User planning path action accessibility analysis method based on digital twinborn model

Abstract

The invention discloses a user planning path action accessibility analysis method based on a digital twinborn model, which comprises the following steps of: constructing a digital twinborn model library which comprises a product digital twinborn model and a maintenance personnel digital twinborn model; establishing a character inherent action database, wherein the character inherent action database comprises different maintenance personnel operation action data corresponding to maintenance task types; according to the product digital twinborn model, the maintenance personnel digital twinborn model and the character inherent action database, fusing reachable space simulation of a user specified path and inherent actions; and when the path specified by the user and the corresponding action are reachable, calculating reachability related data according to a simulation result. The method is based on a digital twinborn technology, adopts a model algorithm to carry out quantitative space analysis, and completes iteration of design, simulation and optimization in a maintenance support characteristic design stage in a low-cost and high-efficiency manner.

Description

Technical Field

[0001] This invention relates to the field of digital twin technology, and more specifically to a method for analyzing the reachability of user-planned path actions based on a digital twin model. Background Technology

[0002] Currently, the development of digital twin technology has made it possible to construct high-fidelity virtual maintenance scenarios. If typical human action data and user-planned paths can be integrated into the digital twin environment for reachability space analysis, the practicality of the analysis results can be significantly improved, providing more precise guidance for product maintenance feature design.

[0003] During the product design phase, maintainability design directly determines the maintenance efficiency and cost throughout the product's entire lifecycle. Accessibility analysis, as a core component of maintainability design, aims to ensure that maintenance personnel can easily access and complete maintenance tasks. Current accessibility analysis methods primarily rely on static geometric space calculations, such as checking maintenance access dimensions based on CAD models, or performing calculations and simulations using simple human models and range of motion.

[0004] However, current technologies perform accessibility analysis based on static model space calculations, which inevitably leads to insufficient consideration of typical actions, spatial constraints, path priority factors, and other aspects in actual maintenance, as well as incomplete calculation factors, resulting in biased results and making it difficult to use for product maintenance and support feature design.

[0005] These methods have the following shortcomings: 1. Deviates from actual movement characteristics: The simulation results do not fully consider the "typical movements" formed by maintenance personnel due to their physiological structure and operating habits, such as the natural range of motion of the arm, the limit of joint movement, and typical maintenance postures, resulting in a large deviation between the simulation results and the actual operation.

[0006] 2. Limited path planning: The system primarily uses pre-defined, fixed paths for analysis, failing to incorporate user-defined personalized paths based on actual maintenance scenarios. For example, it may prioritize access through the front inspection ports of the equipment or avoid high-temperature areas, resulting in insufficient flexibility.

[0007] Therefore, how to solve the problem of the single path planning and detachment from the actual action characteristics in the existing technology is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0008] In view of the above problems, the present invention proposes a user path action reachability analysis method based on a digital twin model to overcome or at least partially solve the above problems; To achieve the above objectives, the present invention adopts the following technical solution: This invention provides a method for analyzing the reachability of user-planned paths based on a digital twin model; including the following steps: S1. Construct a digital twin model library, which includes product digital twin models and maintenance personnel digital twin models; S2. Establish a database of inherent human actions, which includes: data on the operation actions of different maintenance personnel according to the type of maintenance task. S3. Based on the product digital twin model and the maintenance personnel digital twin model, as well as the personnel's inherent action database, simulate the reachability space by integrating the user-specified path and inherent actions; S4. When the user-specified path and corresponding action are reachable, calculate reachability-related data based on the simulation results.

[0009] S5. Based on the accessibility-related data, generate maintenance feature design optimization suggestions.

[0010] Furthermore, in step S1, the product digital twin model is constructed based on product design drawings and 3D scanning data; the product digital twin model includes geometric structure and physical properties; The digital twin model of the maintenance personnel is constructed based on human physiological parameters; the digital twin model of the maintenance personnel includes the degrees of freedom of human skeletal joint movement and muscle constraint data.

[0011] Furthermore, in step S2, a database of inherent character movements is established, including: Motion capture equipment is used to collect motion data of different types of maintenance personnel when performing maintenance tasks; the collected motion data includes joint angle change sequences, motion trajectories, and operational force. The collected actions are then categorized and stored according to maintenance task type and body part.

[0012] Furthermore, step S3 specifically includes: Determine the maintenance path parameters based on the maintenance task; Based on the product digital twin model and the maintenance personnel digital twin model, the maintenance path parameters are geometrically verified, and an initial trajectory is generated; The action sequence matching the maintenance task is called from the character's inherent action database, and the action sequence is fused with the initial trajectory; In a digital twin scenario, the digital twin model of maintenance personnel is used to simulate dynamic maintenance operations.

[0013] Furthermore, the maintenance path parameters include the initial position of the maintenance personnel, the maintenance location, and the path constraints.

[0014] Furthermore, step S3 also includes: When simulating dynamic maintenance operations, if the movement trajectory does not collide with the product's digital twin model and the joint movement does not exceed physiological limits, it is determined that the movement corresponding to the maintenance path meets the reachability condition. If the motion trajectory collides with the product's digital twin model or the joint movement exceeds the physiological limits of joint movement, adjust the motion parameters or local path, and re-simulate the dynamic maintenance operation until the achievable conditions are met. If the reachability condition cannot be met even after adjustments, it is determined to be unreachable.

[0015] Furthermore, the action sequence is fused with the initial trajectory, and the fitting algorithm is implemented as follows: The DH parameter method is used to convert the rotation and translation of each joint into a homogeneous transformation matrix, thus obtaining the end effector; the formula for the homogeneous transformation matrix is:

[0016] The total pose matrix of the end effector is as follows:

[0017] Where θ=[θ1,θ2…,θ n ] T Let T(θ) be the joint angle vector. The first three rows and first three columns of T(θ) represent the attitude matrix R(θ), and the fourth column of the first three rows represents the position vector P(θ) = [x, y, z]. T .

[0018] Furthermore, the calculation process for joint angles includes: Calculate the spatial distance from the target joint to the target position on the path: Based on the structure of the multi-link limbs in the digital twin model of the maintenance personnel, determine the target joint corresponding to the action to be fitted, and combine the target position coordinates on the path to calculate the spatial straight-line distance from the target joint to the target position by the square root. The target angle of the target joint is derived based on the law of cosines: the preset length parameters of each link in the multi-link limb associated with the target joint are obtained, the obtained spatial distance and the length of each link are substituted into the law of cosines, the cosine value of the target angle of the target joint is calculated, and then the target angle of the target joint is determined. Calculate the target angle of the associated joint: Calculate the first auxiliary angle of the line connecting the target joint to the target position relative to the reference direction using the arctangent function. Then, calculate the second auxiliary angle by combining the length of each link with the spatial distance calculated in the first step using the inverse cosine function. Finally, derive the target angle of the associated joint that coordinates with the target joint by using the difference between the first and second auxiliary angles.

[0019] Furthermore, in step S4, the reachability-related data includes action completion rate, spatial redundancy, and path rationality. The degree of action completion refers to the degree of conformity between the actual completed action and the standard inherent action; The spatial redundancy is the size of the buffer space between the maintenance area and surrounding obstacles; The path rationality refers to the matching efficiency between the user-specified path and the inherent action.

[0020] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a user path action reachability analysis method based on a digital twin model, which has the following beneficial effects: 1. This invention constructs a digital twin model that integrates product structure and maintenance personnel actions. By combining real-world collected typical maintenance action data with user-defined path planning, it achieves dynamic, high-fidelity simulation of the maintenance process during the product design phase. Its core effect lies in overcoming the limitations of traditional static geometric analysis. Through action-path fusion simulation, it accurately assesses accessibility, quantitatively analyzes operational space and ergonomic constraints, thereby identifying design flaws in advance, significantly shortening iteration cycles, reducing reliance on physical prototypes and development costs, providing forward-looking, data-driven decision support for optimizing maintenance characteristics, and effectively improving product maintainability and lifecycle maintenance efficiency.

[0021] 2. This invention combines product maintenance and support feature design, digital twin technology, and model algorithms for quantitative spatial analysis, overcoming the shortcomings of being detached from actual actions and having a single path planning method. It completes the design, simulation, and optimization iteration of the maintenance and support feature design stage in a low-cost and high-efficiency manner. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0023] Figure 1 This is a flowchart of the action reachability analysis method provided in the embodiments of the present invention; Figures 2-5 This is a schematic diagram illustrating the construction of an action reachability analysis scenario provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of the action reachability analysis method provided in the embodiments of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] This invention discloses a method for analyzing the reachability of user-planned paths based on a digital twin model; such as... Figure 1 As shown, the specific steps include: S1. Construct a digital twin model library, which includes product digital twin models and maintenance personnel digital twin models; S2. Establish a database of inherent human actions, which includes: data on the operation actions of different maintenance personnel according to the type of maintenance task. S3. Based on the product digital twin model and the maintenance personnel digital twin model, as well as the personnel's inherent action database, simulate the reachability space by integrating the user-specified path and inherent actions; S4. When the user-specified path and corresponding action are reachable, calculate reachability-related data based on the simulation results; S5. Based on the accessibility-related data, generate maintenance feature design optimization suggestions.

[0026] This invention constructs a digital twin model that integrates product structure and maintenance personnel actions. By combining real-world collected typical maintenance action data with user-defined path planning, it achieves dynamic, high-fidelity simulation of the maintenance process during the product design phase. Its core effect lies in overcoming the limitations of traditional static geometric analysis. Through action-path fusion simulation, it accurately assesses accessibility, quantitatively analyzes operational space and ergonomic constraints, thereby identifying design flaws in advance, significantly shortening iteration cycles, reducing reliance on physical prototypes and development costs, providing forward-looking, data-driven decision support for optimizing maintenance characteristics, and effectively improving product maintainability and lifecycle maintenance efficiency.

[0027] The following is a detailed description of each of the above steps: like Figure 2-5 As shown, the first step is the construction of the model, corresponding to step S1; the present invention constructs a digital twin model library, which specifically includes two models: a product digital twin model and a maintenance personnel digital twin model.

[0028] Among them, the product digital twin model is built based on the product's design drawings and 3D scanning data. The product digital twin model includes geometric structure and physical properties, such as material hardness, surface friction, maintenance interface information, bolt positions, and inspection port dimensions.

[0029] The digital twin model of maintenance personnel is built based on human physiological parameters. It includes data such as the degree of freedom of human skeletal joint movement and muscle constraints, and is linked to a database of human physiological parameters, such as the limb length and joint range of motion corresponding to different heights and weights.

[0030] Step S2: After constructing the product digital twin model and the maintenance personnel digital twin model, establish a database of inherent human actions; Motion data acquisition: Through motion capture equipment, inherent motion data of different types of maintenance personnel in typical maintenance tasks are collected, including joint angle change sequences, motion trajectories, and operation force thresholds; the motion capture equipment adopts optical capture systems and inertial measurement units, etc.; data is collected for maintenance personnel with different skill levels and different types of maintenance tasks.

[0031] After data collection is completed, the collected actions are categorized and stored according to maintenance task type and body part, building a structured database of inherent actions, and supporting the invocation and combination of actions.

[0032] Step S3: After the database of the character's inherent actions is built, the reachability space is simulated for the maintenance task. A discretized space for reachability analysis is generated by sampling point clouds, which is then used for subsequent simulation of the reachability space. User path input: Users specify maintenance path parameters through the interactive interface, including the start point, end point, and path constraints; the start point is the initial position of the maintenance personnel; the end point is the maintenance location; and the path constraints are prohibited areas or priority channels.

[0033] Path preprocessing: The digital twin system performs geometric verification on the user-specified path to ensure that there are no obvious obstacles in the static space of the product's digital twin model, and generates an initial path trajectory; Action-path fusion simulation: Action sequences matching the current maintenance task are retrieved from the existing action database, and fused with the initial path trajectory using the A* algorithm. This drives the maintenance personnel model to perform dynamic maintenance operations within the digital twin environment. When simulating dynamic maintenance operations, collision screening is performed by constructing a bounding body. If the action trajectory does not collide with the product digital twin model and the joint movement does not exceed the physiological limit, it is determined that the action corresponding to the maintenance path meets the reachability condition. If the motion trajectory collides with the product's digital twin model or the joint movement exceeds the physiological limits of joint movement, adjust the motion parameters or local path, and re-simulate the dynamic maintenance operation until the achievable conditions are met. If the reachability condition cannot be met even after adjustments, it is determined to be unreachable.

[0034] Step S4 allows for spatial analysis and result output; Based on the simulation results, key accessibility indicators are calculated, including action completion rate, spatial redundancy, and path rationality. Among them, the degree of action completion is the degree of conformity between the actual completed action and the standard inherent action; Spatial redundancy refers to the size of the buffer space between the maintenance area and surrounding obstacles; Path rationality is the matching efficiency between the user-specified path (actual or designed path) and the inherent action path (standard or optimal path), such as whether there are unnecessary action detours; 1. Action completion rate The degree of completion of the action needs to be quantified from two dimensions: trajectory and amplitude.

[0035] Trajectory similarity measures the degree of conformity between the actual operation trajectory of maintenance personnel and the standard action trajectory, reflecting the standardization of the action, such as the rotation trajectory when tightening a bolt, the movement path when installing parts, etc. The calculation formula is as follows:

[0036]

[0037] in, Trajectory similarity (range of values) 1 indicates that the trajectories are completely identical. L It is the total length of the standard motion trajectory. Indicates the distance from the starting point on the actual trajectory. s The position vector, Indicates the distance from the starting point on the standard trajectory. s The position vector, D This represents the total deviation of the user's path from the standard path (calculated by integrating the Euclidean distance between the two points).

[0038] Amplitude matching measures the difference between the actual range of motion of a maintenance worker and the standard range of motion, reflecting the accuracy of the action, such as the rotation angle when a wrench is turned or the depth when a part is inserted. The calculation formula is as follows:

[0039] in, It is the amplitude matching degree (range of values) 1 indicates that the amplitudes are completely consistent. k It is a dimension of the range of motion (such as joint angle, limb extension length, etc.). Indicates the first i The difference between the actual amplitude and the standard amplitude in each dimension. Indicates the first i The standard amplitude variation range of each dimension Indicates the first i Weights for each dimension (e.g., key joints have higher weights).

[0040] 2. Spatial redundancy Spatial redundancy is the "margin" of maintenance operation space, reflecting the amount of space around the maintenance area available for personnel to operate and tools to move.

[0041] Minimum safe distance is the most basic safety constraint in maintenance engineering. It needs to take into account human body size, working posture (such as standing or sitting), and type of hazard (such as contact or crushing). The calculation formula is as follows:

[0042] or

[0043] in, It is the minimum safe distance. L It refers to the range accessible to the human body (such as the maximum accessible distance that the upper limb can reach when extended upwards). It is the maximum accessible range (such as the solid angle rotational accessible distance when the base of the joints such as the palm, wrist, elbow, and shoulder are close to the protective device), and K represents the additional amount coefficient (considering the increment of human body clothing, tool grip or movement error).

[0044] Spatial redundancy calculation needs to consider both available operating space and minimum safe distance, as shown in the following formula:

[0045] in, It is the spatial redundancy (%). It refers to the available operating space of the maintenance area (such as the volume of the internal cavity of the equipment and the volume of the maintenance passage).

[0046] 3. Path rationality The rationality of the path directly affects the convenience and safety of maintenance. Its core logic is the combination of "path matching" and "efficiency gain," encompassing multiple dimensions such as trajectory geometric matching, path efficiency, and key node matching. The calculation formula for trajectory geometric matching is the same as that for trajectory similarity. The remaining formulas are as follows: or

[0047] in, The path efficiency index ( 1 indicates a more efficient user path. 1 indicates less efficient. , These represent the completion times for the standard path and the user path, respectively. These represent the total length of the standard path and the user path, respectively.

[0048]

[0049] in, The key node matching degree (%) This indicates the number of key nodes (such as nodes with the same turning angle or coordinates) that overlap with the standard path. This represents the total number of critical nodes in the standard path (determined through maintenance process decomposition, such as "approaching"). position operate (Evacuation of 4 nodes).

[0050]

[0051] The comprehensive path rationality index (range of values) (1 indicates optimal). This is a weighting coefficient (adjusted according to the type of repair). Indicates the normalized efficiency index ( ), Represents the normalized node matching degree ( ).

[0052] Step S5: Display the analysis results in a visual format and generate suggestions for optimizing maintenance features, such as increasing the size of the access panel and adjusting the layout of maintenance areas.

[0053] like Figure 6 As shown in the schematic diagram, the positions of the maintenance personnel and the target need to be obtained first. After obtaining the positions, the positions of the two are judged by the ray. If the target is not obstructed, the maintenance action is obtained; if the target is obstructed and is covered by the shell, the shell is opened and the maintenance action is obtained. If the target is obscured but not enclosed by the outer shell, adjust the personnel's position, re-acquire the maintenance personnel's position, and reassess the situation. After obtaining the maintenance action, the relationship between the action and the joint is obtained, and the path is planned and fitted to the action; it is determined whether it exceeds the human body limit. If it exceeds the human body limit, the path is replanned; if it does not exceed the human body limit, the action is displayed and a collision is detected. If there is no collision, the reachable conclusion is output; if there is a collision, the path is replanned.

[0054] This invention combines product maintenance and support feature design, digital twin technology, and model algorithms for quantitative spatial analysis, overcoming the shortcomings of being detached from actual actions and having a single path planning approach. It completes the design, simulation, and optimization iterations of the maintenance and support feature design stage in a low-cost and high-efficiency manner.

[0055] The key technologies used in the above steps will be described in detail below: (1) Enclosing body hierarchy: Constructing hierarchical enclosing bodies for product components to accelerate collision detection; In layman's terms, it involves using bounding bodies to replace the original model for initial screening; a bounding body is a simple geometric shape whose volume completely encloses a complex product component or human joint. If the bounding volumes of two components do not collide, then the original models of these two components will definitely not collide. If the bounding volumes of two components collide, then the original models of the two components may also be colliding, requiring further detection.

[0056] For example, an engine spark plug component is enclosed by a cuboid, and a mechanic's finger joint is enclosed by another cuboid. If the two cuboids do not overlap, it is directly determined that there is no collision between the finger and the spark plug; if they overlap, further testing is performed on the fine model of the finger and the fine model of the spark plug.

[0057] The hierarchical structure of the bounding body involves constructing bounding bodies for product components from the whole to the parts. First, the bounding body of the whole component is constructed, and then the bounding bodies of the components are constructed to achieve the hierarchical structure of the bounding body.

[0058] (2) Point cloud and mesh data: Using the vertex data of the product's digital twin model, a discretized space for accessibility analysis is generated through point cloud sampling. For example, the surface of internal engine parts is uniformly sampled, stored as a point set, and used for subsequent inspection.

[0059] (3) Character inherent action database: predefined common operation postures, such as "grasping", "rotating", "plugging and unplugging", as reference templates for accessibility judgment. For example, the "tightening screw" posture requires that the tool axis coincides with the screw axis and the hand is within the operation range.

[0060] (4) Route planning: By obtaining the planned route and referring to the limits of human body, the human movement is fitted; Implementation of the algorithm for fitting the human arm to the planned route: The Denavit-Hartenberg (DH) parameter method is used to describe the transformations between links, converting the rotation and translation of each joint into a homogeneous transformation matrix, ultimately yielding the position and orientation of the end effector, i.e., the arm. The formula for a single homogeneous transformation matrix is:

[0061] The total pose matrix of the end effector is the product of the DH matrices of all links: Where θ=[θ1, θ2…,θ n ] T Let T(θ) be the joint angle vector. The first three rows and first three columns of T(θ) represent the attitude matrix R(θ), and the fourth column of the first three rows represents the position vector P(θ) = [x, y, z]. T .

[0062] The joint angles are calculated as follows: Calculate the spatial distance from the target joint to the target position on the path: Based on the structure of the multi-link limbs in the digital twin model of the maintenance personnel, determine the target joint corresponding to the action to be fitted, and combine the target position coordinates on the path to calculate the spatial straight-line distance from the target joint to the target position by the square root. The target angle of the target joint is derived based on the law of cosines: the preset length parameters of each link in the multi-link limb associated with the target joint are obtained, the obtained spatial distance and the length of each link are substituted into the law of cosines, the cosine value of the target angle of the target joint is calculated, and then the target angle of the target joint is determined. Calculate the target angle of the associated joint: Calculate the first auxiliary angle of the line connecting the target joint to the target position relative to the reference direction using the arctangent function, and then calculate the second auxiliary angle by combining the length of each link with the spatial distance calculated in the first step using the inverse cosine function. Finally, derive the target angle of the associated joint that coordinates with the target joint by using the difference between the first and second auxiliary angles. For example, given the target location P on the planned path... d =[x d ,y d ,z d ] T Find the corresponding joint angles θ1, θ2, ..., θ n (Target joint angle). Taking a planar two-bar arm (simplified to shoulder joint θ1 and elbow joint θ2, ignoring the z-axis) as an example, the inverse solution formula is intuitive and commonly used: Let: Link 1 length L1, Link 2 length L2, Target position (x) d ,y d ).

[0063] Step 1: Calculate the distance r from the shoulder joint to the target point:=

[0064] Step 2: Use the Law of Cosines to determine the elbow joint angle.

[0065] cos ("+" corresponds to elbow flexion, "-" corresponds to elbow extension) Step 3: Determine the shoulder joint angle ; First calculate the auxiliary angle : Then the target angle of the shoulder joint .

[0066] (5) Dijkstra's algorithm: It finds the shortest path from the starting point to all nodes by breadth-first search (BFS), with a time complexity of O(V²+E), where V is the number of nodes and E is the number of edges.

[0067] (6) A* algorithm: Combining Dijkstra's algorithm with the greedy algorithm, a heuristic function is introduced to prioritize searching nodes that are closer to the target, which significantly improves the search efficiency. The time complexity is O((E+V)logV). The logV comes from the fact that "open list" is usually implemented with a priority queue. The operation complexity of taking the minimum f(n) each time is O(logV), while the whole process needs to traverse the edges and vertices, so the final complexity is O((E+V)logV).

[0068] The algorithm implementation process is as follows: The core of the A* algorithm is the evaluation function, which measures the "estimated total cost" from the starting node (i.e., the maintenance personnel's position) through the current node to the target node (i.e., the target location). The formula is: f(n) = g(n) + h(n); f(n) is the estimated total cost from the starting node through node n to the target node, which guides the algorithm to prioritize exploring "more promising" paths; g(n) represents the actual cost incurred from the starting node to node n, similar to the "cumulative path cost from the starting point to the current point" in Dijkstra's algorithm. h(n) is the heuristically estimated cost from node n to the target node, which is key to the algorithm's "intelligence," for example, using Manhattan distance. Euclidean distance Estimated remaining costs.

[0069] a) Initialization: Let the starting node be S and the target node be T.

[0070] Maintain the OpenList and CloseList: The open list is the set of nodes to be explored, initially containing only S; for S, calculate g(S)=0 (no cost from the starting point to itself) and h(S) (heuristic estimate from the starting point to the end point), therefore f(S)=g(S)+h(S).

[0071] The closed list is a collection of nodes that have been explored; it is initially empty.

[0072] b) Loop exploration: until the target is found or the open list is empty.

[0073] If the open list is not empty, repeat the following steps: Step 1: Select the "optimal node"; Select the node n with the smallest f(n) from the open list, that is, the node "currently most likely to approach the target", as the current node.

[0074] Step 2: Check if the target is reached; If the current node n is the target node T, the pathfinding is successful. At this time, the complete path can be obtained by "backtracking from the parent node" (each node records "from which node it came") and finding S in reverse from T.

[0075] Step 3: Expand the "neighbors" of the current node; Traverse all passable neighbor nodes m of the current node n, such as the adjacent grids in the up, down, left, right, and diagonal directions in the grid, and perform the following for each neighbor m: Case 1: m is not in the open list and the closed list; Calculate g(m)=g(n)+cost(n,m) (cost(n,m) is the actual movement cost from n to m, for example, 1 for straight movement and for diagonal movement); Calculate h(m) (heuristic estimate from m to T); Calculate f(m)=g(m)+h(m); Add m to the open list and record the "parent node" of m as n (for subsequent path backtracking); Case 2: m is already in the open list; Calculate the new g value of "from S through n to m": g new =g(n)+cost(n,m); If g new <g(m) (indicating that the path "through n to m" is better than the previously recorded one), then update: g(m)=g new , f(m)=g(m)+h(m); At the same time, update the "parent node" of m as n (for subsequent backtracking to take a better path); Case 3: m is already in the closed list, usually skipped (because the nodes in the closed list are considered "already explored and there is no better path"); for strictness, the logic of "Case 2" can also be repeated. If a better path is found, add m back to the open list.

[0076] Step 4: Mark the current node as "explored"; Move the current node n from the open list to the closed list.

[0077] c) Termination condition If the target node T is found in the loop, the pathfinding is successful and the path is obtained by backtracking.

[0078] If the open list is empty and T is not found, then there is no traversable path from S to T.

[0079] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0080] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A user path action reachability analysis method based on a digital twin model, characterized in that, Includes the following steps: S1. Construct a digital twin model library, which includes product digital twin models and maintenance personnel digital twin models; S2. Establish a database of inherent human actions, which includes: data on the operation actions of different maintenance personnel according to the type of maintenance task. S3. Based on the product digital twin model and the maintenance personnel digital twin model, as well as the personnel's inherent action database, simulate the reachability space by integrating the user-specified path and inherent actions; S4. When the user-specified path and corresponding action are reachable, calculate reachability-related data based on the simulation results.

2. The user path action reachability analysis method based on a digital twin model as described in claim 1, characterized in that, Also includes: S5. Based on the accessibility-related data, generate maintenance feature design optimization suggestions.

3. The user path action reachability analysis method based on a digital twin model as described in claim 1, characterized in that, In step S1, the product digital twin model is constructed based on product design drawings and 3D scanning data; the product digital twin model includes geometric structure and physical properties; The digital twin model of the maintenance personnel is constructed based on human physiological parameters; The digital twin model of the maintenance personnel includes data on the degrees of freedom of human skeletal joint movement and muscle constraints.

4. The user path action reachability analysis method based on a digital twin model as described in claim 1, characterized in that, In step S2, establishing a database of inherent character movements includes: Motion capture equipment is used to collect motion data of different types of maintenance personnel when performing maintenance tasks; the collected motion data includes joint angle change sequences, motion trajectories, and operational force. The collected actions are then categorized and stored according to maintenance task type and body part.

5. The user path action reachability analysis method based on a digital twin model as described in claim 1, characterized in that, Step S3 specifically includes: Determine the maintenance path parameters based on the maintenance task; Based on the product digital twin model and the maintenance personnel digital twin model, the maintenance path parameters are geometrically verified, and an initial trajectory is generated; The action sequence matching the maintenance task is called from the character's inherent action database, and the action sequence is fused with the initial trajectory; In a digital twin scenario, the digital twin model of maintenance personnel is used to simulate dynamic maintenance operations.

6. The user path action reachability analysis method based on a digital twin model as described in claim 5, characterized in that, The maintenance path parameters include the initial position of the maintenance personnel, the maintenance location, and the path constraints.

7. The user path action reachability analysis method based on a digital twin model as described in claim 6, characterized in that, Step S3 further includes: When simulating dynamic maintenance operations, if the movement trajectory does not collide with the product's digital twin model and the joint movement does not exceed physiological limits, it is determined that the movement corresponding to the maintenance path meets the reachability condition. If the motion trajectory collides with the product's digital twin model or the joint movement exceeds the physiological limits of joint movement, adjust the motion parameters or local path, and re-simulate the dynamic maintenance operation until the achievable conditions are met. If the reachability condition cannot be met even after adjustments, it is determined to be unreachable.

8. The user path action reachability analysis method based on a digital twin model as described in claim 5, characterized in that, The action sequence is fused with the initial trajectory, and the fitting algorithm is implemented as follows: The DH parameter method is used to convert the rotation and translation of each joint into a homogeneous transformation matrix, thus obtaining the end effector; the formula for the homogeneous transformation matrix is: The total pose matrix of the end effector is as follows: Where θ=[θ1,θ2…,θ n ] T Let T(θ) be the joint angle vector. The first three rows and first three columns of T(θ) represent the attitude matrix R(θ), and the fourth column of the first three rows represents the position vector P(θ) = [x, y, z]. T .

9. The user path action reachability analysis method based on a digital twin model as described in claim 8, characterized in that, The calculation process for joint angles includes: Calculate the spatial distance from the target joint to the target position on the path: Based on the structure of the multi-link limbs in the digital twin model of the maintenance personnel, determine the target joint corresponding to the action to be fitted, and combine the target position coordinates on the path to calculate the spatial straight-line distance from the target joint to the target position by the square root. The target angle of the target joint is derived based on the law of cosines: the preset length parameters of each link in the multi-link limb associated with the target joint are obtained, the obtained spatial distance and the length of each link are substituted into the law of cosines, the cosine value of the target angle of the target joint is calculated, and then the target angle of the target joint is determined. Calculate the target angle of the associated joint: Calculate the first auxiliary angle of the line connecting the target joint to the target position relative to the reference direction using the arctangent function. Then, calculate the second auxiliary angle by combining the length of each link with the spatial distance calculated in the first step using the inverse cosine function. Finally, derive the target angle of the associated joint that coordinates with the target joint by using the difference between the first and second auxiliary angles.

10. The user path action reachability analysis method based on a digital twin model as described in claim 1, characterized in that, In step S4, the reachability-related data includes action completion rate, spatial redundancy, and path rationality. The degree of action completion refers to the degree of conformity between the actual completed action and the standard inherent action; The spatial redundancy is the size of the buffer space between the maintenance area and surrounding obstacles; The path rationality refers to the matching efficiency between the user-specified path and the inherent action.

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