A terminal strip connection error identification method and system for a low-voltage electric control cabinet

Abstract

The application relates to the technical field of image processing, and discloses a terminal strip connection error identification method and system for a low-voltage electric control cabinet, wherein the method comprises the following steps: acquiring multi-view scanning image data of a terminal strip; generating three-dimensional color reconstruction data of the terminal strip; segmenting a terminal layer position; generating a wire layer position hole position mapping table; analyzing a standard wiring logic description file in the cabinet; performing physical hole error comparison and interlayer logic conflict detection; and outputting a connection error identification result. Compared with the prior art which relies on simple conduction testing, the technical problem that the wire layer position connection error and the standby device logic connection error cannot be accurately identified under the condition that the multi-layer terminal strip is densely arranged and the light in the cabinet is insufficient is solved. Due to the three-dimensional reconstruction, terminal layer position segmentation and standard wiring logic constraint verification, the terminal strip physical hole error and interlayer logic error are simultaneously identified, and the accuracy of the low-voltage electric control cabinet connection detection and the on-site verification efficiency are improved.

Description

Technical Field

[0001] This invention relates to the field of image processing technology, and in particular to a method and system for identifying incorrect wiring of terminal blocks in low-voltage electrical control cabinets. Background Technology

[0002] Currently, in the assembly, commissioning, and maintenance of low-voltage electrical control cabinets, the correct wiring of terminal blocks is crucial. Wiring errors can lead to equipment malfunctions, signal interference, and even safety accidents. Traditional wiring checks mainly rely on manual visual verification or point-to-point testing using tools such as multimeters, which is inefficient and prone to errors or omissions due to visual fatigue or misunderstandings of drawings.

[0003] With the development of machine vision technology, some wiring detection methods based on two-dimensional images have emerged. However, these methods are often difficult to handle dense, heavily obstructed terminal block scenarios, cannot obtain accurate three-dimensional spatial position information, lack the ability to distinguish the layers of multi-layer terminal blocks, and cannot verify the logical constraints that should be followed by the wiring between different layers (such as the layer position difference requirements between backup equipment). Therefore, there is an urgent need for a method and system that can perform non-contact, automated, and high-precision detection of low-voltage electrical control cabinet terminal blocks in complex three-dimensional spatial environments, and can simultaneously perform physical hole position comparison and inter-layer logical conflict verification, in order to improve the efficiency and accuracy of wiring detection and ensure the safe and stable operation of electrical systems. Summary of the Invention

[0004] To address the aforementioned technical shortcomings, the purpose of this invention is to propose a method for identifying incorrect wiring connections in low-voltage electrical control cabinet terminal blocks. This method aims to solve the technical problem that existing technologies rely on simple continuity tests, which are particularly inadequate for accurately identifying incorrect wiring connections at different wire layers and logical misconnections in backup equipment under conditions of densely arranged multi-layer terminal blocks and insufficient lighting inside the cabinet.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides a method for identifying incorrect wiring of terminal blocks in low-voltage electrical control cabinets.

[0006] The method for identifying incorrect wiring connections in low-voltage electrical control cabinet terminal blocks includes: Step S10: Acquire multi-view scanning image data of the terminal block, and perform a three-dimensional reconstruction task based on the multi-view scanning image data of the terminal block using adaptive bilateral filtering and iterative nearest point algorithm, and output three-dimensional color reconstruction data of the terminal block; Step S20: Perform terminal layer segmentation based on the three-dimensional color reconstruction data of the terminal block, and output the terminal layer point cloud; Step S30: Based on the terminal layer site cloud, perform wire hole matching task sequentially using Alpha Shapes boundary extraction, U-Net semantic segmentation and cylindrical fitting, and output wire layer site hole mapping table; Step S40: Based on the preset cabinet terminal standard wiring logic file, execute the standard wiring relationship parsing task and output standard wiring constraint data; Step S50: Perform physical misconnection comparison and inter-layer logical conflict detection based on the wire layer hole mapping table and the standard wiring constraint data, and output the misconnection identification result.

[0007] Preferably, step S10, which involves acquiring multi-view scanning image data of the terminal block, performing a three-dimensional reconstruction task based on the multi-view scanning image data of the terminal block using adaptive bilateral filtering and iterative nearest-point algorithm, and outputting three-dimensional color reconstruction data of the terminal block, specifically includes: Step S101: Simultaneously acquire depth map sequence, color image sequence and pose prior data of the terminal block area of ​​the low-voltage electrical control cabinet through the depth sensor, RGB camera and inertial measurement unit of the preset AR glasses to obtain multi-view scanning image data of the terminal block; Step S102: Perform adaptive bilateral filtering and Gaussian noise suppression on the depth map sequence in the multi-view scanning image data of the terminal block to obtain a denoised depth map sequence; Step S103: Based on the denoised depth map sequence and the pose prior data, multi-frame point cloud registration is performed using the iterative nearest point algorithm, and the color information in the color image sequence is projected onto the registered point cloud to obtain the three-dimensional color reconstruction data of the terminal block.

[0008] Preferably, step S20, which involves performing terminal layer segmentation based on the three-dimensional color reconstruction data of the terminal block and outputting the terminal layer point cluster, specifically includes: Step S201: Perform statistical outlier filtering on the three-dimensional color reconstruction data of the terminal block, and calculate the normal vector of each point in the filtered point cloud to obtain the normal vector point cloud of the terminal block; Step S202: Perform spherical coordinate clustering based on the terminal block normal vector point cloud, and extract candidate plane point clouds that are consistent with the normal direction of the terminal block principal plane; Step S203: Based on the candidate planar point cloud, a random sampling consensus algorithm with interlayer spacing constraints is used to fit multiple parallel terminal planes, and layer marking is performed according to the spatial order of the multiple parallel terminal planes to obtain the terminal layer point cloud set.

[0009] Preferably, in step S203, multiple parallel terminal planes are fitted using a random sampling consensus algorithm with interlayer spacing constraints based on the candidate planar point cloud, specifically including: Using the interlayer spacing in the terminal block CAD model or terminal block specification parameters corresponding to the candidate planar point cloud as the initial interlayer spacing, a planar fitting evaluation function is constructed:

[0010] in, The value represents the plane fitting evaluation, and N represents the number of candidate plane points in the candidate plane point cloud that participated in the plane fitting. Indicates the first Candidate plane points, Indicates the first The terminal plane corresponding to each candidate plane point Indicates the first The distance from each candidate plane point to the corresponding terminal plane. This represents the Huber loss function. Indicates the interlayer spacing constraint weight. Indicates the number of terminal planes. Indicates the first The actual layer spacing between adjacent terminal planes of a group Indicates the reference layer spacing used in the current iteration; After each round of fitting, the median of the actual interlayer spacing between adjacent fitted terminal planes is calculated, and the median is updated as the reference interlayer spacing for the next round of iteration, until the change in the reference interlayer spacing between two adjacent rounds is less than the preset convergence threshold, and finally the terminal layer site cluster is output.

[0011] Preferably, step S30, which involves sequentially performing wire hole matching based on the terminal layer site set using Alpha Shapes boundary extraction, U-Net semantic segmentation, and cylindrical fitting, and outputting a wire layer site mapping table, specifically includes: Step S301: Based on each layer of terminal point cloud in the terminal layer point cloud set, determine the candidate area of ​​the wiring hole by normal mutation and point density change, and use the Alpha Shapes algorithm to extract the boundary contour of the metal connector, and perform circle fitting on the boundary contour of the metal connector to obtain the terminal hole position data. Step S302: Based on the terminal hole position data, extract the corresponding local color image block from the three-dimensional color reconstruction data of the terminal block, and input the local color image block into the preset edge-aware U-Net semantic segmentation network to obtain the two-dimensional mask of the wire; Step S303: Extract the three-dimensional conductor point cloud cluster based on the two-dimensional mask of the conductor, and determine the conductor axis and conductor radius by cylindrical fitting method in combination with the conductor diameter prior library. Generate the conductor layer hole mapping table according to the positional relationship between the conductor axis and the terminal hole data.

[0012] Preferably, step S40, which involves executing a standard wiring relationship parsing task based on a preset cabinet terminal standard wiring logic file and outputting standard wiring constraint data, specifically includes: Step S401: Parse the standard wiring logic file of the terminal inside the cabinet, extract the function label, wire identification, equipment group identification and mutual backup equipment relationship of the wires to be connected to the terminal hole, and obtain the terminal wiring rule data; Step S402: Based on the terminal wiring rule data, establish a expected connection table, which includes expected layer, expected hole position, expected function label and expected wire identification, to obtain expected wiring data; Step S403: Construct a logical constraint matrix based on the expected wiring data and the mutual backup device relationship to obtain standard wiring constraint data.

[0013] Preferably, step S50, which involves comparing physical misconnections and detecting inter-layer logical conflicts based on the conductor layer-hole mapping table and the standard wiring constraint data, and outputting the misconnection identification result, specifically includes: Step S501: Match the wire layer and hole mapping table with the expected connection table in the standard wiring constraint data according to the layer and hole position. If the actual wire function label is inconsistent with the expected function label, generate a physical misalignment identification result. Step S502: Based on the wire layer hole mapping table and the logical constraint matrix in the standard wiring constraint data, perform inter-layer logical conflict detection on the same type of functional lines that are backup devices to obtain the inter-layer logical misconnection identification result; The inter-layer logical conflict detection uses the following judgment formula:

[0014] in, Indicates the first The device and the first Logical conflict indicators between devices Indicates the first The actual access level of the same function line of each device. Indicates the first The actual access level of the same function line of each device. Indicates the first The device and the first Preset layer difference between devices; when When, it is determined that there is a logical misconnection between layers; when When this occurs, it is determined that there are no logical misconnections between layers; Step S503: Based on the physical misconnection identification result and the interlayer logical misconnection identification result, the hole position mark, layer highlight and correction path prompt are superimposed on the terminal block real scene image through AR glasses to obtain the misconnection identification result.

[0015] The present invention also provides a low-voltage electrical control cabinet terminal block wiring misconnection identification system, comprising: The 3D reconstruction module is used to acquire multi-view scan image data of the terminal block, and performs 3D reconstruction task based on the multi-view scan image data of the terminal block using adaptive bilateral filtering and iterative nearest point algorithm, and outputs 3D color reconstruction data of the terminal block; The layer segmentation module is used to perform terminal layer segmentation tasks based on the three-dimensional color reconstruction data of the terminal block and output a cluster of terminal layer points. The wire hole matching module is used to perform wire hole matching tasks based on the terminal layer site set by sequentially using Alpha Shapes boundary extraction, U-Net semantic segmentation and cylindrical fitting, and outputs a wire layer site hole mapping table. The standard wiring parsing module is used to perform standard wiring relationship parsing tasks based on the preset cabinet terminal standard wiring logic file, and output standard wiring constraint data; The misconnection identification module is used to perform physical misconnection comparison and inter-layer logical conflict detection based on the wire layer hole location mapping table and the standard wiring constraint data, and output the misconnection identification result.

[0016] The present invention also provides a low-voltage electrical control cabinet terminal block wiring misconnection identification device, the low-voltage electrical control cabinet terminal block wiring misconnection identification device comprising: a memory, a processor, and a low-voltage electrical control cabinet terminal block wiring misconnection identification program stored in the memory and executable on the processor, the low-voltage electrical control cabinet terminal block wiring misconnection identification program implementing the above method when executed by the processor.

[0017] The present invention also provides a computer program product, the computer program product including a low-voltage electrical control cabinet terminal block wiring misconnection identification program, which implements the above method when executed by a processor.

[0018] The beneficial effects of this invention are as follows: 1. This invention constructs a complete automated wiring misconnection identification process by integrating multi-view 3D reconstruction, layer segmentation, semantic segmentation, and standard logic parsing techniques. First, the method utilizes adaptive bilateral filtering and iterative nearest-point algorithms to reconstruct a high-precision 3D color model of the terminal block from multi-view scan data, providing accurate spatial and visual foundational data for subsequent analysis. Next, layer segmentation technology separates the dense terminal point cloud according to physical layers, solving the identification challenge of multi-layer terminal blocks. Then, combining Alpha Shapes boundary extraction, U-Net semantic segmentation, and cylinder fitting, the physical connection relationship between wires and terminal holes is accurately matched. Finally, by parsing standard wiring logic and comparing it with actual physical connections, not only can incorrect physical hole connections be detected, but also misconnections violating inter-layer logic constraints (such as incorrect wiring layers for backup equipment) can be identified, achieving comprehensive verification from physical to logical levels.

[0019] 2. The technical solution proposed in this invention effectively overcomes the limitations of traditional manual inspection and two-dimensional image detection methods. Compared to manual inspection, this invention achieves automated, non-contact detection, significantly improving detection efficiency and consistency, and reducing labor costs and the risk of human error. Compared to simple two-dimensional image comparison, this invention utilizes three-dimensional information to more accurately handle occlusion, distinguish layers, locate hole positions, and determine the spatial direction of conductors, resulting in higher detection accuracy. Simultaneously, the introduction of standard wiring logic for inter-layer conflict detection expands the dimensions of misconnection identification, enabling the discovery of logical errors that traditional methods cannot identify. This provides a more comprehensive guarantee of wiring quality and offers strong technical support for the intelligent assembly and maintenance of low-voltage electrical control cabinets. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the first embodiment of a method for identifying incorrect wiring of terminal blocks in a low-voltage electrical control cabinet according to the present invention.

[0021] Figure 2 This is a schematic diagram of the multi-plane automatic segmentation result of the first embodiment of the low-voltage electrical control cabinet terminal block wiring misconnection identification method of the present invention.

[0022] Figure 3 This is a schematic diagram of the iterative convergence process of the structured regularization term in the first embodiment of the method for identifying incorrect wiring of terminal blocks in a low-voltage electrical control cabinet according to the present invention.

[0023] Figure 4 This is a schematic diagram of the cylindrical axis fitting residual distribution of a first embodiment of the low-voltage electrical control cabinet terminal block wiring misconnection identification method of the present invention.

[0024] Figure 5 This is a schematic diagram of a segment of the expected connection table of a first embodiment of a method for identifying incorrect wiring of terminal blocks in a low-voltage electrical control cabinet according to the present invention.

[0025] Figure 6 This is a schematic diagram of the mutual exclusion hierarchical relationship constraint matrix of the first embodiment of the low-voltage electrical control cabinet terminal block wiring misconnection identification method of the present invention. Detailed Implementation

[0026] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0027] 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.

[0028] Example 1: As Figure 1 The diagram shown is a flowchart illustrating the first embodiment of a method for identifying incorrect wiring of terminal blocks in a low-voltage electrical control cabinet according to the present invention.

[0029] In the first embodiment, the method for identifying incorrect wiring of terminal blocks in a low-voltage electrical control cabinet includes: Step S10: Acquire multi-view scanning image data of the terminal block, and perform a three-dimensional reconstruction task based on the multi-view scanning image data of the terminal block using adaptive bilateral filtering and iterative nearest point algorithm, and output three-dimensional color reconstruction data of the terminal block; The "terminal strip multi-view scanning image data" in this step refers to the raw perception data set of the terminal strip area of ​​the low-voltage electrical control cabinet, synchronously collected from multiple angles by a device (such as AR glasses) equipped with a depth sensor, RGB camera, and inertial measurement unit (IMU). Specifically, it includes depth map sequences, color image sequences, and the device's own pose prior data. "Adaptive bilateral filtering" is a noise suppression method that adaptively adjusts filtering parameters based on local image features, particularly suitable for removing noise while preserving details such as terminal edges in the depth image. "Iterative nearest point algorithm" is a classic point cloud registration algorithm used to accurately align multiple frames of point cloud data collected from different perspectives with pose priors to the same coordinate system. The "terminal strip 3D color reconstruction data" output in this step is a point cloud or mesh model that integrates accurate 3D geometric information and corresponding color textures. It serves as the basic data input for all subsequent analysis tasks, and its accuracy directly affects the accuracy of the final misconnection identification.

[0030] This step plays a crucial role in data acquisition and preliminary modeling in this invention. Through multi-sensor fusion, it combines two-dimensional visual information with three-dimensional depth and spatial pose information to generate a digital 3D model that accurately reflects the actual spatial layout and appearance of the terminal block. This model not only includes the overall shape and size of the terminal block but also retains the visual features of each terminal, such as color and texture, providing a rich and accurate data foundation for subsequent layer segmentation, hole identification, and wire detection. Without this high-quality 3D reconstruction, subsequent detailed point cloud-based analysis would be difficult to perform.

[0031] Compared to traditional wiring inspection methods that may rely on single-view photography or manual measurement, this step utilizes multi-view scanning and 3D reconstruction technology, offering significant advantages. Traditional methods acquire only localized, two-dimensional information, making it difficult to handle complex terminal block structures, severe wire obstruction, and accurate 3D coordinates. This invention, by actively acquiring multi-view data and performing 3D registration, overcomes the limitations of single-view perspectives and obstruction issues, reconstructing a complete, three-dimensional terminal block model. This ensures that subsequent analysis is based on accurate spatial relationships, a prerequisite for achieving high-precision automated inspection.

[0032] For example, in a typical low-voltage electrical control cabinet, terminal blocks may be installed deep inside the cabinet, possibly partially obstructed by other cables or components. Inspectors wearing AR glasses move around the cabinet, with sensors on the glasses continuously collecting data. Even if the front of a terminal is obstructed, data can still be collected from a side angle. The iterative nearest-point algorithm stitches together these point cloud fragments from different angles (each fragment carrying information about the glasses' pose at the time of acquisition). Figure 1 By precisely piecing them together, a three-dimensional color model is ultimately generated that can fully present the entire appearance of the terminal block even with partial occlusion, just like generating a virtual copy of the terminal block in a computer that can be viewed from any angle.

[0033] Step S20: Perform terminal layer segmentation based on the three-dimensional color reconstruction data of the terminal block, and output the terminal layer point cloud; The "terminal layer segmentation task" in this step refers to the process of extracting and labeling the terminal point clouds belonging to different physical layers (such as upper, middle, and lower layers) from the 3D color reconstruction data obtained in step S10 (usually a dense point cloud containing the entire terminal block and its background). "Statistical outlier filtering" is used to remove discrete and isolated points caused by noise in the reconstructed point cloud. "Normal vector calculation" is the basic operation for obtaining the local surface orientation of each point in the point cloud. "Spherical coordinate clustering" is a preliminary grouping based on the normal vector direction of the points, quickly filtering out point cloud regions with roughly parallel normals (i.e., consistent orientation), which are likely to correspond to the terminal mounting plane. The "random sampling consensus algorithm with interlayer spacing constraints" is a robust model fitting method used here to fit multiple planar models representing the installation positions of different layers of terminals from the candidate point cloud, and uses known interlayer spacing priors (from CAD models or specification parameters) as constraints to ensure that the spacing between the fitted planes conforms to physical reality, thereby accurately distinguishing and labeling each layer. The output "terminal layer site cloud" is a structured dataset that clearly indicates which 3D points belong to the first layer of terminals, which belong to the second layer of terminals, etc., laying the foundation for subsequent detailed analysis on a layer-by-layer basis.

[0034] This step in this invention enables structured analysis of 3D reconstructed data, a crucial step in transforming the overall point cloud into manageable and analyzable hierarchical units. Terminal blocks in low-voltage electrical control cabinets are typically multi-layered, and wire connections may span multiple layers. Without layer segmentation, all terminal points are mixed together, making it impossible to accurately determine which hole a wire is connected to on which layer, and hindering subsequent inter-layer logic conflict detection. Therefore, this step uses advanced point cloud processing algorithms to automatically identify and separate each terminal layer, enabling subsequent hole matching and logic detection to be performed precisely within the "layer" context. It serves as a bridge connecting 3D perception and wiring logic analysis.

[0035] Compared to traditional methods that do not distinguish between layers or rely solely on color or 2D projection for coarse layering, this step's segmentation method based on 3D point cloud geometric features and spatial constraints is more accurate and robust. Traditional methods are prone to failure when terminals are similar in color, interlayer occlusion occurs, or the shooting angle is unfavorable. This invention utilizes the surface normals and spatial distribution patterns of the point cloud, combined with the strong physical prior of interlayer spacing for constraint fitting. Even when terminals in different layers have similar appearances and some point cloud data is missing, it can robustly segment the correct layers, ensuring the accuracy of layer information. This is a necessary condition for achieving accurate hole matching and logical conflict detection.

[0036] For example, consider a three-layer terminal block with 20 terminals per layer. The reconstructed point cloud contains thousands of points. Step S20 first filters out some noise points, then calculates the direction (normal vector) of the tiny plane containing each point. It is found that the normal vectors of most points roughly point to three concentrated directions (corresponding to the orientation of the three mounting surfaces). By clustering, these points are initially grouped to obtain (from specifications or CAD) that the layer spacing of the terminal block is approximately 10 mm. Using this as a reference, a constrained fitting algorithm is applied to find three parallel planes with a spacing of approximately 10 mm from the point cloud, and the terminal points close to each plane are classified into the corresponding layers. Finally, three independent point cloud subsets are obtained, representing the upper, middle, and lower layers of terminals, respectively, allowing for the search and matching of wire hole positions for each layer.

[0037] For example, such as Figure 2 As shown, Figure 2 This is a schematic diagram of the multi-plane automatic segmentation result of the present invention, corresponding to the terminal layer segmentation process in step S20. Figure 2 As can be seen, after statistical outlier filtering, normal vector clustering, and random sampling consistency plane fitting, the flying points, first-layer terminal point clouds, second-layer terminal point clouds, and third-layer terminal point clouds in the 3D color reconstruction data of the terminal block are marked and displayed respectively. It is understandable that flying points mainly originate from metal reflections inside the cabinet, wire obstruction edges, and distance jumps in depth sensor measurements. By removing outliers before plane segmentation, flying points can be avoided from participating in terminal layer fitting and causing layer shift. Figure 2 The point clouds of each layer in the middle exhibit a relatively parallel and stable spatial distribution along the terminal block layer direction, indicating that the present invention can divide the overall terminal block point cloud into a subset of point clouds with clear layer attributes, thereby providing a reliable layer input for locating the hole positions and matching wires layer by layer in the subsequent step S30.

[0038] like Figure 3 As shown, Figure 3 This is a schematic diagram of the iterative convergence process of the structured regularization term of the present invention, which corresponds to the iterative optimization process of the random sampling consensus algorithm with interlayer spacing constraints in step S20. Figure 3The horizontal axis represents the number of iterations, and the vertical axis represents the estimated interlayer spacing. In the initial iteration stage, the estimated interlayer spacing deviates significantly from the actual interlayer spacing. As the median spacing between adjacent terminal planes is continuously updated, the estimated interlayer spacing gradually converges to the actual interlayer spacing. It is understandable that if only a standard random sampling consensus algorithm is used for plane fitting, situations such as installation tilt of the terminal block, missing local point clouds, or wire occlusion can easily lead to misfitting non-terminal planes as terminal planes or incorrectly merging point clouds from adjacent layers. By introducing interlayer spacing constraints and performing iterative updates, this invention enables the terminal plane fitting results to simultaneously satisfy two conditions: minimizing the distance from a point to a plane and maintaining stable interlayer spacing between adjacent layers, thereby improving the stability of the multi-layer terminal block segmentation results.

[0039] Step S30: Based on the terminal layer site cloud, perform wire hole matching task sequentially using Alpha Shapes boundary extraction, U-Net semantic segmentation and cylindrical fitting, and output wire layer site hole mapping table; The "wire hole matching task" in this step is a multi-stage processing flow designed to determine which terminal layer and which specific hole each wire is actually inserted into. "Alpha Shapes boundary extraction" is an algorithm for extracting object contours from scattered point clouds. It is used to identify the boundaries of metal connectors (such as screws or pressure plates) from a certain layer of terminal point cloud, and then obtains the precise center position and radius of all terminal holes in that layer through "circle fitting," i.e., "terminal hole data." "U-Net semantic segmentation network" is a deep learning model commonly used for image segmentation; here it is an "edge-aware" variant, emphasizing fine segmentation of wire edges. Its input is a "local color image patch" extracted from the 3D reconstructed data based on the terminal hole coordinates, and its output is a "2D mask of the wire" that distinguishes wire pixels from background pixels. "Cylinder fitting" fits the 3D point cloud clusters corresponding to the 2D mask (i.e., the points of the wire in 3D space) into a cylindrical model, thereby determining the "wire axis" (spatial direction) and "wire radius." Finally, by analyzing the spatial relationship between the end or nearest point of the conductor axis and each terminal hole, it is determined which hole the conductor was actually inserted into, and recorded as a "conductor layer hole mapping table", which is a table that records "layer X, hole Y, conductor identified as Z is connected".

[0040] This step plays a crucial role in this invention, mapping visual perception to concrete connectivity. It cleverly combines traditional geometric algorithms (Alpha Shapes, cylinder fitting) with modern deep learning (U-Net semantic segmentation), addressing the challenges of wire detection step by step. First, geometric methods robustly locate all possible targets (terminal holes). Then, for the region near each target hole, the powerful feature learning capabilities of deep learning models are used to accurately segment possible wires in complex color images. Finally, geometric methods are used to back-project the segmented two-dimensional wire regions into three-dimensional space and fit their shapes, thereby establishing a deterministic spatial connection between the wire and the hole location.

[0041] Compared to wire identification methods that rely solely on color threshold segmentation or simple template matching, the hybrid strategy employed in this step significantly improves robustness and accuracy in complex backgrounds, varying lighting conditions, diverse wire colors, and situations involving cross-occlusion. Traditional methods are prone to misidentifying shadows, stains, or terminals of different colors as wires, or failing to segment adhered wires. This invention narrows the identification range and reduces interference by first locating the hole position and then specifically analyzing the local area. The U-Net network can learn deep features of the wire and background and is insensitive to color and texture changes. The final cylinder fitting utilizes the spatial geometric prior of the wire to further confirm its existence and location, making the generated mapping table more reliable.

[0042] For example, consider identifying whether a wire is connected to terminal hole #5 in the intermediate layer. Step S301 uses the AlphaShapes algorithm to find the center coordinates of the hole. Step S302 uses these coordinates as the center to extract a small region image from the texture image corresponding to the 3D color reconstruction data and inputs it into the trained U-Net network. The network output shows that a thin strip-shaped region in the image patch is highlighted as a "wire". Step S303 projects these pixels marked as "wires" back into 3D space according to the camera model to obtain a small cluster of 3D point clouds, which are roughly elongated. The algorithm fits this cluster of points with a cylinder. If the fit is successful and the cylinder radius matches a common wire diameter (such as a 1.5 square millimeter wire), it is considered that a wire does exist. Then, the axis of the fitted cylinder is calculated. It is found that one end of the axis is very close to the center of terminal hole #5 in the intermediate layer, and the direction matches. Therefore, it is determined that the wire is connected to this hole, and an entry is recorded in the mapping table: Layer position = Middle, Hole position = 5, Wire ID = a unique identifier.

[0043] like Figure 4 As shown, Figure 4 This is a schematic diagram of the residual distribution of the cylindrical axis fitting in this invention, which corresponds to the cylindrical fitting process of the three-dimensional point cloud cluster of the conductor in step S30. Figure 4In the process, the residual distribution of pure geometric fitting is generally large and highly discrete, while the fitting residual after introducing the wire diameter prior constraint is concentrated in a smaller range. This indicates that under conditions of local occlusion of the conductor, bending of the conductor, or sparse point cloud, fitting based solely on the geometric shape of the point cloud is easily affected by outliers, leading to conductor axis offset. By using the wire diameter prior library as a radius constraint in cylindrical fitting, the fitted conductor axis can be made closer to the actual conductor centerline, and the interference of terminal metal parts, wire gauge edges, or adjacent conductors on the fitting results can be reduced. Therefore, the conductor layer and hole position mapping table generated in step S30 can more accurately reflect the actual terminal layer and hole position where the conductor is connected.

[0044] Step S40: Based on the preset cabinet terminal standard wiring logic file, execute the standard wiring relationship parsing task and output standard wiring constraint data; The "Standard Wiring Logic File for Cabinet Terminals" in this step is a predefined digital file describing the correct wiring relationships of the electrical control cabinet. It is typically derived from electrical design drawings or a configuration database and contains all the rules that wiring should follow. The "Standard Wiring Relationship Analysis Task" involves reading and understanding this file. Specifically, "analysis" includes extracting the "functional labels" (e.g., "Phase A Power Supply," "Control Signal A," "Ground"), "wire identifiers" (e.g., wire number), the "equipment group identifier," and the "mutual backup equipment relationship" between devices for the wires expected to be connected to each terminal hole. These are collectively referred to as "terminal wiring rule data." Based on this rule data, an "expected connection table" can be constructed, which clearly lists the function and identifier of the wires that each terminal (located by layer and hole position) should ideally be connected to. Furthermore, based on the "mutual backup equipment relationship" (e.g., pump A and pump B are mutual backups, and their control signal lines should be connected to different layers), a "logical constraint matrix" can be constructed. This matrix defines the constraints that the same functional lines of different devices should meet at the access layers (e.g., they must be connected to different layers, or a specific layer difference must be maintained). The "standard wiring constraint data" consists of the "expected connection table" and the "logical constraint matrix," representing the standard for wiring correctness in both physical connection and logical relationship dimensions.

[0045] This step establishes a "standard answer" or "golden rule" for judging right and wrong in this invention. It does not process sensor data, but rather prior knowledge data. By parsing unstructured wiring logic files (which may be text, tables, or files in a specific format) into structured constraint data that can be directly used by computer programs, a clear reference system is provided for the comparison in step S50. The "expected connection table" is used for one-to-one comparison of physical holes, while the "logical constraint matrix" is used to verify logical errors involving coordination relationships between multiple devices that cannot be detected through simple hole comparison.

[0046] For example, the standard wiring logic document specifies that terminals 1 to 3 of the first layer (L1) should be connected to the three-phase power lines L1, L2, and L3 (function labels) of the main circuit, respectively; terminal 10 of the second layer (L2) should be connected to the "Cooling Pump A Start Signal" line (wire identification KA1); and terminal 10 of the third layer (L3) should be connected to the "Cooling Pump B Start Signal" line (wire identification KA2). The document also indicates that cooling pumps A and B are backup devices for each other. After parsing, the expected connection table will record: L1-1 connects to phase L1, L1-2 connects to phase L2, L1-3 connects to phase L3, L2-10 connects to KA1, and L3-10 connects to KA2. The logic constraint matrix will record a rule: the "start signal" lines of devices "Cooling Pump A" and "Cooling Pump B" should have a connection layer difference of 1 (i.e., L2 and L3), and cannot be connected to the same layer. These data together constitute the standard wiring constraints for this cabinet.

[0047] For example, such as Figure 5 As shown, Figure 5 This is a schematic diagram of a segment of the expected connection table of the present invention, which corresponds to the process of generating standard wiring constraint data in step S40. Figure 5 The expected connection table uses function labels, layer positions, hole positions, and wire identifiers as fields to structurally represent the standard access locations of different functional wires; specifically, main pump control, standby pump control, main power supply, and standby power supply are mapped to their corresponding layer positions and hole positions, respectively. It should be noted that... Figure 5 Records highlighted with a background color indicate wiring objects with backup relationships or those requiring key participation in logic verification, such as backup pump control lines and backup power lines. By parsing standard wiring logic files into this type of tabular data, this invention can transform wiring rules that originally relied on manual diagram interpretation into directly comparable expected connection relationships, providing a standard reference for physical misalignment comparison in step S50.

[0048] Step S50: Perform physical misconnection comparison and inter-layer logical conflict detection based on the wire layer hole mapping table and the standard wiring constraint data, and output the misconnection identification result.

[0049] This step is the final detection result generation stage of the invention, comprising two core sub-tasks: "physical misconnection comparison" and "inter-layer logical conflict detection." "Physical misconnection comparison" involves matching each item in the "Wire Layer and Hole Position Mapping Table" (actual wiring configuration) generated in step S30 with the "Expected Connection Table" (standard wiring configuration) generated in step S40. The matching is based on the layer and hole position. If a layer and hole position are recorded in the actual mapping table, the functional label of the actual wire inserted into that hole (which can be inferred through wire identification mapping or color features) is compared to the functional label specified in the expected connection table. If they do not match, a "physical misconnection identification result" is generated, indicating that a wire in a certain layer and hole is connected incorrectly. "Inter-layer logical conflict detection" utilizes the "Wire Layer and Hole Position Mapping Table" and the "Logical Constraint Matrix." It focuses on device groups (such as mutually redundant devices) whose relationships are defined in the logical constraint matrix. During inspection, the system first locates the actual layer to which the functional lines of these devices are connected in the actual mapping table. Then, it checks whether these actual connected layers meet the constraints defined in the logical constraint matrix (such as a specific layer difference ΔLab). The judgment formula Cab intuitively expresses this logic: when the actual layer relationship (La and Lb) does not meet the preset layer difference requirement, a logical conflict is considered to exist (Cab=1). Finally, the results of physical misconnections and logical misconnections are summarized and displayed on the real-world image seen by the inspectors in an augmented reality manner using interactive devices such as AR glasses. This forms an intuitive "misconnection identification result."

[0050] This step in the invention achieves closed-loop detection and result presentation. It comprehensively utilizes all the outputs from the preceding steps—the physical mapping of actual wiring and the logical constraints of standard wiring—to perform a comprehensive and in-depth comparative analysis. Its technical advantage lies in its ability to not only detect obvious physical connection errors such as "misattribution," but also to identify wiring that appears correct at each connection point individually (wire function matches hole function), but is logically incorrect when viewed from an overall perspective. For example, all control lines of backup equipment might be incorrectly connected to the same layer, violating the principle of fault isolation. This dual detection mechanism significantly improves the coverage and depth of misconnection identification. Finally, through AR visualization output, the detection results can be quickly and accurately understood by on-site personnel to guide repairs, completing a fully intelligent detection process from perception and analysis to interaction.

[0051] Compared to traditional automated testing methods that only compare physical connections, the addition of inter-layer logical conflict detection in this step is a significant innovation and a major advantage. Traditional methods cannot detect the aforementioned logical errors, which can lead to serious system malfunctions in actual engineering projects. This invention, by introducing a logical constraint matrix and performing detection, expands the definition of wiring correctness from "point-to-point correctness" to "system-level correctness," making the testing standards more closely aligned with the safety and functional requirements of actual engineering projects. Furthermore, the output of results through AR real-scene annotation is more intuitive and efficient than traditional methods of generating test reports or lists, significantly shortening the time from problem discovery to problem localization and improving on-site operational efficiency.

[0052] For example, following the previous example, suppose the mapping table generated in step S30 shows that the actual wire connected to hole L2-10 is identified as KA2 (signal line of coolant pump B), while the actual wire connected to hole L3-10 is identified as KA1 (signal line of coolant pump A). ​​The physical misconnection comparison in step S50 will find that hole L2-10 is expected to be connected to KA1, but is actually connected to KA2, resulting in a functional label mismatch and a physical misconnection report. Simultaneously, the inter-layer logic conflict detection will find that the signal line of coolant pump A is actually in L3, and the signal line of coolant pump B is actually in L2, with a layer difference of |L3-L2|=1, satisfying the requirement of ΔLab=1 in the logic constraint matrix. Therefore, the logic detection passes (Cab=0). The final result will indicate that holes L2-10 and L3-10 are connected in reverse. The AR glasses will highlight these two terminal holes with red circles in the inspector's field of vision, and may use arrows pointing from L2-10 to L3-10 and from L3-10 to L2-10, indicating that these two wires need to be swapped.

[0053] For example, such as Figure 6 As shown, Figure 6 This is a schematic diagram of the mutual exclusion hierarchical relationship constraint matrix of the present invention, which corresponds to the construction result of the logical constraint matrix in step S40 and the inter-layer logical conflict detection process in step S50. Figure 6 The horizontal and vertical axes in the matrix represent different functional wires or equipment control objects, respectively. The values ​​of the matrix cells represent the strength of the hierarchical mutual exclusion constraint or the preset hierarchical difference requirement between the corresponding objects. Among them, there are obvious mutual exclusion hierarchical constraints between objects such as main pump control and standby pump control, and main power supply and standby power supply. It is understandable that for mutually backup equipment, simply determining whether a wire is connected to a certain hole is insufficient to guarantee the correct wiring of the entire cabinet; it is also necessary to determine whether similar functional lines are distributed in different hierarchical locations as designed. Figure 6 As shown by the matrix constraints, the present invention can further identify inter-layer logic misconnections in step S50 where "the hole positions seem to correspond but the backup equipment layer relationship is incorrect", thereby avoiding the problem that traditional continuity tests can only determine the continuity of the line and cannot detect control logic misconnections.

[0054] Example 2: Furthermore, the present invention provides a low-voltage electrical control cabinet terminal block wiring misconnection identification system, which employs a low-voltage electrical control cabinet terminal block wiring misconnection identification method from the above embodiments, and can solve the technical problem of identifying low-voltage electrical control cabinet terminal block wiring misconnections. The beneficial effects of the low-voltage electrical control cabinet terminal block wiring misconnection identification system provided by the present invention are the same as those of the low-voltage electrical control cabinet terminal block wiring misconnection identification method from the above embodiments, and other technical features of the low-voltage electrical control cabinet terminal block wiring misconnection identification system are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0055] Example 3: This invention provides a low-voltage electrical control cabinet terminal block wiring misconnection identification device. The device includes: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores instructions executable by the at least one processor, which, when executed, enable the at least one processor to perform the low-voltage electrical control cabinet terminal block wiring misconnection identification method described in Example 1. The low-voltage electrical control cabinet terminal block wiring misconnection identification device in this invention can include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and vehicle terminals (e.g., vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. This low-voltage electrical control cabinet terminal block wiring misconnection identification device is merely an example and should not limit the functionality or scope of the invention. A low-voltage electrical control cabinet terminal block wiring misconnection identification device may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) that can perform various appropriate actions and processes according to a program stored in a read-only memory or a program loaded from a storage device into a random access memory. The random access memory also stores various programs and data required for the operation of the low-voltage electrical control cabinet terminal block wiring misconnection identification device. The processing unit, the read-only memory, and the random access memory are interconnected via a bus. An I / O interface is also connected to the bus. Typically, the following systems can be connected to the I / O interface: input devices including, for example, touch screens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices including, for example, magnetic tapes, hard disks, etc.; and communication devices. The communication device allows the low-voltage electrical control cabinet terminal block wiring misconnection identification device to communicate wirelessly or wiredly with other devices to exchange data. Although a low-voltage electrical control cabinet terminal block wiring misconnection identification device with various systems has been described, it should be understood that it is not required to implement or possess all the systems shown. It can be implemented alternatively or have more or fewer systems.

[0056] Example 4: This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the low-voltage electrical control cabinet terminal block wiring misconnection identification method described above. The computer program product provided by this invention can solve the technical problem of identifying low-voltage electrical control cabinet terminal block wiring misconnections. Compared with the prior art, the beneficial effects of the computer program product provided by this invention are the same as those of the low-voltage electrical control cabinet terminal block wiring misconnection identification method provided in the above embodiments, and will not be repeated here.

[0057] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from a read-only memory. When the computer program is executed by a processing device, it performs the functions defined in the methods of the embodiments disclosed in this invention.

[0058] It should be understood that the various parts disclosed in this invention can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0059] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the present invention and its equivalents, the present invention also intends to include these modifications and variations.

Claims

1. A terminal strip wiring misconnection identification method for low-voltage electric control cabinet, characterized in that, The methods include: Step S10: Acquire multi-view scanning image data of the terminal block, and perform a three-dimensional reconstruction task based on the multi-view scanning image data of the terminal block using adaptive bilateral filtering and iterative nearest point algorithm, and output three-dimensional color reconstruction data of the terminal block; Step S20: Perform terminal layer segmentation based on the three-dimensional color reconstruction data of the terminal block, and output the terminal layer point set; wherein, the step of performing terminal layer segmentation based on the three-dimensional color reconstruction data of the terminal block and outputting the terminal layer point set specifically includes: Statistical outlier filtering is performed on the three-dimensional color reconstruction data of the terminal block, and the normal vector of each point in the filtered point cloud is calculated to obtain the normal vector point cloud of the terminal block. Based on the point cloud of the terminal block normal vector, spherical coordinate clustering is performed to extract candidate plane point clouds that are consistent with the normal direction of the main plane of the terminal block. Based on the candidate planar point cloud, a random sampling consensus algorithm with interlayer spacing constraints is used to fit multiple parallel terminal planes, and layer marking is performed according to the spatial order of the multiple parallel terminal planes to obtain the terminal layer point cloud set. Step S30: Based on the terminal layer site set, perform wire hole matching task sequentially using Alpha Shapes boundary extraction, U-Net semantic segmentation, and cylindrical fitting, and output a wire layer site hole mapping table; wherein, the step of performing wire hole matching task sequentially using Alpha Shapes boundary extraction, U-Net semantic segmentation, and cylindrical fitting based on the terminal layer site set and outputting a wire layer site hole mapping table specifically includes: Based on each layer of terminal point cloud in the terminal layer point cloud set, candidate regions for wiring holes are determined by normal abrupt change and point density change, and the Alpha Shapes algorithm is used to extract the boundary contour of the metal connector. The boundary contour of the metal connector is then fitted with a circle to obtain terminal hole data. Based on the terminal hole position data, the corresponding local color image block is extracted from the three-dimensional color reconstruction data of the terminal block, and the local color image block is input into a preset edge-aware U-Net semantic segmentation network to obtain a two-dimensional mask for the wire. Based on the two-dimensional mask of the conductor, a three-dimensional conductor point cloud cluster is extracted, and the conductor axis and conductor radius are determined by cylindrical fitting in combination with the conductor diameter prior library. A conductor layer hole mapping table is generated according to the positional relationship between the conductor axis and the terminal hole data. Step S40: Based on the preset cabinet terminal standard wiring logic file, execute the standard wiring relationship parsing task and output standard wiring constraint data; Step S50: Perform physical misconnection comparison and inter-layer logical conflict detection based on the wire layer hole mapping table and the standard wiring constraint data, and output the misconnection identification result.

2. The terminal block connection error identification method for low-voltage electric control cabinet according to claim 1, characterized in that, Step S10, which involves acquiring multi-view scanning image data of the terminal block, performing a 3D reconstruction task based on the multi-view scanning image data of the terminal block using adaptive bilateral filtering and iterative nearest point algorithm, and outputting the 3D color reconstruction data of the terminal block, specifically includes: Step S101: Simultaneously acquire depth map sequence, color image sequence and pose prior data of the terminal block area of ​​the low-voltage electrical control cabinet through the depth sensor, RGB camera and inertial measurement unit of the preset AR glasses to obtain multi-view scanning image data of the terminal block; Step S102: Perform adaptive bilateral filtering and Gaussian noise suppression on the depth map sequence in the multi-view scanning image data of the terminal block to obtain a denoised depth map sequence; Step S103: Based on the denoised depth map sequence and the pose prior data, multi-frame point cloud registration is performed using the iterative nearest point algorithm, and the color information in the color image sequence is projected onto the registered point cloud to obtain the terminal block three-dimensional color reconstruction data.

3. The terminal block connection error identification method of claim 1, wherein, In step S203, multiple parallel terminal planes are fitted using a random sampling consensus algorithm with interlayer spacing constraints based on the candidate plane point cloud, specifically including: Using the interlayer spacing in the terminal block CAD model or terminal block specification parameters corresponding to the candidate planar point cloud as the initial interlayer spacing, a planar fitting evaluation function is constructed: ， in, The value represents the plane fitting evaluation, and N represents the number of candidate plane points in the candidate plane point cloud that participated in the plane fitting. Indicates the first Candidate plane points, Indicates the first The terminal plane corresponding to each candidate plane point Indicates the first The distance from each candidate plane point to the corresponding terminal plane. This represents the Huber loss function. Indicates the interlayer spacing constraint weight. Indicates the number of terminal planes. Indicates the first The actual layer spacing between adjacent terminal planes of a group Indicates the reference layer spacing used in the current iteration; After each round of fitting, the median of the actual interlayer spacing between adjacent fitted terminal planes is calculated, and the median is updated as the reference interlayer spacing for the next round of iteration, until the change in the reference interlayer spacing between two adjacent rounds is less than the preset convergence threshold, and finally the terminal layer site cluster is output.

4. The method for identifying incorrect wiring of terminal blocks in a low-voltage electrical control cabinet as described in claim 1, characterized in that, Step S40, based on the preset cabinet terminal standard wiring logic file, involves executing a standard wiring relationship parsing task to output standard wiring constraint data. This step specifically includes: Step S401: Parse the standard wiring logic file of the terminal inside the cabinet, extract the function label, wire identification, equipment group identification and mutual backup equipment relationship of the wires to be connected to the terminal hole, and obtain the terminal wiring rule data; Step S402: Based on the terminal wiring rule data, establish a expected connection table, which includes expected layer, expected hole position, expected function label and expected wire identification, to obtain expected wiring data; Step S403: Construct a logical constraint matrix based on the expected wiring data and the mutual backup device relationship to obtain standard wiring constraint data.

5. The method for identifying incorrect wiring of terminal blocks in a low-voltage electrical control cabinet as described in claim 1, characterized in that, Step S50, which involves comparing physical misconnections and detecting inter-layer logical conflicts based on the conductor layer-hole mapping table and the standard wiring constraint data, and outputting the misconnection identification result, specifically includes: Step S501: Match the wire layer and hole mapping table with the expected connection table in the standard wiring constraint data according to the layer and hole position. If the actual wire function label is inconsistent with the expected function label, generate a physical misalignment identification result. Step S502: Based on the wire layer hole mapping table and the logical constraint matrix in the standard wiring constraint data, perform inter-layer logical conflict detection on the same type of functional lines that are backup devices to obtain the inter-layer logical misconnection identification result; The inter-layer logical conflict detection uses the following judgment formula: ， in, Indicates the first The device and the first Logical conflict indicators between devices Indicates the first The actual access level of the same function line of each device. Indicates the first The actual access level of the same function line of each device. Indicates the first The device and the first Preset layer difference between devices; when When, it is determined that there is a logical misconnection between layers; when When this occurs, it is determined that there are no logical misconnections between layers; Step S503: Based on the physical misconnection identification result and the interlayer logical misconnection identification result, the hole position mark, layer highlight and correction path prompt are superimposed on the terminal block real scene image through AR glasses to obtain the misconnection identification result.

6. A terminal block connection error identification system for a low-voltage electric control cabinet, applied to the terminal block connection error identification method of any one of claims 1 to 5, characterized in that, The low-voltage electrical control cabinet terminal block wiring misconnection identification system includes: The 3D reconstruction module is used to acquire multi-view scan image data of the terminal block, and performs 3D reconstruction task based on the multi-view scan image data of the terminal block using adaptive bilateral filtering and iterative nearest point algorithm, and outputs 3D color reconstruction data of the terminal block; The layer segmentation module is used to perform terminal layer segmentation tasks based on the three-dimensional color reconstruction data of the terminal block and output terminal layer point sets; wherein, the step of performing terminal layer segmentation tasks based on the three-dimensional color reconstruction data of the terminal block and outputting terminal layer point sets specifically includes: Statistical outlier filtering is performed on the three-dimensional color reconstruction data of the terminal block, and the normal vector of each point in the filtered point cloud is calculated to obtain the normal vector point cloud of the terminal block. Based on the point cloud of the terminal block normal vector, spherical coordinate clustering is performed to extract candidate plane point clouds that are consistent with the normal direction of the main plane of the terminal block. Based on the candidate planar point cloud, a random sampling consensus algorithm with interlayer spacing constraints is used to fit multiple parallel terminal planes, and layer marking is performed according to the spatial order of the multiple parallel terminal planes to obtain the terminal layer point cloud set. The wire hole matching module is used to perform wire hole matching tasks based on the terminal layer site set by sequentially applying Alpha Shapes boundary extraction, U-Net semantic segmentation, and cylindrical fitting, and output a wire layer site hole mapping table; wherein the step of performing wire hole matching tasks based on the terminal layer site set by sequentially applying Alpha Shapes boundary extraction, U-Net semantic segmentation, and cylindrical fitting, and outputting a wire layer site mapping table specifically includes: Based on each layer of terminal point cloud in the terminal layer point cloud set, candidate regions for wiring holes are determined by normal abrupt change and point density change, and the Alpha Shapes algorithm is used to extract the boundary contour of the metal connector. The boundary contour of the metal connector is then fitted with a circle to obtain terminal hole data. Based on the terminal hole position data, the corresponding local color image block is extracted from the three-dimensional color reconstruction data of the terminal block, and the local color image block is input into a preset edge-aware U-Net semantic segmentation network to obtain a two-dimensional mask for the wire. Based on the two-dimensional mask of the conductor, a three-dimensional conductor point cloud cluster is extracted, and the conductor axis and conductor radius are determined by cylindrical fitting in combination with the conductor diameter prior library. A conductor layer hole mapping table is generated according to the positional relationship between the conductor axis and the terminal hole data. The standard wiring parsing module is used to perform standard wiring relationship parsing tasks based on the preset cabinet terminal standard wiring logic file, and output standard wiring constraint data; The misconnection identification module is used to perform physical misconnection comparison and inter-layer logical conflict detection based on the wire layer hole location mapping table and the standard wiring constraint data, and output the misconnection identification result.

7. A terminal block connection error identification device for low-voltage electric control cabinet, characterized in that, The low-voltage electric control cabinet terminal strip connection error identification device comprises a memory, a processor, and a low-voltage electric control cabinet terminal strip connection error identification program stored in the memory and executable on the processor.

8. A computer program product, characterized in that, The computer program product comprises a low-voltage electric control cabinet terminal strip connection error identification program, and the low-voltage electric control cabinet terminal strip connection error identification program, when executed by a processor, implements a low-voltage electric control cabinet terminal strip connection error identification method according to any one of claims 1 to 5.

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