Power grid abnormity identification method and device based on intelligent edge computing gateway

Abstract

The invention discloses a power grid abnormity identification method and device based on an intelligent edge computing gateway. The method comprises the following steps: acquiring a current image of a power grid acquired in a monitoring process of the power grid; comparing the current image with a historical image; when the current image is not different from the historical image, updating the image acquisition time point of the historical image; when the current image is different from the historical image, comparing the current image with a standard image to obtain a coincident image and a non-coincident image between the current image and the standard image; removing the overlapped image; calculating the coincidence degree of the non-coincident images, and performing risk marking according to the coincidence degree to obtain a target image; and transmitting the target image to a server to identify an anomaly in the power grid. According to the method and the device, the technical problems of relatively low data processing efficiency and difficulty in quickly and accurately finding out problems existing in a power grid due to excessive data redundancy in a gateway and no data screening in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid monitoring, and in particular to a power grid anomaly identification method and device based on an intelligent edge computing gateway. Background Art

[0002] Currently, video monitoring is mainly carried out at locations such as substations, distribution stations / stations / towers, distributed new energy and other emerging market entities' grid connection points, and then the data collected at each point is directly transmitted to the server through the corresponding gateway. The server analyzes the data and then makes judgments based on the analysis results to monitor the power grid; however, this method has the following problems: 1) It is easy to cause excessive data redundancy in the gateway, reducing equipment reliability, and thus affecting the long-term stable operation of the edge gateway; 2) The data is not effectively screened and processed, resulting in a significant reduction in the efficiency of the gateway in processing data. At the same time, the information transmission efficiency is low, and the server cannot quickly and accurately find key problems, which is not conducive to quickly finding problems that need to be solved.

[0003] Currently, no effective solution has been proposed for the problem that the data in the gateway in the above-mentioned related technologies is excessively redundant and the data is not screened, resulting in low data processing efficiency and difficulty in quickly and accurately finding problems in the power grid. Summary of the invention

[0004] The embodiments of the present invention provide a method and device for identifying power grid anomalies based on an intelligent edge computing gateway, so as to at least solve the technical problems in the related art that data in the gateway is excessively redundant and data is not screened, resulting in low data processing efficiency and difficulty in quickly and accurately finding problems in the power grid.

[0005] According to one aspect of an embodiment of the present invention, a method for identifying power grid anomalies based on an intelligent edge computing gateway is provided, comprising: obtaining a current image of the power grid collected during monitoring of the power grid; comparing the current image with a historical image to obtain a first comparison result, wherein the historical image is an image collected at a previous collection time point before the current collection time point, and the current collection time point is the collection time point of the current image; when the first comparison result indicates that there is no difference between the current image and the historical image, updating the image collection time point of the historical image; when the first comparison result indicates that there is a difference between the current image and the historical image, Compare the current image with a standard image to obtain an overlapping image and a non-overlapping image between the current image and the standard image, wherein the standard image is a reference image used for comparison and analysis of the current image, the overlapping image is an image of a repeated portion between the current image and the standard image, and the non-overlapping image is an image of a non-overlapping portion between the current image and the standard image; remove the overlapping image from the current image; calculate the degree of overlap of the non-overlapping image, and perform risk marking according to the degree of overlap to obtain a target image; transmit the target image to a server, so that the server can identify abnormalities in the power grid based on the target image.

[0006] Optionally, the current image is compared with a historical image to obtain a first comparison result, including: pixel decomposing the current image to obtain first pixel data, wherein the first pixel data is a set of multiple first pixel points, and the multiple first pixel points are arranged in a matrix form to form the current image; pixel decomposing the historical image to obtain second pixel data, wherein the second pixel data is a set of multiple second pixel points, and the multiple second pixel points are arranged in a matrix form to form the historical image; and multiple first pixel points in the first pixel data are compared one by one with multiple second pixel points in the second pixel data to obtain the first comparison result.

[0007] Optionally, multiple first pixel points in the first pixel point data are compared one by one with multiple second pixel points in the second pixel point data to obtain the first comparison result, including: when all of the first pixel points and the second pixel points correspond to the same, determining that the first comparison result is that there is no difference between the current image and the historical image; when at least one of the pixel points is different from the second pixel point, determining that the first comparison result is that there is a difference between the current image and the historical image.

[0008] Optionally, when the first comparison result indicates that there is a difference between the current image and the historical image, the current image is compared with the standard image to obtain overlapping images and non-overlapping images between the current image and the standard image, including: respectively dividing the current image and the standard image into modules to obtain a plurality of current module images corresponding to the current image and a plurality of standard module images corresponding to the standard image; performing pixel point decomposition on the plurality of current module images to obtain first module pixel point data of each current module image, wherein the first module pixel point data is a collection of a plurality of first module pixel points, and the plurality of first module pixel points are arranged in a matrix form to form the current module image; decomposing the plurality of standard module images to obtain second module pixel point data of each standard module image, wherein the second module pixel point data is a collection of a plurality of second module pixel points. A set of block pixel points, wherein a plurality of the second module pixel points are arranged in a matrix form to form the standard module image; a plurality of the first module pixel points in each group of the first module pixel point data are compared one by one with a plurality of the second module pixel points in the corresponding second module pixel point data to obtain a second comparison result, wherein the first module pixel point data and the second module pixel point data correspond to each other through the position of the current module image in the current image and the position of the standard module image in the standard image; based on the second comparison result, a portion of the first module pixel points in a plurality of groups of the first module pixel point data that are identical to the second module pixel points is determined as the overlapping image; based on the second comparison result, a portion of the first module pixel points in a plurality of groups of the first module pixel point data that are different from the second module pixel points is determined as the non-overlapping image.

[0009] Optionally, the degree of overlap of the non-overlapping images is calculated, and risk marking is performed according to the degree of overlap to obtain a target image, including: calculating the degree of overlap of each current module image according to a second comparison result, wherein the second comparison result is a comparison result obtained by comparing the current image with the standard image, and the current module image is an image obtained by module-dividing the current image; comparing the degree of overlap with a degree of overlap threshold to obtain a risk level corresponding to the degree of overlap; and risk marking each current module image according to the risk level to obtain the target image.

[0010] Optionally, calculating the degree of overlap of each current module image according to the second comparison result includes: generating a comparison matrix corresponding to each current module image according to the second comparison result, wherein the matrix elements of the comparison matrix correspond one-to-one to the first module pixel points in the current module image, and the first module pixel points are pixel points obtained by pixel point decomposition of the current module image; assigning values ​​to the matrix elements of each comparison matrix, wherein the first matrix element corresponding to each first module pixel point in the overlapping image has a value of 0, and the second matrix element corresponding to each first module pixel point in the non-overlapping image has a value of 1; calculating the degree of overlap of each current module image according to the first formula based on the values ​​of the matrix elements, wherein the first formula is: Among them, G represents the overlap degree, n represents the number of row matrices in the comparison matrix, m represents the number of column matrices in the comparison matrix, and H represents the sum of the values ​​of all the first matrix elements and the second matrix elements in the comparison matrix.

[0011] Optionally, the overlap threshold includes: a first overlap threshold, a second overlap threshold, and a third overlap threshold. The overlap is compared with the overlap threshold to obtain the risk level corresponding to the overlap, including: when the overlap is greater than the first overlap threshold but not greater than the second overlap threshold, determining that the risk level corresponding to the overlap is a low risk level, wherein the first overlap threshold is less than the second overlap threshold; when the overlap is greater than the second overlap threshold but not greater than the third overlap threshold, determining that the risk level corresponding to the overlap is a medium risk level, wherein the second overlap threshold is less than the third overlap threshold; when the overlap threshold is greater than the third overlap threshold, determining that the risk level corresponding to the overlap is a high risk level.

[0012] According to another aspect of an embodiment of the present invention, a power grid anomaly identification device based on an intelligent edge computing gateway is also provided, including: a first acquisition unit, used to acquire a current image of the power grid collected during the monitoring of the power grid; a second acquisition unit, used to compare the current image with a historical image to obtain a first comparison result, wherein the historical image is an image collected at a previous collection time point before the current collection time point, and the current collection time point is the collection time point of the current image; an updating unit, used to update the image collection time point of the historical image when the first comparison result indicates that there is no difference between the current image and the historical image; and a third acquisition unit, used to update the image collection time point of the historical image when the first comparison result indicates that there is a difference between the current image and the historical image. In other cases, the current image is compared with a standard image to obtain an overlapping image and a non-overlapping image between the current image and the standard image, wherein the standard image is a reference image for comparing and analyzing the current image, the overlapping image is an image of a repeated portion between the current image and the standard image, and the non-overlapping image is an image of a non-overlapping portion between the current image and the standard image; a removal unit is used to remove the overlapping image in the current image; a fourth acquisition unit is used to calculate the degree of overlap of the non-overlapping images, and perform risk marking according to the degree of overlap to obtain a target image; a transmission unit is used to transmit the target image to a server, so that the server can use the server to identify abnormalities in the power grid based on the target image.

[0013] Optionally, the second acquisition unit includes: a first acquisition module, used to perform pixel decomposition on the current image to obtain first pixel data, wherein the first pixel data is a set of multiple first pixel points, and the multiple first pixel points are arranged in a matrix form to form the current image; a second acquisition module, used to perform pixel decomposition on the historical image to obtain second pixel data, wherein the second pixel data is a set of multiple second pixel points, and the multiple second pixel points are arranged in a matrix form to form the historical image; a third acquisition module, used to compare the multiple first pixel points in the first pixel data with the multiple second pixel points in the second pixel data one by one to obtain the first comparison result.

[0014] Optionally, the third acquisition module includes: a first determination submodule, used to determine that the first comparison result is that there is no difference between the current image and the historical image when all of the first pixel points correspond to the second pixel points; and a second determination submodule, used to determine that the first comparison result is that there is a difference between the current image and the historical image when at least one of the pixel points is different from the second pixel point.

[0015] Optionally, the third acquisition unit includes: a fourth acquisition module, used to perform module division on the current image and the standard image respectively, to obtain a plurality of current module images corresponding to the current image and a plurality of standard module images corresponding to the standard image; a first decomposition module, used to perform pixel decomposition on the plurality of current module images, to obtain first module pixel data of each current module image, wherein the first module pixel data is a set of a plurality of first module pixel points, and the plurality of first module pixel points are arranged in a matrix form to form the current module image; a second decomposition module, used to decompose the plurality of standard module images, to obtain second module pixel data of each standard module image, wherein the second module pixel data is a set of a plurality of second module pixel points, and the plurality of second module pixel points are arranged in a matrix form to form the standard module image. block image; a fifth acquisition module, used to compare the multiple first module pixel points in each group of the first module pixel point data with the multiple second module pixel points in the corresponding second module pixel point data one by one to obtain a second comparison result, wherein the first module pixel point data and the second module pixel point data correspond to each other through the position of the current module image in the current image and the position of the standard module image in the standard image; a first determination module, used to determine, based on the second comparison result, that the portion of the first module pixel points in multiple groups of the first module pixel point data that are identical to the second module pixel points is the overlapping image; and a second determination module, used to determine, based on the second comparison result, that the portion of the first module pixel points in multiple groups of the first module pixel point data that are different from the second module pixel points is the non-overlapping image.

[0016] Optionally, the fourth acquisition unit includes: a calculation module, used to calculate the degree of overlap of each current module image according to the second comparison result, wherein the second comparison result is a comparison result obtained by comparing the current image with the standard image, and the current module image is an image obtained by module-dividing the current image; a sixth acquisition module, used to compare the degree of overlap with a degree of overlap threshold to obtain a risk level corresponding to the degree of overlap; and a seventh acquisition module, used to risk mark each current module image according to the risk level to obtain the target image.

[0017] Optionally, the calculation module includes: a generation submodule, used to generate a comparison matrix corresponding to each of the current module images according to the second comparison result, wherein the matrix elements of the comparison matrix correspond one-to-one to the first module pixel points in the current module image, and the first module pixel points are pixel points obtained by pixel point decomposition of the current module image; an assignment submodule, used to assign values ​​to the matrix elements of each of the comparison matrices, wherein the first matrix element corresponding to each of the first module pixel points in the overlapping image has a value of 0, and the second matrix element corresponding to each of the first module pixel points in the non-overlapping image has a value of 1; a calculation submodule, used to calculate the overlap degree of each of the current module images according to the first formula based on the values ​​of the matrix elements, wherein the first formula is: Among them, G represents the overlap degree, n represents the number of row matrices in the comparison matrix, m represents the number of column matrices in the comparison matrix, and H represents the sum of the values ​​of all the first matrix elements and the second matrix elements in the comparison matrix.

[0018] Optionally, the overlap threshold includes: a first overlap threshold, a second overlap threshold, and a third overlap threshold, and the sixth acquisition module includes: a third determination submodule, used to determine that the risk level corresponding to the overlap is a low risk level when the overlap is greater than the first overlap threshold and not greater than the second overlap threshold, wherein the first overlap threshold is less than the second overlap threshold; a fourth determination submodule, used to determine that the risk level corresponding to the overlap is a medium risk level when the overlap is greater than the second overlap threshold and not greater than the third overlap threshold, wherein the second overlap threshold is less than the third overlap threshold; a fifth determination submodule, used to determine that the risk level corresponding to the overlap is a high risk level when the overlap threshold is greater than the third overlap threshold.

[0019] According to another aspect of an embodiment of the present invention, a power grid anomaly identification system based on an intelligent edge computing gateway is also provided. The power grid anomaly identification system based on an intelligent edge computing gateway uses any of the above-mentioned power grid anomaly identification methods based on an intelligent edge computing gateway.

[0020] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is further provided, wherein the computer-readable storage medium includes a stored program, wherein the program executes any one of the above-mentioned methods for identifying power grid anomalies based on an intelligent edge computing gateway.

[0021] According to another aspect of an embodiment of the present invention, a processor is further provided, wherein the processor is used to run a program, wherein when the program is running, any one of the above-mentioned power grid anomaly identification methods based on an intelligent edge computing gateway is executed.

[0022] According to another aspect of an embodiment of the present invention, a computer program product is also provided, including computer instructions, which, when executed by a processor, execute any of the above-mentioned power grid anomaly identification methods based on an intelligent edge computing gateway.

[0023] In an embodiment of the present invention, a current image of a power grid collected during monitoring of the power grid is obtained; the current image is compared with a historical image to obtain a first comparison result, wherein the historical image is an image collected at a collection time point before the current collection time point, and the current collection time point is the collection time point of the current image; when the first comparison result indicates that there is no difference between the current image and the historical image, the image collection time point of the historical image is updated; when the first comparison result indicates that there is a difference between the current image and the historical image, the current image is compared with a standard image to obtain an overlapping image and a non-overlapping image between the current image and the standard image, wherein the standard image is a reference image for comparison and analysis of the current image, the overlapping image is an image of a repeated portion between the current image and the standard image, and the non-overlapping image is an image of a non-overlapping portion between the current image and the standard image; the overlapping images in the current image are removed; the overlap degree of the non-overlapping images is calculated, and risk marking is performed according to the overlap degree to obtain a target image; the target image is transmitted to a server, so that the server can use the server to identify abnormalities in the power grid based on the target image. Through the above technical scheme, the newly collected image is compared with the previous frame image, and the new image is stored only when there is a difference, and only the part of the new image that does not overlap with the standard image is marked as risk, and the obtained target image is transmitted to the server, so that the server can identify the abnormality in the power grid based on the target image. The technical effect of screening and processing the newly collected image is achieved, so that the server can quickly and accurately find the problems in the power grid, reduce the redundancy of data in the gateway, and improve the efficiency of data processing, thereby solving the technical problems in the related technology that the data in the gateway is excessively redundant and the data is not screened, resulting in low data processing efficiency and difficulty in quickly and accurately finding problems in the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0025] Figure 1It is a hardware structure block diagram of a mobile terminal of a power grid anomaly identification method based on an intelligent edge computing gateway according to an embodiment of the present invention;

[0026] Figure 2 is a flow chart of a method for identifying power grid anomalies based on an intelligent edge computing gateway according to an embodiment of the present invention;

[0027] Figure 3 is a schematic diagram of a power grid anomaly identification system based on an intelligent edge computing gateway according to an embodiment of the present invention;

[0028] Figure 4 is a schematic diagram of an intelligent edge computing gateway according to an embodiment of the present invention;

[0029] Figure 5 is a schematic diagram of a preprocessing module according to an embodiment of the present invention;

[0030] Figure 6 is a flowchart of an optional power grid anomaly identification method based on an intelligent edge computing gateway according to an embodiment of the present invention;

[0031] Figure 7 is a schematic diagram of a data processing and analysis module according to an embodiment of the present invention;

[0032] Figure 8 is a schematic diagram of a power grid anomaly identification device based on an intelligent edge computing gateway according to an embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme 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 only 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 should fall within the scope of protection of the present invention.

[0034] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0035] As introduced in the background technology, in the related art, the data in the gateway is excessively redundant and the data is not screened, resulting in low data processing efficiency and difficulty in quickly and accurately finding problems in the power grid. In view of the above defects, an embodiment of the present invention provides a power grid anomaly identification method and device based on an intelligent edge computing gateway.

[0036] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0037] The method embodiments provided in the embodiments of the present invention can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 1 is a hardware structure block diagram of a mobile terminal of a power grid anomaly identification method based on an intelligent edge computing gateway according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0038] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the power grid anomaly identification method based on the intelligent edge computing gateway in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal 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 a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific network example may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0039] According to an embodiment of the present invention, a method embodiment of a power grid anomaly identification method based on an intelligent edge computing gateway is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0040] Figure 2 is a flow chart of a method for identifying power grid anomalies based on an intelligent edge computing gateway according to an embodiment of the present invention. Figure 2 As shown, the method comprises the following steps:

[0041] Step S202, obtaining a current image of the power grid collected during the monitoring of the power grid.

[0042] In this embodiment, the image acquisition device of the terminal device can be used to capture the current image of the power grid during the process of monitoring the power grid, and the intelligent edge computing gateway can be used to receive the image captured by the terminal device for processing.

[0043] The image acquisition device here may include but is not limited to: cameras, scanners, sensors and other devices that can capture images.

[0044] Combine the following Figure 3 The above embodiments of the present invention are described in detail. Figure 3 is a schematic diagram of a power grid anomaly identification system based on an intelligent edge computing gateway according to an embodiment of the present invention; Figure 3 As shown, the intelligent edge computing gateway has communication connections with the terminal device and the server respectively. The intelligent edge computing gateway can receive images collected by the terminal device when detecting the power grid, and after processing the images, transmit the processed images to the server so that the server can identify abnormal problems in the power grid.

[0045] In the above embodiments of the present invention, the image is mainly processed by the intelligent edge computing gateway. Figure 4 The above embodiments of the present invention are described in detail. Figure 4 is a schematic diagram of an intelligent edge computing gateway according to an embodiment of the present invention, such as Figure 4 As shown, the intelligent edge computing gateway includes: a preprocessing module, a second signal input module, a second signal output module, a storage module and a data processing and analysis module; the intelligent edge computing gateway can realize the following functions: the preprocessing module receives and processes the image collected by the terminal device, and then transmits the processed image to the data processing and analysis module through the first signal input module to further process the preprocessed image. After processing the image, the data processing and analysis module transmits the processed image to the storage module for storage, and transmits the processed image to the server through the second signal output module, so that the server can use the processed image to quickly and accurately identify abnormal problems in the power grid based on the processed image.

[0046] Step S204, comparing the current image with the historical image to obtain a first comparison result, wherein the historical image is an image acquired at a previous acquisition time point before the current acquisition time point, and the current acquisition time point is the acquisition time point of the current image.

[0047] Combine the following Figure 5 and Figure 6 The above embodiments of the present invention are described in detail. Figure 5 is a schematic diagram of a preprocessing module according to an embodiment of the present invention, Figure 6 It is a flowchart of an optional power grid anomaly identification method based on an intelligent edge computing gateway according to an embodiment of the present invention.

[0048] like Figure 5As shown, the preprocessing module in the intelligent edge computing gateway includes a first signal output module for signal output, a data preprocessing module for data comparison and analysis, and a first signal input module for signal output. The data preprocessing module is connected to the first signal input module and the first signal output module; the first signal input module is connected to the acquisition device of the terminal device through the network; the first signal output module is connected to the second signal input module.

[0049] like Figure 6 As shown, the acquisition device of the terminal device transmits the acquired new image to the data preprocessing module of the preprocessing module through the network and the first signal input module. The data preprocessing module compares the newly added image (i.e., the current image) with the previous frame image (i.e., the historical image) to determine whether there is any difference.

[0050] According to the above embodiment of the present invention, in the above step S204, the current image is compared with the historical image to obtain a first comparison result, including: pixel decomposing the current image to obtain first pixel data, wherein the first pixel data is a set of multiple first pixel points, and the multiple first pixel points are arranged in a matrix form to form the current image; pixel decomposing the historical image to obtain second pixel data, wherein the second pixel data is a set of multiple second pixel points, and the multiple second pixel points are arranged in a matrix form to form the historical image; multiple first pixel points in the first pixel data are compared one by one with multiple second pixel points in the second pixel data to obtain a first comparison result.

[0051] In this embodiment, a data preprocessing module can be used to perform pixel decomposition on both the current image and the historical image to decompose them into multiple pixel points arranged in a matrix form, and then the pixel points of the current image and the historical image are compared one by one to determine whether there are any differences.

[0052] It should be noted that when comparing the pixels of the current image and the historical image one by one, the pixels at the same position are compared. Because the pixels of the two images are arranged in a matrix form, the position of each pixel can be compared with the pixel at the corresponding position in the other image to determine whether there is a difference.

[0053] In the above embodiment of the present invention, multiple first pixel points in the first pixel point data are compared one by one with multiple second pixel points in the second pixel point data to obtain a first comparison result, including: when all the first pixel points and the second pixel points correspond to the same, determining that the first comparison result is that there is no difference between the current image and the historical image; when at least one pixel point is different from the second pixel point, determining that the first comparison result is that there is a difference between the current image and the historical image.

[0054] Specifically, after comparing the pixel points of the current image and the historical image one by one, if all the pixel points of the current image and the historical image are the same, it means that there is no difference between the current image and the historical image; otherwise, it means that there is a difference between the current image and the historical image.

[0055] Step S206 , when the first comparison result indicates that there is no difference between the current image and the historical image, the image acquisition time point of the historical image is updated.

[0056] As above Figure 6 As shown, when there is no difference between the current image and the historical image, the data preprocessing module is used to extract the acquisition time of the current image, and the acquisition time is transmitted through the first signal output module, and then transmitted to the data processing and analysis module using the second signal input module, so as to use the information update module in the data processing and analysis module to update the acquisition time of the historical image.

[0057] It should be noted that updating the acquisition time of the historical image here means adding the extracted acquisition time of the current image to the acquisition time of the historical image, and does not directly replace it.

[0058] For example, if the acquisition time of the extracted current image is 2023.12.25.13:00, the original recorded acquisition time of the historical image 2023.12.25.12:00 can be updated to 2023.12.25.12:00; 2023.12.25.13:00. In this way, you only need to modify the last image (historical image) to add the storage time when no changes have occurred. There is no need to add new photo information, and there is no need to save the images collected at each time point. This greatly reduces the amount of data storage, which is beneficial to the reliability of the device and affects the long-term stable operation of the edge gateway.

[0059] Step S208, when the first comparison result indicates that there is a difference between the current image and the historical image, the current image is compared with the standard image to obtain an overlapping image and a non-overlapping image between the current image and the standard image, wherein the standard image is a reference image used for comparison and analysis of the current image, the overlapping image is an image of the repeated portion between the current image and the standard image, and the non-overlapping image is an image of the non-repetitive portion between the current image and the standard image.

[0060] As above Figure 6 As shown, in this embodiment, when there is a difference between the current image and the historical image, the current image can be transmitted to the storage module for storage, and the current image can be compared with the standard image stored in the storage module to analyze possible abnormalities in the power grid based on the difference between the current image and the standard image.

[0061] The standard image here is an image stored in the storage module, and the standard image is used as a benchmark to detect and identify changes or differences in new images; in some cases, the standard image may represent an ideal state or a desired state.

[0062] According to the above embodiment of the present invention, in the above step S208, when the first comparison result indicates that there is a difference between the current image and the historical image, the current image is compared with the standard image to obtain an overlapping image and a non-overlapping image between the current image and the standard image, including: respectively dividing the current image and the standard image into modules to obtain a plurality of current module images corresponding to the current image and a plurality of standard module images corresponding to the standard image; performing pixel point decomposition on the plurality of current module images to obtain first module pixel point data of each current module image, wherein the first module pixel point data is a collection of a plurality of first module pixel points, and the plurality of first module pixel points are arranged in a matrix form to form a current module image; decomposing the plurality of standard module images to obtain second module pixel point data of each standard module image, wherein the second module pixel point data is a collection of a plurality of first module pixel points, and the plurality of first module pixel points are arranged in a matrix form to form a current module image; decomposing the plurality of standard module images to obtain second module pixel point data of each standard module image, wherein the second module pixel point data is a collection of a plurality of first module pixel points, and the plurality of first module pixel points are arranged in a matrix form to form a current module image. The module pixel point data is a collection of multiple second module pixel points, and the multiple second module pixel points are arranged in a matrix form to form a standard module image; the multiple first module pixel points in each group of first module pixel point data are compared one by one with the multiple second module pixel points in the corresponding second module pixel point data to obtain a second comparison result, wherein the first module pixel point data and the second module pixel point data correspond to each other through the position of the current module image in the current image and the position of the standard module image in the standard image; based on the second comparison result, it is determined that the part composed of the first module pixel points that are the same as the second module pixel points in the multiple groups of first module pixel point data is an overlapping image; based on the second comparison result, it is determined that the part composed of the first module pixel points that are different from the second module pixel points in the multiple groups of first module pixel point data is a non-overlapping image.

[0063] Combine the following Figure 7 The above embodiments of the present invention are described in detail. Figure 7 is a schematic diagram of a data processing and analysis module according to an embodiment of the present invention, such as Figure 7 As shown, the data processing and analysis module includes: a comparison and judgment module, an interception module, a level determination module, a calibration module, a separation module and an information update module. The comparison and judgment module is connected to the second signal input module, the interception module and the storage module, the interception module is connected to the separation module, the separation module is connected to the storage module and the level determination module, the level determination module is connected to the calibration module, the calibration module is connected to the storage module and the second signal output module, and the information update module is connected to the storage module and the second signal output module.

[0064] Specifically, the separation module in the data processing and analysis module can be used to divide the current image and the standard image stored in the storage system into modules, so as to divide the current image into multiple current module images and the standard image into multiple standard module images; and then the comparison and judgment module in the data processing and analysis module is used to perform pixel point decomposition and comparison analysis on each current module image and the standard module image at the corresponding position, so as to determine that the parts with the same pixels between the current image and the standard image are overlapping images, and the parts with different pixels are non-overlapping images, and calibrate them respectively.

[0065] Here, the comparison speed can be accelerated by first dividing the current image and the standard image into multiple module images and then comparing them separately. In addition, when the overlap degree of non-overlapping images is subsequently calculated according to the modules, it is also convenient to understand the difference between the current image and the standard image in a more detailed way. That is, the overlap degree is calculated and the risk is marked for the non-overlapping images of each module, rather than calculating the overlap degree of the entire non-overlapping images and marking the risk for the entire non-overlapping images in general. Of course, in the specific implementation process, the current image and the standard image can also be compared first, and then the non-overlapping images obtained by the comparison can be divided into modules to determine the overlap degree of each module and mark the risk. There is no specific restriction here.

[0066] Step S210, removing overlapping images in the current image.

[0067] As above Figure 6 As shown, the interception module in the data processing and analysis module can be used to eliminate the part of the current image marked as the overlapping image, thereby reducing the workload of other modules in the data processing and analysis module for analyzing the part of the current image marked as the non-overlapping image. At the same time, when the overlapping image is eliminated and then transmitted to the server, the transmission speed can be accelerated.

[0068] Step S212, calculating the overlap degree of the non-overlapping images, and performing risk marking according to the overlap degree to obtain a target image.

[0069] In this embodiment, the degree of overlap between the current image (that is, the non-overlapping image) after the interception module removes the overlapping images and the corresponding image in the standard image can be calculated to mark the risk according to the calculated degree of overlap to obtain the target image; the lower the degree of overlap, the greater the change and the more serious the problem; the higher the degree of overlap, the smaller the change and the lighter the problem; the greater the change, the more priority it needs to be handled.

[0070] According to the above embodiment of the present invention, in the above step S212, the overlap of non-overlapping images is calculated, and risk marking is performed according to the overlap, so as to obtain a target image, including: calculating the overlap of each current module image according to the second comparison result, wherein the second comparison result is a comparison result obtained by comparing the current image with the standard image, and the current module image is an image obtained by dividing the current image into modules; comparing the overlap with the overlap threshold to obtain the risk level corresponding to the overlap; and risk marking each current module image according to the risk level to obtain a target image.

[0071] As above Figure 6 As shown, the degree of overlap between each current module image and the corresponding standard module image can be calculated respectively, and then the level determination module in the data processing and analysis module is used to determine the risk level corresponding to the degree of overlap of each current module image. After that, the calibration module is used to calibrate each current module image of the non-overlapping image with the risk level determined by the level determination module to obtain the target image.

[0072] Specifically, in the above embodiment of the present invention, the overlap degree of each current module image is calculated according to the second comparison result, including: generating a comparison matrix corresponding to each current module image according to the second comparison result, wherein the matrix elements of the comparison matrix correspond one-to-one to the first module pixel points in the current module image, and the first module pixel points are pixel points obtained by pixel point decomposition of the current module image; assigning values ​​to the matrix elements of each comparison matrix, wherein the first matrix element corresponding to each first module pixel point in the overlapping image has a value of 0, and the second matrix element corresponding to each first module pixel point in the non-overlapping image has a value of 1; and calculating the overlap degree of each current module image according to the first formula according to the values ​​of the matrix elements, wherein the first formula is: Among them, G represents the degree of overlap, n represents the number of row matrices in the comparison matrix, m represents the number of column matrices in the comparison matrix, and H represents the sum of the values ​​of all first matrix elements and second matrix elements in the comparison matrix.

[0073] In this embodiment, each current module image and the standard module image can be pixel-decomposed to be divided into n rows*m columns of pixels, and then each current module image is compared with the pixels of the corresponding standard module image one by one to generate a corresponding n row*m column matrix, and the values ​​of the matrix elements at the corresponding positions in the matrix are determined according to the pixel comparison results. When the pixels at the corresponding positions of the current module image and the standard module image are the same, the matrix elements at the corresponding positions in the matrix are determined to be 0, and vice versa, when the pixels at the corresponding positions of the current module image and the standard module image are different, the matrix elements at the corresponding positions in the matrix are determined to be 1; then the formula is used: Calculate the overlap of each current module image, where G represents the overlap, n represents the number of row matrices in the comparison matrix, m represents the number of column matrices in the comparison matrix, and H represents the sum of the values ​​of all matrix elements (including the first matrix element and the second matrix element) in the comparison matrix. H can be calculated by the following formula: i represents the number of rows in the matrix, j represents the number of columns in the matrix, and a ij Represents the matrix element at the i-th row and j-th column.

[0074] Specifically, in the above embodiment of the present invention, the overlap threshold includes: a first overlap threshold, a second overlap threshold, and a third overlap threshold. The overlap is compared with the overlap threshold to obtain the risk level corresponding to the overlap, including: when the overlap is greater than the first overlap threshold and not greater than the second overlap threshold, determining that the risk level corresponding to the overlap is a low risk level, wherein the first overlap threshold is less than the second overlap threshold; when the overlap is greater than the second overlap threshold and not greater than the third overlap threshold, determining that the risk level corresponding to the overlap is a medium risk level, wherein the second overlap threshold is less than the third overlap threshold; when the overlap threshold is greater than the third overlap threshold, determining that the risk level corresponding to the overlap is a high risk level.

[0075] For example, the first overlap threshold can be taken as 0, the second overlap threshold can be taken as 8%, and the third overlap threshold can be taken as 15%. Of course, other values ​​can also be taken as the corresponding overlap thresholds according to actual conditions, and there is no specific limitation here; when 0<G≤8%, the risk level corresponding to the current module image can be judged as a low risk level, when 8%<G≤15%, the risk level corresponding to the current module image can be judged as a medium risk level, and when G>15%, the risk level corresponding to the current module image can be judged as a high risk level.

[0076] Step S214: transmitting the target image to the server, so that the server can identify abnormalities in the power grid based on the target image.

[0077] As above Figure 6 As shown, after the intelligent edge computing gateway processes the new image collected by the terminal device, the processed target image can be transmitted to the server through the second signal output module and the network. The target image helps the server to quickly and accurately judge possible abnormal problems in the power grid and provide solutions quickly and accurately.

[0078] As can be seen from the above, through the technical solution provided by the above embodiment of the present invention, the current image of the power grid collected during the monitoring of the power grid is obtained; the current image is compared with the historical image to obtain a first comparison result, wherein the historical image is an image collected at a collection time point before the current collection time point, and the current collection time point is the collection time point of the current image; when the first comparison result indicates that there is no difference between the current image and the historical image, the image collection time point of the historical image is updated; when the first comparison result indicates that there is a difference between the current image and the historical image, the current image is compared with the standard image to obtain an overlapping image and a non-overlapping image between the current image and the standard image, wherein the standard image is a reference image used for comparison and analysis of the current image, and the overlapping image is an image of the repeated portion between the current image and the standard image. The non-overlapping image is an image of the non-repeating part between the current image and the standard image; the overlapping image in the current image is removed; the overlap degree of the non-overlapping images is calculated, and risk marking is performed according to the overlap degree to obtain the target image; the target image is transmitted to the server, so that the server can identify the abnormality in the power grid based on the target image, thereby achieving the purpose of comparing the newly collected image with the previous frame image, storing the new image only when there is a difference, and only marking the risk of the part of the new image that does not overlap with the standard image, and transmitting the obtained target image to the server, so that the server can identify the abnormality in the power grid based on the target image, thereby achieving the purpose of screening and processing the newly collected images, so that the server can quickly and accurately find out the problems in the power grid, reducing the redundancy of data in the gateway and improving the efficiency of data processing.

[0079] Therefore, the technical solution provided by the above-mentioned embodiment of the present invention solves the technical problems in the related technology that the data in the gateway is excessively redundant and the data is not screened, resulting in low data processing efficiency and difficulty in quickly and accurately finding problems in the power grid.

[0080] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0081] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0082] According to an embodiment of the present invention, there is also provided a power grid anomaly identification device based on an intelligent edge computing gateway for implementing the above-mentioned power grid anomaly identification device method based on an intelligent edge computing gateway. Figure 8 is a schematic diagram of a power grid anomaly identification device based on an intelligent edge computing gateway according to an embodiment of the present invention, such as Figure 8 As shown, the device includes: a first acquisition unit 801, a second acquisition unit 803, an update unit 805, a third acquisition unit 807, a removal unit 809, a fourth acquisition unit 811 and a transmission unit 813. The power grid anomaly identification device based on the intelligent edge computing gateway is described in detail below.

[0083] The first acquisition unit 801 is used to acquire the current image of the power grid collected during the monitoring of the power grid.

[0084] The second acquisition unit 803 is used to compare the current image with the historical image to obtain a first comparison result, wherein the historical image is an image acquired at a previous acquisition time point before the current acquisition time point, and the current acquisition time point is an acquisition time point of the current image.

[0085] The updating unit 805 is used to update the image acquisition time point of the historical image when the first comparison result indicates that there is no difference between the current image and the historical image.

[0086] The third acquisition unit 807 is used to compare the current image with the standard image when the first comparison result indicates that there is a difference between the current image and the historical image, so as to obtain an overlapping image and a non-overlapping image between the current image and the standard image, wherein the standard image is a reference image used for comparison and analysis of the current image, the overlapping image is an image of the repeated portion between the current image and the standard image, and the non-overlapping image is an image of the non-repetitive portion between the current image and the standard image.

[0087] The removal unit 809 is used to remove the overlapping images in the current image.

[0088] The fourth acquisition unit 811 is used to calculate the overlap degree of the non-overlapping images, and perform risk marking according to the overlap degree to obtain the target image.

[0089] The transmission unit 813 is used to transmit the target image to the server, so that the server can identify abnormalities in the power grid based on the target image.

[0090] It should be noted here that the above-mentioned first acquisition unit 801, second acquisition unit 803, update unit 805, third acquisition unit 807, removal unit 809, fourth acquisition unit 811 and transmission unit 813 correspond to steps S202 to S214 in the above-mentioned embodiments, and the seven units are the same as the instances and application scenarios implemented by the corresponding steps, but are not limited to the contents disclosed in the above-mentioned embodiments.

[0091] As can be seen from the above, in the scheme recorded in the above-mentioned embodiment of the present invention, a first acquisition unit can be used to acquire a current image of the power grid collected during the monitoring of the power grid; then a second acquisition unit is used to compare the current image with the historical image to obtain a first comparison result, wherein the historical image is an image collected at a previous collection time point before the current collection time point, and the current collection time point is the collection time point of the current image; then an update unit is used to update the image collection time point of the historical image when the first comparison result indicates that there is no difference between the current image and the historical image; then a third acquisition unit is used to compare the current image with the standard image when the first comparison result indicates that there is a difference between the current image and the historical image, to obtain an overlapping image and a non-overlapping image between the current image and the standard image, wherein the standard image is a reference image used for comparison and analysis of the current image, and an overlapping image is an image that is repeated between the current image and the standard image. The non-overlapping image is an image of a non-repeating part between the current image and the standard image; the removal unit is used to remove the overlapping image in the current image; the fourth acquisition unit is then used to calculate the overlap of the non-overlapping images, and risk marking is performed according to the overlap to obtain a target image; finally, the transmission unit is used to transmit the target image to the server, so that the server can identify the abnormality in the power grid based on the target image, thereby achieving the purpose of comparing the newly collected image with the previous frame image, storing the new image only when there is a difference, and only marking the risk of the part of the new image that does not overlap with the standard image, and transmitting the obtained target image to the server, so that the server can identify the abnormality in the power grid based on the target image, thereby achieving the purpose of screening and processing the newly collected images, so that the server can quickly and accurately find out the problems in the power grid, reducing the redundancy of data in the gateway and improving the efficiency of data processing.

[0092] Therefore, the technical solution provided by the above-mentioned embodiment of the present invention solves the technical problems in the related technology that the data in the gateway is excessively redundant and the data is not screened, resulting in low data processing efficiency and difficulty in quickly and accurately finding problems in the power grid.

[0093] Optionally, the second acquisition unit includes: a first acquisition module, used to decompose the current image into pixels to obtain first pixel data, wherein the first pixel data is a set of multiple first pixel points, and the multiple first pixel points are arranged in a matrix form to form the current image; a second acquisition module, used to decompose the historical image into pixels to obtain second pixel data, wherein the second pixel data is a set of multiple second pixel points, and the multiple second pixel points are arranged in a matrix form to form the historical image; a third acquisition module, used to compare the multiple first pixel points in the first pixel data with the multiple second pixel points in the second pixel data one by one to obtain a first comparison result.

[0094] Optionally, the third acquisition module includes: a first determination submodule, used to determine that the first comparison result is that there is no difference between the current image and the historical image when all the first pixel points correspond to the second pixel points; and a second determination submodule, used to determine that the first comparison result is that there is a difference between the current image and the historical image when at least one pixel point is different from the second pixel point.

[0095] Optionally, the third acquisition unit includes: a fourth acquisition module, which is used to perform module division on the current image and the standard image respectively, to obtain multiple current module images corresponding to the current image and multiple standard module images corresponding to the standard image; a first decomposition module, which is used to perform pixel point decomposition on the multiple current module images, to obtain first module pixel point data of each current module image, wherein the first module pixel point data is a collection of multiple first module pixel points, and the multiple first module pixel points are arranged in a matrix form to form the current module image; a second decomposition module, which is used to decompose the multiple standard module images, to obtain second module pixel point data of each standard module image, wherein the second module pixel point data is a collection of multiple second module pixel points, and the multiple second module pixel points are arranged in a matrix form. A standard module image is formed; a fifth acquisition module is used to compare the multiple first module pixel points in each group of first module pixel point data with the multiple second module pixel points in the corresponding second module pixel point data one by one to obtain a second comparison result, wherein the first module pixel point data and the second module pixel point data correspond to each other through the position of the current module image in the current image and the position of the standard module image in the standard image; a first determination module is used to determine, based on the second comparison result, that the portion of the multiple groups of first module pixel point data that is composed of the same first module pixel points as the second module pixel points is an overlapping image; a second determination module is used to determine, based on the second comparison result, that the portion of the multiple groups of first module pixel point data that is composed of the different first module pixel points from the second module pixel points is a non-overlapping image.

[0096] Optionally, the fourth acquisition unit includes: a calculation module, used to calculate the overlap of each current module image according to the second comparison result, wherein the second comparison result is a comparison result obtained by comparing the current image with the standard image, and the current module image is an image obtained by module-dividing the current image; a sixth acquisition module, used to compare the overlap with the overlap threshold to obtain a risk level corresponding to the overlap; and a seventh acquisition module, used to risk mark each current module image according to the risk level to obtain a target image.

[0097] Optionally, the calculation module includes: a generation submodule, which is used to generate a comparison matrix corresponding to each current module image according to the second comparison result, wherein the matrix elements of the comparison matrix correspond one-to-one to the first module pixel points in the current module image, and the first module pixel points are pixel points obtained by pixel point decomposition of the current module image; an assignment submodule, which is used to assign values ​​to the matrix elements of each comparison matrix, wherein the first matrix element corresponding to each first module pixel point in the overlapping image has a value of 0, and the second matrix element corresponding to each first module pixel point in the non-overlapping image has a value of 1; and a calculation submodule, which is used to calculate the degree of overlap of each current module image according to the first formula based on the values ​​of the matrix elements, wherein the first formula is: Among them, G represents the degree of overlap, n represents the number of row matrices in the comparison matrix, m represents the number of column matrices in the comparison matrix, and H represents the sum of the values ​​of all first matrix elements and second matrix elements in the comparison matrix.

[0098] Optionally, the overlap threshold includes: a first overlap threshold, a second overlap threshold, and a third overlap threshold. The seventh acquisition module includes: a third determination submodule, used to determine that the risk level corresponding to the overlap is a low risk level when the overlap is greater than the first overlap threshold and not greater than the second overlap threshold, wherein the first overlap threshold is less than the second overlap threshold; a fourth determination submodule, used to determine that the risk level corresponding to the overlap is a medium risk level when the overlap is greater than the second overlap threshold and not greater than the third overlap threshold, wherein the second overlap threshold is less than the third overlap threshold; a fifth determination submodule, used to determine that the risk level corresponding to the overlap is a high risk level when the overlap threshold is greater than the third overlap threshold.

[0099] According to another aspect of an embodiment of the present invention, a power grid anomaly identification system based on an intelligent edge computing gateway is also provided. The power grid anomaly identification system based on an intelligent edge computing gateway uses any of the above-mentioned power grid anomaly identification methods based on an intelligent edge computing gateway.

[0100] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein the program executes any one of the above-mentioned power grid anomaly identification methods based on an intelligent edge computing gateway.

[0101] Optionally, in this embodiment, the computer-readable storage medium may be located in any one of the computer terminals in a computer terminal group in a computer network, or in any one of the communication devices in a communication device group.

[0102] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: obtaining a current image of the power grid collected during monitoring of the power grid; comparing the current image with a historical image to obtain a first comparison result, wherein the historical image is an image collected at a previous collection time point before the current collection time point, and the current collection time point is the collection time point of the current image; when the first comparison result indicates that there is no difference between the current image and the historical image, updating the image collection time point of the historical image; when the first comparison result indicates that there is a difference between the current image and the historical image, comparing the current image with a standard image to obtain an overlapping image and a non-overlapping image between the current image and the standard image, wherein the standard image is a reference image for comparison and analysis of the current image, the overlapping image is an image of a repeated portion between the current image and the standard image, and the non-overlapping image is an image of a non-overlapping portion between the current image and the standard image; removing overlapping images in the current image; calculating the degree of overlap of the non-overlapping images, and marking the risks according to the degree of overlap to obtain a target image; transmitting the target image to a server so that the server can use the server to identify abnormalities in the power grid based on the target image.

[0103] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: performing pixel decomposition on the current image to obtain first pixel data, wherein the first pixel data is a set of multiple first pixel points, and the multiple first pixel points are arranged in a matrix form to form the current image; performing pixel decomposition on the historical image to obtain second pixel data, wherein the second pixel data is a set of multiple second pixel points, and the multiple second pixel points are arranged in a matrix form to form the historical image; comparing the multiple first pixel points in the first pixel data with the multiple second pixel points in the second pixel data one by one to obtain a first comparison result.

[0104] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: when all the first pixel points correspond to the second pixel points, determining that the first comparison result is that there is no difference between the current image and the historical image; when at least one pixel point is different from the second pixel point, determining that the first comparison result is that there is a difference between the current image and the historical image.

[0105] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: respectively performing module division on the current image and the standard image to obtain a plurality of current module images corresponding to the current image and a plurality of standard module images corresponding to the standard image; performing pixel decomposition on the plurality of current module images to obtain first module pixel point data of each current module image, wherein the first module pixel point data is a collection of a plurality of first module pixel points, and the plurality of first module pixel points are arranged in a matrix form to form the current module image; and decomposing the plurality of standard module images to obtain second module pixel point data of each standard module image, wherein the second module pixel point data is a collection of a plurality of second module pixel points, and the plurality of second module pixel points are arranged in a matrix form to form the current module image. The block pixels are arranged in a matrix form to form a standard module image; the multiple first module pixels in each group of first module pixel data are compared one by one with the multiple second module pixels in the corresponding second module pixel data to obtain a second comparison result, wherein the first module pixel data and the second module pixel data correspond to each other through the position of the current module image in the current image and the position of the standard module image in the standard image; based on the second comparison result, the part composed of the first module pixel points that are the same as the second module pixel points in the multiple groups of first module pixel data is determined to be an overlapping image; based on the second comparison result, the part composed of the first module pixel points that are different from the second module pixel points in the multiple groups of first module pixel data is determined to be a non-overlapping image.

[0106] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: calculating the degree of overlap of each current module image according to the second comparison result, wherein the second comparison result is a comparison result obtained by comparing the current image with the standard image, and the current module image is an image obtained by dividing the current image into modules; comparing the degree of overlap with a degree of overlap threshold to obtain a risk level corresponding to the degree of overlap; and risk marking each current module image according to the risk level to obtain a target image.

[0107] Optionally, in this embodiment, the computer-readable storage medium is configured to store a program code for executing the following steps: generating a comparison matrix corresponding to each current module image according to the second comparison result, wherein the matrix elements of the comparison matrix correspond one-to-one to the first module pixel points in the current module image, and the first module pixel points are pixel points obtained by pixel point decomposition of the current module image; assigning values ​​to the matrix elements of each comparison matrix, wherein the first matrix element corresponding to each first module pixel point in the overlapping image has a value of 0, and the second matrix element corresponding to each first module pixel point in the non-overlapping image has a value of 1; and calculating the overlap degree of each current module image according to the first formula based on the values ​​of the matrix elements, wherein the first formula is: Among them, G represents the degree of overlap, n represents the number of row matrices in the comparison matrix, m represents the number of column matrices in the comparison matrix, and H represents the sum of the values ​​of all first matrix elements and second matrix elements in the comparison matrix.

[0108] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: when the degree of overlap is greater than a first overlap threshold and not greater than a second overlap threshold, determining that the risk level corresponding to the overlap is a low risk level, wherein the first overlap threshold is less than the second overlap threshold; when the degree of overlap is greater than the second overlap threshold and not greater than a third overlap threshold, determining that the risk level corresponding to the overlap is a medium risk level, wherein the second overlap threshold is less than the third overlap threshold; when the overlap threshold is greater than the third overlap threshold, determining that the risk level corresponding to the overlap is a high risk level.

[0109] According to another aspect of an embodiment of the present invention, a processor is further provided, and the processor is used to run a program, wherein when the program is running, any one of the above-mentioned power grid anomaly identification methods based on an intelligent edge computing gateway is executed.

[0110] According to another aspect of an embodiment of the present invention, a computer program product is also provided, including computer instructions, which, when executed by a processor, execute any one of the above-mentioned power grid anomaly identification methods based on an intelligent edge computing gateway.

[0111] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0112] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0113] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0114] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0115] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0116] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0117] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A power grid anomaly identification method based on an intelligent edge computing gateway, characterized in that: include: Acquiring a current image of the power grid collected during monitoring of the power grid; Comparing the current image with a historical image to obtain a first comparison result, wherein the historical image is an image acquired at a previous acquisition time point before the current acquisition time point, and the current acquisition time point is an acquisition time point of the current image; When the first comparison result indicates that there is no difference between the current image and the historical image, updating the image acquisition time point of the historical image; When the first comparison result indicates that there is a difference between the current image and the historical image, the current image is compared with a standard image to obtain an overlapped image and a non-overlapped image between the current image and the standard image, wherein the standard image is a reference image used for comparison and analysis of the current image, the overlapped image is an image of a repeated portion between the current image and the standard image, and the non-overlapped image is an image of a non-overlapped portion between the current image and the standard image; removing the overlapping images in the current image; Calculating the overlap degree of the non-overlapping images, and performing risk marking according to the overlap degree to obtain a target image; The target image is transmitted to a server so as to identify abnormalities in the power grid based on the target image using the server.

2. The power grid anomaly identification method based on the intelligent edge computing gateway according to claim 1 is characterized in that: Comparing the current image with the historical image to obtain a first comparison result includes: Decomposing pixels of the current image to obtain first pixel data, wherein the first pixel data is a set of a plurality of first pixel data, and the plurality of first pixel data are arranged in a matrix form to form the current image; Decomposing the historical image by pixels to obtain second pixel data, wherein the second pixel data is a set of a plurality of second pixel points, and the plurality of second pixel points are arranged in a matrix form to form the historical image; The plurality of first pixel points in the first pixel point data are compared one by one with the plurality of second pixel points in the second pixel point data to obtain the first comparison result.

3. The power grid anomaly identification method based on intelligent edge computing gateway according to claim 2 is characterized in that: Comparing the plurality of first pixel points in the first pixel point data with the plurality of second pixel points in the second pixel point data one by one to obtain the first comparison result includes: When all the first pixel points correspond to the same second pixel points, determining that the first comparison result is that there is no difference between the current image and the historical image; When at least one of the pixel points is different from the second pixel point, it is determined that the first comparison result is that there is a difference between the current image and the historical image.

4. The power grid anomaly identification method based on intelligent edge computing gateway according to claim 1 is characterized in that: When the first comparison result indicates that there is a difference between the current image and the historical image, comparing the current image with a standard image to obtain an overlapping image and a non-overlapping image between the current image and the standard image includes: Performing module division on the current image and the standard image respectively, to obtain a plurality of current module images corresponding to the current image and a plurality of standard module images corresponding to the standard image; Decomposing the multiple current module images by pixels to obtain first module pixel data of each current module image, wherein the first module pixel data is a set of multiple first module pixel points, and the multiple first module pixel points are arranged in a matrix form to form the current module image; Decomposing the plurality of standard module images to obtain second module pixel point data of each of the standard module images, wherein the second module pixel point data is a set of a plurality of second module pixel points, and the plurality of second module pixel points are arranged in a matrix form to form the standard module image; Compare the plurality of first module pixel points in each group of the first module pixel point data with the plurality of second module pixel points in the corresponding second module pixel point data one by one to obtain a second comparison result, wherein the first module pixel point data and the second module pixel point data correspond to each other through a position of the current module image in the current image and a position of the standard module image in the standard image; Based on the second comparison result, determine that the portion of the first module pixel points identical to the second module pixel points in the plurality of groups of the first module pixel point data is the overlapped image; Based on the second comparison result, determine that the portion of the first module pixel points that are different from the second module pixel points in the multiple groups of the first module pixel point data is the non-overlapping image.

5. The method for identifying power grid anomalies based on an intelligent edge computing gateway according to claim 1, characterized in that: Calculating the overlap degree of the non-overlapping images and performing risk marking according to the overlap degree to obtain a target image, including: Calculate the overlap degree of each current module image according to the second comparison result, wherein the second comparison result is a comparison result obtained by comparing the current image with the standard image, and the current module image is an image obtained by dividing the current image into modules; Compare the overlap degree with an overlap degree threshold to obtain a risk level corresponding to the overlap degree; Risk marking is performed on each of the current module images according to the risk level to obtain the target image.

6. The method for identifying power grid anomalies based on an intelligent edge computing gateway according to claim 5 is characterized in that: The overlap degree of each current module image is calculated according to the second comparison result, including: generating a comparison matrix corresponding to each of the current module images according to the second comparison result, wherein the matrix elements of the comparison matrix correspond one-to-one to the first module pixel points in the current module image, and the first module pixel points are pixel points obtained by pixel point decomposition of the current module image; Assigning values ​​to the matrix elements of each comparison matrix, wherein the first matrix element corresponding to each pixel point of the first module in the coincident image has a value of 0, and the second matrix element corresponding to each pixel point of the first module in the non-coincidence image has a value of 1; The overlap degree of each current module image is calculated according to the values ​​of the matrix elements according to a first formula, wherein the first formula is: Among them, G represents the overlap degree, n represents the number of row matrices in the comparison matrix, m represents the number of column matrices in the comparison matrix, and H represents the sum of the values ​​of all the first matrix elements and the second matrix elements in the comparison matrix.

7. The method for identifying power grid anomalies based on an intelligent edge computing gateway according to claim 5, characterized in that: The overlap threshold includes: a first overlap threshold, a second overlap threshold, and a third overlap threshold. The overlap is compared with the overlap threshold to obtain a risk level corresponding to the overlap, including: In a case where the overlap is greater than the first overlap threshold and not greater than the second overlap threshold, determining that the risk level corresponding to the overlap is a low risk level, wherein the first overlap threshold is less than the second overlap threshold; In a case where the overlap is greater than the second overlap threshold and not greater than the third overlap threshold, determining that the risk level corresponding to the overlap is a medium risk level, wherein the second overlap threshold is less than the third overlap threshold; When the overlap threshold is greater than the third overlap threshold, it is determined that the risk level corresponding to the overlap is a high risk level.

8. A power grid anomaly identification device based on an intelligent edge computing gateway, characterized in that: include: A first acquisition unit, used to acquire a current image of the power grid collected during monitoring of the power grid; A second acquisition unit is used to compare the current image with a historical image to obtain a first comparison result, wherein the historical image is an image acquired at a previous acquisition time point before the current acquisition time point, and the current acquisition time point is an acquisition time point of the current image; an updating unit, configured to update an image acquisition time point of the historical image when the first comparison result indicates that there is no difference between the current image and the historical image; a third acquisition unit, configured to compare the current image with a standard image to obtain an overlapped image and a non-overlapped image between the current image and the standard image when the first comparison result indicates that there is a difference between the current image and the historical image, wherein the standard image is a reference image used for comparison and analysis of the current image, the overlapped image is an image of a repeated portion between the current image and the standard image, and the non-overlapped image is an image of a non-overlapped portion between the current image and the standard image; A removing unit, used for removing the overlapping images in the current image; A fourth acquisition unit, used for calculating the overlap degree of the non-overlapping images, and performing risk marking according to the overlap degree to obtain a target image; A transmission unit is used to transmit the target image to a server, so that the server can identify anomalies in the power grid based on the target image.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the power grid anomaly identification method based on an intelligent edge computing gateway as described in any one of claims 1 to 7.

10. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by the processor, the power grid anomaly identification method based on the intelligent edge computing gateway described in any one of claims 1 to 7 is executed.

Images