Event-driven and asset-graph-based no-code programming methods, systems, and media for robots.

Abstract

This application provides a no-code programming method, system, and medium for robots based on event-driven and asset graphs. The method includes: importing physical asset models into a 3D simulation environment; assigning predefined functional labels to each physical asset model; generating basic motion events for non-robot assets based on functional labels and configuration parameters; generating composite events for robot assets based on process types and key work points defined in an interactive interface; constructing an asset graph based on asset functional labels, assembly relationships in 3D space, and defined work space points; traversing the asset graph and automatically generating logical control events in an event tree; compiling the final constructed event tree and logical relationships to generate underlying control code; and automatically deriving and establishing logical dependencies between events by analyzing the graph relationships composed of asset types, spatial position relationships, and work point identifiers, thereby achieving efficient and intuitive no-code programming.

Description

Technical Field

[0001] This application relates to the field of industrial automation simulation and control technology, and more specifically, to a no-code programming method, system, and medium for robots based on event-driven and asset mapping. Background Technology

[0002] In modern intelligent manufacturing, offline simulation technology is widely used in production line planning, virtual debugging, and personnel training. Traditional simulation software, such as Visual Components, primarily defines equipment behavior in two ways:

[0003] 1. Script / code programming: Users need to write scripting languages ​​such as Python and VBScript to control the device's actions and logic, which requires a high level of programming ability from the user.

[0004] 2. Predefined behavior component drag-and-drop: Provides a series of predefined behavior components (such as "MoveTo", "Pick", "Place"), allowing users to build processes by dragging and dropping these components and manually connecting their input and output ports. While this approach lowers the coding threshold, it is still essentially an explicit connection based on flow-based or state machine principles, requiring users to clearly plan the execution order and data transfer path for each step.

[0005] The above methods have obvious shortcomings: for complex multi-device collaboration scenarios, manually connecting all logical relationships is a huge workload and is prone to errors; at the same time, the universality of the preset components is limited, making it difficult to flexibly adapt to various non-standard equipment and processes.

[0006] Pain points in use cases:

[0007] 1. Messy wiring: The collaboration of 10 devices requires hundreds of nodes, with overlapping and intersecting wiring, making maintenance difficult.

[0008] 2. Difficult signal management: The transmission and reception of the same signal need to be defined separately in the flowcharts of multiple devices, which is prone to inconsistency.

[0009] 3. Poor reusability: The logic of a "loading and unloading unit" cannot be copied to a new workstation as a whole because the coordinate system and signal names need to be completely changed.

[0010] 4. Difficult for non-programmers to use: Process engineers cannot understand the meaning of WaitSignal("DI05") == True. Summary of the Invention

[0011] The purpose of this application is to provide a method, system, and medium for no-code programming of robots based on event-driven and asset graphs. By analyzing the graph relationships composed of elements such as asset type, spatial location relationship, and work point identifier, the logical dependencies between events are automatically derived and established, thereby achieving efficient and intuitive no-code programming.

[0012] This application embodiment also provides a no-code programming method for robots based on event-driven and asset graphs, including: constructing a three-dimensional simulation environment, importing physical asset models into the three-dimensional simulation environment, wherein the physical asset models include at least one of robots, servo mechanisms, IO devices, conveyor lines, and sensors, and assigning predefined function tags to each physical asset model, wherein the function tags are used to limit the types of events that can be generated by the corresponding physical asset model;

[0013] For non-robot assets, basic action events for the corresponding assets are automatically generated based on function tags and configuration parameters; for robot assets, composite events containing tool I / O actions are generated based on the process type and key work points defined in the interactive interface.

[0014] Based on the asset's functional tags, assembly relationships in three-dimensional space, and defined work space points, an asset map describing the physical and functional dependencies between assets is automatically constructed.

[0015] In response to user trigger commands, the asset map is traversed, the execution relationship and spatial coordination relationship between events are automatically analyzed, and logical control events are automatically generated in the event tree.

[0016] By using preset tools to supplement, correct, or add complex logic to the automatically associated event logic, adapting it to specific process requirements, the final constructed event tree and logical relationships are compiled into underlying control code.

[0017] Optionally, in the event-driven and asset graph-based robot no-code programming method described in the embodiments of this application, each physical asset model is assigned a predefined functional label, specifically including:

[0018] Import the size parameters and layout coordinates of the real production line, calibrate the 3D simulation environment, and ensure that the spatial proportions and relative positions of the equipment in the 3D simulation environment are consistent with those in the real production line.

[0019] Import the physical asset model, perform an integrity check on the physical asset model, and obtain the check results;

[0020] If the verification result meets the set verification conditions, then assign a functional label to the physical asset model.

[0021] If the test results do not meet the set verification conditions, it is determined that the physical asset model has missing components or abnormal parameters, and the missing components and abnormal parameters are corrected.

[0022] Optionally, in the event-driven and asset graph-based robot no-code programming method described in the embodiments of this application, the non-robot assets include cylinders and conveyor lines, the configuration parameters include motion stroke and speed, and the basic motion events include cylinder extension and conveyor line start-up; the process types of the robot assets include handling, assembly, and destacking / palletizing, and the key work points include gripping point coordinate system and placement point coordinate system.

[0023] Optionally, in the event-driven and asset graph-based robot no-code programming method described in the embodiments of this application, the asset graph construction method specifically includes:

[0024] Extract the functional tag information for each physical asset model;

[0025] Establish a tag association index based on tag information;

[0026] Based on the assembly relationship of physical asset models in three-dimensional space, physical dependency links between assets are generated.

[0027] The operational space locations of each physical asset model are associated with the physical dependency links between assets;

[0028] Based on the dependency relationship of the work space point completion function, the asset map is automatically constructed and the asset map is obtained.

[0029] Optionally, in the event-driven and asset graph-based robot no-code programming method described in the embodiments of this application, the logic control events include waiting events and assignment events;

[0030] When the asset map shows that the target point of a robot composite event is located in the working area of ​​any non-robot asset, the system automatically associates the robot composite event after the basic action event of the non-robot asset and inserts the corresponding waiting event before the robot composite event.

[0031] Optionally, in the event-driven and asset graph-based robot no-code programming method described in the embodiments of this application, the final constructed event tree and logical relationships are compiled to generate low-level control code, and then the method further includes:

[0032] Obtain the underlying control code and analyze whether there are any syntax errors or compatibility issues.

[0033] If present, compilation will be automatically paused and an error message will be generated, along with the error location and suggested modifications. Based on the error location and suggested modifications, the underlying control code will be modified until the code compiles successfully.

[0034] If it does not exist, the final underlying control code will be generated.

[0035] Secondly, embodiments of this application provide a robot no-code programming system based on event-driven and asset graph approaches. The system includes a memory and a processor. The memory includes a program for a robot no-code programming method based on event-driven and asset graph approaches. When the program is executed by the processor, it performs the following steps:

[0036] A 3D simulation environment is constructed, and physical asset models are imported into the 3D simulation environment. The physical asset models include at least one of robots, servo mechanisms, I / O devices, conveyor lines, and sensors. Each physical asset model is assigned a predefined functional label, which is used to limit the types of events that the corresponding physical asset model can generate.

[0037] For non-robot assets, basic action events for the corresponding assets are automatically generated based on function tags and configuration parameters; for robot assets, composite events containing tool I / O actions are generated based on the process type and key work points defined in the interactive interface.

[0038] Based on the asset's functional tags, assembly relationships in three-dimensional space, and defined work space points, an asset map describing the physical and functional dependencies between assets is automatically constructed.

[0039] In response to user trigger commands, the asset map is traversed, the execution relationship and spatial coordination relationship between events are automatically analyzed, and logical control events are automatically generated in the event tree.

[0040] By using preset tools to supplement, correct, or add complex logic to the automatically associated event logic, adapting it to specific process requirements, the final constructed event tree and logical relationships are compiled into underlying control code.

[0041] Optionally, in the event-driven and asset graph-based robot no-code programming system described in this application embodiment, each physical asset model is assigned a predefined functional label, specifically including:

[0042] Import the size parameters and layout coordinates of the real production line, calibrate the 3D simulation environment, and ensure that the spatial proportions and relative positions of the equipment in the 3D simulation environment are consistent with those in the real production line.

[0043] Import the physical asset model, perform an integrity check on the physical asset model, and obtain the check results;

[0044] If the verification result meets the set verification conditions, then assign a functional label to the physical asset model.

[0045] If the test results do not meet the set verification conditions, it is determined that the physical asset model has missing components or abnormal parameters, and the missing components and abnormal parameters are corrected.

[0046] Optionally, in the event-driven and asset graph-based robot no-code programming system described in this application embodiment, the non-robot assets include cylinders and conveyor lines, the configuration parameters include motion stroke and speed, and the basic motion events include cylinder extension and conveyor line start-up; the process types of the robot assets include handling, assembly, and destacking / palletizing, and the key work points include gripping point coordinate system and placement point coordinate system.

[0047] Thirdly, embodiments of this application also provide a computer-readable storage medium, which includes a robot no-code programming method program based on event-driven and asset graph, wherein when the robot no-code programming method program based on event-driven and asset graph is executed by a processor, it implements the steps of the robot no-code programming method based on event-driven and asset graph as described in any of the preceding claims.

[0048] As can be seen from the above, the robot no-code programming method, system, and medium based on event-driven and asset graph provided in this application imports physical asset models into a 3D simulation environment, assigns predefined functional labels to each physical asset model, generates basic motion events for non-robot assets based on functional labels and configuration parameters, and generates composite events for robot assets based on process types and key work points defined in the interactive interface. An asset graph is constructed based on the asset's functional labels, assembly relationships in 3D space, and defined work space points. The asset graph is traversed, and logical control events are automatically generated in the event tree. The final constructed event tree and logical relationships are compiled to generate underlying control code. By analyzing the graph relationships composed of asset types, spatial location relationships, and work point identifiers, the logical dependencies between events are automatically derived and established, thereby achieving efficient and intuitive no-code programming. Attached Figure Description

[0049] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 A flowchart of a robot no-code programming method based on event-driven and asset graph provided in an embodiment of this application;

[0051] Figure 2 A flowchart illustrating the physical asset model definition of a robot no-code programming method based on event-driven and asset graphs, provided in an embodiment of this application.

[0052] Figure 3 A schematic diagram illustrating the principle of asset graph construction and automatic logical association in the event-driven and asset graph-based no-code programming method for robots provided in this application embodiment;

[0053] Figure 4 This is a schematic diagram of the event tree interface for the event-driven and asset graph-based no-code programming method for robots provided in an embodiment of this application. Detailed Implementation

[0054] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0055] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0056] Please refer to Figures 1-4 This event-driven and asset-graph-based no-code robot programming method is used in terminal devices. The method includes the following steps:

[0057] S101, Build a 3D simulation environment, import physical asset models into the 3D simulation environment. The physical asset models include at least one of robots, servo mechanisms, IO devices, conveyor lines, and sensors. Assign predefined function labels to each physical asset model. The function labels are used to limit the types of events that the corresponding physical asset model can generate.

[0058] S102, For non-robot assets, basic action events for the corresponding assets are automatically generated based on function tags and configuration parameters; for robot assets, composite events containing tool IO actions are generated based on the process type and key work points defined in the interactive interface.

[0059] S103 automatically constructs an asset map describing the physical and functional dependencies between assets based on the asset's functional tags, assembly relationships in three-dimensional space, and defined work space points.

[0060] S104 responds to the user's trigger command, traverses the asset map, automatically analyzes the execution relationship and spatial coordination relationship between events, and automatically generates logical control events in the event tree;

[0061] S105 uses preset tools to supplement, correct, or add complex logic to the automatically associated event logic to adapt to specific process requirements, and compiles the final constructed event tree and logical relationships into underlying control code.

[0062] Specifically, this application includes asset modeling and tagging: importing physical asset models such as robots, servo mechanisms, I / O devices, conveyor lines, and sensors into a 3D simulation environment, and assigning predefined functional tags (such as "robot," "cylinder," "conveyor line," "camera," etc.) to each asset. The functional tags determine the types of events that the asset can generate.

[0063] Events are automatically generated:

[0064] 1. For non-robot assets (such as cylinders and conveyor lines), automatically generate default events (such as "cylinder extends" and "conveyor line starts") representing their basic motion capabilities based on their function tags and configuration parameters (stroke and speed).

[0065] 2. For robot assets, guide users through an interactive interface to define their process type (such as handling, assembly, depalletizing) and key work points (such as gripping point and placement point coordinate system), and manually generate a complete composite event containing tool IO actions (e.g., "Robot performs handling from A to B").

[0066] 3. Asset Map Relationship Construction: Based on the asset's functional tags, assembly relationships in three-dimensional space (such as robot tools being bound to fixtures), and defined workspace points (such as workstation coordinate systems on conveyor lines), an "asset map" describing the physical and functional dependencies between assets is automatically constructed.

[0067] 4. Automatic Logical Association: Responding to user trigger commands, the system traverses the asset map, automatically analyzes the event execution relationships or spatial coordination relationships between events, and automatically generates necessary logical control events (such as "wait" and "assignment") in the event tree to ensure that events are executed in the correct sequence and under the correct conditions. For example, if the map shows that the target point of the "robot handling event" is located at "conveyor line station 1", the system will automatically associate the "robot handling event" after the "conveyor line in place" event and insert a logical event "waiting for conveyor line to arrive" before it.

[0068] 5. Manual Logic Fine-tuning: Provides manual association, logic elimination, and logic addition tools, allowing users to supplement, correct, or add complex logic (such as delays and conditional judgments) to the automatic association results to meet specific process requirements.

[0069] Specifically, in the manual logic fine-tuning process, users can customize the parameters of logic control events, including the delay time for waiting events and the trigger threshold for conditional judgment events.

[0070] In the manual logic fine-tuning step, after the event logic is corrected, the system automatically verifies the logical coherence and rationality of the correction. If there are logical conflicts or timing contradictions, a correction prompt will be generated to guide the user to make adjustments.

[0071] 6. Program generation and simulation: The final event tree and its logical relationships are compiled to generate low-level control code, which is used to drive the simulation or exported to a real PLC controller.

[0072] According to an embodiment of the present invention, each physical asset model is assigned a predefined functional label, specifically including:

[0073] Import the size parameters and layout coordinates of the real production line, calibrate the 3D simulation environment, and ensure that the spatial proportions and relative positions of the equipment in the 3D simulation environment are consistent with those in the real production line.

[0074] Import the physical asset model, perform an integrity check on the physical asset model, and obtain the check results;

[0075] If the verification result meets the set verification conditions, then assign a functional label to the physical asset model.

[0076] If the test results do not meet the set verification conditions, it is determined that the physical asset model has missing components or abnormal parameters, and the missing components and abnormal parameters are corrected.

[0077] According to embodiments of the present invention, non-robot assets include cylinders and conveyor lines, and configuration parameters include motion stroke and speed. Basic motion events include cylinder extension and conveyor line start-up. Robot assets include process types such as handling, assembly, and palletizing / depalletizing, and key work points include gripping point coordinate system and placement point coordinate system.

[0078] According to an embodiment of the present invention, the asset mapping method specifically includes:

[0079] Extract the functional tag information for each physical asset model;

[0080] Establish a tag association index based on tag information;

[0081] Based on the assembly relationship of physical asset models in three-dimensional space, physical dependency links between assets are generated.

[0082] The operational space locations of each physical asset model are associated with the physical dependency links between assets;

[0083] Based on the dependency relationship of the work space point completion function, the asset map is automatically constructed and the asset map is obtained.

[0084] It should be noted that in the asset graph relationship construction step, the functional dependencies between assets can be weighted and sorted by the parameter of the asset's work priority, which affects the association priority of event logic.

[0085] During the asset graph relationship construction process, custom dependencies between assets can be added manually to supplement special process association requirements not covered during the automatic construction process.

[0086] The asset map is stored in a graphical structure, with physical asset models as nodes and physical and functional dependencies as edges. Nodes are associated with the asset's functional labels, spatial coordinates, and work location information, while edges are associated with the type and priority parameters of the dependencies.

[0087] According to embodiments of the present invention, logic control events include wait events and assignment events;

[0088] When the asset map shows that the target point of a robot composite event is located in the working area of ​​any non-robot asset, the system automatically associates the robot composite event after the basic action event of the non-robot asset and inserts the corresponding waiting event before the robot composite event.

[0089] According to an embodiment of the present invention, the final constructed event tree and logical relationships are compiled to generate low-level control code, and then the method further includes:

[0090] Obtain the underlying control code and analyze whether there are any syntax errors or compatibility issues.

[0091] If present, compilation will be automatically paused and an error message will be generated, along with the error location and suggested modifications. Based on the error location and suggested modifications, the underlying control code will be modified until the code compiles successfully.

[0092] If it does not exist, the final underlying control code will be generated.

[0093] It should be noted that the compilation process of the underlying control code supports custom compilation parameters. The code format and instruction set can be adjusted according to the target PLC controller model and communication protocol to ensure code compatibility.

[0094] Secondly, embodiments of this application provide a robot no-code programming system based on event-driven and asset graph approaches. The system includes a memory and a processor. The memory contains a program for a robot no-code programming method based on event-driven and asset graph approaches. When the program is executed by the processor, it performs the following steps:

[0095] Construct a 3D simulation environment and import physical asset models into the 3D simulation environment. The physical asset models include at least one of robots, servo mechanisms, IO devices, conveyor lines, and sensors. Assign predefined function tags to each physical asset model. The function tags are used to limit the types of events that the corresponding physical asset model can generate.

[0096] For non-robot assets, basic action events for the corresponding assets are automatically generated based on function tags and configuration parameters; for robot assets, composite events containing tool I / O actions are generated based on the process type and key work points defined in the interactive interface.

[0097] Based on the asset's functional tags, assembly relationships in three-dimensional space, and defined work space points, an asset map describing the physical and functional dependencies between assets is automatically constructed.

[0098] Responding to user trigger commands, the system traverses the asset map, automatically analyzes the execution relationships and spatial coordination relationships between various events, and automatically generates logical control events in the event tree.

[0099] By using preset tools to supplement, correct, or add complex logic to the automatically associated event logic, adapting it to specific process requirements, the final constructed event tree and logical relationships are compiled into underlying control code.

[0100] According to an embodiment of the present invention, each physical asset model is assigned a predefined functional label, specifically including:

[0101] Import the size parameters and layout coordinates of the real production line, calibrate the 3D simulation environment, and ensure that the spatial proportions and relative positions of the equipment in the 3D simulation environment are consistent with those in the real production line.

[0102] Import the physical asset model, perform an integrity check on the physical asset model, and obtain the check results;

[0103] If the verification result meets the set verification conditions, then assign a functional label to the physical asset model.

[0104] If the test results do not meet the set verification conditions, it is determined that the physical asset model has missing components or abnormal parameters, and the missing components and abnormal parameters are corrected.

[0105] According to embodiments of the present invention, non-robot assets include cylinders and conveyor lines, and configuration parameters include motion stroke and speed. Basic motion events include cylinder extension and conveyor line start-up. Robot assets include process types such as handling, assembly, and palletizing / depalletizing, and key work points include gripping point coordinate system and placement point coordinate system.

[0106] Beneficial effects

[0107] Compared to the programming methods of traditional simulation software such as Visual Components, this invention has the following significant advantages:

[0108] A paradigm shift in programming: from explicit process connections to implicit event dependencies. Users no longer need to worry about "how to connect," but only about "what the device can do (events)" and "what the relationships are between devices (graph)," significantly reducing cognitive load.

[0109] High degree of automation: The "asset map" enables intelligent deduction and automatic generation of logical relationships, avoiding tedious and error-prone manual wiring, and is especially suitable for complex production lines with a large number of devices.

[0110] Strong engineering semantics: Events (such as "handling", "loading and unloading", "unstacking and stacking") directly map to real-world process actions, rather than underlying "movement" or "signal triggering", making the programming process closer to the thinking habits of engineers.

[0111] It offers excellent flexibility and scalability: by defining new functional labels and graph rules, it can easily support new or non-standard devices without modifying the underlying programming framework.

[0112] A third aspect of the present invention provides a computer-readable storage medium including a robot no-code programming method program based on event-driven and asset graph, wherein when the robot no-code programming method program based on event-driven and asset graph is executed by a processor, it implements the steps of the robot no-code programming method based on event-driven and asset graph as described above.

[0113] This invention discloses a no-code programming method, system, and medium for robots based on event-driven and asset graph approaches. It imports physical asset models into a 3D simulation environment, assigns predefined functional labels to each physical asset model, generates basic motion events for non-robot assets based on functional labels and configuration parameters, and generates composite events for robot assets based on process types and key work points defined in an interactive interface. An asset graph is constructed based on the asset's functional labels, assembly relationships in 3D space, and defined work space points. The asset graph is traversed, and logical control events are automatically generated in the event tree. The final event tree and logical relationships are compiled to generate underlying control code. By analyzing the graph relationships composed of asset types, spatial relationships, and work point identifiers, the logical dependencies between events are automatically derived and established, thus achieving efficient and intuitive no-code programming.

[0114] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0115] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0116] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0117] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0118] Alternatively, if the integrated units of the present invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A no-code programming method for robots based on event-driven computing and asset graphs, characterized in that, include: A 3D simulation environment is constructed, and physical asset models are imported into the 3D simulation environment. The physical asset models include at least one of robots, servo mechanisms, I / O devices, conveyor lines, and sensors. Each physical asset model is assigned a predefined functional label, which is used to limit the types of events that the corresponding physical asset model can generate. For non-robot assets, basic action events for the corresponding assets are automatically generated based on function tags and configuration parameters; for robot assets, composite events containing tool I / O actions are generated based on the process type and key work points defined in the interactive interface. Based on the asset's functional tags, assembly relationships in three-dimensional space, and defined work space points, an asset map describing the physical and functional dependencies between assets is automatically constructed. In response to user trigger commands, the asset map is traversed, the execution relationship and spatial coordination relationship between events are automatically analyzed, and logical control events are automatically generated in the event tree. By using preset tools to supplement, correct, or add complex logic to the automatically associated event logic, adapting it to specific process requirements, the final constructed event tree and logical relationships are compiled into underlying control code.

2. The event-driven and asset graph-based no-code robot programming method according to claim 1, characterized in that, Each physical asset model is assigned a predefined functional label, specifically including: Import the size parameters and layout coordinates of the real production line, calibrate the 3D simulation environment, and ensure that the spatial proportions and relative positions of the equipment in the 3D simulation environment are consistent with those in the real production line. Import the physical asset model, perform an integrity check on the physical asset model, and obtain the check results; If the verification result meets the set verification conditions, then assign a functional label to the physical asset model. If the test results do not meet the set verification conditions, it is determined that the physical asset model has missing components or abnormal parameters, and the missing components and abnormal parameters are corrected.

3. The event-driven and asset graph-based no-code robot programming method according to claim 2, characterized in that, The non-robot assets include cylinders and conveyor lines; the configuration parameters include stroke and speed; and the basic motion events include cylinder extension and conveyor line startup. The process types of the robot assets include handling, assembly, and palletizing / depalletizing; and the key work points include gripping point coordinate system and placement point coordinate system.

4. The no-code robot programming method based on event-driven and asset graph principles according to claim 3, characterized in that, The specific methods for constructing an asset map include: Extract the functional tag information for each physical asset model; Build a tag association index based on tag information; Based on the assembly relationship of physical asset models in three-dimensional space, physical dependency links between assets are generated. The operational space locations of each physical asset model are associated with the physical dependency links between assets; Based on the dependency relationship of the work space point completion function, the asset map is automatically constructed and the asset map is obtained.

5. The no-code robot programming method based on event-driven and asset graph principles according to claim 4, characterized in that, The logical control events include wait events and assignment events; When the asset map shows that the target point of a robot composite event is located in the working area of ​​any non-robot asset, the system automatically associates the robot composite event after the basic action event of the non-robot asset and inserts the corresponding waiting event before the robot composite event.

6. The no-code robot programming method based on event-driven and asset graph principles according to claim 5, characterized in that, The final event tree and logical relationships are compiled into the underlying control code, which then includes: Obtain the underlying control code and analyze whether there are any syntax errors or compatibility issues. If present, compilation will be automatically paused and error messages will be generated, along with the error locations and suggested modifications. Based on the error locations and suggested modifications, the underlying control code will be modified until the code compiles successfully. If it does not exist, the final underlying control code will be generated.

7. A no-code programming system for robots based on event-driven computing and asset graphs, characterized in that, The system includes a memory and a processor. The memory contains a program for a no-code robot programming method based on event-driven and asset mapping. When the program is executed by the processor, it performs the following steps: A 3D simulation environment is constructed, and physical asset models are imported into the 3D simulation environment. The physical asset models include at least one of robots, servo mechanisms, I / O devices, conveyor lines, and sensors. Each physical asset model is assigned a predefined functional label, which is used to limit the types of events that the corresponding physical asset model can generate. For non-robot assets, basic action events for the corresponding assets are automatically generated based on function tags and configuration parameters; for robot assets, composite events containing tool I / O actions are generated based on the process type and key work points defined in the interactive interface. Based on the asset's functional tags, assembly relationships in three-dimensional space, and defined work space points, an asset map describing the physical and functional dependencies between assets is automatically constructed. In response to user trigger commands, the asset map is traversed, the execution relationship and spatial coordination relationship between events are automatically analyzed, and logical control events are automatically generated in the event tree. By using preset tools to supplement, correct, or add complex logic to the automatically associated event logic, adapting it to specific process requirements, the final constructed event tree and logical relationships are compiled into underlying control code.

8. The event-driven and asset graph-based robot no-code programming system according to claim 7, characterized in that, Each physical asset model is assigned a predefined functional label, specifically including: Import the size parameters and layout coordinates of the real production line, calibrate the 3D simulation environment, and ensure that the spatial proportions and relative positions of the equipment in the 3D simulation environment are consistent with those in the real production line. Import the physical asset model, perform an integrity check on the physical asset model, and obtain the check results; If the verification result meets the set verification conditions, then assign a functional label to the physical asset model. If the test results do not meet the set verification conditions, it is determined that the physical asset model has missing components or abnormal parameters, and the missing components and abnormal parameters are corrected.

9. The event-driven and asset graph-based robot no-code programming system according to claim 8, characterized in that, The non-robot assets include cylinders and conveyor lines; the configuration parameters include stroke and speed; and the basic motion events include cylinder extension and conveyor line startup. The process types of the robot assets include handling, assembly, and palletizing / depalletizing; and the key work points include gripping point coordinate system and placement point coordinate system.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a robot no-code programming method program based on event-driven and asset graph, which, when executed by a processor, implements the steps of the robot no-code programming method based on event-driven and asset graph as described in any one of claims 1 to 6.

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