connection from macroscopic partitioning to microscopic control. Next, the system dynamically allocates energy based on the grid energy mapping table. Considering the acceleration, path curvature changes, and heat conduction effects between adjacent regions during the actual operation of the laser processing head, the energy parameters of each grid are fine-tuned in real time to ensure a smooth transition of energy output during high-speed scanning, avoiding overheating or breakage caused by sudden changes. This generates a set of regional power control parameters linked to spatial location. These parameters not only include static setpoints but also integrate dynamic compensation factors. Finally, the system optimizes the overall energy modulation sequence of the cutting area based on the regional power control parameters, integrates the path sequence, processing direction and energy switching logic, eliminates unnecessary power jumps, improves the continuity and stability of energy output, and further performs time-series correlation analysis on the energy modulation sequence, transforming the spatial partition parameters into a control command stream on the time axis, ensuring that the energy output at each moment during the sequential scanning process of the laser head is precisely matched with the material properties and geometric features of the current location, ultimately forming a structurally complete, time-series clear, and directly laser-driving regional energy parameter table.For example, when processing complex irregular-shaped die-cutting molds for high-end electronic product packaging, the die-cutting mold includes a large area of ​​aluminized PET and local stainless steel reinforcing ribs. The system clearly divides the aluminum film area (medium and low energy), the joint welding area (high energy), and the transition connection area (gradual energy) into an energy density contour map generated by the above process. After regional segmentation, multiple energy clustering areas are formed, and energy mapping is completed by combining gridded data. The final output regional energy parameter table enables the laser to automatically increase the power to 120W when entering the stainless steel section, maintain a stable cutting power of 80W in the straight PET section, and gradually change the power by 5% in the corner transition area. This effectively avoids material cracking or discontinuous cutting caused by sudden energy changes, and realizes integrated high-precision processing of complex composite structures.

[0038] In a specific embodiment, the step of modulating the laser parameters based on the regional energy parameter table and the spot morphology distribution data to obtain a real-time laser output control sequence includes the following steps: Based on the regional energy parameter table, the cutting area of ​​the laser die is serialized to obtain energy parameter serialization data. Then, based on the spot morphology distribution data, the energy parameter serialization data is mapped to spot morphology parameters to obtain spot morphology mapping parameters. The energy and spot parameters are decoupled and calculated for the spot morphology mapping parameters to obtain the parameter decoupling result. Based on the parameter decoupling result, the energy parameter serialization data is reconstructed to obtain the parameter reconstruction sequence. The laser control parameter mapping is performed on the reconstructed parameter sequence to obtain a laser control parameter mapping set; Based on the laser control parameter mapping set, the laser parameters are modulated in real time to obtain a real-time laser output control sequence.

[0039] Specifically, the laser parameters are modulated based on the regional energy parameter table and the spot morphology distribution data to obtain a real-time laser output control sequence. This process first performs energy parameter serialization processing on the cutting area of ​​the laser die based on the regional energy parameter table. That is, the static energy parameters generated in the previous steps and divided by spatial region are arranged in a time sequence according to the execution order of the actual processing path. Combined with the movement trajectory and scanning speed of the laser processing head on the die, the recommended power, pulse frequency, duty cycle and other energy parameters of each region are converted into energy parameter serialization data aligned with the time axis, ensuring that the laser output can dynamically switch with position changes during processing. At the same time, the system further performs spot morphology parameter mapping on the energy parameter serialization data based on the spot morphology distribution data. That is, according to the required spot size, shape (such as circular, elliptical or flat-top) and energy distribution characteristics of each region, the corresponding beam shaping command is matched for each energy parameter segment in the time sequence to form a spot morphology mapping parameter that includes both energy and spot attributes, thereby realizing the coordinated configuration of energy delivery and spot geometry. Based on this, the system performs decoupling calculations on the energy and spot parameters of the light spot morphology mapping parameters. Since in actual laser systems, laser power adjustment and spot morphology control are often implemented through different hardware modules (e.g., power is controlled by laser current, and the spot is controlled by spatial light modulator or variable focus lens group), there may be coupling interference between the two. Therefore, it is necessary to separate and analyze the energy parameters and spot parameters in the control dimension through physical modeling and control algorithms, identify their independent action mechanisms and cross-influence relationships, and then obtain the parameter decoupling results. The results clarify how to independently adjust the energy and spot to achieve the optimal cutting effect under specific processing conditions. Subsequently, based on the parameter decoupling results, the energy parameter serialization data is reconstructed. That is, while retaining the original energy requirements, a compensation factor for spot control and dynamic response delay correction are introduced to optimize the parameter combination at each time node, avoiding processing defects caused by spot switching lag or untimely energy response. Finally, a logically complete, time-accurate, and interference-resistant parameter reconstruction sequence is generated. Next, the system performs laser control parameter mapping on the parameter reconstruction sequence, transforming abstract process parameters into low-level driving signals that the laser processing system can recognize. For example, the target power value is converted into the current output command of the laser power supply, and the spot shape command is converted into the phase-coded data of the spatial light modulator or the focusing offset of the galvanometer system, forming a complete set of laser control parameter mapping. This mapping set not only includes the main control command, but also covers synchronous triggering, status feedback and safety protection mechanisms to ensure the reliability and real-time performance of command execution.Finally, based on the laser control parameter mapping set, the laser parameters are modulated in real time. During processing, the control system continuously reads the command stream from the mapping set and performs closed-loop adjustment based on the position information fed back from the encoder, dynamically adjusting the laser output state to generate a real-time laser output control sequence that is completely synchronized with the die-cutting path. This sequence precisely guides the energy intensity and spot shape that the laser should output within each millisecond. For example, when processing complex irregular-shaped dies for high-end electronic product packaging, when the laser head enters the stainless steel reinforcing rib area, the system increases the power to a high energy level and switches to a small circular spot focusing mode according to the real-time laser output control sequence to ensure penetration. When transitioning to the aluminized PET film area, the energy is automatically reduced and modulated into an elliptical long focal spot to prevent thermal damage. The entire process requires no machine downtime for switching, achieving intelligent continuous processing of multiple materials and features, fully demonstrating the advanced nature and practicality of this method in high-precision laser die-cutting manufacturing.

[0040] The laser energy and spot control method for the precision laser die-cutting mold in the embodiments of the present invention has been described above. The laser energy and spot control system for the precision laser die-cutting mold in the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 2 One embodiment of the laser energy and spot control system for precision laser die-cutting in this invention includes: The acquisition module 21 is used to scan and acquire the laser cutting die and convert it into vector data to obtain the cutting path vector data of the die. Measurement module 22 is used to measure the material properties of the laser die using a material testing device to obtain material property parameters; Calculation module 23 is used to calculate the laser energy demand and spot shape of the laser die based on the cutting path vector data and the material property parameters, and to obtain regional energy parameter table and spot shape distribution data. Modulation module 24 is used to modulate the laser parameters of the laser based on the regional energy parameter table and the spot shape distribution data to obtain a real-time laser output control sequence; The processing module 25 is used to control a preset laser processing system to perform die cutting processing based on the real-time laser output control sequence.

[0041] In this embodiment, the specific implementation of each unit in the above system embodiment is described in the above method embodiment, and will not be repeated here.

[0042] Reference Figure 3 This invention also provides a computer device whose internal structure can be as follows: Figure 3As shown, the computer device includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores the data corresponding to this embodiment. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.

[0043] Those skilled in the art will understand that Figure 3 The structures shown are merely block diagrams of some structures related to the present invention and do not constitute a limitation on the computer devices on which the present invention is applied.

[0044] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0045] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the present invention and embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0046] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0047] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for regulating laser energy and spot of a precision laser knife mold, characterized in that, The method comprises the following steps: The laser cutter is scanned and collected, and vector conversion is performed to obtain cutter cutting path vector data; Material attribute parameters of the laser cutter are measured by a material detection device; Based on the cutter cutting path vector data and the material attribute parameters, laser energy demand calculation and spot shape calculation are performed on the laser cutter to obtain a partition energy parameter table and spot shape distribution data; Based on the real-time laser output control sequence, a preset laser processing system is controlled to perform cutter cutting processing. The laser cutter is scanned and collected, and vector conversion is performed to obtain cutter cutting path vector data, which comprises the following steps:

2. The method of claim 1, wherein the laser energy and spot size are controlled by a computer program. The laser cutter is scanned and collected by an optical scanner to obtain cutter contour image data, and edge contour points of the cutter contour image data are extracted; Bezier curve fitting is performed on the edge contour points to obtain a continuous smooth contour curve, and the continuous smooth contour curve is sampled at equal intervals to obtain a cutter trajectory sampling point sequence; The cutter trajectory sampling point sequence is subjected to piecewise linear interpolation to obtain a vector path data structure, and the vector path data structure is subjected to path optimization sorting to obtain cutter cutting path vector data. The material detection device comprises a multi-wavelength spectrum analyzer and an ultrasonic phased array probe, and the material attribute parameters of the laser cutter are measured by the material detection device, which comprises the following steps:

3. The method of claim 1, wherein the laser energy and spot size are controlled by a computer program. The reflectivity of the metal surface of the laser cutter is measured by the multi-wavelength spectrum analyzer to obtain a material spectral absorption characteristic curve; The laser cutter is subjected to ultrasonic scanning detection based on the ultrasonic phased array probe to obtain material internal acoustic characteristics, and the material microstructure is analyzed based on the material internal acoustic characteristics to obtain material micro characteristics; The material attribute parameters of the laser cutter are analyzed based on the material spectral absorption characteristic curve and the material micro characteristics. The laser cutter is subjected to cutting region grid division based on the cutter cutting path vector data to obtain cutting region grid data, and the cutting region grid data is subjected to material characteristic mapping based on the material attribute parameters to obtain a region material characteristic distribution map; 4. The method of claim 1, wherein the laser energy and spot size are controlled by a computer program. Based on the region material characteristic distribution map, energy demand gradient calculation is performed on the laser cutter in different regions to obtain an energy demand gradient field, and the cutting region grid data is subjected to energy demand partitioning based on the energy demand gradient field to obtain a partition energy parameter table; Based on the partition energy parameter table, spot shape analysis is performed on the laser cutter in different regions to obtain an ideal spot intensity distribution, and the spatial light modulator of the preset laser processing system is subjected to phase encoding based on the ideal spot intensity distribution to obtain a phase modulation function; ​ ​ Parameter control is performed on a beam shaper of the laser processing system based on the phase modulation function, and spot shape distribution data is obtained.

5. The method of claim 4, wherein the laser energy and spot size are controlled by the laser power and the laser spot size. The energy demand gradient field is obtained by performing energy demand gradient calculation on the laser knife model of different regions based on the regional material characteristic distribution map. The material thermal response characteristics are obtained by performing thermal physical parameter decoupling analysis on the regional material characteristic distribution map. The molten pool evolution characteristics are obtained by performing molten pool dynamics calculation on the laser knife model based on the material thermal response characteristics. The cutting area energy density of the laser knife model is calibrated based on the molten pool evolution characteristics, and spatial energy distribution is performed based on the cutting area energy density, and an energy distribution profile is obtained. The multi-source thermal field distribution data of the laser knife model is calculated based on the energy distribution profile, and energy demand analysis is performed based on the multi-source thermal field distribution data, and an energy demand gradient field is obtained.

6. The method of claim 4, wherein the laser energy and spot size are controlled by a computer program. The energy demand partitioning is performed on the cutting region gridding data based on the energy demand gradient field, and a partitioned energy parameter table is obtained, including: The energy density contour map is obtained by performing spatial gradient analysis on the energy demand gradient field, and the energy density contour map is divided into regions to obtain an energy clustering region map; The energy matching calculation is performed on the cutting region gridding data based on the energy clustering region map, and a grid energy mapping table is obtained; The energy dynamic allocation is performed based on the grid energy mapping table, and a regional power control parameter is obtained; The energy modulation sequence of the cutting region is optimized based on the regional power control parameter, and the time sequence correlation analysis is performed on the energy modulation sequence, and a partitioned energy parameter table is obtained.

7. The method of claim 1, wherein the laser energy and spot size are controlled by a computer program. The laser parameter modulation is performed on the laser based on the partitioned energy parameter table and the spot shape distribution data, and a real-time laser output control sequence is obtained, including the following steps: The energy parameter serialization data is obtained by performing energy parameter serialization processing on the cutting region of the laser knife model based on the partitioned energy parameter table, and the spot shape parameter mapping is performed on the energy parameter serialization data based on the spot shape distribution data, and a spot shape mapping parameter is obtained; The energy and spot parameter decoupling calculation is performed on the spot shape mapping parameter, and a parameter decoupling result is obtained, and the parameter reconstruction sequence is obtained by performing parameter reconstruction on the energy parameter serialization data based on the parameter decoupling result; The laser control parameter mapping set is obtained by performing laser control parameter mapping on the parameter reconstruction sequence; The real-time laser parameter modulation is performed on the laser based on the laser control parameter mapping set, and a real-time laser output control sequence is obtained.

8. A laser energy and spot control system for precision laser die-cutting, characterized in that, It includes: The acquisition module is used for scanning and collecting the laser knife model and vector conversion, and the knife model cutting path vector data is obtained; The measurement module is used for measuring the material properties of the laser knife model by a material detection device, and material attribute parameters are obtained; The acquisition module is used for performing laser energy demand calculation and spot shape calculation on the laser knife model based on the knife model cutting path vector data and the material attribute parameters, and a partitioned energy parameter table and a spot shape distribution data are obtained; The acquisition module is configured to modulate laser parameters of the laser based on the partition energy parameter table and the spot shape distribution data, and obtain a real-time laser output control sequence. The acquisition module is configured to control a preset laser processing system to perform die cutting processing based on the real-time laser output control sequence. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8. The processor executes the computer program to implement the steps of the method of any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 7.

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