Material cutting intelligent management and control system based on deep learning

Abstract

The invention discloses a material cutting intelligent management and control system based on deep learning, which comprises a data collection module, a material analysis module and an intelligent control module, and is characterized in that the data collection module is used for collecting appearance characteristic data of a to-be-cut metal material and drawing data of an object; the material analysis module is used for processing materials to enable the materials to meet object cutting conditions and matching drawings for metal materials, the intelligent control module is used for formulating a cutting scheme of the laser cutting machine according to appearance characteristics of the metal materials, and the data collection module, the material analysis module and the intelligent control module are electrically connected with one another. The data collection module comprises a material scanning module, a material information input module, a sample data input module and a visual module, and the system has the advantages of being high in efficiency and practicability.

Description

Technical Field

[0001] The present invention relates to the technical field of metal material cutting, and specifically to an intelligent management and control system for material cutting based on deep learning. Background Art

[0002] A CNC cutting machine is a machine tool that uses a digital program to drive the movement of the machine tool. As the machine tool moves, the cutting tool that comes with it cuts the object.

[0003] With the continuous development of society and the continuous improvement of science and technology, in the process of processing metal materials, most companies still use manually controlled laser cutting machines to complete the cutting tasks of metal materials. Manual control usually means that technical workers set the cutting program on the laser cutting machine based on experience, and the laser cutting machine completes the cutting task through the program. However, metal materials may collide during transportation, resulting in irregular edges of metal materials. At this time, this control cutting method is not only inefficient, but also has the problem of manual processing of irregular burrs caused by collisions on the edges of metal materials. The burrs are not completely processed, resulting in the cut objects being unusable and wasting resources. Moreover, when processing special-shaped metal materials, the laser cutting machine cannot be automatically adjusted according to the appearance characteristics of the special-shaped metal materials. It is difficult to ensure the maximum utilization of metal materials when matching cutting drawings. Therefore, it is very necessary to design a deep learning-based material cutting intelligent management and control system with high efficiency and strong practicality. Summary of the invention

[0004] The purpose of the present invention is to provide a material cutting intelligent management and control system based on deep learning to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a material cutting intelligent management and control system based on deep learning, including a data collection module, a material analysis module and an intelligent control module, characterized in that: the data collection module is used to collect the appearance feature data of the metal material to be cut and the drawing data of the object, the material analysis module is used to process the material to make it meet the conditions for object cutting, and match the object drawings for the metal material, the intelligent control module is used to formulate a cutting plan for the laser cutting machine according to the appearance characteristics of the metal material, and the data collection module, the material analysis module and the intelligent control module are electrically connected to each other.

[0006] According to the above technical solution, the data collection module includes a material scanning module, a material information entry module, a sample data entry module and a visual module. The material scanning module is used to scan and enter the three-dimensional point cloud data of the metal material, the material information entry module is used to enter the data such as the metal material material and cutting drawings, the sample data entry module is used to enter a large amount of sample data of metal materials and processing solutions, and the visual module is used to collect edge visual images of metal materials.

[0007] According to the above technical solution, the material analysis module includes a deep learning module, a material processing module and a drawing matching module. The deep learning module is used to extract the appearance data of the metal material in the sample data and the feature training model of the processing scheme. The material processing module is used to formulate a processing scheme for each special-shaped metal material. The drawing matching module is used to verify whether the metal material to be cut can cut the objects on the drawing and match the appropriate drawing for the metal material.

[0008] According to the above technical solution, the intelligent control module includes a distance control module, a cutting plan formulation module and a machine tool adjustment module. The distance control module is used to control the laser head of the laser cutting machine to always maintain a fixed distance with the metal material to be cut. The cutting plan formulation module is used to formulate a cutting plan based on the appearance characteristics, thickness characteristics, cutting drawings and other data of the metal material to be cut. The machine tool adjustment module is used to adjust the machine tool height for fixing the metal material according to the shape characteristics of the metal material.

[0009] According to the above technical scheme, the deep learning module includes a material feature extraction submodule, a feature fusion submodule and a model training submodule. The material feature extraction submodule is used to extract metal material features and processing method features in sample data, the feature fusion submodule is used to fuse the extracted feature data, and the model training submodule is used to train the material processing model according to the fused feature data to process special-shaped metal materials. The cutting plan formulation module includes a material characterization processing submodule, a speed adjustment submodule and a cutting point calculation submodule. The material characterization processing submodule is used to fit the plane drawing to the surface of the special metal material, the speed adjustment submodule is used to calculate the cutting speed required by the laser cutting machine according to the thickness of the detected metal material, and the cutting point calculation submodule is used to calculate the entry point that can maximize the utilization of the metal material according to the drawing.

[0010] According to the above technical solution, the operation method of the material cutting intelligent management and control system mainly includes the following steps:

[0011] Step S1: The material and cutting drawings of the metal material to be cut are entered into the system through the material information entry module, and a large amount of metal material processing solution data is entered into the system through the sample data entry module, and the appearance data information of the metal material to be cut is further collected, the metal material is scanned, and the point cloud data is scanned and entered into the system;

[0012] Step S2: After the data entry is completed, the system starts the deep learning module, uses a large amount of sample data to train the material processing model and formulate a material processing plan to match the appropriate drawing plan for the metal material;

[0013] Step S3: After matching the drawing plan with the metal material, the system sends an electrical signal to start the cutting plan designation module, starts analyzing various data of the metal material, and formulates a cutting plan;

[0014] Step S4: Further according to the metal material characteristics in the cutting plan, the metal material is fixed on the machine tool of the laser cutting machine, and the machine tool is adjusted. The laser cutting machine cuts the metal material according to the set cutting distance, and measures the distance between the laser head of the laser cutting machine and the metal material in real time.

[0015] According to the above technical solution, step S2 further includes the following steps:

[0016] Step S21: Retrieving a large amount of metal material processing solution sample data from the database, extracting the material of the metal material in the sample data, classifying the sample data according to the material of the metal material, extracting the classified data features, and after the feature value extraction of the metal material sample data is completed, fusing the feature values ​​according to the material category of the metal material, and using the metal material features to train the material processing model;

[0017] Step S22: after the model training is completed, the 3D point cloud data of the scanned metal material is retrieved, a 3D model of the metal material is constructed, an edge visual image of the current metal material is retrieved, the material processing model scans the edge visual image of the metal material, and the irregular protrusions on the edge of the metal material in the visual image are identified, and marked in the 3D model;

[0018] Step S23: Setting the minimum slope threshold of the metal material, marking the irregular protrusions on the metal edge, and measuring the surface slope angle θ of the special-shaped metal material in the three-dimensional model of the metal material. The surface slope angle θ of the metal material is the angle between the plates on both sides of the concave part of the material surface. If the slope angle θ is less than the minimum slope threshold of the system, all the bending nodes on the angle are marked, otherwise they are marked as available materials, and cutting is performed according to the bending nodes identified and marked by the system;

[0019] Step S24: measuring various parameters of the metal material, including metal material, surface area, volume, weight and other data, and matching the material processing model with the object drawing according to the parameters of the metal material.

[0020] According to the above technical solution, step S3 further includes the following steps:

[0021] Step S31: After the object drawing is matched with the metal material, the object drawing is scanned, the part model on the object drawing is extracted, the material processing model is called, and the part model is laid out in the three-dimensional model. After the cutting layout is determined, the outline of each object is marked in the three-dimensional model, and the object drawing is cut according to the outline mark. The remaining material is matched with the object drawing again. If the drawing can be matched, the drawing is tiled and covered on the surface of the three-dimensional model of the metal material. Otherwise, the three-dimensional model is marked as an arbitrary entry point.

[0022] Step S32: Retrieve the object drawing and the matching metal material, read the object drawing information, and if the object drawing belongs to the special-shaped object drawing set by the system, scan the 3D model of the metal material, identify and anchor the material edge nodes in the model, unfold the 3D model parallel to the horizontal plane, and evenly cover its surface with the object drawing, and restore the original state of the 3D model according to the anchored 3D model edge node position;

[0023] Step S33: If the object drawing belongs to the regular object drawing set by the system, the object drawing is directly covered on the surface of the metal material, and the laser cutting machine cuts the metal material matching the drawing according to the drawing.

[0024] According to the above technical solution, step S33 further includes the following steps:

[0025] Step S331: setting the influence coefficient α of the thickness of the metal material on the cutting speed. When cutting metal materials of uneven thickness, the 3D model of the current metal material is retrieved, the system draws a 3D grid model composed of blocks of the same size, merges the 3D model with the 3D grid model, calculates the number of grids M in the longitudinal direction, and calculates the cutting speed that needs to be reduced by the formula V=|M-mλ·α·ν, where V is the laser cutting machine speed that needs to be reduced, M is the thickness of the metal material measured by the system, in millimeters, m is the fixed cutting thickness set by the cutting machine, in millimeters, ν is the cutting speed corresponding to the fixed cutting thickness set by the cutting machine, λ is the thickness influence coefficient of the protrusions or depressions on the surface of the metal material, and α is the influence coefficient of the thickness of the metal material on the cutting speed;

[0026] Step S332: Set the metal material inclined cutting influence coefficient to β. When the metal material is inclined cut, measure the angle Y between the cutting laser and the metal material in the three-dimensional model, and calculate the real-time cutting speed V of the laser cutting machine by the formula:

[0027]

[0028] Wherein, β is the influence coefficient of inclined cutting of metal materials, M is the measured thickness of the metal material, ν is the speed when cutting metal materials with a thickness of M, and Y is the angle between the cutting laser and the upper surface of the metal material.

[0029] According to the above technical solution, step S4 further includes the following steps:

[0030] Step S41: After the cutting plan of the object is perfected, the vertical height of the metal material measured in the three-dimensional model is retrieved. If the current height is greater than the maximum threshold set by the system, it means that the height of the object is too large and there is a risk of damaging the laser head. The machine tool is lowered. Otherwise, cutting is performed directly.

[0031] Step S42: During the process of cutting metal materials, the distance H from the laser head to the metal material is measured in real time in the three-dimensional model. The threshold range of the distance from the laser head to the metal material in the database is retrieved according to the material of the current metal material and compared with the distance H. If the distance H is not within the threshold range, the distance between the laser head and the metal material is adjusted, otherwise the cutting continues.

[0032] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention, by marking the irregular protrusions on the edge of the metal material in the three-dimensional model, the system can quickly find the position of the irregular protrusions, and by matching the drawings for the metal material through the material processing model, it can avoid the manual allocation of drawings, save a lot of labor costs and time, and greatly improve the system efficiency. By simulating the cutting of the object in the three-dimensional model and matching the new object drawings for the waste, it is possible to ensure the maximum utilization of metal material resources during cutting and improve resource utilization. By unfolding the three-dimensional model, fusing the drawings and then restoring them, the material and the drawings can be completely fitted, so that the cutting machine can cut more complex objects and make the cutting machine more practical. By calculating the actual thickness of the protrusions on the surface of the metal material, the system can adjust the cutting power of the cutting machine in real time according to the thickness change of the metal material. The above formula can accurately calculate the cutting speed required for cutting metal materials of any thickness, which can ensure that the metal material can be completely cut, and avoid the phenomenon that the metal material is not completely cut and needs to be reworked. By controlling the lifting of the machine tool to adjust the maximum distance between the metal material and the laser head, the laser head can be protected from damage during cutting. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0034] Figure 1 It is a schematic diagram of the system module composition of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] See also Figure 1 The present invention provides a technical solution: a material cutting intelligent management and control system based on deep learning, including a data collection module, a material analysis module and an intelligent control module, characterized in that: the data collection module is used to collect the appearance feature data of the metal material to be cut and the drawing data of the object, the material analysis module is used to process the material to make it meet the conditions for object cutting, and match the object drawing for the metal material, the intelligent control module is used to formulate a cutting plan for the laser cutting machine according to the appearance characteristics of the metal material, and the data collection module, the material analysis module and the intelligent control module are electrically connected to each other.

[0037] The data collection module includes a material scanning module, a material information entry module, a sample data entry module and a visual module. The material scanning module is used to scan and enter the three-dimensional point cloud data of metal materials. The material information entry module is used to enter data such as metal material materials and cutting drawings. The sample data entry module is used to enter a large amount of sample data of metal materials and processing solutions. The visual module is used to collect edge visual images of metal materials.

[0038] The material analysis module includes a deep learning module, a material processing module and a drawing matching module. The deep learning module is used to extract the appearance data of the metal material in the sample data and the feature training model of the processing scheme. The material processing module is used to formulate a processing scheme for each special-shaped metal material. The drawing matching module is used to verify whether the metal material to be cut can cut the objects on the drawing and match the appropriate drawing for the metal material.

[0039] The intelligent control module includes a distance control module, a cutting plan formulation module and a machine tool adjustment module. The distance control module is used to control the laser head of the laser cutting machine to always maintain a fixed distance with the metal material to be cut. The cutting plan formulation module is used to formulate a cutting plan based on the appearance characteristics, thickness characteristics, cutting drawings and other data of the metal material to be cut. The machine tool adjustment module is used to adjust the machine tool height for fixing the metal material according to the shape characteristics of the metal material.

[0040] The deep learning module includes a material feature extraction submodule, a feature fusion submodule and a model training submodule. The material feature extraction submodule is used to extract metal material features and processing method features from sample data, the feature fusion submodule is used to fuse the extracted feature data, and the model training submodule is used to train the material processing model according to the fused feature data to process special-shaped metal materials. The cutting plan formulation module includes a material characterization processing submodule, a speed adjustment submodule and a cutting point calculation submodule. The material characterization processing submodule is used to fit the plane drawing to the surface of the special metal material, the speed adjustment submodule is used to calculate the cutting speed required by the laser cutting machine according to the thickness of the detected metal material, and the cutting point calculation submodule is used to calculate the entry point that can maximize the utilization of the metal material according to the drawing.

[0041] The operation method of the material cutting intelligent management and control system mainly includes the following steps:

[0042] Step S1: The material and cutting drawings of the metal material to be cut are entered into the system through the material information entry module, and a large amount of metal material processing solution data is entered into the system through the sample data entry module, and the appearance data information of the metal material to be cut is further collected, the metal material is scanned, and the point cloud data is scanned and entered into the system;

[0043] Step S2: After the data entry is completed, the system starts the deep learning module and uses a large amount of sample data to train the material processing model, which is trained using a large number of metal material processing solutions, and formulates a material processing solution to match the metal material with a suitable drawing solution;

[0044] Step S3: After matching the drawing plan with the metal material, the system sends an electrical signal to start the cutting plan designation module, starts analyzing various data of the metal material, and formulates a cutting plan;

[0045] Step S4: Further according to the metal material characteristics in the cutting plan, the metal material is fixed on the machine tool of the laser cutting machine, and the machine tool is adjusted. The laser cutting machine cuts the metal material according to the set cutting distance, and measures the distance between the laser head of the laser cutting machine and the metal material in real time.

[0046] Step S2 further comprises the following steps:

[0047] Step S21: Retrieving a large amount of metal material processing solution sample data from the database, extracting the material of the metal material in the sample data, classifying the sample data according to the material of the metal material, extracting the classified data features, and after the feature value extraction of the metal material sample data is completed, fusing the feature values ​​according to the material category of the metal material, and using the metal material features to train the material processing model;

[0048] Step S22: After the model training is completed, the three-dimensional point cloud data of the scanned metal material is retrieved, a three-dimensional model of the metal material is constructed, and an edge visual image of the current metal material is retrieved. The material processing model scans the edge visual image of the metal material, identifies irregular protrusions on the edge of the metal material in the visual image, and marks them in the three-dimensional model. By marking the irregular protrusions on the edge of the metal material in the three-dimensional model, the system can quickly find the position of the irregular protrusions;

[0049] Step S23: Setting the minimum slope threshold of the metal material, marking the irregular protrusions on the metal edge, and measuring the surface slope angle θ of the special-shaped metal material in the three-dimensional model of the metal material. The surface slope angle θ of the metal material is the angle between the plates on both sides of the depression on the surface of the material. If the slope angle θ is less than the minimum slope threshold of the system, all the bending nodes on the angle are marked, otherwise they are marked as available materials. Cutting is performed according to the bending nodes marked by the system recognition. By measuring the slope of the material and judging whether it meets the cutting standard, the material can be segmented so that it can be cut into objects, reducing the steps of material processing and improving the material utilization rate.

[0050] Step S24: Measure various parameters of the metal material, including metal material, surface area, volume, weight and other data. The material processing model matches the object drawing according to the parameter data of the metal material. Matching the drawing for the metal material through the material processing model can avoid manual allocation of drawings, save a lot of labor costs and time, and greatly improve system efficiency.

[0051] Step S3 further comprises the following steps:

[0052] Step S31: After the object drawing is matched with the metal material, the object drawing is scanned, the part model on the object drawing is extracted, the material processing model is called, and the part model is laid out in the three-dimensional model. After the cutting layout is determined, the outline of each object is marked in the three-dimensional model, and the object drawing is cut according to the outline mark. The remaining material is matched with the object drawing again. If the drawing can be matched, the drawing is flattened and covered on the surface of the three-dimensional model of the metal material. Otherwise, the three-dimensional model is marked as an arbitrary entry point. By simulating the object cutting in the three-dimensional model and matching the waste material with a new object drawing, the utilization of metal material resources can be maximized during cutting, thereby improving resource utilization;

[0053] Step S32: Retrieve the object drawing and the matching metal material, read the object drawing information, if the object drawing belongs to the special-shaped object drawing set by the system, and the special-shaped object drawing is obtained by cutting using a material of a specific shape, then scan the 3D model of the metal material, identify and anchor the edge nodes of the material in the model, unfold the 3D model parallel to the horizontal plane, and the object drawing evenly covers its surface, and restore the original state of the 3D model according to the anchored 3D model edge node position, the original state is the 3D model constructed according to the point cloud data, and by unfolding the 3D model, fusing the drawing and then restoring it, the material and the drawing can be completely fitted, so that the cutting machine can cut more complex objects, making the cutting machine more practical;

[0054] Step S33: If the object drawing belongs to a regular object drawing set by the system, and the regular object drawing is cut on a flat material, the object drawing is directly covered on the surface of the metal material, and the laser cutting machine cuts the metal material matching the drawing.

[0055] Step S33 further includes the following steps:

[0056] Step S331: setting the influence coefficient α of the thickness of the metal material on the cutting speed. When cutting metal materials of uneven thickness, the three-dimensional model of the current metal material is retrieved, and the system draws a three-dimensional grid model composed of blocks of the same size, merges the three-dimensional model with the three-dimensional grid model, calculates the number of grids M in the longitudinal direction, and calculates the cutting speed that needs to be reduced by the formula V=|M-mλ·α·ν, where V is the speed of the laser cutting machine that needs to be reduced, M is the thickness of the metal material measured by the system, in millimeters, m is the fixed cutting thickness set by the cutting machine, in millimeters, ν is the cutting speed corresponding to the fixed cutting thickness set by the cutting machine, λ is the thickness influence coefficient of the protrusions or depressions on the surface of the metal material, and α is the influence coefficient of the thickness of the metal material on the cutting speed. Through the formula, we can know that the larger the protrusions or depressions on the surface of the metal material, the greater the impact on the speed of the cutting machine. By calculating the actual impact thickness of the protrusions on the surface of the metal material, the system can adjust the cutting power of the cutting machine in real time according to the thickness changes of the metal material;

[0057] Step S332: Set the metal material inclined cutting influence coefficient to β. When the metal material is inclined cut, measure the angle Y between the cutting laser and the metal material in the three-dimensional model, and calculate the real-time cutting speed V of the laser cutting machine by the formula:

[0058]

[0059] Where β is the influence coefficient of inclined cutting of metal materials, M is the measured thickness of the metal material, ν is the speed when cutting metal materials with a thickness of M, and Y is the angle between the cutting laser and the upper surface of the metal material. The above formula can accurately calculate the cutting speed required for cutting metal materials of any thickness, ensuring that the metal material can be completely cut, avoiding the phenomenon that the metal material is not completely cut and needs to be reworked.

[0060] Step S4 further comprises the following steps:

[0061] Step S41: After the cutting plan of the object is perfected, the height of the metal material in the vertical direction measured in the three-dimensional model is retrieved. If the current height is greater than the maximum threshold set by the system, it means that the height of the object is too high and there is a risk of damaging the laser head. The machine tool is lowered. Otherwise, cutting is performed directly. By controlling the lifting and lowering of the machine tool to adjust the maximum distance between the metal material and the laser head, the laser head can be protected from being damaged during cutting.

[0062] Step S42: During the process of cutting metal materials, the distance H from the laser head to the metal material is measured in real time in the three-dimensional model. The threshold range of the distance from the laser head to the metal material in the database is retrieved according to the material of the current metal material and compared with the distance H. If the distance H is not within the threshold range, the distance between the laser head and the metal material is adjusted. Otherwise, the cutting is continued. By controlling the distance between the laser head and the metal material to always be within the threshold range, the safety of the laser head during the cutting process can be protected, accidents can be avoided, and the safety of the operator can be protected.

[0063] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0064] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. The material cutting intelligent management and control system based on deep learning includes a data collection module, a material analysis module and an intelligent control module, and is characterized by: The data collection module is used to collect appearance feature data of the metal material to be cut and drawing data of the object, the material analysis module is used to process the material to make it meet the conditions for object cutting and match the object drawing for the metal material, and the intelligent control module is used to formulate a cutting plan of the laser cutting machine according to the appearance features of the metal material. The data collection module, the material analysis module and the intelligent control module are electrically connected to each other; The material analysis module includes a deep learning module, a material processing module and a drawing matching module. The deep learning module is used to extract the appearance data of the metal material in the sample data and the feature training model of the processing scheme. The material processing module is used to formulate a processing scheme for each special-shaped metal material. The drawing matching module is used to verify whether the metal material to be cut can cut the object on the drawing and match the drawing for the metal material. The intelligent control module includes a distance control module and a cutting plan formulation module. The cutting plan formulation module includes a material characterization processing submodule, a speed adjustment submodule and a cutting point calculation submodule. The material characterization processing submodule is used to fit the plane drawing on the surface of the heterogeneous metal material. The speed adjustment submodule is used to calculate the cutting speed required by the laser cutting machine according to the thickness of the detected metal material. The cutting point calculation submodule is used to calculate the entry point that can maximize the utilization of the metal material according to the drawing.

2. The material cutting intelligent management and control system based on deep learning according to claim 1 is characterized by: The data collection module includes a material scanning module, a material information entry module, a sample data entry module and a visual module. The material scanning module is used to scan and enter the three-dimensional point cloud data of metal materials. The material information entry module is used to enter the metal material material and cutting drawing data. The sample data entry module is used to enter a large amount of sample data of metal materials and processing solutions. The visual module is used to collect edge visual images of metal materials.

3. The material cutting intelligent management and control system based on deep learning according to claim 2 is characterized by: The distance control module is used to control the laser head of the laser cutting machine to always maintain a fixed distance with the metal material to be cut, and the cutting plan formulation module is used to formulate a cutting plan according to the appearance characteristics, thickness characteristics and cutting drawings of the metal material to be cut.

4. The material cutting intelligent management and control system based on deep learning according to claim 3 is characterized by: The intelligent control module also includes a machine tool adjustment module, which is used to adjust the height of a machine tool for fixing the metal material according to the shape characteristics of the metal material.

5. The material cutting intelligent management and control system based on deep learning according to claim 4 is characterized by: The deep learning module includes a material feature extraction submodule, a feature fusion submodule and a model training submodule. The material feature extraction submodule is used to extract metal material features and processing method features in sample data, the feature fusion submodule is used to fuse the extracted feature data, and the model training submodule is used to train a material processing model according to the fused feature data to process special-shaped metal materials.

6. The material cutting intelligent management and control system based on deep learning according to claim 5 is characterized by: The operation method of the material cutting intelligent management and control system mainly includes the following steps: Step S1: The material and cutting drawings of the metal material to be cut are entered into the system through the material information entry module, and a large amount of metal material processing solution data is entered into the system through the sample data entry module, and the appearance data information of the metal material to be cut is further collected, the metal material is scanned, and the point cloud data is scanned and entered into the system; Step S2: After the data entry is completed, the system starts the deep learning module, uses a large amount of sample data to train the material processing model, and formulates a material processing plan to match the drawing plan for the metal material; Step S3: After matching the drawing plan with the metal material, the system sends an electrical signal to start the cutting plan designation module, starts analyzing various data of the metal material, and formulates a cutting plan; Step S4: Further according to the metal material characteristics in the cutting plan, the metal material is fixed on the machine tool of the laser cutting machine, and the machine tool is adjusted. The laser cutting machine cuts the metal material according to the set cutting distance, and measures the distance between the laser head of the laser cutting machine and the metal material in real time.

7. The material cutting intelligent management and control system based on deep learning according to claim 6 is characterized by: The step S2 further comprises the following steps: Step S21: Retrieving a large amount of metal material processing solution sample data from the database, extracting the material of the metal material in the sample data, classifying the sample data according to the material of the metal material, extracting the classified data features, and after the feature value extraction of the metal material sample data is completed, fusing the feature values ​​according to the material category of the metal material, and using the metal material features to train the material processing model; Step S22: after the model training is completed, the 3D point cloud data of the scanned metal material is retrieved, a 3D model of the metal material is constructed, an edge visual image of the current metal material is retrieved, the material processing model scans the edge visual image of the metal material, and the irregular protrusions on the edge of the metal material in the visual image are identified, and marked in the 3D model; Step S23: Setting the minimum slope threshold of the metal material, marking the irregular protrusions on the metal edge, and measuring the slope angle θ of the surface of the special-shaped metal material in the three-dimensional model of the metal material. If the slope angle θ is less than the minimum slope threshold of the system, all the bending nodes on the angle are marked, otherwise they are marked as available materials, and cutting is performed according to the bending nodes marked by the system recognition; Step S24: measuring various parameters of the metal material, including metal material, surface area, volume and weight data, and matching the material processing model with the object drawing according to the parameters of the metal material.

8. The material cutting intelligent management and control system based on deep learning according to claim 7 is characterized by: The step S3 further comprises the following steps: Step S31: After the object drawing is matched with the metal material, the object drawing is scanned, the part model on the object drawing is extracted, the material processing model is called, and the part model is laid out in the three-dimensional model. After the cutting layout is determined, the outline of each object is marked in the three-dimensional model, and the object drawing is cut according to the outline mark. The remaining material is matched with the object drawing again. If the drawing can be matched, the drawing is tiled and covered on the surface of the three-dimensional model of the metal material. Otherwise, the three-dimensional model is marked as an arbitrary entry point. Step S32: Retrieve the object drawing and the matching metal material, read the object drawing information, and if the object drawing belongs to the special-shaped object drawing set by the system, scan the 3D model of the metal material, identify and anchor the material edge nodes in the model, unfold the 3D model parallel to the horizontal plane, and evenly cover its surface with the object drawing, and restore the original state of the 3D model according to the anchored 3D model edge node position; Step S33: If the object drawing belongs to the regular object drawing set by the system, the object drawing is directly covered on the surface of the metal material, and the laser cutting machine cuts the metal material matching the drawing according to the drawing.

9. The material cutting intelligent management and control system based on deep learning according to claim 8 is characterized by: The step S33 further comprises the following steps: Step S331: setting the influence coefficient α of the thickness of the metal material on the cutting speed. When cutting metal materials of uneven thickness, the 3D model of the current metal material is retrieved, the system draws a 3D grid model composed of blocks of the same size, merges the 3D model with the 3D grid model, calculates the number of grids M in the longitudinal direction, and calculates the cutting speed that needs to be reduced by the formula V=|M-mλ·α·ν, where V is the laser cutting machine speed that needs to be reduced, M is the thickness of the metal material measured by the system, in millimeters, m is the fixed cutting thickness set by the cutting machine, in millimeters, ν is the cutting speed corresponding to the fixed cutting thickness set by the cutting machine, λ is the thickness influence coefficient of the protrusions or depressions on the surface of the metal material, and α is the influence coefficient of the thickness of the metal material on the cutting speed; Step S332: Set the metal material inclined cutting influence coefficient to β. When the metal material is inclined cut, measure the angle Y between the cutting laser and the metal material in the three-dimensional model, and calculate the real-time cutting speed V of the laser cutting machine by the formula: Wherein, β is the influence coefficient of inclined cutting of metal materials, M is the measured thickness of the metal material, ν is the speed when cutting metal materials with a thickness of M, and Y is the angle between the cutting laser and the upper surface of the metal material.

10. The material cutting intelligent management and control system based on deep learning according to claim 9 is characterized in that: The step S4 further comprises the following steps: Step S41: After the cutting plan of the object is perfected, the vertical height of the metal material measured in the three-dimensional model is retrieved. If the current height is greater than the maximum threshold set by the system, it means that the height of the object is too large and there is a risk of damaging the laser head. The machine tool is lowered. Otherwise, cutting is performed directly. Step S42: During the process of cutting metal materials, the distance H from the laser head to the metal material is measured in real time in the three-dimensional model. The threshold range of the distance from the laser head to the metal material in the database is retrieved according to the material of the current metal material and compared with the distance H. If the distance H is not within the threshold range, the distance between the laser head and the metal material is adjusted, otherwise the cutting continues.

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