Embedded real-time operating system deployment method and system for industrial control

Abstract

The application relates to the technical field of industrial control, in particular to an embedded real-time operating system deployment method and system for industrial control. The method comprises the following steps: laser processing control point division is performed on a workpiece to be processed to generate laser control point coordinate data; laser beam testing is performed according to the laser control point coordinate data, and an embedded real-time operation monitoring network is deployed to perform monitoring signal synchronous collection to obtain laser pre-scanning monitoring data; theoretical focal point position adjustment and laser power density processing are performed according to the laser pre-scanning monitoring data, and real-time laser thermal processing operation is performed on the workpiece to be processed to obtain a real-time thermal gradient vector field; and real-time regulation and control of the industrial processing control process is performed according to the real-time thermal gradient vector field to realize industrial processing real-time operation system deployment. The embedded real-time operation system is used to realize real-time operation control of the industrial laser processing process, and the processing precision and efficiency are significantly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial control, and in particular to an embedded real-time operating system deployment method and system for industrial control. BACKGROUND

[0002] Industrial laser processing technology is widely used in metal cutting, welding, surface treatment and precision machining fields. However, the traditional laser processing control system usually adopts fixed parameter setting, which presets key parameters such as laser power, focal point position and scanning speed. This method performs well when processing single material or simple shape workpiece, but its limitations are increasingly prominent when facing the processing needs of modern industrial production. Especially when processing different materials, variable thickness workpieces or complex geometric shapes, fixed parameter setting cannot be adjusted in real time according to material properties and processing state, resulting in uneven laser energy transmission and causing a series of quality problems. Specifically, when the laser beam acts on the surface of different materials, due to the significant differences in thermal conductivity and surface reflectivity of materials, if the laser parameters cannot be adjusted in real time, it will lead to uneven energy absorption. For example, high reflectivity materials such as aluminum alloy or copper alloy will reflect most of the laser energy, requiring higher power density; while materials with poor thermal conductivity such as some ceramics or composites are prone to local overheating, requiring more uniform energy distribution. The current real-time operating system of laser processing cannot effectively cope with these differences, and cannot meet the needs of high-precision processing, which leads to problems such as excessive ablation or insufficient energy in the processing process, often resulting in unstable processing quality, and further causing a series of problems such as decreased processing precision and expanded heat affected zone. SUMMARY

[0003] Therefore, the present application provides an embedded real-time operating system deployment method and system for industrial control to solve at least one of the above technical problems.

[0004] To achieve the above purpose, an embedded real-time operating system deployment method for industrial control comprises the following steps:

[0005] Step S1: identifying the surface processing area of the workpiece to be processed to obtain the workpiece surface processing area; dividing the workpiece surface processing area into laser processing control points to generate laser control point coordinate data;

[0006] Step S2: testing the laser beam according to the laser control point coordinate data, and performing synchronous acquisition of monitoring signals through embedded deployment of a real-time operating monitoring network to obtain laser pre-scanning monitoring data; analyzing the temperature characteristics of the measurement points according to the laser pre-scanning monitoring data to generate measurement point temperature distribution characteristic data;

[0007] Step S3: Obtain the workpiece processing task type; based on the workpiece processing task type, identify the heat accumulation risk area of the workpiece surface processing area through the measuring point temperature distribution characteristic data, then adjust the theoretical focal point position to obtain the theoretical focal point position data; process the laser power density according to the laser pre-scanning monitoring data to generate the processing laser power density data;

[0008] Step S4: Perform real-time laser thermal processing operation on the workpiece to be processed through the theoretical focal point position data and the processing laser power density data to obtain a real-time thermal gradient vector field; control the industrial processing control process in real time according to the real-time thermal gradient vector field to realize the deployment of the industrial processing real-time operation system.

[0009] The present application realizes fine control of laser processing by identifying and dividing the control points of the processing area on the workpiece surface. Compared with traditional global parameter setting, this method can set different laser parameters for different processing areas, thus realizing precise energy delivery. This is particularly important for workpieces with complex shapes and uneven thickness, as it can effectively avoid problems such as over-ablation and insufficient energy caused by uneven energy distribution. For example, when processing workpieces with complex geometric shapes such as sharp corners and narrow gaps, this method can fine-tune laser power and scanning speed according to the geometric characteristics of different areas, ensuring that each area receives the best processing results and avoiding over-ablation of sharp corners or insufficient energy in narrow gaps. Through the real-time operation monitoring network deployed in an embedded manner, the system can obtain laser pre-scanning monitoring data in real time and analyze temperature characteristics, thus accurately grasping the temperature distribution on the workpiece surface. For example, when processing materials with poor thermal conductivity, the system can monitor temperature changes in real time and adjust laser power or scanning speed immediately if local overheating is detected, preventing material damage and improving processing quality while reducing material waste. According to different processing task types (such as cutting, welding, surface treatment, etc.) and real-time monitoring of temperature distribution characteristic data, potential heat accumulation risk areas are predicted, and laser focal point position and power density are adjusted accordingly. This parameter adjustment strategy based on real-time data and workpiece characteristics can effectively avoid processing quality problems caused by material property differences and geometric complexity. For example, for high-reflectivity materials, the system can automatically increase laser power density to compensate for reflection loss; for heat-sensitive materials, the system can reduce power density and optimize the scanning path to avoid overheating and thermal damage. According to the real-time generated thermal gradient vector field, laser parameters are dynamically adjusted to ensure that laser energy always acts on the workpiece surface in the best state. This closed-loop control mechanism can effectively respond to various complex dynamic changes during processing, such as material thickness changes and surface reflectivity changes, thus maintaining optimal processing results at all times and achieving high-precision, high-quality laser processing. Therefore, the embedded real-time operating system deployment method for industrial control of the present application realizes real-time operation control of the industrial laser processing control process by identifying and dividing the control points of the processing area on the workpiece surface to be processed, using a real-time operation monitoring network deployed in an embedded manner to obtain laser pre-scanning monitoring data in real time, analyzing temperature characteristics, and adjusting laser parameters in a timely manner to ensure precise delivery of laser energy. Real-time thermal gradient vector field regulation is performed to execute laser thermal processing operations, significantly improving processing precision and efficiency.

[0010] Preferably, the laser processing control point division of the workpiece surface processing area in step S1 includes:

[0011] According to the workpiece surface processing area, the geometric information of the to-be-processed area is extracted.

[0012] According to the shape complexity calculation of the to-be-processed region geometry information, a region shape complexity coefficient is generated; the surface texture direction, surface roughness and surface curvature of the workpiece surface processing region are extracted;

[0013] The initial grid density data of the workpiece surface processing region is calculated by the region shape complexity coefficient, and the initial region grid density data is generated;

[0014] According to the surface texture direction, the grid direction adjustment coefficient is obtained; and according to the surface roughness and surface curvature, the grid spacing adjustment coefficient is calculated;

[0015] Based on the grid direction adjustment coefficient and the grid spacing adjustment coefficient, the initial region grid density data is adaptively adjusted, and then the workpiece surface processing region is processed grid division, and the gridded processing region is obtained;

[0016] The gridded processing region is numbered in rows and columns, and then the laser processing control point is discretized, and the laser control point coordinate data is generated.

[0017] The geometry information of the to-be-processed region is extracted and the shape complexity coefficient is calculated, the grid density can be adaptively adjusted according to the shape complexity of the workpiece. For the region with complex shape, such as the region with sharp angle, curve or small feature, the system will automatically increase the grid density to ensure that these regions can be processed more finely; and for the region with simple shape, the grid density will be reduced, thereby reducing the calculation amount and processing time. The method of adaptively adjusting the grid density according to the geometric shape can improve the processing efficiency while ensuring the processing precision. By numbering and discretizing the gridded processing region, the laser processing system can finely adjust the parameters of each control point, such as adjusting the laser power, scanning speed and focal point position of each control point, so as to realize the fine processing of the workpiece surface, and finally obtain higher processing quality and precision.

[0018] Preferably, the laser beam test according to the laser control point coordinate data in step S2 comprises:

[0019] The infrared thermal imager array and the environment temperature sensor are installed around the laser, the beam splitter and the photoelectric sensor are arranged in the laser light path inside the laser, and the laser power meter is connected in series at the output end of the laser; the infrared thermal imager array, the environment temperature sensor, the photoelectric sensor and the laser are connected in communication through industrial Ethernet or special bus, so as to embed the real-time operation monitoring network;

[0020] The material attribute parameters of the workpiece surface processing region are extracted, and the processing region material attribute parameters are generated;

[0021] extracting a material ablation threshold according to the material attribute parameter of the processing area;

[0022] switching the laser to a low-power pre-scanning mode using a laser power of 10% of the material ablation threshold to obtain low-power pre-scanning parameters, wherein the low-power pre-scanning parameters include pre-scanning laser power and pre-scanning speed;

[0023] controlling the laser to perform point-by-point or short-line laser beam testing on the processing area of the workpiece surface based on the low-power pre-scanning parameters through laser control point coordinate data, and then performing synchronous acquisition of monitoring signals through a real-time operation monitoring network to obtain laser pre-scanning monitoring data, wherein the laser pre-scanning monitoring data includes pre-scanning environmental temperature data, pre-scanning workpiece processing temperature data, pre-scanning surface reflected light signals, pre-scanning laser power, and pre-scanning speed, and the pre-scanning surface reflected light signals are reflected to a photoelectric sensor through a beam splitter for signal acquisition.

[0024] The present application constructs a multi-sensor fusion real-time monitoring network by installing an infrared thermal imager array and an environmental temperature sensor around the laser, setting a beam splitter and a photoelectric sensor in the laser light path, and combining a laser power meter, which can synchronously acquire key data such as environmental temperature, workpiece processing temperature, surface reflected light signals, and laser power, thereby comprehensively reflecting energy changes and material responses in the laser processing process. Through the real-time operation monitoring network, monitoring signals are synchronously acquired, and pre-scanning data is associated with material attribute parameters, so that a mapping relationship between material characteristics and laser parameters can be established. For example, by analyzing the temperature changes and reflected light signals of the workpiece surface during the pre-scanning process, the thermal conductivity and reflectivity of the material can be inferred, and the subsequent laser processing parameters can be optimized accordingly, such as adjusting the laser power, scanning speed, and focal point position.

[0025] Preferably, the measurement point temperature characteristic analysis according to the laser pre-scanning monitoring data in step S2 includes:

[0026] correcting the pre-scanning workpiece processing temperature data for outliers through the pre-scanning environmental temperature data to obtain corrected workpiece processing temperature monitoring data;

[0027] calculating the laser action time of each measurement point or short line segment according to the pre-scanning speed and laser control point coordinate data;

[0028] segmenting the corrected workpiece processing temperature monitoring data into sub-sequences based on the laser action time to obtain single-point / short-line temperature sequence data;

[0029] processing the single-point / short-line temperature sequence data based on the workpiece surface processing area to generate measurement temperature field distribution data;

[0030] According to the measured temperature field distribution data and the single point / short line segment temperature sequence data, the temperature characteristics of the measurement points are analyzed, and the temperature distribution characteristic data of the measurement points are generated.

[0031] The present application can eliminate the influence of environmental temperature fluctuation on the measurement results and improve the accuracy of temperature measurement by correcting the pre-scan workpiece processing temperature data with the pre-scan environmental temperature data. The laser action time of each measurement point or short line segment is calculated according to the pre-scan speed and laser control point coordinate data, and the corrected temperature data is divided into sub-sequences according to the laser action time, so that a more detailed temperature change curve can be obtained. This fine time division can more accurately reflect the temperature change process of the material under the action of the laser, so that the thermal response characteristics of the material can be better understood. Based on the single point / short line segment temperature sequence data, the temperature field distribution of the measurement points in the workpiece surface processing area is processed, so that the potential overheating risk in the laser processing process can be more comprehensively evaluated. By combining the measured temperature field distribution data and the single point / short line segment temperature sequence data, the thermal response characteristics of the material can be more comprehensively reflected.

[0032] Preferably, the temperature characteristics of the measurement points are analyzed according to the measured temperature field distribution data and the single point / short line segment temperature sequence data, including:

[0033] According to the single point / short line segment temperature sequence data, the temperature rise rate is calculated;

[0034] According to the single point / short line segment temperature sequence data, the temperature curve of each measurement point / short line segment is extracted, and then the peak temperature point is located to obtain the peak temperature point and the peak temperature data, respectively;

[0035] According to the measured temperature field distribution data, the spatial positions on both sides of the peak temperature point where the temperature drops to 1 / e of the peak temperature data are searched, and the measurement temperature boundary point positions are obtained; wherein e is a mathematical natural constant;

[0036] The pre-scan environmental temperature data+3℃ is taken as the heat affected threshold, and the heat affected judgment is performed on the measurement temperature boundary point positions through the heat affected threshold; when the temperature of the measurement temperature boundary point position is higher than the heat affected threshold, the measurement temperature boundary point position is marked as effective boundary point position data;

[0037] According to the effective boundary point position data, the heat affected zone width is calculated;

[0038] The temperature distribution characteristics of the temperature rise rate, the peak temperature data and the heat affected zone width are integrated to obtain the temperature distribution characteristic data of the measurement points.

[0039] The present application can evaluate the absorption speed of laser energy and the thermal response characteristics of the material by calculating the temperature rise rate. Higher temperature rise rate indicates that the material absorbs laser energy faster, and overheating phenomenon is more likely to occur, which requires corresponding adjustment of laser parameters, such as reducing laser power or increasing scanning speed. By extracting peak temperature data, the highest temperature of the material under the action of laser can be determined, and whether it exceeds the damage threshold of the material can be evaluated. If the peak temperature is too high, the laser parameters need to be adjusted to avoid overheating or ablation of the material. The width of the heat-affected zone is an important indicator for evaluating the quality of laser processing. Excessive heat-affected zone width will cause material deformation or performance degradation, and corresponding parameter adjustment is needed. At the same time, using the ambient temperature plus 3℃ as the heat-affected threshold for effective boundary judgment can more accurately determine the heat-affected area, exclude the interference of ambient temperature, and improve the calculation accuracy of the heat-affected zone width. The temperature distribution characteristic data of the measurement point is obtained by integrating the temperature rise rate, peak temperature data and heat-affected zone width, which can construct a more comprehensive temperature feature data set, and can more accurately reflect the thermal response characteristics of the material and the temperature variation law in the laser processing process.

[0040] Preferably, the heat accumulation risk area identification of the workpiece surface processing area based on the workpiece processing task type through the measurement point temperature distribution characteristic data in step S3, and then the theoretical focal point position adjustment comprises:

[0041] According to the temperature rise rate in the measurement point temperature distribution characteristic data, the spatial gradient is evaluated to generate temperature rise gradient data;

[0042] According to the peak temperature data in the measurement point temperature distribution characteristic data, the scanning center point offset evaluation is performed to generate peak temperature offset data;

[0043] According to the heat-affected zone width in the measurement point temperature distribution characteristic data, the heat-affected zone shape analysis is performed to obtain heat-affected zone shape analysis data;

[0044] The process requirement comparison of the temperature rise gradient data, the peak temperature offset data and the heat-affected zone shape analysis data is performed through the workpiece processing task type, and then the heat accumulation risk area identification is performed to obtain the heat accumulation risk area;

[0045] According to the heat accumulation risk area, the heat diffusion direction analysis is performed to generate processing heat diffusion direction data;

[0046] Based on the processing heat diffusion direction data, the laser control point coordinate data is subjected to minimum heat accumulation risk processing, and then the theoretical focal point position adjustment is performed to obtain the theoretical focal point position data.

[0047] The present application can identify areas with rapid temperature changes, which are usually areas with higher heat accumulation risks, by evaluating the spatial gradient of the temperature rise rate. By scanning the center point offset of the peak temperature data, it can be determined whether the actual point of action of the laser energy deviates from the predetermined position. If there is an offset, the focal point position needs to be adjusted to ensure that the laser energy can accurately act on the target area. By analyzing the shape of the heat-affected zone based on the width of the heat-affected zone, it can be evaluated whether the shape and size of the laser action area meet the processing requirements. If the heat-affected zone is too large or irregular in shape, the laser parameters need to be adjusted to optimize the energy distribution and reduce the size of the heat-affected zone. By comparing the temperature rise gradient data, peak temperature offset data, and heat-affected zone shape analysis data with the process requirements of the workpiece processing task type, the heat accumulation risk area can be more accurately identified. According to the heat accumulation risk area, the laser scanning path and focal point position are optimized to minimize the heat accumulation risk and improve the efficiency and quality of laser processing. By adjusting the theoretical focal point position to the optimal position, the distribution of heat can be more effectively controlled to avoid overheating or insufficient heat, and ultimately achieve high-quality laser processing.

[0048] Preferably, the laser power density processing in step S3 includes:

[0049] Setting a laser processing standard energy according to the workpiece processing task type;

[0050] Calculating the single-point reflectivity according to the pre-scan surface reflected light signal to generate single-point reflectivity data;

[0051] Mapping the single-point reflectivity data with the laser control point coordinate data to generate surface reflectivity distribution data;

[0052] Dividing the surface reflectivity distribution data into low reflectivity areas, medium reflectivity areas, and high reflectivity areas through a pre-set reflectivity threshold;

[0053] Calculating the standard unit area laser energy of the medium reflectivity area through the laser processing standard energy to generate medium reflectivity area energy demand;

[0054] Based on the laser processing standard energy, the low reflectivity area and the high reflectivity area are weighted unit area laser energy calculated respectively using a pre-set energy demand weight, to obtain low reflectivity area energy demand and high reflectivity area energy demand respectively;

[0055] Processing the laser power density of the medium reflectivity area energy demand, the low reflectivity area energy demand, and the high reflectivity area energy demand to generate processing laser power density data.

[0056] The present application sets the laser processing standard energy according to the type of workpiece processing task, ensuring that the energy needs of different processing tasks are met. For example, cutting tasks usually require higher energy density, while welding tasks require lower energy density. By calculating the single-point reflectivity of the pre-scanned surface reflection light signal and mapping it with the laser control point coordinate data, surface reflectivity distribution data is generated. By dividing the surface reflectivity distribution data into reflection regions using a pre-set reflectivity threshold, the laser energy density in different regions can be controlled more finely. According to the laser processing standard energy and the pre-set energy demand weight, the unit area laser energy demand of different reflectivity regions is calculated. For high reflectivity regions, the system will automatically increase the energy demand to compensate for the energy loss caused by reflection; while for low reflectivity regions, the energy demand will be reduced to avoid excessive ablation or thermal damage caused by excess energy. Processing the energy demand of different reflectivity regions into laser power density can effectively control the distribution of laser energy on the workpiece surface, ensuring that each region can obtain the required energy, thereby improving the efficiency and quality of laser processing.

[0057] Preferably, the real-time laser thermal processing operation on the workpiece to be processed in step S4 includes:

[0058] planning a laser processing control path according to the theoretical focal point position data;

[0059] processing the theoretical focal point position data and the processing laser power density data to obtain laser processing control parameters;

[0060] encoding the processing control instructions based on the laser processing control path and the laser processing control parameters to generate laser processing control instructions;

[0061] starting the laser based on the laser processing control instructions and performing laser processing on the workpiece to be processed according to the laser processing control path, and recording the temperature distribution in the laser processing process in real time through the infrared thermal imager array to obtain real-time temperature distribution field data;

[0062] calculating the single-point temperature gradient component according to the real-time temperature distribution field data to construct a real-time thermal gradient vector field.

[0063] The present invention plans the laser processing control path according to the theoretical focus position data to ensure that the laser can be processed according to the predetermined trajectory. The theoretical focus position data and the processing laser power density data are processed as processing parameters to achieve precise control of the laser power and focus position, thereby ensuring the precise delivery of laser energy. Processing control instructions are encoded based on the laser processing control path and laser processing control parameters to convert complex processing processes into precise control instructions. The laser is started based on the laser processing control instructions, and laser processing is performed on the workpiece to be processed according to the laser processing control path to obtain real-time temperature distribution field data. A real-time thermal gradient vector field is constructed. The real-time thermal gradient vector field can reflect the real-time changes in the workpiece surface temperature field. By real-time monitoring of the temperature gradient, potential overheating risks can be discovered in a timely manner, and laser parameters can be dynamically adjusted, such as reducing the laser power or changing the scanning speed, thereby effectively avoiding material damage and improving processing quality.

[0064] Preferably, the real-time regulation of the industrial processing control process according to the real-time thermal gradient vector field in step S4 includes:

[0065] The over-temperature, low-temperature and ultra-wide heat-affected zones are marked on the workpiece surface through the real-time thermal gradient vector field to obtain the defect marking area data;

[0066] Defect area priority assessment is performed based on defect marking area data, and then processing parameters are dynamically adjusted to generate a dynamic processing control strategy;

[0067] Real-time control of industrial processing control processes is carried out according to dynamic processing control strategies to realize the deployment of real-time operating systems for industrial processing.

[0068] The present application can mark the over-temperature, low-temperature and over-wide heat affected zone of the workpiece surface machining area by real-time thermal gradient vector field, and can monitor various defects in the machining process in real time, such as overburning, insufficient energy and excessive heat affected zone. According to the defect marking area data, the priority of the defect area can be evaluated, and the severity of different defects can be distinguished. For example, the over-temperature area will cause material ablation, and the laser power or scanning speed needs to be reduced immediately; while the low-temperature area will cause incomplete machining, and the laser power or scanning speed needs to be increased. According to the evaluation result of the priority of the defect area, the processing parameters are dynamically adjusted, and the laser parameters such as laser power, scanning speed and focal point position can be automatically adjusted according to the real-time monitoring of the defects, so as to effectively correct the machining defects. According to the dynamic processing control strategy, the industrial processing control process is real-time regulated, realizing the closed-loop control of laser processing. This closed-loop control system can continuously adjust the processing parameters according to real-time feedback information, so as to maintain the best processing state at all times, and finally realize the deployment of the real-time operation system of industrial processing. This not only improves the processing quality and efficiency, but also reduces the need for human intervention, reduces the skill requirements of the operator, and improves the production efficiency and product consistency.

[0069] Preferably, the present application also provides an embedded real-time operation system deployment system for industrial control, which executes the embedded real-time operation system deployment method for industrial control as described above, and comprises:

[0070] A machining area identification module is configured to identify the surface machining area of a workpiece to be machined to obtain a workpiece surface machining area, and divide the workpiece surface machining area into laser machining control points to generate laser control point coordinate data.

[0071] An industrial pre-scanning analysis module is configured to test a laser beam according to the laser control point coordinate data, and perform synchronous acquisition of monitoring signals through an embedded deployment real-time operation monitoring network to obtain laser pre-scanning monitoring data, and analyze the temperature characteristics of the measurement points according to the laser pre-scanning monitoring data to generate measurement point temperature distribution characteristic data.

[0072] A thermal control optimization module is configured to obtain a workpiece machining task type, identify the heat accumulation risk area of the workpiece surface machining area based on the workpiece machining task type and the measurement point temperature distribution characteristic data, and then adjust the theoretical focal point position to obtain theoretical focal point position data, and process the laser power density according to the laser pre-scanning monitoring data to generate machining laser power density data.

[0073] A real-time thermal processing control module is used to perform real-time laser thermal processing operation on a workpiece to be processed by using theoretical focal point position data and processing laser power density data to obtain a real-time thermal gradient vector field; and an industrial processing control process is real-time regulated according to the real-time thermal gradient vector field to realize industrial processing real-time operation system deployment. BRIEF DESCRIPTION OF DRAWINGS

[0074] Figure 1 A step flowchart of an embedded real-time operating system deployment method for industrial control of the present application;

[0075] Figure 2 A detailed implementation flowchart of the theoretical focal point position adjustment in step S3; Figure 1

[0076] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0077] The technical method of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0078] In addition, the accompanying drawings are only schematic illustrations of the present application, and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated description thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities, which do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0079] It should be understood that although the terms "first", "second" and the like can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of the exemplary embodiments, a first element can be referred to as a second element, and similarly a second element can be referred to as a first element. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0080] To achieve the above-mentioned purposes, please refer to Figures 1 to 2 The present application provides an embedded real-time operating system deployment method for industrial control, comprising the following steps: ​

[0081] Step S1: surface processing area recognition is performed on the workpiece to be processed to obtain a workpiece surface processing area; laser processing control point division is performed on the workpiece surface processing area to generate laser control point coordinate data;

[0082] Step S2: laser beam testing is performed according to the laser control point coordinate data, and an embedded real-time operation monitoring network is deployed to perform monitoring signal synchronous acquisition to obtain laser pre-scanning monitoring data; measurement point temperature feature analysis is performed according to the laser pre-scanning monitoring data to generate measurement point temperature distribution feature data;

[0083] Step S3: obtain the workpiece processing task type; based on the workpiece processing task type, heat accumulation risk area identification is performed on the workpiece surface processing area by using the measurement point temperature distribution feature data, and then theoretical focal point position adjustment is performed to obtain theoretical focal point position data; laser power density processing is performed according to the laser pre-scanning monitoring data to generate processing laser power density data;

[0084] Step S4: real-time laser thermal processing operation is performed on the workpiece to be processed by using the theoretical focal point position data and the processing laser power density data to obtain a real-time thermal gradient vector field; industrial processing control process real-time regulation and control are performed according to the real-time thermal gradient vector field to realize industrial processing real-time operation system deployment.

[0085] In the embodiment of the present application, the embedded real-time operation system deployment method for industrial control comprises the following steps:

[0086] Step S1: surface processing area recognition is performed on the workpiece to be processed to obtain a workpiece surface processing area; laser processing control point division is performed on the workpiece surface processing area to generate laser control point coordinate data;

[0087] In an embodiment of the present invention, for example, this case uses turbine blade repair as an example, including but not limited to turbine blade repair. A structured light 3D scanner (e.g., GOM ATOS Core) is used to scan a worn turbine blade (made of Inconel 718 nickel-based superalloy). The scanner projects structured light stripes onto the blade surface. Two or more cameras capture the stripe images, and triangulation is used to calculate 3D point cloud data of the blade surface. The point cloud data is processed using supporting software (e.g., GOMInspect), including noise removal, smoothing, and hole filling. The processed point cloud data is imported into CAD software (e.g., CATIA V5), where surface reconstruction is used to fit a NURBS surface model of the blade surface. Within the CAD model, the worn area at the blade tip is selected; this area serves as the workpiece surface processing area. The boundary curve is discretized into a series of points. The ratio of the chord length to the arc length between these discrete points is then calculated to determine the region's shape complexity coefficient (e.g., 1.8). Images of the blade tip wear area were captured using a high-resolution industrial camera (e.g., Basler acA2500-14gc, 5 megapixels). Image processing was performed using the OpenCV library to extract the surface texture orientation (e.g., 30° relative to the blade chord). The surface roughness Ra (e.g., 1.2 μm) of the blade tip wear area was measured using a surface roughness meter (e.g., Taylor Hobson Form Talysurf i-Series). The curvature analysis tool in the CAD software (CATIA V5) was used to calculate the average curvature of the blade tip wear area (e.g., 0.05 mm⁻¹). The initial mesh density and mesh adjustment factor were calculated based on the shape complexity factor, texture orientation, roughness, and curvature. For example, the initial mesh spacing was set to 0.5 mm, which was adjusted to 0.28 mm based on the complexity factor. The mesh was rotated 30° based on the texture orientation, and the mesh spacing was further adjusted to 0.25 mm based on the roughness and curvature. The blade tip wear area was meshed using a rectangular grid in the CAD software. The center point of each mesh cell served as a control point for laser processing. The coordinates (X, Y, Z coordinates) of these control points are extracted and arranged in a scanning order (e.g., from left to right, row by row) to generate laser control point coordinate data.

[0088] Step S2: Performing laser beam testing based on the laser control point coordinate data, and synchronously collecting monitoring signals through an embedded deployment real-time operation monitoring network to obtain laser pre-scan monitoring data; performing temperature characteristic analysis of the measurement point based on the laser pre-scan monitoring data to generate temperature distribution characteristic data of the measurement point;

[0089] In this embodiment of the present invention, four infrared thermal imagers (e.g., FLIR A655sc) are installed in a circular array around a laser (e.g., an IPG YLR-2000 fiber laser). An ambient temperature sensor (e.g., a Pt100 platinum resistor) is also installed. A beam splitter (95% transmittance, 5% reflectivity) and a photoelectric sensor (e.g., Thorlabs DET10A / M) are placed in the laser light path. A laser power meter (e.g., Ophir 10A-V2-SH) is connected in series to the laser output. These devices are connected to a real-time industrial control system (e.g., Beckhoff TwinCAT 3) using industrial Ethernet (e.g., PROFINET). The material handbook for Inconel 718 alloy was consulted to obtain parameters such as its density, specific heat capacity, thermal conductivity, melting point, vaporization point, and absorptivity. A laser-material interaction model was developed using COMSOL Multiphysics, and the ablation threshold of the material was determined to be 2.5 J / mm². The laser was switched to low-power prescan mode, with the power set to 1.96 W (10% of the ablation threshold) and the scanning speed set to 1 mm / s. Based on the laser control point coordinate data, the laser is controlled to scan the blade tip wear area point by point. Simultaneously, a real-time operation monitoring network collects data: an infrared thermal imager captures temperature images at a 30Hz frame rate, an ambient temperature sensor measures ambient temperature at a 1Hz frequency, a photoelectric sensor collects reflected light signals at a 10kHz sampling rate, and a laser power meter measures actual output power at a 1Hz frequency. The control system records this data, along with the laser scan position and timestamp, to generate laser pre-scan monitoring data. Pre-scan ambient temperature data (e.g., 25.5°C) is extracted from the laser pre-scan monitoring data, and outlier correction is performed on the pre-scan workpiece processing temperature data (e.g., removing data points outside the range of 25.5°C ± 5°C). The laser action time (e.g., 0.1s) is calculated for each point based on the pre-scan speed (1mm / s) and the laser control point coordinate data. Based on the laser action time, temperature series data for each point are extracted from the corrected workpiece processing temperature monitoring data. The average, maximum, minimum, and temperature rise rates of each point are calculated using MATLAB and interpolated to generate the measured temperature field distribution data. The temperature series data for each point is smoothed to locate the peak temperature point and calculate the peak temperature data. With the peak temperature point as the center, the measured temperature field distribution data is searched for locations where the temperature drops to 1 / e of the peak temperature to determine the location of the measured temperature boundary point. Using the ambient temperature +3°C (28.5°C) as the heat impact threshold, the measured temperature boundary point is determined to be a valid boundary point. The distance between valid boundary points is calculated to determine the width of the heat-affected zone. The temperature rise rate, peak temperature data, and heat-affected zone width are integrated to generate the temperature distribution characteristic data for the measurement point.

[0090] Step S3: Obtain the workpiece processing task type; based on the workpiece processing task type, identify the heat accumulation risk area of the workpiece surface processing area through the measurement point temperature distribution characteristic data, then adjust the theoretical focus position, and obtain the theoretical focus position data; according to the laser pre-scanning monitoring data, process the laser power density to generate the processing laser power density data;

[0091] In the embodiment of the application, the spatial gradient of the temperature rise rate is calculated according to the measurement point temperature distribution characteristic data (for example, using the central difference method). The time offset of the peak temperature point is evaluated (for example, the ideal peak temperature point time is 0.05s, the actual peak temperature point time is 0.06s, and the offset is 0.01s). The shape of the heat-affected zone is analyzed (for example, the variance of the distance from the heat-affected zone boundary point to the laser action point is calculated). The temperature rise gradient threshold (for example, 400℃ / s / mm), the peak temperature offset threshold (for example, 0.02s), and the heat-affected zone shape requirement (for example, the heat-affected zone shape should be close to a circle and is not allowed to expand beyond the measurement range) are set. Compare these data with the thresholds and requirements to identify the heat accumulation risk area (for example, the side of the blade root close to the center of the disc). Analyze the heat diffusion direction of the heat accumulation risk area (for example, along the radial direction of the blade). Adjust the scanning path and use short line segments perpendicular to the heat diffusion direction in the heat accumulation risk area), introduce a cooling time (for example, increase a 0.08s delay between each short line segment), and move the laser focus point up by 0.2mm. For example, according to the laser cladding repair task, the standard energy density of laser processing is set to 50J / mm². According to the pre-scanning surface reflected light signal, the reflectivity of each point is calculated. For example, the photovoltaic sensor output voltage is 0.1V, the load resistance is 50Ω, the responsivity of the photovoltaic sensor is 0.5A / W, the pre-scanning laser power is 1.96W, and the reflectivity of the beam splitter is 5%, then the reflectivity of this point is (0.1V / (0.5A / W ×50Ω)) / (1.96W ×0.05)≈0.41. Map the single-point reflectivity data with the laser control point coordinate data to generate the surface reflectivity distribution data. Set the reflectivity threshold: low reflectivity area (reflectivity<0.2), medium reflectivity area (0.2≤reflectivity≤0.4), and high reflectivity area (reflectivity>0.4). According to the area division, the required unit area laser energy of each area is calculated respectively: medium reflectivity area energy requirement=50J / mm², low reflectivity area energy requirement=40J / mm² (weight 0.8), and high reflectivity area energy requirement=60J / mm² (weight 1.2).

[0092] Step S4: Real-time laser thermal processing operation is performed on the workpiece to be processed through the theoretical focus position data and the processing laser power density data, and a real-time thermal gradient vector field is obtained; the real-time thermal gradient vector field is used for real-time regulation and control of the industrial processing control process to realize the deployment of the industrial processing real-time operation system.

[0093] In the embodiment of the present application, the laser processing control path (spiral path, and optimized for heat accumulation risk area) is planned according to the theoretical focal position data. The theoretical focal position data and the processing laser power density data are integrated to obtain the laser processing control parameters (including the coordinates of each control point, focal position, laser power, scanning speed, etc.). The control parameters are encoded into G code instructions. Start the laser, and the control system controls the laser head movement and laser output according to the processing control instructions to perform laser cladding repair on the blade tip wear area. The infrared thermal imager array monitors the temperature distribution of the workpiece surface in real time, and transmits the thermal image data to the control system through PROFINET. The control system synchronously records the thermal image data with the timestamp and position information of the laser processing to generate real-time temperature distribution field data. For the real-time temperature distribution field data, the finite difference method is used to calculate the temperature gradient components of each point to construct a real-time thermal gradient vector field. Set the upper temperature threshold (1200℃), the lower temperature threshold (1000℃) and the heat affected zone width threshold (0.5mm). Traverse the real-time thermal gradient vector field data to mark the over-temperature area, low-temperature area and over-wide heat affected area to generate defect marked area data. According to the defect type, determine the priority: over-temperature > low-temperature > over-wide heat affected area. According to the priority and the position of the defect area, develop a dynamic processing control strategy: reduce the power of the over-temperature area by 5%-10%, increase the power of the low-temperature area by 3%-5%, and adjust the scanning path or add a delay in the over-wide heat affected area. In the embedded real-time operating system (Beifang TwinCAT), a real-time task is created to execute the following at a frequency of 100Hz: read the real-time thermal gradient vector field and defect marked area data, search the control strategy table, modify the control parameters (power, scanning speed) of the laser or the motion parameters (path, delay) of the numerical control system, and send the modified control parameters to the laser and the numerical control system. In this way, closed-loop control of the laser cladding repair process based on real-time temperature feedback is realized, the repair quality is guaranteed, and the deployment of the industrial processing real-time operating system is completed.

[0094] Preferably, the laser processing control point division of the workpiece surface processing area in step S1 comprises:

[0095] According to the workpiece surface processing area, extract the geometry information of the to-be-processed area;

[0096] According to the geometry information of the to-be-processed area, calculate the shape complexity to generate a region shape complexity coefficient; extract the surface texture direction, surface roughness and surface curvature of the workpiece surface processing area;

[0097] Perform initial grid density calculation on the workpiece surface processing area by the region shape complexity coefficient to generate initial region grid density data;

[0098] According to the surface texture direction, a grid direction adjustment coefficient is obtained; and according to the surface roughness and the surface curvature, a grid spacing adjustment coefficient is calculated;

[0099] Based on the grid direction adjustment coefficient and the grid spacing adjustment coefficient, the initial regional grid density data is adaptively adjusted, and then the workpiece surface machining region is divided into machining grids to obtain a gridded machining region.

[0100] The gridded machining region is numbered in rows and columns, and then the laser machining control points are discretized to generate laser control point coordinate data.

[0101] In an embodiment of the present invention, in industrial production, for example, laser repair of an engine blade may involve a concave area on the blade surface that requires repair. First, a coordinate measuring machine (CMM) is used to scan the blade surface to obtain three-dimensional point cloud data for the entire blade. The surface reconstruction function of the CAD software is then used to fit and generate a NURBS surface model of the blade surface. From the NURBS surface model, a region requiring laser cladding repair (e.g., a blade tip wear region) is selected, and the boundary curve of the region is extracted. This boundary curve represents the geometric contour information of the region to be processed. The boundary curve is discretized into a series of points. Then, the ratio of the chord length to the arc length between these discrete points is calculated. Specifically, three adjacent points A, B, and C on the curve are selected, and the chord length (straight-line distance) of AB and BC, as well as the arc length (curve length) of AB and BC, is calculated. The chord length to arc length ratio = (AB chord length + BC chord length) / (AB arc length + BC arc length). The chord length to arc length ratio is calculated for all combinations of three adjacent points on the curve, and the minimum value of all ratios is taken as the shape complexity coefficient of the curve segment. The entire boundary curve of the area to be processed is segmented (for example, every 1 mm). The shape complexity coefficient of each segment is calculated. Finally, the average shape complexity coefficient of all segments is taken and multiplied by a weighting factor (for example, 1.2, set based on experience) to obtain the final regional shape complexity coefficient. High-resolution images of the pitted area on the blade surface are acquired using an optical microscope or laser confocal microscope. Image processing algorithms (such as Gabor filter banks) are used to analyze the texture features in the image and extract the principal direction of the surface texture. A surface roughness measuring instrument (such as a stylus profilometer) is used to scan the pitted area and obtain the surface roughness parameter Ra (arithmetic mean deviation). Using coordinate measuring machine (CMM) scan data or CAD model data, the surface curvature (such as Gaussian curvature or mean curvature) is calculated for each point within the pitted area. For example, if the blade surface has tool marks left during manufacturing, the texture direction will be along the tool mark direction. A rougher surface will result in a larger Ra value, while a sharper pit bottom will result in a larger curvature value. A base grid spacing (for example, 0.5 mm) is set, which corresponds to a shape complexity coefficient of 1. Initial grid spacing = baseline grid spacing / regional shape complexity coefficient. If the complexity coefficient is greater than 1, the initial grid spacing is smaller than the baseline spacing; if the complexity coefficient is less than 1, the initial grid spacing is larger than the baseline spacing. After determining the initial grid spacing, the initial number of grid cells can be calculated based on the area of ​​the area to be processed (for example, the area of ​​the area divided by the area of ​​a single grid cell), thereby obtaining the initial regional grid density (number of grid cells / area of ​​the area). Calculate the angle between the texture direction and the horizontal direction. If the angle is between 45 and 135 degrees, the grid direction adjustment coefficient is 1, indicating that grid direction adjustment is required; otherwise, the grid direction adjustment coefficient is 0, indicating that no adjustment is required.The grid spacing adjustment factor = base spacing × (1-k1 × surface roughness) × (1-k2 × surface curvature), where k1 and k2 are weighting factors (e.g., k1=0.1, k2=0.2, set based on experience). Roughness and curvature are normalized. The greater the roughness and curvature, the smaller the adjustment factor and the smaller the grid spacing. The initial area mesh density data is adjusted based on the grid orientation adjustment factor and the grid spacing adjustment factor. First, the initial mesh is rotated according to the grid orientation adjustment factor to align the grid lines with the grain direction. Then, the grid spacing is scaled according to the grid spacing adjustment factor. For example, if the grid spacing adjustment factor for a certain area is 0.8, the grid spacing in that area is reduced to 80% of its original value. After this adjustment, the workpiece surface processing area (the pit area) is meshed to obtain a meshed processing area. A uniform or non-uniform mesh is generated within the pit area using the Delaunay triangulation algorithm or the quadrilateral mesh generation algorithm. The meshed processing area is numbered in rows and columns. For example, the grid cells are numbered from left to right and from top to bottom. The center point or vertex of each grid cell is then used as a laser processing control point. The 3D coordinates of these control points are extracted to generate laser control point coordinate data.

[0102] Preferably, the step S2 of performing laser beam testing according to the laser control point coordinate data and performing synchronous acquisition of monitoring signals through an embedded deployment real-time operating monitoring network includes:

[0103] An infrared thermal imager array and ambient temperature sensor are installed around the laser. A beam splitter and photoelectric sensor are installed in the laser optical path inside the laser. A laser power meter is connected in series at the laser output. The infrared thermal imager array, ambient temperature sensor, photoelectric sensor, and laser are connected and communicated via industrial Ethernet or a dedicated bus, thus deploying an embedded real-time operation monitoring network.

[0104] Extract material property parameters of the workpiece surface processing area and generate material property parameters of the processing area;

[0105] Extract material ablation threshold according to material property parameters of processing area;

[0106] Using a laser power of 10% of the material ablation threshold, the laser is switched to a low-power pre-scan mode to obtain low-power pre-scan parameters; wherein the low-power pre-scan parameters include pre-scan laser power and pre-scan speed;

[0107] The laser is controlled by the low-power pre-scanning parameter to test the workpiece surface machining area point by point or short line segment laser beam through the laser control point coordinate data, and then the laser pre-scanning monitoring data is obtained through the real-time operation monitoring network for monitoring signal synchronous acquisition; wherein the laser pre-scanning monitoring data includes pre-scanning environmental temperature data, pre-scanning workpiece machining temperature data, pre-scanning surface reflected light signal, pre-scanning laser power and pre-scanning speed, and the pre-scanning surface reflected light signal is reflected to the photoelectric sensor for signal acquisition through the beam splitter.

[0108] In the embodiments of the present application, eight FLIR A655sc infrared thermographs are evenly distributed around the laser to form an infrared thermograph array for monitoring the temperature field changes during laser processing. An OMEGA HHM290 digital thermometer is installed near the laser to measure the ambient temperature. In the laser light path, a Thorlabs BS013 beam splitter is installed to reflect a small part of the laser beam (e.g. 5%) onto a Thorlabs S120C photosensor for monitoring the fluctuations in laser power. At the output end of the laser, an Ophir F150A-BB-26 laser power meter is connected in series to measure the laser output power in real time. The infrared thermograph array, the ambient temperature sensor, the photosensor, and the laser power meter are connected to a Beckhoff CX2020 embedded controller through a PROFINET industrial Ethernet network, which runs the TwinCAT 3 real-time operating system to form a real-time operation monitoring network. Consult a material manual or database (e.g. MatWeb, ASM Handbook) to obtain the key material property parameters of the alloy. These parameters include: density (e.g. 8.19 g / cm³), specific heat capacity (e.g. 435 J / kg·K), thermal conductivity (e.g. 11.4 W / m·K at 25°C), melting point (e.g. 1260-1336°C), vaporization point (e.g. 2700°C), and absorption rate (e.g. about 0.3 at a wavelength of 1064 nm). The formula Fth=ρ × H × δ / A can be used, where Fth is the ablation threshold, ρ is the density, H is the sum of the latent heat of melting and vaporization, δ is the optical penetration depth, and A is the absorption rate. The ablation threshold of Inconel 718 at a specific laser wavelength (e.g. 1064 nm) is calculated to be 10 J / cm². Alternatively, single-point ablation experiments at different laser energy densities can be performed to observe the lowest energy density at which ablation marks appear on the material surface as the ablation threshold. Based on the obtained material ablation threshold (2.5 J / mm²), the laser power for the low-power pre-scan mode is calculated. Pre-scan laser power = ablation threshold × spot area × 10%. Assuming a spot diameter of 0.1 mm, the spot area is π × (0.05 mm)² ≈ 0.00785 mm². Pre-scan laser power ≈ 2.5 J / mm² × 0.00785 mm² × 0.1 ≈ 0.00196 J / s ≈ 1.96 W. In the control software of the laser (e.g. the NC PTP function of Beckhoff TwinCAT), the laser power is set to 1.96 W. The pre-scan speed can be set to a lower value, e.g. 1 mm / s, to ensure sufficient time for data collection. The laser is started, and the control software controls the laser to emit a low-power laser beam (1.96 W) to scan the workpiece surface point by point (or connect adjacent points to form short line segments) according to the pre-set laser control point coordinates.The network synchronously collects data in real time while the laser scans. The infrared thermal imager array continuously captures temperature images of the workpiece surface at a certain frame rate (e.g., 30 Hz) and transmits the temperature data to the control system in real time through industrial Ethernet. The ambient temperature sensor measures the ambient temperature at a sampling frequency of 1 Hz. The photoelectric sensor receives 5% of the reflected laser signal through the beam splitter and converts it into a voltage signal, which is collected at a high sampling rate (e.g., 10 kHz). The laser power meter measures the actual output laser power at a sampling rate of 1 Hz. The control system synchronously records these data with the scanning position of the laser (control point coordinates) and the timestamp, generating a multi-channel data file, i.e., laser pre-scan monitoring data.

[0109] Preferably, the measuring point temperature feature analysis based on the laser pre-scan monitoring data in step S2 includes:

[0110] The pre-scan workpiece processing temperature data is corrected for outliers by the pre-scan ambient temperature data to obtain corrected workpiece processing temperature monitoring data;

[0111] The laser action time for each measuring point or short line segment is calculated based on the pre-scan speed and laser control point coordinate data;

[0112] The corrected workpiece processing temperature monitoring data is divided into sub-sequences based on the laser action time to obtain single point / short line segment temperature sequence data;

[0113] The single point / short line segment temperature sequence data is processed for measuring point temperature field distribution based on the workpiece surface processing area to generate measuring temperature field distribution data;

[0114] The measuring point temperature feature analysis is performed based on the measuring temperature field distribution data and the single point / short line segment temperature sequence data to generate measuring point temperature distribution feature data.

[0115] In the embodiment of the present application, the pre-scan environmental temperature data is extracted from the laser pre-scan monitoring data (for example, the average value of the temperature measured by the environmental temperature sensor within 5 seconds before the start of laser scanning is 25.2°C). The pre-scan workpiece processing temperature data is extracted (all temperature data collected by the infrared thermal imager array). Due to the influence of environmental light, electromagnetic interference and other factors, individual measurement values may be abnormal (for example, a point suddenly appears much higher or lower than the surrounding temperature). A temperature threshold is set, which is based on the environmental temperature and empirical value (for example, ±5°C of the environmental temperature). Traverse the pre-scan workpiece processing temperature data, and mark the temperature data points exceeding the threshold as abnormal values. For abnormal values, use the average value of the adjacent non-abnormal temperature data points (for example, the average value of the two points before and after) to replace them. The pre-scan speed (for example, 1mm / s) and laser control point coordinate data are extracted from the laser pre-scan monitoring data. If the laser scanning mode is point-by-point scanning, the laser action time of each control point = spot diameter / pre-scan speed. Assuming the spot diameter is 0.1mm, the action time of each point = 0.1mm / 1mm / s = 0.1s. If the laser scanning mode is short line segment scanning (connecting adjacent control points), the laser action time of each short line segment = short line segment length / pre-scan speed. The short line segment length can be obtained by calculating the Euclidean distance between the coordinates of adjacent control points. For example, the distance between point A(1, 1, 0) and point B(1, 2, 0) is 1mm, so the laser action time of this short line segment = 1mm / 1mm / s = 1s. Calculate for all control points or short line segments to get the laser action time of each measurement point or short line segment. According to the starting coordinates of each measurement point / short line segment and the laser action time, the temperature data within the corresponding time period is extracted from the corrected workpiece processing temperature monitoring data. For example, for control point A, the laser action time is 0.1s, and assuming its scanning start timestamp is T0, the temperature data between T0 and T0+0.1s is extracted from the corrected workpiece processing temperature monitoring data to form an independent temperature sequence data, i.e. single-point temperature sequence data. For short line segment AB, the laser action time is 1s, and assuming its scanning start timestamp is T1, the temperature data between T1 and T1+1s is extracted to form a short line segment temperature sequence data. According to the control point coordinates corresponding to each single-point / short line segment temperature sequence data, it is placed at the corresponding position of the workpiece surface processing area. For each single-point / short line segment temperature sequence data, calculate its average temperature, maximum temperature, minimum temperature and temperature rising rate (for example, the difference between the maximum temperature and the starting temperature divided by the laser action time). These calculation results are taken as the temperature characteristic values of the point. Use interpolation algorithm (for example, inverse distance weighted interpolation or Kriging interpolation) to extend these discrete temperature characteristic values to the entire processing area to generate a continuous temperature field distribution map.Calculate statistical features of the temperature field distribution data, such as the maximum temperature, the minimum temperature, the average temperature, the temperature standard deviation, etc. Analyze the change trend of each single point / short line segment temperature sequence data, such as the temperature rise rate, the temperature drop rate, the temperature peak value occurrence time, etc. Combine these feature parameters to generate the measurement point temperature distribution feature data.

[0116] Preferably, the measurement point temperature feature analysis according to the measured temperature field distribution data and the single point / short line segment temperature sequence data comprises:

[0117] Calculate the temperature rise rate according to the single point / short line segment temperature sequence data;

[0118] Extract the temperature curve of each measurement point / short line segment according to the single point / short line segment temperature sequence data, and then locate the peak temperature point to obtain the peak temperature point and the peak temperature data respectively;

[0119] Search for the spatial position where the temperature drops to 1 / e proportion of the peak temperature data on both sides of the peak temperature point according to the measured temperature field distribution data, to obtain the measurement temperature boundary point position; wherein e is the mathematical natural constant;

[0120] Take the pre-scanning environmental temperature data+3℃ as the heat influence threshold, and perform heat influence judgment on the measurement temperature boundary point position through the heat influence threshold. When the temperature of the measurement temperature boundary point position is higher than the heat influence threshold, mark the measurement temperature boundary point position as the effective boundary point position data;

[0121] Calculate the heat influence zone width according to the effective boundary point position data;

[0122] Integrate the temperature rise rate, the peak temperature data, and the heat influence zone width to obtain the measurement point temperature distribution feature data.

[0123] In the embodiment of the present application, the initial temperature (the first temperature value of the sequence data) and the maximum temperature (the maximum value in the sequence data) of each measuring point / short line segment are extracted. The temperature rise rate = (maximum temperature - initial temperature) / laser action time. The laser action time has been calculated in the previous step. For example, for control point A, the initial temperature is 26°C, the maximum temperature is 85°C, and the laser action time is 0.1s, then the temperature rise rate = (85°C - 26°C) / 0.1s = 590°C / s. Calculate the temperature sequence data of all single points / short line segments to obtain the temperature rise rate of each measuring point / short line segment. Smooth the temperature sequence data (for example, use moving average filtering or Savitzky-Golay filtering) to remove high-frequency noise. Then, find the maximum value in the smoothed temperature sequence data, which is the peak temperature data, and the corresponding time point is the peak temperature point. Record the peak temperature data and the time coordinate of the peak temperature point of each measuring point / short line segment. For example, for short line segment AB, after processing the temperature sequence data, the maximum value found is 125°C, and the corresponding time coordinate is T = 0.5s (relative to the start time of short line segment scanning). Take each peak temperature point as the center, search in the measured temperature field distribution data along the laser scanning direction and its opposite direction. In each direction, find the position where the temperature drops to 1 / e (e≈2.718, natural constant) of the peak temperature data. For example, for control point A, the peak temperature is 150°C, and 1 / e × 150°C ≈ 55.2°C. In the measured temperature field distribution data, search along the scanning direction to find the position with a temperature of 55.2°C, and record the coordinates (X, Y) of this position; search in the opposite direction to also find the position with a temperature of 55.2°C, and record its coordinates. These two positions are the measured temperature boundary point positions of the control point. Operate on all measuring points / short line segments to obtain all measured temperature boundary point positions. Extract the pre-scanning environmental temperature data (for example, 25.2°C) from the laser pre-scanning monitoring data. Calculate the heat influence threshold = pre-scanning environmental temperature data + 3°C = 25.2°C + 3°C = 28.2°C. Traverse all the measured temperature boundary point positions obtained in step 3, and extract the temperature values of these positions from the measured temperature field distribution data. If the temperature of a certain measured temperature boundary point position is higher than 28.2°C, mark this position as valid boundary point position data; otherwise, consider that this position is not affected by the laser heat and do not record it. For each measuring point / short line segment, calculate the heat influence zone width according to the valid boundary point position data. If a measuring point / short line segment has two valid boundary points, the heat influence zone width = the distance between the two boundary points (calculate the Euclidean distance between the coordinates of the two points). If there is only one valid boundary point, the heat influence zone width is infinite, indicating that the heat influence has spread beyond the measurement range, and is recorded as a special value (for example, -1). If there is no valid boundary point, the heat influence zone width is 0.The temperature rise rate, peak temperature, scanning direction heat affected zone width, vertical direction heat affected zone width and other data of each measuring point or short line segment are combined into a feature vector as the temperature distribution feature data of the point.

[0124] Preferably, the theoretical focal point position adjustment in step S3 comprises:

[0125] According to the temperature rise rate in the measuring point temperature distribution feature data, spatial gradient evaluation is performed to generate temperature rise gradient data;

[0126] According to the peak temperature data in the measuring point temperature distribution feature data, scanning center point offset evaluation is performed to generate peak temperature offset data;

[0127] According to the heat affected zone width in the measuring point temperature distribution feature data, heat affected zone shape analysis is performed to obtain heat affected zone shape analysis data;

[0128] By comparing the temperature rise gradient data, peak temperature offset data and heat affected zone shape analysis data according to the workpiece processing task type, process requirement comparison is performed, and then heat accumulation risk area identification is performed to obtain the heat accumulation risk area;

[0129] According to the heat accumulation risk area, heat diffusion direction analysis is performed to generate processing heat diffusion direction data;

[0130] Based on the processing heat diffusion direction data, minimum heat accumulation risk processing is performed on the laser control point coordinate data, and then theoretical focal point position adjustment is performed to obtain the theoretical focal point position data.

[0131] As an example of the present application, referring to FIG. 1, a detailed implementation flowchart of the theoretical focal point position adjustment in step S3 in the present example is shown, and the theoretical focal point position adjustment comprises: Figure 2 Figure 1 S31: According to the temperature rise rate in the measuring point temperature distribution feature data, spatial gradient evaluation is performed to generate temperature rise gradient data;

[0132] S31: According to the temperature rise rate in the measuring point temperature distribution feature data, spatial gradient evaluation is performed to generate temperature rise gradient data;

[0133] ​In the embodiment of the present application, the finite difference method is used to calculate the gradient. For example, for a two-dimensional gridded processing area, the central difference method can be used to calculate the temperature rise rate gradient of each point in the X direction and the Y direction: X direction gradient=(temperature rise rate of the right point-temperature rise rate of the left point) / (2*grid spacing), Y direction gradient=(temperature rise rate of the upper point-temperature rise rate of the lower point) / (2*grid spacing), the module length of the gradient vector of each point (sqrt(X direction gradient^2+Y direction gradient^2)) is calculated as the temperature rise gradient value of the point. The temperature rise gradient values of all points form a matrix corresponding to the grid of the processing area, which is the temperature rise gradient data. For example, if the processing area is divided into a 10x10 grid, the temperature rise gradient data is a 10x10 matrix, and each element represents the temperature rise rate gradient value of the corresponding grid point.

[0134] S32: According to the peak temperature data in the measurement point temperature distribution characteristic data, the scanning center point offset evaluation is performed to generate the peak temperature offset data;

[0135] In the embodiment of the present application, under ideal conditions, the peak temperature point should appear at the middle time of the laser action time (for point-by-point scanning) or at the center position of the short line segment scanning path (for short line segment scanning). The deviation of the actual peak temperature point time coordinate and the ideal peak temperature point time coordinate (or position) is calculated. For example, for point-by-point scanning, if the laser action time is 0.1s, the ideal peak temperature point time coordinate is 0.05s; if the actual peak temperature point time coordinate is 0.07s, the time deviation is 0.02s. For short line segment scanning, if the short line segment length is 1mm and the pre-scanning speed is 1mm / s, the ideal peak temperature point position should be at the center of the short line segment (0.5mm away from the starting point); if the actual peak temperature point position is 0.7mm away from the starting point, the position deviation is 0.2mm. The peak temperature point time (or position) deviation of all measurement points / short line segments forms a vector, which is the peak temperature offset data.

[0136] S33: According to the heat-affected zone width and heat-affected zone shape analysis in the measurement point temperature distribution characteristic data, the heat-affected zone shape analysis data is obtained;

[0137] In the embodiment of the present application, for each measurement point, the width of the heat-affected zone in the scanning direction and the width of the heat-affected zone perpendicular to the scanning direction are compared. The difference or ratio between the two widths is calculated as the heat-affected zone shape analysis data of the point. For example, the heat-affected zone should be circular or elliptical centered on the laser action point. The distance from the heat-affected zone boundary point to the laser action point is calculated, and the variance or standard deviation of these distances is calculated. The larger the variance / standard deviation, the more irregular the shape of the heat-affected zone. If there is a special value (for example, -1, indicating that the heat-affected zone extends beyond the measurement range) in the heat-affected zone width data, it indicates that the heat-affected zone in this area is severe. These analysis results (the regularity of the shape of the heat-affected zone, whether there is an extension beyond the measurement range) are arranged into descriptive data, such as "heat-affected zone shape regular, no extension", "heat-affected zone shape irregular, with extension", etc., as heat-affected zone shape analysis data.

[0138] S34: Process requirement comparison is performed on the thermal rise gradient data, the peak temperature offset data, and the heat-affected zone shape analysis data by workpiece processing task type, and then heat accumulation risk area identification is performed to obtain a heat accumulation risk area;

[0139] In the embodiment of the present application, the heat accumulation risk area is identified by comparing the process requirements of the thermal rise gradient data, the peak temperature offset data, and the heat-affected zone shape analysis data by workpiece processing task type (for example, laser cladding repair or laser surface modification). For example, for laser cladding repair, it is usually required that the thermal rise gradient is small, the peak temperature offset is small, and the heat-affected zone shape is close to circular. Corresponding threshold values are set, for example, the thermal rise gradient threshold value is 8℃ / ms, the peak temperature offset threshold value is 0.5, and the heat-affected zone shape analysis data (ratio) threshold value is 1.2. The thermal rise gradient data, the peak temperature offset data, and the heat-affected zone shape analysis data are compared with the corresponding threshold values respectively, and if the data of a certain measurement point exceeds the corresponding threshold value, the area where the point is located is marked as a heat accumulation risk area. For example, if a point exceeds all threshold values, the area is marked as the highest heat accumulation risk.

[0140] S35: Heat diffusion direction analysis is performed according to the heat accumulation risk area to generate processing heat diffusion direction data;

[0141] In the embodiment of the present application, in the heat accumulation risk area, the point with the highest temperature is found, and then the temperature gradient direction between the point and its surrounding adjacent points is calculated. The direction with the largest temperature gradient is taken as the main heat diffusion direction of the area. For example, in a certain heat accumulation risk area, the temperature of point A is the highest, and the temperatures of its four surrounding adjacent points B, C, D and E are TB, TC, TD and TE respectively. The temperature gradients between A and B, C, D and E are calculated, and the direction with the largest gradient is determined, for example, the temperature gradient between A and C is the largest, and then the AC direction is taken as the main heat diffusion direction of the area. The processing heat diffusion direction data is generated.

[0142] S36: Based on the processing heat diffusion direction data, the laser control point coordinate data is subjected to minimum heat accumulation risk processing, and then the theoretical focal point position is adjusted to obtain the theoretical focal point position data.

[0143] In the embodiment of the present application, in order to reduce the accumulation of heat along the radial direction of the blade, the scanning path is modified. The "Z" scanning strategy is adopted, that is, the first row is scanned from left to right, the second row is scanned from right to left, and so on. For the area with heat accumulation risk (the side of the blade root close to the center of the disc), the scanning order is further adjusted. The scanning path of the area is divided into a plurality of short line segments, and the direction of each short line segment is perpendicular to the heat diffusion direction (the radial direction of the blade), that is, the scanning is performed along the circumferential direction of the blade. The control points in the sub-area are sorted according to the distance from point C, and the closer to point C, the higher the priority. Starting from the control point closest to point C, the adjacent control points are connected in turn along the opposite direction of heat diffusion (that is, toward point A), to form a new scanning path. If a boundary or other obstacles are encountered, the direction with the smallest angle with the heat diffusion direction is selected to continue connecting the control points, until all the control points in the sub-area are traversed. The solution algorithm (such as genetic algorithm, simulated annealing algorithm, etc.) of the traveling salesman problem (TSP) can be used to further optimize the re-planned path. The objective function is set as: the total length of the path and the weighted sum of the heat accumulation risk of each point on the path. The heat accumulation risk can be estimated according to the distance of each point from the heat source point C and the projection distance of each point in the heat diffusion direction.

[0144] Preferably, the laser power density processing according to the laser pre-scanning monitoring data in step S3 comprises:

[0145] setting the laser processing standard energy according to the type of the workpiece processing task;

[0146] calculating the single-point reflectivity according to the pre-scanning surface reflected light signal to generate single-point reflectivity data;

[0147] mapping the single-point reflectivity data and the laser control point coordinate data to generate surface reflectivity distribution data;

[0148] The surface reflectivity distribution data is divided into a low reflectivity region, a medium reflectivity region and a high reflectivity region by a preset reflectivity threshold value;

[0149] The medium reflectivity region is calculated by a standard unit area laser energy of a standard laser processing energy, to generate a medium reflectivity region energy requirement;

[0150] The low reflectivity region and the high reflectivity region are respectively calculated by a weighted unit area laser energy based on the laser processing standard energy and a preset energy requirement weight, to respectively generate a low reflectivity region energy requirement and a high reflectivity region energy requirement;

[0151] The medium reflectivity region energy requirement, the low reflectivity region energy requirement and the high reflectivity region energy requirement are processed by a laser power density, to generate processing laser power density data.

[0152] In the embodiments of the present application, according to the workpiece processing task type (such as laser cladding, laser welding, laser cutting) and the workpiece material (such as Inconel 718 nickel-based superalloy), the relevant process manual, literature or database is consulted to determine the standard energy density required for laser processing. The standard energy density refers to the laser energy required per unit area, and the unit is usually J / mm². For example, for laser cladding repair of Inconel 718 alloy, the standard energy density can be set to 50 J / mm². The pre-scanning surface reflected light signal (voltage signal collected by the photoelectric sensor) is extracted from the laser pre-scanning monitoring data. It is known that the reflectivity of the beam splitter is 5%, that is, 5% of the incident laser is reflected to the photoelectric sensor. Assuming that the responsivity of the photoelectric sensor is R, that is, R amperes of current is generated per watt of incident light power. The voltage signal output by the photoelectric sensor is V (V), and the load resistance is RL (Ω). Then the reflected laser power Pr = V / (R × RL). The incident laser power Pi can be obtained from the pre-scanning laser power data (measured by a laser power meter). The single-point reflectivity p = Pr / Pi = (V / (R × RL)) / Pi. The pre-scanning surface reflected light signal and the pre-scanning laser power data of each measurement point / short line segment are calculated to obtain the single-point reflectivity data of each point. These discrete single-point reflectivity data are interpolated into a continuous reflectivity distribution map. The interpolation method can be selected from nearest neighbor interpolation, linear interpolation or cubic spline interpolation, etc. After interpolation, a matrix corresponding to the workpiece surface processing region grid is obtained, and each element in the matrix represents the reflectivity value at the corresponding position, that is, the surface reflectivity distribution data. The surface reflectivity distribution data is divided into three regions: low reflectivity region, medium reflectivity region and high reflectivity region by setting a preset reflectivity threshold. The threshold needs to be determined according to the specific material and surface state. For example, the low reflectivity region can be set as: reflectivity <0.2; the medium reflectivity region can be set as: 0.2≤reflectivity≤0.4; and the high reflectivity region can be set as: reflectivity >0.4. The unit area laser energy required by each grid point in the medium reflectivity region is calculated. Since the reflectivity of the medium reflectivity region is close to the average absorption rate of the material, the standard energy density can be directly used. The medium reflectivity region energy requirement = laser processing standard energy density = 50 J / mm². The unit area laser energy required by each grid point in the two regions is calculated respectively. Since the low reflectivity region has a high absorption rate and the high reflectivity region has a low absorption rate, the standard energy density needs to be adjusted by weighting. The energy requirement weight is preset, for example: low reflectivity region: energy requirement weight = 0.8 (reduce energy input to avoid overburning); high reflectivity region: energy requirement weight = 1.2 (increase energy input to ensure melting); low reflectivity region energy requirement = laser processing standard energy density × energy requirement weight = 50 J / mm² × 0.8 = 40 J / mm².High reflection zone energy requirement = laser processing standard energy density × energy requirement weight = 50 J / mm2× 1.2 = 60 J / mm2. Combined with laser control point coordinate data and pre-scanning speed data, the required laser power of each control point is calculated. For each control point, first determine the reflectivity area it belongs to (low, medium, high). Then, according to the energy requirement of the area it belongs to and the laser action time (calculated in the previous step), the required laser power is calculated. Laser power = energy requirement / laser action time. Assuming the spot area is A (mm2), then the laser power density = laser power / A. All the laser power density data of the control points are combined into a matrix corresponding to the processing area grid, which is the processing laser power density data.

[0153] Preferably, the real-time laser thermal processing operation on the workpiece to be processed in step S4 includes:

[0154] planning a laser processing control path according to the theoretical focal point position data;

[0155] processing the theoretical focal point position data and the processing laser power density data to obtain laser processing control parameters;

[0156] encoding the processing control instructions based on the laser processing control path and the laser processing control parameters to generate laser processing control instructions;

[0157] starting the laser based on the laser processing control instructions, and performing laser processing on the workpiece to be processed according to the laser processing control path, and recording the temperature distribution in the laser processing process in real time through the infrared thermal imager array to obtain real-time temperature distribution field data;

[0158] calculating the single-point temperature gradient component according to the real-time temperature distribution field data to construct a real-time thermal gradient vector field.

[0159] In the embodiment of the present application, in the software, the tool path for laser processing is generated according to the geometric model (CAD model) of the workpiece and the kinematic model of the laser head (for example, a five-axis linkage numerical control system). The tool path defines the motion trajectory of the laser head during processing, including moving speed, acceleration, jerk, etc. At the same time, the attitude change of the laser head needs to be considered (for example, for non-perpendicular incidence laser processing, the tilt angle of the laser head needs to be adjusted). The theoretical focal point position data contains the coordinates of each control point, the scanning speed, the delay time, and the focal point position compensation value. The processing laser power density data contains the required laser power density of each control point. According to the model and performance parameters of the laser (such as maximum power, minimum power, power adjustment step, response time, etc.), the laser power density data is converted into the actual laser power setting value. Laser power = laser power density × spot area. The spot area can be calculated according to the optical parameters of the laser (such as focal length, beam quality), or measured by experiment. Use laser processing special software (such as Befor TwinCAT NC I) or numerical control system programming software (such as Siemens SINUMERIK Operate) to import the tool path file and parameter file. In the software, associate the tool path instructions (such as G01, G02, G03, etc. in G code) with the control parameters in the parameter file. For example, for each control point, write the corresponding X, Y, Z coordinate values, scanning speed, delay time, focal point position compensation value, and laser power setting value into the corresponding G code instructions. For areas that need to accurately control the laser power, special laser power control instructions (such as analog output control or PWM control supported by some lasers) can be used. The control system controls the laser head motion and laser output according to the processing control instructions. The laser beam processes the workpiece surface according to the predetermined path and parameters. During laser processing, an infrared thermal imager array (which has been installed and configured in step S2) is used to monitor the temperature distribution of the workpiece surface in real time. The infrared thermal imager continuously takes thermal images of the workpiece surface at a certain frame rate (such as 30 Hz or higher). The thermal image data is transmitted in real time to the control system or data acquisition system through industrial Ethernet (such as PROFINET). The central difference method can be used to calculate the temperature gradient of each point in the X and Y directions: X-direction gradient = (temperature of the right point - temperature of the left point) / (2 × spatial interval); Y-direction gradient = (temperature of the upper point - temperature of the lower point) / (2 × spatial interval); the spatial interval refers to the actual distance between adjacent pixel points. Calculate the modulus (sqrt(X-direction gradient^2 + Y-direction gradient^2)) and direction (arctan(Y-direction gradient / X-direction gradient)) of the gradient vector of each point. The gradient vectors (modulus and direction) of all points form a vector field corresponding to the real-time temperature distribution field data, which is the real-time thermal gradient vector field.

[0160] Preferably, the real-time regulation of the industrial processing control process according to the real-time thermal gradient vector field in step S4 comprises:

[0161] Marking the over-temperature, low-temperature and over-wide heat affected zones of the workpiece surface processing area by the real-time thermal gradient vector field to obtain defect marking area data;

[0162] According to the defect marking area data, the priority of the defect area is evaluated, and then the processing parameter is dynamically adjusted to generate a dynamic processing regulation strategy;

[0163] According to the dynamic processing regulation strategy, the real-time regulation of the industrial processing control process is performed to realize the deployment of the industrial processing real-time operation system.

[0164] In the embodiments of the present application, a temperature upper threshold is set. The threshold is usually based on the melting point or phase transition temperature of the material, and a certain safety margin is left. For example, for Inconel718 alloy, the temperature upper threshold can be set to 1200℃ (lower than its melting point 1260℃). Traverse the real-time thermal gradient vector field data (actually temperature field data), mark the area where the temperature exceeds 1200℃ as an overheating area. Set a temperature lower threshold. The threshold is usually based on process requirements, such as the minimum temperature required to ensure good metallurgical bonding between the cladding layer and the base material. For example, the temperature lower threshold can be set to 1000℃. Traverse the real-time thermal gradient vector field data, mark the area where the temperature is lower than 1000℃ as a low temperature area. Set a heat affected zone width threshold. The threshold is usually based on the control requirements of workpiece deformation and residual stress. For example, the heat affected zone width threshold can be set to 0.5mm. Based on the real-time thermal gradient vector field, calculate the temperature gradient of each point. Find the location where the temperature gradient drops to a certain specific value (for example, the gradient value corresponding to the ambient temperature +3℃), determine the boundary of the heat affected zone. Calculate the width of the heat affected zone, mark the area where the width exceeds 0.5mm as an overheated heat affected zone. The priority of the defect area is determined in the following order: overheating area> low temperature area> overheated heat affected zone. Overheating area will cause serious defects such as overburning, porosity, etc., with the highest priority; low temperature area will cause defects such as poor fusion, unmelting, etc., with the second priority; overheated heat affected zone will cause excessive deformation and residual stress, with the lowest priority. The dynamic processing control strategy is: for the overheating area: immediately reduce the laser power or increase the scanning speed. The amplitude of reducing power or increasing speed is determined according to the degree of overheating. For example, if the temperature exceeds the threshold by 10%, reduce the power by 5%; if the temperature exceeds the threshold by 20%, reduce the power by 10%. PID control algorithm can also be used to dynamically adjust the laser power according to the deviation of real-time temperature and set temperature. For the low temperature area: appropriately increase the laser power or reduce the scanning speed. The amplitude of increasing power or reducing speed is determined according to the degree of low temperature. For example, if the temperature is lower than the threshold by 5%, increase the power by 3%; if the temperature is lower than the threshold by 10%, increase the power by 5%. For the overheated heat affected zone: adjust the scanning path or introduce cooling time. For example, in the area where the heat affected zone is wide, change the scanning direction, or increase the delay time between two scans. According to the defect marking area data, find the dynamic processing control strategy table to determine the control measures to be executed at present. According to the control measures, modify the control parameters of the laser (power, scanning speed) or the motion parameters of the numerical control system (path, delay). For example, it can be realized by modifying the F value (feed speed), S value (spindle speed, which is used to control the laser power here) or G04 instruction (delay) in the NC program. For more complex control, PLC programming (such as structured text) can be used.The modified control parameters are sent to the laser and the numerical control system to realize real-time control.

[0165] Preferably, the present application also provides an embedded real-time operating system deployment system for industrial control, which executes the embedded real-time operating system deployment method for industrial control as described above, and the embedded real-time operating system deployment system for industrial control comprises:

[0166] The processing area identification module is configured to identify the surface processing area of the workpiece to be processed, to divide the laser processing control points of the surface processing area of the workpiece, and to generate laser control point coordinate data.

[0167] The industrial pre-scanning analysis module is configured to test the laser beam according to the laser control point coordinate data, to perform synchronous acquisition of monitoring signals through the embedded deployment real-time operation monitoring network, to obtain laser pre-scanning monitoring data, to analyze the temperature characteristics of the measurement points according to the laser pre-scanning monitoring data, and to generate measurement point temperature distribution characteristic data.

[0168] The thermal control optimization module is configured to obtain the workpiece processing task type, to identify the heat accumulation risk area of the surface processing area of the workpiece based on the workpiece processing task type and the measurement point temperature distribution characteristic data, to adjust the theoretical focal point position, to obtain theoretical focal point position data, and to process the laser power density according to the laser pre-scanning monitoring data, to generate processing laser power density data.

[0169] The real-time thermal processing control module is configured to perform real-time laser thermal processing operation on the workpiece to be processed based on the theoretical focal point position data and the processing laser power density data, to obtain a real-time thermal gradient vector field, and to perform real-time regulation and control of the industrial processing control process according to the real-time thermal gradient vector field, so as to realize the deployment of the industrial processing real-time operation system.

[0170] The present application can accurately determine the control points of laser processing according to the geometric characteristics and surface state of the workpiece by detailed processing area recognition and laser processing control point division of the workpiece surface. This fine control point division method can effectively avoid the uneven energy distribution problem caused by improper parameter setting in traditional methods, ensuring that each area can obtain the best processing effect. For example, when processing workpieces with complex geometric shapes, this method can dynamically adjust the grid density and direction according to the shape complexity, surface texture direction, surface roughness, and surface curvature of different regions, thereby achieving accurate control of each control point and avoiding over-ablation or insufficient energy. Through the real-time operation monitoring network deployed in embedded mode, the system can obtain laser pre-scanning monitoring data in real time and perform temperature feature analysis. This multi-sensor fusion real-time monitoring network can synchronously collect environmental temperature, workpiece processing temperature, surface reflected light signal, and laser power, and other key data, comprehensively reflecting the energy change and material response in the laser processing process. Through analysis of the pre-scanning data, the system can accurately grasp the temperature distribution of the workpiece surface, timely adjust the laser power and scanning speed, prevent material damage, and improve the processing quality. According to different processing task types and real-time monitoring temperature distribution feature data, potential heat accumulation risk areas can be predicted, and the laser focal point position and power density can be adjusted accordingly. This parameter adjustment strategy based on real-time data and workpiece characteristics can effectively avoid processing quality problems caused by material property differences and geometric shape complexity. For example, for high reflectivity materials, the system can automatically increase the laser power density to compensate for the loss of reflection; for heat-sensitive materials, the system can reduce the power density and optimize the scanning path to avoid overheating and thermal damage. By analyzing the real-time thermal gradient vector field, the over-temperature, low-temperature, and super-wide heat affected zone of the workpiece surface processing area can be identified, and the data of these defect areas can be used for priority evaluation and dynamic adjustment of processing parameters. This real-time regulation strategy can automatically adjust the laser parameters according to the actual processing situation, ensuring the stability and consistency of the processing process, and improving the processing efficiency and quality.

[0171] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the present application being defined by the appended claims and not by the above description, therefore all variations falling within the meaning and scope of the equivalent elements of the application file are intended to be included in the present application.

[0172] The foregoing is considered as illustrative only of the principles of the application. Numerous modifications and changes will readily occur to those skilled in the art, and it is intended to embrace all such modifications and changes that fall within the scope of the application. Accordingly, the application is not to be restricted in scope to the specific embodiments disclosed herein but is to be accorded the full scope that the principles and novel features request appropriately granted.

Claims

1. A method for deploying an embedded real-time operating system for industrial control, characterized in that: The following steps are involved: Step S1: Identify the surface processing area of ​​the workpiece to be processed to obtain the workpiece surface processing area; divide the workpiece surface processing area into laser processing control points to generate laser control point coordinate data; Step S2: performing a laser beam test based on the laser control point coordinate data, and synchronously collecting monitoring signals through an embedded deployment real-time operation monitoring network to obtain laser pre-scan monitoring data; wherein the laser pre-scan monitoring data includes pre-scanning ambient temperature data, pre-scanning workpiece processing temperature data, pre-scanning surface reflected light signal, pre-scanning laser power, and pre-scanning speed; performing a measurement point temperature characteristic analysis based on the laser pre-scanning monitoring data to generate measurement point temperature distribution characteristic data; Step S3: Obtaining the workpiece processing task type; based on the workpiece processing task type, identifying the heat accumulation risk area in the workpiece surface processing area using the temperature distribution characteristic data of the measurement point, and then adjusting the theoretical focus position to obtain theoretical focus position data; performing laser power density processing based on the laser pre-scan monitoring data to generate processing laser power density data; Step S4: performing real-time laser thermal processing on the workpiece to be processed using the theoretical focus position data and the processing laser power density data to obtain a real-time thermal gradient vector field; performing real-time control of the industrial processing control process based on the real-time thermal gradient vector field to implement deployment of a real-time operating system for industrial processing; The laser power density processing according to the laser pre-scan monitoring data in step S3 includes: Set the laser processing standard energy according to the workpiece processing task type; Calculate single-point reflectivity based on the reflected light signal of the pre-scanned surface to generate single-point reflectivity data; Mapping single-point reflectivity data with laser control point coordinate data to generate surface reflectivity distribution data; The surface reflectivity distribution data is divided into reflective areas using a preset reflectivity threshold, and low reflectivity areas, medium reflectivity areas, and high reflectivity areas are obtained respectively; The standard laser energy per unit area is calculated for the medium reflectivity area through the laser processing standard energy to generate the energy requirement for the medium reflectivity area; Based on the laser processing standard energy, the preset energy demand weights are used to calculate the weighted laser energy per unit area for the low reflectivity area and the high reflectivity area, respectively, to obtain the energy demand for the low reflectivity area and the energy demand for the high reflectivity area; The energy requirements of the medium reflection area, the low reflection area, and the high reflection area are processed by laser power density to generate processing laser power density data.

2. The method for deploying an embedded real-time operating system for industrial control according to claim 1, wherein: The step S1 of dividing the workpiece surface processing area into laser processing control points includes: Extracting geometric information of the area to be processed according to the processing area of ​​the workpiece surface; Calculate the shape complexity based on the geometric information of the area to be processed and generate the regional shape complexity coefficient; extract the surface texture direction, surface roughness and surface curvature of the workpiece surface processing area; Calculate the initial grid density of the workpiece surface processing area through the regional shape complexity coefficient to generate initial regional grid density data; Analyze the grid adjustment direction according to the surface texture direction to obtain the grid direction adjustment coefficient; calculate the grid spacing adjustment coefficient according to the surface roughness and surface curvature; Adaptively adjust the grid density of the initial area grid density data based on the grid direction adjustment coefficient and the grid spacing adjustment coefficient, and then divide the workpiece surface processing area into processing grids to obtain a gridded processing area; The gridded processing area is numbered in rows and columns, and then the laser processing control points are discretized to generate the laser control point coordinate data.

3. The method for deploying an embedded real-time operating system for industrial control according to claim 1, wherein: The step S2 of performing laser beam testing according to the laser control point coordinate data and performing synchronous acquisition of monitoring signals through an embedded deployment real-time operating monitoring network includes: An infrared thermal imager array and ambient temperature sensor are installed around the laser. A beam splitter and photoelectric sensor are installed in the laser optical path inside the laser. A laser power meter is connected in series at the laser output. The infrared thermal imager array, ambient temperature sensor, photoelectric sensor, and laser are connected and communicated via industrial Ethernet or a dedicated bus, thus deploying an embedded real-time operation monitoring network. Extract material property parameters of the workpiece surface processing area and generate material property parameters of the processing area; Extract material ablation threshold according to material property parameters of processing area; Using a laser power of 10% of the material ablation threshold, the laser is switched to a low-power pre-scan mode to obtain low-power pre-scan parameters; wherein the low-power pre-scan parameters include pre-scan laser power and pre-scan speed; Based on the low-power pre-scan parameters, the laser is controlled by the laser control point coordinate data to perform point-by-point or short-segment laser beam testing on the workpiece surface processing area, and then the monitoring signal is synchronously collected through the real-time operation monitoring network to obtain laser pre-scan monitoring data; wherein, the laser pre-scan monitoring data includes the ambient temperature data before scanning, the pre-scan workpiece processing temperature data, the pre-scan surface reflected light signal, the pre-scan laser power and the pre-scan speed. The pre-scan surface reflected light signal is reflected by a beam splitter to reflect 5% of the laser to the photoelectric sensor for signal collection.

4. The method for deploying an embedded real-time operating system for industrial control according to claim 1, wherein: The step S2 of analyzing the temperature characteristics of the measurement points based on the laser pre-scan monitoring data includes: The abnormal value of the pre-scanned workpiece processing temperature data is corrected by the pre-scanning ambient temperature data to obtain the corrected workpiece processing temperature monitoring data; Calculate the laser action time of each measuring point or short line segment according to the pre-scan speed and the coordinate data of the laser control point; The workpiece processing temperature monitoring data is sub-sequenced by laser action time to obtain single point / short segment temperature series data; Based on the workpiece surface processing area, the single point / short segment temperature series data is processed for the temperature field distribution of the measurement point to generate the measured temperature field distribution data; The temperature characteristics of the measuring point are analyzed based on the measured temperature field distribution data and the single point / short segment temperature sequence data to generate the temperature distribution characteristic data of the measuring point.

5. The method for deploying an embedded real-time operating system for industrial control according to claim 4, wherein: The temperature characteristic analysis of the measurement point according to the measured temperature field distribution data and the single point / short segment temperature sequence data includes: Calculate the temperature rise rate based on single point / short segment temperature series data; Extract the temperature curve of each measuring point / short line segment based on the single point / short line segment temperature series data, and then locate the peak temperature point to obtain the peak temperature point and peak temperature data respectively; Taking the peak temperature point as the center, search for the spatial position where the temperature drops to 1 / e of the peak temperature data on both sides of the measured temperature field distribution data to obtain the position of the measured temperature boundary point; where e is a mathematical constant; The ambient temperature data before scanning +3°C is used as the heat impact threshold, and the heat impact of the measured temperature boundary point position is judged by the heat impact threshold. When the temperature of the measured temperature boundary point position is higher than the heat impact threshold, the measured temperature boundary point position is marked as the valid boundary point position data; Calculate the width of the heat-affected zone based on the effective boundary point position data; The temperature distribution characteristics of the temperature rise rate, peak temperature data and heat affected zone width are integrated to obtain the temperature distribution characteristic data of the measuring point.

6. The method for deploying an embedded real-time operating system for industrial control according to claim 1, wherein: The theoretical focus position adjustment in step S3 includes: Perform spatial gradient evaluation based on the temperature rise rate in the temperature distribution characteristic data of the measurement point to generate temperature rise gradient data; Perform scan center point offset evaluation based on peak temperature data in the temperature distribution characteristic data of the measurement point to generate peak temperature offset data; According to the heat affected zone width and heat affected zone shape analysis in the temperature distribution characteristic data of the measuring point, the heat affected zone shape analysis data is obtained; The process requirements are compared with the temperature rise gradient data, peak temperature offset data, and heat-affected zone shape analysis data based on the workpiece processing task type, and then the heat accumulation risk area is identified to obtain the heat accumulation risk area; Analyze the heat diffusion direction based on the heat accumulation risk area and generate processing heat diffusion direction data; Based on the processing heat diffusion direction data, the laser control point coordinate data is processed to minimize the heat accumulation risk, and then the theoretical focus position is adjusted to obtain the theoretical focus position data.

7. The method for deploying an embedded real-time operating system for industrial control according to claim 1, wherein: The step S4 of performing real-time laser thermal processing on the workpiece using the theoretical focus position data and the processing laser power density data includes: Plan the laser processing control path based on the theoretical focus position data; The theoretical focus position data and processing laser power density data are processed into processing parameters to obtain laser processing control parameters; Encoding processing control instructions based on the laser processing control path and laser processing control parameters to generate laser processing control instructions; The laser is started based on the laser processing control instruction, and the workpiece is laser processed according to the laser processing control path. The temperature distribution during the laser processing is recorded in real time through the infrared thermal imager array to obtain real-time temperature distribution field data; The single-point temperature gradient component is calculated based on the real-time temperature distribution field data to construct the real-time thermal gradient vector field.

8. The method for deploying an embedded real-time operating system for industrial control according to claim 1, wherein: The real-time regulation of the industrial processing control process according to the real-time thermal gradient vector field in step S4 includes: The over-temperature, low-temperature and ultra-wide heat-affected zones are marked on the workpiece surface through the real-time thermal gradient vector field to obtain the defect marking area data; Defect area priority assessment is performed based on defect marking area data, and then processing parameters are dynamically adjusted to generate a dynamic processing control strategy; Real-time control of industrial processing control processes is carried out according to dynamic processing control strategies to realize the deployment of real-time operating systems for industrial processing.

9. An embedded real-time operating system deployment system for industrial control, characterized in that: The embedded real-time operating system deployment method for industrial control according to claim 1 is used to execute the embedded real-time operating system deployment method for industrial control, and the embedded real-time operating system deployment system for industrial control comprises: The processing area recognition module is used to identify the surface processing area of ​​the workpiece to be processed and obtain the surface processing area of ​​the workpiece; divide the surface processing area of ​​the workpiece into laser processing control points and generate laser control point coordinate data; The industrial pre-scan analysis module performs laser beam testing based on the laser control point coordinate data and synchronously collects monitoring signals through an embedded real-time operation monitoring network to obtain laser pre-scan monitoring data. It also analyzes the temperature characteristics of the measurement points based on the laser pre-scan monitoring data to generate temperature distribution characteristic data of the measurement points. The thermal control optimization module is used to obtain the workpiece processing task type; based on the workpiece processing task type, it uses the temperature distribution characteristic data of the measurement point to identify the heat accumulation risk area in the processing area of ​​the workpiece surface, and then adjusts the theoretical focus position to obtain the theoretical focus position data; it processes the laser power density based on the laser pre-scan monitoring data to generate the processing laser power density data; The real-time thermal processing control module is used to perform real-time laser thermal processing operations on the workpiece to be processed based on theoretical focus position data and processing laser power density data to obtain a real-time thermal gradient vector field; based on the real-time thermal gradient vector field, the industrial processing control process is controlled in real time to realize the deployment of a real-time operating system for industrial processing.

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