Furnace front quick separation robot teaching method and related equipment

Abstract

The invention discloses a stokehole quick separation robot teaching method and related equipment, and relates to the technical field of industrial robot motion control, the method comprises the following steps: determining a target process flow of a stokehole quick separation robot according to a processing task type; according to the target process flow, the preset equipment station is searched and matched, and a working point space module corresponding to the stokehole quick separation robot is obtained; performing motion path logic debugging on the working point space module in the virtual simulation environment to obtain a debugged teaching program; and in response to an input signal of the human-computer interaction interface, executing a teaching program so as to complete teaching operation on the stokehole quick separation robot. Through flow-driven working point space module construction, virtual simulation debugging and man-machine interaction control, high efficiency, visualization and intelligentization of furnace front fast separation robot teaching can be achieved, and the operation efficiency and the system adaptability are remarkably improved.

Description

Technical Field

[0001] The present application relates to the technical field of industrial robot motion control, and more specifically, to a furnace-front quick-splitting robot teaching method and related equipment. Background Art

[0002] With the continuous evolution of intelligent manufacturing systems, furnace-front quick-separation robots have become crucial equipment for improving automation and ensuring personnel safety in industrial sites like steelmaking, which are subject to high temperatures, strong interference, and high risk. The pre-operation teaching phase for robots is a core preparatory step for completing complex task processes, directly impacting the rationality of subsequent task paths, the continuity of movements, and the efficiency of equipment operation. In production environments with diverse task types and complex equipment layouts, how to quickly and accurately complete teaching operations has become a pressing technical challenge in industrial sites.

[0003] In the existing technology, the teaching of the furnace-front quick-splitting robot mostly relies on on-site manual settings, that is, the operator searches for the equipment stations one by one and sets the working points according to the task process, and then manually debugs the motion path. This method is not only time-consuming and labor-intensive, but also easily leads to unreasonable teaching paths and frequent repeated adjustments due to operational errors or lack of experience; in addition, in the face of rapid switching between different types of tasks, traditional teaching is difficult to achieve efficient reuse and rapid response, which seriously restricts the intelligence level and application value of the furnace-front quick-splitting robot in flexible production scenarios. In other words, there are technical problems in the relevant technology that the teaching process relies on manual labor, the path setting efficiency is low, and it is difficult to adapt to the diversification of tasks. Summary of the Invention

[0004] The Summary of the Invention section of this application introduces a series of simplified concepts that will be further described in detail in the Detailed Description of the Invention section. The Summary of the Invention section of this application is not intended to limit the key features and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0005] The teaching method and related equipment of the furnace-front quick-splitting robot provided in this application can realize the efficient, visual and intelligent teaching of the furnace-front quick-splitting robot through process-driven work point space module construction, virtual simulation debugging and human-computer interaction control, thereby significantly improving work efficiency and system adaptability.

[0006] In the first aspect, the present application provides a teaching method for a furnace-front quick-splitting robot, comprising: determining a target process flow of the furnace-front quick-splitting robot according to a processing task type; searching and matching preset equipment workstations according to the target process flow to obtain a work point space module corresponding to the furnace-front quick-splitting robot, wherein the work point space module includes an equipment number, workstation coordinates and preset process steps; performing motion path logic debugging on the work point space module in a virtual simulation environment to obtain a debugged teaching program; executing the teaching program in response to an input signal from a human-computer interaction interface to complete the teaching operation of the furnace-front quick-splitting robot.

[0007] In some embodiments, the processing task types include circular sample processing type, racket sample processing type and conical column sample processing type; the target process flow of the furnace-front quick-splitting robot is determined according to the processing task type, including: for the circular sample type, determining that the target process flow includes suction cup sampling, air conveying device processing, grinding machine processing, visual discrimination, fluorescence analysis and laser marking in sequence; for the racket sample type, determining that the target process flow includes suction cup sampling, air conveying device processing, milling machine processing, visual discrimination, spectral analysis and laser marking in sequence; for the conical column sample type, determining that the target process flow includes spectral analysis process chain and gas analysis process chain, wherein the spectral analysis process chain includes gripper sampling, air conveying device processing, cutting and milling processing, sample cooling, visual discrimination, spectral analysis and laser marking in sequence, and the gas analysis process chain includes gripper sampling, air conveying device processing, chip sample preparation, chip sample collection, measurement and weighing, gas analysis and laser marking in sequence.

[0008] In some embodiments, the preset equipment workstations are searched and matched according to the target process flow to obtain a work point space module corresponding to the furnace-front quick-splitting robot, including: matching the equipment number and the workstation coordinates in the preset equipment workstation library according to the target process flow; sorting the equipment number and the workstation coordinates according to the execution order of the target process flow to obtain the nested structure of the work point space module.

[0009] In some embodiments, the motion path logic debugging of the work point space module in the virtual simulation environment to obtain the debugged teaching program includes: constructing the virtual simulation environment consistent with the working environment of the furnace-front quick-splitting robot through digital twin technology; simulating the teaching process of the furnace-front quick-splitting robot in the virtual simulation environment according to the work point space module to obtain a simulation result; when there is a path conflict signal or an equipment interference signal in the simulation result, adjusting the work station coordinates or the preset process sequence in the work point space module until the simulation result meets the preset safety conditions.

[0010] In some embodiments, the teaching method of the furnace-front fast-splitting robot also includes: obtaining the steelmaking stage in which the furnace-front fast-splitting robot is located; when the steelmaking stage is the primary refining stage, determining that the processing task type is a round sample processing type; when the steelmaking stage is the refining stage, determining that the processing task type is a racket sample processing type; when the steelmaking stage is the post-processing and continuous casting stage, determining that the processing task type is a cone-cylinder sample processing type.

[0011] In some embodiments, the human-computer interaction interface includes a function button set, a coordinate adjustment window, and a parallel task selection pop-up window; the function button set is displayed in order of operation frequency; the coordinate adjustment window supports numerical input, which is used to fine-tune the workstation coordinates in the workpoint space module; the parallel task selection pop-up window is used to provide a process chain switching selection prompt when multiple process conflicts are detected.

[0012] In some embodiments, the teaching method of the furnace-front quick-splitting robot also includes: collecting the actual trajectory of the furnace-front quick-splitting robot to obtain actual motion trajectory data; comparing the actual motion trajectory data with the expected trajectory data in the virtual simulation environment to obtain a trajectory deviation value; when the trajectory deviation value exceeds a preset tolerance range, the teaching program rolls back to the previous safety position and issues an abnormal alarm.

[0013] On the second aspect, the present application also provides a teaching device for a furnace-front quick-splitting robot, comprising: a process determination unit, for determining the target process flow of the furnace-front quick-splitting robot according to the type of processing task; a module determination unit, for searching and matching preset equipment workstations according to the target process flow, and obtaining a work point space module corresponding to the furnace-front quick-splitting robot, wherein the work point space module includes an equipment number, workstation coordinates and preset process steps; a program debugging unit, for performing motion path logic debugging on the work point space module in a virtual simulation environment to obtain a debugged teaching program; a teaching unit, for executing the teaching program in response to an input signal of a human-computer interaction interface to complete the teaching operation of the furnace-front quick-splitting robot.

[0014] In a third aspect, the present application further provides an electronic device comprising: a memory and a processor, wherein the processor is configured to implement the steps of the furnace front quick separation robot teaching method described in the first aspect when executing the computer program stored in the memory.

[0015] In a fourth aspect, the present application further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the furnace front quick separation robot teaching method described in the first aspect.

[0016] In a fifth aspect, the present application also provides a computer program product, including a computer program or computer executable instructions. When the computer program or computer executable instructions are executed by a processor, the furnace front fast separation robot teaching method provided in the embodiment of the present application is implemented.

[0017] In summary, this application automatically determines the target process flow according to the type of processing task, and intelligently matches the equipment workstations according to the process, which can quickly locate the relevant work points and generate a workpoint space module. This structured processing method avoids manual point-by-point search and teaching, can greatly reduce the teaching preparation work, and improve overall work efficiency; with the help of the human-computer interaction interface and virtual simulation environment, the operator can complete path debugging and motion simulation without actually running the equipment, which can not only reduce the dependence on the professional skills of the personnel, but also reduce the risk during equipment debugging, and prevent the furnace-front quick-splitting robot from colliding or executing the wrong path; the equipment number, workstation coordinates and process steps are integrated into a workpoint space module to achieve decoupling and reconstruction of the process and space, making the adaptation and adjustment of different types of tasks more flexible and efficient. The corresponding teaching program can be quickly called by inputting a signal, which is convenient for rapid response to task changes and can improve the automation and intelligence capabilities of the entire furnace-front quick-splitting robot. To sum up, the teaching method of the furnace-front quick-splitting robot provided in this application can realize the efficiency, visualization and intelligence of the teaching of the furnace-front quick-splitting robot through process-driven work point space module construction, virtual simulation debugging and human-computer interaction control, thereby significantly improving work efficiency and system adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present description. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0019] Figure 1 A flow chart of a teaching method for a furnace-front quick separation robot provided in an embodiment of the present application;

[0020] Figure 2A schematic diagram of the structure of the targets targeted by different types of processing tasks provided in the embodiments of the present application;

[0021] Figure 3 A schematic diagram of the structure of a furnace-front quick separation robot teaching device provided in an embodiment of the present application;

[0022] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0023] Among them, 110 is the target targeted by the circle-like processing type, 120 is the target targeted by the racket-like processing type, and 130 is the target targeted by the cone-like processing type. DETAILED DESCRIPTION

[0024] Terms in the specification, claims, and drawings of this application, such as "first," "second," "third," "fourth," and the like (if any), are used to distinguish between similar objects, rather than to describe a particular order or precedence. Therefore, it is understood that these terms can be used interchangeably where appropriate, so that the embodiments described can be implemented in a different order, unless otherwise specified in the drawings or descriptions. In addition, the terms "is" and "has" and any variations thereof in this application are intended to cover all possible constituent elements on a non-exclusive basis. For example, a process, method, system, product, or apparatus that includes several steps or units is not necessarily limited to the steps or units that are explicitly listed, but may also include other steps or units that are not explicitly listed, or steps or units that are inherent to the process, method, product, or apparatus.

[0025] In this application, a "module" or "unit" refers to a computer program or part of a computer program that has a specific function and works in conjunction with other related parts to achieve a predetermined goal. These modules or units can be implemented by software, hardware (such as processing circuits or memories), or a combination of the two. One or more processors or memories can implement one or more modules or units. At the same time, each module or unit can also be part of a larger module or unit.

[0026] The technical solutions in this application will be described in detail below in conjunction with the accompanying drawings in the embodiments. It should be noted that the embodiments described are only part of this application, not all embodiments. In the following description, the "some embodiments" mentioned are only a subset of all possible embodiments, which may be the same or different subsets, and different embodiments can be combined with each other without conflict.

[0027] Figure 1 This is a flow chart of a method for teaching a furnace-front quick separation robot provided by an embodiment of the present application. Figure 1The teaching method of the furnace front quick separation robot provided in the embodiment of the present application may include the following steps 101 to 104:

[0028] Step 101, determining the target process flow of the furnace front quick separation robot according to the processing task type;

[0029] In some examples, a furnace-front fast-splitting robot refers to an automated robot deployed in a furnace-front fast-splitting laboratory for tasks such as automatic sample distribution, precise sorting, and standardized processing. The furnace-front fast-splitting robot primarily performs directional processing on metallurgical samples of different shapes, and possesses high-precision motion control, multi-axis collaborative operation, and rapid task response capabilities. The furnace-front fast-splitting robot of the embodiment of the present application can also perform tasks such as sampling, temperature measurement, slag and iron identification, and separation. The processing task type is the specific task sample type that the furnace-front fast-splitting robot needs to complete, and can be classified according to different sample shapes, analysis needs, or processing requirements. The processing task type can include circular sample processing type, racket sample processing type, cone-cylinder sample processing type, and the like. The target process flow is the sequence of process steps that the furnace-front fast-splitting robot should execute according to the task type. It is the execution blueprint of the task within the furnace-front fast-splitting robot system. Different processing task types correspond to different target process flows. For example, if the processing task type is a circular sample type, the target process flow is suction cup sampling, air conveying processing, grinding machine processing, visual discrimination, fluorescence analysis and laser marking in sequence; if the processing task type is a racket sample type, the target process flow is suction cup sampling, air conveying processing, milling machine processing, visual discrimination, spectral analysis and laser marking in sequence; if the processing task type is a conical cylinder sample type, the target process flow is gripper sampling, cutting and milling processing, visual discrimination, spectral analysis and laser marking in sequence.

[0030] Through the implementation of step 101, the processing task type is automatically identified, and the corresponding target process flow can be quickly generated, avoiding the inefficiency and error-proneness of manual step-by-step process planning, and improving the efficiency of task deployment; at the same time, ensuring that the robot's actions are highly matched with the task requirements, can guarantee the accuracy and consistency of subsequent operations, and provide basic support for realizing flexible and diversified production.

[0031] Step 102: Search and match the preset equipment stations according to the target process flow to obtain a work point space module corresponding to the furnace front quick separation robot. The work point space module may include the equipment number, station coordinates, and preset process steps.

[0032] In some examples, the preset equipment stations are the various equipment and their locations that are pre-configured and entered in the workshop production layout for operation by the furnace-front quick-splitting robot. Each equipment station includes information such as specific processing equipment, physical location, and process capability. The workpoint space module refers to the collection of all station information related to the current furnace-front quick-splitting robot task that is extracted after screening and matching. It is the basic data unit for path planning and teaching. It contains fields such as equipment number, station coordinates, and preset process steps. It can structuredly describe the key locations and action content that the furnace-front quick-splitting robot should visit throughout the entire processing flow. The equipment number refers to a code that uniquely identifies each device and is used for quick identification and calling; for example, the number "D102" represents "Milling Machine No. 2", the number "D301" represents "Spectrum Analyzer No. 1", etc. Workstation coordinates refer to the three-dimensional position of the equipment in space, which is used for positioning and path calculation of the furnace-front quick-splitting robot. They can be determined during equipment modeling or installation through laser measurement, manual input, or simulation calibration. For example, the coordinates (X=1200, Y=500, Z=100) represent the precise position of the equipment in the workshop. A preset process step is the specific processing task performed at a workstation and is an action node in the target process flow. For example, the preset process for a workstation may be "fluorescence analysis" or "laser marking."

[0033] For example, according to the determined target process flow, equipment with corresponding process capabilities can be searched in the preset equipment station library in sequence, and its number and spatial coordinates can be obtained; then, all matching results are packaged into a structured work point space module according to the process sequence, providing an accurate data basis for subsequent simulation path planning and robot teaching.

[0034] Through the implementation of step 102, the process logic and spatial information are combined to automatically match and generate a structured work point space module, which can avoid the tedious operation of manually setting the work station point by point; the work point space module integrates the equipment number, coordinate information and step process, so that the furnace front fast separation robot can be executed efficiently in a clear space and task context, thereby improving the standardization and accuracy of the teaching configuration.

[0035] Step 103: Debugging the motion path logic of the working point space module in a virtual simulation environment to obtain a debugged teaching program;

[0036] In some examples, a virtual simulation environment refers to a visual simulation system constructed using technologies such as 3D modeling, physics engines, and digital twins. It is used to recreate the physical layout and motion conditions of the furnace-front quick-splitting robot's work site. It can be constructed based on CAD drawings, BIM models, robot parameter files, or generated using industrial simulation software. The virtual simulation environment can reproduce the actual positions of equipment such as laser marking machines and air conveying devices, and the furnace-front quick-splitting robot can perform motion testing and path verification in this virtual simulation environment. The motion path of the furnace-front quick-splitting robot from one workpoint to another can be planned, collision detected, sequence optimized, and motion coordinated in this virtual simulation environment to ensure safe, continuous, and accurate movement. If the furnace-front quick-splitting robot is found to have path interference with the bracket when moving to the milling machine, a prompt will be given to modify the path or adjust the station coordinates to obtain a debugged teaching program. A debugged teaching program refers to a set of program instructions generated based on simulation debugging and can be directly sent to the furnace-front quick-splitting robot's execution control mechanism to guide the furnace-front quick-splitting robot's motion execution. It can include information such as path points, speed, motion type, and execution order.

[0037] By implementing step 103 and introducing a virtual simulation environment, path debugging and action verification can be completed without activating actual equipment, significantly reducing trial and error costs and equipment wear risks; at the same time, it is easy to identify path logic errors or spatial conflicts in advance, which helps to improve the rationality of path design and the safety of execution, and enhance the intelligence and robustness of the furnace-front quick-separation robot.

[0038] Step 104, in response to the input signal of the human-machine interface, executing the teaching program to complete the teaching operation of the furnace front fast separation robot;

[0039] In some examples, the human-machine interface (HMI) refers to a visual operating platform for the operator to input commands, monitor status, and interact with the furnace-front quick-splitting robot. It can be implemented in the form of a touch screen, industrial tablet, or PC software. For example, the human-machine interface may include function buttons such as "task start," "path jump," "pause teaching," and "coordinate fine-tuning," and be equipped with a real-time status display and log information panel. The input signal refers to the control command or triggering behavior issued by the operator through the human-machine interface, which is used to drive the furnace-front quick-splitting robot to perform a task, adjust the program, or change the process status. Based on the operator's input signal, the furnace-front quick-splitting robot can be driven to automatically perform a series of actions according to the pre-debugged teaching program, including path movement, posture adjustment, operation simulation, etc., thereby completing the entire teaching process.

[0040] By implementing step 104, control is achieved through a human-machine interactive interface, making the operation process more intuitive and convenient. The operator can start or adjust the teaching process with one click, reducing the complexity of the operation and the dependence on human skills. Combined with the previous virtual debugging results, the optimized teaching program can be directly executed, improving deployment efficiency and accuracy, accelerating the speed of robot commissioning, and enhancing the ability to quickly respond to changes on site.

[0041] In summary, the embodiment of the present application automatically determines the target process flow according to the type of processing task, and intelligently matches the equipment workstations according to the process, which can quickly locate the relevant work points and generate a workpoint space module. This structured processing method avoids manual point-by-point search and teaching, can greatly reduce the teaching preparation work, and improve overall work efficiency; with the help of the human-computer interaction interface and virtual simulation environment, the operator can complete path debugging and motion simulation without actually running the equipment, which can not only reduce the dependence on the professional skills of the personnel, but also reduce the risk during equipment debugging, and prevent the furnace-front quick-splitting robot from colliding or executing the wrong path; the equipment number, workstation coordinates and process steps are integrated into a workpoint space module to achieve decoupling and reconstruction of the process and space, making the adaptation and adjustment of different types of tasks more flexible and efficient. The corresponding teaching program can be quickly called through the input signal, which is convenient for rapid response to task changes and can improve the automation and intelligent capabilities of the entire furnace-front quick-splitting robot. In summary, the teaching method of the furnace-front quick-splitting robot provided in the embodiment of the present application can realize the efficiency, visualization and intelligence of the teaching of the furnace-front quick-splitting robot through process-driven work point space module construction, virtual simulation debugging and human-computer interaction control, thereby significantly improving work efficiency and system adaptability.

[0042] In some embodiments, the aforementioned processing task types may include circular sample processing type, racket sample processing type and conical column sample processing type; the aforementioned step 101 may include: for the aforementioned circular sample type, determining the target process flow may include suction cup sampling, air conveying device processing, grinding machine processing, visual discrimination, fluorescence analysis and laser marking in sequence; for the aforementioned racket sample type, determining the target process flow may include the aforementioned suction cup sampling, air conveying device processing, milling machine processing, visual discrimination, spectral analysis and laser marking in sequence; for the conical column sample type, determining the target process flow may include spectral analysis process chain and gas analysis process chain, wherein the aforementioned spectral analysis process chain may include jaw sampling, the aforementioned air conveying device processing, cutting and milling processing, sample cooling, visual discrimination, spectral analysis and laser marking in sequence, and the gas analysis process chain may include jaw sampling, air conveying device processing, chip sample preparation, chip sample collection, measurement and weighing, gas analysis and laser marking in sequence.

[0043] In some cases, the round sample processing type refers to processing tasks that target disc-shaped specimens, usually used for preliminary analysis of steel samples; see Figure 2, 110 in the figure is the target of the round sample processing type. The racket sample processing type refers to the processing task with the sample shape close to the racket structure, which is suitable for strength or surface detection and analysis; see Figure 2 , 120 in the figure is the target of the racket-like processing type. The cone-cylinder processing type refers to the processing task targeting composite structure samples with conical or cylindrical geometric features, which is used for more detailed or process-specific analysis, such as gas analysis and spectral analysis; see Figure 2130 in the figure represents the target for conical sample processing. Suction cup sampling is the process of grasping round or flat samples using a vacuum cup. Automatic recognition of the sample's plane and position allows for suction cup sampling. For example, the collection of round samples in the desulfurization process is a suction cup sampling operation. Gripper sampling is the process of grasping conical, cylindrical, or irregularly shaped samples using mechanical grippers. This gripping is typically achieved through automated control of the clamping force and jaw angle. For example, the extraction of cylindrical, conical samples is a typical example of gripper sampling. Air conveyor processing uses airflow to transport samples to downstream equipment. Automated path planning can be used to achieve directional sample transfer. For example, air conveyor processing involves conveying samples to a laboratory's pre-furnace quick-sorting system and then dispatching them to a grinder via a pre-furnace quick-sorting robot. Grinding is the process of grinding the sample surface. Grinding accuracy is typically controlled by setting the grinding depth and time. For example, fine grinding of round samples is a typical example of grinding. Milling is the process of using cutting and milling equipment to perform multi-faceted processing on a sample. For example, surface processing of a conical cylindrical sample is milling. Sample cooling is the process of rapidly cooling a hot sample to facilitate subsequent analysis. This can be achieved by setting a cooling time or using infrared temperature control. For example, cooling after milling is considered sample cooling. Visual inspection uses machine vision to identify sample position or machining quality, often in conjunction with image recognition algorithms. For example, using a vision system to detect surface defects such as bubbles, cracks, and slag inclusions after machining to determine if they exceed acceptable thresholds and to determine if the surface quality meets process requirements is considered visual inspection. Chip preparation is the process of removing small chips from a parent sample for gas analysis. This can be achieved by adjusting cutting tool parameters. For example, generating chip samples for gas testing is considered chip preparation. Chip collection is the process of collecting and transferring chip samples to a weighing or analysis unit. This can be achieved automatically through suction or conveying devices. For example, collecting chip samples into a sample collection bucket is considered chip collection. Weighing is a process for quality inspection of test samples or chips, and is often completed by the linkage of an electronic scale and a robot controller. For example, automatically weighing a chip sample to 3 grams is a weighing operation. Gas analysis is a process for detecting gas elements such as oxygen, nitrogen, and hydrogen in a sample, and is usually performed using infrared or thermal conductivity analyzers. For example, nitrogen content analysis is a gas analysis process. Fluorescence analysis is a process for detecting the elemental composition of a material by generating a hole effect through the excitation of the inner electrons of the sample by X-rays. It is particularly suitable for samples that require high excitation energy, such as pig iron samples and slag samples. It can analyze a wide range of elements such as silicon, manganese, and chromium (0.001%-100%). For example, detecting carbon and sulfur elements on the surface of a pig iron sample is a fluorescence analysis operation. The detection process requires the planarization of the sample, and standardized positioning is usually completed by an automatic sample loader.Laser marking is the process of inscribing numbers or identification information on the surface of a sample using a laser device. Usually, the system automatically generates the code and performs the marking action. For example, printing "Furnace No. 1234" on the surface of a sample is a laser marking operation.

[0044] Through the implementation of the above embodiments, different task types such as round samples, racket samples, and cone-cylinder samples are mapped one-to-one with specific process flows, and adaptive switching of teaching logic is realized, which can enhance the compatibility of the furnace-front fast-splitting robot with diversified production needs; the process structure is refined, which is conducive to the modularization of the furnace-front fast-splitting robot's behavior, controllability of the process, and refinement of task management.

[0045] In some embodiments, the aforementioned step 102 may include: matching the equipment numbers and workstation coordinates in the preset equipment workstation library according to the target process flow; sorting the equipment numbers and workstation coordinates according to the execution order of the target process flow to obtain a nested structure workpoint space module.

[0046] In some examples, the preset equipment workstation library is a pre-established database used to store all key equipment in the workshop and their location data. The preset equipment workstation library can include information such as equipment number, spatial coordinates, and the functional process to which it belongs. For example, the grinder numbered "GJ001" has coordinates (X:1000, Y:200, Z:800) and is stored in the preset equipment workstation library. Based on each process corresponding to the processing task, such as grinding machine processing or spectral analysis, the equipment number and its spatial position coordinates that match the process function can be screened from the preset equipment workstation library. The matched equipment information can then be arranged in order from the front to the back of the target process. The sorted equipment numbers and coordinates are organized into a unified spatial task unit in a hierarchical and structured data format to describe the robot operation process, that is, a nested workpoint space module.

[0047] Through the implementation of the above embodiments, nested work point space modules are automatically searched and sorted from the equipment workstation library based on the target process flow, which can avoid manual point-by-point path setting and greatly improve the workstation planning efficiency; the modular structure facilitates later maintenance, adjustment and cross-task migration, and improves the scalability and adaptability of the method.

[0048] In some embodiments, the aforementioned step 103 may include: constructing a virtual simulation environment consistent with the working environment of the furnace-front quick-splitting robot through digital twin technology; simulating the teaching process of the furnace-front quick-splitting robot in the virtual simulation environment according to the work point space module to obtain a simulation result; when there is a path conflict signal or equipment interference signal in the simulation result, adjusting the workstation coordinates or the preset process sequence in the work point space module until the simulation result meets the preset safety conditions.

[0049] In some examples, digital twin technology can be used to "mirror" and reconstruct the physical equipment, spatial layout, and robot structure in a real workshop environment in a virtual space, forming an interactive and dynamically simulated virtual simulation environment. The aforementioned work point space module data can be used to simulate the motion path and process flow of the furnace-front quick-splitting robot in a virtual simulation environment, and output simulation results such as motion trajectory, collision detection, and time analysis. For example, in the virtual simulation environment, the furnace-front quick-splitting robot follows the teaching program, sequentially grabbing samples from coordinate A, sending them to coordinate B for grinding, and returning to coordinate C for detection. The path conflict signal is an error alarm that violates the motion rules when the motion path of the furnace-front quick-splitting robot overlaps or intersects with its own structure or the path of other objects in the virtual simulation environment. For example, the robot arm of the furnace-front quick-splitting robot collides with a positioned fixture during rotation, triggering a path conflict signal. The equipment interference signal is when the furnace-front quick-splitting robot spatially interferes with surrounding fixed or movable equipment while performing its tasks. For example, when the gripper of the furnace-front quick-splitting robot approaches the spectrometer, it interferes with the instrument casing due to an unreasonable path, generating this signal. Preset safety conditions refer to operating safety standards pre-defined in a virtual simulation environment to determine whether the teaching path is reasonable. They may include minimum safety distance, speed limit, movement angle limit, etc. When there are anomalies in the simulation results, the system or operator can adjust the position parameters of each workstation in the workpoint space module or rearrange the execution order until the simulation feedback is correct and the safety standards are met.

[0050] Through the implementation of the above embodiments, the use of digital twins to build a virtual environment can not only detect path conflicts or equipment interference problems before actual operation, make optimization adjustments in advance to ensure safety, but also significantly reduce physical debugging time and errors, and improve the accuracy and reliability of path planning.

[0051] In some embodiments, the aforementioned teaching method for the furnace-front fast-splitting robot also includes: obtaining the steelmaking stage in which the furnace-front fast-splitting robot is located; when the steelmaking stage is the primary refining stage, determining that the processing task type is a round sample processing type; when the steelmaking stage is the refining stage, determining that the processing task type is a racket sample processing type; when the steelmaking stage is the post-processing and continuous casting stage, determining that the processing task type is a cone-column sample processing type.

[0052] In some examples, the steelmaking stage refers to the general term for the major process stages in the steelmaking process, from molten iron treatment to the preparation of finished molten steel, which usually includes primary refining, refining, post-processing and continuous casting. The primary refining stage is the earliest smelting stage in the steelmaking process, including preliminary purification and composition adjustment processes such as tank pouring and desulfurization. The goal is to preliminarily remove impurities, desulfurize and dephosphorize, and adjust the composition. The refining stage is the stage of further purification, composition and temperature adjustment after the primary refining stage, including argon blowing, ladle treatment and other composition fine-tuning and homogenization processes. The post-processing and continuous casting stage refers to the process of degassing, wrapping, and continuous casting after the molten steel composition adjustment is completed, including final treatment and molding processes such as vacuum treatment and continuous casting.

[0053] Through the implementation of the above embodiments, the steelmaking stage of the furnace-front quick-separation robot can be obtained, and the corresponding processing task type can be automatically matched to avoid manual intervention. The task flow can be dynamically adapted according to the on-site process, which can improve the adaptability and versatility of the method in multiple scenarios. After automatically determining the processing task type, the matching process flow, equipment station and teaching path template can be accurately called, which significantly reduces the path planning, process configuration and subsequent debugging time.

[0054] In some embodiments, the aforementioned human-computer interaction interface may include a function button set, a coordinate adjustment window, and a parallel task selection pop-up window; the function button set is displayed in order of operation frequency; the coordinate adjustment window supports numerical input, which is used to fine-tune the workstation coordinates in the work point space module; the parallel task selection pop-up window is used to provide a process chain switching selection prompt when multiple process conflicts are detected.

[0055] In some examples, a function button set refers to a group of commonly used operation buttons presented in a human-computer interaction interface, which are sorted by frequency of use and importance to facilitate operators to quickly call common functions. It can be generated by UI design specifications and user operation log analysis, and the button sorting can be dynamically adjusted according to the frequency of function calls. For example, buttons can include "Execute Teaching", "Pause Process", "Path Jump", "Equipment Reset", etc., and the commonly used "Execute Teaching" is displayed at the top. The coordinate adjustment window is an interface module for inputting or fine-tuning the robot work point position (X / Y / Z coordinates). It allows the operator to make numerical adjustments to the work station coordinates in the spatial module, which can be set through interface controls such as digital input boxes and sliders. It supports connection to on-site posture feedback for deviation correction. For example, the original position is (300mm, 210mm, 150mm), and the user enters "X+5mm". The adjusted position is updated to (305mm, 210mm, 150mm). The parallel task selection pop-up window is used to pop up when multiple possible process execution path conflicts or resource preemption are detected, allowing the operator to select the current priority process chain branch; for example, if "laser marking" and "fluorescence analysis" occupy the same platform, the parallel task selection pop-up window prompts "Please select the priority execution task."

[0056] Through the implementation of the above embodiments, interface optimization such as high-frequency sorting of function buttons, coordinate fine-tuning windows, and parallel task conflict prompts is utilized to improve operational intuitiveness and interaction efficiency; the operator can more conveniently intervene and adjust the operation path, improve teaching accuracy, and reduce dependence on professional technicians.

[0057] In some embodiments, the aforementioned teaching method for the furnace-front quick-splitting robot may further include: collecting the actual trajectory of the furnace-front quick-splitting robot to obtain actual motion trajectory data; comparing the actual motion trajectory data with the expected trajectory data in the virtual simulation environment to obtain a trajectory deviation value; when the trajectory deviation value exceeds a preset tolerance range, the teaching program rolls back to the previous safe position and issues an abnormal alarm.

[0058] In some examples, actual motion trajectory data is the time-series position data of the end effector (e.g., gripper or suction cup) of a furnace-front quick-sorting robot in three-dimensional space during real-world operation. This data can be collected in real time using the robot's built-in encoders, servo motor feedback, or an external three-dimensional tracking system. Expected trajectory data is the standard spatial path that the robot should ideally follow, generated by a teaching program in a virtual simulation environment. This data is generated by the digital twin simulation system based on spatial models and process logic and saved as expected trajectory data. The trajectory deviation value is the spatial distance error between the actual motion trajectory data and the expected trajectory data at each sampling point. This can be calculated using Euclidean distance and used to measure trajectory execution accuracy. The preset tolerance range is the acceptable trajectory error threshold within which the furnace-front quick-sorting robot's motion is considered to meet acceptable accuracy. This tolerance can be set based on process requirements, such as ±3mm or ±2°, by the process engineer or automatically adjusted by an algorithm. When the trajectory deviation value exceeds the preset tolerance range, the current operation can be automatically terminated, retracing to the last safe stop point, and an alarm can be issued through an audible, visual, or visual interface to indicate the abnormality.

[0059] Through the implementation of the above embodiment, the teaching accuracy can be verified by comparing the actual trajectory with the simulated path; once the deviation exceeds the tolerance, it will automatically roll back and alarm, which greatly improves the safety assurance capability during the teaching process and prevents equipment damage or operation failure caused by path deviation.

[0060] Furthermore, as an implementation of the aforementioned method embodiment, the present application also provides a furnace-front fast separation robot teaching device for implementing the aforementioned method embodiment. This device embodiment corresponds to the aforementioned method embodiment. For ease of reading, this furnace-front fast separation robot teaching device embodiment will no longer repeat the details of the aforementioned method embodiment one by one, but it should be clear that the device in the embodiment of the present application can correspond to and implement all the contents of the aforementioned method embodiment. Figure 3As shown, the teaching device 20 of the furnace-front quick-splitting robot includes: a process determination unit 201, a module determination unit 202, a program debugging unit 203 and a teaching unit 204, wherein the process determination unit 201 is used to determine the target process flow of the furnace-front quick-splitting robot according to the type of processing task; the module determination unit 202 is used to search and match the preset equipment workstations according to the aforementioned target process flow, and obtain a work point space module corresponding to the furnace-front quick-splitting robot, wherein the work point space module may include equipment number, workstation coordinates and preset process steps; the program debugging unit 203 is used to perform motion path logic debugging on the work point space module in a virtual simulation environment to obtain a debugged teaching program; the teaching unit 204 is used to execute the teaching program in response to the input signal of the human-computer interaction interface to complete the teaching operation of the furnace-front quick-splitting robot.

[0061] In some embodiments, the processing task types include circular sample processing type, racket sample processing type and conical column sample processing type; the process determination unit 201 is also used to determine, for the circular sample type, that the target process flow includes suction cup sampling, air conveying device processing, grinding machine processing, visual discrimination, fluorescence analysis and laser marking in sequence; for the racket sample type, the target process flow includes suction cup sampling, air conveying device processing, milling machine processing, visual discrimination, spectral analysis and laser marking in sequence; for the conical column sample type, the target process flow includes spectral analysis process chain and gas analysis process chain, wherein the spectral analysis process chain includes jaw sampling, air conveying device processing, cutting and milling processing, sample cooling, visual discrimination, spectral analysis and laser marking in sequence, and the gas analysis process chain includes jaw sampling, air conveying device processing, chip sample preparation, chip sample collection, measurement and weighing, gas analysis and laser marking in sequence.

[0062] In some embodiments, the module determination unit 202 is also used to match the equipment number and workstation coordinates in the preset equipment workstation library according to the target process flow; sort the equipment number and workstation coordinates according to the execution order of the target process flow to obtain a nested structure workpoint space module.

[0063] In some embodiments, the program debugging unit 203 is also used to construct a virtual simulation environment that is consistent with the working environment of the furnace-front quick-splitting robot through digital twin technology; according to the work point space module, the teaching process of the furnace-front quick-splitting robot is simulated in the virtual simulation environment to obtain a simulation result; when there is a path conflict signal or equipment interference signal in the simulation result, the work station coordinates or the preset process sequence in the work point space module are adjusted until the simulation result meets the preset safety conditions.

[0064] In some embodiments, the steelmaking stage in which the furnace-front fast-splitting robot is located is obtained; when the steelmaking stage is the primary refining stage, the processing task type is determined to be the round sample processing type; when the steelmaking stage is the refining stage, the processing task type is determined to be the racket sample processing type; when the steelmaking stage is the post-processing and continuous casting stage, the processing task type is determined to be the cone-cylinder sample processing type.

[0065] In some embodiments, the human-computer interaction interface includes a function button set, a coordinate adjustment window, and a parallel task selection pop-up window; the function button set is displayed in order of operation frequency; the coordinate adjustment window supports numerical input, which is used to fine-tune the workstation coordinates in the workpoint space module; the parallel task selection pop-up window is used to provide a process chain switching selection prompt when multiple process conflicts are detected.

[0066] In some embodiments, the teaching unit 204 is also used to collect the actual trajectory of the furnace-front quick-splitting robot to obtain actual motion trajectory data; compare the actual motion trajectory data with the expected trajectory data in the virtual simulation environment to obtain a trajectory deviation value; when the trajectory deviation value exceeds the preset tolerance range, the teaching program rolls back to the previous safety position and issues an abnormal alarm.

[0067] The present application also provides a computer-readable storage medium, which stores computer-executable instructions or computer programs. When the computer-executable instructions or computer programs are executed by a processor, the processor will execute any step of the furnace front fast separation robot teaching method provided in the present application.

[0068] In some embodiments, the computer-readable storage medium may be a random access memory (RAM), a read-only memory (ROM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); or it may be various devices including one or any combination of the above memories.

[0069] In some embodiments, computer-executable instructions may be in the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0070] In some embodiments, computer-executable instructions may, but need not, correspond to a file in a file system, may be stored as part of a file that stores other programs or data, for example, in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinating files (for example, files storing one or more modules, subroutines, or code portions).

[0071] In some embodiments, computer-executable instructions may be deployed to be executed on one electronic device, or on multiple electronic devices located at one site, or on multiple electronic devices distributed across multiple sites and interconnected by a communication network.

[0072] like Figure 4 As shown, the present application also provides an electronic device 30, including a memory 310, a processor 320 and a computer program 311 stored in the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, any step of the above-mentioned furnace front fast separation robot teaching method is implemented.

[0073] The present application also provides a computer program product, which includes a computer program or computer-executable instructions, wherein the computer program or computer-executable instructions are stored in a computer-readable storage medium. A processor of an electronic device reads the computer program or computer-executable instructions from the computer-readable storage medium, and the processor executes the computer program or computer-executable instructions, causing the electronic device to perform any step of the method for teaching a furnace-front fast-sorting robot described above.

[0074] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A teaching method for a furnace-front quick separation robot, characterized in that: include: Determine the target process flow of the furnace-front quick separation robot according to the processing task type; According to the target process flow, the preset equipment stations are searched and matched to obtain a work point space module corresponding to the furnace front quick separation robot, wherein the work point space module includes the equipment number, station coordinates and preset process steps; Performing motion path logic debugging on the working point space module in a virtual simulation environment to obtain a debugged teaching program; In response to the input signal of the human-computer interaction interface, the teaching program is executed to complete the teaching operation of the furnace-front fast-separating robot.

2. The teaching method of the furnace front quick separation robot according to claim 1, characterized in that: The processing task types include circle-like processing type, racket-like processing type and cone-like processing type; Determining the target process flow of the furnace-front quick separation robot according to the processing task type includes: For the circular sample type, determining that the target process flow includes suction cup sampling, air conveying device processing, grinding machine processing, visual identification, fluorescence analysis and laser marking in sequence; For the racket sample type, determining that the target process flow includes the suction cup sampling, the air conveying device processing, the milling machine processing, the visual identification, the spectral analysis and the laser marking in sequence; For the cone-cylinder sample type, it is determined that the target process flow includes a spectral analysis process chain and a gas analysis process chain, wherein the spectral analysis process chain includes, in sequence, gripper sampling, the air conveying device processing, cutting and milling, sample cooling, the visual judgment, the spectral analysis and the laser marking; and the gas analysis process chain includes, in sequence, gripper sampling, the air conveying device processing, chip sample preparation, chip sample collection, measurement and weighing, gas analysis and the laser marking.

3. The teaching method for the furnace front quick separation robot according to claim 1, characterized in that: According to the target process flow, the preset equipment positions are searched and matched to obtain the working point space module corresponding to the furnace front fast separation robot, including: According to the target process flow, matching the equipment number and the workstation coordinates in the preset equipment workstation library; The equipment numbers and the workstation coordinates are sorted according to the execution order of the target process flow to obtain the workpoint space module with a nested structure.

4. The teaching method for the furnace front quick separation robot according to claim 1, characterized in that: The step of performing motion path logic debugging on the working point space module in a virtual simulation environment to obtain a debugged teaching program includes: Constructing the virtual simulation environment consistent with the working environment of the furnace-front quick separation robot through digital twin technology; According to the working point space module, the teaching process of the furnace front quick separation robot is simulated in the virtual simulation environment to obtain a simulation result; When a path conflict signal or an equipment interference signal exists in the simulation result, the workstation coordinates or the preset process sequence in the workpoint space module are adjusted until the simulation result meets a preset safety condition.

5. The teaching method for the furnace front quick separation robot according to claim 1, characterized in that: The furnace front quick separation robot teaching method also includes: Obtaining the steelmaking stage of the furnace-front fast separation robot; When the steelmaking stage is the primary steelmaking stage, determining that the processing task type is a round sample processing type; When the steelmaking stage is a refining stage, determining that the processing task type is a racket-like processing type; When the steelmaking stage is the post-processing and continuous casting stage, the processing task type is determined to be a cone-cylinder processing type.

6. The teaching method for the furnace-front quick separation robot according to claim 1, characterized in that: The human-computer interaction interface includes a set of function buttons, a coordinate adjustment window and a parallel task selection pop-up window; the function button set is displayed in order of operation frequency; the coordinate adjustment window supports numerical input and is used to fine-tune the workstation coordinates in the workpoint space module; the parallel task selection pop-up window is used to provide a process chain switching selection prompt when multiple process conflicts are detected.

7. The teaching method for the furnace front quick separation robot according to claim 1, characterized in that: The furnace front quick separation robot teaching method also includes: Performing actual trajectory collection on the furnace-front quick separation robot to obtain actual motion trajectory data; Comparing the actual motion trajectory data with the expected trajectory data in the virtual simulation environment to obtain a trajectory deviation value; When the trajectory deviation value exceeds a preset tolerance range, the teaching program rolls back to the previous safe position and issues an abnormality alarm.

8. A furnace-front quick separation robot teaching device, characterized in that: include: The process determination unit is used to determine the target process flow of the furnace-front quick separation robot according to the processing task type; A module determination unit is configured to search and match preset equipment stations according to the target process flow to obtain a work point space module corresponding to the furnace front quick separation robot, wherein the work point space module includes an equipment number, station coordinates, and preset process steps; A program debugging unit, configured to perform motion path logic debugging on the working point space module in a virtual simulation environment to obtain a debugged teaching program; The teaching unit is used to execute the teaching program in response to the input signal of the human-computer interaction interface to complete the teaching operation of the furnace-front fast-separating robot.

9. An electronic device comprising: A memory and a processor, characterized in that the processor is used to implement the steps of the furnace front fast separation robot teaching method as described in any one of claims 1 to 7 when executing the computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the furnace front quick separation robot teaching method according to any one of claims 1 to 7 are implemented.

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