A kind of engraving control method and system

Abstract

The present invention relates to the field of engraving control technology, and in particular to an engraving control method and system. The method comprises the following steps: obtaining a sculpture design plan, marking its morphological outline and generating a three-dimensional sculpture model. Next, the engraving demand data is obtained, and the material raw materials are classified into two categories: wood and stone. If a wood raw material is selected, temperature and humidity monitoring is performed, the stress warping is analyzed, and compensation planning is performed through linear regression. If a stone raw material is selected, its internal cracks are analyzed through X-ray scanning, the cutting fracture limit stress is identified, and the engraving logic design is performed based on this. Finally, combining the wood compensation planning with the stone engraving logic, an automated engraving control firmware is designed, and it is uploaded to a cloud platform to execute the engraving control method to achieve automated and precise engraving. The present invention makes the engraving control technology more perfect by optimizing the engraving control technology.

Description

Technical Field

[0001] The present invention relates to the technical field of engraving control, and in particular to an engraving control method and system. Background Art

[0002] With the introduction of automation and digital technology, CNC engraving has gradually become an important development trend in the engraving industry. Through the combination of digital design and CNC equipment, the engraving process can be automated, which greatly improves production efficiency, accuracy and repeatability, and can meet more complex and delicate engraving needs. However, although CNC engraving has made significant progress in many fields, it still faces technical difficulties such as the particularity of engraving materials, the adaptability of equipment, and the inevitable material deformation during the engraving process. Different engraving materials (such as wood, stone, etc.) have different physical properties. These differences will cause problems such as warping, cracks, and deformation during the engraving process, affecting the engraving effect and the quality of the finished product. Wood will warp or crack due to its natural humidity and temperature changes; and during the engraving process, the internal cracks and uneven texture of stone will cause fractures or stress concentration during the cutting process. These factors will have an adverse effect on the engraving effect. Therefore, how to carry out targeted engraving control according to the characteristics of different materials has become a key issue in improving engraving accuracy and quality. However, a traditional engraving control method has the problem of low accuracy in identifying the state of raw materials, resulting in large engraving accuracy errors. Summary of the invention

[0003] Based on this, it is necessary to provide an engraving control method and system to solve at least one of the above technical problems.

[0004] To achieve the above object, a carving control method is provided, the method comprising the following steps:

[0005] Step S1: obtaining a sculpture design plan; marking the shape contour of the sculpture design plan to obtain multi-angle shape contour data; constructing a three-dimensional sculpture model according to the multi-angle shape contour data to obtain a three-dimensional sculpture model;

[0006] Step S2: Obtain carving demand data; classify carving material raw materials according to the carving demand data to obtain carving material raw materials, wherein the carving material raw materials include wood material raw materials and stone material raw materials;

[0007] Step S3: When it is determined that the carving material is a wooden material, the temperature and humidity of the wooden material is monitored to obtain wooden material temperature and humidity monitoring data; carving force warping analysis is performed based on the wooden material temperature and humidity monitoring data to obtain wooden carving force warping data; wooden material carving compensation planning is performed based on the force warping linear regression data to obtain wooden material carving compensation planning data;

[0008] Step S4: When it is determined that the engraving material is a stone material, an X-ray scan is performed on the stone material, and the internal crack structure of the material is analyzed to obtain the internal crack structure data of the stone material; the cutting fracture limit stress is identified according to the internal crack structure data of the stone material to obtain the cutting fracture limit stress data; the stone material engraving logic is designed based on the cutting fracture limit stress data to obtain the stone material engraving logic;

[0009] Step S5: Design the automatic engraving control firmware based on the wood material engraving compensation planning data and the stone material engraving logic to obtain the automatic engraving control firmware; send the automatic engraving control firmware to the cloud platform to execute the engraving control method.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: Obtaining a sculpture design plan;

[0012] Step S12: performing multi-angle view analysis on the sculpture design plan to obtain multi-angle views of the sculpture;

[0013] Step S13: marking the shape contour of the multi-angle views of the sculpture to obtain multi-angle shape contour data;

[0014] Step S14: constructing a three-dimensional sculpture model for the sculpture design plan according to the multi-angle morphological contour data to obtain a three-dimensional sculpture model.

[0015] Preferably, step S3 comprises the following steps:

[0016] Step S31: When it is determined that the engraving material is a wooden material, the temperature and humidity of the wooden material are monitored to obtain wooden material temperature and humidity monitoring data;

[0017] Step S32: extracting texture patterns from the three-dimensional sculpture model to obtain sculpture texture pattern data;

[0018] Step S33: performing carving force warping analysis on the sculpture texture pattern data according to the temperature and humidity monitoring data of the wood raw material to obtain the wood carving force warping data;

[0019] Step S34: performing linear regression analysis on the warping data of the wood carving to obtain linear regression data of the warping;

[0020] Step S35: Performing wood material carving compensation planning based on the force warping linear regression data to obtain wood material carving compensation planning data.

[0021] Preferably, step S33 includes the following steps:

[0022] Step S331: estimating the moisture content of the wood raw material based on the temperature and humidity monitoring data of the wood raw material to obtain the moisture content of the wood raw material; measuring the expansion coefficient of the wood raw material based on the moisture content of the wood raw material to obtain the expansion coefficient of the wood raw material;

[0023] Step S332: Performing texture geometric curve analysis on the sculpture texture pattern data to obtain a texture geometric curve; performing three-dimensional effect geometric surface drop calculation on the sculpture texture pattern data based on the texture geometric curve to obtain a three-dimensional effect geometric surface drop;

[0024] Step S333: performing a theoretical required engraving force analysis based on the drop of the three-dimensional effect geometric surface to obtain the theoretical required engraving force; performing a continuous fluctuation analysis of the engraving force on the theoretical required engraving force based on the drop of the three-dimensional effect geometric surface to obtain continuous fluctuation data of the engraving force;

[0025] Step S334: analyzing the expansion anisotropy distribution of the wood raw material according to the expansion coefficient of the wood raw material to obtain expansion anisotropy distribution data; calculating the mean difference of internal stress distribution on the expansion anisotropy distribution data to obtain the mean difference of expansion internal stress distribution;

[0026] Step S335: numerically integrate the engraving force resistance instability according to the engraving force continuous fluctuation data on the expansion internal stress distribution mean difference to obtain the engraving force resistance instability data;

[0027] Step S336: Perform a carving force warping analysis based on the carving force resistance instability data to obtain the wood carving force warping data.

[0028] Preferably, step S35 includes the following steps:

[0029] Step S351: performing a thinning process on the pressure in the warping region based on the stress warping linear regression data to obtain thinning data on the pressure in the warping region;

[0030] Step S352: performing warping amplitude correlation matching according to the warping region pressure sparse data to obtain pressure-correlated warping amplitude matching data;

[0031] Step S353: adjusting the engraving force in stages based on the sparse pressure data of the warping area and the pressure-associated warping amplitude matching data to obtain the engraving force in stages;

[0032] Step S354: performing phased adjustment of the local material removal amount according to the phased engraving applied force to obtain phased local material removal amount adjustment data;

[0033] Step S355: Perform wood material carving compensation planning based on the staged carving applied force and the staged local material removal amount adjustment data to obtain wood material carving compensation planning data.

[0034] Preferably, step S4 comprises the following steps:

[0035] Step S41: When it is determined that the engraving material is a stone material, an X-ray scan is performed on the stone material to obtain an X-ray image of the stone material; based on the X-ray image of the stone material, an internal crack structure analysis is performed on the material to obtain internal crack structure data of the stone material;

[0036] Step S42: performing crack depth distribution morphology recognition on the internal crack structure data of the stone raw material to obtain crack depth distribution morphology data;

[0037] Step S43: calculating the curvature of the sculpture surface texture of the three-dimensional sculpture model to obtain the curvature of the sculpture surface texture;

[0038] Step S44: identifying the cutting fracture limit stress according to the crack depth distribution morphology data and the curvature of the engraved surface texture, and obtaining the cutting fracture limit stress data;

[0039] Step S45: Design the stone material carving logic based on the cutting fracture limit stress data to obtain the stone material carving logic.

[0040] Preferably, step S44 includes the following steps:

[0041] Step S441: Calculating the lateral spacing of crack bifurcation based on the crack depth distribution morphology data to obtain the lateral spacing of crack bifurcation;

[0042] Step S442: performing lateral spacing expansion power law distribution identification on the crack depth distribution morphology data according to the lateral spacing of the crack bifurcation to obtain crack spacing expansion distribution data;

[0043] Step S443: Analyze the cutting parameters required by the engraving theory for the curvature of the engraving surface texture to obtain the cutting parameters required by the engraving theory;

[0044] Step S444: performing cutting fracture force approximate error fitting on the crack spacing expansion distribution data according to the cutting parameters required by the engraving theory to obtain cutting fracture force approximate fitting data;

[0045] Step S445: Identify the cutting fracture limit stress based on the cutting fracture force approximate fitting data to obtain the cutting fracture limit stress data.

[0046] Preferably, step S444 includes the following steps:

[0047] Extract the cutting angle and cutting force of the cutting parameters required by engraving theory, and obtain the theoretically required cutting angle and the theoretically required cutting force respectively;

[0048] Perform crack connection splitting density analysis on the crack spacing expansion distribution data to obtain crack connection splitting density data;

[0049] Based on the crack connection splitting density data, the mean difference of crack section radius is calculated for the crack spacing expansion distribution data, and the mean difference of crack section radius is obtained;

[0050] According to the theoretically required cutting angle and the theoretically required cutting force, the cutting stress concentration instability analysis is performed on the mean difference of the crack section radius to obtain the crack cutting stress concentration instability data;

[0051] According to the crack connection splitting density data, the force dispersion gradient of the crack cutting stress concentration instability data is quantified to obtain the cutting stress dispersion gradient data;

[0052] Based on the instability data of cutting stress concentration and cutting stress dispersion gradient data, the approximate error fitting of cutting fracture force is performed to obtain the approximate fitting data of cutting fracture force.

[0053] Preferably, step S45 includes the following steps:

[0054] Step S451: assigning fracture risk weight regions to stone raw materials based on cutting fracture limit stress data to obtain fracture risk weight regions;

[0055] Step S452: performing cutting angle correction on the fracture risk weight region according to the cutting fracture limit stress data to obtain limit stress cutting angle correction data;

[0056] Step S453: adjusting the cutting rate and the lateral cutting feed amount according to the limit stress cutting angle correction data and the cutting fracture limit stress data, and obtaining the cutting rate adjustment data and the lateral cutting feed amount adjustment data respectively;

[0057] Step S456: Based on the limiting stress cutting angle correction data, the cutting rate adjustment data and the lateral cutting feed adjustment data, the stone material carving logic is designed to obtain the stone material carving logic.

[0058] Preferably, the present invention further provides an engraving control system for executing the engraving control method as described above, the engraving control system comprising:

[0059] The three-dimensional sculpture model building module is used to obtain a sculpture design plan; mark the shape contour of the sculpture design plan to obtain multi-angle shape contour data; and build a three-dimensional sculpture model according to the multi-angle shape contour data to obtain a three-dimensional sculpture model;

[0060] The carving demand type classification module is used to obtain carving demand data; classify carving material raw materials according to the carving demand data to obtain carving material raw materials, wherein the carving material raw materials include wood material raw materials and stone material raw materials;

[0061] The wood material carving compensation planning module is used to monitor the temperature and humidity of the wood material when the carving material is determined to be a wood material, and obtain the wood material temperature and humidity monitoring data; perform carving force warping analysis based on the wood material temperature and humidity monitoring data, and obtain the wood carving force warping data; perform wood material carving compensation planning based on the force warping linear regression data, and obtain the wood material carving compensation planning data;

[0062] The stone material carving logic design module is used to determine that the carving material is a stone material, perform X-ray scanning on the stone material, and analyze the internal crack structure of the material to obtain the internal crack structure data of the stone material; identify the cutting fracture limit stress based on the internal crack structure data of the stone material to obtain the cutting fracture limit stress data; perform stone material carving logic design based on the cutting fracture limit stress data to obtain the stone material carving logic;

[0063] The automatic engraving control firmware design module is used to design the automatic engraving control firmware based on the wood material engraving compensation planning data and the stone material engraving logic to obtain the automatic engraving control firmware; the automatic engraving control firmware is sent to the cloud platform to execute the engraving control method.

[0064] The beneficial effect of the present invention is that by obtaining the sculpture design plan and marking the multi-angle morphological contour, the complex shape and structure of the sculpture can be captured more accurately. By converting the multi-angle morphological contour data into a three-dimensional sculpture model, the system can provide an accurate digital design basis for subsequent carving, ensuring the morphological consistency and high precision during the carving process. By obtaining the carving demand data and classifying the material raw materials, different control strategies can be adopted for different carving materials (such as wood and stone materials) to ensure that the carving requirements of each material are best met. The classification of material raw materials helps to identify the unique properties of each material, providing a scientific basis for subsequent carving planning and control, thereby avoiding errors and losses caused by differences in material properties. Temperature and humidity monitoring and stress warping analysis of wood material raw materials. By real-time monitoring of the temperature and humidity of wood raw materials, the system can grasp the physical change law of wood, predict and analyze the warping problem of wood during carving. Carving compensation planning through a linear regression model can effectively correct the stress deformation of wood during carving, thereby improving the accuracy and quality of wood carving and reducing errors caused by warping. Optimize control for the characteristics of stone material raw materials. By analyzing the internal crack structure through X-ray scanning technology, the internal defects of the stone can be accurately identified before engraving, avoiding fracture or damage during the engraving process caused by cracks. Based on the identification of crack data and cutting fracture limit stress, the system can generate stone engraving logic, rationally plan the engraving path and strength, minimize stone damage, and improve the success rate and quality of engraving. By combining the compensation planning of wood raw material engraving and the engraving logic of stone raw materials, an automated engraving control firmware that meets different materials is designed. This firmware can accurately control the engraving equipment, realize real-time adjustment and optimization during the automated engraving process, and further improve the accuracy, speed and quality of engraving. The support of the cloud platform makes the deployment and update of the firmware more efficient, and the monitoring and adjustment of the engraving process can also be realized remotely, which improves the overall intelligent level of production. Therefore, the present invention is an improved processing of a traditional engraving control method, which solves the problem that a traditional engraving control method has low accuracy in identifying the state of the raw materials, thereby causing large errors in the engraving accuracy, improves the accuracy of identifying the state of the raw materials, and reduces the error in the engraving accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A schematic diagram of the steps of an engraving control method;

[0066] Figure 2 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0067] Figure 3 for Figure 1 Detailed implementation steps of step S4 in FIG. DETAILED DESCRIPTION

[0068] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0069] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0070] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0071] To achieve this, please refer to Figures 1 to 3 , a carving control method, the method comprising the following steps:

[0072] Step S1: obtaining a sculpture design plan; marking the shape contour of the sculpture design plan to obtain multi-angle shape contour data; constructing a three-dimensional sculpture model according to the multi-angle shape contour data to obtain a three-dimensional sculpture model;

[0073] Step S2: Obtain carving demand data; classify carving material raw materials according to the carving demand data to obtain carving material raw materials, wherein the carving material raw materials include wood material raw materials and stone material raw materials;

[0074] Step S3: When it is determined that the carving material is a wooden material, the temperature and humidity of the wooden material is monitored to obtain wooden material temperature and humidity monitoring data; carving force warping analysis is performed based on the wooden material temperature and humidity monitoring data to obtain wooden carving force warping data; wooden material carving compensation planning is performed based on the force warping linear regression data to obtain wooden material carving compensation planning data;

[0075] Step S4: When it is determined that the engraving material is a stone material, an X-ray scan is performed on the stone material, and the internal crack structure of the material is analyzed to obtain the internal crack structure data of the stone material; the cutting fracture limit stress is identified according to the internal crack structure data of the stone material to obtain the cutting fracture limit stress data; the stone material engraving logic is designed based on the cutting fracture limit stress data to obtain the stone material engraving logic;

[0076] Step S5: Design the automatic engraving control firmware based on the wood material engraving compensation planning data and the stone material engraving logic to obtain the automatic engraving control firmware; send the automatic engraving control firmware to the cloud platform to execute the engraving control method.

[0077] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of an engraving control method of the present invention. In this example, the engraving control method includes the following steps:

[0078] Step S1: obtaining a sculpture design plan; marking the shape contour of the sculpture design plan to obtain multi-angle shape contour data; constructing a three-dimensional sculpture model according to the multi-angle shape contour data to obtain a three-dimensional sculpture model;

[0079] In the embodiment of the present invention, first, after obtaining the sculpture design plan, the sculpture design plan is digitized using computer-aided design (CAD) software. The plan includes the main shape contour, detail texture, etc. of the sculpture. The geometric information of each part of the figure is extracted through the design software and converted into digital graphic data. Next, the plan is marked with multi-angle morphological contours, and the contour information of the sculpture at different angles is collected. These marked data not only include the main shape contour of the sculpture, but also include changes in the details, such as the ups and downs of the sculpture surface, fine concave and convex textures, etc. These multi-angle morphological contour data are obtained through data fusion technology and processed by linear interpolation to ensure that the contours under different viewing angles are seamlessly connected to avoid morphological loss. Based on these multi-angle morphological contour data, a three-dimensional sculpture model is constructed. This step uses a three-dimensional modeling algorithm to first convert the two-dimensional contour data into three-dimensional space coordinates, and generate a complete three-dimensional model of the sculpture through the "boundary surface filling method" combined with the "stereoscopic geometric modeling" technology. In this process, the details of the sculpture need to be optimized to ensure the accuracy of the details. Through reasonable precision control, it is ensured that all surfaces and structures of the sculpture model meet the design requirements and are suitable for subsequent engraving operations.

[0080] Step S2: Obtain carving demand data; classify carving material raw materials according to the carving demand data to obtain carving material raw materials, wherein the carving material raw materials include wood material raw materials and stone material raw materials;

[0081] In the embodiment of the present invention, after obtaining the engraving demand data from the manufacturer or contractor, the data first needs to be preprocessed to ensure the integrity and validity of the data. These demand data include the specific material of the engraving, the size requirements, the complexity of the engraving, etc. Next, the engraving material raw materials are classified. By analyzing the material information in the engraving demand data, the "feature extraction-based classification algorithm" is used to automatically classify the materials. The specific operation is to compare each raw material (such as wood and stone) with the predefined classification standard, and accurately classify the material using the characteristic parameters such as density, hardness, and texture. For the distinction between wooden material raw materials and stone material raw materials, a method based on the "multidimensional feature space clustering algorithm" is adopted. According to the specific physical properties of each material, the material raw materials are classified as wooden or stone. Wooden material raw materials generally have high humidity dependence and large expansion, while stone material raw materials are usually relatively hard and have complex internal structures. The classification results directly affect the subsequent engraving scheme design to ensure that each material obtains a suitable engraving method.

[0082] Step S3: When it is determined that the carving material is a wooden material, the temperature and humidity of the wooden material is monitored to obtain wooden material temperature and humidity monitoring data; carving force warping analysis is performed based on the wooden material temperature and humidity monitoring data to obtain wooden carving force warping data; wooden material carving compensation planning is performed based on the force warping linear regression data to obtain wooden material carving compensation planning data;

[0083] In the embodiment of the present invention, when it is determined that the raw material of the engraving material is a wood raw material, the temperature and humidity of the wood raw material are first monitored. This process is implemented by a sensor network, and the sensors are evenly distributed at various key points of the wood raw material, and the ambient temperature and humidity of the wood raw material are monitored in real time by a data acquisition device. The temperature and humidity data are filtered for noise by a Kalman filter algorithm to obtain accurate temperature and humidity values. Next, the engraving force warping analysis is performed based on the temperature and humidity monitoring data. The force warping analysis uses the "finite element method" (FEM) combined with the physical properties of the wood raw material to establish a mechanical model. By analyzing the influence of the engraving force on the wood raw material under different temperature and humidity conditions, the degree of warping of the raw material during the engraving process is calculated. Subsequently, the wood raw material engraving compensation planning is performed based on the linear regression data of the force warping. In this step, a linear relationship between the force and the degree of warping is established using a regression analysis algorithm to determine the compensation strategy. A compensation scheme is designed by analyzing the force magnitude of the warping area. The scheme includes adjusting the engraving tool path and the engraving force applied to ensure that the warping of the raw material during the engraving process is effectively controlled, thereby ensuring that the engraved shape meets the design requirements. This process ensures the accuracy and quality of the engraving.

[0084] Step S4: When it is determined that the engraving material is a stone material, an X-ray scan is performed on the stone material, and the internal crack structure of the material is analyzed to obtain the internal crack structure data of the stone material; the cutting fracture limit stress is identified according to the internal crack structure data of the stone material to obtain the cutting fracture limit stress data; the stone material engraving logic is designed based on the cutting fracture limit stress data to obtain the stone material engraving logic;

[0085] In the embodiment of the present invention, when it is determined that the raw material of the engraving material is a stone material, an X-ray scan of the stone raw material is first performed. Specifically, an industrial X-ray scanning device is used to perform an all-round scan of the stone raw material. The device uses high-frequency X-ray radiation and obtains information such as cracks, cavities, and stress concentration areas inside the raw material by detecting reflection and penetration signals. The X-ray image obtained after scanning is processed using digital image processing technology. Edge detection and grayscale analysis are performed on the image to further extract cracks and tiny defects in the internal structure of the raw material. In this process, a "threshold segmentation algorithm" is applied to distinguish different density areas of the stone material, so as to clarify the position, size and distribution of the cracks. The morphology and distribution information of the cracks are converted into structural data, which include the depth, width and relative position of the cracks. Based on this crack structure data, the cutting fracture limit stress is further identified. The "finite element analysis method" (FEA) is used to simulate the stress distribution of the stone under different cutting conditions. Specifically, a three-dimensional model of the stone raw material is created and discretized into a finite number of elements, and numerical calculations are performed using the principles of elastic mechanics. Using this model, the stress value of each node is calculated, especially in the stress concentration area near the crack, to determine the cutting fracture limit stress of the stone. At this time, the cutting fracture limit stress is comprehensively calculated by considering multiple factors such as the influence of internal cracks in the stone, material hardness, and temperature effect. Finally, the obtained cutting fracture limit stress data provides an indicator of the fracture risk of the stone during the engraving process. Next, based on the obtained cutting fracture limit stress data, the stone raw material engraving logic design is carried out. First, by analyzing the cutting fracture limit stress data, the weak areas of the stone (i.e., the stress concentration area and the area around the crack) are identified. Then, according to these weak areas, combined with the "cutting force distribution optimization algorithm", the engraving path and feed method are designed. The algorithm dynamically optimizes the force conditions of each cutting path to ensure that the cutting force of the tool is evenly distributed on each section of the path, avoid excessive cutting force in the stress concentration area, and thus prevent the stone from breaking or breaking during the engraving process. After the engraving path is optimized, the engraving speed and depth are further adjusted to ensure that the force of the stone during the engraving process is always within a safe range to avoid excessive cutting deformation. Finally, generate specific engraving logic, including details such as cutting tool type, motion trajectory, speed control, etc. This engraving logic will be used to control the actual operation of the engraving machine in subsequent steps.

[0086] Step S5: Design the automatic engraving control firmware based on the wood material engraving compensation planning data and the stone material engraving logic to obtain the automatic engraving control firmware; send the automatic engraving control firmware to the cloud platform to execute the engraving control method.

[0087] In the embodiment of the present invention, the automatic engraving control firmware is designed based on the wood material engraving compensation planning data and the stone material engraving logic. First, the wood material engraving compensation planning data is integrated with the stone material engraving logic. The wood material engraving compensation planning data mainly involves the compensation of the influence of temperature and humidity changes on the morphology of wood materials, while the stone material engraving logic focuses on the control of avoiding stone fracture during high-intensity cutting. To this end, the designed control firmware needs to consider the difference between the two materials and adjust the engraving parameters through feedback during the cutting process. In the firmware design, the "adaptive control algorithm" is applied to adjust the feed speed and cutting depth during the engraving process in real time. The algorithm corrects the target engraving path in real time according to the actual movement of the tool to ensure that the tool performs the engraving operation according to the predetermined mechanical requirements. Then, in the design process, the "PID control algorithm" is used to adjust the accuracy and speed of the tool during the engraving process. The PID controller dynamically adjusts the speed and position of the tool according to the input deviation (i.e., the error between the current engraving path and the target path) to ensure that the error during the engraving process is minimized. In addition, a real-time monitoring system should be introduced to monitor the working status of the engraving machine in real time using sensor feedback. If the tool is found to deviate or abnormal pressure occurs during the engraving process, the firmware will immediately adjust the operating parameters to avoid engraving errors or equipment damage. All these control strategies are embedded in the control firmware to form a complete engraving operation logic. After the control firmware design is completed, it is uploaded to the cloud platform. The cloud platform is responsible for unified management and scheduling of multiple engraving devices. Through the cloud platform, users can remotely monitor the execution of engraving tasks and adjust engraving strategies as needed. The cloud platform not only provides real-time data storage and processing functions, but also performs task scheduling and optimizes resource allocation for multiple engraving tasks. Through the "load balancing algorithm" in the cloud, the distribution of tasks can be automatically adjusted according to the working status of the equipment to ensure that each engraving machine operates within a reasonable load range. With the help of the cloud platform, the control firmware can realize cross-device and cross-task automated management, improve the intelligence level of the engraving process, and ensure the consistency and high precision of engraving quality.

[0088] Step S1 includes the following steps:

[0089] Step S11: Obtaining a sculpture design plan;

[0090] Step S12: performing multi-angle view analysis on the sculpture design plan to obtain multi-angle views of the sculpture;

[0091] Step S13: marking the shape contour of the multi-angle views of the sculpture to obtain multi-angle shape contour data;

[0092] Step S14: constructing a three-dimensional sculpture model for the sculpture design plan according to the multi-angle morphological contour data to obtain a three-dimensional sculpture model.

[0093] In the embodiment of the present invention, obtaining the sculpture design plan is the first step of the engraving control method, which mainly relies on high-precision scanning and digital rendering technology. First, the sculpture plan design is photographed using a high-resolution digital camera to ensure that every part of the drawing can be clearly displayed. All design details should be marked on the drawing, including information such as the proportion, shape and size of the sculpture. When shooting, a professional digital scanner is used for accurate scanning, and the resolution of the scanner should be at least 600DPI to ensure that every stroke and every detail on the drawing is accurately captured. The scanned image data will be converted into a vector format for subsequent computer processing. After obtaining the sculpture design plan, the image is cropped using image processing software to remove the redundant edge parts to ensure that only the design content of the sculpture is retained. In addition, the color and brightness of the image need to be adjusted to improve the contrast of the image, so that the design details are more prominent, which is convenient for subsequent parsing and analysis. At this time, the obtained plan data contains the two-dimensional coordinate information of the sculpture, which provides basic data support for subsequent multi-angle view parsing and three-dimensional modeling. The process of multi-angle view parsing of the sculpture design plan mainly relies on the "projection transformation algorithm" in computer graphics. In this process, the two-dimensional image data in the plane map is first converted into projection images under multiple viewing angles. In order to simulate the three-dimensional spatial sense of the sculpture, multiple specific viewing angles (such as the front, side, top and diagonal, etc.) are selected, and the graphics under each viewing angle are transformed in perspective. When performing the projection transformation, the perspective projection model is used to ensure that the scaling and deformation of the graphics under each viewing angle conform to the observation effect of the real world. Specifically, the two-dimensional plane map is mapped into the three-dimensional space using the projection matrix, and the graphic projections in different directions are obtained by adjusting the angle and distance of the viewing angle. In this way, the three-dimensional effect of the sculpture can be presented on the computer screen without losing its original design details. The projection image generated at each viewing angle will be converted into standardized two-dimensional data for subsequent processing and analysis. Finally, the obtained multi-angle views include the outline and detail information of the sculpture observed from different directions, which will provide support for the subsequent morphological contour marking and three-dimensional modeling. The morphological contour marking of the multi-angle views of the sculpture involves the "edge detection" technology in image processing. The "Canny edge detection algorithm" is used to process the views obtained from multiple angles. The Canny algorithm first performs Gaussian blurring on the image to remove noise, and then identifies the edges by calculating the gradient value of the image. By detecting changes in pixel values ​​in the image, the algorithm can determine the edge position of the graphic and mark the outline for each view. Further, the "Hough transform" technology is used for straight line detection to extract the straight line outline of the sculpture's appearance. For the curved part, the "Bezier curve fitting" method is used to calculate the precise curve of the sculpture surface through fitting. These marked contours are saved in vector data format, including details such as the starting point, end point, and curvature information of each line segment.In order to further improve the accuracy of the marking, it is also possible to combine multi-view data for cross-validation, and use the least squares method to fine-tune the contours of each view to eliminate errors. Finally, the obtained multi-angle morphological contour data presents the complete appearance information of the sculpture at each perspective, and provides important geometric data support for the subsequent three-dimensional model construction. The process of constructing a three-dimensional sculpture model based on multi-angle morphological contour data relies on the "stereo reconstruction algorithm" in computer graphics. The core of this algorithm is to synthesize multi-angle contour data into a unified three-dimensional space model. In this process, the contour data extracted from multiple perspectives is first converted into three-dimensional point cloud data, and the point cloud model is constructed using the spatial coordinates of each contour point. Then, the point cloud data is meshed using the "Delaunay triangulation algorithm". The algorithm can generate triangular meshes according to the distribution of the point cloud, and establish the three-dimensional structure of the sculpture surface through these meshes. In order to ensure a smooth and seamless surface, the "Laplace smoothing algorithm" is used to optimize the generated three-dimensional mesh so that each vertex of the mesh is kept within a reasonable smooth curvature range to avoid unnatural angles or protrusions. As the optimization proceeds, the resulting three-dimensional sculpture model will gradually approach the real sculpture design form. In order to ensure the accuracy of the 3D model, the marked morphological contour data is combined to repair the details of each key part of the model to ensure that the size and proportion of each part of the model meet the design requirements. Finally, the 3D sculpture model is completed and saved in a standardized 3D data format (such as STL, OBJ, etc.), providing an accurate digital reference for automated control during the carving process.

[0094] Step S3 includes the following steps:

[0095] Step S31: When it is determined that the engraving material is a wooden material, the temperature and humidity of the wooden material are monitored to obtain wooden material temperature and humidity monitoring data;

[0096] Step S32: extracting texture patterns from the three-dimensional sculpture model to obtain sculpture texture pattern data;

[0097] Step S33: performing carving force warping analysis on the sculpture texture pattern data according to the temperature and humidity monitoring data of the wood raw material to obtain the wood carving force warping data;

[0098] Step S34: performing linear regression analysis on the warping data of the wood carving to obtain linear regression data of the warping;

[0099] Step S35: Performing wood material carving compensation planning based on the force warping linear regression data to obtain wood material carving compensation planning data.

[0100] As an example of the present invention, refer to Figure 3As shown, in this example, step S3 includes:

[0101] Step S31: When it is determined that the engraving material is a wooden material, the temperature and humidity of the wooden material are monitored to obtain wooden material temperature and humidity monitoring data;

[0102] In an embodiment of the present invention, the temperature and humidity of the wood material raw material are first monitored in real time. A high-precision temperature and humidity sensor (for example, a digital temperature and humidity sensor DHT22 or SHT30) is used to continuously record the surface temperature and humidity of the wood. The temperature and humidity sensor is fixedly mounted on the surface of the wood and transmits the monitoring data to the data acquisition system wirelessly. The sampling frequency of the sensor is set to once per minute to ensure that the temperature and humidity fluctuations of the wood during the carving process can be captured. The collected data include temperature (unit: Celsius) and relative humidity (unit: percentage), which will form a temperature and humidity monitoring data set for the wood raw material. During the monitoring process, multi-point measurements need to be performed in areas where the ambient temperature and humidity change greatly and in different parts of the wood to ensure that the acquired data is representative. These raw material temperature and humidity monitoring data will be used for the analysis of the force and deformation of the wood during the subsequent carving process, so as to carry out accurate carving compensation design. The monitoring data is summarized through the data processing platform, and the obtained temperature and humidity data will provide a basis for the optimization and adjustment of wood carving.

[0103] Step S32: extracting texture patterns from the three-dimensional sculpture model to obtain sculpture texture pattern data;

[0104] In the embodiment of the present invention, the step of extracting texture patterns from a three-dimensional sculpture model relies on the "UV mapping technology" in computer graphics. First, the three-dimensional sculpture model is converted into a standard polygonal mesh (usually in OBJ format). Then, the surface of the three-dimensional model is unfolded into a two-dimensional plane using a texture mapping algorithm. By calculating the UV coordinates of the model surface, the corresponding area of ​​each part of the model on the two-dimensional plane is obtained. Next, texture information is extracted from the design drawings or existing patterns of the sculpture. The design drawings are usually analyzed using image processing technology to extract the required texture patterns. The texture pattern can be a simple geometric pattern or a complex artistic decoration, depending on the design requirements of the sculpture. When extracting the texture, an algorithm based on edge detection and image segmentation, such as the Canny edge detection algorithm or the adaptive threshold algorithm, is used to clearly separate the details in the pattern from the background. After the texture pattern extraction is completed, the texture pattern is matched one by one with the UV coordinates of the three-dimensional sculpture model to form the texture pattern data of the sculpture surface. These texture pattern data will be used as a reference for surface decoration and detail carving in the subsequent carving process.

[0105] Step S33: performing carving force warping analysis on the sculpture texture pattern data according to the temperature and humidity monitoring data of the wood raw material to obtain the wood carving force warping data;

[0106] In the embodiment of the present invention, the purpose of the warping analysis of the carving force is to evaluate the deformation caused by the temperature and humidity changes of the wood during the wood carving process. First, the temperature and humidity monitoring data of the wood raw material is used, combined with the physical properties of the wood (such as swelling, shrinkage coefficient, etc.), to perform a simulation analysis of the wood force warping. At this time, the finite element analysis (FEA) method is used to apply carving force to the sculpture surface to simulate the influence of temperature and humidity changes on the wood. Specifically, the physical changes of each part of the wood raw material under different temperature and humidity conditions are modeled, and the texture pattern data of the three-dimensional sculpture model is combined to form a three-dimensional deformation model including the influence of temperature and humidity. This model will take into account the deformation characteristics of the wood during the force carving process, including deformation modes such as compression, stretching, and bending. By analyzing the force at each point, the warping value of the wood at different parts can be obtained, and then the wood carving force warping data can be generated. The data includes the degree of warping, deformation direction, and deformation speed of each area, providing an accurate basis for mechanical adjustment during the carving process.

[0107] Step S34: performing linear regression analysis on the warping data of the wood carving to obtain linear regression data of the warping;

[0108] In an embodiment of the present invention, in order to further optimize the force warping compensation of wood during the engraving process, it is necessary to perform linear regression analysis on the force warping data. This step uses a linear regression algorithm to mathematically model the force warping data of wood engraving. First, the warping data of wood at different engraving depths and different temperature and humidity conditions are collected to form a multivariate data set. Based on these data, regression analysis is performed using the Least Squares Method to establish a relationship model between warping deformation and factors such as engraving depth, temperature and humidity. Through regression analysis, the optimal coefficient value is obtained, thereby describing the linear relationship between temperature and humidity changes and warping deformation during the engraving process. Through this model, the deformation trend of wood under different environmental conditions during actual engraving can be predicted. Finally, the obtained linear regression data contains the amplitude and direction of wood deformation under different conditions, providing a basis for subsequent engraving compensation planning.

[0109] Step S35: Performing wood material carving compensation planning based on the force warping linear regression data to obtain wood material carving compensation planning data.

[0110] In an embodiment of the present invention, according to the linear regression analysis results of the force warping, the carving compensation planning is carried out. First, according to the warping prediction model obtained by regression analysis, the deformation of the wood during the carving process is calculated. According to the deformation direction and amplitude of the wood, combined with the carving texture pattern data, the carving compensation strategy is designed. The core of the compensation strategy is to adjust the carving path to ensure that the shape of the finished product after carving is not affected by warping. By compiling a compensation algorithm in the carving control system and adopting a "real-time correction" strategy, the carving path is dynamically adjusted. Specifically, during the carving process, the real-time feedback system is used to monitor the force and temperature and humidity changes of the wood, and the data is input into the compensation algorithm, and the carving path is fine-tuned according to the calculation results. This process gradually corrects the position of each node of the carving path through continuous calculation to ensure that the finished carving product can meet the design requirements. The results of the compensation planning will be saved as a carving compensation data set and used to control the carving machine for precise carving. Through this compensation planning, the warping and deformation of the wood caused by temperature and humidity changes can be effectively offset to ensure stable carving quality.

[0111] Step S33 includes the following steps:

[0112] Step S331: estimating the moisture content of the wood raw material based on the temperature and humidity monitoring data of the wood raw material to obtain the moisture content of the wood raw material; measuring the expansion coefficient of the wood raw material based on the moisture content of the wood raw material to obtain the expansion coefficient of the wood raw material;

[0113] Step S332: Performing texture geometric curve analysis on the sculpture texture pattern data to obtain a texture geometric curve; performing three-dimensional effect geometric surface drop calculation on the sculpture texture pattern data based on the texture geometric curve to obtain a three-dimensional effect geometric surface drop;

[0114] Step S333: performing a theoretical required engraving force analysis based on the drop of the three-dimensional effect geometric surface to obtain the theoretical required engraving force; performing a continuous fluctuation analysis of the engraving force on the theoretical required engraving force based on the drop of the three-dimensional effect geometric surface to obtain continuous fluctuation data of the engraving force;

[0115] Step S334: analyzing the expansion anisotropy distribution of the wood raw material according to the expansion coefficient of the wood raw material to obtain expansion anisotropy distribution data; calculating the mean difference of internal stress distribution on the expansion anisotropy distribution data to obtain the mean difference of expansion internal stress distribution;

[0116] Step S335: numerically integrate the engraving force resistance instability according to the engraving force continuous fluctuation data on the expansion internal stress distribution mean difference to obtain the engraving force resistance instability data;

[0117] Step S336: Perform a carving force warping analysis based on the carving force resistance instability data to obtain the wood carving force warping data.

[0118] In an embodiment of the present invention, the moisture content of the wood raw material is estimated by temperature and humidity monitoring data, and the expansion coefficient of the wood raw material is calculated accordingly. First, according to the temperature and humidity monitoring data of the wood raw material, the moisture content is estimated by applying the "humidity-moisture content relationship model". The model is established based on the relationship between the dry weight and wet weight of the wood. First, the temperature and humidity data of the wood are collected, and the humidity is converted by the empirical formula between the humidity and the moisture content of the wood under the known standard humidity. For example, the real-time moisture content of the wood is obtained by using Fick's law or the empirical relationship between the humidity and the moisture ratio of the wood, combined with the actual temperature and humidity data. Then, the expansion coefficient of the wood is further derived by combining the estimated value with the expansion characteristics of the wood. The expansion coefficient is the ratio of the volume change of the wood when the humidity changes, which has an important influence on the morphological deformation of the wood during the carving process. The expansion coefficient is verified by the "dry-wet shrinkage test method", and multiple wood samples are selected to conduct moisture change experiments, and a regression model between the expansion coefficient and the humidity change is established by the experimental data. The moisture content and expansion coefficient data of the wood raw material obtained will be used for mechanical simulation and warping compensation in the subsequent wood carving process. The texture pattern data of the sculpture is geometrically analyzed to extract the geometric features of the texture surface. The geometric modeling technology based on curve analysis is used. The specific method includes converting the texture pattern data from a two-dimensional plane to a three-dimensional surface, and analyzing the geometric curve of the pattern through the "curve fitting" algorithm. First, each pattern of the sculpture surface texture is discretized and decomposed into a series of curve segments. The morphology of these curve segments is fitted by the "least squares method" to establish the geometric equation of each texture pattern and obtain the geometric curve of the texture pattern. Further, by performing curvature analysis on the geometric curve, the curvature value of each point is calculated to determine the convex and concave shape of the curve. This analysis method can accurately describe the geometric shape of each texture pattern and provide data support for the optimization of the three-dimensional effect in the subsequent carving process. Finally, the texture geometric curve data obtained will provide basic data for carving force analysis and three-dimensional effect calculation. Based on the characteristics of the texture geometric curve, the texture effect of the sculpture can be further spatially transformed and reconstructed, and the surface can be converted into a geometric surface using the "shape interpolation" algorithm. This processing method can retain all the details and three-dimensional effects of the texture pattern and ensure the accuracy of the surface morphology during the carving process. In this step, the theoretical engraving force required in the engraving process is first calculated using the drop data of the three-dimensional effect geometric surface obtained from the texture geometric curve analysis. The engraving force calculation method is based on the mechanical properties of the material and the engraving depth. Here, the friction force, cutting force and yield strength of the wood are considered. During the engraving process, after the tool contacts the wood surface, a certain cutting force will be generated on the wood, which will be affected by the texture surface. By calculating the contact area and cutting depth of the engraving tool on the three-dimensional effect geometric surface, a mechanical analysis is performed to obtain the engraving force required at each position.In the calculation, the engraving force F can be expressed as the following formula: F = σ × A, where σ is the yield strength of the wood and A is the contact area. In the calculation process, taking into account the elasticity, plastic deformation and other characteristics of the wood, the linear elastic theory is used for correction to obtain a more accurate theoretical engraving force. The theoretical value of the engraving force will provide a basis for the subsequent fluctuation analysis of the engraving force. Based on the previous engraving force calculation results, the continuous fluctuation analysis of the engraving force is then carried out. This step mainly considers the force changes during the engraving process, as well as the non-uniformity and dynamic changes during the engraving process. First, according to the distribution of the engraving force required by the theory, an engraving force fluctuation model is established. The model adopts the "mechanical vibration analysis" method to analyze the force changes during the engraving process and obtain the fluctuation curve of the engraving force. At this time, the frequency response of the engraving tool, the deformation characteristics of the wood and the changes in the engraving environment are considered. Through the "time domain analysis" method, the engraving force at each moment in the engraving process is dynamically calculated to obtain the force fluctuation frequency, amplitude and fluctuation trend. Further, the Fourier transform method is used to perform spectrum analysis on the engraving force fluctuation to obtain the frequency component and amplitude characteristics of the engraving force. The analysis results show that the engraving force will fluctuate during the engraving process. This fluctuation comes from the change in friction between the tool and the wood, the expansion of the wood caused by temperature and humidity, and the change in the engraving path. By filtering the fluctuation data, high-frequency noise can be removed, and a stable engraving force curve can be obtained, providing an accurate force reference for the control in the actual engraving process. Finally, the continuous fluctuation data of the engraving force obtained will help the engraving control system to adjust the force fluctuation in the engraving process in real time to ensure the engraving accuracy and surface quality. The expansion anisotropy distribution is analyzed based on the expansion coefficient data of the wood raw material. The expansion coefficient of wood is different in different directions, and this difference is usually related to the fiber structure and moisture content of the wood. In order to obtain the expansion anisotropy distribution data of the wood raw material, the known physical properties of wood are first used to combine the fiber direction of the wood to model it. The anisotropy of the expansion coefficient usually shows different values ​​between the radial, chordal and axial directions, which can be obtained through experimental tests or mechanical models based on wood structures. On this basis, the anisotropic expansion coefficient model is applied for analysis. For example, the "expansion strain matrix" defined by the directional characteristics of the wood is used to calculate the expansion in different directions. For each direction, the expansion strain of wood in different parts is calculated through the known expansion coefficient and moisture content changes, and then the expansion anisotropy distribution data is obtained. These data reflect the morphological changes of wood caused by moisture changes during the carving process, and provide a basis for the subsequent calculation of internal stress distribution. After obtaining the expansion anisotropy distribution data, the next step is to further analyze the internal stress of the wood raw material. Due to the directional differences in the expansion coefficient of wood, internal stress will be generated during the carving process. These internal stresses will affect the mechanical behavior of the carving, and then affect the carving effect.The mean difference calculation of the internal stress distribution is performed on the expansion anisotropy distribution data obtained from step S334. When wood is subjected to humidity changes or external forces, it will produce different expansion degrees in different directions, and this expansion difference will cause the generation of internal stress. The calculation of internal stress is based on the stress-strain relationship of elastic mechanics, especially based on the anisotropic mechanical model of wood. First, by analyzing the expansion anisotropy distribution data, the linear relationship between strain and stress is used to estimate the internal stress distribution of wood. For the expansion coefficient in each direction, the corresponding stress value is calculated according to the material properties of the wood (such as Young's modulus and Poisson's ratio). Then, the internal stress data in each direction are spatially integrated to obtain the overall internal stress distribution. Next, the "mean difference calculation method" is applied to perform mean difference analysis on the obtained internal stress distribution data, aiming to measure the difference in stress distribution in different regions. The calculation results reveal the uniformity of the internal stress of the wood, thereby providing a quantitative basis for the force analysis during the engraving process. If the mean difference value is large, it indicates that the stress distribution inside the wood is uneven, which will lead to warping or cracking problems during the engraving process. First, based on the continuous fluctuation data of engraving force in the previous step, the numerical integration analysis of engraving force resistance instability is performed. The fluctuation of engraving force not only affects the processing effect of wood, but also leads to mechanical instability of wood, especially under the repeated action of force during processing, wood will experience resistance instability. In order to accurately analyze the mechanical behavior of wood during engraving, the "numerical integration" method is used to analyze the fluctuation of engraving force. The numerical integration process includes discretizing the continuous fluctuation data of engraving force in the time domain and converting the fluctuation data into a computable mechanical load model. By integrating the force fluctuation, the dynamic effect of engraving force on wood can be simulated. Specifically, the "Euler method" or "Runge-Kutta method" is applied for numerical integration to obtain the change of internal force at each time step. The engraving force resistance instability data obtained by integration can reveal the instability phenomenon of wood caused by the fluctuation of force during engraving, such as warping and cracking. This process takes into account the anisotropy of wood and the nonlinear effects of force, thereby ensuring the accuracy and stability of the model. By analyzing the numerical integration results of the carving force resistance instability, the key mechanical conditions that cause the deformation instability of wood during the carving process can be identified, providing theoretical support for the optimization of the carving control method. Based on the data in steps S335 and S336, a force warping analysis of wood carving is performed. Through the numerical integration results of the carving force resistance instability, combined with the elastic mechanics model of wood, the warping phenomenon of wood caused by internal stress and carving force fluctuations during the carving process is further analyzed. First, the "mechanical warping analysis model" is used to simulate the warping of wood under different carving forces. This model converts the pressure field generated during the carving process into warping deformation, taking into account the effects of changes in the elastic modulus, yield strength and expansion coefficient of the wood on the warping.Specifically, based on the principles of mechanics, the "thin plate warping equation" is used to describe the geometric characteristics of wood deformation after being subjected to force, and the degree of warping in each area of ​​the wood during the carving process is calculated. For each carving position, the stress-strain relationship is applied to analyze the warping stress of the wood, and then the deformation is obtained through the elastic deformation theory. The warping analysis process uses numerical simulation to simulate the stress state of the wood in a refined manner, and uses the "finite element analysis method" to perform local force analysis in order to accurately predict the warping effect under a specific carving force. Through this series of analyses, the wood carving force warping data obtained provides a theoretical basis for controlling the deformation of wood morphology during the carving process, and can especially provide scientific support for carving path optimization and carving force adjustment.

[0119] Step S35 includes the following steps:

[0120] Step S351: performing a thinning process on the pressure in the warping region based on the stress warping linear regression data to obtain thinning data on the pressure in the warping region;

[0121] Step S352: performing warping amplitude correlation matching according to the warping region pressure sparse data to obtain pressure-correlated warping amplitude matching data;

[0122] Step S353: adjusting the engraving force in stages based on the sparse pressure data of the warping area and the pressure-associated warping amplitude matching data to obtain the engraving force in stages;

[0123] Step S354: performing phased adjustment of the local material removal amount according to the phased engraving applied force to obtain phased local material removal amount adjustment data;

[0124] Step S355: Perform wood material carving compensation planning based on the staged carving applied force and the staged local material removal amount adjustment data to obtain wood material carving compensation planning data.

[0125] In an embodiment of the present invention, based on the linear regression data of force warping, the pressure thinning process of the warped area is performed. During the engraving process of wood, some areas are warped due to uneven force, and these warped areas will produce higher local pressure. In order to reduce the impact of warping on the engraving quality and reasonably distribute the engraving force, it is necessary to thin out the pressure of these areas. The purpose of the thinning process is to convert the local excessive pressure value into a more uniform pressure distribution through mathematical methods. First, based on the linear regression model, the pressure change trend of the warped area is determined by analyzing the force changes of the wood at each engraving stage. These pressure data contain the force intensity and variation range of different positions, and are usually manifested as significant data of the pressure concentration area. In the thinning process, a method based on "Laplace smoothing" is used to process these data. The specific steps are: first, the high pressure points in the warped area are identified, and the pressure gradient of the surrounding area is calculated. The high pressure points are smoothed by the Laplace operator to eliminate unnecessary excessive concentrated pressure. That is, in the warped area, the pressure of the high pressure area is reduced to a level consistent with the surrounding area by averaging. Through this treatment, it is possible to effectively avoid excessive local engraving force during the engraving process, which may cause further damage to the wood surface or uneven engraving. Based on the sparse pressure data of the warping area obtained in step S351, the warping amplitude is associated and matched. The warping amplitude is a quantitative indicator describing the degree of warping of wood, which is usually determined by the deformation of the wood after being subjected to force. The goal of this step is to find the warping amplitude that matches the pressure distribution through the analysis of the pressure data, thereby providing a more accurate adjustment basis for engraving control. First, through the in-depth analysis of the pressure sparse data, the relationship between the area with higher pressure and the area with greater warping is identified. According to the mechanical response of the wood during the engraving process, the "pressure-deformation" relationship model is used to infer the change of the warping amplitude in different areas. In the specific implementation, the "least squares method" is used for curve fitting to associate and match the pressure sparse data with the known warping amplitude data. The fitting model calculates the functional relationship between pressure and warping amplitude, and obtains the warping amplitude of each area by matching the pressure data of different areas. This process can be fitted by polynomial regression, or by using more complex curve fitting techniques, such as piecewise linear regression. The final result is that the warping amplitude corresponding to each warping area is obtained, which provides a basis for the precise adjustment of the engraving force. Based on the previously obtained sparse data of the warping area pressure and the pressure-related warping amplitude matching data, the staged engraving force is adjusted. The purpose of this step is to adjust the force applied during the engraving process according to the force conditions at different engraving stages to avoid excessive warping or cracking of the wood. First, by analyzing the pressure distribution in the warping area, determine at which locations the engraving force is too large, resulting in unnecessary warping. Then, based on the correlation data of the warping amplitude and engraving force in each area, the most suitable engraving force is calculated.For areas with less warping, an appropriate amount of engraving force is applied; for areas with larger warping amplitudes, the engraving force needs to be reduced to reduce the possibility of wood warping. Specifically, the engraving force is adjusted using the "mechanical balance method". The force of each engraving area is comprehensively adjusted using the weighted average method based on the pressure sparse data and the warping amplitude matching data. The weighting coefficient is set according to the size of the warping amplitude. The area with a larger warping amplitude has a higher weight and a corresponding lower engraving force. The calculated engraving force will be adjusted in real time at each engraving stage to ensure that the engraving force at each stage can be within the optimal range to avoid excessive stress. Based on the staged engraving force obtained in step S353, the local raw material removal amount is adjusted in stages. The purpose of this step is to ensure that the amount of wood removed during the engraving process matches the distribution of the engraving force to avoid excessive removal or uneven engraving in local areas. First, the removal amount of the local area is calculated based on the force applied at each engraving stage. The engraving force is proportional to the removal amount. When the engraving force is large, more raw materials need to be removed; for areas with smaller engraving forces, the removal amount needs to be reduced. By analyzing the engraving force data, the "progressive removal algorithm" is used to adjust the removal amount. The algorithm first preliminarily estimates the removal amount of each area, and then adjusts the raw material removal amount according to the force change during the engraving process to achieve the best engraving effect. In the specific implementation process, the removal amount of each engraving position is adjusted using an iterative method. After each stage of engraving is completed, the mechanical data of that stage is calculated in real time, and then the removal amount of the next stage is updated in combination with the change in the removal amount. This process ensures that the removal amount of wood is most appropriately adjusted at each stage during the engraving process to avoid deformation or damage of the wood due to excessive removal. Based on the staged engraving force and staged local raw material removal adjustment data obtained in steps S353 and S354, wood raw material engraving compensation planning is performed. During the engraving process, due to the anisotropy and uneven force of the wood, the wood surface will warp or be unevenly removed. In order to compensate for these errors, the engraving path and force need to be optimized by calculating compensation data. First, the "compensation planning algorithm" is used to deduce the most appropriate compensation path based on the staged engraving force and removal amount data. The design of the compensation path is dynamically adjusted according to the force conditions in each area during the engraving process. The process uses a "feedback control" method to evaluate the engraving effect at each stage and adjust the engraving force and path according to the difference between the actual engraving force and the theoretical engraving force. Ultimately, the resulting wood material engraving compensation planning data provides a specific compensation strategy for each engraving stage. These compensation data will directly affect the execution of the engraving control system to ensure that the engraving results meet expectations and avoid excessive warping or damage to the wood.

[0126] Step S4 includes the following steps:

[0127] Step S41: When it is determined that the engraving material is a stone material, an X-ray scan is performed on the stone material to obtain an X-ray image of the stone material; based on the X-ray image of the stone material, an internal crack structure analysis is performed on the material to obtain internal crack structure data of the stone material;

[0128] Step S42: performing crack depth distribution morphology recognition on the internal crack structure data of the stone raw material to obtain crack depth distribution morphology data;

[0129] Step S43: calculating the curvature of the sculpture surface texture of the three-dimensional sculpture model to obtain the curvature of the sculpture surface texture;

[0130] Step S44: identifying the cutting fracture limit stress according to the crack depth distribution morphology data and the curvature of the engraved surface texture, and obtaining the cutting fracture limit stress data;

[0131] Step S45: Design the stone material carving logic based on the cutting fracture limit stress data to obtain the stone material carving logic.

[0132] As an example of the present invention, refer to Figure 3 As shown, in this example, step S4 includes:

[0133] Step S41: When it is determined that the engraving material is a stone material, an X-ray scan is performed on the stone material to obtain an X-ray image of the stone material; based on the X-ray image of the stone material, an internal crack structure analysis is performed on the material to obtain internal crack structure data of the stone material;

[0134] In the embodiment of the present invention, when it is determined that the material used for engraving is a stone material, an X-ray scan is performed to obtain an X-ray image of the stone raw material. During the X-ray scanning process, by utilizing the characteristics of X-rays penetrating materials and combining with X-ray detectors, the structural characteristics inside the stone raw material, including cracks, cavities, mineral impurities, etc., can be effectively revealed. These internal information have an important impact on the structural fracture or deterioration that occurs during the engraving process. X-ray scanning uses a high-resolution X-ray source. When penetrating through stone raw materials, there are differences in the ability to penetrate materials of different densities. The more fragile crack areas often appear as darker shadows, while the areas with higher density appear as lighter shadows. After obtaining the X-ray image, image preprocessing is performed, including denoising, contrast enhancement, and other operations to ensure that the image is clear and that detailed features such as cracks can be accurately captured. After preprocessing, image segmentation technology, such as the Otsu threshold segmentation method, is used to separate the crack area in the image from the normal area. In this process, the location and shape of the cracks are extracted based on the X-ray images differentiated by different densities to obtain a detailed X-ray image of the stone raw material. Through in-depth analysis of these images, the internal crack structure of the stone raw material can be accurately extracted.

[0135] Step S42: performing crack depth distribution morphology recognition on the internal crack structure data of the stone raw material to obtain crack depth distribution morphology data;

[0136] In the embodiment of the present invention, after obtaining the X-ray image of the stone raw material, the crack depth distribution morphology is identified. The crack depth distribution is an important parameter for evaluating the internal crack characteristics of the stone raw material, and is crucial to the safety and stability of the subsequent engraving process. First, an image processing algorithm, such as an edge detection algorithm (such as Canny edge detection), is used to process the X-ray image to identify the crack edge. For the identification of the crack depth, a layered scanning technology is used to divide the X-ray image into multiple depth layers, and the crack position and depth in the stone raw material are scanned layer by layer. Each layer is analyzed, and the depth of each crack is calculated in combination with the intensity attenuation law of the X-ray. In this way, the distribution depth of the crack in the stone raw material can be accurately obtained. Then, the "Hough transform" in image processing is used to fit the crack morphology. The Hough transform can effectively detect linear cracks and curved cracks, and help determine the geometric morphology of the cracks. By comparing the crack data with the known mechanical properties of the material, the crack depth distribution morphology data of the stone raw material is finally obtained. These data include important information such as the number, depth distribution, and morphology of the cracks, providing an accurate basis for subsequent engraving control.

[0137] Step S43: calculating the curvature of the sculpture surface texture of the three-dimensional sculpture model to obtain the curvature of the sculpture surface texture;

[0138] In an embodiment of the present invention, after obtaining the crack depth distribution morphological data, the curvature of the engraved surface texture is calculated. The curvature of the engraved surface texture affects the contact force between the tool and the material during the engraving process, thereby directly affecting the engraving quality. First, according to the geometric form of the three-dimensional sculpture model, the texture features of the model surface are extracted. In this process, the "surface fitting" technology is used to model the texture of the engraved surface through methods such as spline interpolation to generate a mathematical expression of the surface texture. Then, the "curvature calculation" method in computational geometry is used to calculate the curvature value of each point on the engraved surface. Curvature is an important indicator to describe the degree of curvature of the surface. For irregular surfaces, areas with larger curvature values ​​usually represent larger surface curvatures and are prone to engraving deviations. Specifically, a high-order differential geometry method is used to calculate the surface of the three-dimensional sculpture model point by point to obtain the curvature value of each point. Finally, these curvature values ​​are integrated into the texture curvature data of the engraved surface, providing a basis for the subsequent calculation of the engraving force.

[0139] Step S44: identifying the cutting fracture limit stress according to the crack depth distribution morphology data and the curvature of the engraved surface texture, and obtaining the cutting fracture limit stress data;

[0140] In an embodiment of the present invention, after obtaining the crack depth distribution morphological data and the curvature of the engraved surface texture, the cutting fracture limit stress is identified. The cutting fracture limit stress refers to the maximum stress that the material can withstand during the engraving process. After exceeding this stress, the material will be broken or irreparably damaged. First, according to the crack depth distribution morphological data, the stress distribution of each crack area of ​​the stone raw material is simulated. The stress distribution simulation adopts the finite element analysis (FEA) method to simulate the stress changes in different engraving stages to obtain the stress value of each area. Next, combined with the engraving surface texture curvature data, the mechanical response analysis of the material is performed. The curvature information calculated by the curvature can infer the change trend of the force in different areas during the engraving process. Using these data, the stress distribution in the cutting process is calculated based on the stress-strain relationship, and which areas will exceed the cutting fracture limit of the stone raw material are identified. Finally, based on these data, the cutting fracture limit stress data is obtained to ensure that the stone will not be broken due to excessive stress during the engraving process.

[0141] Step S45: Design the stone material carving logic based on the cutting fracture limit stress data to obtain the stone material carving logic.

[0142] In the embodiment of the present invention, finally, according to the cutting fracture limit stress data obtained in step S44, the stone material engraving logic design is performed. The purpose of the engraving logic design is to optimize the engraving path, select appropriate engraving tools and adjust the engraving force to ensure that the stress distribution during the engraving process does not exceed the cutting fracture limit of the stone material, thereby avoiding fracture or damage. First, according to the cutting fracture limit stress data, the stress distribution of each part of the stone material is optimized. For those areas with greater stress, the engraving speed is reduced or the engraving angle is adjusted to avoid excessive stress concentration. For areas with relatively hard or deep cracks, a more sophisticated engraving method is used to reduce the contact area of ​​the engraving tool. Then, based on the characteristics of crack depth and texture curvature, the engraving path is designed to avoid crack concentration areas or areas with large texture curvature. The path design adopts a "path smoothing optimization algorithm" to ensure that the engraving tool moves along a reasonable trajectory throughout the engraving process. By accurately controlling the engraving force and path planning, a complete stone material engraving logic is finally obtained to ensure that every step in the engraving process meets the mechanical properties and cutting requirements of the stone material.

[0143] Step S44 includes the following steps:

[0144] Step S441: Calculating the lateral spacing of crack bifurcation based on the crack depth distribution morphology data to obtain the lateral spacing of crack bifurcation;

[0145] Step S442: performing lateral spacing expansion power law distribution identification on the crack depth distribution morphology data according to the lateral spacing of the crack bifurcation to obtain crack spacing expansion distribution data;

[0146] Step S443: Analyze the cutting parameters required by engraving theory for the curvature of the engraving surface texture to obtain the cutting parameters required by engraving theory;

[0147] Step S444: performing cutting fracture force approximate error fitting on the crack spacing expansion distribution data according to the cutting parameters required by the engraving theory to obtain cutting fracture force approximate fitting data;

[0148] Step S445: Identify the cutting fracture limit stress based on the cutting fracture force approximate fitting data to obtain the cutting fracture limit stress data.

[0149] In an embodiment of the present invention, after obtaining the crack depth distribution morphological data, the lateral spacing of the crack is first calculated. The lateral spacing of the crack bifurcation refers to the distance between the bifurcation points at both ends of the crack. This parameter is crucial for evaluating the brittleness of stone raw materials and the risk of fracture during cutting. The crack bifurcation point is where the crack branches, usually located at the turning point of the crack path. To ensure the accurate calculation of the lateral spacing of the crack bifurcation, the edge detection algorithm in image processing, such as the Canny edge detection method, is first used to accurately locate each bifurcation point of the crack. Then, based on the spatial position of each bifurcation point, its lateral spacing is calculated. Specifically, the crack bifurcation points in the three-dimensional space are first projected onto a two-dimensional plane through coordinate transformation, and then the lateral distance between the crack bifurcation points is calculated according to the geometric distance formula. By traversing each pair of adjacent crack bifurcation points, the crack bifurcation lateral spacing data is obtained. These data provide basic information for the subsequent crack spacing expansion and carving parameter optimization, and further evaluate the expansion trend of the crack during the carving process. After obtaining the crack bifurcation lateral spacing, the next step is to identify the power law distribution of the crack spacing. That is, the cracks grow slowly within a smaller spacing range, while the crack growth rate will increase significantly within a larger spacing range. Therefore, by analyzing the power-law distribution of the lateral spacing data of the cracks, the expansion trend of the cracks at different depths can be effectively identified. First, the lateral spacing data of the cracks are statistically analyzed, and the "least squares method" is used to fit these data to verify whether they conform to the characteristics of the power-law distribution. Through fitting, the exponent of the power-law distribution can be obtained, and the expansion distribution data of the crack spacing can be further obtained. These data reflect the law of the change of the crack spacing with depth, which is helpful for stress analysis and control in the subsequent cutting process. The curvature of the engraving surface texture is an important factor affecting the engraving tool path, contact force and cutting efficiency. After obtaining the texture curvature of the engraving surface, the cutting parameters are analyzed. First, according to the surface data of the three-dimensional sculpture model, the curvature calculation method is used to obtain the local curvature value of each point. Through these curvature values, the curvature characteristics of the engraving surface are analyzed, and the cutting parameters required in the engraving process, such as cutting depth, feed speed, cutting angle, etc., are further deduced. The cutting depth usually depends on the curvature of the surface texture. Areas with larger curvatures require smaller cutting depths, while areas with smaller curvatures can increase the cutting depth appropriately. The feed speed is closely related to the cutting depth and the mechanical properties of the material. Usually, in areas with larger curvatures, the feed speed should be appropriately slowed down to ensure the stability of the cutting process. Through these analyses, the cutting parameters required by the engraving theory are obtained to ensure the stability of the contact between the tool and the surface and the engraving quality. After obtaining the cutting parameters required by the engraving theory, the approximate error fitting of the cutting fracture force is performed. The force distribution during the cutting process is a very complex nonlinear problem, which is affected by multiple factors, including the mechanical properties of the material, the shape of the tool, the engraving depth and the feed speed.According to the curvature of the engraved surface texture and the cutting parameters, a force model is established, and the force analysis is performed in combination with the crack spacing expansion distribution data. The force analysis can be simulated by the finite element analysis (FEA) method. During the simulation process, the distribution of the cracks and the cutting parameters are input into the force model to obtain the force conditions in different areas during the cutting process. By analyzing the force data, the errors generated during the cutting process, especially the force fluctuations near the cracks, are obtained. Then, the least squares method or other regression analysis methods are used to fit the force approximation error to obtain the cutting fracture force approximate fitting data. These data can accurately describe the change law of the fracture force during the engraving process under different cutting parameters and crack distribution conditions, and provide a basis for the adjustment of cutting parameters. Finally, based on the cutting fracture force approximate fitting data, the cutting fracture limit stress is identified. The cutting fracture limit stress refers to the maximum stress that the material can withstand during the engraving process. When this stress is exceeded, the material will break or break. First, the force conditions in different areas are obtained through force analysis, and then the mechanical properties of the material, such as tensile strength and compressive strength, are used to determine whether the stress in each area exceeds the ultimate bearing capacity of the material. In this process, the "yield criterion" is used to further analyze the stress data. Specifically, for each crack area, its theoretical cutting fracture stress is calculated and compared with the material's fracture resistance limit. If the calculated cutting stress exceeds the fracture resistance limit of the area, the engraving parameters of the area should be adjusted, such as reducing the feed speed, reducing the cutting depth, or replanning the engraving path. Finally, the cutting fracture limit stress data is obtained, which is used to guide tool selection, path planning, and parameter adjustment during the engraving process to ensure the safety of the material and the quality of the engraving process.

[0150] Step S444 includes the following steps:

[0151] Extract the cutting angle and cutting force of the cutting parameters required by engraving theory, and obtain the theoretically required cutting angle and the theoretically required cutting force respectively;

[0152] Perform crack connection splitting density analysis on the crack spacing expansion distribution data to obtain crack connection splitting density data;

[0153] Based on the crack connection splitting density data, the mean difference of crack section radius is calculated for the crack spacing expansion distribution data, and the mean difference of crack section radius is obtained;

[0154] According to the theoretically required cutting angle and the theoretically required cutting force, the cutting stress concentration instability analysis is performed on the mean difference of the crack section radius to obtain the crack cutting stress concentration instability data;

[0155] According to the crack connection splitting density data, the force dispersion gradient of the crack cutting stress concentration instability data is quantified to obtain the cutting stress dispersion gradient data;

[0156] Based on the instability data of cutting stress concentration and cutting stress dispersion gradient data, the approximate error fitting of cutting fracture force is performed to obtain the approximate fitting data of cutting fracture force.

[0157] In the embodiment of the present invention, the cutting parameters required by the engraving theory are analyzed to extract the cutting angle and cutting force. The cutting angle refers to the angle between the tool and the engraving surface, which directly affects the size of the cutting force and the contact condition of the tool during the cutting process. The cutting force is the actual contact force between the tool and the material, which determines the cutting effect and surface quality of the material. First, based on the geometric features of the three-dimensional engraving model, the cutting angle of the tool at different positions is calculated. To this end, the normal vector calculation method in geometry is used to calculate the cutting angle of each cutting point using the angle between the normal line of the engraving surface and the cutting direction of the tool. After obtaining the cutting angle, the cutting force is extracted. The cutting force is closely related to the feed speed, cutting depth and mechanical properties of the tool. First, according to the cutting depth and feed speed required by the engraving theory, combined with the mechanical parameters such as the compressive strength and shear strength of the material, the stress calculation formula in material mechanics is applied to obtain the cutting force of each cutting point. Through this series of calculations, the theoretical cutting angle and cutting force of each cutting point in the engraving process can be obtained, and these data provide a basis for subsequent cutting stress analysis and instability analysis. After obtaining the crack spacing expansion distribution data, the crack connection splitting density is analyzed. The crack connection splitting density refers to the relationship between the number of crack connections generated by crack splitting in a certain area and the density of the area where these cracks are located. The crack spacing expansion distribution data can be used to further understand the expansion of cracks in different areas. First, the spatial distribution analysis method is used to calculate the connectivity between cracks, that is, which crack branches are connected to each other. By delineating the analysis area, the cracks are classified according to their bifurcation points, and the number of crack connections in each area is counted. Then, the density analysis method in statistics is used to normalize the number of crack connections to obtain the crack connection splitting density in each area. This density value reflects the degree of crack expansion in stone materials and provides a basis for subsequent cutting stress analysis and instability analysis. In the crack connection splitting density analysis process, the morphological characteristics of the cracks, such as the opening angle of the cracks, the expansion direction of the cracks, and other factors can be considered to further accurately calculate the distribution density of the cracks. Based on the crack connection splitting density data, the mean difference of the crack section radius is calculated for the crack spacing expansion distribution data. The mean difference of the crack section radius is a key indicator to measure the crack changes during the cutting process, especially the crack expansion trend and crack morphology during the cutting process. First, based on the crack connectivity splitting density data, the cross-sectional morphology of each crack is analyzed and the geometric features of the crack are extracted. For each crack cross section, its radius is calculated and its mean is obtained. Then, the difference between the cross-sectional radius of each crack and the mean is calculated, that is, the mean difference of the crack cross-sectional radius. Through this process, the mean difference of the crack cross-sectional radius can be obtained, which reflects the morphological changes during the crack expansion process.The area with a large mean difference in the crack section radius indicates that the crack expansion trend in this area is more intense, which has a great impact on the stress distribution during the cutting process. This data provides the necessary geometric information for the subsequent cutting stress concentration instability analysis. After obtaining the mean difference in the crack section radius, the next step is to perform cutting stress concentration instability analysis on the crack. The cutting stress concentration instability analysis mainly evaluates whether stress concentration instability will occur in the crack area during the cutting process. Stress concentration instability usually occurs at the tip of the crack or where the cross-sectional radius is large, especially when the cutting angle and cutting force are large, the expansion of the crack will be aggravated. First, according to the cutting angle and cutting force required by the theory, the stress condition of each crack area during the cutting process is calculated. Through the finite element analysis method (FEA), a stress analysis model is established, and a stress analysis is performed on each cutting point during the cutting process. In the stress analysis, the geometric characteristics of the crack (such as the depth, width, cross-sectional radius, etc.) and the changes in the cutting angle and force are considered to evaluate the distribution of stress in the crack area. Through this analysis, areas with excessive stress can be identified, and further analysis can be performed on whether these areas will experience instability. The obtained cutting stress concentration instability data will reflect the potential risk of crack extension during cutting, suggesting that the cutting parameters need to be adjusted to avoid material damage or further crack extension. After obtaining the crack cutting stress concentration instability data, the force dispersion gradient is quantified. The force dispersion gradient is an indicator that describes the speed and degree of force dispersion from the concentrated area to the surrounding area during cutting. A higher force dispersion gradient means that the stress in the stress concentration area is transferred to the surrounding area faster, resulting in uneven stress distribution, which affects the cutting quality and engraving accuracy. First, by analyzing the crack cutting stress concentration instability data, the gradient of stress dispersion from the concentrated area to the outside is calculated. This gradient can be obtained by solving the rate of change in the stress field. Specifically, a numerical simulation method is used to calculate the stress distribution in different areas based on the force conditions in the crack area during cutting, and the transfer speed of stress from the crack area to the surrounding area is quantified. The obtained cutting stress dispersion gradient data can reflect the force characteristics of the material and the stability of the cutting process under different cutting conditions. This data plays an important role in optimizing the engraving path and cutting parameters. Finally, according to the crack cutting stress concentration instability data and the cutting stress dispersion gradient data, the cutting fracture force approximate error fitting is performed. The purpose of cutting fracture force approximate error fitting is to describe the force error in the cutting process by fitting the model and predict the future engraving operation. First, using the existing cutting stress concentration instability data and cutting stress dispersion gradient data, the force error is fitted by regression analysis to obtain a mathematical model of cutting fracture force error. This model can be constructed by the least squares method or other error optimization methods to obtain the best fitting parameters. The goal of minimizing the error in the fitting process is to make the model fit the actual force situation.The obtained approximate fitting data of cutting fracture force provides a scientific basis for the subsequent adjustment of engraving parameters, ensuring that cracks will not break due to excessive stress during the cutting process, thereby improving the accuracy and stability of engraving.

[0158] Step S45 includes the following steps:

[0159] Step S451: assigning fracture risk weight regions to stone raw materials based on cutting fracture limit stress data to obtain fracture risk weight regions;

[0160] Step S452: performing cutting angle correction on the fracture risk weight region according to the cutting fracture limit stress data to obtain limit stress cutting angle correction data;

[0161] Step S453: adjusting the cutting rate and the lateral cutting feed amount according to the limit stress cutting angle correction data and the cutting fracture limit stress data, and obtaining the cutting rate adjustment data and the lateral cutting feed amount adjustment data respectively;

[0162] Step S456: Based on the limiting stress cutting angle correction data, the cutting rate adjustment data and the lateral cutting feed adjustment data, the stone material carving logic is designed to obtain the stone material carving logic.

[0163] In the embodiment of the present invention, the cutting fracture limit stress data is first used to identify the fracture risk area in the stone raw material. The cutting fracture limit stress data provides the limit value of the stress that the stone raw material bears during the cutting process. Based on the data, it can be evaluated which areas are fractured or damaged when subjected to the cutting force. By analyzing the surface geometric information of the stone raw material, the stone raw material can be divided into multiple areas. The stress value of each area is calibrated according to the cutting fracture limit stress data. Specifically, the raw material is simulated using the finite element method according to the geometric shape and crack distribution of the stone raw material, and the stress distribution of each area under different cutting conditions is calculated. Then, the maximum stress of each area is compared with its corresponding cutting fracture limit stress value. If the stress of a certain area exceeds its limit stress value, the area is judged as a high-risk area. Different risk weight values ​​are assigned according to the stress conditions of each area. The risk weight value can be linearly mapped according to the degree to which the stress exceeds the limit stress to obtain the fracture risk weight data of each area. These data can intuitively show which areas have a high fracture risk, providing a basis for parameter adjustment in the subsequent cutting process. After obtaining the fracture risk weight area, the next step is to correct the cutting angle of the risk area according to the cutting fracture limit stress data. The choice of cutting angle directly affects the stress distribution of the raw material during the cutting process, and then affects the fracture risk of the raw material. In order to reduce the fracture risk, the cutting angle can be adjusted according to the risk weight of different areas. In the specific implementation, the stress distribution of each risk area is firstly refined and analyzed, and the high stress area is identified according to the cutting fracture limit stress data. These areas are usually in the parts that are subjected to greater stress during the cutting process, so it is necessary to reduce the stress concentration by adjusting the cutting angle. The correction method of the cutting angle is to calculate the angle between the normal vector of the cutting area and the cutting direction, and optimize it in combination with the stress data. The cutting angle of the high-risk area can be appropriately increased to reduce the contact area between the tool and the material, thereby reducing the cutting force and stress concentration. On the contrary, in the low-risk area, the original cutting angle can be maintained or adjusted appropriately. The corrected cutting angle can be simulated and verified by the finite element analysis method to ensure that the stress in these areas does not exceed its fracture limit during the cutting process. Finally, the obtained limit stress cutting angle correction data provides clear correction values ​​for the cutting angle of each cutting area, which will provide a basis for the next adjustment of cutting rate and cutting feed. After obtaining the limit stress cutting angle correction data, the cutting rate and lateral cutting feed are adjusted next. Cutting rate and lateral cutting feed are key parameters that affect cutting stress and surface quality. Reasonable adjustment of these two parameters can effectively avoid excessive stress concentration in high-risk areas, thereby reducing the risk of fracture. First, according to the cutting fracture limit stress data and the limit stress cutting angle correction data, the cutting load conditions in each area are analyzed.In high-risk areas, due to stress concentration, the cutting rate needs to be appropriately reduced to avoid excessive cutting force causing the stone material to break. The transverse cutting feed refers to the transverse feed of the tool along the surface of the material. Excessive feed will lead to greater cutting force, thereby causing crack expansion. For high-risk areas, the transverse cutting feed should be appropriately reduced, while the feed can be increased in low-risk areas to improve processing efficiency. During the adjustment process, the cutting force model can be used to predict the stress distribution under different cutting parameters, and the optimization adjustment can be made based on the model. Through this method, the cutting rate adjustment data and the transverse cutting feed adjustment data can be obtained, and the integrity of the stone material during the cutting process can be ensured. After completing the adjustment of the cutting angle, cutting rate and transverse cutting feed, the stone material engraving logic design is finally carried out. This design combines the various parameters obtained in the previous steps and provides a cutting strategy for the actual engraving process. The core task of the stone material engraving logic is to reasonably arrange the engraving path and cutting parameters to ensure that the stress in different areas does not exceed its fracture limit during the entire engraving process. First, the engraving path is designed according to the adjusted cutting angle, cutting rate and transverse cutting feed. When designing the path, the geometry of the material and the distribution of cracks should be taken into account, and priority should be given to adjusting the cutting parameters in high-risk areas to avoid stress concentration. At the same time, in order to improve the engraving efficiency, the cutting parameters in the low-risk areas can be optimized, and the feed rate and cutting speed can be reasonably arranged. In the engraving logic design, it is also necessary to intelligently plan the tool path according to the actual situation of the stone raw material to avoid excessive contact between the tool and the material surface, thereby reducing the risk of material damage. At this time, a path planning algorithm (such as the A* algorithm or the Dijkstra algorithm) is used to optimize the path to ensure that the tool passes through each area in the most appropriate path during the engraving process. Finally, the obtained stone raw material engraving logic provides a detailed cutting strategy for the actual operation, making the engraving process more precise and stable, and effectively avoiding the risk of fracture caused by excessive cutting stress.

[0164] In another embodiment, the fracture risk weight area of ​​the stone raw material is assigned based on the cutting fracture limit stress data to obtain the fracture risk weight area. In this step, the cutting fracture limit stress data needs to be mapped to the three-dimensional model of the stone raw material first, and the stress concentration degree of each area is determined by the stress distribution analysis algorithm. The stone raw material is divided into several small units by the finite element analysis method, and its fracture risk coefficient is calculated according to the stress value of each unit. The fracture risk coefficient is determined by the ratio of the stress value to the tensile strength of the material, and the formula is: fracture risk coefficient = unit stress value / material tensile strength. According to the size of the fracture risk coefficient, the stone raw material is divided into low-risk area, medium-risk area and high-risk area, and each area is assigned a corresponding weight value. The weight value of the high-risk area is set to 1, the medium-risk area is 0.5, and the low-risk area is 0.2. By marking the weight value, a fracture risk weight area distribution map is formed. According to the cutting fracture limit stress data, the cutting angle of the fracture risk weight area is corrected to obtain the limit stress cutting angle correction data. In this step, the angle correction algorithm is used to adjust the cutting angle for different weight values ​​of the fracture risk weight area. For high-risk areas, the cutting angle correction value is the initial cutting angle minus 10 degrees to ensure that the cutting force is dispersed and to avoid stress concentration leading to fracture; for medium-risk areas, the cutting angle correction value is the initial cutting angle minus 5 degrees; for low-risk areas, the cutting angle remains unchanged. The angle correction algorithm is based on the stress distribution gradient calculation, and the formula is: Corrected cutting angle = initial cutting angle - (fracture risk coefficient × 10 degrees). Through this algorithm, the limit stress cutting angle correction data is generated to ensure that the cutting angle of each area matches its fracture risk. The cutting rate and lateral cutting feed are adjusted according to the limit stress cutting angle correction data and the cutting fracture limit stress data, and the cutting rate adjustment data and lateral cutting feed adjustment data are obtained respectively. In this step, the rate and feed linkage adjustment algorithm is adopted to dynamically adjust the cutting rate and lateral cutting feed according to the limit stress cutting angle correction data and the distribution of the fracture risk weight area. For high-risk areas, the cutting rate is reduced to 50% of the initial rate and the transverse cutting feed is reduced to 30% of the initial feed to reduce the impact of cutting force on the material; for medium-risk areas, the cutting rate is reduced to 70% of the initial rate and the transverse cutting feed is reduced to 50% of the initial feed; for low-risk areas, the cutting rate and transverse cutting feed remain unchanged. The adjustment algorithm is based on the linear relationship between the fracture risk coefficient and the cutting parameters, and the formula is: adjusted cutting rate = initial cutting rate × (1-fracture risk coefficient × 0.5), adjusted transverse cutting feed = initial transverse cutting feed × (1-fracture risk coefficient × 0.7). Through this algorithm, cutting rate adjustment data and transverse cutting feed adjustment data are generated.The logic design of stone material engraving is carried out based on the limiting stress cutting angle correction data, cutting rate adjustment data and transverse cutting feed adjustment data to obtain the stone material engraving logic. In this step, the limiting stress cutting angle correction data, cutting rate adjustment data and transverse cutting feed adjustment data generated in the previous step are integrated to form a complete engraving logic. The engraving logic design adopts a rule-based control algorithm to associate the cutting parameters of each area with its corresponding fracture risk weight area. The specific operation is: first read the distribution map of the fracture risk weight area, and then call the corresponding cutting angle correction data, cutting rate adjustment data and transverse cutting feed adjustment data according to the regional weight value to generate the engraving instruction sequence for each area. The engraving instruction sequence includes the specific values ​​of the cutting angle, cutting rate and transverse cutting feed, as well as the planning of the cutting path. Through this algorithm, the engraving logic of stone material is generated to ensure that the cutting parameters of each area in the engraving process match its fracture risk, avoiding material fracture and engraving failure.

[0165] The present invention also provides an engraving control system for executing the engraving control method as described above, the engraving control system comprising:

[0166] The three-dimensional sculpture model building module is used to obtain a sculpture design plan; mark the shape contour of the sculpture design plan to obtain multi-angle shape contour data; and build a three-dimensional sculpture model according to the multi-angle shape contour data to obtain a three-dimensional sculpture model;

[0167] The carving demand type classification module is used to obtain carving demand data; classify carving material raw materials according to the carving demand data to obtain carving material raw materials, wherein the carving material raw materials include wood material raw materials and stone material raw materials;

[0168] The wood material carving compensation planning module is used to monitor the temperature and humidity of the wood material when the carving material is determined to be a wood material, and obtain the wood material temperature and humidity monitoring data; perform carving force warping analysis based on the wood material temperature and humidity monitoring data, and obtain the wood carving force warping data; perform wood material carving compensation planning based on the force warping linear regression data, and obtain the wood material carving compensation planning data;

[0169] The stone material carving logic design module is used to determine that the carving material is a stone material, perform X-ray scanning on the stone material, and analyze the internal crack structure of the material to obtain the internal crack structure data of the stone material; identify the cutting fracture limit stress based on the internal crack structure data of the stone material to obtain the cutting fracture limit stress data; perform stone material carving logic design based on the cutting fracture limit stress data to obtain the stone material carving logic;

[0170] The automatic engraving control firmware design module is used to design the automatic engraving control firmware based on the wood material engraving compensation planning data and the stone material engraving logic to obtain the automatic engraving control firmware; the automatic engraving control firmware is sent to the cloud platform to execute the engraving control method.

[0171] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0172] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for engraving control, characterized in that: The following steps are involved: Step S1: obtaining a sculpture design plan; marking the shape contour of the sculpture design plan to obtain multi-angle shape contour data; constructing a three-dimensional sculpture model according to the multi-angle shape contour data to obtain a three-dimensional sculpture model; Step S2: Obtain carving demand data; classify carving material raw materials according to the carving demand data to obtain carving material raw materials, wherein the carving material raw materials include wood material raw materials and stone material raw materials; Step S3 includes: Step S31: When it is determined that the engraving material is a wooden material, the temperature and humidity of the wooden material are monitored to obtain wooden material temperature and humidity monitoring data; Step S32: extracting texture patterns from the three-dimensional sculpture model to obtain sculpture texture pattern data; Step S33: performing carving force warping analysis on the sculpture texture pattern data according to the temperature and humidity monitoring data of the wood raw material to obtain the wood carving force warping data; Step S34: performing linear regression analysis on the warping data of the wood carving to obtain linear regression data of the warping; Step S35: Performing wood material carving compensation planning based on the force warping linear regression data to obtain wood material carving compensation planning data; Step S4: When it is determined that the engraving material is a stone material, an X-ray scan is performed on the stone material, and the internal crack structure of the material is analyzed to obtain the internal crack structure data of the stone material; the cutting fracture limit stress is identified according to the internal crack structure data of the stone material to obtain the cutting fracture limit stress data; the stone material engraving logic is designed based on the cutting fracture limit stress data to obtain the stone material engraving logic; Step S5: Design the automatic engraving control firmware based on the wood material engraving compensation planning data and the stone material engraving logic to obtain the automatic engraving control firmware; send the automatic engraving control firmware to the cloud platform to execute the engraving control method.

2. The engraving control method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Obtaining a sculpture design plan; Step S12: performing multi-angle view analysis on the sculpture design plan to obtain multi-angle views of the sculpture; Step S13: marking the shape contour of the multi-angle views of the sculpture to obtain multi-angle shape contour data; Step S14: constructing a three-dimensional sculpture model for the sculpture design plan according to the multi-angle morphological contour data to obtain a three-dimensional sculpture model.

3. The engraving control method according to claim 1, characterized in that: Step S35 includes the following steps: Step S351: performing a thinning process on the pressure in the warping region based on the stress warping linear regression data to obtain thinning data on the pressure in the warping region; Step S352: performing warping amplitude correlation matching according to the warping region pressure sparse data to obtain pressure-correlated warping amplitude matching data; Step S353: adjusting the engraving force in stages based on the sparse pressure data of the warping area and the pressure-associated warping amplitude matching data to obtain the engraving force in stages; Step S354: performing phased adjustment of the local material removal amount according to the phased engraving applied force to obtain phased local material removal amount adjustment data; Step S355: Perform wood material carving compensation planning based on the staged carving applied force and the staged local material removal amount adjustment data to obtain wood material carving compensation planning data.

4. The engraving control method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: When it is determined that the engraving material is a stone material, an X-ray scan is performed on the stone material to obtain an X-ray image of the stone material; based on the X-ray image of the stone material, an internal crack structure analysis is performed on the material to obtain internal crack structure data of the stone material; Step S42: performing crack depth distribution morphology recognition on the internal crack structure data of the stone raw material to obtain crack depth distribution morphology data; Step S43: calculating the curvature of the sculpture surface texture of the three-dimensional sculpture model to obtain the curvature of the sculpture surface texture; Step S44: identifying the cutting fracture limit stress according to the crack depth distribution morphology data and the curvature of the engraved surface texture, and obtaining the cutting fracture limit stress data; Step S45: Design the stone material carving logic based on the cutting fracture limit stress data to obtain the stone material carving logic.

5. The engraving control method according to claim 4, characterized in that: Step S45 includes the following steps: Step S451: assigning fracture risk weight regions to stone raw materials based on cutting fracture limit stress data to obtain fracture risk weight regions; Step S452: performing cutting angle correction on the fracture risk weight region according to the cutting fracture limit stress data to obtain limit stress cutting angle correction data; Step S453: adjusting the cutting rate and the lateral cutting feed amount according to the limit stress cutting angle correction data and the cutting fracture limit stress data, and obtaining the cutting rate adjustment data and the lateral cutting feed amount adjustment data respectively; Step S456: Based on the limiting stress cutting angle correction data, the cutting rate adjustment data and the lateral cutting feed adjustment data, the stone material carving logic is designed to obtain the stone material carving logic.

6. An engraving control system, characterized in that: Used to execute the engraving control method according to claim 1, the engraving control system comprises: The three-dimensional sculpture model building module is used to obtain a sculpture design plan; mark the shape contour of the sculpture design plan to obtain multi-angle shape contour data; and build a three-dimensional sculpture model according to the multi-angle shape contour data to obtain a three-dimensional sculpture model; The carving demand type classification module is used to obtain carving demand data; classify carving material raw materials according to the carving demand data to obtain carving material raw materials, wherein the carving material raw materials include wood material raw materials and stone material raw materials; The wood material carving compensation planning module is used to monitor the temperature and humidity of the wood material when the carving material is determined to be a wood material, and obtain the wood material temperature and humidity monitoring data; perform carving force warping analysis based on the wood material temperature and humidity monitoring data, and obtain the wood carving force warping data; perform wood material carving compensation planning based on the force warping linear regression data, and obtain the wood material carving compensation planning data; The stone material carving logic design module is used to determine that the carving material is a stone material, perform X-ray scanning on the stone material, and analyze the internal crack structure of the material to obtain the internal crack structure data of the stone material; identify the cutting fracture limit stress based on the internal crack structure data of the stone material to obtain the cutting fracture limit stress data; perform stone material carving logic design based on the cutting fracture limit stress data to obtain the stone material carving logic; The automated engraving control firmware design module is used to design automated engraving control firmware based on wood material engraving compensation planning data and stone material engraving logic to obtain automated engraving control firmware; the automated engraving control firmware is sent to the cloud platform to execute the engraving control method.

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