Intelligent electric appliance cabinet infrared wire arrangement system and method based on machine vision

Abstract

The application relates to the technical field of electric appliance cabinets, in particular to an intelligent electric appliance cabinet infrared wire arrangement system and method based on machine vision, which comprises a collection layer, an analysis layer and an indication layer; the system is executed under the running state of the electric appliance cabinet; the electric appliance cabinet internal circuit image data collection logic is set in the collection layer, the electric appliance cabinet internal circuit image data is collected based on the collection logic, the collected electric appliance cabinet internal circuit image data is stored synchronously, the electric appliance cabinet internal circuit image data stored in the collection layer is received by the analysis layer, the machine vision technology is applied to collect the electric appliance cabinet internal circuit image data, the comprehensive analysis of the electric appliance cabinet internal image data and the circuit temperature information is further based on, the high-precision wire searching technology is realized, the necessary prerequisite of the wire arrangement operation of the staff is met, the required wire to be arranged is searched, and the wire arrangement work is more quickly realized.

Description

Technical Field

[0001] This invention relates to the field of electrical cabinet technology, and specifically to an intelligent electrical cabinet infrared cable management system and method based on machine vision. Background Technology

[0002] Infrared cables are specifically designed for connecting to infrared-related devices, primarily for transmitting signals and supplying power to these devices. Examples include the cable connections in infrared remote control, infrared sensors, and infrared imaging equipment. Their design and function are tailored to the operating characteristics of infrared devices, meeting requirements for signal transmission quality and interference resistance. Infrared cable cabinets, on the other hand, are enclosures used in infrared technology applications to integrate, manage, and protect infrared-related equipment and cables.

[0003] The invention patent application with application number 202110055366.9 discloses a cable management method for an automatic fiber optic distribution device. The method includes: acquiring the position information of a target optical fiber, the position information of which includes: the target optical fiber head corresponding to the target optical fiber, its position on the current interface of the plug panel, and the position of the target interface; moving a clamp to the position of the target optical fiber head on the plug panel, causing the clamp to engage the target optical fiber; moving the clamp along the target optical fiber, aligning the clamp with the notch between hooks on the side wall of the hook groove, the clamp driving the target optical fiber to the middle of the hook groove and removing it outwards, causing the target optical fiber to detach from the hook of the hook groove; moving the clamp along the buried groove, removing the target optical fiber from the buried groove, and then hooking the target optical fiber onto the transition groove; moving the clamp to the current interface position of the target optical fiber head, clamping the target optical fiber head, and then removing it outwards, causing the target optical fiber head to be inserted into the target interface; the clamp engaging and removing the target optical fiber from the transition groove, then burying the target optical fiber into the buried groove, and finally hooking the target optical fiber into the hook groove.

[0004] The application aims to address the problem that "in the existing fiber optic cable management method, during the process of 'the clamp moves to the side above the hook of the first buried cable trough and pulls the cable out from the hook,' the clamp cannot ensure that the cable is safely pulled out from the hook of the buried cable trough. This may result in the fiber optic cable being snapped during the pulling process, causing communication delays, and also increasing maintenance costs and the workload of staff."

[0005] For electrical cabinets equipped with infrared cables that have been put into use, the wiring inside the cabinet will become messy due to maintenance operations during daily maintenance, which will gradually increase the difficulty of subsequent maintenance. To solve this problem, relevant personnel have developed a large number of cable management tools and equipment. However, finding the "head and tail" of the tangled cables still requires manual operation based on their own experience and visual tracking to carry out cable management work.

[0006] To address this, we propose an infrared cable management system and method for intelligent electrical appliance cabinets based on machine vision. Summary of the Invention

[0007] In view of the above-mentioned shortcomings of the existing technology, the present invention provides an infrared cable management system and method for intelligent electrical cabinets based on machine vision, which solves the technical problems mentioned in the background.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] The first aspect is an infrared cable management system for intelligent electrical cabinets based on machine vision, which includes: a data acquisition layer, an analysis layer, and an indication layer.

[0010] The system operates while the electrical cabinet is running.

[0011] The acquisition layer sets up the logic for acquiring internal wiring image data of the electrical cabinet. Based on the acquisition logic, it acquires internal wiring image data of the electrical cabinet and stores the acquired internal wiring image data of the electrical cabinet simultaneously. The analysis layer receives the internal wiring image data of the electrical cabinet stored in the acquisition layer, converts the internal wiring image data of the electrical cabinet into a contour image, selects a wiring contour in the contour image, and analyzes its associated wiring contours. The system user selects a wiring contour in the contour image converted by the analysis layer in the indication layer. The indication layer simultaneously indicates the associated wiring contour in the contour image based on the wiring contour selection result of the system user. The system user refers to the associated wiring contour to determine the internal wiring of the electrical cabinet corresponding to the associated wiring contour.

[0012] The analysis layer includes a sensing module, a conversion module, a selection module, and an analysis module. The sensing module is used to sense the surface temperature of the circuit. The conversion module receives the internal circuit image data of the electrical cabinet stored in the acquisition layer and converts the internal circuit image data of the electrical cabinet into a contour image. The selection module is used to traverse the contour images obtained by the conversion module and select the circuit contour in the contour image. The analysis module is used to receive the circuit contour selected by the selection module and analyze the associated circuit contours.

[0013] The associated line contour analysis logic in the analysis module is represented as follows:

[0014] ;

[0015] In the formula: Line outline With line outline The correlation; Line outline The average values ​​of the R, G, and B channels in a defined area of ​​the internal wiring image data of the electrical cabinet; Line outline The average values ​​of the R, G, and B channels in a defined area of ​​the internal wiring image data of the electrical cabinet; Line outline With line outline Distance between adjacent ends; , Line outline With line outline Surface temperature; For coordination index; Line outline With line outline Surface temperature sensing location distance; As a correction factor;

[0016] Among them, the line outline With line outline correlation The smaller the value, the higher the correlation between the two sets of route profiles; conversely, the larger the value, the lower the correlation between the two sets of route profiles. In the above formula... As the target for obtaining the contour of the associated line, it is continuously changed synchronously. Find the line profile with the highest correlation and the coordination index. The value can be 1 or -1. ≤ At that time, the coordination index =1, > At that time, the coordination index =-1.

[0017] Furthermore, the acquisition layer includes a logic module, an acquisition module, and a storage module. The logic module is used to set the operating logic of the acquisition module, the acquisition module is used to acquire image data of the internal wiring of the electrical cabinet, and the storage module is used to receive the image data of the internal wiring of the electrical cabinet acquired by the acquisition module and store the image data of the internal wiring of the electrical cabinet.

[0018] During the acquisition module's operation phase, the acquisition module synchronously acquires the operating logic from the logic module and performs the acquisition operation of the internal wiring image data of the electrical cabinet based on the operating logic. After receiving the internal wiring image data of the electrical cabinet, the storage module synchronously performs enhancement processing on the internal wiring image data of the electrical cabinet, and after completing the enhancement processing, it performs the storage of the internal wiring image data of the electrical cabinet.

[0019] Furthermore, the operating logic of the acquisition module set in the logic module is as follows:

[0020] The application acquisition module collects a set of image data of the internal wiring of the electrical cabinet in the positive direction. The complexity of the collected image data is analyzed, and the number of image data to be collected is set based on the complexity analysis results.

[0021] ;

[0022] In the formula: The complexity of the internal wiring image data of the electrical cabinet; The size of the wiring image area in the internal wiring image data of the electrical cabinet; The global size of the internal wiring image data of the electrical cabinet; This represents the total number of wiring intersections in the internal wiring image data of the electrical cabinet. The total number of contour images representing the circuit contours in the internal wiring image data of the electrical cabinet; Let be the length of the i-th group of contour images; The number of image data samples collected from the internal wiring of the electrical cabinet; This serves as the baseline for collecting image data of the internal wiring of the electrical cabinet.

[0023] Among them, the number of data collection bases for internal wiring image data of electrical cabinets Customized by the system user, and >1, Number of image data collected of internal wiring in electrical cabinet Based on the further rounding method.

[0024] Furthermore, the acquisition module integrates lighting equipment and a high-definition camera. The lighting equipment provides supplemental illumination to the internal wiring of the electrical cabinet, and the high-definition camera acquires image data of the internal wiring of the electrical cabinet based on the number of images collected. A corresponding number of images of the internal wiring of electrical cabinets were collected, and the viewing angle of each collected image of the internal wiring of electrical cabinets was different. Furthermore, the relative distance between the camera end of the high-definition camera and the opposite side of the electrical cabinet was equal during the collection of each image of the internal wiring of electrical cabinets.

[0025] The enhanced processing logic for the internal wiring image data of the electrical cabinet in the storage module is represented as follows:

[0026] ;

[0027] In the formula: , , The enhancement coefficients of the R, G, and B channels of a group of pixels in the image data during the enhancement processing of the internal wiring image data of the electrical cabinet. For adjustment factors; , , This refers to the R, G, and B channel values ​​of a set of pixels in the image data of the internal wiring of the electrical cabinet. The average values ​​of the R, G, and B channels of the internal wiring image data of the electrical cabinet;

[0028] Among them, setting 0 < <10, adjustment factor The value setting is used to constrain color overflow caused by pixel enhancement processing. Based on the above formula, the value of each pixel in the internal wiring image data of the electrical cabinet is... , , If we perform the calculation, the enhanced output result for this pixel will be: , This represents the values ​​of the R, B, and G channels after pixel enhancement processing.

[0029] Furthermore, the sensing module is integrated with several sets of infrared sensors. The several sets of infrared sensors operate to sense the surface temperature of each line corresponding to the outline image in the internal wiring image data of the electrical cabinet. The surface temperature sensing results of each set of line outlines are marked on their corresponding line outlines.

[0030] Among them, the conversion module runs before the sensing module. After the conversion module obtains the outline image of the internal circuit image data of the electrical cabinet, it synchronously feeds back the outline image to the sensing module. The sensing module uses the circuits corresponding to the outlines of each circuit in the outline image as the sensing targets and performs the sensing operation of the surface temperature of the circuits.

[0031] Before converting the internal wiring image data of the electrical cabinet into a contour image, the conversion module sets a color threshold to represent the wiring. Pixels in the internal wiring image data that do not meet the set color threshold are deleted, and the remaining pixels form an image to represent the wiring. The conversion operation of the contour image is then performed using the wiring image.

[0032] Furthermore, each time the selection module runs, it selects a group of line contours in the contour image. When the analysis module analyzes the associated line contours of the line contours, it uses all unselected line contours in the current contour image as the analysis target.

[0033] The analysis layer operates by processing image data of the internal wiring of each electrical cabinet.

[0034] The correction factor The value follows:

[0035] ;

[0036] In the formula: Line outline The average grayscale value; Line outline The average grayscale value;

[0037] in, This indicates taking the minimum value within the parentheses.

[0038] Furthermore, the indication layer includes a selection module, an indication module, and an output module. The selection module is used to select a line contour in the contour image. The indication module is used to receive the line contour selected by the selection module, use the selected line contour as the query target, query the line contour with the highest correlation with the line contour in the analysis layer, render the query line contour with the highest correlation to indicate the line contour, and the output module is used to receive the line contour in the rendered contour image in the indication module, place the contour image with the rendered line contour in its original position in the corresponding electrical cabinet internal line image data, and output the electrical cabinet internal line image data.

[0039] The rendering operation is performed synchronously on the line outline corresponding to the query target. The selection module is manually operated by the system user to select the line outline in the outline image.

[0040] When a system user selects a line contour in a contour image, at least one set of contour images is used, and the number is customized by the system user. The line contours selected in each set of contour images all point to the same line. The operation of selecting a line contour in a contour image follows the following rules: the higher the accuracy requirement of the associated line contour query of the contour image, the more contour images are used; conversely, the less contour image data is used, and at least one set is required.

[0041] Furthermore, the instruction module renders the outline of the most relevant line found, that is, it renders the line color in the internal wiring image data of the electrical cabinet corresponding to the outline of the most relevant line.

[0042] Furthermore, the sensing module is interconnected with a conversion module via a wireless network. The conversion module is interconnected with a selection module and an analysis module via a wireless network. The conversion module is interconnected with a logic module via a wireless network. The logic module is interconnected with a data acquisition module and a storage module via a wireless network. The data acquisition module is interconnected with lighting equipment and a high-definition camera via a wireless network. The sensing module and the data acquisition module are interconnected via a wireless network. The analysis module is interconnected with a selection module via a wireless network. The selection module is interconnected with an indication module and an output module via a wireless network.

[0043] Secondly, a machine vision-based infrared cable management method for intelligent appliance cabinets includes:

[0044] Set up the logic for acquiring image data of the internal wiring of the electrical cabinet, and acquire image data of the internal wiring of the electrical cabinet based on the acquisition logic;

[0045] Acquire the collected image data of the internal wiring of the electrical cabinet, set the image data enhancement processing logic, and apply the image data enhancement processing logic to enhance the image data of the internal wiring of the electrical cabinet;

[0046] Obtain the image data of the internal wiring of the electrical cabinet after enhancement processing, and extract the outline image of the internal wiring of the electrical cabinet from the image data of the internal wiring of the electrical cabinet.

[0047] In the image of the wiring outline inside the electrical cabinet, select the wiring outline as the target for querying related wiring outlines, and then select the set of related wiring outlines with the highest correlation.

[0048] Using the selected line contour and the most relevant line contour found in the query as the rendering targets, render the two sets of line contours and represent them in the contour image.

[0049] The contour image representing the selected line contours and the group of line contours with the highest correlation is placed at the contour image source position in the line image data inside the electrical cabinet.

[0050] The internal wiring data of the appliance cabinet, which contains outline images, is fed back to the user terminal.

[0051] Compared with known public technologies, the technical solution provided by this invention has the following beneficial effects:

[0052] This invention provides an intelligent electrical cabinet infrared cable management system and method based on machine vision. During operation, the system uses machine vision technology to collect image data of the internal wiring of the electrical cabinet. Based on the comprehensive analysis of the internal image data and wiring temperature information, it achieves high-precision cable finding technology, thereby meeting the necessary prerequisites for cable management operations and locating the wiring to be sorted, thus achieving cable management work more quickly.

[0053] Furthermore, based on image display, the system operation results are output to provide reference for technicians engaged in cable management, thereby minimizing the difficulty of cable management in electrical cabinets. At the same time, by configuring cable management methods into the cable management system, the technical solution is further improved to ensure that the technical solution can stably serve cable management work. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0055] Figure 1 This is a schematic diagram of an infrared cable management system for an intelligent appliance cabinet based on machine vision.

[0056] Figure 2 This is a flowchart illustrating the infrared cable management method for intelligent appliance cabinets based on machine vision.

[0057] Figure 3 This is a schematic diagram showing the direction of the acquisition module's initial acquisition of image data of the internal wiring of the electrical cabinet in this invention;

[0058] Figure 4 This is a schematic diagram illustrating the logical process of the system in this invention for finding circuits based on the image data of the internal circuitry of the output electrical cabinet. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0060] The present invention will be further described below with reference to embodiments.

[0061] Example 1:

[0062] The infrared cable management system for the intelligent appliance cabinet based on machine vision in this embodiment, such as... Figure 1 As shown, it includes: a data acquisition layer, an analysis layer, and an indicator layer;

[0063] The system executes while the electrical cabinet is in operation;

[0064] The acquisition layer sets up the logic for acquiring internal wiring image data of the electrical cabinet. Based on the acquisition logic, it acquires internal wiring image data of the electrical cabinet and stores the acquired internal wiring image data of the electrical cabinet simultaneously. The analysis layer receives the internal wiring image data of the electrical cabinet stored in the acquisition layer, converts the internal wiring image data of the electrical cabinet into a contour image, selects a wiring contour in the contour image, and analyzes its associated wiring contours. The system user selects a wiring contour in the contour image converted by the analysis layer in the indication layer. The indication layer simultaneously indicates the associated wiring contour in the contour image based on the wiring contour selection result of the system user. The system user refers to the associated wiring contour to determine the internal wiring of the electrical cabinet corresponding to the associated wiring contour.

[0065] The acquisition layer includes a logic module, an acquisition module, and a storage module. The logic module is used to set the operating logic of the acquisition module, the acquisition module is used to acquire image data of the internal wiring of the electrical cabinet, and the storage module is used to receive and store the image data of the internal wiring of the electrical cabinet acquired by the acquisition module.

[0066] During the acquisition module's operation phase, the acquisition module synchronously acquires the operating logic in the logic module and performs the acquisition operation of the internal wiring image data of the electrical cabinet based on the operating logic. After receiving the internal wiring image data of the electrical cabinet, the storage module synchronously performs enhancement processing on the internal wiring image data of the electrical cabinet. After completing the enhancement processing, the storage of the internal wiring image data of the electrical cabinet is then performed.

[0067] The logic for the acquisition module's operation is defined in the logic module as follows:

[0068] The application acquisition module collects a set of image data of the internal wiring of the electrical cabinet in the positive direction. The complexity of the collected image data is analyzed, and the number of image data to be collected is set based on the complexity analysis results.

[0069] ;

[0070] In the formula: The complexity of the internal wiring image data of the electrical cabinet; The size of the wiring image area in the internal wiring image data of the electrical cabinet; The global size of the internal wiring image data of the electrical cabinet; This represents the total number of wiring intersections in the internal wiring image data of the electrical cabinet. The total number of contour images representing the circuit contours in the internal wiring image data of the electrical cabinet; Let be the length of the i-th group of contour images; The number of image data samples collected from the internal wiring of the electrical cabinet; This serves as the baseline for collecting image data of the internal wiring of the electrical cabinet.

[0071] Among them, the number of data collection bases for internal wiring image data of electrical cabinets Customized by the system user, and >1, Number of image data collected of internal wiring in electrical cabinet Based on the further rounding method;

[0072] The acquisition module integrates lighting equipment and a high-definition camera. The lighting equipment provides supplemental illumination to the internal wiring of the electrical cabinet, and the high-definition camera acquires image data of the internal wiring of the electrical cabinet. A corresponding number of images of the internal wiring of electrical cabinets were collected, and the viewing angle of each collected image of the internal wiring of electrical cabinets was different. Furthermore, the relative distance between the camera end of the high-definition camera and the opposite side of the electrical cabinet was equal during the collection of each image of the internal wiring of electrical cabinets.

[0073] The augmentation processing logic for the internal wiring image data of the electrical cabinet in the storage module is represented as follows:

[0074] ;

[0075] In the formula: , , The enhancement coefficients of the R, G, and B channels of a group of pixels in the image data during the enhancement processing of the internal wiring image data of the electrical cabinet. For adjustment factors; , , This refers to the R, G, and B channel values ​​of a set of pixels in the image data of the internal wiring of the electrical cabinet. The average values ​​of the R, G, and B channels of the internal wiring image data of the electrical cabinet;

[0076] Among them, setting 0 < <10, adjustment factor The value setting is used to constrain color overflow caused by pixel enhancement processing. Based on the above formula, the value of each pixel in the internal wiring image data of the electrical cabinet is... , , If we perform the calculation, the enhanced output result for this pixel will be: , This represents the values ​​of the R, B, and G channels after pixel enhancement processing.

[0077] The analysis layer includes a sensing module, a conversion module, a selection module, and an analysis module. The sensing module is used to sense the surface temperature of the circuit. The conversion module receives the image data of the internal circuit of the electrical cabinet stored in the acquisition layer and converts the image data of the internal circuit of the electrical cabinet into a contour image. The selection module is used to traverse the contour images obtained by the conversion module and select the circuit contour in the contour image. The analysis module is used to receive the circuit contour selected by the selection module and analyze the associated circuit contours.

[0078] The associated line contour analysis logic in the analysis module is represented as follows:

[0079] ;

[0080] In the formula: Line outline With line outline The correlation; Line outline The average values ​​of the R, G, and B channels in a defined area of ​​the internal wiring image data of the electrical cabinet; Line outline The average values ​​of the R, G, and B channels in a defined area of ​​the internal wiring image data of the electrical cabinet; Line outline With line outline Distance between adjacent ends; , Line outline With line outline Surface temperature; For coordination index; Line outline With line outline Surface temperature sensing location distance; As a correction factor;

[0081] Among them, the line outline With line outline correlation The smaller the value, the higher the correlation between the two sets of route profiles; conversely, the larger the value, the lower the correlation between the two sets of route profiles. In the above formula... As the target for obtaining the contour of the associated line, it is continuously changed synchronously. Find the line profile with the highest correlation and the coordination index. The value can be 1 or -1. ≤ At that time, the coordination index =1, > At that time, the coordination index =-1;

[0082] Each time the selection module runs, it selects a group of line contours in the contour image. When the analysis module analyzes the associated line contours, it uses all unselected line contours in the current contour image as the analysis target.

[0083] The analysis layer operates by processing image data of the internal wiring of each electrical cabinet.

[0084] Correction factor The value follows:

[0085] ;

[0086] In the formula: Line outline The average grayscale value; Line outline The average grayscale value;

[0087] in, This indicates taking the minimum value within the parentheses;

[0088] The indicator layer includes a selection module, an indicator module, and an output module. The selection module is used to select the line contour in the contour image. The indicator module is used to receive the line contour selected by the selection module, use the selected line contour as the query target, query the line contour with the highest correlation with the line contour in the analysis layer, render the query line contour with the highest correlation to indicate the line contour. The output module is used to receive the line contour in the rendered contour image in the indicator module, place the contour image with the rendered line contour in its original position in the corresponding electrical cabinet internal line image data, and output the electrical cabinet internal line image data.

[0089] The rendering operation is performed synchronously on the line outline corresponding to the query target. The selection module is manually operated by the system user to select the line outline in the outline image.

[0090] When a system user selects a line contour in a contour image, at least one set of contour images is used, and the number is customized by the system user. The line contours selected in each set of contour images all point to the same line. The operation of selecting a line contour in a contour image follows the following rules: the higher the accuracy requirement of the associated line contour query of the contour image, the more contour images are used; conversely, the less contour image data is used, and at least one set is required.

[0091] The sensing module is interconnected with a conversion module via a wireless network. The conversion module is interconnected with a selection module and an analysis module via a wireless network. The conversion module is interconnected with a logic module via a wireless network. The logic module is interconnected with a data acquisition module and a storage module via a wireless network. The data acquisition module is interconnected with lighting equipment and a high-definition camera via a wireless network. The sensing module and the data acquisition module are interconnected via a wireless network. The analysis module is interconnected with a selection module via a wireless network. The selection module is interconnected with an indication module and an output module via a wireless network.

[0092] In this embodiment, the logic module sets the operating logic of the acquisition module. The acquisition module then collects image data of the internal wiring of the electrical cabinet. The storage module receives the image data of the internal wiring of the electrical cabinet collected by the acquisition module and stores it. The sensing module further senses the surface temperature of the wiring. The conversion module collects the image data of the internal wiring of the electrical cabinet stored in the real-time acquisition layer and converts it into a contour image. The selection module traverses the contour images obtained by the conversion module and selects the wiring contours in the contour images. The analysis module receives the wiring contours selected by the selection module, analyzes the associated wiring contours, and finally selects wiring contours in the contour images through the selection module. The indication module receives the wiring contours selected by the selection module, uses the selected wiring contours as the query target, and queries the analysis layer for the wiring contour with the highest correlation to the selected wiring contour. The most correlated wiring contour is rendered to indicate the wiring contour, and the output module receives the wiring contours in the rendered contour images from the indication module. The contour image with the rendered wiring contours is placed in its original position in the corresponding internal wiring image data of the electrical cabinet, and the internal wiring image data of the electrical cabinet is output.

[0093] Through the system operation in the above embodiments, an effective visual cable tracing reference is provided for electrical cabinet cable management, which helps cable management staff to quickly find cables and complete the cable management work.

[0094] The above system determines the number of image data acquisitions of the internal wiring of the electrical cabinet by specifying the number of acquisitions, thereby achieving accurate acquisition of image data of the internal wiring of the electrical cabinet and providing sufficient necessary data support for system operation. At the same time, image enhancement processing logic is configured to improve the quality of the image data of the internal wiring of the electrical cabinet, so as to improve the accuracy of the system's output results. Meanwhile, the logic of calculating the correlation between the wiring contours is limited to ensure that the wiring contours are output stably with the highest correlation.

[0095] See Figure 3 As shown in the figure, this diagram further illustrates the direction in which the acquisition module initially acquires image data of the internal wiring of the electrical cabinet. (See also...) Figure 4As shown in the figure, based on the map sheet number, the process of continuously tracing a line using this system is demonstrated, which provides assistance for cable management in electrical cabinets.

[0096] Example 2:

[0097] At the implementation level, based on Example 1, this example refers to... Figure 1 The infrared cable management system for intelligent appliance cabinets based on machine vision in Example 1 will be further described in detail below:

[0098] The sensing module is integrated by several sets of infrared sensors. The operation of these infrared sensors senses the surface temperature of each line in the contour image corresponding to the internal wiring data of the electrical cabinet. The surface temperature sensing results of each line are marked on its corresponding line contour.

[0099] Among them, the conversion module runs before the sensing module. After the conversion module obtains the outline image of the internal circuit image data of the electrical cabinet, it synchronously feeds back the outline image to the sensing module. The sensing module uses the circuits corresponding to the outlines of each circuit in the outline image as the sensing targets and performs the sensing operation of the surface temperature of the circuits.

[0100] Before converting the internal wiring image data of the electrical cabinet into a contour image, the conversion module sets a color threshold to represent the wiring. Pixels in the internal wiring image data that do not meet the set color threshold are deleted, and the remaining pixels form an image to represent the wiring. The conversion operation of the contour image is then performed using the wiring image.

[0101] Through the above settings, an infrared sensor is configured in the system of Embodiment 1 to obtain the temperature information of the circuit inside the electrical cabinet, which helps the system to perform the line tracing task. At the same time, the logic of converting the image data of the circuit inside the electrical cabinet into a contour image provides the necessary operating data support for the operation of subsequent modules of the system in Embodiment 1.

[0102] like Figure 1 As shown, the instruction module renders the outline of the most relevant line found, that is, it renders the line color in the internal wiring image data of the electrical cabinet corresponding to the outline of the most relevant line.

[0103] The above settings further limit the logic of the indicator module in the system indicator layer, ensuring stable output of the internal circuit image data of the electrical cabinet with target circuit indication.

[0104] Example 3:

[0105] At the implementation level, based on Example 1, this example refers to... Figure 2 The infrared cable management system for intelligent appliance cabinets based on machine vision in Example 1 will be further described in detail below:

[0106] A machine vision-based method for infrared cable management in smart appliance cabinets includes:

[0107] Set up the logic for acquiring image data of the internal wiring of the electrical cabinet, and acquire image data of the internal wiring of the electrical cabinet based on the acquisition logic;

[0108] Acquire the collected image data of the internal wiring of the electrical cabinet, set the image data enhancement processing logic, and apply the image data enhancement processing logic to enhance the image data of the internal wiring of the electrical cabinet;

[0109] Obtain the image data of the internal wiring of the electrical cabinet after enhancement processing, and extract the outline image of the internal wiring of the electrical cabinet from the image data of the internal wiring of the electrical cabinet.

[0110] In the image of the wiring outline inside the electrical cabinet, select the wiring outline as the target for querying related wiring outlines, and then select the set of related wiring outlines with the highest correlation.

[0111] Using the selected line contour and the most relevant line contour found in the query as the rendering targets, render the two sets of line contours and represent them in the contour image.

[0112] The contour image representing the selected line contours and the group of line contours with the highest correlation is placed at the contour image source position in the line image data inside the electrical cabinet.

[0113] The internal wiring data of the appliance cabinet, which contains outline images, is fed back to the user terminal.

[0114] In summary, during operation, the system in the above embodiments uses machine vision technology to collect image data of the wiring inside the electrical cabinet. Further, based on a comprehensive analysis of the internal image data and wiring temperature information, it achieves a high-precision wiring locating technique. This meets the necessary prerequisites for wiring management operations, allowing the system to find the wiring to be organized and thus expedite the wiring process. Furthermore, the system outputs the results based on image display, providing reference for technicians performing wiring management tasks and minimizing the difficulty of wiring in the electrical cabinet. Simultaneously, by configuring the wiring management method within the system, the technical solution is further improved, ensuring its stable service for wiring management.

[0115] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A machine vision-based intelligent appliance cabinet infrared cable management system, characterized in that, include: Data acquisition layer, analysis layer, and indicator layer; The system operates while the electrical cabinet is running. The acquisition layer sets up the logic for acquiring internal wiring image data of the electrical cabinet. Based on the acquisition logic, it acquires internal wiring image data of the electrical cabinet and stores the acquired internal wiring image data of the electrical cabinet simultaneously. The analysis layer receives the internal wiring image data of the electrical cabinet stored in the acquisition layer, converts the internal wiring image data of the electrical cabinet into a contour image, selects a wiring contour in the contour image, and analyzes its associated wiring contours. The system user selects a wiring contour in the contour image converted by the analysis layer in the indication layer. The indication layer simultaneously indicates the associated wiring contour in the contour image based on the wiring contour selection result of the system user. The system user refers to the associated wiring contour to determine the internal wiring of the electrical cabinet corresponding to the associated wiring contour. The analysis layer includes a sensing module, a conversion module, a selection module, and an analysis module. The sensing module is used to sense the surface temperature of the circuit. The conversion module receives the internal circuit image data of the electrical cabinet stored in the acquisition layer and converts the internal circuit image data of the electrical cabinet into a contour image. The selection module is used to traverse the contour images obtained by the conversion module and select the circuit contour in the contour image. The analysis module is used to receive the circuit contour selected by the selection module and analyze the associated circuit contours. The associated line contour analysis logic in the analysis module is represented as follows: ； In the formula: Line outline With line outline The correlation; Line outline The average values ​​of the R, G, and B channels in a defined area of ​​the internal wiring image data of the electrical cabinet; Line outline The average values ​​of the R, G, and B channels in a defined area of ​​the internal wiring image data of the electrical cabinet; Line outline With line outline Distance between adjacent ends; , Line outline With line outline Surface temperature; For coordination index; Line outline With line outline Surface temperature sensing location distance; As a correction factor; Among them, the line outline With line outline correlation The smaller the value, the higher the correlation between the two sets of route profiles; conversely, the larger the value, the lower the correlation between the two sets of route profiles. In the above formula... As the target for obtaining the contour of the associated line, it is continuously changed synchronously. Find the line profile with the highest correlation and the coordination index. The value can be 1 or -1. ≤ At that time, the coordination index =1, > At that time, the coordination index =-1.

2. The machine vision-based intelligent appliance cabinet infrared cable management system according to claim 1, characterized in that, The acquisition layer includes a logic module, an acquisition module, and a storage module. The logic module is used to set the operating logic of the acquisition module, the acquisition module is used to acquire image data of the internal wiring of the electrical cabinet, and the storage module is used to receive the image data of the internal wiring of the electrical cabinet acquired by the acquisition module and store the image data of the internal wiring of the electrical cabinet. During the acquisition module's operation phase, the acquisition module synchronously acquires the operating logic from the logic module and performs the acquisition operation of the internal wiring image data of the electrical cabinet based on the operating logic. After receiving the internal wiring image data of the electrical cabinet, the storage module synchronously performs enhancement processing on the internal wiring image data of the electrical cabinet, and after completing the enhancement processing, it performs the storage of the internal wiring image data of the electrical cabinet.

3. The machine vision-based intelligent appliance cabinet infrared cable management system according to claim 2, characterized in that, The logic for the acquisition module in the logic module is as follows: The application acquisition module collects a set of image data of the internal wiring of the electrical cabinet in the positive direction. The complexity of the collected image data is analyzed, and the number of image data to be collected is set based on the complexity analysis results. ； In the formula: The complexity of the internal wiring image data of the electrical cabinet; The size of the wiring image area in the internal wiring image data of the electrical cabinet; The global size of the internal wiring image data of the electrical cabinet; This represents the total number of wiring intersections in the internal wiring image data of the electrical cabinet. The total number of contour images representing the circuit contours in the internal wiring image data of the electrical cabinet; Let be the length of the i-th group of contour images; The number of image data samples collected from the internal wiring of the electrical cabinet; This serves as the baseline for collecting image data of the internal wiring of the electrical cabinet. Among them, the number of data collection bases for internal wiring image data of electrical cabinets Customized by the system user, and >1, Number of image data collected of internal wiring in electrical cabinet Based on the further rounding method.

4. The machine vision-based intelligent appliance cabinet infrared cable management system according to claim 2, characterized in that, The acquisition module integrates lighting equipment and a high-definition camera. The lighting equipment provides supplemental illumination to the internal wiring of the electrical cabinet, and the high-definition camera acquires image data of the internal wiring of the electrical cabinet. A corresponding number of images of the internal wiring of electrical cabinets were collected, and the viewing angle of each collected image of the internal wiring of electrical cabinets was different. Furthermore, the relative distance between the camera end of the high-definition camera and the opposite side of the electrical cabinet was equal during the collection of each image of the internal wiring of electrical cabinets. The enhanced processing logic for the internal wiring image data of the electrical cabinet in the storage module is represented as follows: ； In the formula: , , The enhancement coefficients of the R, G, and B channels of a group of pixels in the image data during the enhancement processing of the internal wiring image data of the electrical cabinet. For adjustment factors; , , This refers to the R, G, and B channel values ​​of a set of pixels in the image data of the internal wiring of the electrical cabinet. The average values ​​of the R, G, and B channels of the internal wiring image data of the electrical cabinet; Among them, setting 0 < <10, adjustment factor The value setting is used to constrain color overflow caused by pixel enhancement processing. Based on the above formula, the value of each pixel in the internal wiring image data of the electrical cabinet is... , , If we perform the calculation, the enhanced output result for this pixel will be: , This represents the values ​​of the R, B, and G channels after pixel enhancement processing.

5. The machine vision-based intelligent appliance cabinet infrared cable management system according to claim 1, characterized in that, The sensing module is integrated by several sets of infrared sensors. The several sets of infrared sensors operate to sense the surface temperature of each line in the contour image corresponding to the internal wiring data of the electrical cabinet. The surface temperature sensing results of each line in the contour image are marked on its corresponding line contour. Among them, the conversion module runs before the sensing module. After the conversion module obtains the outline image of the internal circuit image data of the electrical cabinet, it synchronously feeds back the outline image to the sensing module. The sensing module uses the circuits corresponding to the outlines of each circuit in the outline image as the sensing targets and performs the sensing operation of the surface temperature of the circuits. Before converting the internal wiring image data of the electrical cabinet into a contour image, the conversion module sets a color threshold to represent the wiring. Pixels in the internal wiring image data that do not meet the set color threshold are deleted, and the remaining pixels form an image to represent the wiring. The conversion operation of the contour image is then performed using the wiring image.

6. The machine vision-based intelligent appliance cabinet infrared cable management system according to claim 1, characterized in that, Each time the selection module runs, it selects a set of line contours from the contour image. When the analysis module analyzes the associated line contours of the line contours, it uses all unselected line contours in the current contour image as the analysis target. The analysis layer operates by processing image data of the internal wiring of each electrical cabinet. The correction factor The value follows: ； In the formula: Line outline The average grayscale value; Line outline The average grayscale value; in, This indicates taking the minimum value within the parentheses.

7. The machine vision-based intelligent appliance cabinet infrared cable management system according to claim 1, characterized in that, The indicator layer includes a selection module, an indicator module, and an output module. The selection module is used to select a line contour in the contour image. The indicator module is used to receive the line contour selected by the selection module, use the selected line contour as the query target, query the line contour with the highest correlation with the line contour in the analysis layer, render the query line contour with the highest correlation to indicate the line contour, and the output module is used to receive the line contour in the rendered contour image in the indicator module, place the contour image with the rendered line contour in its original position in the corresponding electrical cabinet internal line image data, and output the electrical cabinet internal line image data. The rendering operation is performed synchronously on the line outline corresponding to the query target. The selection module is manually operated by the system user to select the line outline in the outline image. When a system user selects a line contour in a contour image, at least one set of contour images is used, and the number is customized by the system user. The line contours selected in each set of contour images all point to the same line. The operation of selecting a line contour in a contour image follows the following rules: the higher the accuracy requirement of the associated line contour query of the contour image, the more contour images are used; conversely, the less contour image data is used, and at least one set is required.

8. The machine vision-based intelligent appliance cabinet infrared cable management system according to claim 7, characterized in that, The instruction module renders the outline of the most relevant line found, that is, it renders the line color in the internal wiring image data of the electrical cabinet corresponding to the outline of the most relevant line.

9. The machine vision-based intelligent appliance cabinet infrared cable management system according to claim 1, characterized in that, The sensing module is interconnected with a conversion module via a wireless network. The conversion module is interconnected with a selection module and an analysis module via a wireless network. The conversion module is interconnected with a logic module via a wireless network. The logic module is interconnected with a data acquisition module and a storage module via a wireless network. The data acquisition module is interconnected with lighting equipment and a high-definition camera via a wireless network. The sensing module and the data acquisition module are interconnected via a wireless network. The analysis module is interconnected with a selection module via a wireless network. The selection module is interconnected with an indication module and an output module via a wireless network.

10. A machine vision-based method for infrared cable management in intelligent appliance cabinets, wherein the method is an implementation method of the machine vision-based infrared cable management system for intelligent appliance cabinets as described in any one of claims 1-9, characterized in that... include: Set up the logic for acquiring image data of the internal wiring of the electrical cabinet, and acquire image data of the internal wiring of the electrical cabinet based on the acquisition logic; Acquire the collected image data of the internal wiring of the electrical cabinet, set the image data enhancement processing logic, and apply the image data enhancement processing logic to enhance the image data of the internal wiring of the electrical cabinet; Obtain the image data of the internal wiring of the electrical cabinet after enhancement processing, and extract the outline image of the internal wiring of the electrical cabinet from the image data of the internal wiring of the electrical cabinet. In the image of the wiring outline inside the electrical cabinet, select the wiring outline as the target for querying related wiring outlines, and then select the set of related wiring outlines with the highest correlation. Using the selected line contour and the most relevant line contour found in the query as the rendering targets, render the two sets of line contours and represent them in the contour image. The contour image representing the selected line contours and the group of line contours with the highest correlation is placed at the contour image source position in the line image data inside the electrical cabinet. The internal wiring data of the appliance cabinet, which contains outline images, is fed back to the user terminal.

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