Three-dimensional waste cutting and boxing collaborative simulation method and system for nuclear facility decommissioning project

Abstract

Embodiments of the present application provide a method and system for three-dimensional waste cutting and packing simulation for nuclear facility decommissioning engineering. The method comprises obtaining three-dimensional model data, waste storage container data and cutting constraint data of a target nuclear facility; performing format conversion on the three-dimensional model data and the waste storage container data to generate target three-dimensional data and target container data; performing cutting and packing simulation based on a preset optimization engine, the target three-dimensional data, the target container data and the cutting constraint data to generate a simulation scheme; optimizing the simulation scheme based on a preset optimization parameter to generate a Pareto frontier solution set; determining a final selection result in response to user selection in the Pareto frontier solution set; and generating a cutting and packing simulation engineering report based on the final selection result. In this way, waste can be efficiently processed flexibly.

Description

Technical Field

[0001] The embodiments of this application relate to the field of nuclear decommissioning engineering, and more particularly to a method and system for three-dimensional waste cutting and packing co-simulation in nuclear facility decommissioning engineering. Background Technology

[0002] Nuclear decommissioning refers to the radioactive decontamination and site restoration actions taken after a nuclear facility has reached the end of its service life or ceased operation, in order to protect personnel health, environmental safety, and remove regulatory controls. The ultimate goal is to achieve limited or unrestricted access to the current site.

[0003] The decommissioning of nuclear facilities generates a large amount of radioactive waste that is huge and irregularly shaped, such as reactor pressure vessels, pipes, and heat exchangers. To facilitate transportation and final disposal, these large components must be cut into smaller waste pieces on-site and then packed into standard disposal containers. This makes the "cut-and-pack" process a critical part of the decommissioning project, and its efficiency directly affects the cost and timeline of the entire project.

[0004] Currently, planning the "cut-and-pack" process faces an inherent dilemma. On the one hand, to maximize the volume utilization of disposal containers and reduce the number of expensive containers used and the space occupied by the final disposal facility, it is generally desirable to cut large components into smaller, more regular shapes. Smaller, more regular waste blocks are easier to stack tightly, reducing gaps. On the other hand, each cutting operation consumes a significant amount of time and involves complex robot operations, tool changes, and safety verification. Therefore, excessive cutting drastically increases the total cutting time, leading to longer decommissioning cycles and increased costs.

[0005] Existing technologies typically employ a sequential, separate planning approach when addressing this problem: engineers or operators develop cutting plans based on experience, safety regulations, or simple geometric rules. This approach primarily ensures that the cut waste blocks can fit into containers, but rarely considers the overall packing effect of these waste blocks combined. In other words, a seemingly reasonable plan at the cutting stage may produce a set of waste blocks that are extremely difficult to pack efficiently, or the waste block sizes required for an ideal packing layout may necessitate a time-consuming and impractical cutting plan. Because the cutting and packing stages are interdependent, yet the planning process is fragmented, there is an urgent need for a technology that can flexibly and efficiently handle waste. Summary of the Invention

[0006] According to embodiments of this application, a method and system for collaborative simulation of three-dimensional waste cutting and packing in nuclear facility decommissioning projects are provided, which can flexibly and efficiently process waste.

[0007] In a first aspect of this application, a method for co-simulation of three-dimensional waste cutting and packing in nuclear facility decommissioning projects is provided. The method includes:

[0008] Acquire 3D model data of the target nuclear facility, waste storage container data, and cutting constraint data;

[0009] The format of the 3D model data and the waste storage container data is converted to generate target 3D data and target container data;

[0010] Based on the preset optimization engine, the target 3D data, the target container data, and the cutting constraint data, a cutting and packing co-simulation is performed to generate a simulation scheme;

[0011] The simulation scheme is optimized based on preset optimization parameters to generate a Pareto front solution set.

[0012] In response to the user's selection in the Pareto front solution set, the final selection result is determined;

[0013] A cutting and packing simulation engineering report is generated based on the final selection results.

[0014] In one possible implementation, the process of converting the format of the 3D model data and the waste storage container data to generate target 3D data and target container data includes:

[0015] Acquire the data type and target processing format of the 3D model data and the waste storage container data;

[0016] Based on the target processing format, the data type is converted to generate a conversion result.

[0017] In response to the user's adjustment operation on the conversion processing result, the position angle adjustment parameters are obtained;

[0018] The conversion processing result is adjusted based on the angle and position adjustment parameters to generate target 3D data and target container data.

[0019] In one possible implementation, the step of performing cut-and-pack co-simulation based on a preset optimization engine, the target 3D data, the target container data, and the cut constraint data to generate a simulation scheme includes:

[0020] Multiple candidate cutting schemes are generated based on the preset optimization engine, the target 3D data, and the cutting constraints.

[0021] Obtain the cutting parameters of the candidate cutting individual scheme;

[0022] Based on the cutting parameters and preset calculation formulas, a cutting simulation is performed to determine the cutting time for each candidate cutting individual scheme;

[0023] The candidate cutting individual schemes are processed into voxel segments to generate voxel cutting schemes;

[0024] Based on the target container data, the cutting parameters, and the preset packing algorithm, a packing simulation is performed to determine the space utilization rate of each voxel cutting scheme.

[0025] Multiple candidate cutting schemes are generated based on the cutting time and the space utilization rate.

[0026] In one possible implementation, determining the cutting time for each candidate cutting scheme by performing cutting simulation based on the cutting parameters and a preset calculation formula includes:

[0027] Obtain the cutting path direction of the candidate cutting individual scheme;

[0028] Based on the cutting parameters and the preset calculation formula, a cutting simulation is performed to determine the directional path length of each cutting path direction.

[0029] The total length of the cutting path is determined based on the cutting path direction and the length of the directional path.

[0030] Obtain the cutting speed during the cutting simulation;

[0031] The cutting time for each candidate cutting individual scheme is determined based on the total length of the cutting path and the cutting speed.

[0032] In one possible implementation, the step of performing bin packing simulation based on the target container data, the cutting parameters, and a preset bin packing algorithm to determine the space utilization of each voxel cutting scheme includes:

[0033] Collision detection is performed based on the preset bin packing algorithm to generate collision detection results;

[0034] Based on the collision detection results, the target container data, and the cutting parameters, a packing simulation is performed to generate a packing simulation result.

[0035] The container volume is calculated based on the packing simulation results, the target container data, and the cutting parameters.

[0036] The space utilization rate of each voxel cutting scheme is determined based on the cutting parameters and the volume occupied by the container.

[0037] In one possible implementation, optimizing the simulation scheme based on preset optimization parameters to generate a Pareto front solution set includes:

[0038] The crowding degree of each simulation scheme is calculated based on the cutting time and the space utilization rate;

[0039] The simulation schemes are selected based on the congestion level and the preset optimization parameters to obtain the parent scheme;

[0040] The parent scheme is used for mutation merging and screening to generate a new scheme result;

[0041] Based on the results of the new scheme and the preset optimization parameters, a Pareto front solution set is generated.

[0042] In one possible implementation, generating the cutting and packing simulation engineering report based on the final selection result includes:

[0043] Obtain the final simulation scheme corresponding to the final selection result and the scheme data corresponding to the final simulation scheme;

[0044] A cutting and packing simulation engineering report is generated based on the data from the proposed scheme.

[0045] In a second aspect of this application, a three-dimensional waste cutting and packing co-simulation system for nuclear facility decommissioning projects is provided. The system includes:

[0046] The relevant data acquisition module is used to acquire the three-dimensional model data of the target nuclear facility, the waste storage container data, and the cutting constraint data;

[0047] The data format conversion module is used to convert the format of the three-dimensional model data and the waste storage container data to generate target three-dimensional data and target container data.

[0048] The simulation scheme generation module is used to perform cutting and packing co-simulation based on a preset optimization engine, the target 3D data, the target container data, and the cutting constraint data to generate a simulation scheme.

[0049] The scheme solution set generation module is used to optimize the simulation scheme based on preset optimization parameters and generate a Pareto front solution set.

[0050] The final result determination module is used to determine the final selection result in response to the user's selection in the Pareto front solution set;

[0051] The engineering report generation module is used to generate a cutting and packing simulation engineering report based on the final selection result.

[0052] In a third aspect of this application, an electronic device is provided. The electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described above.

[0053] In a fourth aspect of this application, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the method according to the first aspect of this application.

[0054] The method and system for three-dimensional waste cutting and packing co-simulation in nuclear facility decommissioning projects provided in this application effectively solves the inherent problem of the separation between cutting planning and packing planning in nuclear facility decommissioning projects by constructing an integrated co-simulation optimization framework for cutting and packing. By automatically processing and optimizing the target three-dimensional model through a preset optimization engine, it can automatically find the optimal balance between cutting operation time and waste packing volume utilization rate and generate a Pareto front solution set representing the best balance relationship, thereby significantly improving planning efficiency and economic benefits. The Pareto solution set-based scheme selection improves the scientificity and intuitiveness of scheme execution, and the final generated engineering report ensures that the optimization results can directly guide the engineering implementation, forming a complete closed loop from virtual simulation to actual operation, realizing the need for flexible and efficient waste treatment, and significantly reducing the overall cost and project cycle of nuclear decommissioning projects.

[0055] It should be understood that the description in the Summary Section is not intended to limit the key or essential features of the embodiments of this application, nor is it intended to restrict the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0056] The above and other features, advantages, and aspects of the embodiments of this application will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0057] Figure 1 A flowchart of a three-dimensional waste cutting and packing co-simulation method for nuclear facility decommissioning projects, according to an embodiment of this application;

[0058] Figure 2 This is a block diagram of a three-dimensional waste cutting and packing co-simulation system for nuclear facility decommissioning projects, according to an embodiment of this application.

[0059] Figure 3 This is a schematic diagram of the structure of a terminal device or server suitable for implementing the embodiments of this application. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0061] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0062] Figure 1 A flowchart is shown for a three-dimensional waste cutting and packing co-simulation method for nuclear facility decommissioning projects, according to an embodiment of this disclosure.

[0063] like Figure 1 As shown, the main process of this method is described below (steps S101 to S106):

[0064] Step S101: Obtain the three-dimensional model data, waste storage container data, and cutting constraint data of the target nuclear facility.

[0065] In some embodiments, model data of the target nuclear facility that needs to be cut is collected, and the collected model data is used as three-dimensional model data. Similarly, model data of the container used to hold the cut target and facility is also collected, and the collected model data is used as waste storage container data. Both the three-dimensional model data and the waste storage container data are three-dimensional solid data. In addition, cutting constraint data also needs to be obtained to impose relevant constraints during simulated cutting. Cutting constraint data includes, but is not limited to, the upper and lower limits of axial cutting height, radial cutting number, and circumferential cutting number. Specific cutting constraint data can be added according to actual needs, and the specific content of cutting constraint data is not specifically limited here.

[0066] Step S102: Convert the format of the 3D model data and waste storage container data to generate target 3D data and target container data.

[0067] For step S102, the data types and target processing formats of the 3D model data and waste storage container data are obtained; the data types are converted based on the target processing format to generate conversion results; in response to the user's adjustment operation on the conversion results, position and angle adjustment parameters are obtained; the conversion results are adjusted based on the angle and position adjustment parameters to generate target 3D data and target container data.

[0068] In some embodiments, the data types of the 3D model data and waste storage container data are basically in CAD standard formats, such as STEP for precise geometry exchange, IGES for surface representation, and FBX for direct compatibility with UE5. These formats include not only the 3D geometry of the components but also key information such as topological connections, material properties, and radiation characteristics. The target processing format is a mesh format supported by UE5, which converts the CAD model to a UE5-supported mesh format through a dedicated geometry conversion pipeline. During the conversion process, the system automatically identifies the geometric features of the model and generates a multi-level detail representation suitable for real-time rendering, ensuring optimized rendering performance while maintaining visual quality. Furthermore, the waste storage container data is constructed as a parametric blueprint asset in UE5, with its internal space precisely defined through bounding box components, supporting dynamic size adjustment to adapt to different specification requirements. The processed 3D model data and waste storage container data are imported into the UE5 3D scene as the target objects for cutting and packing optimization. In the UE5 system architecture, complex 3D geometric models are converted into procedural mesh structures that support dynamic cutting through blueprint visualization scripts. Users can directly adjust their position and orientation using 3D manipulation tools. After the user completes the adjustment, the final target 3D data and target container data are obtained.

[0069] To facilitate retrieval and use, after the above processing, a JSON file containing this configuration information is generated. This JSON file includes detailed data such as geometric information, cutting parameter configurations, container specifications, and optimization settings. The JSON file is then parsed, and the data is categorized and stored according to a predetermined data structure, such as model data, parameter data, and result data, and synchronized to the database. In practical applications, the configuration information and model data in the database are used for co-simulation of cutting and packing. By matching parameter configurations and optimization history in the database, the system can quickly initiate new optimization tasks and ensure efficient and accurate identification of the optimal solution in complex nuclear decommissioning scenarios.

[0070] Step S103: Based on the preset optimization engine, target 3D data, target container data and cutting constraint data, perform cutting and packing co-simulation to generate a simulation scheme.

[0071] For step S103, multiple candidate cutting schemes are generated based on a preset optimization engine, target 3D data, and cutting constraints; cutting parameters of the candidate cutting schemes are obtained; cutting simulation is performed based on the cutting parameters and preset calculation formulas to determine the cutting time of each candidate cutting scheme; the candidate cutting schemes are processed into voxels to generate voxel cutting schemes; binning simulation is performed based on target container data, cutting parameters, and a preset binning algorithm to determine the space utilization of each voxel cutting scheme; multiple candidate cutting schemes are generated based on the cutting time and space utilization.

[0072] In some embodiments, during the initialization phase of the nuclear decommissioning engineering cutting and packing co-simulation system, relevant Actors need to be created in the BeginPlay event to achieve co-optimization of cutting and packing. The following is an introduction and function of the core Actors: BP_CuttingTargetActor: This Actor represents the large radioactive component to be processed. It encapsulates the model and cutting-related attributes. By loading STEP or FBX files, a precise geometric representation of the component can be obtained. BP_CuttingSchemeActor: Based on the parameters generated by the optimization algorithm, this Actor dynamically creates cutting schemes. It maintains a set of cutting planes and defines how to decompose the target component into blocks. By adjusting the cutting parameters, cutting schemes of different granularities can be generated. BP_PackingContainerActor: The Actor representation of a standard waste container, responsible for defining the spatial constraints of packing. It contains the container's size information and loading limits, providing boundary conditions for packing optimization. BP_OptimizationEngineActor: The core control Actor for multi-objective optimization, responsible for executing the NSGA-II algorithm, managing the population evolution process, coordinating cutting simulation and packing evaluation, and maintaining the Pareto front. By creating these Actors in the BeginPlay event, a complete collaborative optimization framework is established. The collaboration and data exchange between these Actors will drive the entire optimization process, ensuring that the system can efficiently and accurately find the optimal cutting and packing scheme.

[0073] Before starting the simulation, the user manually sets cutting constraints according to actual needs, such as axial cutting height ∈ [300, 800] mm, to ensure that the parameter combination can generate an engineering-feasible cutting scheme. Then, the preset optimization engine randomly generates an initial population based on the set cutting constraints, i.e., generates multiple candidate cutting schemes. Each candidate cutting scheme is an independent individual with a set of cutting parameters. Subsequently, cutting simulation and bin packing simulation are performed using the cutting parameters, preset calculation formulas, target container data, and preset bin packing algorithms to generate cutting time and space utilization. The cutting time, corresponding space utilization, and cutting parameters are then bound together to obtain multiple candidate cutting schemes.

[0074] Furthermore, based on the cutting parameters and preset calculation formulas, cutting simulation is performed to determine the cutting time of each candidate cutting scheme, including: obtaining the cutting path direction of the candidate cutting scheme; performing cutting simulation based on the cutting parameters and preset calculation formulas to determine the directional path length of each cutting path direction; determining the total cutting path length based on the cutting path direction and directional path length; obtaining the cutting speed during the cutting simulation; and determining the cutting time of each candidate cutting scheme based on the total cutting path length and cutting speed.

[0075] First, based on the current individual's cutting parameters, a cutting scheme is generated using a geometry engine. Axial cutting uses equally spaced horizontal planes to layer the component along its height. Radial cutting is evenly distributed outward from the central axis, forming a fan-shaped segmentation. Circumferential cutting generates concentric cylindrical surfaces at different radii. These cutting planes are then subjected to Boolean operations with the original model to generate independent cut blocks. The cutting time is calculated based on the total length of all cutting paths, taking into account the different complexities of straight-line and curved cutting. The formula for calculating the cutting time is:

[0076] ;

[0077] in, The total length of all cutting paths; This refers to the cutting speed.

[0078] The formula for calculating the total length of the cutting path is as follows:

[0079] ;

[0080] in, This represents the axial cutting path length. This represents the radial cutting path length. This represents the circumferential cutting path length. Therefore, the cutting time for each candidate cutting scheme is calculated based on the total cutting path length and the cutting speed.

[0081] Furthermore, based on the target container data, cutting parameters, and a preset packing algorithm, packing simulation is performed to determine the space utilization rate of each voxel cutting scheme. This includes: performing collision detection based on the preset packing algorithm and generating collision detection results; performing packing simulation based on the collision detection results, target container data, and cutting parameters and generating packing simulation results; calculating the container volume occupied based on the packing simulation results, target container data, and cutting parameters; and determining the space utilization rate of each voxel cutting scheme based on the cutting parameters and the container volume occupied.

[0082] After each cut, the generated blocks are voxelized. The voxelization process is implemented using a GPU-accelerated rasterization algorithm, which converts irregular 3D blocks into regular voxel meshes. The appropriate voxel resolution is automatically selected according to the accuracy requirements, achieving a balance between computational efficiency and representation accuracy. The voxel-based 3D bin packing algorithm adopts the innovative NFV (Nofit Voxel) technology. Due to the irregular shape of the blocks generated by cutting, the collision detection computation of traditional bin packing algorithms is huge. Considering the real-time requirements, a pre-computed NFV method is adopted. This method first calculates the NFV sets of all pairs of blocks with different shapes offline. These sets define all relative positions where two blocks do not collide. During bin packing, collisions can be quickly determined by looking up the table, reducing the complexity from O(n²) to O(1). The bin packing algorithm uses the Bottom-Left-Back heuristic to generate the initial solution, and then iteratively optimizes it through variable neighborhood search (VNS). The optimization process considers minimizing the container height and reducing the number of containers used by close arrangement, generating bin packing simulation results.

[0083] The container's occupied volume is calculated by combining the packing simulation results with the target container data and cutting parameters. The ratio of the total volume of the cut pieces to the container's occupied volume is then calculated, and this ratio represents the space utilization rate. The calculation formula is as follows:

[0084] ;

[0085] in, This represents the total volume of all the cut pieces; It is expressed as the volume of the container.

[0086] Step S104: Optimize the simulation scheme based on preset optimization parameters to generate the Pareto front solution set.

[0087] For step S104, the congestion degree of each simulation scheme is calculated based on the cutting time and space utilization; the simulation schemes are screened based on the congestion degree and preset optimization parameters to obtain the parent schemes; the parent schemes are used for mutation merging and screening to generate new scheme results; the Pareto front solution set is generated based on the new scheme results and preset optimization parameters.

[0088] In some embodiments, population diversity is maintained to avoid excessive concentration of solutions in local regions, thereby optimizing the simulation scheme. All individual simulation schemes are treated as a single entity for computation, and crowding is calculated for individuals at the same non-dominated level. Optimization parameters are pre-set during optimization, including the number of iterations, convergence criteria, and selection criteria used for mutation merging and screening. These parameters need to be set according to actual needs and will not be elaborated here. First, the crowding of individuals at the same non-dominated level is calculated based on cutting time and space utilization, i.e., the crowding of simulation schemes at the same non-dominated level. Crowding reflects the distribution density of individuals in the target space, and is calculated as follows: First, the crowding of all individuals at that level is initialized to 0; then, individuals are sorted in ascending order along both the cutting time and space utilization dimensions; for each target dimension, the crowding of individuals at the boundary positions (maximum and minimum values) after sorting is set to infinity to ensure extreme solutions are preserved; for a non-boundary individual i, its crowding contribution on target m is the difference between two adjacent individuals on that target divided by the value range of that target, i.e.:

[0089] ;

[0090] in and Individuals In the target Adjacent larger and smaller values, and The maximum and minimum values ​​for this objective at the current level are calculated; finally, the crowding contributions of each individual across all objective dimensions are summed to obtain the total crowding.

[0091] ;

[0092] Individuals with high crowding density are relatively isolated in the target space, with fewer surrounding solutions, and will be given priority in subsequent selection operations.

[0093] Since individuals have different non-dominated levels and the crowding degree of individuals in each non-dominated level has been calculated, a tournament selection mechanism is used to select the best individuals as parents. That is, the parent scheme is selected from the simulation scheme. Simulated binary crossover and polynomial mutation are performed on the selected parents to generate new offspring individuals. The parents and offspring are merged, and the best N individuals are retained through environmental selection to form a new population. It is determined whether the maximum number of iterations or the convergence criterion has been reached. If not, the simulation cutting step is returned to continue iterative processing until the parameter value set in the preset optimization parameters is reached, and the Pareto front solution set is generated.

[0094] Step S105: In response to the user's selection in the Pareto front solution set, determine the final selection result.

[0095] In some embodiments, the user selects a suitable solution based on actual needs on the Pareto front, the system loads and displays the corresponding detailed information, and after the user determines the required solution, the solution selected by the user is taken as the final selection result.

[0096] Step S106: Generate a cutting and packing simulation engineering report based on the final selection results.

[0097] For step S106, obtain the final simulation scheme corresponding to the final selection result and the scheme data corresponding to the final simulation scheme; generate a cutting and packing simulation engineering report based on the scheme data.

[0098] In some embodiments, after obtaining the final selection result selected by the user, the simulation scheme corresponding to the final selection result is called as the final simulation scheme, and the scheme data corresponding to the final simulation scheme is collected. Based on the scheme data, a complete cutting and packing simulation engineering report containing cutting parameters, a block list, and a packing layout diagram is generated. The cutting and packing simulation engineering report can be directly used to guide actual operations.

[0099] According to the embodiments of this disclosure, the following technical effects are achieved: By constructing an integrated collaborative simulation optimization framework for cutting and packing, the inherent problem of the separation between cutting planning and packing planning in nuclear facility decommissioning projects is effectively solved. By automatically processing and optimizing the target 3D model through a preset optimization engine, it can automatically find the optimal balance between cutting operation time and waste packing volume utilization rate and generate a Pareto front solution set representing the best balance relationship, thereby significantly improving planning efficiency and economic benefits. The Pareto solution set-based scheme selection improves the scientificity and intuitiveness of scheme execution, and the final generated engineering report ensures that the optimization results can directly guide the engineering implementation, forming a complete closed loop from virtual simulation to actual operation, realizing the need for flexible and efficient waste treatment, and significantly reducing the overall cost and project cycle of nuclear decommissioning projects.

[0100] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0101] The above is an introduction to the method embodiments. The following describes the solution of this application further through device embodiments.

[0102] Figure 2 A block diagram of a three-dimensional waste cutting and packing co-simulation system for nuclear facility decommissioning projects, according to an embodiment of this application, is shown. Figure 2 The following are included:

[0103] The relevant data acquisition module 201 is used to acquire the three-dimensional model data of the target nuclear facility, the waste storage container data, and the cutting constraint data;

[0104] The data format conversion module 202 is used to convert the format of the 3D model data and the waste storage container data to generate the target 3D data and the target container data.

[0105] The simulation scheme generation module 203 is used to perform collaborative simulation of cutting and packing based on a preset optimization engine, target 3D data, target container data and cutting constraint data, and generate a simulation scheme.

[0106] The solution set generation module 204 is used to optimize the simulation scheme based on preset optimization parameters and generate the Pareto front solution set.

[0107] The final result determination module 205 is used to determine the final selection result in response to the user's selection in the Pareto front solution set;

[0108] The engineering report generation module 206 is used to generate a cutting and packing simulation engineering report based on the final selection results.

[0109] As an optional implementation of this embodiment, the data format conversion module 202 is specifically used to acquire the data types and target processing formats of the three-dimensional model data and waste storage container data; convert the data types based on the target processing format to generate conversion processing results; in response to the user's adjustment operation on the conversion processing results, acquire position and angle adjustment parameters; adjust the conversion processing results based on the angle and position adjustment parameters to generate target three-dimensional data and target container data.

[0110] As an optional implementation of this embodiment, the simulation scheme generation module 203 includes:

[0111] The individual cutting scheme generation module is used to generate multiple candidate individual cutting schemes based on a preset optimization engine, target 3D data, and cutting constraints.

[0112] The cutting parameter acquisition module is used to acquire the cutting parameters of individual candidate cutting schemes;

[0113] The cutting time determination module is used to perform cutting simulation based on cutting parameters and preset calculation formulas to determine the cutting time of each candidate cutting scheme.

[0114] The voxel scheme generation module is used to perform block voxelization processing on candidate cutting individual schemes to generate voxel cutting schemes;

[0115] The space utilization determination module is used to perform packing simulation based on target container data, cutting parameters and preset packing algorithms to determine the space utilization rate of each voxel cutting scheme.

[0116] The cutting scheme generation module is used to generate multiple candidate cutting schemes based on cutting time and space utilization.

[0117] In this optional embodiment, the cutting duration determination module is specifically used to obtain the cutting path direction of the candidate cutting individual scheme; perform cutting simulation based on cutting parameters and preset calculation formulas to determine the directional path length of each cutting path direction; determine the total length of the cutting path based on the cutting path direction and the directional path length; obtain the cutting speed during the cutting simulation; and determine the cutting duration of each candidate cutting individual scheme based on the total length of the cutting path and the cutting speed.

[0118] In this optional embodiment, the space utilization determination module is specifically used to perform collision detection based on a preset packing algorithm and generate collision detection results; perform packing simulation based on the collision detection results, target container data and cutting parameters and generate packing simulation results; calculate the container occupied volume based on the packing simulation results, target container data and cutting parameters; and determine the space utilization rate of each voxel cutting scheme based on the cutting parameters and the container occupied volume.

[0119] As an optional implementation of this embodiment, the solution set generation module 204 is specifically used to calculate the congestion degree of each simulation solution based on the cutting time and space utilization; to filter the simulation solutions based on the congestion degree and preset optimization parameters to obtain the parent solution; to perform mutation merging and filtering processing on the parent solution to generate new solution results; and to generate the Pareto front solution set based on the new solution results and preset optimization parameters.

[0120] As an optional implementation of this embodiment, the engineering report generation module 206 is specifically used to obtain the final simulation scheme corresponding to the final selection result and the scheme data corresponding to the final simulation scheme; and to generate a cutting and packing simulation engineering report based on the scheme data.

[0121] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the described module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0122] Figure 3 A schematic diagram of a terminal device or server suitable for implementing embodiments of this application is shown.

[0123] like Figure 3As shown, the terminal device or server includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 302 or a program loaded from storage section 508 into random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the terminal device or server. The CPU 301, ROM 302, and RAM 303 are interconnected via bus 304. An input / output (I / O) interface 305 is also connected to bus 304.

[0124] The following components are connected to I / O interface 305: an input section 306 including a keyboard, mouse, etc.; an output section 307 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN card, modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to I / O interface 305 as needed. A removable medium 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 310 as needed so that computer programs read from it can be installed into storage section 308 as needed.

[0125] Specifically, according to embodiments of this application, the above method flow steps can be implemented as a computer software program. For example, embodiments of this application include a computer program product comprising a computer program carried on a machine-readable medium, the computer program containing program code for performing the methods shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the functions defined in the system of this application.

[0126] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0127] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0128] The units or modules described in the embodiments of this application can be implemented in software or hardware. The described units or modules can also be located in a processor. The names of these units or modules do not, in certain circumstances, constitute a limitation on the unit or module itself.

[0129] In another aspect, this application also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable storage medium stores one or more programs that, when used by one or more processors, execute the methods described in this application.

[0130] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A method for three-dimensional waste cutting and boxing co-simulation for nuclear facility decommissioning engineering, characterized in that, The method comprises the following steps: acquiring three-dimensional model data, waste storage container data and cutting constraint data of a target nuclear facility; format converting the three-dimensional model data and the waste storage container data to generate target three-dimensional data and target container data; performing cutting and packing co-simulation based on a preset optimization engine, the target three-dimensional data, the target container data and the cutting constraint data to generate a simulation scheme; optimizing candidate cutting individual schemes based on preset optimization parameters to generate a Pareto frontier solution set; determining a final selection result in response to user selection in the Pareto frontier solution set; generating a cutting and packing simulation engineering report based on the final selection result; the cutting and packing co-simulation based on the preset optimization engine, the target three-dimensional data, the target container data and the cutting constraint data to generate a simulation scheme comprises: generating a plurality of candidate cutting individual schemes based on the preset optimization engine, the target three-dimensional data and the cutting constraint; acquiring cutting parameters of the candidate cutting individual schemes; performing cutting simulation based on the cutting parameters and a preset operation formula to determine the cutting time length of each candidate cutting individual scheme; performing voxelization processing on the candidate cutting individual schemes to generate voxel cutting schemes; performing packing simulation based on the target container data, the cutting parameters and a preset packing algorithm to determine the space utilization rate of each voxel cutting scheme; generating a plurality of candidate cutting individual schemes based on the cutting time length and the space utilization rate.

2. The method of claim 1, wherein, the format conversion of the three-dimensional model data and the waste storage container data to generate target three-dimensional data and target container data comprises: acquiring the data types and target processing formats of the three-dimensional model data and the waste storage container data; performing conversion processing on the data types based on the target processing formats to generate a conversion processing result; acquiring position and angle adjustment parameters in response to user adjustment operations on the conversion processing result; adjusting the conversion processing result based on the angle and position adjustment parameters to generate target three-dimensional data and target container data.

3. The method of claim 1, wherein, the cutting simulation based on the cutting parameters and a preset operation formula to determine the cutting time length of each candidate cutting individual scheme comprises: acquiring cutting path directions of the candidate cutting individual schemes; performing cutting simulation based on the cutting parameters and the preset operation formula to determine the direction path length of each cutting path direction; determining the total length of the cutting path based on the cutting path direction and the direction path length; acquiring the cutting speed during cutting simulation; determining the cutting time length of each candidate cutting individual scheme based on the total length of the cutting path and the cutting speed.

4. The method of claim 1, wherein, the packing simulation based on the target container data, the cutting parameters and a preset packing algorithm to determine the space utilization rate of each voxel cutting scheme comprises: performing collision detection based on the preset packing algorithm to generate a collision detection result; performing packing simulation based on the collision detection result, the target container data and the cutting parameters to generate a packing simulation result; Calculate a container occupation volume based on the container loading simulation result, the target container data, and the cutting parameter; Determine a space utilization of each voxel cutting scheme based on the cutting parameter and the container occupation volume.

5. The method of claim 1, wherein, The optimization of the candidate cutting individual scheme based on the preset optimization parameter includes: Calculate a congestion degree of each simulation scheme based on the cutting duration and the space utilization; Screen the simulation scheme based on the congestion degree and the preset optimization parameter to obtain a parent scheme; Generate a new scheme result by using the parent scheme for mutation merging and screening processing; Generate a Pareto frontier solution set based on the new scheme result and the preset optimization parameter.

6. The method of claim 1, wherein, The generation of the cutting container loading simulation engineering report based on the final selection result includes: Obtain a final simulation scheme corresponding to the final selection result and scheme data corresponding to the final simulation scheme; Generate a cutting container loading simulation engineering report based on the scheme data.

7. A three-dimensional waste cutting and boxing co-simulation system for nuclear facility decommissioning engineering, characterized in that, It includes: The related data acquisition module is used for acquiring the three-dimensional model data, the waste storage container data and the cutting constraint data of the target nuclear facility; The data format conversion module is used for format conversion of the three-dimensional model data and the waste storage container data, and generation of target three-dimensional data and target container data; The simulation scheme generation module is used for cutting and loading simulation based on a preset optimization engine, the target three-dimensional data, the target container data and the cutting constraint data, and generation of a simulation scheme; The scheme solution set generation module is used for optimization of a candidate cutting individual scheme based on a preset optimization parameter, and generation of a Pareto frontier solution set; The final result determination module is used for determining a final selection result in response to user selection in the Pareto frontier solution set; The engineering report generation module is used for generating a cutting container loading simulation engineering report based on the final selection result; The simulation scheme generation module includes: The individual scheme generation module is used for generating a plurality of candidate cutting individual schemes based on a preset optimization engine, target three-dimensional data and cutting constraints; The cutting parameter acquisition module is used for acquiring the cutting parameter of the candidate cutting individual scheme; The cutting duration determination module is used for cutting simulation based on the cutting parameter and a preset operation formula to determine the cutting duration of each candidate cutting individual scheme; The voxel scheme generation module is used for cutting voxelization processing of the candidate cutting individual scheme to generate a voxel cutting scheme; The space utilization determination module is used for container loading simulation based on the target container data, the cutting parameter and a preset loading algorithm to determine the space utilization of each voxel cutting scheme; The cutting scheme generation module is used for generating a plurality of candidate cutting individual schemes based on the cutting duration and the space utilization.

8. An electronic device comprising a memory and a processor, said memory having stored thereon a computer program, characterized in that, The processor executes the computer program to realize the method of any one of claims 1-6.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the method of any one of claims 1-6.

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