Intelligent path planning simulation method and system for nuclear reactor decommissioning engineering equipment

Abstract

The invention discloses an intelligent path planning simulation method and system for nuclear reactor decommissioning engineering equipment. The method comprises the following steps: calling scene data and model data from a field database of a nuclear reactor decommissioning engineering; the model data comprises a structure model, a task equipment model and an operation limiting condition of the nuclear reactor; based on the scene data and the model data, assembling parameters of a path planning algorithm; the parameters comprise path safety requirements, path optimization targets and environment constraint conditions; calling a path planning algorithm based on the parameters; executing a path planning algorithm and outputting a result; and simulating an intelligent path of the dynamic decommissioning engineering equipment based on the result, and generating and outputting a UE animation. According to the method, a high-precision three-dimensional simulation model is established by deeply combining a simulation technology and combining decommissioning environment data of a nuclear reactor, a simulation platform supports multi-scene switching, different decommissioning process requirements are met, an optimal path verified through simulation can directly guide actual operation, and a clear navigation instruction is provided for equipment.

Description

Technical Field

[0001] The present invention relates to the field of nuclear decommissioning engineering, and particularly to an intelligent path planning simulation method and system for nuclear reactor decommissioning engineering equipment. Background Art

[0002] Nuclear decommissioning refers to the safe decommissioning of nuclear facilities, nuclear power plants or other nuclear-related sites from the operating state to eliminate the hazards of radioactive substances, restore the environment and ensure the safety of the surrounding areas. The nuclear reactor decommissioning project is a complex and challenging task, involving a large number of safety hazards and environmental impacts. In the traditional nuclear decommissioning process, workers face risks such as high radiation, complex structures and dangerous environments, which require careful design and construction to ensure safety and efficiency. Nowadays, advanced automation and robotic technologies are used to assist the nuclear decommissioning process and have become an important means to improve safety and efficiency. The application of these technologies can significantly reduce the exposure time of humans in high-radiation areas and reduce the risk of human errors through precise operations. These devices are usually equipped with advanced sensor technologies and intelligent path planning algorithms to ensure the accuracy and flexibility of operations. The introduction of intelligent path planning simulation technology further enhances the utility of these automated systems. By simulating path planning in the environment, the system can efficiently navigate in complex spaces, avoid obstacles and optimize the operation path. This not only improves the speed of task completion but also reduces equipment wear and energy consumption. The simulation method allows different strategies to be tested and optimized in a virtual environment, reducing risks and uncertainties in actual operations.

[0003] Therefore, how to design a method to improve the safety, efficiency and cost-effectiveness of the decommissioning process is a technical problem to be solved. Summary of the Invention

[0004] Based on this, it is necessary to provide an intelligent path planning simulation method and system for nuclear reactor decommissioning engineering equipment in view of the problems of the prior art.

[0005] In a first aspect, an embodiment of the present application provides an intelligent path planning simulation method for nuclear reactor decommissioning engineering equipment, including the following steps: S1: Call scenario data and model data from the on-site database of the nuclear reactor decommissioning project; the scenario data includes a three-dimensional map of the interior of the nuclear reactor, the radiation distribution, the equipment location and known obstacles; the model data includes the structural model of the nuclear reactor, the task equipment model and the operation restriction conditions; S2: Based on the scenario data and model data, assemble the algorithm parameters required for the path planning algorithm; the algorithm parameters include path safety requirements, path optimization objectives and environmental constraint conditions; S3: Call the pre-stored path planning algorithm based on the algorithm parameters; S4: Execute the path planning algorithm and output the algorithm result, where the algorithm result includes the data information of the optimal path; S5: Based on the algorithm result, simulate the intelligent path of the dynamic nuclear reactor decommissioning engineering equipment, generate a UE animation and output it for display.

[0006] Preferably, the on-site database is obtained in the following manner: Construct a model file based on the nuclear facility parameters of the nuclear reactor decommissioning project; Import the model file into the UE5 engine for robot joint configuration to obtain joint configuration data; Perform data processing on the model file and the joint configuration data and save them to generate an on-site database.

[0007] Preferably, performing data processing on the model file and the joint configuration data includes the following steps: [[ID=..]] Parse the Json files of the model file and the joint configuration data to obtain intermediate data; Classify and bind the intermediate data according to a preset coding format to obtain the processed model file and joint configuration data.

[0008] Preferably, the data information of the optimal path includes the starting point, ending point, key nodes passed, path length, estimated time, and possible dangerous areas of the path.

[0009] Preferably, executing the path planning algorithm includes the following steps Obtain the initial state data of the robot, where the initial state data includes the position, attitude, and motion parameters at the initial moment; Select a first target point and call the XYZ gantry three-dimensional path planning algorithm to send the robot to the first target point; Obtain the state data of the robot at the first target point to obtain the first state data of the robot; Select a second target point and call the XYZ gantry three-dimensional path planning algorithm to send the robot to the second target point; The robot grabs the engineering equipment at the second target point and returns to the initial state; Select a third target point and call the XYZ gantry three-dimensional path planning algorithm to send the robot to the third target point; Select a fourth target point and call the XYZ gantry three-dimensional path planning algorithm to send the robot to the fourth target point; The robot places the engineering equipment into the recycling bin at the fourth target point, and the robot returns to the initial state.

[0010] Preferably, the third target point is a position near the recycling bin of the engineering equipment.

[0011] Preferably, the fourth target point is the position where the recycling bin of the engineering equipment is located.

[0012] Preferably, the XYZ gantry three-dimensional path planning algorithm is an algorithm based on the combination of the three-dimensional Manhattan distance algorithm and the improved A* algorithm; The improved A* algorithm has the following cost function: (1); Wherein, is the cost required to reach the target point from the starting point through the node , is the cumulative actual cost from the starting point to the current point, is the estimated cost from the current point to the target point; The cumulative actual cost is represented by the following formula: (2); Wherein, is the length adjustment factor, is the movement distance, is the movement speed, and are respectively the average nuclear dose rates of the grids and the grid ; The estimated cost is represented by the following formula: (3); Wherein, ([[]] , ) is the coordinate of the current point grid N, ([[]] ) are the coordinates of the target point grid M respectively; is the minimum movement distance; The calculation formula of the three-dimensional Manhattan distance algorithm is as follows: (4); Wherein, ([[]] ) is the three-dimensional coordinate of the robotic arm at the current point, ([[]] ) is the three-dimensional coordinate of the robotic arm at the target point, .

[0013] In a second aspect, an automatic grading system for computer exams provided by an embodiment of the present application includes: A data acquisition module for calling scenario data and model data from the on-site database of the nuclear reactor decommissioning project; the scenario data includes a three-dimensional map inside the nuclear reactor, radiation distribution, equipment locations, and known obstacles; the model data includes the structural model of the nuclear reactor, task equipment models, and operation limit conditions. A parameter acquisition module for assembling the algorithm parameters required for the path planning algorithm based on the scenario data and model data; the algorithm parameters include path safety requirements, path optimization objectives, and environmental constraint conditions. An algorithm calling module for calling a pre-stored path planning algorithm based on the algorithm parameters. An algorithm execution module for executing the path planning algorithm and outputting the algorithm result, where the algorithm result includes the data information of the optimal path. An animation generation module for simulating the intelligent path of the nuclear reactor decommissioning project equipment dynamically based on the algorithm result, generating a UE animation and outputting it.

[0014] Compared with the prior art, the present invention has the following beneficial effects: (1) High degree of automation: The present invention realizes the full-process automation of nuclear facility decommissioning projects from obstacle recognition, path planning to simulation animation generation by integrating three-dimensional modeling, path planning, and simulation technologies. Compared with traditional manual operations, it significantly reduces manual intervention and improves operation efficiency and accuracy.

[0015] (2) Intelligent path planning: The present invention realizes intelligent path planning based on the nuclear decommissioning environment by combining the computing power of the path planning algorithm and the three-dimensional simulation environment of UE5. The system can generate the optimal path according to the actual scenario and automatically adjust the movement trajectory of the robotic arm to ensure the efficiency and safety of path planning.

[0016] (3) Optimized user experience: The system provides an intuitive front-end interface, and users can complete complex path simulation tasks through simple interface operations. The automated simulation, emulation, and result display functions greatly simplify the user's operation process and improve the user experience.

[0017] (4) High precision and high reliability: The present invention can achieve high-precision path planning and simulation calculations. At the same time, the structural design and data management of the system ensure the high reliability of operations and reduce errors that may be caused by manual operations.

[0018] (5) Strong scalability: The present invention adopts a modular design and a plug-in architecture, making the system have strong scalability. Users can flexibly add or modify system function modules according to the requirements of different nuclear decommissioning scenarios to meet the needs of nuclear decommissioning projects with different complexities. Description of the Drawings

[0019] The exemplary embodiments of the present invention can be more fully understood by referring to the following drawings. The drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. They are used together with the embodiments of the present application to explain the present invention and do not constitute a limitation to the present invention. In the drawings, the same reference numerals generally represent the same components or steps.

[0020] Figure 1 It is a flowchart of an intelligent path planning simulation method for equipment in a nuclear reactor decommissioning project provided by an embodiment of the present application; Figure 2 It is a schematic diagram of an intelligent path planning simulation system for equipment in a nuclear reactor decommissioning project provided by an embodiment of the present application. Detailed embodiments

[0021] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0022] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings. They are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present invention. In addition, the terms "first", "second", "third" are only used for descriptive purposes and should not be construed as indicating or implying relative importance.

[0023] In addition, the technical features involved in different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0024] Embodiment 1 Referring to Figure 1 , this embodiment discloses an intelligent path planning simulation method for equipment in a nuclear reactor decommissioning project, including the following steps: S1: Call the scenario data and model data from the on-site database of the nuclear reactor decommissioning project; the scenario data includes the three-dimensional map inside the nuclear reactor, the radiation distribution, the equipment location, and the known obstacles; the model data includes the structural model of the nuclear reactor, the task equipment model, and the operation limit conditions; Specifically, the on-site database is obtained in the following manner: Construct a model file based on the nuclear facility parameters of the nuclear reactor decommissioning project; Import the model file into the UE5 engine for robot joint configuration to obtain joint configuration data; Process and save the model file and the joint configuration data to generate a site database.

[0025] Specifically, the model data includes the structural model of the nuclear reactor, the task equipment model, and the operation limit conditions. The invocation of these data provides the necessary input basis for the subsequent path planning algorithm.

[0026] In practical applications, the system uses the robot model and joint configuration data in the database for path planning and motion control. By matching the joint configuration and model data in the database, the system can generate accurate motion instructions and ensure that the robot performs tasks safely and accurately in complex nuclear decommissioning scenarios.

[0027] Specifically, the nuclear facility parameters include geometric information such as the shape, length, width, height, and radius of the facility, the size and location of the radiation field, and the radiation distribution.

[0028] S2: Assemble the algorithm parameters required for the path planning algorithm based on the scenario data and model data; the algorithm parameters include path safety requirements, path optimization objectives, and environmental constraint conditions; After obtaining the scenario data and model data, the system begins to assemble the parameters required for the path planning algorithm. These parameters include, but are not limited to: Path safety requirements: such as radiation dose limits, avoidance of dangerous areas, etc.; Path optimization objectives: such as the shortest path, minimum energy consumption, least time, etc.; Environmental constraint conditions: such as equipment size limits, channel width limits, etc. By assembling these parameters, the system can provide clear calculation objectives and constraint conditions for the path planning algorithm.

[0029] S3: Invoke the pre-stored path planning algorithm based on the algorithm parameters; After completing the parameter assembly, the system begins to invoke the path planning algorithm.

[0030] S4: Execute the path planning algorithm and output the algorithm result, where the algorithm result includes the data information of the optimal path; After the path planning algorithm is executed, the system will output the calculation result. The result includes the detailed information of the generated optimal path, such as the starting point, ending point, key nodes passed, path length, estimated time, and possible dangerous areas of the path.

[0031] Specifically, executing the path planning algorithm includes the following steps: Obtain the initial state data of the robot, where the initial state data includes the position, attitude, and motion parameters at the initial moment; Select the first target point and call the XYZ gantry three-dimensional path planning algorithm to send the robot to the first target point; Obtain the status data of the robot at the first target point to obtain the first status data of the robot; Select the second target point and call the XYZ gantry three-dimensional path planning algorithm to send the robot to the second target point; The robot grabs the engineering equipment at the second target point and returns to the initial state; Select the third target point and call the XYZ gantry three-dimensional path planning algorithm to send the robot to the third target point; Select the fourth target point and call the XYZ gantry three-dimensional path planning algorithm to send the robot to the fourth target point; The robot places the engineering equipment into the recycling bin at the fourth target point, and the robot returns to the initial state.

[0032] The above entire process completes a set of grasping and placing operations.

[0033] In this embodiment, the movement path of the robotic arm is controlled by the XYZ gantry three-dimensional path planning algorithm, and is responsible for moving the robotic arm from the initial position to the position where the item is located. The XYZ algorithm includes an improved A* algorithm. In the path planning of nuclear decommissioning robots, traditional A* and RRT algorithms are difficult to meet the actual requirements due to problems such as large three-dimensional space calculation burden and non-smooth paths. Therefore, combined with the structural characteristics of nuclear decommissioning robots and the radiation field environment, an improved path planning method is proposed. The nuclear decommissioning robot adopts a gantry structure, can move stably in the X-Y plane, and accurately position on the Z axis through the telescopic arm, so that its movement can be decomposed into independent linear movements in the X, Y, and Z directions, thus significantly enhancing the path stability and control accuracy. For this structure, the three-dimensional Manhattan distance algorithm is combined with the improved A* algorithm, showing significant advantages. The three-dimensional Manhattan distance algorithm simplifies path planning into linear movements in the X, Y, and Z axis directions. The path consists of straight line segments aligned with the coordinate axes, which fits the segmented movement characteristics of the robot. At the same time, it uses a distance calculation method to limit the movement vector to be parallel to the coordinate axes, significantly reducing the calculation amount and being suitable for real-time applications. The improved A* algorithm uses the cumulative radiation dose received by personnel as the cost value, combines the non-uniformity of the radiation field, the distribution of obstacles, and the radiation absorption dose, and uses an 8-way search method to perform path planning with the lowest radiation dose as the goal. Combining the advantages of the two algorithms, this method can quickly plan a smooth path with the lowest dose, effectively meeting the path planning requirements of nuclear decommissioning robots in complex radiation environments.

[0034] The improved A* algorithm has the following cost function: (1); Among them, is the cost required to reach the target point from the starting point through the node and reach the target point. is the cumulative actual cost from the starting point to the current point, is the estimated cost from the current point to the target point; The cumulative actual cost is represented by the following formula: (2); Among them, is the length adjustment factor, is the movement distance, is the movement speed, and are respectively the grille and the grille average nuclear dose rate; The estimated cost is represented by the following formula: (3); Among them, ( , ) is the coordinate of the current point grille N, ([[]] ) are respectively the coordinates of the target point grille M; is the minimum movement distance; The calculation formula of the three-dimensional Manhattan distance algorithm is as follows: (4); Among them, ([[]] ) is the three-dimensional coordinate of the robotic arm at the current point, ([[]] ) is the three-dimensional coordinate of the robotic arm at the target point, .

[0035] S5: Based on the algorithm results, simulate the intelligent path of the dynamic nuclear reactor decommissioning engineering equipment, generate UE animations and output for display.

[0036] Through visual display, engineers can intuitively understand the details of path planning and the operation process. The animation effect shows the path, speed and time of equipment movement, helping engineers better control and operate.

[0037] The core technology of the present invention lies in the deep integration of outstanding achievements in the fields of robotics, automation, and computer simulation. By integrating robotic kinematics, path planning optimization algorithms, and virtual simulation technology, it provides intelligent and visual support for the execution of complex tasks in the nuclear reactor decommissioning environment. Based on robotic kinematics, the algorithm can accurately describe the motion capabilities and limitations of equipment. Combining with the path planning optimization algorithm, it designs the optimal path to meet the requirements of efficiency and safety. With the help of advanced computer simulation technology, the system can perform high-fidelity modeling of the three-dimensional decommissioning scenario of the nuclear reactor, simulate the operation process of equipment in an environment with distributed obstacles and radiation, thereby analyzing and optimizing the path planning scheme to ensure that the planning results are still efficient and reliable under complex conditions. In addition, through multiple iterative tests of the simulation scenario, the system can verify the applicability of different path planning schemes and discover potential problems, providing data support and optimization suggestions for actual deployment. This comprehensive method integrating robotics and simulation analysis not only significantly improves the accuracy and flexibility of path planning but also provides a new visual and intelligent solution for equipment operation in the nuclear reactor decommissioning project.

[0038] The intelligent path planning simulation method for equipment in the nuclear reactor decommissioning project of the present invention significantly improves the navigation efficiency and operation safety of equipment in the nuclear reactor decommissioning project. By integrating robotics and computer simulation technology, this method not only reduces the dependence on manual participation and lowers safety risks but also provides an innovative and advanced solution for the nuclear decommissioning project, with broad application prospects and profound social significance.

[0039] Embodiment 2 Refer to Figure 2 , this embodiment discloses an intelligent path planning simulation system 20 for equipment in a nuclear reactor decommissioning project, including: A data acquisition module 201, which is used to call scenario data and model data from the on-site database of the nuclear reactor decommissioning project; the scenario data includes the three-dimensional map inside the nuclear reactor, radiation distribution, equipment location, and known obstacles; the model data includes the structural model of the nuclear reactor, task equipment model, and operation limit conditions; In this embodiment, in the nuclear facility decommissioning path planning system, the modeling and configuration of the robotic arm are one of the key steps. The robot model is usually a three-dimensional model file designed by CAD software (such as STEP, IGES, FBX, etc. formats), and these model files contain the geometric structure, material information, etc. of each part of the robot.

[0040] Before import, these model files need to be converted into a format suitable for the UE5 engine and system database through a dedicated conversion tool or plugin (for example, converted into a static mesh or skeletal mesh format supported by UE5).

[0041] Import the pre - processed robot model into UE5 as the 3D assets of the system.

[0042] In UE5, use the BP_RobotArmActor blueprint to add each part of the model (such as the robotic arm and joints) as components to build the structure of the robotic arm. In the system, the joint configuration is implemented through a tree structure. Each joint serves as a node of the tree, and the hierarchical relationship between the nodes corresponds to the joint dependency relationship (parent - child relationship) in the actual robot. This configuration method not only helps with kinematic control but also simplifies the implementation of path - planning algorithms.

[0043] Specifically, in the BP_RobotArmActor blueprint, build the tree structure of the robotic arm by adding components to make it consistent with the hierarchical structure in the algorithm. To ensure the accuracy of kinematics, the system also ensures that the Mesh (i.e., the actual model of the node) is consistent with the corresponding Joint position and rotation. In UE5, the Mesh represents the actual model part of the joint. Bind each Mesh to the corresponding Joint (joint) to make its position and rotation consistent with the Joint. This ensures that when the robot moves, the visual effect is consistent with the actual movement. Thus, precise motion control and path planning of the robotic arm are achieved in a 3D environment. This configuration process ensures the consistency and high precision of the robotic arm in virtual simulation and actual operation.

[0044] After completing the robot model and joint configuration, the system generates a Json file containing this configuration information. The Json file includes details such as the position information, rotation angle, and parent - child relationship of each joint. The system parses the Json file and classifies and binds this data (such as joint data, kinematic data, etc.) according to a predetermined coding specification and synchronizes it to the database.

[0045] The parameter acquisition module 202 is used to assemble the algorithm parameters required for the path - planning algorithm based on the scene data and model data; the algorithm parameters include path safety requirements, path optimization objectives, and environmental constraint conditions; The algorithm call module 203 is used to call the pre - stored path - planning algorithm based on the algorithm parameters; In this embodiment, in the path planning of the nuclear decommissioning engineering software, we need to create relevant Actors (objects) in the BeginPlay event to implement the grasping and moving operations of the robotic arm. The following is an introduction and function of the above three Actors: BP_RobotArmSourceActor: This Actor represents the item to be grasped, and it can simulate the materials or equipment to be processed in the nuclear decommissioning project. By setting the position and attributes of BP_RobotArmSourceActor, we can define the target to be grasped. BP_RobotArmTargetActor: After grasping the target, the robotic arm needs to be moved to a specified position for operation.

[0046] BP_RobotArmTargetActor represents the grasped target position, which defines the final target position and orientation of the robotic arm. By setting the position and rotation angle of BP_RobotArmTargetActor, we can accurately specify the movement path of the robotic arm.

[0047] BP_RobotArmActor: This is the Actor of the robotic arm itself, responsible for simulating the movement and grasping operation of the robotic arm. BP_RobotArmActor is closely related to the previous two Actors, and it performs path planning and control based on the positions and attributes of BP_RobotArmSourceActor and BP_RobotArmTargetActor. By adjusting the joint positions and rotation angles of BP_RobotArmActor, we can achieve precise grasping and movement of the robotic arm.

[0048] By creating these Actors in the BeginPlay event, we can simulate the grasping and movement tasks in the nuclear decommissioning project. The cooperation and interaction among these Actors will provide a basis for path planning and optimization, ensuring that the robotic arm can complete the tasks efficiently and accurately.

[0049] The algorithm execution module 204 is used to execute the path planning algorithm and output the algorithm result, and the algorithm result includes the data information of the optimal path; The animation generation module 205 is used to simulate the intelligent path of the dynamic nuclear reactor decommissioning project equipment based on the algorithm result, generate a UE animation and output it.

[0050] In this embodiment, before starting the simulation, the user can manually or automatically move the item (represented by BP_RobotArmSourceActor) and the target position (represented by BP_RobotArmTargetActor) to the specified positions through the front-end interface.

[0051] After the user clicks the "Simulate" button, the front - end interface sends the OnSimulateRobotArm message to the back - end. After receiving this message, the BP_RobotArmRailActor (rail Actor) starts to execute the simulation. The rail Actor controls the movement path of the robotic arm and is responsible for moving the robotic arm from the initial position to the position where the item is located.

[0052] The user can also click the "Reset" button, and the system will reset the simulation state to the initial state for a new simulation.

[0053] In each frame, the BP_RobotArmRailActor updates the X, Y, and Z axis positions of the rail, moving the robotic arm to the item position. When the robotic arm starts to move, the system sets the current simulation mode to "MoveToSource", indicating that the robotic arm is moving towards the item position.

[0054] When the robotic arm reaches the item position, the system constructs the input data required for the robotic arm path - planning algorithm through NPRobotArmSystem and saves this data to the Content / RobotArm / input.json file. Subsequently, the system calls a program to perform calculations, generates calculation results, and saves them to the output.json file. At the same time, the system sets the current simulation state to "CaculateDrag", indicating that the calculation of the grasping action is in progress.

[0055] After the calculation is completed, when the user clicks the "Simulate" button again, the system will load the calculation results in the output.json file through the UNPRobotArmSystem::LoadOutputFile function. The BP_RobotArmActor (robotic arm Actor) performs an animation simulation of the grasping process based on the loaded animation data and sets the current simulation state to "Draging".

[0056] After the grasping animation simulation is completed, the user can click the "Simulate" button again, and the system will enter the placement operation process. Similar to the grasping operation, the rail first moves the robotic arm to the target position, and then calculates the animation data of the placement operation through Matlab. The system generates and loads new animation data, and the robotic arm performs an animation simulation of the placement action.

[0057] Through the above steps, the system realizes the complete path simulation of the robotic arm from item grasping to target placement. This process integrates multiple links such as the front-end interface, track control, path planning calculation, and animation simulation, ensuring that the system can operate and simulate efficiently and accurately in complex nuclear facility decommissioning scenarios. Users can view the whole process of grasping and placement step by step by clicking the "simulation" button multiple times, improving the usability and user experience of the system.

[0058] The system provided by the embodiments of the present application can implement the above method, and the system can be implemented in a software, hardware, or a combination of software and hardware manner. For example, the system can include integrated or separate functional modules or units to execute the corresponding steps in the above various methods. In some embodiments of the embodiments of the present application, the system provided by the embodiments of the present application and the method provided by the foregoing embodiments of the present application are based on the same inventive concept and have the same beneficial effects. Details are not described herein again.

[0059] It should be noted that the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of systems, methods, and computer program products according to multiple embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0060] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments, and details are not described herein again.

[0061] In 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 the units is only a logical function division. In actual implementation, there may be other division methods. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some communication interfaces. The indirect coupling or communication connection of the devices or units can be in electrical, mechanical or other forms.

[0062] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0063] In addition, in each embodiment of this application, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0064] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of this application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0065] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of this application, rather than to limit it; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of each embodiment of this application, and they should all be covered by the scope of the claims and the description of this application.

Claims

1. An intelligent path planning simulation method for nuclear reactor decommissioning engineering equipment, characterized in that, It includes the following steps: S1: Call the scenario data and model data from the on-site database of the nuclear reactor decommissioning project; the scenario data includes the three-dimensional map inside the nuclear reactor, the radiation distribution, the equipment location, and the known obstacles; the model data includes the structural model of the nuclear reactor, the task equipment model, and the operation restriction conditions; S2: Based on the scenario data and model data, assemble the algorithm parameters required for the path planning algorithm; the algorithm parameters include the path safety requirements, the path optimization objectives, and the environmental constraint conditions; S3: Call the pre-stored path planning algorithm based on the algorithm parameters; S4: Execute the path planning algorithm and output the algorithm result, and the algorithm result includes the data information of the optimal path; S5: Based on the algorithm result, simulate the intelligent path of the dynamic nuclear reactor decommissioning project equipment, generate a UE animation and output it for display.

2. The method according to claim 1, wherein The on-site database is obtained in the following way: Construct a model file based on the nuclear facility parameters of the nuclear reactor decommissioning project; Import the model file into the UE5 engine for robot joint configuration to obtain joint configuration data; Perform data processing on the model file and the joint configuration data and save them to generate an on-site database.

3. The method according to claim 2, characterized in that, Performing data processing on the model file and the joint configuration data includes the following steps: Parse the Json files of the model file and the joint configuration data to obtain intermediate data; Classify and bind the intermediate data according to a preset encoding format to obtain the processed model file and joint configuration data.

4. The method according to claim 3, wherein The data information of the optimal path includes the starting point, ending point, key nodes passed, path length, estimated time, and possible dangerous areas of the path.

5. The method according to claim 4, characterized in that, Executing the path planning algorithm includes the following steps: Obtain the initial state data of the robot, and the initial state data includes the position, attitude, and motion parameters at the initial moment; Select a first target point and call the XYZ gantry three-dimensional path planning algorithm to send the robot to the first target point; Obtain the state data of the robot at the first target point to obtain the first state data of the robot; Select a second target point and call the XYZ gantry three-dimensional path planning algorithm to send the robot to the second target point; The robot grabs the engineering equipment at the second target point and returns to the initial state; Select a third target point and call the XYZ gantry three-dimensional path planning algorithm to send the robot to the third target point; Select a fourth target point and call the XYZ gantry three-dimensional path planning algorithm to send the robot to the fourth target point; The robot places the engineering equipment in the recycling bin at the fourth target point, and the robot returns to the initial state.

6. The method according to claim 5, wherein The third target point is a position near the recycling bin of the engineering equipment.

7. The method according to claim 6, characterized in that, The fourth target point is the location of the recycling bin of the engineering equipment.

8. The method according to claim 7, wherein The XYZ gantry three-dimensional path planning algorithm is an algorithm based on the combination of the three-dimensional Manhattan distance algorithm and the improved A* algorithm; The improved A* algorithm has the following cost function: （1）； Among them, is the cost required to reach the target point from the starting point through the node and is the cumulative actual cost from the starting point to the current point, while is the estimated cost from the current point to the target point; Accumulated actual cost It is expressed by the following formula: （2）； Among them, is the length adjustment factor, is the movement distance, is the movement speed, and are respectively the average nuclear dose rates of the grille and the grille ; Estimated cost It is represented by the following formula: （3）； Among them, ( , ) are the coordinates of the current point grid N, and ( ) are the coordinates of the target point grid M respectively; is the minimum movement distance; The calculation formula of the three-dimensional Manhattan distance algorithm is as follows: （4）； Among them, ( ) is the three-dimensional coordinate of the robotic arm at the current point, ( ) is the three-dimensional coordinate of the robotic arm at the target point, .

9. An intelligent path planning simulation system for nuclear reactor decommissioning engineering equipment, characterized in that, It includes: A data acquisition module for calling scenario data and model data from the on-site database of the nuclear reactor decommissioning project; the scenario data includes a three-dimensional map inside the nuclear reactor, radiation distribution, equipment locations, and known obstacles; the model data includes the structural model of the nuclear reactor, task equipment models, and operation limit conditions; A parameter acquisition module for assembling algorithm parameters required for the path planning algorithm based on the scenario data and model data; the algorithm parameters include path safety requirements, path optimization objectives, and environmental constraint conditions; An algorithm call module for calling a pre-stored path planning algorithm based on the algorithm parameters; An algorithm execution module for executing the path planning algorithm and outputting algorithm results, where the algorithm results include data information on the optimal path; An animation generation module for simulating the intelligent path of the nuclear reactor decommissioning project equipment dynamically based on the algorithm results, generating a UE animation and outputting it.

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