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

Abstract

The invention 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, and the system comprises a collection layer, an analysis layer and an indication layer. The system is executed in the operation state of the electric appliance cabinet. An electrical appliance cabinet internal circuit image data acquisition logic is set in the acquisition layer, electrical appliance cabinet internal circuit image data is acquired based on the acquisition logic, the acquired electrical appliance cabinet internal circuit image data is synchronously stored, and the analysis layer receives the electrical appliance cabinet internal circuit image data stored in the acquisition layer. According to the invention, the image data of the internal circuit of the electric appliance cabinet is collected, and the high-precision line tracking technology is further realized based on the comprehensive analysis of the internal image data of the electric appliance cabinet and the circuit temperature information, so that the necessary prerequisite conditions for the line management operation of workers are met, the lines needing to be managed are searched, and the line management work is realized more quickly.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical cabinets, and particularly to an intelligent electrical cabinet infrared wire management system and method based on machine vision. Background Art

[0002] Infrared electric wire cables are specially used for connecting with infrared-related devices, with the focus on transmitting signals and supplying power to infrared devices, such as the cable connection parts in devices like infrared remote control, infrared sensors, and infrared imaging. Their design and functions revolve around the working characteristics of infrared devices and need to meet requirements such as signal transmission quality and anti-interference. An electrical cabinet for infrared electric wire cables is a cabinet that integrally manages and protects infrared-related devices and cables in an infrared technology application scenario.

[0003] A wire management method for an optical fiber automatic wiring device is disclosed in the invention patent with the application number 202110055366.9. It is characterized by including: obtaining the position information of the target optical fiber, where the position information of the target optical fiber includes: the target optical fiber head corresponding to the target optical fiber, its position at the current interface corresponding to the plug-in panel, and the position of the target interface; moving the fixture to the position where the target optical fiber head is located on the plug-in panel to make the fixture buckle the target optical fiber; moving the fixture along the target optical fiber to align the fixture with the gap between the hooks on the side wall of the hook wire groove, and the fixture drives the target optical fiber to translate to the middle of the hook wire groove and take it outwards, so that the target optical fiber is separated from the hooks of the hook wire groove; the fixture moves along the buried wire groove to take out the target optical fiber from the buried wire groove, and then hangs the target optical fiber on the transition wire groove: the fixture moves to the current interface position of the target optical fiber head, clamps the target optical fiber head and takes it outwards, driving the target optical fiber head to be inserted into the target interface; the fixture buckles and removes the target optical fiber from the transition wire groove, then the fixture buries the target optical fiber into the buried wire groove, and finally the fixture hooks the target optical fiber into the hook wire groove.

[0004] This application aims to solve the problem that: "In the wire management method of the existing optical fiber wire management rack, during the process of'moving the fixture above the hook opening side of the first buried wire groove and pulling out the cable from the hook opening', the fixture cannot ensure that the cable is safely pulled out from the hook opening of the buried wire groove, and may break the optical fiber during the pulling process, thereby causing communication delays, and increasing the maintenance cost and the workload of the staff."

[0005] For the electrical cabinet loaded with infrared electric wire cables that has been put into use, during its daily maintenance process, the lines in the cabinet will be in a messy state due to maintenance operations, resulting in an increasing difficulty in subsequent maintenance of the electrical cabinet. To solve this problem, a large number of wire management tools and equipment have been developed by relevant personnel, but finding the "heads and tails" of the intertwined cables still requires manual operation based on personal experience and visual pursuit to carry out the wire management work;

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

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

[0008] To achieve the above objectives, the present invention is realized through the following technical solutions:

[0009] In a first aspect, an infrared wire management system for intelligent electrical cabinets based on machine vision includes: an acquisition layer, an analysis layer, and an indication layer;

[0010] The system operates in the operating state of the electrical cabinet;

[0011] In the acquisition layer, the acquisition logic of the internal circuit image data of the electrical cabinet is set, and based on the acquisition logic, the internal circuit image data of the electrical cabinet is acquired, and the acquired internal circuit image data of the electrical cabinet is stored synchronously. The analysis layer receives the internal circuit image data of the electrical cabinet stored in the acquisition layer, converts the internal circuit image data of the electrical cabinet into a contour image, selects a circuit contour in the contour image, and analyzes its associated circuit contour. The system-end user selects the circuit contour in the contour image obtained by conversion in the analysis layer in the indication layer, and the indication layer synchronously indicates the associated circuit contour in the contour image based on the circuit contour selection result of the system-end user. The system-end user determines the internal circuit of the electrical cabinet corresponding to the associated circuit contour with reference to the associated circuit contour;

[0012] The analysis layer includes a perception module, a conversion module, a selection module, and an analysis module. The perception module is used to perceive 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 image obtained by conversion in the conversion module and select a circuit contour in the contour image. The analysis module is used to receive the circuit contour selected in the selection module and analyze the associated circuit contour of the circuit contour;

[0013] The analysis logic of the associated circuit contour for the circuit contour in the analysis module is expressed as:

[0014]

[0015] In the formula: ε(α, β) is the relevance between circuit contour α and circuit contour β; R α 、G α 、B α are the average values of the R, G, and B channels in the defined area of circuit contour α in the internal circuit image data of the electrical cabinet; R β 、G β 、B βR, G, and B channel means of the line profile β in the defined area of the electrical cabinet internal line image data; D near (α, β) is the distance between the proximal ends of the line profiles α and β; α T 、β T are the surface temperatures of the line profiles α and β; τ is the coordination index; D far (α, β) is the distance of the surface temperature sensing position between the line profiles α and β; λ is the correction factor;

[0016] Among them, the smaller the value of the correlation ε(α, β) between the line profiles α and β, the higher the correlation between the two sets of line profiles. Conversely, it means that the correlation between the two sets of line profiles is lower. In the above formula, α is the target for obtaining the associated line profile, and β is continuously replaced synchronously to obtain the set of line profiles with the highest correlation. The coordination index τ takes the value of 1 or -1, α T ≤β T When, the coordination index τ = 1, α T >β T When, the coordination index τ = -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 operation logic of the acquisition module. The acquisition module is used to acquire the electrical cabinet internal line image data. The storage module is used to receive the electrical cabinet internal line image data acquired by the operation of the acquisition module and store the electrical cabinet internal line image data;

[0018] Among them, during the operation stage of the acquisition module, the operation logic is obtained synchronously in the logic module, and the acquisition operation of the electrical cabinet internal line image data is performed based on the operation logic. After receiving the electrical cabinet internal line image data, the storage module synchronously performs enhancement processing on the electrical cabinet internal line image data, and then performs storage of the electrical cabinet internal line image data after the enhancement processing is completed.

[0019] Furthermore, the operation logic of the acquisition module set in the logic module is:

[0020] Apply the acquisition module to acquire a set of electrical cabinet internal line image data in the positive direction of the electrical cabinet internal line, analyze the complexity of the acquired electrical cabinet internal line image data, and set the acquisition quantity of the electrical cabinet internal line image data according to the complexity analysis result;

[0021]

[0022] In the formula: C is the complexity of the electrical cabinet internal line image data; S Lis the size of the line image area in the internal circuit image data of the electrical cabinet; S is the global size of the internal circuit image data of the electrical cabinet; q is the total number of line intersections in the internal circuit image data of the electrical cabinet; n is the total number of contour images representing the line contours in the contour image of the internal circuit image data of the electrical cabinet; L i is the length of the i-th group of contour images; M is the number of acquisitions of the internal circuit image data of the electrical cabinet; M0 is the acquisition quantity base of the internal circuit image data of the electrical cabinet;

[0023] Among them, the acquisition quantity base M0 of the internal circuit image data of the electrical cabinet is user-defined by the system end user, and M0 > 1. The acquisition quantity M of the internal circuit image data of the electrical cabinet is rounded up based on the further method.

[0024] Furthermore, the acquisition module is integrated by lighting equipment and a high-definition camera. The lighting equipment operates to supplement light for the internal circuit of the electrical cabinet. The high-definition camera acquires the internal circuit image data of the electrical cabinet in the corresponding quantity based on the acquisition quantity M of the internal circuit image data of the electrical cabinet. And the acquisition perspectives of each acquired internal circuit image data of the electrical cabinet are all different. And when each internal circuit image data of the electrical cabinet is acquired, the relative distance from the camera end of the high-definition camera to the opposite side of the electrical cabinet is equal;

[0025] The enhancement processing logic for the internal circuit image data of the electrical cabinet in the storage module is expressed as:

[0026]

[0027] In the formula: a R 、a G 、a B are the R, G, and B channel enhancement coefficients of a group of pixels in the image data during the enhancement processing of the internal circuit image data of the electrical cabinet; k is the adjustment factor; R, G, and B are the R, G, and B channel values of a group of pixels in the internal circuit image data of the electrical cabinet; is the average value of the R, G, and B channels of the internal circuit image data of the electrical cabinet;

[0028] Among them, 0 < k < 10 is set. The setting of the value of the adjustment factor k is used to constrain the situation of color overflow caused by pixel enhancement processing. Based on the above formula, a R 、a G 、a B of each pixel in the internal circuit image data of the electrical cabinet are obtained. Then the enhancement output result of this pixel is: R′, G′, and B′ represent the values of the R, B, and G channels after pixel enhancement processing.

[0029] Furthermore, the sensing module is integrated by several groups of infrared sensors. The several groups of infrared sensors operate to sense the surface temperature of each line corresponding to the line contour in the contour image of the internal line image data of the electrical cabinet. The surface temperature sensing results of each group of line contours corresponding to the lines are marked on their corresponding line contours;

[0030] Among them, the conversion module runs prior to the sensing module. After the conversion module runs to obtain the contour image of the internal line image data of the electrical cabinet, it synchronously feeds back the contour image to the sensing module. The sensing module uses the lines corresponding to each line contour in the contour image as the sensing targets and executes the sensing operation of the line surface temperature;

[0031] Before the conversion module converts the internal line image data of the electrical cabinet into a contour image, it sets a color threshold representing the line, deletes the pixels in the internal line image data of the electrical cabinet that do not meet the set color threshold, and the remaining pixels form an image representing the line image. The contour image conversion operation is performed using the line 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 contour, it uses all the unselected line contours in the current contour image as the analysis targets;

[0033] The analysis layer runs with each group of internal line image data of the electrical cabinet as the processing target;

[0034] The value of the correction factor λ follows:

[0035]

[0036] In the formula: is the average gray value of the line contour α; is the average gray value of the line contour β;

[0037] Among them, means taking the minimum value within the brackets.

[0038] Furthermore, the indication layer includes a selection module, an indication module, and an output module. The selection module is used to select line contours in the contour image. The indication module is used to receive the line contours selected by the selection module, uses the selected line contours as the query targets, queries the line contours with the highest relevance to the line contour in the analysis layer, and renders the queried line contours with the highest relevance to indicate the line contour. The output module is used to receive the line contours in the contour image rendered by the indication module, place the contour image with the rendered line contours in the original position in the corresponding internal line image data of the electrical cabinet, and output the internal line image data of the electrical cabinet;

[0039] The rendering operation is synchronously performed for the line profile corresponding to the query target. The selection module is manually operated by the system-side user to select the line profile in the profile image;

[0040] Among them, when the system-side user selects the line profile in the profile image, no less than one group of profile images is applied, and the quantity is user-defined by the system-side user. Moreover, the line profiles selected in each group of profile images all point to the same line. At the same time, the operation of selecting the line profile in the profile image follows that the higher the accuracy requirement for querying the associated line profile of the profile image, the more profile images are applied; conversely, the less the amount of profile image data is applied, and no less than one group.

[0041] Furthermore, the operation of the indication module for rendering the line profile with the highest relevance is the operation of rendering with the line color in the line image data inside the electrical cabinet corresponding to the line profile with the highest relevance.

[0042] Furthermore, the sensing module is wirelessly interactively connected to a conversion module. The conversion module is wirelessly interactively connected to a selection module and an analysis module. The conversion module is wirelessly interactively connected to a logic module. The logic module is wirelessly interactively connected to a collection module and a storage module. The lower level of the collection module is wirelessly interactively connected to lighting equipment and a high-definition camera. The sensing module and the collection module are wirelessly interactively connected. The analysis module is wirelessly interactively connected to a selection module. The selection module is wirelessly interactively connected to an indication module and an output module.

[0043] In a second aspect, an intelligent electrical cabinet infrared wire management method based on machine vision includes:

[0044] Set the acquisition logic for the line image data inside the electrical cabinet, and acquire the line image data inside the electrical cabinet based on the acquisition logic;

[0045] Obtain the acquired line image data inside the electrical cabinet, set the image data enhancement processing logic, and apply the image data enhancement processing logic to enhance the line image data inside the electrical cabinet;

[0046] Obtain the line image data inside the electrical cabinet after the enhancement processing is completed, and extract the line profile image inside the electrical cabinet from the line image data inside the electrical cabinet;

[0047] Select the line profile in the line profile image inside the electrical cabinet as the query target for the associated line profile, and query a group of associated line profiles with the highest relevance to the selected line profile;

[0048] Use the selected line profile and the group of associated line profiles with the highest relevance queried as the rendering targets, render the two groups of line profiles, and represent them in the profile image where they are located;

[0049] Place the contour image representing the selected line contour and the group of line contours with the highest relevance at the source position of the contour image in the internal circuit image data of the electrical cabinet;

[0050] Feed back the internal circuit image data of the electrical cabinet with the contour image placed therein to the user side.

[0051] Adopting the technical solution provided by the present invention, compared with the known public technology, it has the following

[0052] Beneficial effects:

[0053] The present invention provides an intelligent electrical cabinet infrared wire management system and method based on machine vision. During the operation of the system, machine vision technology is applied to collect the internal circuit image data of the electrical cabinet. Further, based on the comprehensive analysis of the internal image data of the electrical cabinet and the line temperature information, a high-precision line search technology is realized, so as to meet the necessary prerequisite conditions for the wire management operation of the staff, search for the wires to be sorted, and thus realize the wire management work more quickly;

[0054] Furthermore, based on image display, the operation result of the system is output, so that technicians engaged in wire management work can obtain references, and the difficulty of wire management in the electrical cabinet is reduced to a great extent. At the same time, by configuring the wire management method in the wire management system, the technical solution is further improved to ensure that the technical solution can stably serve the wire management work. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0056] Figure 1 It is a schematic structural diagram of an intelligent electrical cabinet infrared wire management system based on machine vision;

[0057] Figure 2 It is a schematic flow diagram of an intelligent electrical cabinet infrared wire management method based on machine vision;

[0058] Figure 3 It is a schematic direction diagram of the first acquisition of the internal circuit image data of the electrical cabinet by the acquisition module in the present invention;

[0059] Figure 4 It is a schematic logical process diagram of the system for searching for wires based on the output of the internal circuit image data of the electrical cabinet in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0061] The following further describes the present invention with reference to embodiments.

[0062] Embodiment 1:

[0063] The intelligent electrical cabinet infrared wire management system based on machine vision in this embodiment, as Figure 1 shown, includes: an acquisition layer, an analysis layer, and an indication layer;

[0064] The system executes in the operating state of the electrical cabinet;

[0065] Set the acquisition logic of the internal circuit image data of the electrical cabinet in the acquisition layer, acquire the internal circuit image data of the electrical cabinet based on the acquisition logic, and synchronously store the acquired internal circuit image data of the electrical cabinet. The analysis layer receives the internal circuit image data of the electrical cabinet stored in the acquisition layer, converts the internal circuit image data of the electrical cabinet into a contour image, selects the circuit contour in the contour image, and analyzes its associated circuit contour. The system-side user selects the circuit contour in the contour image obtained by the conversion of the analysis layer in the indication layer, and the indication layer synchronously indicates the associated circuit contour in the contour image based on the circuit contour selection result of the system-side user. The system-side user determines the internal circuit of the electrical cabinet corresponding to the associated circuit contour with reference to the associated circuit contour.

[0066] The acquisition layer includes a logic module, an acquisition module, and a storage module. The logic module is used to set the operation logic of the acquisition module. The acquisition module is used to acquire the internal circuit image data of the electrical cabinet. The storage module is used to receive the internal circuit image data acquired by the operation of the acquisition module and store the internal circuit image data of the electrical cabinet.

[0067] Among them, during the operation stage of the acquisition module, the operation logic is obtained from the logic module synchronously, and the acquisition operation of the internal circuit image data of the electrical cabinet is executed based on the operation logic. After receiving the internal circuit image data of the electrical cabinet, the storage module synchronously performs enhancement processing on the internal circuit image data of the electrical cabinet, and then executes the storage of the internal circuit image data of the electrical cabinet after the enhancement processing is completed.

[0068] The operation logic of the acquisition module set in the logic module is:

[0069] Apply the acquisition module to collect a set of electrical cabinet internal circuit image data in the positive direction of the internal circuit of the electrical cabinet, analyze the complexity of the collected electrical cabinet internal circuit image data, and set the acquisition quantity of the electrical cabinet internal circuit image data according to the analysis result of the complexity.

[0070]

[0071] In the formula: C is the complexity of the electrical cabinet internal circuit image data; S L is the size of the circuit image area in the electrical cabinet internal circuit image data; S is the global size of the electrical cabinet internal circuit image data; q is the total number of circuit intersections in the electrical cabinet internal circuit image data; n is the total number of contour images representing the circuit contours in the contour image of the electrical cabinet internal circuit image data; L i is the length of the i-th group of contour images; M is the acquisition quantity of the electrical cabinet internal circuit image data; M0 is the acquisition quantity base of the electrical cabinet internal circuit image data.

[0072] Among them, the acquisition quantity base M0 of the electrical cabinet internal circuit image data is user-defined by the system end user, and M0 > 1. The acquisition quantity M of the electrical cabinet internal circuit image data is rounded up based on the further method.

[0073] The acquisition module is integrated by lighting equipment and a high-definition camera. The lighting equipment operates to supplement light for the internal circuit of the electrical cabinet. The high-definition camera collects the corresponding quantity of electrical cabinet internal circuit image data based on the acquisition quantity M of the electrical cabinet internal circuit image data. And the acquisition perspectives of each collected electrical cabinet internal circuit image data are different. And when each electrical cabinet internal circuit image data is collected, the relative distance from the camera end of the high-definition camera to the opposite side of the electrical cabinet is equal.

[0074] The enhancement processing logic for the electrical cabinet internal circuit image data in the storage module is expressed as:

[0075]

[0076] In the formula: a R 、a G 、a B are the R, G, and B channel enhancement coefficients of a group of pixels in the image data during the enhancement processing of the electrical cabinet internal circuit image data; k is the adjustment factor; R, G, and B are the R, G, and B channel values of a group of pixels in the electrical cabinet internal circuit image data. is the average value of the R, G, and B channels of the electrical cabinet internal circuit image data;

[0077] Among them, 0 < k < 10 is set. The setting of the value of the adjustment factor k is used to constrain the situation of color overflow caused by pixel enhancement processing. Based on the above formula, for each pixel in the electrical cabinet internal circuit image data, aR and a G and a B If calculated, the enhanced output result of this pixel is: R′, G′, and B′ represent the values of the R, B, and G channels after pixel enhancement processing

[0078] 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 image obtained by conversion in the conversion module and select the circuit contour in the contour image. The analysis module is used to receive the circuit contour selected in the selection module and analyze the associated circuit contours of the circuit contour;

[0079] The analysis logic for the associated circuit contours of the circuit contour in the analysis module is expressed as:

[0080]

[0081] In the formula: ε(α, β) is the correlation between circuit contour α and circuit contour β; R α , G α , B α are the average values of the R, G, and B channels in the defined area of circuit contour α in the image data of the internal circuit of the electrical cabinet; R β , G β , B β are the average values of the R, G, and B channels in the defined area of circuit contour β in the image data of the internal circuit of the electrical cabinet; D near (α, β) is the distance between the proximal ends of circuit contour α and circuit contour β; α T , β T are the surface temperatures of circuit contour α and circuit contour β; τ is the coordination index; D far (α, β) is the distance of the surface temperature sensing position between circuit contour α and circuit contour β; λ is the correction factor;

[0082] Among them, the smaller the value of the correlation ε(α, β) between circuit contour α and circuit contour β, the higher the correlation between the two sets of circuit contours. Conversely, it means that the correlation between the two sets of circuit contours is lower. In the above formula, α is the target for obtaining the associated circuit contour, and β is continuously replaced synchronously to obtain the set of circuit contours with the highest correlation. The coordination index τ takes the value of 1 or -1. When α T ≤β T , the coordination index τ = 1. When α T >β T , the coordination index τ = -1;

[0083] Each time the selection module runs, a set of line contours is selected from the contour image. When the analysis module analyzes the associated line contours of the line contours, all unselected line contours in the current contour image are used as the analysis target;

[0084] The analysis layer runs with each set of internal circuit image data of the electrical cabinet as the processing target;

[0085] The value of the correction factor λ follows:

[0086]

[0087] In the formula: is the average gray value of the line contour α; is the average gray value of the line contour β;

[0088] Among them, means taking the minimum value within the brackets;

[0089] The indication layer includes a selection module, an indication module, and an output module. The selection module is used to select line contours in the contour image. The indication module is used to receive the line contours selected by the selection module, use the selected line contours as the query target, query the line contour with the highest relevance to the line contour in the analysis layer, and render the queried line contour with the highest relevance to indicate the line contour. The output module is used to receive the line contours in the contour image rendered in the indication module, place the contour image with the rendered line contours in the original position of the corresponding internal circuit image data of the electrical cabinet, and output the internal circuit image data of the electrical cabinet;

[0090] The rendering operation is synchronously performed on the line contours corresponding to the query target. The selection module is manually operated by the system-side user to select line contours in the contour image;

[0091] Among them, when the system-side user selects line contours in the contour image, no less than one set of contour images is applied, and the quantity is user-defined by the system-side user. Moreover, the line contours selected in each group of contour images all point to the same line. At the same time, the operation of selecting line contours in the contour image follows: the higher the query accuracy requirement for the associated line contours of the contour image, the more contour images are applied; conversely, the less the amount of contour image data is applied, and no less than one set;

[0092] The sensing module is connected to a conversion module through wireless network interaction. The conversion module is connected to a selection module and an analysis module through wireless network interaction. The conversion module is connected to a logic module through wireless network interaction. The logic module is connected to a collection module and a storage module through wireless network interaction. The lower level of the collection module is connected to lighting equipment and a high-definition camera through wireless network interaction. The sensing module and the collection module are connected through wireless network interaction. The analysis module is connected to a selection module through wireless network interaction. The selection module is connected to an indication module and an output module through wireless network interaction.

[0093] In this embodiment, the logic module runs to set the operation logic of the collection module. The collection module runs later to collect the image data of the internal lines of the electrical cabinet. The storage module then receives the image data of the internal lines of the electrical cabinet collected by the collection module and stores the image data of the internal lines of the electrical cabinet. The sensing module further senses the surface temperature of the lines. The conversion module collects in real time the image data of the internal lines of the electrical cabinet stored in the layer and converts the image data of the internal lines of the electrical cabinet into a contour image. The selection module runs through the contour image obtained by conversion in the conversion module and selects the line contour in the contour image. The analysis module is used to receive the line contour selected in the selection module and analyze the associated line contours of the line contour. Finally, the selection module selects the line contour in the contour image. The indication module receives the line contour selected by the selection module, uses the selected line contour as the query target, queries in the analysis layer for the line contour with the highest correlation with the line contour, renders the line contour with the highest correlation queried to indicate the line contour, and the output module receives the line contour in the contour image rendered by the indication module, places the contour image with the rendered line contour at the original position of the corresponding image data of the internal lines of the electrical cabinet, and outputs the image data of the internal lines of the electrical cabinet.

[0094] Through the operation of the system in the above embodiment, it provides an effective visual line-finding reference for cable management in the electrical cabinet, which helps cable management staff quickly find the lines and complete the cable management work.

[0095] In the above system, the logic for determining the collection quantity of the image data of the internal lines of the electrical cabinet through the specified one is implemented, achieving the precise collection of the image data of the internal lines of the electrical cabinet, providing sufficient prior data support for the system operation. At the same time, it is configured with image enhancement processing logic to improve the quality of the image data of the internal lines of the electrical cabinet, so as to achieve the purpose of improving the accuracy of the system operation output result. At the same time, the correlation calculation logic for the associated line contours of the line contours is defined to ensure the stable output of the line contour with the highest correlation of the line contours.

[0096] See Figure 3 As shown, this figure further shows the direction in which the collection module first collects the image data of the internal lines of the electrical cabinet. See Figure 4As shown, based on the drawing numbers in the figure, the process of continuously tracing a line based on this system is demonstrated, which provides assistance for the cable management work of the electrical cabinet.

[0097] Embodiment 2:

[0098] At the specific implementation level, based on Embodiment 1, this embodiment refers to Figure 1 to further specifically describe the intelligent infrared cable management system for electrical cabinets based on machine vision in Embodiment 1:

[0099] The sensing module is integrated by several groups of infrared sensors. The several groups of infrared sensors operate to sense the surface temperature of each line corresponding to the line contour in the contour image of the internal line image data of the electrical cabinet. The surface temperature sensing results of each group of line contours corresponding to the lines are marked on their corresponding line contours;

[0100] Among them, the conversion module runs prior to the sensing module. After the conversion module runs to obtain the contour image of the internal line image data of the electrical cabinet, it synchronously feeds back the contour image to the sensing module. The sensing module takes the lines corresponding to each line contour in the contour image as the sensing targets and performs the sensing operation of the line surface temperature;

[0101] Before converting the internal line image data of the electrical cabinet into a contour image, the conversion module sets a color threshold representing the line, deletes the pixels in the internal line image data of the electrical cabinet that do not meet the set color threshold, and the remaining pixels form an image representing the line image, and applies the line image to perform the conversion operation of the contour image.

[0102] Through the above settings, infrared sensors are configured for the system in Embodiment 1 to obtain the internal line temperature information of the electrical cabinet, assist the system to complete the line tracing task, and further clarify the logic of converting the internal line image data of the electrical cabinet into a contour image, providing necessary operation data support for the subsequent operation of the modules in Embodiment 1.

[0103] As Figure 1 shown, the indication module performs an operation of rendering the line contour with the highest relevance, that is, an operation of rendering with the line color in the internal line image data of the electrical cabinet corresponding to the line contour with the highest relevance.

[0104] Through the above settings, the logic of the operation of the indication module in the system indication layer is further defined to ensure the stable output of the internal line image data of the electrical cabinet with the target line indication.

[0105] Embodiment 3:

[0106] At the specific implementation level, based on Embodiment 1, this embodiment refers to Figure 2 to further specifically describe the intelligent infrared cable management system for electrical cabinets based on machine vision in Embodiment 1:

[0107] An infrared wire management method for intelligent electrical cabinets based on machine vision, including:

[0108] Setting the image data acquisition logic for the internal wiring of the electrical cabinet, and acquiring the image data of the internal wiring of the electrical cabinet based on the acquisition logic;

[0109] Obtaining the acquired image data of the internal wiring of the electrical cabinet, setting the image data enhancement processing logic, and applying the image data enhancement processing logic to enhance the image data of the internal wiring of the electrical cabinet;

[0110] Obtaining the image data of the internal wiring of the electrical cabinet after the enhancement processing, and extracting the contour image of the internal wiring of the electrical cabinet from the image data of the internal wiring of the electrical cabinet;

[0111] Selecting a line contour as the target for querying associated line contours in the contour image of the internal wiring of the electrical cabinet, and querying a group of associated line contours with the highest relevance to the selected line contour;

[0112] Using the selected line contour and the group of associated line contours with the highest relevance as the rendering targets, rendering the two groups of line contours, and representing them in the contour image where they are located;

[0113] Placing the contour image representing the selected line contour and the group of line contours with the highest relevance to it at the source position of the contour image in the image data of the internal wiring of the electrical cabinet;

[0114] Feeding back the image data of the internal wiring of the electrical cabinet with the contour image placed to the user side.

[0115] In summary, during the operation of the system in the above embodiments, machine vision technology is applied to collect the image data of the internal wiring of the electrical cabinet. Further, based on the comprehensive analysis of the internal image data of the electrical cabinet and the line temperature information, a wire tracing technology with higher accuracy is realized, so as to meet the necessary prerequisite conditions for the wire management operation of the staff, search for the wires to be sorted, and thus realize the wire management work more quickly. Further, based on the image display, the operation results of the system are output, so that the technical personnel engaged in wire management work can obtain references, greatly reducing the difficulty of wire management in the electrical cabinet. At the same time, by configuring the wire management method in the wire management system, the technical solution is further improved to ensure that the technical solution can stably serve the wire management work.

[0116] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements 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. Intelligent electrical cabinet infrared cable management system based on machine vision, characterized by: include: Collection layer, analysis layer and indication layer; The system is executed when the electrical cabinet is in operation; The acquisition layer sets the acquisition logic of the internal circuit image data of the electrical cabinet, acquires the internal circuit image data of the electrical cabinet based on the acquisition logic, and synchronously stores the acquired internal circuit image data of the electrical cabinet; the analysis layer receives the internal circuit image data of the electrical cabinet stored in the acquisition layer, converts the internal circuit image data of the electrical cabinet into a contour image, selects the circuit contour in the contour image, and analyzes its associated circuit contour; the system end user selects the circuit contour in the contour image obtained by the analysis layer conversion in the indication layer; the indication layer synchronously indicates the associated circuit contour in the contour image based on the line contour selection result of the system end user; the system end user refers to the associated circuit contour to determine that the associated circuit contour corresponds to the internal circuit of the electrical cabinet; The analysis layer includes a perception module, a conversion module, a selection module and an analysis module. The perception module is used to perceive 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 image converted in the conversion module and select the circuit contour in the contour image. The analysis module is used to receive the circuit contour selected in the selection module and analyze the associated circuit contour of the circuit contour. The associated line profile analysis logic for the line profile in the analysis module is expressed as: Where: ε(α, β) is the correlation between line profile α and line profile β; R α , G α , B α is the mean value of the three channels R, G, and B of the line profile α in the limited area of ​​the line image data inside the electrical cabinet; R β , G β , B β is the mean value of the three channels R, G, and B of the line profile β in the limited area of ​​the line image data inside the electrical cabinet; D near (α, β) is the distance between the proximal ends of line profile α and line profile β; α T , β T is the surface temperature of line profile α and line profile β; τ is the coordination index; D far (α, β) is the distance between the surface temperature sensing positions of line profile α and line profile β; λ is the correction factor; Among them, the smaller the correlation ε(α, β) value between the line profile α and the line profile β, the higher the correlation between the two groups of line profiles. Conversely, the lower the correlation between the two groups of line profiles, α is used as the target for obtaining the associated line profiles, and β is continuously changed synchronously to obtain a group of line profiles with the highest correlation. The coordination index τ takes the value of 1 or -1, and α T ≤β T When the coordination index τ=1, α T >β T When , the coordination index τ = -1.

2. The infrared cable management system for intelligent electrical cabinets based on machine vision according to claim 1 is 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 operation logic of the acquisition module. The acquisition module is used to acquire the internal circuit image data of the electrical cabinet. The storage module is used to receive the internal circuit image data of the electrical cabinet acquired by the acquisition module and store the internal circuit image data of the electrical cabinet. Among them, during the operation stage of the acquisition module, the operation logic is obtained synchronously in the logic module, and the acquisition operation of the internal circuit image data of the electrical cabinet is executed based on the operation logic. After receiving the internal circuit image data of the electrical cabinet, the storage module synchronously enhances the internal circuit image data of the electrical cabinet, and after completing the enhancement processing, the internal circuit image data of the electrical cabinet is stored.

3. The infrared cable management system for intelligent electrical cabinets based on machine vision according to claim 2 is characterized in that: The acquisition module operation logic set in the logic module is: Using the acquisition module to acquire a set of internal circuit image data of the electrical cabinet in the positive direction of the internal circuit of the electrical cabinet, analyzing the complexity of the acquired internal circuit image data of the electrical cabinet, and using the complexity analysis result to set the number of acquired internal circuit image data of the electrical cabinet; Where: C is the complexity of the circuit image data inside the electrical cabinet; S L is the size of the line image area in the line image data inside the electrical cabinet; S is the global size of the line image data inside the electrical cabinet; q is the total number of line intersections in the line image data inside the electrical cabinet; n is the total number of contour images representing the contour of the circuit in the contour image of the internal circuit image data of the electrical cabinet; L i is the length of the i-th group of contour images; M is the number of collected line image data inside the electrical cabinet; M0 is the base number of collected line image data inside the electrical cabinet; The cardinality M0 of the collected number of line image data inside the electrical cabinet is customized by the system end user, and M0>1, and the collected number M of line image data inside the electrical cabinet is rounded up based on the further normal.

4. The infrared cable management system for intelligent electrical cabinets based on machine vision according to claim 2 is characterized in that: The acquisition module is integrated with lighting equipment and a high-definition camera. The lighting equipment is operated to fill light for the internal circuit of the electrical cabinet. The high-definition camera acquires a corresponding number of internal circuit image data of the electrical cabinet based on the acquisition number M of the internal circuit image data of the electrical cabinet. The acquisition angle of each acquired internal circuit image data of the electrical cabinet is different. When each internal circuit image data of the electrical cabinet is acquired, the relative distance between the camera end of the high-definition camera and the opposite surface of the electrical cabinet is equal. The enhanced processing logic for the internal circuit image data of the electrical cabinet in the storage module is expressed as follows: Where: a R 、a G 、a B is the R, G, B three-channel enhancement coefficient of a group of pixels in the image data when the image data of the internal circuit of the electrical cabinet is enhanced; k is the adjustment factor; R, G, B are the R, G, B three-channel values ​​of a group of pixels in the image data of the internal circuit of the electrical cabinet; The mean value of the three channels R, G, and B of the internal circuit image data of the electrical cabinet; Wherein, 0<k<10 is set, and the setting of the value of the adjustment factor k is used to constrain the situation where the pixel enhancement process causes color overflow. Based on the above formula, the a of each pixel in the internal circuit image data of the electrical cabinet is R 、a G 、a B The enhanced output result of the pixel is: R′, G′, and B′ represent the values ​​of the three channels of R, B, and G after pixel enhancement processing.

5. The infrared cable management system for intelligent electrical cabinets based on machine vision according to claim 1 is characterized in that: The sensing module is integrated by a plurality of infrared sensors, which sense the surface temperature of each line corresponding to each line contour in the contour image corresponding to the line image data inside the electrical cabinet, and the surface temperature sensing result of each group of line contours corresponding to the line is marked on its corresponding line contour; Among them, the conversion module takes precedence over the perception module. After the conversion module obtains the contour image of the internal circuit image data of the electrical cabinet, it synchronously feeds the contour image back to the perception module. The perception module takes the circuit corresponding to each circuit contour in the contour image as the perception target and performs the perception operation of the circuit surface temperature. Before converting the internal circuit image data of the electrical cabinet into a contour image, the conversion module sets a color threshold representing the circuit, deletes pixels in the internal circuit image data of the electrical cabinet that do not meet the set color threshold, and the remaining pixels form an image representing the circuit image, and the circuit image is used to perform the contour image conversion operation.

6. The infrared cable management system for intelligent electrical cabinets based on machine vision according to claim 1 is characterized in that: Each time the selection module runs, a group of line contours are selected in the contour image, and when the analysis module analyzes the associated line contours of the line contours, all the unselected line contours in the current contour image are used as analysis targets; The analysis layer runs with each set of internal circuit image data of the electrical cabinet as the processing target; The correction factor λ is subject to the following value: Where: is the mean gray value of line profile α; is the mean gray value of line profile β; in, Indicates taking the minimum value between brackets.

7. The infrared cable management system for intelligent electrical cabinets based on machine vision according to claim 1 is characterized in that: 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, take the selected line contour as a query target, query the line contour with the highest correlation with the line contour in the analysis layer, render the queried 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 to the original position in the corresponding electrical cabinet internal line image data, and output the electrical cabinet internal line image data; The query target corresponds to the line contour and performs rendering operation synchronously. The selection module is manually operated by the system end user to select the line contour in the contour image; Among them, when the system-side user selects the line contour in the contour image, no less than one group of contour images is applied, and the number is customized by the system-side user, and the line contours selected in each group of contour images all point to the same line. At the same time, the operation of selecting the line contour in the contour image is subject to: the higher the query accuracy requirement of the associated line contour of the contour image, the more contour images are applied; conversely, the smaller the amount of contour image data is applied, and no less than one group.

8. The machine vision-based intelligent electrical cabinet infrared cable management system according to claim 7, characterized in that: The instructing module renders the retrieved line contour with the highest correlation, that is, renders the line contour with the highest correlation corresponding to the line color in the line image data inside the electrical cabinet.

9. The infrared cable management system for intelligent electrical cabinets based on machine vision according to claim 1 is characterized in that: The perception module is interactively connected to the conversion module through a wireless network, the conversion module is interactively connected to the selection module and the analysis module through a wireless network, the conversion module is interactively connected to the logic module through a wireless network, the logic module is interactively connected to the acquisition module and the storage module through a wireless network, the acquisition module is interactively connected to the lighting equipment and the high-definition camera through a wireless network, the perception module and the acquisition module are interactively connected through a wireless network, the analysis module is interactively connected to the selection module through a wireless network, and the selection module is interactively connected to the indication module and the output module through a wireless network.

10. An infrared wire management method for an intelligent electrical cabinet based on machine vision, the method being an implementation method of an infrared wire management system for an intelligent electrical cabinet based on machine vision as claimed in any one of claims 1 to 9, characterized in that: include: Setting the image data acquisition logic of the internal circuit of the electrical cabinet, and acquiring the image data of the internal circuit of the electrical cabinet based on the acquisition logic; Acquire the collected image data of the internal circuit 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 circuit of the electrical cabinet; Acquire the internal circuit image data of the electrical cabinet after the enhancement processing, and extract the internal circuit contour image of the electrical cabinet from the internal circuit image data of the electrical cabinet; Selecting a line contour in the line contour image inside the electrical cabinet as a correlated line contour query target, querying and selecting a group of correlated line contours with the highest correlation; Taking the selected line contour and the group of related line contours with the highest correlation found as rendering targets, the two groups of line contours are rendered and represented in the contour image where they are located; Placing the contour image representing the selected line contour and a group of line contours with the highest correlation at the contour image source position in the line image data inside the electrical cabinet; The internal circuit image data of the electrical cabinet with the outline image placed thereon is fed back to the user end.

Images