Method and system for identifying abnormity of electrical element in metering box

Abstract

The invention discloses a method and a system for identifying abnormity of an electrical element in a metering box. The method comprises the following steps of: judging whether each infrared image in a certain infrared image set has an association relationship or not; if the certain infrared image does not have the association relationship, segmenting the certain infrared image based on a preset segmentation rule to obtain at least one infrared sub-image corresponding to the certain infrared image, and searching other infrared images corresponding to the at least one infrared sub-image in a certain infrared image set, and splicing other visible light images associated with other infrared images to obtain a target visible light image corresponding to a certain infrared image, and fusing the target visible light image with the certain infrared image to obtain a target mixed image. According to the invention, the application scenarios of identifying the abnormity of the metering box based on double spectrums are effectively increased, and the accuracy of identifying the abnormity of the electrical element can be improved.

Description

Technical Field

[0001] The invention belongs to the technical field of meter box abnormality identification, and in particular relates to a method and system for identifying abnormalities of electrical components in a meter box. Background Art

[0002] The meter box is used to detect the power data of electrical appliances. When the meter box operates normally, the power data detected by it is the same as the real power data, so it can convey accurate power data to people. When the meter box operates abnormally, there is a deviation between the collected power data and the real power data, and it cannot convey accurate power data to people. Therefore, it is necessary to monitor the electrical components in the meter box so as to detect abnormalities in time to avoid causing greater losses.

[0003] In the prior art, by acquiring visible light images and infrared images, fusing the two, and then identifying abnormal temperatures on the fused images, high-precision temperature identification can be achieved compared to the traditional single infrared image recognition method. For example, a dual-spectrum camera is installed in the meter box, and the visible light image and infrared image in the meter box are respectively collected by the dual-spectrum camera, and subsequent identification is performed. It has the advantages of non-contact, high precision, real-time monitoring, simple installation and maintenance, etc., and can effectively improve the accuracy and real-time performance of temperature monitoring of electrical components in the meter box.

[0004] However, when multiple meter boxes are monitored simultaneously, if the visible light images of one or several meter boxes are missing due to obstruction or partial damage to the equipment, the visible light image and infrared image cannot be fused, resulting in insufficient subsequent recognition accuracy. Summary of the invention

[0005] The present invention provides a method and system for identifying abnormalities of electrical components in a meter box, which are used to solve the technical problem that the visible light image and the infrared image cannot be fused due to the missing visible light image, resulting in insufficient subsequent identification accuracy.

[0006] In a first aspect, the present invention provides a method for identifying abnormalities of electrical components in a meter box, comprising: Obtaining visible light images and infrared images in a plurality of meter boxes, and associating the visible light image and the infrared image of the same meter box to obtain an association relationship, wherein each of the visible light image and the infrared image contains at least one component object, and the association relationship contains an association sub-relationship corresponding to the at least one component object; Preprocessing each infrared image, and clustering each preprocessed infrared image according to a preset division strategy to obtain at least one infrared image set; Determine whether all infrared images in a certain infrared image set have a correlation relationship; If there is no association relationship for a certain infrared image, segment the certain infrared image based on a preset segmentation rule to obtain at least one infrared sub-image corresponding to the certain infrared image, and search for other infrared images corresponding to the at least one infrared sub-image in the certain infrared image set; splicing other visible light images associated with the other infrared images to obtain a target visible light image corresponding to the certain infrared image, and fusing the target visible light image with the certain infrared image to obtain a target mixed image; The target mixed image is input into a preset image recognition model, and the image recognition model outputs abnormal component objects in the target mixed image.

[0007] In a second aspect, the present invention provides a system for identifying abnormalities of electrical components in a meter box, comprising: an acquisition module configured to acquire visible light images and infrared images in a plurality of meter boxes, and associate the visible light image and the infrared image of the same meter box to obtain an association relationship, wherein each of the visible light image and the infrared image contains at least one component object, and the association relationship contains an association sub-relationship corresponding to the at least one component object; A clustering module is configured to pre-process each infrared image, and cluster each pre-processed infrared image according to a preset division strategy to obtain at least one infrared image set; A judgment module is configured to judge whether all infrared images in a certain infrared image set have a correlation relationship; a segmentation module configured to segment a certain infrared image based on a preset segmentation rule to obtain at least one infrared sub-image corresponding to the certain infrared image if there is no association relationship between the certain infrared images, and search for other infrared images corresponding to the at least one infrared sub-image in the certain infrared image set; a splicing module configured to splice other visible light images associated with the other infrared images to obtain a target visible light image corresponding to the certain infrared image, and to fuse the target visible light image with the certain infrared image to obtain a target mixed image; The output module is configured to input the target mixed image into a preset image recognition model, and the image recognition model outputs abnormal component objects in the target mixed image.

[0008] According to a third aspect, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the steps of the method for identifying abnormalities of electrical components in a meter box of any embodiment of the present invention.

[0009] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor executes the steps of the method for identifying abnormalities of electrical components in a meter box of any embodiment of the present invention.

[0010] The method and system for identifying abnormalities of electrical components in a meter box of the present application, if there is no association relationship for a certain infrared image, then segment a certain infrared image based on a preset segmentation rule to obtain at least one infrared sub-image corresponding to the certain infrared image, and search for other infrared images corresponding to the at least one infrared sub-image in a certain infrared image set, splice other visible light images associated with other infrared images to obtain a target visible light image corresponding to a certain infrared image, and fuse the target visible light image with a certain infrared image to obtain a target mixed image. The target mixed image can be obtained even in the absence of a single visible light image or several visible light images, thereby effectively increasing the application scenarios of identifying abnormalities of meter boxes based on dual spectrums and improving the accuracy of identifying abnormalities of electrical components. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 A flow chart of a method for identifying abnormalities of electrical components in a meter box provided by one embodiment of the present invention; Figure 2 A structural block diagram of a system for identifying abnormalities of electrical components in a meter box provided by an embodiment of the present invention; Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0013] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0014] See also Figure 1 , which shows a flow chart of a method for identifying abnormalities of electrical components in a meter box of the present application.

[0015] like Figure 1 As shown, the method for identifying abnormalities of electrical components in a meter box specifically includes the following steps: Step S101, obtaining visible light images and infrared images in multiple meter boxes, and associating the visible light image and the infrared image of the same meter box to obtain an association relationship, wherein the visible light image and the infrared image each contain at least one component object, and the association relationship contains an association sub-relationship corresponding to the at least one component object.

[0016] In this step, a first marking frame is assigned to each component object in a certain visible light image to obtain a target visible light image, and a second marking frame is assigned to each component object in a certain infrared image after grayscale processing to obtain a target grayscale image; a two-dimensional coordinate system is constructed, and the target visible light image and the target grayscale image are respectively imported into the two-dimensional coordinate system to obtain the first coordinate information of the center point of each first marking frame and the second coordinate information of the center point of each second marking frame; it is determined whether the difference between a certain first coordinate and a certain second coordinate is greater than a preset threshold; if it is greater than the preset threshold, a certain first marking frame is not associated with a certain second marking frame; if it is not greater than the preset threshold, a certain first marking frame is associated with a certain second marking frame to obtain a certain associated sub-relationship, and all first coordinate information and all second coordinate information are traversed to obtain the associated relationship between the certain visible light image and the certain infrared image.

[0017] It should be noted that when a visible light image is input into a trained convolutional neural network model, the convolutional neural network model can annotate each component object in a visible light image, thereby outputting a target visible light image, wherein the target visible light image contains at least one first marking frame. Furthermore, when a grayscale infrared image is input into a trained VGGNet network model, the VGGNet network model can annotate each component object in an infrared image, thereby outputting a target grayscale image, wherein the target grayscale image contains at least one second marking frame. Both the convolutional neural network model and the VGGNet network model are obtained through machine learning using multiple sets of training images and detection images.

[0018] Specifically, the target visible light image and the target grayscale image are imported into a two-dimensional coordinate system, and the lower left corner vertices of the target visible light image and the target grayscale image are aligned with the coordinate origin, so that the coordinates of the center points of each first marking frame in the target visible light image and the coordinates of the center points of each second marking frame in the target grayscale image can be obtained. When the difference between a certain first coordinate and a certain second coordinate is greater than a preset threshold, it means that the component objects corresponding to the first marking frame and the second marking frame are most likely not the same, so a certain first marking frame is not associated with a certain second marking frame.

[0019] Step S102 , preprocessing each infrared image, and clustering each preprocessed infrared image according to a preset division strategy to obtain at least one infrared image set.

[0020] In this step, image enhancement processing is performed on each infrared image, and each processed infrared image is classified according to the type of meter box, and all infrared images of a certain category are sorted based on the location information of each meter box on a preset electronic map to obtain a certain infrared image sequence; a preset first sliding window is used to slide on a certain infrared image sequence, and each infrared image obtained by each sliding is clustered to obtain at least one infrared image set.

[0021] In this embodiment, since the internal electrical component structures of the same meter boxes type may be the same, the infrared images of the same meter boxes type are clustered, and based on the installation distance of the meter boxes, the infrared images of the meter boxes with the same internal electrical component structures can be clustered as accurately as possible, thereby facilitating the subsequent acquisition of visible light images.

[0022] Step S103: determine whether all infrared images in a certain infrared image set have an associated relationship.

[0023] In a specific embodiment, after determining whether all infrared images in a certain infrared image set are associated, if an infrared image is associated, the infrared image is directly fused with a corresponding visible light image to obtain a target mixed image.

[0024] Step S104, if there is no association relationship for a certain infrared image, segment the certain infrared image based on a preset segmentation rule to obtain at least one infrared sub-image corresponding to the certain infrared image, and search for other infrared images corresponding to the at least one infrared sub-image in the certain infrared image set.

[0025] In this step, an infrared image is converted into a grayscale image, and a second marking frame is assigned to each component object in the grayscale image; a preset second sliding window is slid along the edge of a second marking frame, and the area covered by the second sliding window is merged with the second marking frame to obtain a target area; the target area is segmented to obtain an infrared sub-image corresponding to the infrared image, wherein the infrared sub-image only contains the target area.

[0026] In this embodiment, the second sliding window slides along the edge of a second marking frame, and the area covered by the second sliding window is merged with the second marking frame to obtain the target area, so that more pixel features can be obtained, which is convenient for reflecting the difference in subsequent similarities.

[0027] Furthermore, the similarity between a certain infrared sub-image and other infrared images in a certain infrared image set is calculated; and another infrared image with the greatest similarity is selected as the infrared image corresponding to the certain infrared sub-image.

[0028] Step S105 , stitching other visible light images associated with the other infrared images to obtain a target visible light image corresponding to the certain infrared image, and fusing the target visible light image with the certain infrared image to obtain a target mixed image.

[0029] In this step, the visible light image corresponding to each infrared sub-image is obtained according to the associated sub-relationship, and it is determined whether the difference between the number of identical visible light images and the total number of visible light images corresponding to each infrared sub-image is greater than a preset number threshold; if it is greater than the preset number threshold, a certain visible light image is directly defined as the target visible light image; if it is not greater than the preset number threshold, other visible light images are cropped based on the associated sub-relationship, and each first marking frame obtained by cropping is fused into the visible light image to be processed based on a two-dimensional coordinate system, wherein the visible light image to be processed is: the visible light image with the largest number of identical visible light images corresponding to each infrared sub-image.

[0030] Step S106: input the target mixed image into a preset image recognition model, and the image recognition model outputs abnormal component objects in the target mixed image.

[0031] In this step, the convolutional neural network is iteratively trained according to the target mixed image to obtain an image recognition model. The network structure of the convolutional neural network includes components such as convolutional layer, activation layer, pooling layer and fully connected layer. During the training process, the visible light image and infrared image are respectively extracted through the network to extract deep features, and then the connection layer is used to fuse features, and the object recognition results are output through the fully connected layer.

[0032] In summary, the method of the present application, if there is no association relationship for a certain infrared image, then segments a certain infrared image based on a preset segmentation rule to obtain at least one infrared sub-image corresponding to a certain infrared image, and searches for other infrared images corresponding to at least one infrared sub-image in a certain infrared image set, splices other visible light images associated with other infrared images to obtain a target visible light image corresponding to a certain infrared image, and fuses the target visible light image with a certain infrared image to obtain a target mixed image. The target mixed image can be obtained even in the absence of a single visible light image or several visible light images, thereby effectively increasing the application scenarios of meter box abnormality identification based on dual spectrum, and can improve the accuracy of electrical component abnormality identification.

[0033] See also Figure 2 , which shows a structural block diagram of a system for identifying abnormalities of electrical components in a meter box of the present application.

[0034] like Figure 2 As shown, the electrical component abnormality identification system 200 in the meter box includes an acquisition module 210, a clustering module 220, a judgment module 230, a segmentation module 240, a splicing module 250 and an output module 260.

[0035] Among them, the acquisition module 210 is configured to acquire visible light images and infrared images in multiple meter boxes, and associate the visible light image and the infrared image of the same meter box to obtain an association relationship, wherein the visible light image and the infrared image each contain at least one component object, and the association relationship contains an association sub-relationship corresponding to the at least one component object; the clustering module 220 is configured to pre-process each infrared image, and cluster each pre-processed infrared image according to a preset division strategy to obtain at least one infrared image set; the judgment module 230 is configured to judge whether each infrared image in a certain infrared image set has an association relationship; the segmentation module 240 is configured to determine whether a certain infrared image If there is no association relationship, the infrared image is segmented based on a preset segmentation rule to obtain at least one infrared sub-image corresponding to the infrared image, and other infrared images corresponding to the at least one infrared sub-image are searched in the infrared image set; a stitching module 250 is configured to stitch other visible light images associated with the other infrared images to obtain a target visible light image corresponding to the infrared image, and fuse the target visible light image with the infrared image to obtain a target mixed image; an output module 260 is configured to input the target mixed image into a preset image recognition model, and the image recognition model outputs the abnormal component object in the target mixed image.

[0036] It should be understood that Figure 2 Modules and references documented in Figure 1 Therefore, the operations and features described above for the method and the corresponding technical effects are also applicable to Figure 2 The modules in it will not be described in detail here.

[0037] In other embodiments, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor executes the method for identifying abnormalities of electrical components in a meter box in any of the above method embodiments; As an implementation mode, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are configured as follows: Obtaining visible light images and infrared images in a plurality of meter boxes, and associating the visible light image and the infrared image of the same meter box to obtain an association relationship, wherein each of the visible light image and the infrared image contains at least one component object, and the association relationship contains an association sub-relationship corresponding to the at least one component object; Preprocessing each infrared image, and clustering each preprocessed infrared image according to a preset division strategy to obtain at least one infrared image set; Determine whether all infrared images in a certain infrared image set have a correlation relationship; If there is no association relationship for a certain infrared image, segment the certain infrared image based on a preset segmentation rule to obtain at least one infrared sub-image corresponding to the certain infrared image, and search for other infrared images corresponding to the at least one infrared sub-image in the certain infrared image set; splicing other visible light images associated with the other infrared images to obtain a target visible light image corresponding to the certain infrared image, and fusing the target visible light image with the certain infrared image to obtain a target mixed image; The target mixed image is input into a preset image recognition model, and the image recognition model outputs abnormal component objects in the target mixed image.

[0038] The computer-readable storage medium may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the abnormal identification system for electrical components in the meter box, etc. In addition, the computer-readable storage medium may include a high-speed random access memory, and may also include a memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the abnormal identification system for electrical components in the meter box via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0039] Figure 3 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 3 As shown, the device includes: a processor 310 and a memory 320. The electronic device may also include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330 and the output device 340 may be connected via a bus or other means. Figure 3 In the example, the bus connection is used. The memory 320 is the above-mentioned computer-readable storage medium. The processor 310 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 320, that is, the above-mentioned method embodiment of the method for identifying abnormal electrical components in the meter box is implemented. The input device 330 can receive input digital or character information, and generate key signal input related to user settings and function control of the abnormal electrical component identification system in the meter box. The output device 340 may include display devices such as display screens.

[0040] The electronic device can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided by the embodiment of the present invention.

[0041] As an implementation mode, the electronic device is applied to an abnormality identification system of electrical components in a meter box, and is used for a client, and includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can: Obtaining visible light images and infrared images in a plurality of meter boxes, and associating the visible light image and the infrared image of the same meter box to obtain an association relationship, wherein each of the visible light image and the infrared image contains at least one component object, and the association relationship contains an association sub-relationship corresponding to the at least one component object; Preprocessing each infrared image, and clustering each preprocessed infrared image according to a preset division strategy to obtain at least one infrared image set; Determine whether all infrared images in a certain infrared image set have a correlation relationship; If there is no association relationship for a certain infrared image, segment the certain infrared image based on a preset segmentation rule to obtain at least one infrared sub-image corresponding to the certain infrared image, and search for other infrared images corresponding to the at least one infrared sub-image in the certain infrared image set; splicing other visible light images associated with the other infrared images to obtain a target visible light image corresponding to the certain infrared image, and fusing the target visible light image with the certain infrared image to obtain a target mixed image; The target mixed image is input into a preset image recognition model, and the image recognition model outputs abnormal component objects in the target mixed image.

[0042] Through the description of the above implementation modes, those skilled in the art can clearly understand that each implementation mode can be implemented by means of software plus a necessary general hardware platform, or of course by hardware. Based on such an understanding, the above technical solution can essentially or in other words be embodied in the form of a software product that contributes to the prior art. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiment.

[0043] Finally, it should be noted that 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying abnormalities of electrical components in a meter box, characterized in that: include: Obtaining visible light images and infrared images in a plurality of meter boxes, and associating the visible light image and the infrared image of the same meter box to obtain an association relationship, wherein each of the visible light image and the infrared image contains at least one component object, and the association relationship contains an association sub-relationship corresponding to the at least one component object; Preprocessing each infrared image, and clustering each preprocessed infrared image according to a preset division strategy to obtain at least one infrared image set; Determine whether all infrared images in a certain infrared image set have a correlation relationship; If there is no association relationship for a certain infrared image, segment the certain infrared image based on a preset segmentation rule to obtain at least one infrared sub-image corresponding to the certain infrared image, and search for other infrared images corresponding to the at least one infrared sub-image in the certain infrared image set; splicing other visible light images associated with the other infrared images to obtain a target visible light image corresponding to the certain infrared image, and fusing the target visible light image with the certain infrared image to obtain a target mixed image; The target mixed image is input into a preset image recognition model, and the image recognition model outputs abnormal component objects in the target mixed image.

2. A method for identifying abnormalities of electrical components in a meter box according to claim 1, characterized in that: The associating the visible light image and the infrared image of the same metering box to obtain the association relationship includes: Assigning a first marking frame to each component object in a visible light image to obtain a target visible light image, and assigning a second marking frame to each component object in a grayscale processed infrared image to obtain a target grayscale image; Constructing a two-dimensional coordinate system, and importing the target visible light image and the target grayscale image into the two-dimensional coordinate system respectively, to obtain first coordinate information of the center point of each first marking frame, and second coordinate information of the center point of each second marking frame; Determine whether the difference between a first coordinate and a second coordinate is greater than a preset threshold; If it is greater than a preset threshold, a first marking box is not associated with a second marking box; If it is not greater than the preset threshold, a first marking box is associated with a second marking box to obtain a certain associated sub-relationship, and all the first coordinate information and all the second coordinate information are traversed to obtain the associated relationship between the certain visible light image and the certain infrared image.

3. A method for identifying abnormalities of electrical components in a meter box according to claim 1, characterized in that: The method of clustering the preprocessed infrared images according to the preset division strategy to obtain at least one infrared image set includes: Classify each infrared image according to the type of meter box, and sort all infrared images of a certain category based on the location information of each meter box on a preset electronic map to obtain a certain infrared image sequence; A preset first sliding window is used to slide on the certain infrared image sequence, and each infrared image obtained by each sliding is clustered to obtain at least one infrared image set.

4. A method for identifying abnormalities of electrical components in a meter box according to claim 1, characterized in that: After determining whether all infrared images in a certain infrared image set have a correlation relationship, the method further includes: If a certain infrared image has a correlation relationship, the infrared image is directly fused with a corresponding visible light image to obtain a target mixed image.

5. The method for identifying abnormality of electrical components in a meter box according to claim 1, characterized in that: The step of segmenting the infrared image based on a preset segmentation rule to obtain at least one infrared sub-image corresponding to the infrared image includes: Converting the infrared image into a grayscale image, and assigning a second marking frame to each component object in the grayscale image; Slide along the edge of a second marking frame according to a preset second sliding window, and merge the area covered by the second sliding window with the second marking frame to obtain a target area; The target area is segmented to obtain an infrared sub-image corresponding to the infrared image, wherein the infrared sub-image only includes the target area.

6. A method for identifying abnormalities of electrical components in a meter box according to claim 1, characterized in that: The searching for other infrared images corresponding to the at least one infrared sub-image in the certain infrared image set comprises: Calculating the similarity between a certain infrared sub-image and other infrared images in the certain infrared image set; Another infrared image with the greatest similarity is selected as the infrared image corresponding to the certain infrared sub-image.

7. A method for identifying abnormalities of electrical components in a meter box according to claim 6, characterized in that: The step of stitching other visible light images associated with the other infrared images to obtain a target visible light image corresponding to the certain infrared image comprises: Obtaining visible light images corresponding to each infrared sub-image according to the associated sub-relationship, and determining whether a difference between the number of identical visible light images and the total number of visible light images corresponding to each infrared sub-image is greater than a preset number threshold; If the number is greater than a preset threshold, the certain visible light image is directly defined as a target visible light image; If it is not greater than the preset quantity threshold, the other visible light images are cropped based on the associated sub-relationship, and the first marking frames obtained by cropping are fused into the visible light image to be processed based on the two-dimensional coordinate system, wherein the visible light image to be processed is: the visible light image with the largest number of the same visible light images corresponding to each infrared sub-image.

8. A system for identifying abnormalities of electrical components in a meter box, characterized in that: include: an acquisition module configured to acquire visible light images and infrared images in a plurality of meter boxes, and associate the visible light image and the infrared image of the same meter box to obtain an association relationship, wherein each of the visible light image and the infrared image contains at least one component object, and the association relationship contains an association sub-relationship corresponding to the at least one component object; A clustering module is configured to pre-process each infrared image, and cluster each pre-processed infrared image according to a preset division strategy to obtain at least one infrared image set; A judgment module is configured to judge whether all infrared images in a certain infrared image set have a correlation relationship; a segmentation module configured to segment a certain infrared image based on a preset segmentation rule to obtain at least one infrared sub-image corresponding to the certain infrared image if there is no association relationship between the certain infrared images, and search for other infrared images corresponding to the at least one infrared sub-image in the certain infrared image set; a splicing module configured to splice other visible light images associated with the other infrared images to obtain a target visible light image corresponding to the certain infrared image, and to fuse the target visible light image with the certain infrared image to obtain a target mixed image; The output module is configured to input the target mixed image into a preset image recognition model, and the image recognition model outputs abnormal component objects in the target mixed image.

9. An electronic device, characterized in that: include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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