Automatic wiring method and system of secondary screen cabinet, medium and product

Abstract

The invention discloses an automatic wiring method and system of a secondary screen cabinet, a medium and a product, and belongs to the field of automatic wiring of the secondary screen cabinet, and the method comprises the steps: obtaining the design drawing data of the secondary screen cabinet, and extracting the installation position of each component and the insertion starting point and end point of a wiring harness; setting a plurality of bundling middle points for each wire harness based on the positions of the components, and forming a node sequence by combining starting and ending points; generating path fragments for each pair of adjacent nodes in the sequence by adopting a fast exploration random tree connection algorithm with sampling region constraint and an obstacle approaching penalty term; and sequentially splicing all the segments into a complete wiring path, and controlling a wiring device to automatically complete wiring harness arrangement and fixation according to the complete wiring path. By implementing the method, the problems of low efficiency and poor safety of automatic wiring path planning of the wiring harnesses in the secondary screen cabinet in the prior art can be solved.

Description

Technical Field

[0001] This invention belongs to the field of automatic wiring for secondary power supply cabinets, and relates to an automated wiring method, system, medium and product for secondary power supply cabinets. Background Technology

[0002] Secondary power control cabinets, as crucial hubs connecting primary equipment with secondary equipment such as control, protection, and metering systems, play a vital role in ensuring the reliable operation of the power grid in modern power systems. Their internal structure is compact, filled with relays, terminal blocks, DIN rails, and cable trays, resulting in complex wiring connections. Currently, wiring operations for secondary power control cabinets still heavily rely on manual labor, accounting for over 50% of the total production time and becoming a major bottleneck restricting capacity expansion and automation upgrades.

[0003] Existing technologies mainly focus on specific aspects such as gripper mechanism optimization, visual positioning, or virtual simulation of wiring harnesses, and have not yet formed a complete automated cabling solution that can connect digital design and physical execution. In small-batch, multi-variety production models, cabling paths need to be adjusted frequently, and existing automation attempts are unable to flexibly cope with the complex spatial constraints brought about by the dense components inside the cabinet, still requiring a lot of manual intervention, which restricts the simultaneous improvement of cabling efficiency and safety. Summary of the Invention

[0004] This application provides an automated cabling method, system, medium, and product for secondary cabinets, which can solve the problems of low efficiency and poor security in the automated cabling path planning of wire harnesses in the prior art.

[0005] To achieve the above objectives, in a first aspect, the present invention provides an automated cabling method for a secondary power distribution cabinet, comprising:

[0006] Obtain the design drawing data of the secondary cabinet, and extract the installation positions of each component inside the secondary cabinet, as well as the insertion start and end points of each wire harness.

[0007] Based on the installation position of each component, several bundle intermediate points are set for each wire harness, and they are arranged sequentially in combination with the insertion start point and insertion end point to obtain the node sequence corresponding to each wire harness.

[0008] For each line bundle, any two adjacent nodes in the corresponding node sequence are extracted sequentially. After each extraction, the two extracted adjacent nodes are used as the starting point and the ending point, respectively. Combined with a preset fast exploration random tree connection algorithm, the corresponding path segment is generated. The fast exploration random tree connection algorithm is configured with sampling area constraints during sampling and obstacle proximity penalty term when calculating path cost.

[0009] All path segments corresponding to each wire harness are spliced ​​together in sequence to obtain the complete wiring path of the wire harness. Based on each complete wiring path, the wiring device is controlled to automatically perform wire harness arrangement and fixing operations to complete the wiring.

[0010] Compared to existing technologies, the embodiments of this application have the following beneficial effects: They acquire design drawing data of the secondary cabinet and extract component installation positions and wire harness insertion start and end point positions, digitizing the physical cabinet structure and providing accurate geometric and topological input for subsequent automated path planning; they set wire harness bundling intermediate points according to component installation positions and arrange the insertion start, intermediate, and end points sequentially to form a node sequence, restoring the forced constraint path in the actual wiring process and ensuring that the generated path conforms to engineering specifications; they sequentially extract adjacent nodes as start and end points for each wire harness, and combine this with a fast exploration random tree connection algorithm configured with sampling area constraints and obstacle proximity penalties to generate path segments, significantly improving efficiency by limiting the sampling space. By reducing invalid searches and improving planning efficiency, and by introducing an obstacle proximity penalty term into the path cost to guide the path away from components to enhance safety, the system achieves segmented planning and overall reconstruction of multi-pathway paths by sequentially splicing all path segments into a complete wiring path. Based on the complete wiring path, the system controls the wiring device to automatically perform harness placement and fixing operations, completing a closed loop from digital planning to physical execution. The synergistic effect of these features enables the efficient generation of collision-free wiring paths that meet both process constraints and safety margins in complex environments such as secondary cabinets where components are dense and space is limited. This solves the core technical problem of low path planning efficiency and poor safety in existing automated methods for small-batch, multi-variety scenarios.

[0011] In some embodiments of the first aspect of this application, the step of generating corresponding path segments by using two extracted adjacent nodes as the starting point and the ending point, and combining them with a preset fast exploratory random tree connection algorithm, includes:

[0012] Initialize and construct the first tree structure and the second tree structure using the coordinates of the starting point and the ending point as roots respectively;

[0013] According to the fast exploratory random tree connection algorithm, the first tree structure and the second tree structure are iterated until a preset termination condition is met, and the path segment with the minimum sum of path costs of all nodes is output. In each iteration, one of the first tree structure and the second tree structure is alternately selected as the current expansion tree. A probability value is randomly generated and compared with a preset adaptive threshold. If the probability value is less than the adaptive threshold, the root node coordinates of the other tree are used as the target point for this sampling. Otherwise, a sampling point is generated within an elliptical region centered on the starting and ending coordinates. The distance from the sampling point or target point is then searched in the current expansion tree. Find the nearest existing node and extend a new node from that nearest existing node toward the sampling point or target point; determine whether the line connecting the nearest existing node to the new node collides with an obstacle. If no collision occurs, select all existing nodes in the current extended tree that are within a preset neighborhood of the new node as candidate parent nodes. For each candidate parent node, calculate the path cost of the new node and select the candidate parent node with the minimum path cost as the final parent node of the new node; determine whether there is a connectable node pair between the current extended tree and another tree. If so, splice them together to obtain a complete path, which is the currently found path segment.

[0014] The path cost is obtained by weighted summation of the path cost of the candidate parent node, the Euclidean distance between the candidate parent node and the new node, and the obstacle proximity penalty.

[0015] Compared to existing technologies, the above embodiments have the following advantages: First and second tree structures are constructed using the start and end point coordinates as roots, establishing a bidirectional path search framework to accelerate convergence; in each iteration, the expansion tree is alternately selected, and the sampling strategy is determined by comparing a random probability value with an adaptive threshold. If the probability value is less than the threshold, target point-guided sampling is used to enhance directionality; otherwise, sampling points are generated within an elliptical region centered on the start and end points. Geometric constraints focus the search on the potential optimal path region, significantly improving sampling efficiency; the nearest existing node is found in the current expansion tree, and new nodes are expanded in the direction of the sampling points, achieving incremental growth of the tree structure; the connection between the new node and its expansion source node is determined... The system checks whether the line collides with obstacles to ensure the path is collision-free. If no collision occurs, all existing nodes in the neighborhood of the new node are selected as candidate parent nodes, and the path cost of the new node is calculated. This cost is obtained by weighted summation of the cumulative path cost of the candidate parent nodes, Euclidean distance, and obstacle proximity penalty. Taking into account historical costs, path length, and safety risks, the node with the lowest cost is selected as the final parent node, thus achieving dynamic optimization of the local path. The system determines whether there is a connectable node pair between two trees and, if so, splices the paths together as the currently found path segment, thus completing the fusion of bidirectional paths. The entire iterative process integrates elliptical region constraints and obstacle penalty terms to ensure path feasibility while also considering search efficiency and safety.

[0016] In some embodiments of the first aspect of this application, the elliptical region is constructed with the starting point coordinates and the ending point coordinates as foci, including:

[0017] Calculate the Euclidean distance between the starting point coordinates and the ending point coordinates, and combine it with the preset scaling factor to calculate the length of the major axis and the minor axis of the ellipse;

[0018] By combining the lengths of the major and minor axes of the ellipse, and using the coordinates of the starting and ending points as foci, an elliptical region is constructed.

[0019] Compared to existing technologies, the above embodiments have the following advantages: the Euclidean distance between the start and end points is calculated and the lengths of the major and minor axes of the ellipse are determined by combining the preset scaling factor, and an elliptical region is constructed with the start and end points as foci; based on the geometric characteristics of the ellipse—that is, the sum of the distances from any point on the ellipse to the two foci is a constant—this shape naturally covers all possible shortest path candidate regions, and the major axis extends along the direction of the line connecting the start and end points, while the minor axis vertically constrains the lateral expansion range, thereby accurately focusing on the efficient and feasible search domain within the narrow space of the cabinet; compared with sampling regions of circles, squares, or other shapes, the ellipse can more tightly enclose the potential optimal path, significantly reducing invalid sampling far from the main path direction, while avoiding insufficient path diversity due to excessive constraints, thus balancing search efficiency and solution space integrity.

[0020] In some embodiments of the first aspect of this application, the obstacle proximity penalty is the sum of the overlapping areas of a circular region centered on the candidate parent node and with a preset safety radius, and the geometric models of all components and wiring harnesses within the secondary cabinet.

[0021] Compared with the prior art, the above embodiments have the following beneficial effects: the obstacle proximity penalty term is defined as the sum of the overlapping areas of a circular area centered on the candidate parent node and with a preset safety radius and the geometric models of all components and wiring harnesses in the cabinet. By quantifying the spatial intrusion degree between path nodes and obstacles, the safety distance constraint is transformed into a calculable numerical term. This penalty term is directly embedded in the path cost function, enabling the algorithm to actively avoid high-risk areas during the expansion process. The generated path has sufficient safety margin, effectively reducing the risk of interference or wear between wiring harnesses and components during the wiring process.

[0022] In some embodiments of the first aspect of this application, the step of sequentially splicing together all path segments corresponding to each wire harness to obtain the complete wiring path of the wire harness includes:

[0023] By sequentially splicing together all the path segments corresponding to each wire harness, the original wiring path of the wire harness can be obtained.

[0024] The original wiring path is smoothed by applying a third-order Bézier curve to obtain a continuous and smooth complete wiring path.

[0025] Compared with the prior art, the above embodiments have the following beneficial effects: after splicing the path segments to obtain the original wiring path, a third-order Bézier curve is applied for trajectory smoothing. By constructing a high-order continuous curve in segments to replace the original polyline path, sharp inflection points in the path are effectively eliminated, and the smoothness of the wiring operation is improved.

[0026] In some embodiments of the first aspect of this application, controlling the cabling device to automatically perform harness placement and fixing operations based on each of the complete cabling paths includes:

[0027] The complete wiring path is converted into a sequence of motion commands for the wiring device, and control commands are generated by combining preset speed and acceleration constraints.

[0028] The control command is sent to the wiring device, which then drives the wiring device to complete the wiring harness arrangement and fixing operation along the complete wiring path.

[0029] Compared with the prior art, the above embodiments have the following beneficial effects: converting the complete wiring path into a sequence of motion instructions for the wiring device and generating control instructions by combining preset speed and acceleration constraints, ensuring that the movement of the wiring device, like a robot, conforms to dynamic constraints; sending the control instructions to the wiring device to drive it to complete the wiring harness arrangement and fixing operations along the path, realizing a precise mapping from the planned trajectory to physical execution, and ensuring the reliability and repeatability of the wiring operation.

[0030] Secondly, the present invention also provides an automated cabling system for a secondary cabinet, comprising: a data acquisition module, a sequence generation module, a path segment generation module, and an execution module;

[0031] The data acquisition module is used to acquire the design drawing data of the secondary cabinet, and extract the installation position of each component inside the secondary cabinet, as well as the position data of the insertion start point and insertion end point of each wire harness.

[0032] The sequence generation module is used to set several wire harness bundling intermediate points for each wire harness according to the installation position of each component, and arrange them sequentially in combination with the insertion start point and insertion end point to obtain the node sequence corresponding to each wire harness.

[0033] The path segment generation module is used to extract any two adjacent nodes from the corresponding node sequence for each line bundle, and after each extraction, the two extracted adjacent nodes are used as the starting point and the ending point, and a preset fast exploration random tree connection algorithm is used to generate the corresponding path segment; wherein, the fast exploration random tree connection algorithm is configured with sampling area constraints during sampling and with obstacle proximity penalty term when calculating path cost;

[0034] The execution module is used to sequentially splice all path segments corresponding to each wire harness to obtain the complete wiring path of the wire harness, and based on each complete wiring path, control the wiring device to automatically perform wire harness arrangement and fixing operations to complete the wiring.

[0035] Compared with the prior art, the above embodiments of this application have the following beneficial effects: They acquire the design drawing data of the secondary cabinet and extract the component installation positions and the start and end points of the wire harness insertion, digitizing the physical cabinet structure and providing accurate geometric and topological input for subsequent automated path planning; they set the intermediate points of the wire harness bundling according to the component installation positions and arrange the insertion start, intermediate, and end points sequentially to form a node sequence, restoring the forced constraint path in the actual wiring process and ensuring that the generated path conforms to engineering specifications; they extract adjacent nodes as start and end points for each wire harness sequentially, and combine this with a fast exploration random tree connection algorithm configured with sampling area constraints and obstacle proximity penalties to generate path segments, thereby limiting the sampling space... This approach significantly reduces invalid searches and improves planning efficiency. Simultaneously, it introduces an obstacle proximity penalty in the path cost to guide the path away from components, enhancing safety. By sequentially piecing together all path segments to obtain a complete wiring path, it achieves segmented planning and overall reconstruction of multi-pathway paths. Based on the complete wiring path, the wiring device automatically executes harness placement and fixing operations, completing a closed loop from digital planning to physical execution. The synergistic effect of these features enables efficient generation of collision-free wiring paths that meet both process constraints and safety margins in complex environments like secondary cabinets with dense components and limited space. This solves the core technical problem of low path planning efficiency and poor safety in existing automated methods for small-batch, multi-variety scenarios.

[0036] In some embodiments of the second aspect of this application, the path segment generation module includes: an initialization unit and an iteration unit;

[0037] The initialization unit is used to initialize and construct the first tree structure and the second tree structure respectively, using the coordinates of the starting point and the ending point as the root.

[0038] The iterative unit is used to iterate over the first tree structure and the second tree structure according to the fast exploratory random tree connection algorithm until a preset termination condition is met, and output the path segment with the minimum sum of path costs for each node; and in each iteration, one of the first tree structure and the second tree structure is alternately selected as the current expansion tree, a probability value is randomly generated and compared with a preset adaptive threshold. If the probability value is less than the adaptive threshold, the root node coordinates of the other tree are used as the target point for this sampling; otherwise, a sampling point is generated in the elliptical region with the starting point coordinates and the ending point coordinates as the focus; and the distance to the sampling point is found in the current expansion tree. Find the nearest existing node to the sampling point or target point, and extend a new node from the nearest existing node toward the sampling point or target point; determine whether the connection from the nearest existing node to the new node collides with an obstacle. If no collision occurs, select all existing nodes in the current extended tree that are within a preset neighborhood of the new node as candidate parent nodes. For each candidate parent node, calculate the path cost of the new node, and select the candidate parent node with the minimum path cost as the final parent node of the new node; determine whether there is a connectable node pair between the current extended tree and another tree. If so, splice them together to obtain a complete path, which is the currently found path segment.

[0039] The path cost is obtained by weighted summation of the path cost of the candidate parent node, the Euclidean distance between the candidate parent node and the new node, and the obstacle proximity penalty.

[0040] Compared to existing technologies, the above embodiments have the following advantages: First and second tree structures are constructed using the start and end point coordinates as roots, establishing a bidirectional path search framework to accelerate convergence; in each iteration, the expansion tree is alternately selected, and the sampling strategy is determined by comparing a random probability value with an adaptive threshold. If the probability value is less than the threshold, target point-guided sampling is used to enhance directionality; otherwise, sampling points are generated within an elliptical region centered on the start and end points. Geometric constraints focus the search on the potential optimal path region, significantly improving sampling efficiency; the nearest existing node is found in the current expansion tree, and new nodes are expanded in the direction of the sampling points, achieving incremental growth of the tree structure; the connection between the new node and its expansion source node is determined... The system checks whether the line collides with obstacles to ensure the path is collision-free. If no collision occurs, all existing nodes in the neighborhood of the new node are selected as candidate parent nodes, and the path cost of the new node is calculated. This cost is obtained by weighted summation of the cumulative path cost of the candidate parent nodes, Euclidean distance, and obstacle proximity penalty. Taking into account historical costs, path length, and safety risks, the node with the lowest cost is selected as the final parent node, thus achieving dynamic optimization of the local path. The system determines whether there is a connectable node pair between two trees and, if so, splices the paths together as the currently found path segment, thus completing the fusion of bidirectional paths. The entire iterative process integrates elliptical region constraints and obstacle penalty terms to ensure path feasibility while also considering search efficiency and safety.

[0041] Thirdly, the present invention also provides a computer program product, including a computer program or instructions, characterized in that, when the computer program or instructions are executed, they implement any one of the automated wiring methods for secondary cabinets of the present invention.

[0042] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any one of the automated wiring methods for secondary cabinets of the present invention. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating an automated cabling method for a secondary cabinet provided in some embodiments of the present invention.

[0044] Figure 2 This is a structural schematic diagram of an automated cabling system for a secondary cabinet provided in some embodiments of the present invention. Detailed Implementation

[0045] 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.

[0046] Example 1:

[0047] Please refer to Figure 1 To address the problems of low efficiency and poor security in automated wiring path planning for secondary cabinets in existing technologies, an embodiment of the present invention provides an automated wiring method for secondary cabinets, comprising steps S1 to S4:

[0048] Step S1: Obtain the design drawing data of the secondary cabinet, and extract the installation positions of each component inside the secondary cabinet, as well as the position data of the insertion start and end points of each wire harness.

[0049] In practice, based on the design drawings, a dedicated parser can be used to extract information such as the location of internal components and wiring harness connection ports of the cabinet.

[0050] Step S2: Based on the installation position of each component, set several intermediate points for each wire harness, and arrange them sequentially in combination with the insertion start point and insertion end point to obtain the node sequence corresponding to each wire harness.

[0051] In this application, to address the narrow and obstruction-prone environment inside the secondary cabinet, multiple wire harness bundling points are set inside the cabinet based on the insertion start and end points of each wire harness, serving as intermediate nodes. This decomposes the wiring process of a single wire into two logically and kinematically independent sub-task segments: the first segment (insertion start point → wire harness bundling intermediate point); and the second segment (wire harness bundling intermediate point → insertion end point).

[0052] Step S3: For each line bundle, extract any two adjacent nodes from the corresponding node sequence in sequence, and after each extraction, use the two extracted adjacent nodes as the starting point and the ending point, and combine them with the preset fast exploration random tree connection algorithm to generate the corresponding path segment; wherein, the fast exploration random tree connection algorithm is configured with sampling area constraints when sampling and with obstacle proximity penalty term when calculating path cost.

[0053] Furthermore, the generation of path segments can be achieved through the following preferred implementation method, including steps S31-S32, as detailed below:

[0054] S31: Initialize and construct the first tree structure and the second tree structure using the coordinates of the starting point and the ending point as roots respectively;

[0055] S32: According to the fast exploratory random tree connection algorithm, iterate through the first tree structure and the second tree structure until a preset termination condition is met, and output the path segment with the minimum sum of path costs of each node; and in each iteration, alternately select one of the first tree structure and the second tree structure as the current expansion tree, randomly generate a probability value and compare it with a preset adaptive threshold. If the probability value is less than the adaptive threshold, then the root node coordinates of the other tree are used as the target point for this sampling. Otherwise, a sampling point is generated in the elliptical region with the starting point coordinates and the ending point coordinates as the focus; based on the sampling point or the target point, perform a node expansion operation in the current expansion tree that includes collision detection and path cost optimization with obstacle penalty, and generate a path segment when the two trees can be connected.

[0056] The path cost is obtained by weighted summation of the path cost of the candidate parent node, the Euclidean distance between the candidate parent node and the new node, and the obstacle proximity penalty.

[0057] Further, step S32 can be specifically expanded as follows: According to the fast exploratory random tree connection algorithm, iterate through the first tree structure and the second tree structure until a preset termination condition is met, and output the path segment with the minimum sum of path costs for each node; and in each iteration, alternately select one of the first tree structure and the second tree structure as the current expanded tree, randomly generate a probability value and compare it with a preset adaptive threshold. If the probability value is less than the adaptive threshold, then the root node coordinates of the other tree are used as the target point for this sampling; otherwise, a sampling point is generated within the elliptical region centered on the starting point coordinates and the ending point coordinates; search in the current expanded tree... Find the existing node closest to the sampling point or target point, and extend a new node from the nearest existing node toward the sampling point or target point; determine whether the connection from the nearest existing node to the new node collides with an obstacle. If no collision occurs, select all existing nodes in the current extended tree that are within a preset neighborhood of the new node as candidate parent nodes. For each candidate parent node, calculate the path cost of the new node, and select the candidate parent node with the minimum path cost as the final parent node of the new node; determine whether there is a connectable node pair between the current extended tree and another tree. If so, splice them together to obtain a complete path, which is the currently found path segment.

[0058] The path cost is obtained by weighted summation of the path cost of the candidate parent node, the Euclidean distance between the candidate parent node and the new node, and the obstacle proximity penalty.

[0059] In this preferred embodiment, a first tree structure and a second tree structure are constructed with the start and end point coordinates as roots, respectively, establishing a bidirectional path search framework to accelerate convergence. In each iteration, the expansion tree is alternately selected, and the sampling strategy is determined by comparing a random probability value with an adaptive threshold. If the probability value is less than the threshold, the target point guides the sampling to enhance directionality; otherwise, sampling points are generated within an elliptical region with the start and end points as foci. Geometric constraints focus the search on the potential optimal path region, thereby significantly improving sampling efficiency. The nearest existing node is found in the current expansion tree, and new nodes are expanded in the direction of the sampling points to achieve incremental growth of the tree structure. The connection between the new node and its source node is determined by whether it is an obstacle. Object collision detection ensures the path is collision-free. If no collision occurs, all existing nodes in the neighborhood of the new node are considered as candidate parent nodes, and the path cost of the new node is calculated. This cost is obtained by weighted summation of the cumulative path cost of the candidate parent nodes, Euclidean distance, and obstacle proximity penalty. Taking into account historical costs, path length, and safety risks, the node with the lowest cost is selected as the final parent node, thus achieving dynamic optimization of local paths. The system determines whether there is a connectable node pair between two trees and, if so, splices the paths together as the currently found path segment, completing the fusion of bidirectional paths. The entire iterative process integrates elliptical region constraints and obstacle penalty terms to ensure path feasibility while balancing search efficiency and safety.

[0060] Furthermore, in step S32, the elliptical region is constructed with the starting point coordinates and the ending point coordinates as the focal points. This can be achieved through the following preferred implementation method, including steps S321-S322, as follows:

[0061] S321: Calculate the Euclidean distance between the starting point coordinates and the ending point coordinates, and combine it with the preset scaling factor to calculate the length of the major axis and the minor axis of the ellipse;

[0062] S322: Combining the lengths of the major and minor axes of the ellipse, and using the coordinates of the starting point and the coordinates of the ending point as the focal points, an elliptical region is constructed.

[0063] In this preferred embodiment, the Euclidean distance between the start and end points is calculated and the lengths of the major and minor axes of the ellipse are determined by combining the preset scaling factor. An elliptical region is constructed with the start and end points as foci. Based on the geometric characteristics of the ellipse—that is, the sum of the distances from any point on the ellipse to the two foci is a constant—this shape naturally covers all possible shortest path candidate regions. The major axis extends along the direction of the line connecting the start and end points, and the minor axis vertically constrains the lateral expansion range, thereby accurately focusing on the efficient and feasible search domain within the narrow space of the cabinet. Compared with sampling regions of circles, squares, or other shapes, the ellipse can more tightly enclose the potential optimal path, significantly reducing invalid sampling far from the main path direction, while avoiding insufficient path diversity due to excessive constraints, thus balancing search efficiency and solution space integrity.

[0064] Furthermore, the obstacle proximity penalty term is the sum of the overlapping areas of a circular region centered on the candidate parent node and with a preset safety radius, and the geometric models of all components and wiring harnesses within the secondary cabinet.

[0065] In this preferred embodiment, the obstacle proximity penalty term is defined as the sum of the overlapping areas of a circular region centered on the candidate parent node and with a preset safety radius, and the geometric models of all components and wiring harnesses within the cabinet. By quantifying the spatial intrusion degree between path nodes and obstacles, the safety distance constraint is transformed into a calculable numerical term. This penalty term is directly embedded in the path cost function, enabling the algorithm to actively avoid high-risk areas during the expansion process. The generated path has sufficient safety margin, effectively reducing the risk of interference or wear between wiring harnesses and components during the wiring process.

[0066] In practical implementation, the optimized Rapid Exploration Random Tree Connect (RRT-connect) algorithm of this application is used for robot motion path planning, as shown below:

[0067] I. Initialize the bidirectional tree:

[0068] (1) Create two trees: StartTree grows from the starting point; Goal Tree grows from the goal point.

[0069] (2) Set the maximum number of iterations, fixed basic step size s, obstacle safety distance d, and weight parameter w. area .

[0070] II. Optimized Sampling: In each iteration, sampling is performed alternately on the StartTree and Goal Tree. The sampling point X_rand is generated as follows:

[0071] (1) Adaptive target bias sampling:

[0072] 1.1 Introduce a random probability P∈[0,1] and an adaptive threshold P a Compare;

[0073] 1.2 Calculate the adaptive threshold using the following formula:

[0074]

[0075] Where P max The maximum threshold value is preferably 0.8, P. min The minimum threshold value is preferably 0.3, max is the maximum number of iterations, itr is the current iteration number, and P... S The success rate of collision-free samples;

[0076] 1.3, If P <P aThen let X_rand = the root node X_goal of GoalTree (or conversely, if the tree to be sampled is GoalTree, then X_rand = the root node X_start of StartTree), that is, select the target point as the random point and guide the expansion in the target direction;

[0077] 1.4 Otherwise, random sampling is performed within the elliptical region;

[0078] (2) Elliptic space-constrained sampling:

[0079] 2.1. The sampling is restricted to an elliptical space with the starting point and the target as foci. The preferred parameters of the ellipse are major axis L = 3d and minor axis W = 0.8d, where d = ||X_goal - X_start||.

[0080] 2.2 Generate points within the ellipse using polar coordinates, and convert them to Cartesian coordinates as X_rand.

[0081] 3. Find the nearest node: Use Euclidean distance to find the node X_near that is closest to X_rand in the current tree.

[0082] IV. Expanding to New Nodes: For each tree, expand from the nearest neighbor node X_near in the direction of X_rand to obtain a new node X. new .

[0083] V. Parent node reselection and path rerouting:

[0084] 5.1 Search X new For nearby nodes, calculate the path cost using an optimized cost function, and select the parent node X with the lowest cost. parent :

[0085] The optimized cost function is as follows:

[0086] C(X new )=C(X parent )+d(X parent ,X new )+w area Intersection(X) parent ,X new );

[0087] Where C(X) is the path cost of node X, d(X,Y) is the Euclidean distance between nodes X and Y, and Intersection(X,Y) is the total area of ​​overlap between a circle centered at X and with radius r (e.g., the radius of a robotic arm link) and all obstacles. areaTo control obstacle avoidance weights, i.e., to penalize paths that are close to obstacles during parent node reselection and path rewiring, the algorithm is guided to plan paths that are far away from obstacles.

[0088] 5.2 Rewiring: Check if nearby nodes can be accessed via X. new Find the lowest-cost path, and if so, update the parent nodes of those nodes.

[0089] VI. Bidirectional Tree Connection and Path Extraction:

[0090] 6.1 After each expansion, check if the two trees can be connected (i.e., X). new Is the distance to a node in the GoalTree less than the step size?

[0091] 6.2 Once the connection is successful, the splicing path is: X_start → intermediate point → X_goal.

[0092] 6.3 Continue iterating to the maximum number of iterations, and retain the connection path with the minimum total cost.

[0093] Step S4: Sequentially splice all path segments corresponding to each wire harness to obtain the complete wiring path of the wire harness, and based on each complete wiring path, control the wiring device to automatically perform wire harness arrangement and fixing operations to complete the wiring.

[0094] Furthermore, the path segments are sequentially spliced ​​together to obtain a complete wiring path, which can be achieved through the following preferred implementation method, including steps S41-S42:

[0095] S41: Concatenate all path segments corresponding to each wire harness in sequence to obtain the original wiring path of the wire harness;

[0096] S42: Apply a third-order Bézier curve to the original wiring path for trajectory smoothing to obtain a continuous and smooth complete wiring path.

[0097] In this preferred embodiment, after splicing the path segments to obtain the original wiring path, a third-order Bézier curve is applied for trajectory smoothing. By constructing a high-order continuous curve in segments to replace the original polyline path, sharp inflection points in the path are effectively eliminated, and the smoothness of the wiring operation is improved.

[0098] Furthermore, based on the complete wiring path, controlling the wiring device to automatically perform wire harness arrangement and fixing operations can be achieved through the following preferred implementation method, including steps S43-S44, as follows:

[0099] S43: Convert the complete wiring path into a sequence of motion commands for the wiring device, and generate control commands by combining preset speed and acceleration constraints;

[0100] S44: Send the control command to the wiring device to drive the wiring device to complete the wiring harness arrangement and fixing operation along the complete wiring path.

[0101] In practical implementation, a robot can be used as a wiring device, and speed and acceleration constraints can be added to ensure that the robot's motion conforms to dynamic constraints.

[0102] In this preferred embodiment, the complete wiring path is converted into a sequence of motion instructions for the wiring device and combined with preset speed and acceleration constraints to generate control instructions, ensuring that the movement of the wiring device, like that of a robot, conforms to dynamic constraints; the control instructions are sent to the wiring device to drive it to complete the wiring harness arrangement and fixing operations along the path, realizing a precise mapping from the planned trajectory to physical execution, and ensuring the reliability and repeatability of the wiring operation.

[0103] In summary, compared with the prior art, the above embodiments of this application have the following beneficial effects: They acquire the design drawing data of the secondary cabinet and extract the component installation positions and the start and end points of the wire harness insertion, digitizing the physical cabinet structure and providing accurate geometric and topological input for subsequent automated path planning; they set the intermediate points of the wire harness bundling according to the component installation positions and arrange the insertion start, intermediate, and end points sequentially to form a node sequence, restoring the forced constraint path in the actual wiring process and ensuring that the generated path conforms to engineering specifications; they extract adjacent nodes as start and end points for each wire harness sequentially, and combine this with a fast exploration random tree connection algorithm configured with sampling area constraints and obstacle proximity penalties to generate path segments, thereby limiting the sampling space. This significantly reduces invalid searches and improves planning efficiency. Simultaneously, it introduces an obstacle proximity penalty in the path cost to guide the path away from components, enhancing safety. By sequentially splicing all path segments to obtain a complete wiring path, it achieves segmented planning and overall reconstruction of multi-pathway paths. Based on the complete wiring path, it controls the wiring device to automatically perform harness placement and fixing operations, completing a closed loop from digital planning to physical execution. The synergistic effect of these features enables efficient generation of collision-free wiring paths that meet both process constraints and safety margins in complex environments like secondary cabinets with dense components and limited space. This solves the core technical problem of low path planning efficiency and poor safety in existing automated methods for small-batch, multi-variety scenarios.

[0104] Example 2:

[0105] Please refer to Figure 2 Based on the same inventive concept, the present invention discloses an automated cabling system for a secondary cabinet, comprising: a data acquisition module M1, a sequence generation module M2, a path segment generation module M3, and an execution module M4;

[0106] The data acquisition module M1 is used to acquire the design drawing data of the secondary cabinet, and extract the installation positions of each component inside the secondary cabinet, as well as the position data of the insertion start and end points of each wire harness.

[0107] The sequence generation module M2 is used to set several wire harness bundling intermediate points for each wire harness according to the installation position of each component, and arrange them sequentially in combination with the insertion start point and insertion end point to obtain the node sequence corresponding to each wire harness.

[0108] The path segment generation module M3 is used to extract any two adjacent nodes from the corresponding node sequence for each line bundle in sequence, and after each extraction, the two extracted adjacent nodes are used as the starting point and the ending point, and combined with the preset fast exploration random tree connection algorithm, to generate the corresponding path segment; wherein, the fast exploration random tree connection algorithm is configured with sampling area constraints during sampling and obstacle proximity penalty term when calculating path cost.

[0109] Furthermore, the path segment generation module M3 includes: an initialization unit and an iteration unit;

[0110] The initialization unit is used to initialize and construct the first tree structure and the second tree structure respectively, using the coordinates of the starting point and the ending point as the root.

[0111] The iterative unit is used to iterate over the first tree structure and the second tree structure according to the fast exploratory random tree connection algorithm until a preset termination condition is met, and output the path segment with the minimum sum of path costs for each node; and in each iteration, one of the first tree structure and the second tree structure is alternately selected as the current expansion tree, a probability value is randomly generated and compared with a preset adaptive threshold. If the probability value is less than the adaptive threshold, the root node coordinates of the other tree are used as the target point for this sampling; otherwise, a sampling point is generated in the elliptical region with the starting point coordinates and the ending point coordinates as the focus; and the distance to the sampling point is found in the current expansion tree. Find the nearest existing node to the sampling point or target point, and extend a new node from the nearest existing node toward the sampling point or target point; determine whether the connection from the nearest existing node to the new node collides with an obstacle. If no collision occurs, select all existing nodes in the current extended tree that are within a preset neighborhood of the new node as candidate parent nodes. For each candidate parent node, calculate the path cost of the new node, and select the candidate parent node with the minimum path cost as the final parent node of the new node; determine whether there is a connectable node pair between the current extended tree and another tree. If so, splice them together to obtain a complete path, which is the currently found path segment.

[0112] The path cost is obtained by weighted summation of the path cost of the candidate parent node, the Euclidean distance between the candidate parent node and the new node, and the obstacle proximity penalty.

[0113] In this preferred embodiment, a first tree structure and a second tree structure are constructed with the start and end point coordinates as roots, respectively, establishing a bidirectional path search framework to accelerate convergence. In each iteration, the expansion tree is alternately selected, and the sampling strategy is determined by comparing a random probability value with an adaptive threshold. If the probability value is less than the threshold, the target point guides the sampling to enhance directionality; otherwise, sampling points are generated within an elliptical region with the start and end points as foci. Geometric constraints focus the search on the potential optimal path region, thereby significantly improving sampling efficiency. The nearest existing node is found in the current expansion tree, and new nodes are expanded in the direction of the sampling points to achieve incremental growth of the tree structure. The connection between the new node and its source node is determined by whether it is an obstacle. Object collision detection ensures the path is collision-free. If no collision occurs, all existing nodes in the neighborhood of the new node are considered as candidate parent nodes, and the path cost of the new node is calculated. This cost is obtained by weighted summation of the cumulative path cost of the candidate parent nodes, Euclidean distance, and obstacle proximity penalty. Taking into account historical costs, path length, and safety risks, the node with the lowest cost is selected as the final parent node, thus achieving dynamic optimization of local paths. The system determines whether there is a connectable node pair between two trees and, if so, splices the paths together as the currently found path segment, completing the fusion of bidirectional paths. The entire iterative process integrates elliptical region constraints and obstacle penalty terms to ensure path feasibility while balancing search efficiency and safety.

[0114] Furthermore, the iterative unit includes: an ellipse parameter calculation subunit and an ellipse construction subunit;

[0115] The ellipse parameter calculation subunit is used to calculate the Euclidean distance between the starting point coordinates and the ending point coordinates, and, in combination with a preset scaling factor, to calculate the length of the major axis and the length of the minor axis of the ellipse.

[0116] The ellipse construction subunit is used to combine the lengths of the major and minor axes of the ellipse and construct an elliptical region with the starting and ending coordinates as the focal points.

[0117] In this preferred embodiment, the Euclidean distance between the start and end points is calculated and the lengths of the major and minor axes of the ellipse are determined by combining the preset scaling factor. An elliptical region is constructed with the start and end points as foci. Based on the geometric characteristics of the ellipse—that is, the sum of the distances from any point on the ellipse to the two foci is a constant—this shape naturally covers all possible shortest path candidate regions. The major axis extends along the direction of the line connecting the start and end points, and the minor axis vertically constrains the lateral expansion range, thereby accurately focusing on the efficient and feasible search domain within the narrow space of the cabinet. Compared with sampling regions of circles, squares, or other shapes, the ellipse can more tightly enclose the potential optimal path, significantly reducing invalid sampling far from the main path direction, while avoiding insufficient path diversity due to excessive constraints, thus balancing search efficiency and solution space integrity.

[0118] Furthermore, the obstacle proximity penalty term is the sum of the overlapping areas of a circular region centered on the candidate parent node and with a preset safety radius, and the geometric models of all components and wiring harnesses within the secondary cabinet.

[0119] In this preferred embodiment, the obstacle proximity penalty term is defined as the sum of the overlapping areas of a circular region centered on the candidate parent node and with a preset safety radius, and the geometric models of all components and wiring harnesses within the cabinet. By quantifying the spatial intrusion degree between path nodes and obstacles, the safety distance constraint is transformed into a calculable numerical term. This penalty term is directly embedded in the path cost function, enabling the algorithm to actively avoid high-risk areas during the expansion process. The generated path has sufficient safety margin, effectively reducing the risk of interference or wear between wiring harnesses and components during the wiring process.

[0120] The execution module M4 is used to sequentially splice all path segments corresponding to each wire harness to obtain the complete wiring path of the wire harness, and based on each complete wiring path, control the wiring device to automatically perform wire harness arrangement and fixing operations to complete the wiring.

[0121] Furthermore, the execution module M4 includes: a splicing unit and a smoothing unit;

[0122] The splicing unit is used to splice all path segments corresponding to each wire bundle in sequence to obtain the original wiring path of the wire bundle.

[0123] The smoothing unit is used to apply a third-order Bézier curve to the original wiring path for trajectory smoothing to obtain a continuous and smooth complete wiring path.

[0124] In this preferred embodiment, after splicing the path segments to obtain the original wiring path, a third-order Bézier curve is applied for trajectory smoothing. By constructing a high-order continuous curve in segments to replace the original polyline path, sharp inflection points in the path are effectively eliminated, and the smoothness of the wiring operation is improved.

[0125] Furthermore, the execution module M4 also includes: an instruction generation unit and an instruction execution unit;

[0126] The instruction generation unit is used to convert the complete wiring path into a sequence of motion instructions for the wiring device, and generate control instructions by combining preset speed and acceleration constraints.

[0127] The instruction execution unit is used to send the control instruction to the wiring device, driving the wiring device to complete the wiring harness arrangement and fixing operation along the complete wiring path.

[0128] In this preferred embodiment, the complete wiring path is converted into a sequence of motion instructions for the wiring device and combined with preset speed and acceleration constraints to generate control instructions, ensuring that the movement of the wiring device, like that of a robot, conforms to dynamic constraints; the control instructions are sent to the wiring device to drive it to complete the wiring harness arrangement and fixing operations along the path, realizing a precise mapping from the planned trajectory to physical execution, and ensuring the reliability and repeatability of the wiring operation.

[0129] In summary, compared with the prior art, the embodiments of this application have the following beneficial effects: They acquire the design drawing data of the secondary cabinet and extract the component installation positions and the start and end points of the wire harness insertion, digitizing the physical cabinet structure and providing accurate geometric and topological input for subsequent automated path planning; they set the intermediate points of the wire harness bundling according to the component installation positions and arrange the insertion start, intermediate, and end points sequentially to form a node sequence, restoring the forced constraint path in the actual wiring process and ensuring that the generated path conforms to engineering specifications; they sequentially extract adjacent nodes as start and end points for each wire harness, and combine this with a fast exploration random tree connection algorithm configured with sampling area constraints and obstacle proximity penalties to generate path segments, thereby limiting the sampling space... This approach significantly reduces invalid searches and improves planning efficiency. Simultaneously, it introduces an obstacle proximity penalty in the path cost to guide the path away from components, enhancing safety. By sequentially piecing together all path segments to obtain a complete wiring path, it achieves segmented planning and overall reconstruction of multi-pathway paths. Based on the complete wiring path, the wiring device automatically executes harness placement and fixing operations, completing a closed loop from digital planning to physical execution. The synergistic effect of these features enables efficient generation of collision-free wiring paths that meet both process constraints and safety margins in complex environments like secondary cabinets with dense components and limited space. This solves the core technical problem of low path planning efficiency and poor safety in existing automated methods for small-batch, multi-variety scenarios.

[0130] Example 3:

[0131] This invention also provides a computer program product, including a computer program or instructions, capable of running on a computing device or stored in any available medium. When the computer program product is run on at least one computing device, it causes the at least one computing device to execute any of the automated cabling methods for secondary cabinets of this invention.

[0132] Example 4:

[0133] This invention also provides a computer-readable storage medium storing at least one executable instruction. When the executable instruction is executed on the automated cabling system of the secondary cabinet, the automated cabling system of the secondary cabinet performs an automated cabling method for a secondary cabinet according to any of the above method embodiments.

[0134] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. Similarly, for the purpose of simplification and aiding understanding of one or more aspects of the invention, in the above description of exemplary embodiments of this application, various features of the embodiments are sometimes grouped together in a single embodiment, figure, or description thereof. The claims, which follow the detailed description, are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this application.

[0135] Those skilled in the art will understand that the modules in the system of the embodiments can be adaptively changed and placed in one or more systems different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components, except that at least some of such features and / or processes or units are mutually exclusive.

Claims

1. A method of automated wiring of a secondary containment cabinet, characterized by, The method comprises the following steps: obtaining design drawing data of a secondary screen cabinet, and extracting installation positions of each component inside the secondary screen cabinet, and position data of a plug-in starting point and a plug-in ending point of each wire harness; setting a plurality of wire harness bundling intermediate points for each wire harness according to the installation positions of each component, and sequentially arranging the plug-in starting point and the plug-in ending point to obtain a node sequence corresponding to each wire harness; for each wire harness, sequentially extracting any two adjacent nodes in the corresponding node sequence, and after each extraction, taking the two adjacent nodes as a starting point and an ending point respectively, and combining a preset rapid exploration random tree connection algorithm to generate a corresponding path segment; wherein the rapid exploration random tree connection algorithm is configured with a sampling area constraint when sampling, and is configured with an obstacle proximity penalty term when calculating a path cost. sequentially splicing all path segments corresponding to each wire harness to obtain a complete wiring path of the wire harness, and based on each complete wiring path, controlling a wiring device to automatically perform wire harness arrangement and fixing operations to complete wiring.

2. The method of claim 1, wherein, the step of taking the two adjacent nodes as a starting point and an ending point respectively, and combining a preset rapid exploration random tree connection algorithm to generate a corresponding path segment, comprises: respectively taking the coordinates of the starting point and the ending point as roots to initialize construction of a first tree structure and a second tree structure; iterating the first tree structure and the second tree structure according to the rapid exploration random tree connection algorithm until a preset end condition is met, and outputting a path segment with the smallest sum of path costs of each node; and in each iteration, alternately selecting one of the first tree structure and the second tree structure as a current expansion tree, generating a probability value at random and comparing it with a preset adaptive threshold value, if the probability value is less than the adaptive threshold value, taking the root node coordinates of the other tree as a target point of this sampling, otherwise, generating a sampling point in an elliptical region with the starting point coordinates and the ending point coordinates as foci; finding the nearest existing node to the sampling point or the target point in the current expansion tree, and expanding a new node in the direction from the nearest existing node to the sampling point or the target point; judging whether the connecting line from the nearest existing node to the new node collides with an obstacle, if not, taking all existing nodes within a preset neighborhood range of the new node in the current expansion tree as candidate parent nodes, calculating the path cost of the new node for each candidate parent node, and selecting the candidate parent node with the smallest path cost as the final parent node of the new node; judging whether there is a connectable node pair between the current expansion tree and the other tree, if there is, splicing to obtain a complete path as the path segment found currently; wherein the path cost is obtained by weighted summation of the path cost of the candidate parent node, the Euclidean distance between the candidate parent node and the new node, and the obstacle proximity penalty term.

3. The automated wiring method for a secondary cabinet as described in claim 2, characterized in that, the elliptical region is constructed with the starting point coordinates and the ending point coordinates as foci, comprising: calculating the Euclidean distance between the starting point coordinates and the ending point coordinates, and calculating the elliptical major axis length and the elliptical minor axis length by combining a preset proportion coefficient; By combining the lengths of the major and minor axes of the ellipse, and using the coordinates of the starting and ending points as foci, an elliptical region is constructed.

4. The method of claim 2, wherein the secondary containment cabinet is a glove box. 5 The obstacle proximity penalty is the sum of the overlapping areas of the geometric models of all components and wiring harnesses within the secondary cabinet, with the candidate parent node as the center and a preset safety radius as the radius.

5. The method of claim 1, wherein, The step of sequentially splicing together all path segments corresponding to each wire harness to obtain the complete wiring path of the wire harness includes: By sequentially splicing together all the path segments corresponding to each wire harness, the original wiring path of the wire harness can be obtained. The original wiring path is smoothed by applying a third-order Bézier curve to obtain a continuous and smooth complete wiring path.

6. The method of claim 1, wherein, Based on each of the complete wiring paths, the control of the wiring device to automatically perform wire harness arrangement and fixing operations includes: The complete wiring path is converted into a sequence of motion commands for the wiring device, and control commands are generated by combining preset speed and acceleration constraints. The control command is sent to the wiring device, which then drives the wiring device to complete the wiring harness arrangement and fixing operation along the complete wiring path.

7. An automated wiring system for a secondary containment cabinet, characterized by, include: The module comprises a data acquisition module, a sequence generation module, a path fragment generation module, and an execution module. The data acquisition module is used to acquire the design drawing data of the secondary cabinet, and extract the installation position of each component inside the secondary cabinet, as well as the position data of the insertion start point and insertion end point of each wire harness. The sequence generation module is used to set several wire harness bundling intermediate points for each wire harness according to the installation position of each component, and arrange them sequentially in combination with the insertion start point and insertion end point to obtain the node sequence corresponding to each wire harness. The path segment generation module is used to extract any two adjacent nodes from the corresponding node sequence for each line bundle, and after each extraction, the two extracted adjacent nodes are used as the starting point and the ending point, and a preset fast exploration random tree connection algorithm is used to generate the corresponding path segment; wherein, the fast exploration random tree connection algorithm is configured with sampling area constraints during sampling and with obstacle proximity penalty term when calculating path cost; The execution module is used to sequentially splice all path segments corresponding to each wire harness to obtain the complete wiring path of the wire harness, and based on each complete wiring path, control the wiring device to automatically perform wire harness arrangement and fixing operations to complete the wiring.

8. An automated wiring system for a secondary containment cabinet as defined in claim 7, wherein, The path segment generation module includes: an initialization unit and an iteration unit; The initialization unit is used to initialize and construct the first tree structure and the second tree structure respectively, using the coordinates of the starting point and the ending point as the root. The iteration unit is configured to perform iteration on the first tree structure and the second tree structure according to the fast exploration random tree connection algorithm until a preset ending condition is met, and output a path segment with a minimum sum of path costs of nodes; and in each iteration, one of the first tree structure and the second tree structure is selected as a current expansion tree, a probability value is randomly generated and compared with a preset adaptive threshold, if the probability value is less than the adaptive threshold, a root node coordinate of the other tree is taken as a target point of this sampling, otherwise, a sampling point is generated in an elliptical region with the start point coordinate and the end point coordinate as foci; a nearest existing node to the sampling point or the target point is searched in the current expansion tree, and a new node is expanded from the nearest existing node to the sampling point or the target point; it is judged whether a line from the nearest existing node to the new node collides with an obstacle, if not, all existing nodes in a preset neighborhood range of the new node in the current expansion tree are taken as candidate parent nodes, for each candidate parent node, a path cost of the new node is calculated, and a candidate parent node with a minimum path cost is selected as a final parent node of the new node; it is judged whether there is a connectable node pair between the current expansion tree and the other tree, if yes, a complete path is obtained by splicing, as a path segment currently found. The path cost is obtained by weighted summation of a path cost of the candidate parent node, an Euclidean distance between the candidate parent node and the new node, and an obstacle proximity penalty term.

9. A computer program product comprising computer programs or instructions, characterized in that, The computer program or instructions, when executed, implement the secondary screen cabinet automatic wiring method of any one of claims 1-6.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program, when executed by the processor, implements the secondary screen cabinet automatic wiring method of any one of claims 1-6.

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