A method and device for detecting defects in compensation capacitors

Abstract

The embodiment of the present invention provides a compensation capacitor defect detection method and device, the method comprising: acquiring the current state data of the target compensation capacitor; determining the current ambient temperature of the area where the target compensation capacitor is located, the current mains power situation and the standard visual image corresponding to the target compensation capacitor according to the acquisition position; determining the current correction parameter according to the current ambient temperature and the acquisition position; determining the deformation of the target compensation capacitor according to the current visual image, the current mains power situation, the current correction parameter, the acquisition position and the standard visual image; specifically, determining the image difference value according to the current visual image and the standard visual image; generating the deformation according to the current correction parameter and the image difference value. Reduce the interference of people on the road to driving and reduce the probability of personal injury accidents.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to a compensation capacitor defect detection method and device. Background Art

[0002] The compensation capacitor in the non-insulated track circuit system is used to achieve electrical isolation between adjacent track circuits, ensure the effective transmission of signals in this section, and achieve the balance of the power frequency traction return current. It is a very critical component in the non-insulated track circuit system. At present, the non-insulated track circuits used in my country's large railway lines are generally tuned non-insulated track circuits.

[0003] The compensation capacitor detection method based on visual technology mainly adopts the manual inspection method. The inspectors not only have to walk dozens of kilometers at night, but also have to inspect the signal equipment. The inspection cycle is half a year and one month.

[0004] However, the manual inspection method is labor-intensive and has a long inspection cycle. Once the equipment in the section is damaged, it is not easy to discover it in time. In addition, increasing the number of people on the road will interfere with driving and increase the probability of personal injury accidents. Summary of the invention

[0005] In view of the above problems, the present application is proposed to provide a compensation capacitor defect detection method and device that overcomes the above problems or at least partially solves the above problems, including:

[0006] A compensation capacitor defect detection method, the method is used to determine the deformation of a target compensation capacitor through a visual image acquired by an image acquisition device loaded on the bottom of a train; wherein the deformation condition includes warning or normal, including:

[0007] Acquiring current state data of the target compensation capacitor, wherein the current state data includes a current visual image of the target compensation capacitor and an acquisition position of acquiring the current state data;

[0008] Determine the current ambient temperature of the area where the target compensation capacitor is located, the current mains power situation, and a standard visual image corresponding to the target compensation capacitor according to the acquisition position;

[0009] Determining a current correction parameter according to the current ambient temperature and the acquisition position;

[0010] Determine the deformation of the target compensation capacitor based on the current visual image, the current mains power situation, the current correction parameters, the acquisition position and the standard visual image; specifically, determine an image difference value based on the current visual image and the standard visual image; generate the deformation based on the current correction parameters and the image difference value.

[0011] Furthermore, the step of determining the current correction parameter according to the current ambient temperature and the acquisition position includes:

[0012] Determining a target compensation capacitor model according to the acquired position;

[0013] Determining a target correction relationship curve according to the target compensation capacitor model;

[0014] The current correction parameter is determined according to the correction relationship curve and the current ambient temperature.

[0015] Furthermore, it also includes:

[0016] Acquire sample data for establishing a corresponding relationship between visual image parameters of the compensation capacitor and the ground temperature; specifically, the visual image parameters include the compensation capacitor model and the dimensions of each side of the compensation capacitor in the visual image; the ground temperature includes at least 5 temperature values;

[0017] Analyze the characteristics and laws of the specification parameters, and determine the network structure and network parameters of the artificial neural network based on the characteristics and laws;

[0018] Using the sample data, training and testing the network structure and the network parameters, and determining the corresponding relationship between the visual image parameters of the compensation capacitor and the ground temperature;

[0019] A correction relationship curve is generated according to the corresponding relationship and the compensation capacitor model.

[0020] Further, the step of obtaining the current state data of the target compensation capacitor includes:

[0021] Acquire a real-time image of the target compensation capacitor, and generate a current visual image of the target compensation capacitor based on the real-time image and a foreground model corresponding to the target compensation capacitor and a background model corresponding to the target compensation capacitor;

[0022] The acquisition position is acquired, and current state data of the target compensation capacitor is generated according to the acquisition position and the current visual image.

[0023] Furthermore, it also includes:

[0024] Acquire a standard visual image of the target compensation capacitor, and determine a target area corresponding to the target compensation capacitor in the standard visual image of the target compensation capacitor;

[0025] Acquire pixel points in the target area in the standard visual image, and generate the foreground model according to the pixel points in the target area;

[0026] Pixel points outside the target area in the standard visual image are obtained, and the background model is generated according to the pixel points outside the target area.

[0027] Further, the step of acquiring a real-time image of the target compensation capacitor, and generating a current visual image of the target compensation capacitor according to the real-time image and a foreground model corresponding to the target compensation capacitor and a background model corresponding to the target compensation capacitor includes:

[0028] Determining a first probability that each pixel point in the real-time image belongs to the target area according to the foreground model and the real-time image;

[0029] Determining a second probability that each pixel in the real-time image belongs to outside the target area according to the background model and the real-time image;

[0030] A current visual image of the target compensation capacitance is generated according to the first probability, the second probability and the real-time image.

[0031] Furthermore, the step of determining the current ambient temperature of the area where the target compensation capacitor is located, the current mains power situation, and the standard visual image corresponding to the target compensation capacitor according to the acquisition position includes:

[0032] Determine the real-time monitoring temperatures of the three temperature monitoring points closest to the acquisition location, and set the average value of the real-time monitoring temperatures of the three temperature monitoring points closest to the acquisition location as the current ambient temperature;

[0033] Determine the current city power situation of the corresponding area according to the acquired location;

[0034] Determining the target compensation capacitor identifier according to the acquired position;

[0035] The standard visual image is determined according to the target compensation capacitance identifier.

[0036] A compensation capacitor defect detection device, the device is used to determine the deformation of a target compensation capacitor through a visual image acquired by an image acquisition device loaded on the bottom of a train; wherein the deformation condition includes warning or normal, including:

[0037] A current state data acquisition module, used to acquire current state data of the target compensation capacitor, wherein the current state data includes a current visual image of the target compensation capacitor and an acquisition position for acquiring the current state data;

[0038] An ambient temperature and standard visual image determination module, used to determine the current ambient temperature of the area where the target compensation capacitor is located, the current city power situation and the standard visual image corresponding to the target compensation capacitor according to the acquisition position;

[0039] A current correction parameter determination module, used to determine the current correction parameter according to the current ambient temperature and the acquisition position;

[0040] A deformation condition determination module is used to determine the deformation condition of the target compensation capacitor based on the current visual image, the current mains condition, the current correction parameter, the acquisition position and the standard visual image; specifically, to determine the image difference value based on the current visual image and the standard visual image; and to generate the deformation condition based on the current correction parameter and the image difference value.

[0041] A computer device comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, a compensation capacitance detection method based on visual technology is implemented.

[0042] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, a compensation capacitance detection method based on visual technology is implemented.

[0043] This application has the following advantages:

[0044] In an embodiment of the present application, by acquiring the current state data of the target compensation capacitor, wherein the current state data includes the current visual image of the target compensation capacitor and the acquisition position of the current state data; determining the current ambient temperature of the area where the target compensation capacitor is located, the current city power situation and the standard visual image corresponding to the target compensation capacitor according to the acquisition position; determining the current correction parameter according to the current ambient temperature and the acquisition position; determining the deformation of the target compensation capacitor according to the current visual image, the current city power situation, the current correction parameter, the acquisition position and the standard visual image; specifically, determining the image difference value according to the current visual image and the standard visual image; generating the deformation according to the current correction parameter and the image difference value. The deformation of the compensation capacitor is detected by the monitoring device installed on the train, so that the detection of the compensation capacitor has low labor intensity and short inspection cycle. Once the section equipment is damaged, it is easy to find it in time, and the interference of personnel on the road to driving and the probability of personal injury accidents are reduced; by adding temperature parameters to detect the transponder, the detection result is more accurate, the probability of false alarm is reduced, and the maintenance cost is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solution of the present application, the drawings required for use in the description of the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0046] Figure 1 is a flowchart of a compensation capacitor defect detection method provided by an embodiment of the present application;

[0047] Figure 2 It is a structural block diagram of a compensation capacitor defect detection device provided by an embodiment of the present application;

[0048] Figure 3 It is a structural schematic diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the objects, features and advantages of the present application more obvious and understandable, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0050] It should be noted that the compensation capacitor detection method based on visual technology in any embodiment of the present invention determines the deformation of the target compensation capacitor through the visual image obtained by the monitoring device loaded on the train; wherein the deformation condition includes needing maintenance or normal.

[0051] Reference Figure 1 , showing the method provided by an embodiment of the present application for determining the deformation of the target compensation capacitor through the visual image acquired by the image acquisition device loaded on the bottom of the train; wherein the deformation condition includes warning or normal, and is characterized in that it includes:

[0052] S110, obtaining current state data of the target compensation capacitor, wherein the current state data includes a current visual image of the target compensation capacitor and an acquisition position of the current state data;

[0053] S120, determining the current ambient temperature of the area where the target compensation capacitor is located, the current city power situation, and a standard visual image corresponding to the target compensation capacitor according to the acquired position;

[0054] S130, determining a current correction parameter according to the current ambient temperature and the acquisition position;

[0055] S140, determining the deformation of the target compensation capacitor based on the current visual image, the current mains power situation, the current correction parameters, the acquisition position and the standard visual image; specifically, determining an image difference value based on the current visual image and the standard visual image; generating the deformation based on the current correction parameters and the image difference value.

[0056] In an embodiment of the present application, by acquiring the current state data of the target compensation capacitor, wherein the current state data includes the current visual image of the target compensation capacitor and the acquisition position of the current state data; determining the current ambient temperature of the area where the target compensation capacitor is located, the current city power situation and the standard visual image corresponding to the target compensation capacitor according to the acquisition position; determining the current correction parameter according to the current ambient temperature and the acquisition position; determining the deformation of the target compensation capacitor according to the current visual image, the current city power situation, the current correction parameter, the acquisition position and the standard visual image; specifically, determining the image difference value according to the current visual image and the standard visual image; generating the deformation according to the current correction parameter and the image difference value. The deformation of the compensation capacitor is detected by the monitoring device installed on the train, so that the detection of the compensation capacitor has low labor intensity and short inspection cycle. Once the section equipment is damaged, it is easy to find it in time, and the interference of personnel on the road to driving and the probability of personal injury accidents are reduced; by adding temperature parameters to detect the transponder, the detection result is more accurate, the probability of false alarm is reduced, and the maintenance cost is reduced.

[0057] Next, the compensation capacitance detection method based on vision technology in this exemplary embodiment will be further described.

[0058] As described in the above step S110, the current state data of the target compensation capacitor is obtained, wherein the current state data includes a current visual image of the target compensation capacitor and an acquisition position for acquiring the current state data.

[0059] In one embodiment of the present invention, the specific process of "obtaining current state data of the target compensation capacitor, wherein the current state data includes a current visual image of the target compensation capacitor and an acquisition location of the current state data" in step S110 can be further explained in combination with the following description.

[0060] As described in the following steps, a real-time image of the target compensation capacitor is acquired, and a current visual image of the target compensation capacitor is generated based on the real-time image and a foreground model corresponding to the target compensation capacitor and a background model corresponding to the target compensation capacitor.

[0061] As described in the following steps, the acquisition position is acquired, and the current state data of the target compensation capacitor is generated according to the acquisition position and the current visual image.

[0062] In one embodiment of the present invention, the specific process of the step of "obtaining a real-time image of the target compensation capacitor, and generating a current visual image of the target compensation capacitor based on the real-time image and a foreground model corresponding to the target compensation capacitor and a background model corresponding to the target compensation capacitor" can be further explained in combination with the following description.

[0063] As described in the following steps, a first probability that each pixel point in the real-time image belongs to the target area is determined according to the foreground model and the real-time image.

[0064] In one embodiment of the present invention, the method further includes acquiring a standard visual image of the target compensation capacitor, and determining a target area corresponding to the target compensation capacitor in the standard visual image of the target compensation capacitor.

[0065] As an example, the target area can be implemented by obtaining the outer contour identification points of the target area corresponding to the target compensation capacitor in the standard visual image, and constructing the target area based on the outer contour identification points; wherein the outer contour identification points are identified using artificial intelligence recognition technology.

[0066] It should be noted that the artificial intelligence recognition technology is to establish an outer contour identification point model using an artificial neural network method, and train the outer contour identification point model using a standard visual image. The outline identification point is an inflection point, which can be set according to the user's range.

[0067] As an example, constructing the target area based on the outer contour identification points can be implemented through the following steps: establishing a pixel coordinate system based on the standard visual image, and taking the point in the upper left corner of the standard visual image as the origin; assigning a first function to each of the outer contour identification points to obtain a first parameter; identifying the maximum value and the minimum value among multiple first parameters as the contour starting point and the contour end point of the target image respectively; taking the contour starting point and the contour end point as the starting point and the pixel coordinates of the contour identification points and sorting them clockwise; connecting two adjacent contour identification points after sorting to form the target area.

[0068] As an example, the first function may be:

[0069] (point.y*imgW)+(point.x+1)

[0070] Among them, point.y represents the coordinate of the pixel point along the Y axis in the pixel coordinate system, point.x represents the coordinate of the pixel point along the X axis in the pixel coordinate system, and imgW represents the width of the original image.

[0071] In one embodiment of the present invention, the method further includes acquiring pixel points within the target area in the standard visual image, and generating the foreground model based on the pixel points within the target area.

[0072] In one embodiment of the present invention, the specific process of the step of "obtaining pixel points in the target area in the standard visual image, and generating the foreground model based on the pixel points in the target area" can be further explained in combination with the following description.

[0073] As an example, the background model can be obtained by obtaining the pixel points in the target area in the standard visual image as the first pixel point set, performing cluster analysis according to the color value of each pixel point in the first pixel point set, and obtaining at least one first category. For each first category, the first weight corresponding to the first category is obtained, and the first Gaussian distribution model is determined according to the color value of each pixel point in the first category; the foreground model is determined by weighted summation according to the determined first Gaussian distribution models and the obtained first weights.

[0074] As an example, the first category can be implemented by classifying pixels with color differences less than a preset color difference into one category, or dividing the first pixel set into a preset number of first categories. The clustering analysis method can be determined according to the prior art, and the present invention does not limit this.

[0075] As an optional example, the first weight may be determined according to one of the following methods; for example, the ratio of the number of pixels included in the first category to the number of pixels in the first pixel set is used as the first weight corresponding to the first category.

[0076] In a specific implementation, the first category A includes 10 pixels, and the first pixel set includes 100 pixels, and the first weight corresponding to the first category A is 0.1 (ie, 10 / 100).

[0077] As another optional example, the first weight may be determined according to one of the following methods; such as calculating the average color difference of the pixels included in each first category, and calculating the sum of the average color differences of all first categories; for each first category, subtracting the average color difference of the first category from the sum to obtain a difference, and using the ratio of the difference to the sum as the first weight corresponding to the first category.

[0078] In a specific implementation, after cluster analysis of the first pixel set, three first categories are obtained, which are numbered as category 1, category 2 and category 3. The average color difference of the pixels included in category 1 is a; the average color difference of the pixels included in category 2 is b; the average color difference of the pixels included in category 3 is c; then the sum is (a+b+c) (that is, H), and for category 1, its corresponding first weight is (Ha) / H. Similarly, for category 2, its corresponding first weight is (Hb) / H; for category 3, its corresponding first weight is (Hc) / H.

[0079] As an example, the Gaussian distribution model can be implemented using the maximum likelihood estimation method, specifically by determining the Gaussian distribution model, that is, determining the mean and variance of the Gaussian distribution. The maximum likelihood estimation method is used to determine the mean and variance of the Gaussian distribution model. It can also be determined according to the existing technical methods, and the present invention is not limited to this.

[0080] As an example, the foreground module may be determined according to the following formula:

[0081]

[0082] In the formula, f represents the foreground model; i represents the i-th first category; λ i represents the first weight corresponding to the i-th first category; Represents the Gaussian distribution model corresponding to the i-th first category.

[0083] In one embodiment of the present invention, the specific process of the step of "generating a current visual image of the target compensation capacitor based on the visual image and a foreground model corresponding to the target compensation capacitor and a background model corresponding to the target compensation capacitor" can be further explained in combination with the following description.

[0084] As described in the following steps, a second probability that each pixel point in the real-time image belongs to outside the target area is determined based on the background model and the real-time image.

[0085] In one embodiment of the present invention, the method further includes acquiring pixel points outside the target area in the standard visual image, and generating the background model based on the pixel points outside the target area.

[0086] In one embodiment of the present invention, the specific process of the step of "obtaining pixel points outside the target area in the standard visual image, and generating the background model based on the pixel points outside the target area" can be further explained in combination with the following description.

[0087] As an example, the background model can be implemented by the following steps, specifically obtaining pixel points outside the target area in the standard visual image as a second pixel point set, performing cluster analysis based on the color values ​​of each pixel point in the second pixel point set, and obtaining at least one second category. For each second category, obtain the second weight corresponding to the second category, and determine the second Gaussian distribution model based on the color value of each pixel point in the second category. According to the determined second Gaussian distribution models and the obtained second weights, the background model is determined by weighted summation. Since the construction methods of the foreground model and the background model are the same, they will not be repeated here.

[0088] In one embodiment of the present invention, the specific process of the step of "generating a current visual image of the target compensation capacitor based on the visual image and a foreground model corresponding to the target compensation capacitor and a background model corresponding to the target compensation capacitor" can be further explained in combination with the following description.

[0089] As described in the following steps, a current visual image of the target compensation capacitance is generated according to the first probability, the second probability and the real-time image.

[0090] As an example, for any pixel in the visual image, if the first probability that the pixel belongs to the target area is greater than the second probability that the pixel belongs to outside the target area, the pixel is regarded as a pixel belonging to the target area; or, for any pixel in the visual image, if the first probability that the pixel belongs to the target area is greater than the preset probability, the pixel is regarded as a pixel belonging to the target area; or, for any pixel in the visual image, if the ratio of the first probability that the pixel belongs to the target area to the second probability that the pixel belongs to outside the target area is greater than the preset ratio, the pixel is regarded as a pixel belonging to the target area; or, for any pixel in the visual image, if the difference between the first probability that the pixel belongs to the target area and the second probability that the pixel belongs to outside the target area is greater than the preset difference, the pixel is regarded as a pixel belonging to the target area, and the pixel belonging to the target area is used as the current visual image of the target compensation capacitor.

[0091] In one embodiment of the present invention, the specific process of "obtaining current state data of the target compensation capacitor, wherein the current state data includes a current visual image of the target compensation capacitor and an acquisition location of the current state data" in step S110 can be further explained in combination with the following description.

[0092] As described in the following steps, an acquisition position of the target compensation capacitor is acquired, and current state data of the target compensation capacitor is generated according to the acquisition position of the target compensation capacitor and a current visual image of the target compensation capacitor.

[0093] It should be noted that the timestamp of the current visual image of the target compensation capacitor is the acquisition time point, that is, the acquisition time point of the current state data, and the acquisition position is the longitude and latitude coordinates of the target compensation capacitor.

[0094] As described in the above step S120, the current ambient temperature of the area where the target compensation capacitor is located, the current city power situation and the standard visual image corresponding to the target compensation capacitor are determined according to the acquisition position.

[0095] In one embodiment of the present invention, the specific process of "determining the current ambient temperature of the area where the target compensation capacitor is located, the current city power conditions and the standard visual image corresponding to the target compensation capacitor based on the acquisition position" in step S120 can be further explained in combination with the following description.

[0096] As described in the following steps, the real-time monitoring temperatures of the three temperature monitoring points closest to the acquisition location are determined, and the average value of the real-time monitoring temperatures of the three temperature monitoring points closest to the acquisition location is set as the current ambient temperature.

[0097] It should be noted that, based on the acquisition location of the current state data, that is, the longitude and latitude coordinates of the target compensation capacitor, the three temperature monitoring points with the closest straight-line distance to the longitude and latitude coordinates are selected as the data source for acquiring the real-time detection temperature, and the real-time monitoring temperatures of the three detection points are averaged to prevent detection errors due to instrument failures; it should be noted that when the temperature of a certain detection point is obviously significantly different from the temperatures of the other two detection points (for example, ±5°C), the temperature monitoring point is temporarily removed from the temperature data collection queue, and the maintenance personnel are notified to confirm the situation, and it will be reinserted into the queue after repair.

[0098] As described in the following steps, the current city power situation of the corresponding area is determined according to the acquired location.

[0099] It should be noted that the current city power situation includes whether there is power construction in the area and whether there is a power outage in the area, where the area is an area with a radius of one kilometer of the longitude and latitude coordinates corresponding to the acquisition location.

[0100] As described in the following steps, the target compensation capacitor identifier is determined according to the acquired position.

[0101] It should be noted that the target compensation capacitor maintenance record corresponding to the latitude and longitude coordinates is found from the server through the aforementioned latitude and longitude coordinates, and the identification information of the target compensation capacitor is determined through the record.

[0102] As described in the following steps, the standard visual image is determined according to the target compensation capacitance identifier.

[0103] It should be noted that the model of the compensation capacitor corresponding to the identification is found from the database, and the pre-recorded standard visual image corresponding to the model is found, and the image can be a three-dimensional image or several two-dimensional photos.

[0104] As described in the above step S130, the current correction parameter is determined according to the current ambient temperature and the acquisition position.

[0105] In an embodiment of the present invention, the specific process of "determining the current correction parameter according to the current ambient temperature and the acquisition position" in step S130 can be further explained in combination with the following description.

[0106] As described in the following steps, a target compensation capacitor model is determined according to the acquired position;

[0107] As described in the following steps, a target correction relationship curve is determined according to the target compensation capacitor model;

[0108] It should be noted that the heating or cooling of outdoor temperature is simulated under specified conditions for compensation capacitors of different models, and the data on changes in the shell during the process are recorded, including whether the shell is deformed and the deformation amount of the shell (recorded in percentage); each model is tested several times and the parameters are recorded, and finally the relationship curve between temperature and shell deformation is calculated through statistical principles.

[0109] As described in the following steps, the current correction parameter is determined according to the correction relationship curve and the current ambient temperature.

[0110] As described in the following steps, sample data for establishing a corresponding relationship between visual image parameters of the compensation capacitor and the ground temperature is obtained; specifically, the visual image parameters include the compensation capacitor model and the dimensions of each side of the compensation capacitor in the visual image; the ground temperature includes at least 5 temperature values;

[0111] As described in the following steps, the characteristics and laws of the specification parameters are analyzed, and the network structure and network parameters of the artificial neural network are determined according to the characteristics and laws;

[0112] As described in the following steps, the network structure and the network parameters are trained and tested using the sample data to determine the corresponding relationship between the visual image parameters of the compensation capacitor and the ground temperature;

[0113] As described in the following steps, a correction relationship curve is generated according to the corresponding relationship and the compensation capacitor model.

[0114] As described in the above step S140, the deformation of the target compensation capacitor is determined based on the current visual image, the current mains power situation, the current correction parameters, the acquisition position and the standard visual image; specifically, the image difference value is determined based on the current visual image and the standard visual image; and the deformation is generated based on the current correction parameters and the image difference value.

[0115] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0116] Reference Figure 2 , shows a compensation capacitor defect detection method provided by an embodiment of the present application, the method is used to determine the deformation of the target compensation capacitor through a visual image acquired by an image acquisition device loaded on the bottom of a train; wherein the deformation condition includes warning or normal, including:

[0117] A current state data acquisition module 210 is used to acquire current state data of the target compensation capacitor, wherein the current state data includes a current visual image of the target compensation capacitor and an acquisition position for acquiring the current state data;

[0118] The ambient temperature and standard visual image determination module 220 is used to determine the current ambient temperature of the area where the target compensation capacitor is located, the current city power situation and the standard visual image corresponding to the target compensation capacitor according to the acquisition position;

[0119] A current correction parameter determination module 230, configured to determine a current correction parameter according to the current ambient temperature and the acquisition position;

[0120] The deformation condition determination module 240 is used to determine the deformation condition of the target compensation capacitor based on the current visual image, the current mains power condition, the current correction parameter, the acquisition position and the standard visual image; specifically, determine the image difference value based on the current visual image and the standard visual image; generate the deformation condition based on the current correction parameter and the image difference value.

[0121] In one embodiment of the present invention, the current correction parameter determination module 230 includes:

[0122] A target compensation capacitor model determination submodule, used to determine the target compensation capacitor model according to the acquisition position;

[0123] A target correction relationship curve determination submodule, used to determine a target correction relationship curve according to the target compensation capacitor model;

[0124] The current correction parameter determination submodule is used to determine the current correction parameter according to the correction relationship curve and the current ambient temperature.

[0125] In one embodiment of the present invention, it also includes:

[0126] A sample data acquisition module, used to acquire sample data for establishing a corresponding relationship between visual image parameters of the compensation capacitor and the ground temperature; specifically, the visual image parameters include the compensation capacitor model and the dimensions of each side of the compensation capacitor in the visual image; the ground temperature includes at least 5 temperature values;

[0127] The network structure and network parameter determination module is used to analyze the characteristics and laws of the specification parameters, and determine the network structure and network parameters of the artificial neural network according to the characteristics and laws;

[0128] A corresponding relationship determination module, used to use the sample data to train and test the network structure and the network parameters, and determine the corresponding relationship between the visual image parameters of the compensation capacitor and the ground temperature;

[0129] A correction relationship curve generating module is used to generate a correction relationship curve according to the corresponding relationship and the compensation capacitor model.

[0130] In one embodiment of the present invention, the current state data acquisition module 210 includes:

[0131] A current visual image generation submodule, used to obtain a real-time image of the target compensation capacitor, and generate a current visual image of the target compensation capacitor based on the real-time image and a foreground model corresponding to the target compensation capacitor and a background model corresponding to the target compensation capacitor;

[0132] The current state data generating submodule is used to obtain the acquisition position, and generate the current state data of the target compensation capacitor according to the acquisition position and the current visual image.

[0133] In one embodiment of the present invention, it also includes:

[0134] a target area determination module, configured to obtain a standard visual image of the target compensation capacitor and determine a target area corresponding to the target compensation capacitor in the standard visual image of the target compensation capacitor;

[0135] A foreground model generation module, used to obtain pixel points in the target area in the standard visual image, and generate the foreground model according to the pixel points in the target area;

[0136] The background model generation module is used to obtain pixel points outside the target area in the standard visual image and generate the background model based on the pixel points outside the target area.

[0137] In one embodiment of the present invention, the current visual image generation submodule includes:

[0138] A first probability determination submodule, configured to determine a first probability that each pixel point in the real-time image belongs to the target area according to the foreground model and the real-time image;

[0139] A second probability determination submodule, configured to determine a second probability that each pixel in the real-time image belongs to outside the target area according to the background model and the real-time image;

[0140] A current visual image determination submodule is used to generate a current visual image of the target compensation capacitor according to the first probability, the second probability and the real-time image.

[0141] In one embodiment of the present invention, the ambient temperature and standard visual image determination module 220 includes:

[0142] A target compensation capacitance identification determination submodule, configured to determine the target compensation capacitance identification according to the acquisition position;

[0143] The standard visual image determination submodule is used to determine the standard visual image according to the target compensation capacitance identifier.

[0144] In one embodiment of the present invention, the deformation condition determination module 240 includes:

[0145] A first grayscale binary image generating submodule, used for generating a first grayscale binary image corresponding to the standard visual image according to the standard visual image;

[0146] A second grayscale binary image generating submodule, configured to generate a second grayscale binary image corresponding to the current visual image based on the current visual image;

[0147] A standard pixel value acquisition submodule is used to acquire standard pixel values ​​of preset coordinates in the first grayscale binary image; wherein the preset coordinates are arranged in nine grids;

[0148] A current pixel value acquisition submodule, used to acquire the current pixel value of the preset coordinate in the second grayscale binary image;

[0149] The image difference value determination submodule is used to determine the image difference value according to the standard pixel value and the current pixel value.

[0150] In one embodiment of the present invention, the deformation condition determination module 240 includes:

[0151] A first deformation condition determination submodule is used to determine a first product of the current correction parameter and the image difference value when the pixel value difference between the current pixel value and the standard pixel value in the nine-square grid is greater than a preset threshold value, and determine the deformation condition according to the first product; wherein the symmetry includes axial symmetry or central symmetry;

[0152] or;

[0153] A proportion value determination submodule, used for determining a proportion value of the target coordinate position in the nine-square grid when the pixel value difference between the current pixel value and the standard pixel value in the nine-square grid is greater than a preset threshold and the target coordinate position is asymmetric;

[0154] The second deformation condition determination submodule is used to determine a second product of the current correction parameter, the proportion value and the image difference value, and determine the deformation condition according to the second product.

[0155] Reference Figure 3 , showing a computer device of a compensation capacitor defect detection method of the present application, which may specifically include the following:

[0156] The computer device 12 is in the form of a general-purpose computing device, and the components of the computer device 12 may include but are not limited to: one or more processors or processing units 16, a memory 28, and a bus 18 connecting different system components (including the memory 28 and the processing unit 16).

[0157] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, an Audio Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0158] The computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0159] Memory 28 may include computer system readable media in the form of volatile memory, such as random access memory 30 and / or cache memory 32. Computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write to non-removable, non-volatile magnetic media (commonly referred to as a "hard drive"). Although Figure 3 Not shown, a disk drive for reading and writing to a removable non-volatile disk (such as a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (such as a CD-ROM, DVD-ROM or other optical medium) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory may include at least one program product having a set (e.g., at least one) of program modules 42, which are configured to perform the functions of the various embodiments of the present application.

[0160] A program / utility 40 having a set (at least one) of program modules 42 may be stored in, for example, a memory, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules 42, and program data, each of which or some combination may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0161] The computer device 12 may also communicate with one or more external devices 14 (e.g., keyboards, pointing devices, displays 24, cameras, etc.), one or more devices that enable an operator to interact with the computer device 12, and / or any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed through the I / O interface 22. Furthermore, the computer device 12 may also communicate with one or more networks (e.g., local area networks (LANs)), wide area networks (WANs), and / or public networks (e.g., the Internet) through a network adapter 20. Figure 3 As shown, the network adapter 20 communicates with other modules of the computer device 12 via the bus 18. It should be understood that although Figure 3 Not shown, other hardware and / or software modules may be used in conjunction with the computer device 12, including but not limited to: microcode, device drivers, redundant processing units 16, external disk drive arrays, RAID systems, tape drives, and data backup storage systems 34, etc.

[0162] The processing unit 16 executes various functional applications and data processing by running the programs stored in the memory 28, such as implementing a compensation capacitor defect detection method provided in an embodiment of the present application.

[0163] That is, when the processing unit 16 executes the program, it realizes: obtaining the current state data of the target compensation capacitor, wherein the current state data includes the current visual image of the target compensation capacitor and the acquisition position of the current state data; determining the current ambient temperature of the area where the target compensation capacitor is located, the current mains power situation and the standard visual image corresponding to the target compensation capacitor based on the acquisition position; determining the current correction parameter based on the current ambient temperature and the acquisition position; determining the deformation of the target compensation capacitor based on the current visual image, the current mains power situation, the current correction parameter, the acquisition position and the standard visual image; specifically, determining the image difference value based on the current visual image and the standard visual image; generating the deformation based on the current correction parameter and the image difference value.

[0164] In an embodiment of the present application, the present application further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, a compensation capacitor defect detection method as provided in all embodiments of the present application is implemented.

[0165] That is, when the program is executed by the processor, it is implemented as follows: obtaining the current state data of the target compensation capacitor, wherein the current state data includes the current visual image of the target compensation capacitor and the acquisition position of obtaining the current state data; determining the current ambient temperature of the area where the target compensation capacitor is located, the current mains power situation and the standard visual image corresponding to the target compensation capacitor based on the acquisition position; determining the current correction parameter based on the current ambient temperature and the acquisition position; determining the deformation of the target compensation capacitor based on the current visual image, the current mains power situation, the current correction parameter, the acquisition position and the standard visual image; specifically, determining an image difference value based on the current visual image and the standard visual image; and generating the deformation based on the current correction parameter and the image difference value.

[0166] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0167] Computer-readable signal media may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0168] The computer program code for performing the operation of the present application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the operator's computer, partially on the operator's computer, as an independent software package, partially on the operator's computer, partially on the remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the operator's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet). The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the various embodiments can be referred to each other.

[0169] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present application.

[0170] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0171] The above is a detailed introduction to a compensation capacitor defect detection method and device provided by the present application. This article uses specific examples to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for general technical personnel in this field, based on the ideas of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for detecting a compensation capacitor defect, the method being used to determine the deformation of a target compensation capacitor through a visual image acquired by an image acquisition device loaded on the bottom of a train; wherein: The deformation condition includes warning or normal, which is characterized by including: Acquiring current state data of the target compensation capacitor, wherein the current state data includes a current visual image of the target compensation capacitor and an acquisition position of acquiring the current state data; Determine the current ambient temperature of the area where the target compensation capacitor is located, the current mains power situation, and a standard visual image corresponding to the target compensation capacitor according to the acquisition position; Determine the current correction parameter according to the current ambient temperature and the acquisition position; wherein, determine the target compensation capacitor model according to the acquisition position; determine the target correction relationship curve according to the target compensation capacitor model; determine the current correction parameter according to the correction relationship curve and the current ambient temperature; obtain sample data for establishing a corresponding relationship between the visual image parameters of the compensation capacitor and the ground temperature; determine the corresponding relationship between the visual image parameters of the compensation capacitor and the ground temperature; generate a correction relationship curve according to the corresponding relationship and the compensation capacitor model; Determine the deformation of the target compensation capacitor based on the current visual image, the current mains power situation, the current correction parameters, the acquisition position and the standard visual image; specifically, determine an image difference value based on the current visual image and the standard visual image; generate the deformation based on the current correction parameters and the image difference value.

2. The method according to claim 1, characterized in that The visual image parameters include the compensation capacitor model and the dimensions of each side of the compensation capacitor in the visual image; the temperature values ​​of the ground temperature include at least 5; the step of determining the corresponding relationship between the visual image parameters of the compensation capacitor and the ground temperature includes: Analyze the characteristics and laws of the specification parameters, and determine the network structure and network parameters of the artificial neural network based on the characteristics and laws; The network structure and the network parameters are trained and tested using the sample data to determine the corresponding relationship between the visual image parameters of the compensation capacitor and the ground temperature.

3. The method according to claim 1, characterized in that The step of obtaining the current state data of the target compensation capacitor includes: Acquire a real-time image of the target compensation capacitor, and generate a current visual image of the target compensation capacitor based on the real-time image and a foreground model corresponding to the target compensation capacitor and a background model corresponding to the target compensation capacitor; The acquisition position is acquired, and current state data of the target compensation capacitor is generated according to the acquisition position and the current visual image.

4. The method according to claim 3, characterized in that Also includes: Acquire a standard visual image of the target compensation capacitor, and determine a target area corresponding to the target compensation capacitor in the standard visual image of the target compensation capacitor; Acquire pixel points in the target area in the standard visual image, and generate the foreground model according to the pixel points in the target area; Pixel points outside the target area in the standard visual image are obtained, and the background model is generated according to the pixel points outside the target area.

5. The method according to claim 4, characterized in that The step of acquiring a real-time image of the target compensation capacitor, and generating a current visual image of the target compensation capacitor according to the real-time image and a foreground model corresponding to the target compensation capacitor and a background model corresponding to the target compensation capacitor comprises: Determining a first probability that each pixel point in the real-time image belongs to the target area according to the foreground model and the real-time image; Determining a second probability that each pixel in the real-time image belongs to outside the target area according to the background model and the real-time image; A current visual image of the target compensation capacitance is generated according to the first probability, the second probability and the real-time image.

6. The method according to claim 1, characterized in that The step of determining the current ambient temperature of the area where the target compensation capacitor is located, the current mains power situation, and the standard visual image corresponding to the target compensation capacitor according to the acquisition position includes: Determine the real-time monitoring temperatures of the three temperature monitoring points closest to the acquisition location, and set the average value of the real-time monitoring temperatures of the three temperature monitoring points closest to the acquisition location as the current ambient temperature; Determine the current city power situation of the corresponding area according to the acquired location; Determining the target compensation capacitor identifier according to the acquired position; The standard visual image is determined according to the target compensation capacitance identifier.

7. A compensation capacitor defect detection device, the device is used to determine the deformation of the target compensation capacitor through the visual image acquired by the image acquisition device loaded on the bottom of the train; wherein, The deformation condition includes warning or normal, which is characterized by including: A current state data acquisition module, used to acquire current state data of the target compensation capacitor, wherein the current state data includes a current visual image of the target compensation capacitor and an acquisition position for acquiring the current state data; An ambient temperature and standard visual image determination module, used to determine the current ambient temperature of the area where the target compensation capacitor is located, the current city power situation and the standard visual image corresponding to the target compensation capacitor according to the acquisition position; A current correction parameter determination module, used to determine the current correction parameter according to the current ambient temperature and the acquisition position; wherein, a target compensation capacitor model is determined according to the acquisition position; a target correction relationship curve is determined according to the target compensation capacitor model; the current correction parameter is determined according to the correction relationship curve and the current ambient temperature; sample data for establishing a corresponding relationship between the visual image parameters of the compensation capacitor and the ground temperature is obtained; the corresponding relationship between the visual image parameters of the compensation capacitor and the ground temperature is determined; and a correction relationship curve is generated according to the corresponding relationship and the compensation capacitor model; A deformation condition determination module is used to determine the deformation condition of the target compensation capacitor based on the current visual image, the current mains condition, the current correction parameter, the acquisition position and the standard visual image; specifically, to determine the image difference value based on the current visual image and the standard visual image; and to generate the deformation condition based on the current correction parameter and the image difference value.

8. A computer device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program implements the method according to any one of claims 1 to 6 when executed by the processor.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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