Converter valve water leakage fault monitoring method, device, equipment and storage medium

Abstract

The invention provides a converter valve water leakage fault monitoring method and device, equipment and a storage medium. The converter valve water leakage fault monitoring method comprises the steps of obtaining laser water leakage explicit time sequence image data in a to-be-judged time period; the laser water leakage explicit time sequence image data are preprocessed, and binarized time sequence image difference result data are obtained; and determining whether a water leakage fault exists or not based on the binarized time sequence image difference result data. According to the invention, on the premise of not increasing requirements of special computing resources such as an artificial intelligence chip, a general computing architecture is adopted to complete laser water leakage explicit time sequence image processing, and early slight leakage and water leakage fault detection of the converter valve is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power generation and power transmission and transformation, and in particular to a method, device, equipment and storage medium for monitoring water leakage fault of a converter valve. Background Art

[0002] The converter valve is the core equipment of the power transmission project. It is mainly composed of large-scale multi-stage series-connected power electronic units. It can continuously pass a large current of up to thousands of amperes during operation, which will generate large losses and heat in the core power electronic devices of the converter valve. In order to timely discharge the loss and heat of the converter valve, ensure that the core power electronic devices are at the designed operating temperature, and avoid the loss and heat affecting the normal operation of the converter valve, a cooling system is specially set up for the converter valve in the project. The water machine provides circulating power, and the cooling water flows through the heat dissipation surface of the power electronic power unit through the cooling water path to quickly remove the heat.

[0003] The internal water connection points of the converter valve use polymer material sealing rings to achieve sealing through extrusion deformation. Under long-term engineering stress, the polymer material sealing rings deform due to material aging, resulting in reduced extrusion force and reduced sealing performance; regular maintenance requires disassembly of water connection points, which affects the sealing of the connection points. These two main factors will cause water leakage failures at the water connection points of the converter valve during the service life. At the same time, the converter valve components such as radiators and valve saturation reactors through which cooling water flows may also cause leakage failures due to local structural damage caused by corrosion, mechanical vibration, wear, etc. When the converter valve leaks, it will directly affect the operating reliability of the equipment itself, and in severe cases, it may cause the DC transmission line to stop operating. In the early stage of the converter valve leakage failure, the leakage form is usually slight leakage. As time goes by, the leakage failure becomes more and more serious, the leakage flow rate becomes larger and larger, and the leakage form gradually develops into leakage, until a serious leakage failure occurs that affects the operation of the converter valve. Therefore, it is of great engineering significance to adopt certain technical means to realize the early leakage fault monitoring of the converter valve.

[0004] Currently, there is no technology that can realize the early leakage fault monitoring of the converter valve and put it into engineering application. The flip-type leakage detection device and test paper color change method used in the project cannot monitor early leakage faults, and cannot meet the project needs in terms of detection sensitivity and advance time.

[0005] The document CN202110680882.0 proposes a laser radar-based flow control valve leakage monitoring system and method. However, since the laser radar scans at an angle, it cannot scan all positions in the space in real time. At the same time, its resolution will decrease rapidly when the distance is far, and it is easy to miss detection. The document 1CN202311048594.9 proposes a spatial liquid drop detection system, method and computer equipment, and proposes a method and equipment that uses a line laser to make leakage events in space explicit, and realizes leakage fault monitoring through image analysis. Due to the use of line laser scanning, it has the ability to scan spatial areas in real time, and the resolution is less affected by distance. Summary of the invention

[0006] In order to solve the problem that the prior art cannot detect early leakage faults and cannot meet the engineering needs in terms of detection sensitivity and advance, the present invention proposes a method for monitoring and locating a converter valve leakage fault, including:

[0007] Obtaining laser water leakage explicit time-series image data within the time period to be determined;

[0008] Preprocessing the laser water leakage explicit time-series image data to obtain binarized time-series image difference result data;

[0009] Determine whether there is a water leakage fault based on the binarized time-series image difference result data.

[0010] Optionally, the preprocessing of the laser water leakage explicit time series image data to obtain binarized time series image difference result data includes:

[0011] Perform channel conversion on the laser water leakage explicit time series image data to obtain a water leakage time series image represented by a four-dimensional matrix;

[0012] Performing image difference calculation on the water leakage time series image represented by the four-dimensional matrix to obtain a time series image difference result;

[0013] The time-series image difference result is subjected to image binarization processing to obtain binarized time-series image difference result data.

[0014] Optionally, the step of performing channel conversion on the laser water leakage explicit time-series image data to obtain a water leakage time-series image represented by a four-dimensional matrix includes:

[0015] Based on the laser water leakage explicit time-series image data format, converting the laser water leakage explicit time-series image data into a plurality of consecutive frames of continuous time-series image data represented by a numerical matrix;

[0016] Convert each frame of the continuous time-series image data into a three-dimensional numerical matrix with multiple color channels;

[0017] A water leakage time series image represented by a four-dimensional matrix is ​​constructed by combining the three-dimensional data matrix with the time series dimension.

[0018] Optionally, the time series image difference result is calculated as follows:

[0019] X fm-s =[x fm(1) ,x fm(2) ,…,x fm(n-s-1) ,x fm(n-s) ]

[0020] =|[x1-x 1+s ,x2-x 2+s ,…,x n-s-1 -x n-1 ,x n-s -x n ]|

[0021] Where, X fm-s is the time series image difference result after the original time series image data X is differentiated according to the step size s, x fm(1) 、x fm(2) 、x fm(n-s-1) 、x fm(n-s) X fm-s The first, second, third, up to the nth element in x fm(i) =x i -x i+s ,i=1,2,…,ns,x1、x 1+s ,x2,x 2+s 、x n-s-1 、x n-1 、x n-s 、x n are elements in the original time series image data X, and n is a positive integer.

[0022] Optionally, performing image binarization processing on the time series image difference result to obtain binarized time series image difference result data includes:

[0023] The elements in the time sequence image difference results that are greater than the set binarization threshold are assigned the maximum value, and the elements in the time sequence image difference results that are not greater than the set binarization threshold are assigned the minimum value, so as to obtain the binarized time sequence image difference result data.

[0024] Optionally, the determining whether there is a water leakage fault based on the binarized time-series image difference result data includes:

[0025] Traverse all binarized time series image difference result data, retain the frames with connected image regions, and form binary difference time series image data containing connected regions;

[0026] Traversing the connected regions corresponding to the binary differential time series image data containing connected regions, calculating the centroid distance between each connected region in each frame and other connected regions in the frame, and the centroid distance between each connected region in each frame and connected regions in other frames;

[0027] Determine whether the connected area is a water leakage characteristic spot based on the centroid distance between each connected area in each frame and the connected areas in the same frame and other frames and a preset coordinate error threshold and a discrimination number threshold;

[0028] The water leakage characteristic light spot determines that the converter valve has a water leakage fault.

[0029] Optionally, after determining that the converter valve has a water leakage fault, the method further includes:

[0030] The sampling image processing tool obtains the number of pixels in the region corresponding to the connected region and the pixel coordinates in the image;

[0031] The mean or median of the pixel coordinates of the water leakage characteristic spots in all connected areas in the image is taken as the water leakage spot position;

[0032] The valve layer where the water leakage is located is determined by the position of the water leakage light spot.

[0033] Optionally, the determining whether a connected area is a water leakage characteristic spot based on the centroid distance between each connected area in each frame and connected areas in the same frame and other frames and a preset coordinate error threshold and a discrimination number threshold includes:

[0034] Determine whether the centroid distance is less than the set coordinate error threshold. If the centroid distance is less than the set coordinate error threshold, and the number of occurrences of the centroid distance being less than the set coordinate error threshold exceeds the set judgment number threshold, then the connected area is a water leakage characteristic light spot, otherwise the connected area is a non-water leakage characteristic light spot.

[0035] Optionally, after determining whether there is a water leakage fault of the converter valve based on the binarized time-series image difference result data, the method further includes:

[0036] The characteristic light spots judged as non-leakage in the multi-period time series image data are compared to monitor and locate long-interval leakage faults.

[0037] Optionally, comparing the characteristic light spots determined as non-leakage in the multi-period time series image data to monitor the long-interval leakage fault includes:

[0038] Traverse all connected regions in multiple time series image data and calculate the centroid distance between each connected region and other connected regions;

[0039] Traversing the connected regions corresponding to the binary differential time series image data containing connected regions, calculating the centroid distance between each connected region in each frame and other connected regions in the frame, and the centroid distance between each connected region in each frame and connected regions in other frames;

[0040] Determine whether the connected area is a water leakage characteristic spot based on the centroid distance between each connected area in each frame and the connected areas in the same frame and other frames and a preset coordinate error threshold and a discrimination number threshold;

[0041] Based on the water leakage characteristic light spot, it is determined that the converter valve has a long-interval water leakage fault.

[0042] Optionally, after determining that the converter valve has a long-interval water leakage fault, the method further includes:

[0043] The sampling image processing tool obtains the number of pixels in the region corresponding to the connected region and the pixel coordinates in the image;

[0044] The mean or median of the pixel coordinates of the water leakage characteristic spots in all connected areas in the image is taken as the water leakage spot position.

[0045] In another aspect, the present invention further provides a converter valve water leakage fault monitoring device, comprising:

[0046] A communication unit, used for acquiring laser water leakage explicit time-series image data within a period to be determined;

[0047] The calculation unit is used to pre-process the laser water leakage explicit time-series image data to obtain binarized time-series image difference result data; and determine whether the flow control valve is leaking based on the binarized time-series image difference result data.

[0048] Optionally, the calculation unit includes:

[0049] A channel conversion subunit, used for performing channel conversion on the laser water leakage explicit time series image data to obtain a water leakage time series image represented by a four-dimensional matrix;

[0050] A differential processing subunit is used to perform image differential calculation on the water leakage time series image represented by the four-dimensional matrix to obtain a time series image differential result;

[0051] A binarization processing subunit, used for performing image binarization processing on the time-series image difference result to obtain binarized time-series image difference result data;

[0052] The positioning processing subunit is used to determine whether the converter valve is leaking based on the binary time-series image difference result data.

[0053] Optionally, the channel conversion subunit is specifically used to:

[0054] Based on the laser water leakage explicit time-series image data format, converting the laser water leakage explicit time-series image data into a plurality of consecutive frames of continuous time-series image data represented by a numerical matrix;

[0055] Convert each frame of the continuous time-series image data into a three-dimensional numerical matrix with multiple color channels;

[0056] A water leakage time series image represented by a four-dimensional matrix is ​​constructed by combining the three-dimensional data matrix with the time series dimension.

[0057] Optionally, the time series image difference result in the difference processing subunit is calculated as follows:

[0058] X fm-s =[x fm(1) ,x fm(2) ,…,x fm(n-s-1) ,x fm(n-s) ]

[0059] =|[x1-x 1+s ,x2-x 2+s ,…,x n-s-1 -x n-1 ,x n-s -x n ]|

[0060] Where, X fm-s is the time series image difference result after the original time series image data X is differentiated according to the step size s, x fm(1) 、x fm(2) 、x fm(n-s-1) 、x fm(n-s) X fm-s The first, second, third, up to the nth element in x fm(i) =x i -x i+s ,i=1,2,…,ns,x1、x2、x 2+s 、x n-s-1 、x n-1 、x n-s 、x n are elements in the original time series image data X, and n is a positive integer.

[0061] Optionally, the binarization processing subunit is specifically used for:

[0062] The elements in the time sequence image difference results that are greater than the set binarization threshold are assigned the maximum value, and the elements in the time sequence image difference results that are not greater than the set binarization threshold are assigned the minimum value, so as to obtain the binarized time sequence image difference result data.

[0063] Optionally, the positioning processing subunit is specifically configured to:

[0064] Traverse all binarized time series image difference result data, retain the frames with connected image regions, and form binary difference time series image data containing connected regions;

[0065] Traversing the connected regions corresponding to the binary differential time series image data containing connected regions, calculating the centroid distance between each connected region in each frame and other connected regions in the frame, and the centroid distance between each connected region in each frame and connected regions in other frames;

[0066] Determine whether the connected area is a water leakage characteristic spot based on the centroid distance between each connected area in each frame and the connected areas in the same frame and other frames and a preset coordinate error threshold and a discrimination number threshold;

[0067] The water leakage characteristic light spot determines that the converter valve has a water leakage fault.

[0068] Optionally, the calculation unit further includes: a water leakage locating subunit configured to:

[0069] The sampling image processing tool obtains the number of pixels in the region corresponding to the connected region and the pixel coordinates in the image;

[0070] The mean or median of the pixel coordinates of the water leakage characteristic spots in all connected areas in the image is taken as the water leakage spot position;

[0071] The valve layer where the water leakage is located is determined by the position of the water leakage light spot.

[0072] Optionally, the positioning processing subunit determines whether a connected area is a water leakage characteristic spot based on the centroid distance between each connected area in each frame and the connected areas in the same frame and other frames and a preset coordinate error threshold and a discrimination number threshold as follows:

[0073] Determine whether the centroid distance is less than the set coordinate error threshold. If the centroid distance is less than the set coordinate error threshold, and the number of occurrences of the centroid distance being less than the set coordinate error threshold exceeds the set judgment number threshold, then the connected area is a water leakage characteristic light spot, otherwise the connected area is a non-water leakage characteristic light spot.

[0074] Optionally, the calculation unit further includes: a long interval water leakage judgment subunit, which is used to:

[0075] The characteristic light spots judged as non-leakage in the multi-period time series image data are compared to monitor long-interval leakage faults.

[0076] Optionally, the long-interval water leakage locating subunit is specifically used for:

[0077] Traverse all connected regions in multiple time series image data and calculate the centroid distance between each connected region and other connected regions;

[0078] Traversing the connected regions corresponding to the binary differential time series image data containing connected regions, calculating the centroid distance between each connected region in each frame and other connected regions in the frame, and the centroid distance between each connected region in each frame and connected regions in other frames;

[0079] Determine whether the connected area is a water leakage characteristic spot based on the centroid distance between each connected area in each frame and the connected areas in the same frame and other frames and a preset coordinate error threshold and a discrimination number threshold;

[0080] Based on the water leakage characteristic light spot, it is determined that the converter valve has a long-interval water leakage fault.

[0081] Optionally, the calculation unit further includes: a long-interval water leakage fault locating subunit, configured to:

[0082] The sampling image processing tool obtains the number of pixels in the region corresponding to the connected region and the pixel coordinates in the image;

[0083] The mean or median of the pixel coordinates of the water leakage characteristic spots in all connected areas in the image is taken as the water leakage spot position.

[0084] In another aspect, the present application further provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0085] The memory is used to store one or more programs;

[0086] When the one or more programs are executed by the at least one processor, a method for monitoring water leakage fault of a converter valve as described above is implemented.

[0087] On the other hand, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed, a method for monitoring a water leakage fault of a converter valve as described above is implemented.

[0088] Compared with the prior art, the present invention has the following beneficial effects:

[0089] The present invention provides a method for monitoring a water leakage fault of a converter valve, comprising: obtaining laser water leakage explicit time-series image data within a time period to be determined; preprocessing the laser water leakage explicit time-series image data to obtain binary time-series image differential result data; and determining whether there is a water leakage fault based on the binary time-series image differential result data. The present invention can complete the laser water leakage explicit time-series image processing using a general computing architecture without increasing the demand for special computing resources such as artificial intelligence chips, thereby realizing early detection of slight water leakage faults of converter valves. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 A flow chart of a method for monitoring a water leakage fault of a converter valve according to the present invention;

[0091] Figure 2 It is a flow chart of the single-stage-multi-stage combined converter valve laser water leakage explicit time-series image data processing and water leakage identification method of the present invention;

[0092] Figure 3 It is a flow chart of the single-stage converter valve laser water leakage explicit time-series image data processing and water leakage identification method of the present invention;

[0093] Figure 4 It is a flow chart of the method for processing time-series image data and identifying water leakage of a multi-stage converter valve laser water leakage explicitness according to the present invention;

[0094] Figure 5 It is a structural schematic diagram of a converter valve water leakage fault monitoring device of the present invention;

[0095] Figure 6 The figure is a schematic diagram of the structure of an electronic device of the present invention. DETAILED DESCRIPTION

[0096] The present invention provides a method for monitoring a converter valve water leakage fault, which realizes the identification and determination of a converter valve water leakage fault by processing and calculating laser water leakage explicit time series image data acquired by an image acquisition device.

[0097] In order to better understand the present invention, the content of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0098] Embodiment 1:

[0099] A method for monitoring a water leakage fault of a converter valve, such as Figure 1 As shown, including:

[0100] Step S1: Acquire laser water leakage explicit time-series image data within the time period to be determined;

[0101] Step S2: preprocessing the laser water leakage explicit time series image data to obtain binary time series image difference result data;

[0102] Step S3: Determine whether there is a water leakage fault based on the binarized time-series image difference result data.

[0103] like Figure 2 As shown, the technical solution provided by the present invention can realize the monitoring of the water leakage event of the converter valve and the positioning of the pixel coordinates in the image by processing the time-series image data. It is also possible to process the single-segment-multi-segment combined time-series image data of the laser water leakage of the converter valve, with the purpose of ensuring the real-time nature of the water leakage monitoring while avoiding the problem of missed detection of water leakage faults due to the long interval between two repeated water leakage events.

[0104] Specifically, Figure 3 As shown, by transforming and calculating one segment of time-series image data, the identification of the water leakage event of the converter valve corresponding to this segment of time-series image data is realized, and the position coordinates of the laser water leakage explicit light spot in the image data are fed back; when it is determined that no water leakage event occurs in this segment of time-series image data, the water leakage light spot in each frame image and its position coordinates in the image data are recorded.

[0105] like Figure 4 As shown, by comparing and calculating the positions of light spots that are not judged as water leakage features in the processing results of multiple single-segment time-series image data, long-interval water leakage faults distributed in multiple segments of time-series image data can be identified, avoiding missed detection of water leakage faults caused by water leakage events that do not contain enough repetitions in one segment of time-series image data.

[0106] The technical solution provided by the present invention is a single-segment-multi-segment combined time-series image data processing method for laser water leakage of converter valves. The method is characterized by comprising the steps of data segmentation, single-segment data recognition, and multi-segment data joint recognition.

[0107] According to the preset data length value, the continuous laser water leakage explicit time series image data of a certain length is sorted into a piece of data in a certain format. The data length value can be in units of time or in units of time series data frames.

[0108] By calculating and processing a continuous time-series image data, it is determined whether a water leakage fault event has occurred, and the pixel coordinates of the water leakage spot in the image are fed back, and the single-segment data water leakage fault identification result is output. It is characterized by including four main links: time-series image data acquisition, time-series image data preprocessing, water leakage identification and spot coordinate calculation, and single-segment data identification result output.

[0109] Step S1: Obtaining laser water leakage explicit time-series image data within the time period to be determined, including:

[0110] The time-series image data acquisition link refers to obtaining a certain length of original time-series image data from the image acquisition device and determining the format of the original time-series image data at the same time. The original time-series image data is usually a long continuous sequence represented by numerical values. The format of the original time-series image data usually includes information such as the length, width, color format, frame rate, etc. of the image. The number of frames of the original time-series image data should be greater than 1.

[0111] Step S2: preprocessing the laser water leakage explicit time-series image data to obtain binary time-series image difference result data, including:

[0112] Perform channel conversion on the laser water leakage explicit time series image data to obtain a water leakage time series image represented by a four-dimensional matrix;

[0113] Performing image difference calculation on the water leakage time series image represented by the four-dimensional matrix to obtain a time series image difference result;

[0114] The time-series image difference result is subjected to image binarization processing to obtain binarized time-series image difference result data.

[0115] Further, it includes: performing channel conversion on the laser water leakage explicit time series image data to obtain a water leakage time series image represented by a four-dimensional matrix, including:

[0116] Based on the laser water leakage explicit time-series image data format, converting the laser water leakage explicit time-series image data into a plurality of consecutive frames of continuous time-series image data represented by a numerical matrix;

[0117] Convert each frame of the continuous time-series image data into a three-dimensional numerical matrix with multiple color channels;

[0118] A water leakage time series image represented by a four-dimensional matrix is ​​constructed by combining the three-dimensional data matrix with the time series dimension.

[0119] Furthermore, the time series image difference result is calculated as follows:

[0120] X fm-s =[x fm(1) ,x fm(2) ,…,x fm(n-s-1) ,x fm(n-s) ]

[0121] =|[x1-x 1+s ,x2-x 2+s ,…,x n-s-1 -x n-1 ,xn-s -x n ]|

[0122] In the formula, X fm-s is the time series image difference result after the original time series image data X is differentiated according to the step size s, x fm(1) 、x fm(2) 、x fm(n-s-1) 、x fm(n-s) X fm-s The first, second, third, up to the nth element in x fm(i) =x i -x i+s ,i=1,2,…,ns. x1,x2,x 2+s 、x n-s-1 、x n-1 、x n-s 、x n are elements in the original time series image data X, and n is a positive integer.

[0123] Furthermore, the performing of image binarization processing on the time series image difference result to obtain binarized time series image difference result data includes:

[0124] The elements in the time sequence image difference results that are greater than the set binarization threshold are assigned the maximum value, and the elements in the time sequence image difference results that are not greater than the set binarization threshold are assigned the minimum value, so as to obtain the binarized time sequence image difference result data.

[0125] Step S2 specifically includes: a time series image data preprocessing phase, including the following main steps:

[0126] (1) Image channel transformation.

[0127] 1) First, the original data is converted into time-series image data of continuous images represented by a numerical matrix for a number of consecutive frames according to the length, width, color format, and frame rate information of the original time-series image data format;

[0128] 2) Each frame of the continuous time-series image data is converted into a three-dimensional numerical matrix with multiple color channels through a channel conversion operation, wherein two dimensions represent the width and length of the image data, and the other dimension represents the number of the color channel. The time-series image data after the image channel conversion is a four-dimensional matrix, including a time-series dimension.

[0129] (2) Image difference processing.

[0130] 1) Selection of main color channels. According to the laser wavelength range selected for water leakage visualization, the color channel that is sensitive to the laser in the selected wavelength range is selected as the subsequent main processing color channel. The number of main processing color channels can be one or more. After image channel transformation and selection, the obtained water leakage time series image data is in the form of X = [x1, x2, ..., x n-1 ,x n ], where n represents the frame number of the image data in the overall time series data, and x i (i=1,2,…,n) is a three-dimensional matrix, which contains two length and width dimensions and one color channel dimension, representing a frame of image in the time series image data;

[0131] 2) Image difference calculation. First, determine the image difference time sequence step s, which is an integer greater than or equal to 1; secondly, calculate the time sequence image difference result. The specific calculation formula is X fm-s =[x fm(1) ,x fm(2) ,…,x fm(n-s-1) ,x fm(n-s) ]=|[x1-x 1+s ,x2-x 2+s ,…,x n-s-1 -x n-1 ,x n-s -x n ]|;

[0132] (3) Image Binarization

[0133] 1) Binarization threshold setting. Set the image data binarization threshold th b .

[0134] 2) Image

[0135] Binarization processing. If the binarization threshold th is exceeded b All elements in the time series image difference result data are assigned the maximum value, which will not exceed the binarization processing threshold th b All elements in the time series image difference result data are assigned to the minimum value, and the binary time series image difference result data X is obtained. fmb =[x fmb(1) ,x fmb(2) ,…,x fmb(n-s-1) ,x fmb(n-s) ], x fmb(1) ,x fmb(2) ,x fmb(n-s-1) ,x fmb(n-s) They respectively represent the time series image difference results after binarization with time series step sizes of 1, 2, ns-1 and ns.

[0136] Step S3: determining whether there is a water leakage fault based on the binarized time-series image difference result data, including:

[0137] Traverse all binarized time series image difference result data, retain the frames with connected image regions, and form binary difference time series image data containing connected regions;

[0138] Traversing the connected regions corresponding to the binary differential time series image data containing connected regions, calculating the centroid distance between each connected region in each frame and other connected regions in the frame, and the centroid distance between each connected region in each frame and connected regions in other frames;

[0139] Determine whether the connected area is a water leakage characteristic spot based on the centroid distance between each connected area in each frame and the connected areas in the same frame and other frames and a preset coordinate error threshold and a discrimination number threshold;

[0140] The water leakage characteristic light spot determines that the converter valve has a water leakage fault.

[0141] Furthermore, after determining that the converter valve has a water leakage fault, the method further includes:

[0142] The sampling image processing tool obtains the number of pixels in the region corresponding to the connected region and the pixel coordinates in the image;

[0143] The mean or median of the pixel coordinates of the water leakage characteristic spots in all connected areas in the image is taken as the water leakage spot position;

[0144] The valve layer where the water leakage is located is determined by the position of the water leakage light spot.

[0145] Further, the method of determining whether a connected area is a water leakage characteristic spot based on the centroid distance between each connected area in each frame and the connected areas in the same frame and other frames and a preset coordinate error threshold and a discrimination number threshold includes:

[0146] Determine whether the centroid distance is less than the set coordinate error threshold. If the centroid distance is less than the set coordinate error threshold, and the number of occurrences of the centroid distance being less than the set coordinate error threshold exceeds the set judgment number threshold, then the connected area is a water leakage characteristic light spot, otherwise the connected area is a non-water leakage characteristic light spot.

[0147] Further, after determining whether there is a water leakage fault based on the binarized time-series image difference result data, the method further includes:

[0148] The characteristic light spots judged as non-leakage in the multi-period time series image data are compared to monitor long-interval leakage faults.

[0149] Furthermore, the method of comparing the characteristic light spots determined as non-leakage in the multi-period time series image data to monitor long-interval leakage faults includes:

[0150] Traverse all connected regions in multiple time series image data and calculate the centroid distance between each connected region and other connected regions;

[0151] Traversing the connected regions corresponding to the binary differential time series image data containing connected regions, calculating the centroid distance between each connected region in each frame and other connected regions in the frame, and the centroid distance between each connected region in each frame and connected regions in other frames;

[0152] Determine whether the connected area is a water leakage characteristic spot based on the centroid distance between each connected area in each frame and the connected areas in the same frame and other frames and a preset coordinate error threshold and a discrimination number threshold;

[0153] Based on the water leakage characteristic light spot, it is determined that the converter valve has a long-interval water leakage fault.

[0154] Furthermore, after determining that there is a long-interval water leakage fault, the following steps are also included:

[0155] The sampling image processing tool obtains the number of pixels in the region corresponding to the connected region and the pixel coordinates in the image;

[0156] The mean or median of the pixel coordinates of the water leakage characteristic spots in all connected areas in the image is taken as the water leakage spot position.

[0157] Step S3 specifically includes: a water leakage identification step, including one or more of the following main steps:

[0158] (1) Image connected region detection

[0159] The binary time series image difference result data X obtained from the time series image data preprocessing step fmb , using mature image processing tools to process each frame of image x in the binary time series image difference result data fmb(i) (i=1,2,…,ns) performs image connected region detection, and obtains several connected image regions A after excluding the background for a single frame image. ik (i=1,2,…,ns,k=1,2,…o) (It should be noted that o is not a fixed value, and the m values ​​corresponding to different frame images may be different), and obtain the number of regional pixels m corresponding to these image regions ik and the coordinates of the center of mass position in the image (u ik ,v ik ).

[0160] (2) Water leakage identification and light spot position calculation

[0161] 1) Filter the time-series image frames with connected image regions. Traverse all binary time-series image difference result data X from 1 to ns fmb Number, only keep the frames with connected image regions, and form binary differential time series image data X containing connected regions fmba =[x fmba(1) ,x fmba(2) ,…,x fmba(m-1) ,z fmba(m) ], contains m image frames with connected regions, z fmba(m) Represents the image difference result frame data of the mth connected area.

[0162] 2) Set the coordinate error threshold th loc And the discrimination threshold th count . Set the coordinate error threshold th loc is a positive number. This threshold is mainly used to determine whether the light spots detected in two frames of images are in the same position. Set the judgment threshold th count is a positive integer. This threshold is mainly used to determine whether the number of repetitions of water leakage droplets at the same position in the processed time series image data reaches a preset value.

[0163] 3) Comparison and judgment of distance and number thresholds. Traverse the connected regions A corresponding to m binary differential time series image frames containing connected regions jk (j=1,2,…,m,k=1,2,…o), calculate the centroid distance L of each connected region in the jth frame to the connected regions in other frames jlk (j,l=1,2,…,m,j≠l,k=1,2,…o), when the distance between the centroid of a connected area in a certain frame and the connected areas in other frames is L jlk There are more than th count satisfies L jlk <th loc If it is established, then this connected area is considered to be a water leakage characteristic spot, and all the distances between this characteristic spot and the connected area satisfy L jlk <th loc The connected areas are all identified as water leakage characteristic spots, and a water leakage characteristic spot set is formed. According to the above steps, multiple sets of water leakage spot sets may be obtained by analyzing a single segment of time series image data.

[0164] 4) Calculation of the position of the characteristic light spot of water leakage. Analyze the single-segment time-series image data to obtain one or more sets of water leakage light spot sets, and count the characteristic values ​​of the light spot coordinate distribution in the set, and use the mean or median of the pixel coordinates of all the light spots in the set in the image as the output of the water leakage light spot position. Preferably, a standard deviation statistical characteristic parameter can be provided to describe the pixel coordinate distribution characteristics of the light spot corresponding to a single set of water leakage light spots in the image.

[0165] The invented single-stage converter valve leakage explicit image data recognition result output link refers to the output of the identified leakage event and the leakage characteristic light spot position or distribution characteristics, and at the same time outputs all other connected areas in this section of data that are not identified as leakage characteristic light spots and the corresponding pixel coordinate positions in the image.

[0166] A method for joint recognition of multi-segment laser water leakage explicit time-series image data of a converter valve is invented. Its function is to process the recognition results of a preset number of multi-segment laser water leakage explicit time-series image data, and further determine whether a water leakage event has occurred by combining the recognition results of the multi-segment data. It includes the steps of obtaining the recognition results of the multi-segment data, determining the joint recognition results of the multi-segment data, and outputting the joint recognition results of the multi-segment data.

[0167] The invented multi-segment data recognition result acquisition link refers to obtaining all connected areas that are not identified as water leakage characteristic spots in the recognition results of multi-segment laser water leakage explicit image data according to a preset number and the corresponding pixel coordinate position data in the image.

[0168] The invented multi-segment data joint recognition result determination process is similar to the recognition process for a single segment of data, and includes the following steps:

[0169] 1) Set the coordinate error threshold th loc And the discrimination threshold th count . Set the coordinate error threshold th loc is a positive number. This threshold is mainly used to determine whether the light spots detected in two frames of images are in the same position. Set the judgment threshold th count is a positive integer, and this threshold is mainly used to determine whether the number of repetitions of water droplets at the same position in the processed time series image data reaches a preset value. It should be pointed out that the coordinate error threshold and the number of times threshold for joint determination of multiple segments of data here can be different from the threshold used for single segment data determination.

[0170] 2) Comparison and judgment of distance and number thresholds. Traverse all connected areas A in the acquired multi-segment data jk (j=1,2,…,m,k=1,2,…o), calculate the centroid distance L of each connected area to other connected areas jlk (j,l=1,2,…,m,j≠l,k=1,2,…o), when the distance between the centroid of a connected area in a certain frame and the connected areas in other frames is L jlk There are more than th count satisfies L jlk <th loc If it is established, then this connected area is considered to be a water leakage characteristic spot, and all the distances between this characteristic spot and the connected area satisfy L jlk <th locThe connected areas are all identified as water leakage characteristic spots, and a water leakage characteristic spot set is formed. According to the above steps, multiple groups of water leakage spot sets may be obtained by analyzing multiple time series image data.

[0171] 3) Calculation of the position of the characteristic spot of water leakage. Analyze the data of multiple time series images to obtain one or more sets of water leakage spots, and count the characteristic values ​​of the spot coordinate distribution in the set, and use the mean or median of the pixel coordinates of all the spots in the set in the image as the output of the water leakage spot position. Preferably, a standard deviation statistical characteristic parameter can be provided to describe the pixel coordinate distribution characteristics of the spot corresponding to a single set of water leakage spots in the image.

[0172] The invented multi-segment data joint recognition result output link refers to outputting the leakage spot position or the pixel coordinate distribution characteristics of the spot in the image in the joint recognition result of the multi-segment laser leakage explicit time series image data of the converter valve.

[0173] Compared with the prior art, the present invention has the following beneficial effects:

[0174] (1) The prior art does not provide a specific method for processing the laser water leakage explicit time-series image of the converter valve. The present invention provides a technical solution that can complete the laser water leakage explicit time-series image processing using a general computing architecture without increasing the demand for special computing resources such as artificial intelligence chips, thereby realizing the early detection of slight leakage faults of the converter valve.

[0175] (2) The present invention provides a time-series image processing method for explicit laser water leakage in a converter valve that combines simple image processing algorithms such as difference frames, binarization, connected area recognition, and target distance calculation, thereby realizing the detection of water leakage events in single-segment data and the positioning of pixel coordinates in the image.

[0176] (3) The present invention provides a single-segment-multi-segment combined converter valve laser leakage explicit time-series image processing method, which further identifies and determines leakage events by jointly distinguishing the results of multi-segment data recognition, thereby avoiding the problem of missed detection of leakage faults due to the long interval between two repeated leakage events.

[0177] (4) The present invention provides a device that can implement the method proposed by the present invention and mark the characteristic light spot of the water leakage by displaying the laser water leakage explicit image for the water leakage fault.

[0178] Example 2

[0179] A converter valve leakage fault monitoring device, such as Figure 5 As shown, including:

[0180] A communication unit, used for acquiring laser water leakage explicit time-series image data within a period to be determined;

[0181] The calculation unit is used to pre-process the laser water leakage explicit time-series image data to obtain binarized time-series image difference result data; and determine whether there is a water leakage fault based on the binarized time-series image difference result data.

[0182] Furthermore, the communication unit is used to communicate with one or more converter valve leakage monitoring devices based on the laser visualization principle to obtain laser leakage visualization time-series image data. The invented communication unit can also communicate and exchange data with other devices or systems.

[0183] Furthermore, the calculation unit is used to complete the preprocessing of the laser water leakage explicit time-series image data described in Example 1, and monitor whether there is a water leakage fault, which will not be repeated here.

[0184] The converter valve water leakage fault monitoring device described in the present invention also includes a control unit and a human-computer interaction unit;

[0185] The invented control unit is used to connect other functional units and to coordinate and control the work of other functional units.

[0186] The human-computer interaction unit is used to realize the interaction between the device and the management personnel or the converter station operating personnel, and supports the personnel to view the device information and manage the device. At the same time, when a water leakage fault is detected, the laser water leakage explicit image is displayed, and the position of the identified water leakage characteristic light spot is marked in the image for the reference of the operating personnel.

[0187] Example 3

[0188] The embodiment of the present invention provides a time-series image data processing device for detecting a water leakage fault of a converter valve, such as Figure 4 shown. Figure 4 Only the external communication unit is shown, the internal communication unit is indicated by arrows.

[0189] In the embodiment provided by the present invention, an Ethernet with a twisted pair cable as the physical layer connection and a corresponding network adapter are used as a communication unit to exchange data information with a laser leakage explicit image acquisition device of a converter valve to obtain time-series image data. In the implementation of the present invention, any other communication method that can realize data exchange, such as WIFI, 5G, satellite communication, Bluetooth, etc., can be used.

[0190] In the embodiment provided by the present invention, the computing unit and the control unit are implemented by a general-purpose X86 architecture microcomputer, a microprocessor is used as the computing unit and the control unit, and corresponding software programs are used to implement specific control and calculation. In the implementation of the present invention, a single-chip microcomputer, an embedded device, a centralized cloud computing device, etc. can be used as the computing unit and the control unit.

[0191] In the embodiments provided by the present invention, a display, a speaker, a keyboard, a mouse, and a microphone are used in conjunction with corresponding software programs as a human-computer interaction unit. A display is used to output corresponding laser water leakage explicit image data and mark the position of the characteristic light spot of the water leakage fault; a speaker is used to provide a sound alarm; a keyboard, a mouse, and a microphone are used to receive information and instructions input by management and operation personnel to execute corresponding commands. In the specific implementation of the present invention, human-computer interaction can also be achieved by means of VR virtual reality output, somatosensory input, etc.

[0192] In an embodiment of the present invention, a single-segment and multi-segment data joint identification method is specifically adopted to realize the converter valve leakage fault monitoring based on time series image data analysis.

[0193] In the embodiment provided by the present invention, the length of a single segment of data is set to 1 minute, and the number of multiple segments of data is set to 1440 segments, corresponding to the length of 24 hours. In the implementation of the present invention, the specific length of a single segment of data and the number of multiple segments of data can be set to different values ​​according to the site conditions.

[0194] In the embodiment provided by the present invention, the resolution of a single-segment time-series image data is 640×480, the data frame rate is 5 frames / second, the number of single-segment data frames is 300 frames, and the original time-series image data format is BayerBG8. In the implementation of the present invention, the specific image data resolution and frame rate can be set to different values ​​according to the site conditions, and the original time-series image data format can adopt any data format that can realize image data expression, such as RGB, BayerGB, BayerGR, Mono, etc.

[0195] In the embodiment provided by the present invention, OpenCV tool software is specifically used to implement the image channel conversion of the original data BayerBG8 format, which is converted into RGB three-channel format, and each pixel point of each channel is represented by an 8-bit unsigned integer. In the implementation of the present invention, the image channel conversion can also be implemented by other tool software or by the staff's own coding.

[0196] In the embodiment provided by the present invention, the main color channel specifically used before the image difference processing is the R channel. In the implementation of the present invention, other color channels may also be selected. In the embodiment provided by the present invention, the difference timing step length s=1 is set. In the implementation of the present invention, the difference timing step length may also be set to other values.

[0197] In the embodiment provided by the present invention, the binarization threshold th is set b = 50. In the implementation of the present invention, the binarization threshold may also be set to other values.

[0198] In the embodiment provided by the present invention, OpenCV tool software is specifically used to implement the detection and marking of connected regions of image result data after differential binarization. In the implementation of the present invention, the detection of connected regions of image data can also be implemented by other tool software or by self-coding by staff.

[0199] In the embodiment provided by the present invention, the coordinate error threshold th is set loc =20, discrimination times threshold th count =5. In the implementation of the present invention, the coordinate error threshold and the discrimination times threshold may also be set to other values.

[0200] In the embodiment provided by the present invention, the distance between each connected region is calculated by using the Euclidean distance method. In the implementation of the present invention, other distance calculation methods may also be used to calculate the distance between each connected region.

[0201] In the embodiment provided by the present invention, for the set of water leakage characteristic light spots in the single-segment data recognition results, the average of the water leakage characteristic light spot coordinates in the set is used as the pixel position coordinates of the water leakage characteristic light spots in the image, and the standard deviation is used as the characteristic value of the pixel position coordinate distribution of the water leakage characteristic light spots in the image.

[0202] The present invention provides an embodiment, for the problem of determining the joint recognition results of multiple segments of data, specifically setting a coordinate error threshold th loc =15, discrimination times threshold th count = 10. In the implementation of the present invention, the coordinate error threshold and the number of times threshold of the joint discrimination result of the majority of data may also be set to other values.

[0203] In the embodiment provided by the present invention, for the problem of joint recognition result recognition of multiple segments of data, the distance between each connected region is calculated by using the Euclidean distance method. In the implementation of the present invention, other distance calculation methods may also be used to calculate the distance between each connected region.

[0204] In the embodiment provided by the present invention, for the set of leakage characteristic light spots in the joint identification results of multiple segments of data, the average of the coordinates of the leakage characteristic light spots in the set is used as the pixel position coordinates of the leakage characteristic light spots in the image, and the standard deviation is used as the characteristic value of the distribution of the pixel position coordinates of the leakage characteristic light spots in the image.

[0205] The embodiments of the present invention also provide storage media such as CDs and USB flash drives, which contain programs capable of implementing the methods proposed by the present invention and can be distributed to facilitate engineering application implementation.

[0206] Example 4

[0207] like Figure 6As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0208] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in a storage medium to implement a corresponding method flow or a corresponding function, so as to implement the steps of a method for monitoring a water leakage fault of a converter valve in the above-mentioned embodiment.

[0209] Example 5

[0210] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in the electronic device for storing programs and data. It can be understood that the storage medium here can include both the built-in storage medium in the electronic device and the extended storage medium supported by the electronic device. The storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of a method for monitoring water leakage fault of a converter valve in the above embodiment.

[0211] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0212] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0213] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0214] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0215] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A method for monitoring a water leakage fault of a converter valve, characterized in that: include: Obtaining laser water leakage explicit time-series image data within the time period to be determined; Preprocessing the laser water leakage explicit time-series image data to obtain binarized time-series image difference result data; Determine whether there is a water leakage fault based on the binarized time-series image difference result data.

2. The method according to claim 1, characterized in that The preprocessing of the laser water leakage explicit time series image data to obtain binarized time series image difference result data includes: Perform channel conversion on the laser water leakage explicit time series image data to obtain a water leakage time series image represented by a four-dimensional matrix; Performing image difference calculation on the water leakage time series image represented by the four-dimensional matrix to obtain a time series image difference result; The time-series image difference result is subjected to image binarization processing to obtain binarized time-series image difference result data.

3. The method according to claim 2, characterized in that The step of performing channel conversion on the laser water leakage explicit time series image data to obtain a water leakage time series image represented by a four-dimensional matrix includes: Based on the laser water leakage explicit time-series image data format, converting the laser water leakage explicit time-series image data into a plurality of consecutive frames of continuous time-series image data represented by a numerical matrix; Convert each frame of the continuous time-series image data into a three-dimensional numerical matrix with multiple color channels; A water leakage time series image represented by a four-dimensional matrix is ​​constructed by combining the three-dimensional data matrix with the time series dimension.

4. The method according to claim 2, characterized in that The time series image difference result is calculated as follows: X fm-s =[x fm(1) ,x fm(2) ,…,x fm(n-s-1) ,x fm(n-s) ] |[x1-x 1+s ,x2-x 2+s ,…,x n-s-1 -x n-1 ,x n-s -x n ]| Where, X fm-s is the time series image difference result after the original time series image data X is differentiated according to the step size s, x fm(1) 、x fm(2) 、x fm(n-s-1) 、x fm(n-s) X fm-s The first, second, third, up to the nth element in x fm(i) =x i -x i+s ,i=1,2,…,ns,x1、x 1+s ,x2,x 2+s 、x n-s-1 、x n-1 、x n-s 、x n are elements in the original time series image data X, and n is a positive integer.

5. The method according to claim 2, characterized in that The step of performing image binarization processing on the time series image difference result to obtain binarized time series image difference result data includes: The elements in the time sequence image difference results that are greater than the set binarization threshold are assigned the maximum value, and the elements in the time sequence image difference results that are not greater than the set binarization threshold are assigned the minimum value, so as to obtain the binarized time sequence image difference result data.

6. The method according to claim 1, characterized in that The determining whether there is a water leakage fault based on the binarized time-series image difference result data includes: Traverse all binarized time series image difference result data, retain the frames with connected image regions, and form binary difference time series image data containing connected regions; Traversing the connected regions corresponding to the binary differential time series image data containing connected regions, calculating the centroid distance between each connected region in each frame and other connected regions in the frame, and the centroid distance between each connected region in each frame and connected regions in other frames; Determine whether the connected area is a water leakage characteristic spot based on the centroid distance between each connected area in each frame and the connected areas in the same frame and other frames and a preset coordinate error threshold and a discrimination number threshold; The water leakage characteristic light spot determines that the converter valve has a water leakage fault.

7. The method according to claim 6, characterized in that After confirming that there is a water leakage fault in the converter valve, the following steps are also required: Use image processing tools to obtain the number of pixels in the connected area and the pixel coordinates in the image; The mean or median of the pixel coordinates of the water leakage characteristic spots in all connected areas in the image is taken as the water leakage spot position; The valve layer where the water leakage is located is determined by the position of the water leakage light spot.

8. The method according to claim 6, characterized in that The method of determining whether a connected area is a water leakage characteristic spot based on the centroid distance between each connected area in each frame and the connected areas in the same frame and other frames and a preset coordinate error threshold and a discrimination number threshold comprises: Determine whether the centroid distance is less than the set coordinate error threshold. If the centroid distance is less than the set coordinate error threshold, and the number of occurrences of the centroid distance being less than the set coordinate error threshold exceeds the set judgment number threshold, then the connected area is a water leakage characteristic light spot, otherwise the connected area is a non-water leakage characteristic light spot.

9. The method according to claim 6, characterized in that After determining whether there is a water leakage fault based on the binarized time-series image difference result data, the method further includes: The characteristic light spots judged as non-leakage in the multi-period time series image data are compared to monitor long-interval leakage faults.

10. The method according to claim 9, characterized in that The method of comparing the characteristic light spots determined as non-leakage in the multi-period time series image data to monitor the long-interval leakage fault includes: Traverse all connected regions in multiple time series image data and calculate the centroid distance between each connected region and other connected regions; Traversing the connected regions corresponding to the binary differential time series image data containing connected regions, calculating the centroid distance between each connected region in each frame and other connected regions in the frame, and the centroid distance between each connected region in each frame and connected regions in other frames; Determine whether the connected area is a water leakage characteristic spot based on the centroid distance between each connected area in each frame and the connected areas in the same frame and other frames and a preset coordinate error threshold and a discrimination number threshold; Based on the water leakage characteristic light spot, it is determined that the converter valve has a long-interval water leakage fault.

11. The method according to claim 10, characterized in that After confirming that there is a long-interval water leakage fault, the following also need to be done: The sampling image processing tool obtains the number of pixels in the region corresponding to the connected region and the pixel coordinates in the image; The mean or median of the pixel coordinates of the water leakage characteristic spots in all connected areas in the image is taken as the water leakage spot position.

12. A converter valve leakage fault monitoring device, characterized in that: include: A communication unit, used for acquiring laser water leakage explicit time-series image data within a period to be determined; The calculation unit is used to pre-process the laser water leakage explicit time-series image data to obtain binarized time-series image difference result data; and determine whether there is a water leakage fault based on the binarized time-series image difference result data.

13. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a method for monitoring water leakage fault of a converter valve according to any one of claims 1 to 11 is implemented.

14. A readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, a method for monitoring water leakage fault of a converter valve as described in any one of claims 1 to 11 is implemented.

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