A vibration measurement method and system for heavy-load electric locomotive based on machine vision

Abstract

The present invention relates to the field of vibration measurement technology, and includes a machine vision-based vibration measurement method and system for a heavy-load electric locomotive. The method and system comprise the following steps: obtaining a vibration function set and a velocity gradient set for simulating vibration; obtaining a vibration combination set based on the vibration function set and velocity gradient set; obtaining a target detection locomotive; obtaining a vibration detection video stream based on the vibration combination set and the target detection locomotive; obtaining a detected vibration displacement set based on the vibration detection video stream; screening the detected vibration displacement set to obtain a target vibration displacement set; obtaining a fitted vibration displacement set and a size parameter set; obtaining a fitted detection model based on the fitted vibration displacement set, the size parameter set, and the target vibration displacement set; and utilizing a vibration feedback unit to send the fitted detection model to an initiator of a vibration test instruction to achieve vibration measurement of the heavy-load electric locomotive. The present invention can improve the accuracy and intelligence of vibration measurement of heavy-load electric locomotives.

Description

Technical Field

[0001] The present invention relates to the field of vibration measurement technology, and in particular to a machine vision-based vibration measurement method, system, electronic equipment and computer-readable storage medium for a heavy-load electric locomotive. Background Art

[0002] During operation, heavy-load electric locomotives inevitably generate vibrations, which can have a significant impact on the locomotive itself and the entire transportation system. Vibration often degrades passenger experience and can even lead to safety accidents. Therefore, accurate and comprehensive testing of heavy-load electric locomotives is crucial to ensuring their safe and reliable operation.

[0003] Currently, vibration measurement methods mainly include contact measurement, that is, vibration measurement of the object to be measured is achieved by placing a large number of sensors on the surface of the object to be measured.

[0004] Although the above method can realize vibration measurement of heavy-loaded electric locomotives, it consumes a lot of resources and has the problem of inaccurate vibration measurement. Therefore, the intelligence and accuracy of vibration measurement of heavy-loaded electric locomotives need to be improved. Summary of the Invention

[0005] The present invention provides a heavy-load electric locomotive vibration measurement method based on machine vision and a computer-readable storage medium, the main purpose of which is to improve the accuracy and intelligence of heavy-load electric locomotive vibration measurement.

[0006] To achieve the above objectives, the present invention provides a method for measuring vibration of a heavy-load electric locomotive based on machine vision, comprising:

[0007] receiving a vibration test instruction, and determining a vibration test environment for the vibration test based on the vibration test instruction, wherein the vibration test environment includes: a heavy-load electric locomotive, a simulated vibration table, and a vibration measurement system, wherein the vibration measurement system includes: a video capture unit and a vibration feedback unit;

[0008] Acquire a vibration function set and a velocity gradient set for simulating vibration, wherein the vibration function set includes a plurality of vibration functions, and the velocity gradient set includes a plurality of reference velocities, and acquire a vibration combination set based on the vibration function set and the velocity gradient set;

[0009] A reference coordinate system is constructed, and a target detection locomotive is obtained using the reference coordinate system and the heavy-load electric locomotive, wherein the target detection locomotive includes a plurality of target detection points;

[0010] Acquire a vibration detection video stream based on the vibration combination set, the target detection locomotive, the simulated vibration platform, and the video capture unit; and acquire a detected vibration displacement set based on the vibration detection video stream, wherein the detected vibration displacement set includes multiple detected vibration displacements;

[0011] screening the detected vibration displacement set to obtain a target vibration displacement set;

[0012] Acquire a fitted vibration displacement set of the target detection locomotive, wherein the fitted vibration displacement set includes a plurality of fitted vibration displacements, and the fitted vibration displacements correspond one-to-one to the target detection points;

[0013] A size parameter set of a target detection locomotive is obtained, a fitting detection model is obtained based on the fitting vibration displacement set, the size parameter set and the target vibration displacement set, and the fitting detection model is sent to an initiator of a vibration test instruction using a vibration feedback unit to realize vibration measurement of a heavy-load electric locomotive.

[0014] Optionally, acquiring the vibration combination set based on the vibration function set and the velocity gradient set includes:

[0015] Extract vibration functions from the vibration function set in sequence, and perform the following operations on the extracted vibration functions:

[0016] The extracted vibration function is matched with the velocity gradient set to obtain a vibration combination, and the vibration combinations are summarized to obtain a vibration combination set, wherein the vibration combination is as follows:

[0017] ;

[0018] in, Indicates the vibration combination concentration A vibration combination, Represents the first A vibration function, represents the velocity gradient set, They represent the first reference speed, the second reference speed, and the The reference speed and A reference speed, Indicates that the velocity gradient is concentrated A reference speed.

[0019] Optionally, the method of acquiring the target detection locomotive by using a reference coordinate system and a heavy-load electric locomotive includes:

[0020] identifying a detection area in the heavy-load electric locomotive, and acquiring a plurality of target detection points based on the reference coordinate system and the detection area;

[0021] Extract target detection points from the multiple target detection points in sequence, and perform the following operations on the extracted target detection points:

[0022] Obtaining a detection grayscale value based on the extracted target detection point, using the detection grayscale value as the starting point of a pre-constructed region growing algorithm, and using the region growing algorithm to obtain a target growth region, wherein the grayscale values of the pixels corresponding to the target growth region are all the same;

[0023] In a detection area corresponding to the target growth area, a target positioning pattern is identified based on the detected grayscale value, wherein the target positioning pattern is a square checkerboard pattern having a preset first grayscale value and a preset second grayscale value alternating therebetween, and both the first grayscale value and the second grayscale value are different from the detected grayscale value, and a side length of the square checkerboard pattern is preset to be (2q+1), where q is an integer greater than or equal to 1;

[0024] Fix the center of the target positioning pattern to the target detection point to obtain the target detection locomotive.

[0025] Optionally, the step of obtaining a vibration detection video stream based on the vibration combination set, the target detection locomotive, the simulated vibration table, and the video capture unit includes:

[0026] Extract vibration combinations from the vibration combination set in sequence, and perform the following operations on the extracted vibration combinations:

[0027] A plurality of target vibration combinations are obtained based on the extracted vibration combinations, wherein the target vibration combinations are as follows:

[0028] ;

[0029] in, Indicates the vibration combination concentration The vibration combination corresponds to the target vibration combination;

[0030] Extract target vibration combinations from multiple target vibration combinations in sequence and perform the following operations on the extracted target vibration combinations:

[0031] The extracted target vibration combination is input into a simulated vibration table to obtain a target vibration table, and a vibration operation is performed on the target detection locomotive using the target vibration table, wherein the duration of the vibration operation is preset, and a video shooting unit is used to shoot the target vibration table in the vibration operation to obtain a vibration detection video stream.

[0032] Optionally, acquiring a detected vibration displacement set based on the vibration detection video stream includes:

[0033] Based on the target detection locomotive, an initial detection image is obtained, an initial frame number of the vibration detection video stream is obtained, target detection images are sequentially extracted from the vibration detection video stream based on the initial frame number, and the following operations are performed on each of the extracted target detection images:

[0034] Extract target positioning patterns from the target detection locomotive in sequence, and perform the following operations on the extracted target positioning patterns:

[0035] Performing a grayscale operation on the initial detection image to obtain an initial grayscale image, and obtaining an initial detection window based on the initial grayscale image and the extracted target positioning pattern, wherein the initial detection window is a pixel area corresponding to the target positioning pattern in the initial grayscale image;

[0036] Performing a grayscale operation on the extracted target detection image to obtain a target grayscale image, and obtaining a plurality of different first positioning patterns using the initial detection window and the target grayscale image;

[0037] Extracting first positioning patterns sequentially from the multiple different first positioning patterns, and performing the following operations on the extracted first positioning patterns:

[0038] Calculating reference similarities using a pre-constructed similarity evaluation relationship and the extracted first positioning pattern, summarizing the reference similarities to obtain a reference similarity set, identifying a second positioning pattern using the reference similarity set, wherein the second positioning pattern is the first positioning pattern corresponding to the maximum reference similarity in the reference similarity set, and obtaining a detected vibration displacement based on the second positioning pattern;

[0039] The detected vibration displacements are summarized to obtain a detected vibration displacement set.

[0040] Optionally, the similarity evaluation relationship is as follows:

[0041] ;

[0042] in, represents the reference similarity, are all preset coefficients. Represents the grayscale value of the pixel corresponding to the target positioning pattern in the initial grayscale image, Represents the mean grayscale value of the pixel points corresponding to the target positioning pattern in the initial grayscale image, represents the grayscale value of the pixel corresponding to the first positioning pattern, represents the mean grayscale value of the pixel points corresponding to the first positioning pattern, Indicates the first positioning pattern corresponding to the Rank The pixel point or target positioning pattern of the column corresponds to the first Rank Column pixels, Indicates the coordinates of the target detection point corresponding to the target positioning pattern in the reference coordinate system, Represents the coordinates of the pre-built reference point in the reference coordinate system, The pixel area corresponding to the target positioning pattern in the initial grayscale image is OK The pixel area of the column.

[0043] Optionally, acquiring and detecting vibration displacement based on the second positioning pattern includes:

[0044] The central pixel point is retrieved from the second positioning pattern, and the pixel point is used as the displacement pixel point. The displacement coordinates are calculated using the pre-built displacement conversion relationship and the displacement pixel point. The initial detection coordinates are retrieved from the target detection locomotive using the displacement pixel point. The vibration displacement is calculated based on the detection displacement coordinates and the initial detection coordinates. The displacement conversion relationship is as follows:

[0045] ;

[0046] in, Indicates the calibration parameters of the video capture unit, Represents the coordinates of the displaced pixel points, represents the intrinsic parameter matrix of the video capture unit, represents the extrinsic parameter matrix of the video capture unit, Indicates the detection displacement coordinates.

[0047] Optionally, screening the detected vibration displacement set to obtain a target vibration displacement set includes:

[0048] The screening value is calculated using the detected vibration displacement set and the pre-built screening relationship, where the screening relationship is as follows:

[0049] ;

[0050] in, Indicates the detection of vibration displacement concentration The screening value corresponding to the detected vibration displacement, Indicates the detection of vibration displacement concentration A detection vibration displacement, Indicates the detection of vibration displacement concentration A detection vibration displacement, Indicates the detection of vibration displacement concentration Detect vibration displacement;

[0051] Summarizing the screening values to obtain a screening value set, and performing a sorting operation on the screening values in the screening value set in descending order to obtain a screening sequence, wherein the screening values correspond one-to-one to the detected vibration displacements;

[0052] The detected vibration displacements corresponding to the screening values are sequentially extracted from the screening sequence, and the following operations are performed on the extracted detected vibration displacements:

[0053] The extracted detected vibration displacement is eliminated from the detected vibration displacement set to obtain an updated vibration displacement set, and an estimated value is calculated based on the updated vibration displacement set and a pre-constructed estimation relationship, wherein the estimation relationship is as follows:

[0054] ;

[0055] in, Indicates estimated value, represents the coordinates corresponding to the extracted detection vibration displacement, Indicates the update of the vibration displacement concentration Update vibration displacement, Indicates the updated vibration displacement concentration total Update vibration displacement, Indicates the update of the vibration displacement concentration Update the coordinates corresponding to the vibration displacement;

[0056] Obtaining a first evaluation value using the estimated value and the extracted detected vibration displacement, wherein the first evaluation value is an absolute difference between the estimated value and the extracted detected vibration displacement;

[0057] comparing the first evaluation value with a preset evaluation threshold;

[0058] If the first evaluation value is less than or equal to the evaluation threshold, confirming that the detected vibration displacement set is a target vibration displacement set;

[0059] Otherwise, the extracted detected vibration displacement is eliminated from the detected vibration displacement set to obtain a eliminated detected displacement set, the eliminated detected displacement set is used as the detected vibration displacement set, and the step of calculating the screening value using the detected vibration displacement set and the pre-constructed screening relationship is returned until the first evaluation value is less than or equal to the evaluation threshold, thereby obtaining the target vibration displacement set.

[0060] Optionally, acquiring a fitting detection model based on the fitting vibration displacement set, the size parameter set, and the target vibration displacement set includes:

[0061] constructing an initial locomotive model based on the size parameter set, and retrieving a plurality of model detection points in the initial locomotive model according to the position of the target detection point in the target detection locomotive, wherein the model detection points correspond one-to-one to the target detection points;

[0062] According to the position of the target detection point, the fitting vibration displacement in the fitting vibration displacement set is matched with the target vibration displacement in the target vibration displacement set to obtain a plurality of matching displacement groups, wherein the matching displacement groups correspond to the target detection points one by one;

[0063] Matching displacement groups are sequentially extracted from the multiple matching displacement groups, and the following operations are performed on the extracted matching displacement groups:

[0064] If the target vibration displacement does not exist in the extracted matching displacement group, the matching displacement group is proposed as an empty set;

[0065] Summarize the matching displacement groups that are not empty sets to obtain multiple target displacement groups;

[0066] extracting target displacement groups from the plurality of target displacement groups in sequence, and obtaining target vibration values based on the extracted target displacement groups, wherein the target vibration value is a difference between the fitting vibration displacement and the target vibration displacement;

[0067] The fitting vibration displacement, target vibration displacement and target vibration value corresponding to the target displacement group are used to perform a marking operation on the model detection points corresponding to the target displacement group to obtain a fitting detection model.

[0068] To achieve the above object, the present invention further provides a heavy-load electric locomotive vibration measurement system based on machine vision, comprising:

[0069] a vibration test environment confirmation module, configured to receive a vibration test instruction and, based on the vibration test instruction, confirm a vibration test environment for the vibration test, wherein the vibration test environment includes: a heavy-load electric locomotive, a simulated vibration table, and a vibration measurement system, wherein the vibration measurement system includes: a video capture unit and a vibration feedback unit;

[0070] a vibration test preparation module, configured to obtain a vibration function set and a velocity gradient set for simulating vibration, wherein the vibration function set includes a plurality of vibration functions, and the velocity gradient set includes a plurality of reference velocities, and to obtain a vibration combination set based on the vibration function set and the velocity gradient set;

[0071] A reference coordinate system is constructed, and a target detection locomotive is obtained using the reference coordinate system and the heavy-load electric locomotive, wherein the target detection locomotive includes a plurality of target detection points;

[0072] a vibration testing module, configured to obtain a vibration detection video stream based on the vibration combination set, the target detection locomotive, the simulated vibration platform, and the video capture unit, and to obtain a detected vibration displacement set based on the vibration detection video stream, wherein the detected vibration displacement set includes a plurality of detected vibration displacements;

[0073] screening the detected vibration displacement set to obtain a target vibration displacement set;

[0074] a vibration test analysis module, configured to obtain a fitted vibration displacement set of a target detection locomotive, wherein the fitted vibration displacement set includes a plurality of fitted vibration displacements, and the fitted vibration displacements correspond one-to-one to the target detection points;

[0075] A size parameter set of a target detection locomotive is obtained, a fitting detection model is obtained based on the fitting vibration displacement set, the size parameter set and the target vibration displacement set, and the fitting detection model is sent to an initiator of a vibration test instruction using a vibration feedback unit to realize vibration measurement of a heavy-load electric locomotive.

[0076] In order to solve the above problem, the present invention further provides an electronic device, comprising:

[0077] a memory storing at least one instruction; and

[0078] The processor executes the instructions stored in the memory to implement the above-mentioned heavy-load electric locomotive vibration measurement method based on machine vision.

[0079] In order to solve the above problems, the present invention also provides a computer-readable storage medium, which stores at least one instruction. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned machine vision-based heavy-load electric locomotive vibration measurement method.

[0080] In order to solve the problems described in the background technology, the present invention obtains a vibration function set and a velocity gradient set for simulating vibration, wherein the vibration function set includes multiple vibration functions, and the velocity gradient set includes multiple reference speeds. A vibration combination set is obtained based on the vibration function set and the velocity gradient set. It can be seen that the embodiment of the present invention takes into account that different speeds and different vibration conditions will cause heavy-loaded electric locomotives to produce different vibrations. Therefore, the form of a vibration combination set is adopted to analyze the situation in which different reference speeds cause vibrations of heavy-loaded electric locomotives under the same vibration function, thereby improving the intelligence level of vibration measurement of heavy-loaded electric locomotives. The present invention obtains a vibration detection video stream based on the vibration combination set, the target detection locomotive, the simulated vibration table, and the video capture unit, and obtains a detected vibration displacement set based on the vibration detection video stream, wherein the detected vibration displacement set includes multiple detected vibration displacements. It can be seen that the present invention not only considers the different vibrations that may exist at different target detection points, but also obtains the vibration conditions of the heavy-load electric locomotive at different times, different reference speeds, different vibration functions, or different target detection points in the form of a vibration detection video stream. In addition, during vibration detection, not only does it consider the difference in the color of the heavy-load electric locomotive itself, but it also formulates different target positioning patterns at different target detection points. In addition, when obtaining the second positioning pattern, coordinate elements are incorporated to improve the accuracy of obtaining the second positioning pattern, thereby improving the accuracy of the vibration measurement of the heavy-load electric locomotive. The present invention filters the detected vibration displacement set to obtain a target vibration displacement set. It can be seen that the present invention, while being able to accurately obtain the second positioning image, also considers the problem of inaccurate detected vibration displacement due to image recognition. Furthermore, it further filters the detected vibration displacement set to obtain a target vibration displacement set to improve the accuracy of the vibration measurement of the heavy-load electric locomotive. The present invention obtains a set of dimensional parameters for the target detection locomotive, obtains a fitting detection model based on the fitting vibration displacement set, the dimensional parameter set, and the target vibration displacement set, and uses a vibration feedback unit to send the fitting detection model to the initiator of the vibration test instruction to achieve vibration measurement of the heavy-load electric locomotive. It can be seen that the present invention also uses the target vibration displacement set to construct vibration-related parameters for characterizing the heavy-load electric locomotive at different target detection points, thereby improving the intelligent level of vibration measurement for heavy-load electric locomotives. Therefore, the present invention can improve the accuracy and intelligent level of vibration measurement for heavy-load electric locomotives. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 A schematic flow chart of a method for measuring vibration of a heavy-load electric locomotive based on machine vision according to an embodiment of the present invention;

[0082] Figure 2 A functional module diagram of a machine vision-based heavy-load electric locomotive vibration measurement system provided by one embodiment of the present invention;

[0083] Figure 3 A schematic structural diagram of an electronic device for implementing the machine vision-based heavy-load electric locomotive vibration measurement method provided in one embodiment of the present invention.

[0084] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0085] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0086] The embodiment of the present application provides a method for measuring the vibration of a heavy-load electric locomotive based on machine vision. The execution subject of the method for measuring the vibration of a heavy-load electric locomotive based on machine vision includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for measuring the vibration of a heavy-load electric locomotive based on machine vision can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0087] Reference Figure 1 FIG. 1 is a flow chart of a method for measuring vibration of a heavy-load electric locomotive based on machine vision according to an embodiment of the present invention. In this embodiment, the method for measuring vibration of a heavy-load electric locomotive based on machine vision includes:

[0088] S1. Receive a vibration test instruction, and determine a vibration test environment for the vibration test based on the vibration test instruction, wherein the vibration test environment includes: a heavy-load electric locomotive, a simulated vibration table, and a vibration measurement system, wherein the vibration measurement system includes: a video capture unit and a vibration feedback unit.

[0089] It should be explained that the vibration test instruction refers to the instruction issued by the heavy-load electric locomotive tester to the heavy-load electric locomotive that is to undergo vibration testing. The vibration test system refers to an APP or software for vibration detection and analysis of heavy-load electric locomotives, and the vibration measurement system includes: a video shooting unit and a vibration feedback unit. For the application of specific units, please refer to the subsequent embodiments. Optionally, the simulated vibration table is a rolling vibration test table. The same effect can be achieved by using other technologies, which will not be repeated here.

[0090] For example, in order to test the vibration resistance performance of a certain model of heavy-load electric locomotive at different speeds and different vibration modes, the tester of the heavy-load electric locomotive issues the vibration test instruction to the heavy-load electric locomotive and confirms the vibration test environment based on the vibration test instruction.

[0091] S2. Obtain a vibration function set and a velocity gradient set for simulating vibration, wherein the vibration function set includes multiple vibration functions, and the velocity gradient set includes multiple reference speeds, and obtain a vibration combination set based on the vibration function set and the velocity gradient set.

[0092] It is understood that the vibration function is a function used to simulate the vibration conditions that a heavy-load electric locomotive may encounter in real life. The reference speed is the speed at which a heavy-load electric locomotive may actually operate in real life. Optionally, a simple harmonic vibration function is selected as the vibration function, wherein the simple harmonic vibration function is as follows:

[0093] ;

[0094] in, represents the simple harmonic oscillation function, represents the amplitude, represents the phase angle, Indicates time, Indicates the angular frequency.

[0095] It should be explained that the obtaining of the vibration combination set based on the vibration function set and the velocity gradient set includes:

[0096] Extract vibration functions from the vibration function set in sequence, and perform the following operations on the extracted vibration functions:

[0097] The extracted vibration function is matched with the velocity gradient set to obtain a vibration combination, and the vibration combinations are summarized to obtain a vibration combination set, wherein the vibration combination is as follows:

[0098] ;

[0099] in, Indicates the vibration combination concentration A vibration combination, Represents the first A vibration function, represents the velocity gradient set, They represent the first reference speed, the second reference speed, and the The reference speed and A reference speed, Indicates that the velocity gradient is concentrated A reference speed.

[0100] It should be understood that under the same vibration function, the vibration amount generated by a heavy-load electric locomotive may vary depending on the reference speed. Therefore, by matching the vibration function with the velocity gradient set, a vibration combination is obtained for vibration simulation. This vibration combination can characterize the impact of different reference speeds on the vibration of the heavy-load electric locomotive under the same vibration function. Please refer to the subsequent examples for specific characterization methods.

[0101] S3. Construct a reference coordinate system, and use the reference coordinate system and the heavy-load electric locomotive to obtain a target detection locomotive, wherein the target detection locomotive includes multiple target detection points.

[0102] It is understood that the target detection point refers to the point where the vibration change of the heavy-load electric locomotive is observed. Optionally, the world coordinate system is selected as the reference coordinate system. Other technologies can achieve the same effect and will not be described in detail here.

[0103] It should be explained that the method of using a reference coordinate system and a heavy-load electric locomotive to obtain a target detection locomotive includes:

[0104] identifying a detection area in the heavy-load electric locomotive, and acquiring a plurality of target detection points based on the reference coordinate system and the detection area;

[0105] Extract target detection points from the multiple target detection points in sequence, and perform the following operations on the extracted target detection points:

[0106] Obtaining a detection grayscale value based on the extracted target detection point, using the detection grayscale value as the starting point of a pre-constructed region growing algorithm, and using the region growing algorithm to obtain a target growth region, wherein the grayscale values of the pixels corresponding to the target growth region are all the same;

[0107] In a detection area corresponding to the target growth area, a target positioning pattern is identified based on the detected grayscale value, wherein the target positioning pattern is a square checkerboard pattern having a preset first grayscale value and a preset second grayscale value alternating therebetween, and both the first grayscale value and the second grayscale value are different from the detected grayscale value, and a side length of the square checkerboard pattern is preset to be (2q+1), where q is an integer greater than or equal to 1;

[0108] Fix the center of the target positioning pattern to the target detection point to obtain the target detection locomotive.

[0109] It should be noted that the detection area refers to the area used to observe the vibration changes of heavy-loaded electric locomotives, and the selection of the detection area needs to be combined with the specific vibration function. For example: the selected vibration function is used to characterize the vibration of the heavy-loaded electric locomotive in the up and down directions, then the area that cannot characterize the heavy-loaded electric locomotive perpendicular to the up and down directions should not be selected as the detection area. The target detection point refers to the point used to observe the vibration changes of the heavy-loaded electric locomotive. For example: the figure corresponding to the detection area is a 3 by 5 rectangle, and the detection area is divided into 3 equal points and 5 equal points respectively, and the intersection of the divisions is used as the target detection point, then 24 target detection points are obtained. The detection gray value refers to the gray value of the image corresponding to the target detection point in the heavy-loaded electric locomotive. The purpose of the region growing algorithm in the embodiment of the present invention is to identify the target growth area composed of pixel points with the same detection gray value, and formulate different target positioning patterns according to different target growth areas, which can improve the accuracy of positioning the target detection point in the image of vibration changes. The purpose of presetting the first and second grayscale values is to distinguish between the target positioning pattern and the detection area corresponding to the target production area, thereby improving the accuracy of identifying the target positioning pattern at the target detection point, and further improving the accuracy of vibration measurement of heavy-load electric locomotives. The region growing algorithm described above is existing technology and will not be described in detail here.

[0110] It is understandable that if the grayscale value of the selected target positioning pattern is slightly different from the grayscale value of the target growth area, it may be difficult to identify the target positioning pattern at the target detection point. The side length unit of the square checkerboard pattern can be set according to the size of the target growth area, which will not be further explained here.

[0111] For example, before conducting a vibration test on a heavy-load electric locomotive, a tester selects a surface perpendicular to the vibration test direction as the detection area, and evenly distributes multiple target detection points within the detection area. For ease of understanding, the target positioning pattern is represented herein in coordinate form, where the target positioning pattern is a square area enclosed by vertices (0, 0), (0, 5), (5, 0), and (5, 5), with the center of the square chessboard image at (3, 3).

[0112] S4. Obtain a vibration detection video stream based on the vibration combination set, the target detection locomotive, the simulated vibration platform, and the video shooting unit; and obtain a detected vibration displacement set based on the vibration detection video stream, wherein the detected vibration displacement set includes multiple detected vibration displacements.

[0113] It should be explained that the step of obtaining a vibration detection video stream based on the vibration combination set, the target detection locomotive, the simulated vibration platform, and the video capture unit includes:

[0114] Extract vibration combinations from the vibration combination set in sequence, and perform the following operations on the extracted vibration combinations:

[0115] A plurality of target vibration combinations are obtained based on the extracted vibration combinations, wherein the target vibration combinations are as follows:

[0116] ;

[0117] in, Indicates the vibration combination concentration The vibration combination corresponds to the target vibration combination;

[0118] Extract target vibration combinations from multiple target vibration combinations in sequence and perform the following operations on the extracted target vibration combinations:

[0119] The extracted target vibration combination is input into a simulated vibration table to obtain a target vibration table, and a vibration operation is performed on the target detection locomotive using the target vibration table, wherein the duration of the vibration operation is preset, and a video shooting unit is used to shoot the target vibration table in the vibration operation to obtain a vibration detection video stream.

[0120] It is understandable that the multiple target vibration combinations obtained in the vibration combination can characterize the effects of different speeds on the heavy-loaded electric locomotive under the same vibration function. Vibration operation refers to the operation of vibrating the heavy-loaded electric locomotive using the vibration that can be generated by the simulated vibration table under the extracted vibration function. Duration refers to the duration of the vibration operation, and the duration should be greater than or equal to the period of the vibration function. This is to facilitate the complete observation of the vibration conditions of the heavy-loaded electric locomotive throughout the entire cycle. The vibration detection video stream refers to the video of the detection area captured by the video shooting unit during the vibration process. Optionally, a camera is used as the video shooting unit, and other technologies can achieve the same effect, which will not be repeated here.

[0121] Furthermore, the acquiring of the detected vibration displacement set based on the vibration detection video stream includes:

[0122] Based on the target detection locomotive, an initial detection image is obtained, an initial frame number of the vibration detection video stream is obtained, target detection images are sequentially extracted from the vibration detection video stream based on the initial frame number, and the following operations are performed on each of the extracted target detection images:

[0123] Extract target positioning patterns from the target detection locomotive in sequence, and perform the following operations on the extracted target positioning patterns:

[0124] Performing a grayscale operation on the initial detection image to obtain an initial grayscale image, and obtaining an initial detection window based on the initial grayscale image and the extracted target positioning pattern, wherein the initial detection window is a pixel area corresponding to the target positioning pattern in the initial grayscale image;

[0125] Performing a grayscale operation on the extracted target detection image to obtain a target grayscale image, and obtaining a plurality of different first positioning patterns using the initial detection window and the target grayscale image;

[0126] Extracting first positioning patterns sequentially from the multiple different first positioning patterns, and performing the following operations on the extracted first positioning patterns:

[0127] Calculating reference similarities using a pre-constructed similarity evaluation relationship and the extracted first positioning pattern, summarizing the reference similarities to obtain a reference similarity set, identifying a second positioning pattern using the reference similarity set, wherein the second positioning pattern is the first positioning pattern corresponding to the maximum reference similarity in the reference similarity set, and obtaining a detected vibration displacement based on the second positioning pattern;

[0128] The detected vibration displacements are summarized to obtain a detected vibration displacement set.

[0129] It should be noted that the initial detection image refers to the image of the target detection locomotive before it vibrates. The initial frame number refers to the frame number of the vibration detection video stream. The target detection image refers to the image extracted from the vibration detection video stream using the initial frame number. The grayscale operation refers to the operation of converting a color image into a grayscale image. The technology for grayscale operation is well-known and will not be further described here.

[0130] It should be understood that the purpose of obtaining the initial detection window in the initial grayscale image is to retrieve a pixel area in the target grayscale image that is similar to or even identical to the target positioning pattern, so as to obtain the displacement of the pixel points generated by the target detection points corresponding to the target positioning pattern through the target grayscale image and the initial grayscale image, and then convert the displacement into a vibration value.

[0131] It is understandable that the similarity evaluation relationship is as follows:

[0132] ;

[0133] in, represents the reference similarity, are all preset coefficients. Represents the grayscale value of the pixel corresponding to the target positioning pattern in the initial grayscale image, Represents the mean grayscale value of the pixel points corresponding to the target positioning pattern in the initial grayscale image, represents the grayscale value of the pixel corresponding to the first positioning pattern, represents the mean grayscale value of the pixel points corresponding to the first positioning pattern, Indicates the first positioning pattern corresponding to the Rank The pixel point or target positioning pattern of the column corresponds to the first Rank Column pixels, Indicates the coordinates of the target detection point corresponding to the target positioning pattern in the reference coordinate system, Represents the coordinates of the pre-built reference point in the reference coordinate system, The pixel area corresponding to the target positioning pattern in the initial grayscale image is OK The pixel area of the column.

[0134] It should be explained that if, under a certain vibration function, the target vibration platform actually generates little vibration on the target detection locomotive, or is unable to generate vibration on the target detection locomotive, then the reference similarity corresponding to the second positioning pattern is maximized. Alternatively, a laser generator can be used to monitor the vibrating target detection locomotive in real time. At the same time as the first positioning pattern, the laser generator can be used to obtain the displacement of the target detection locomotive during vibration. The coordinates of the reference point in the reference coordinate system are calculated based on this displacement. Other techniques can achieve the same effect and are not further elaborated here. For example, if the line connecting the laser generator and the reference point is parallel to the vertical axis of the reference coordinate system, and the distance between the laser generator and the reference point is 50 cm when not vibrating, and the coordinates of the reference point in the reference coordinate system are (10, 20, 100), and at a certain moment, the distance between the laser generator and the reference point is detected to be 49 cm, then the coordinates of the reference point in the reference coordinate system are (10, 20, 51). Optionally, the laser generator is a fixed laser generator, and other technologies can achieve the same effect, which will not be described here.

[0135] Furthermore, the coordinates of the reference point in the reference coordinate system can only represent the vibration displacement of the target locomotive as a whole, and cannot express the vibration conditions at a specific location on the target locomotive. For example, a shock absorber failure at a specific location on the target locomotive may result in a larger displacement value at that location under the same vibration conditions.

[0136] It should be understood that, generally, there are a large number of areas with consistent grayscale values in the target detection locomotive, and the target detection image in the vibration detection video stream is easily affected by environmental interference, which may result in inaccurate matching results when the extracted target positioning pattern is used to match the first positioning pattern. For example, because the target positioning patterns have similar grayscale values and are affected by the environment, the extracted target positioning pattern can be used to match multiple different first positioning patterns. Therefore, the similarity evaluation relationship formulated by the present invention also takes into account changes in overall vibration, thereby improving the accuracy of matching the first positioning pattern using the extracted target positioning pattern.

[0137] Furthermore, acquiring and detecting the vibration displacement based on the second positioning pattern includes:

[0138] The central pixel point is retrieved from the second positioning pattern, and the pixel point is used as the displacement pixel point. The displacement coordinates are calculated using the pre-built displacement conversion relationship and the displacement pixel point. The initial detection coordinates are retrieved from the target detection locomotive using the displacement pixel point. The vibration displacement is calculated based on the detection displacement coordinates and the initial detection coordinates. The displacement conversion relationship is as follows:

[0139] ;

[0140] in, Indicates the calibration parameters of the video capture unit, Represents the coordinates of the displaced pixel points, represents the intrinsic parameter matrix of the video capture unit, represents the extrinsic parameter matrix of the video capture unit, Indicates the detection displacement coordinates.

[0141] It can be understood that, in theory, the pixel point in the center of the second positioning pattern is the pixel point corresponding to the target detection point. Therefore, by converting the displaced pixel point into the detection displacement coordinate, the detection vibration displacement can be calculated by detecting the displacement coordinate and the detection initial coordinate, wherein the detection initial coordinate is the coordinate of the target detection point in the reference coordinate system. Optionally, the distance between the detection initial coordinate and the detection displacement coordinate is calculated using the distance formula between the two points, and this distance is used as the detection vibration displacement.

[0142] S5. Filter the detected vibration displacement set to obtain a target vibration displacement set.

[0143] It is understandable that the screening of the detected vibration displacement set to obtain the target vibration displacement set includes:

[0144] The screening value is calculated using the detected vibration displacement set and the pre-built screening relationship, where the screening relationship is as follows:

[0145] ;

[0146] in, Indicates the detection of vibration displacement concentration The screening value corresponding to the detected vibration displacement, Indicates the detection of vibration displacement concentration A detection vibration displacement, Indicates the detection of vibration displacement concentration A detection vibration displacement, Indicates the detection of vibration displacement concentration Detect vibration displacement;

[0147] Summarizing the screening values to obtain a screening value set, and performing a sorting operation on the screening values in the screening value set in descending order to obtain a screening sequence, wherein the screening values correspond one-to-one to the detected vibration displacements;

[0148] The detected vibration displacements corresponding to the screening values are sequentially extracted from the screening sequence, and the following operations are performed on the extracted detected vibration displacements:

[0149] The extracted detected vibration displacement is eliminated from the detected vibration displacement set to obtain an updated vibration displacement set, and an estimated value is calculated based on the updated vibration displacement set and a pre-constructed estimation relationship, wherein the estimation relationship is as follows:

[0150] ;

[0151] in, Indicates estimated value, represents the coordinates corresponding to the extracted detection vibration displacement, Indicates the update of the vibration displacement concentration Update vibration displacement, Indicates the updated vibration displacement concentration total Update vibration displacement, Indicates the update of the vibration displacement concentration Update the coordinates corresponding to the vibration displacement;

[0152] Obtaining a first evaluation value using the estimated value and the extracted detected vibration displacement, wherein the first evaluation value is an absolute difference between the estimated value and the extracted detected vibration displacement;

[0153] comparing the first evaluation value with a preset evaluation threshold;

[0154] If the first evaluation value is less than or equal to the evaluation threshold, confirming that the detected vibration displacement set is a target vibration displacement set;

[0155] Otherwise, the extracted detected vibration displacement is eliminated from the detected vibration displacement set to obtain a eliminated detected displacement set, the eliminated detected displacement set is used as the detected vibration displacement set, and the step of calculating the screening value using the detected vibration displacement set and the pre-constructed screening relationship is returned until the first evaluation value is less than or equal to the evaluation threshold, thereby obtaining the target vibration displacement set.

[0156] It should be understood that, ideally, the detected vibration displacements in the detected vibration displacement set corresponding to the target detection locomotive should be roughly the same. The screening value in the screening sequence can express the degree of deviation of the detected vibration displacement from the overall situation, and the screening value in the screening sequence corresponds one-to-one to the detected vibration displacement. The estimated value refers to the value of the detected vibration displacement in the updated vibration displacement set that estimates the extracted detected vibration displacement, and when estimating, the distance factor is taken into account. The farther the distance between the detected vibration displacement in the updated vibration displacement set and the extracted detected vibration displacement, the smaller the influence of the detected vibration displacement in the updated vibration displacement set on the extracted detected vibration displacement. When the first evaluation value is less than or equal to the evaluation threshold, it indicates that the extracted detected vibration displacement is equivalent to the estimated detected vibration displacement, indicating that there is no abnormal detected vibration displacement in the detected vibration displacement set, that is, the matched second positioning patterns are all expected second positioning patterns.

[0157] S6. Obtain a fitting vibration displacement set of the target detection locomotive, wherein the fitting vibration displacement set includes a plurality of fitting vibration displacements, and the fitting vibration displacements correspond one-to-one to the target detection points.

[0158] It should be explained that a fitted vibration displacement set refers to the set of vibration values that can be generated by the target vibration combination under ideal conditions, using the target vibration combination and the time corresponding to the extracted target detection image. For example, if the vibration value generated by the target vibration combination under ideal conditions is the amplitude of a simple harmonic vibration function, then the fitted vibration displacement set includes multiple fitted vibration displacements that correspond one-to-one to the target detection points, and the values of these fitted vibration displacements are amplitudes.

[0159] S7. Obtain a size parameter set of the target detection locomotive, obtain a fitting detection model based on the fitting vibration displacement set, the size parameter set and the target vibration displacement set, and use a vibration feedback unit to send the fitting detection model to the initiator of the vibration test instruction to realize vibration measurement of the heavy-load electric locomotive.

[0160] It is understood that in the embodiment of the present invention, the size parameter set refers to the size parameters of the surface of the target detection vehicle, for example, the length, width and height parameters of the target detection vehicle.

[0161] It should be explained that the step of obtaining the fitting detection model based on the fitting vibration displacement set, the size parameter set, and the target vibration displacement set includes:

[0162] constructing an initial locomotive model based on the size parameter set, and retrieving a plurality of model detection points in the initial locomotive model according to the position of the target detection point in the target detection locomotive, wherein the model detection points correspond one-to-one to the target detection points;

[0163] According to the position of the target detection point, the fitting vibration displacement in the fitting vibration displacement set is matched with the target vibration displacement in the target vibration displacement set to obtain a plurality of matching displacement groups, wherein the matching displacement groups correspond to the target detection points one by one;

[0164] Matching displacement groups are sequentially extracted from the multiple matching displacement groups, and the following operations are performed on the extracted matching displacement groups:

[0165] If there is any non-target vibration displacement in the extracted matching displacement group, the matching displacement group is proposed as an empty set;

[0166] Summarize the matching displacement groups that are not empty sets to obtain multiple target displacement groups;

[0167] extracting target displacement groups from the plurality of target displacement groups in sequence, and obtaining target vibration values based on the extracted target displacement groups, wherein the target vibration value is a difference between the fitting vibration displacement and the target vibration displacement;

[0168] The fitting vibration displacement, target vibration displacement and target vibration value corresponding to the target displacement group are used to perform a marking operation on the model detection points corresponding to the target displacement group to obtain a fitting detection model.

[0169] It should be explained that when the initial locomotive model is constructed using 3D modeling software, the dimensional parameters in the dimensional parameter set are proportionally enlarged or reduced. Therefore, the point used to represent the target detection point can be retrieved in the initial locomotive model through the position of the target detection point in the target detection machine. This point is the model detection point. Optionally, the model detection point can be retrieved in the initial locomotive model using the coordinates corresponding to the target detection point. Other technologies can achieve the same effect and will not be repeated here. Optionally, the initial locomotive model can be constructed using SolidWorks and the dimensional parameter set. Other technologies can achieve the same effect and will not be repeated here.

[0170] It should be understood that because model detection points correspond one-to-one with target detection points, they can be matched. Ideally, the matched fitted vibration displacements should correspond one-to-one with the target vibration displacements, and ideally, the matched fitted vibration displacements and target vibration displacements constitute the target displacement group. Since the acquired target vibration displacement set does not necessarily contain a target vibration displacement at the coordinates corresponding to each target detection point, when a matched displacement group lacks a target vibration displacement, the matched displacement group is meaningless. Therefore, the matched displacement group is set to an empty set, and a matched displacement group containing both the target vibration displacement and the fitted vibration displacement is taken as the target displacement group. The purpose of labeling the model detection points corresponding to the target displacement group with the fitted vibration displacement, target vibration displacement, and target vibration value corresponding to the target displacement group is to intuitively reflect the vibration conditions in the target detection locomotive. For example, the model detection points corresponding to the target displacement group are annotated with the fitted vibration displacement, target vibration displacement, and target vibration value to achieve this labeling operation. Other techniques can achieve the same effect, and will not be elaborated on here.

[0171] In order to solve the problems described in the background technology, the present invention obtains a vibration function set and a velocity gradient set for simulating vibration, wherein the vibration function set includes multiple vibration functions, and the velocity gradient set includes multiple reference speeds. A vibration combination set is obtained based on the vibration function set and the velocity gradient set. It can be seen that the embodiment of the present invention takes into account that different speeds and different vibration conditions will cause heavy-loaded electric locomotives to produce different vibrations. Therefore, the form of a vibration combination set is adopted to analyze the situation in which different reference speeds cause vibrations of heavy-loaded electric locomotives under the same vibration function, thereby improving the intelligence level of vibration measurement of heavy-loaded electric locomotives. The present invention obtains a vibration detection video stream based on the vibration combination set, the target detection locomotive, the simulated vibration table, and the video capture unit, and obtains a detected vibration displacement set based on the vibration detection video stream, wherein the detected vibration displacement set includes multiple detected vibration displacements. It can be seen that the present invention not only considers the different vibrations that may exist at different target detection points, but also obtains the vibration conditions of the heavy-load electric locomotive at different times, different reference speeds, different vibration functions, or different target detection points in the form of a vibration detection video stream. In addition, during vibration detection, not only does it consider the difference in the color of the heavy-load electric locomotive itself, but it also formulates different target positioning patterns at different target detection points. In addition, when obtaining the second positioning pattern, coordinate elements are incorporated to improve the accuracy of obtaining the second positioning pattern, thereby improving the accuracy of the vibration measurement of the heavy-load electric locomotive. The present invention filters the detected vibration displacement set to obtain a target vibration displacement set. It can be seen that the present invention, while being able to accurately obtain the second positioning image, also considers the problem of inaccurate detected vibration displacement due to image recognition. Furthermore, it further filters the detected vibration displacement set to obtain a target vibration displacement set to improve the accuracy of the vibration measurement of the heavy-load electric locomotive. The present invention obtains a set of dimensional parameters for the target detection locomotive, obtains a fitting detection model based on the fitting vibration displacement set, the dimensional parameter set, and the target vibration displacement set, and uses a vibration feedback unit to send the fitting detection model to the initiator of the vibration test instruction to achieve vibration measurement of the heavy-load electric locomotive. It can be seen that the present invention also uses the target vibration displacement set to construct vibration-related parameters for characterizing the heavy-load electric locomotive at different target detection points, thereby improving the intelligent level of vibration measurement for heavy-load electric locomotives. Therefore, the present invention can improve the accuracy and intelligent level of vibration measurement for heavy-load electric locomotives.

[0172] like Figure 2 , which is a functional module diagram of a heavy-load electric locomotive vibration measurement system based on machine vision provided by one embodiment of the present invention.

[0173] The machine vision-based vibration measurement system 100 for heavy-duty electric locomotives described in the present invention can be installed in an electronic device. Depending on the functionality implemented, the machine vision-based vibration measurement system 100 for heavy-duty electric locomotives can include a vibration test environment confirmation module 101, a vibration test preparation module 102, a vibration test module 103, and a vibration test analysis module 104. A module, also referred to as a unit, refers to a series of computer program segments that can be executed by an electronic device processor and perform a fixed function. These modules are stored in the electronic device's memory.

[0174] The vibration test environment confirmation module 101 is used to receive a vibration test instruction and confirm a vibration test environment for the vibration test based on the vibration test instruction, wherein the vibration test environment includes: a heavy-load electric locomotive, a simulated vibration table and a vibration measurement system, wherein the vibration measurement system includes: a video capture unit and a vibration feedback unit;

[0175] The vibration test preparation module 102 is configured to obtain a vibration function set and a velocity gradient set for simulating vibration, wherein the vibration function set includes a plurality of vibration functions, and the velocity gradient set includes a plurality of reference velocities, and to obtain a vibration combination set based on the vibration function set and the velocity gradient set;

[0176] A reference coordinate system is constructed, and a target detection locomotive is obtained using the reference coordinate system and the heavy-load electric locomotive, wherein the target detection locomotive includes a plurality of target detection points;

[0177] The vibration testing module 103 is configured to obtain a vibration detection video stream based on the vibration combination set, the target detection locomotive, the simulated vibration platform, and the video capture unit, and obtain a detected vibration displacement set based on the vibration detection video stream, wherein the detected vibration displacement set includes multiple detected vibration displacements;

[0178] screening the detected vibration displacement set to obtain a target vibration displacement set;

[0179] The vibration test analysis module 104 is configured to obtain a fitted vibration displacement set of the target detection locomotive, wherein the fitted vibration displacement set includes a plurality of fitted vibration displacements, and the fitted vibration displacements correspond one-to-one to the target detection points;

[0180] A size parameter set of a target detection locomotive is obtained, a fitting detection model is obtained based on the fitting vibration displacement set, the size parameter set and the target vibration displacement set, and the fitting detection model is sent to an initiator of a vibration test instruction using a vibration feedback unit to realize vibration measurement of a heavy-load electric locomotive.

[0181] In detail, the modules in the heavy-duty electric locomotive vibration measurement system 100 based on machine vision in the embodiment of the present invention are used in the same manner as above. Figure 1 The same technical means are used as the heavy-load electric locomotive vibration measurement method based on machine vision described in , and can produce the same technical effects, so they will not be repeated here.

[0182] like Figure 3 FIG. 1 is a schematic diagram of the structure of an electronic device for implementing a vibration measurement method for a heavy-load electric locomotive based on machine vision according to an embodiment of the present invention.

[0183] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a program for a vibration measurement method for a heavy-load electric locomotive based on machine vision.

[0184] The memory 11 includes at least one type of readable storage medium, including flash memory, a removable hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in removable hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the memory 11 includes both the internal storage unit of the electronic device 1 and an external storage device. The memory 11 can be used not only to store application software installed in the electronic device 1 and various data, such as the code of a machine vision-based heavy-load electric locomotive vibration measurement method program, but also to temporarily store data that has been output or is about to be output.

[0185] In some embodiments, the processor 10 may be comprised of an integrated circuit, such as a single packaged integrated circuit or a combination of multiple packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control core (control unit) of the electronic device, connecting the various components of the electronic device using various interfaces and circuits. It executes programs or modules stored in the memory 11 (e.g., a machine vision-based vibration measurement method program for heavy-load electric locomotives) and accesses data stored in the memory 11 to perform various functions and process data.

[0186] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0187] Figure 3 Only the electronic device with components is shown, and it can be understood by those skilled in the art that Figure 3 The structure shown does not constitute a limitation on the electronic device 1 , and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0188] For example, although not shown, the electronic device 1 may further include a power supply (e.g., a battery) to power various components. Preferably, the power supply may be logically connected to the at least one processor 10 via a power management device, thereby enabling functions such as charge management, discharge management, and power consumption management via the power management device. The power supply may further include any components such as one or more DC or AC power supplies, a recharging device, a power failure detection circuit, a power converter or inverter, and a power status indicator. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which are not further described here.

[0189] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0190] Optionally, the electronic device 1 may further include a user interface, which may be a display or an input unit (such as a keyboard). Optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display may also be appropriately referred to as a display screen or a display unit, and is used to display information processed by the electronic device 1 and to display a visual user interface.

[0191] The program of the heavy-load electric locomotive vibration measurement method based on machine vision stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve the following:

[0192] receiving a vibration test instruction, and determining a vibration test environment for the vibration test based on the vibration test instruction, wherein the vibration test environment includes: a heavy-load electric locomotive, a simulated vibration table, and a vibration measurement system, wherein the vibration measurement system includes: a video capture unit and a vibration feedback unit;

[0193] Acquire a vibration function set and a velocity gradient set for simulating vibration, wherein the vibration function set includes a plurality of vibration functions, and the velocity gradient set includes a plurality of reference velocities, and acquire a vibration combination set based on the vibration function set and the velocity gradient set;

[0194] A reference coordinate system is constructed, and a target detection locomotive is obtained using the reference coordinate system and the heavy-load electric locomotive, wherein the target detection locomotive includes a plurality of target detection points;

[0195] Acquire a vibration detection video stream based on the vibration combination set, the target detection locomotive, the simulated vibration platform, and the video capture unit; and acquire a detected vibration displacement set based on the vibration detection video stream, wherein the detected vibration displacement set includes multiple detected vibration displacements;

[0196] screening the detected vibration displacement set to obtain a target vibration displacement set;

[0197] Acquire a fitted vibration displacement set of the target detection locomotive, wherein the fitted vibration displacement set includes a plurality of fitted vibration displacements, and the fitted vibration displacements correspond one-to-one to the target detection points;

[0198] A size parameter set of a target detection locomotive is obtained, a fitting detection model is obtained based on the fitting vibration displacement set, the size parameter set and the target vibration displacement set, and the fitting detection model is sent to an initiator of a vibration test instruction using a vibration feedback unit to realize vibration measurement of a heavy-load electric locomotive.

[0199] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0200] Furthermore, if the modules / units integrated into the electronic device 1 are implemented as software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium. The computer-readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0201] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by a processor of an electronic device, the computer program can implement:

[0202] receiving a vibration test instruction, and determining a vibration test environment for the vibration test based on the vibration test instruction, wherein the vibration test environment includes: a heavy-load electric locomotive, a simulated vibration table, and a vibration measurement system, wherein the vibration measurement system includes: a video capture unit and a vibration feedback unit;

[0203] Acquire a vibration function set and a velocity gradient set for simulating vibration, wherein the vibration function set includes a plurality of vibration functions, and the velocity gradient set includes a plurality of reference velocities, and acquire a vibration combination set based on the vibration function set and the velocity gradient set;

[0204] A reference coordinate system is constructed, and a target detection locomotive is obtained using the reference coordinate system and the heavy-load electric locomotive, wherein the target detection locomotive includes a plurality of target detection points;

[0205] Acquire a vibration detection video stream based on the vibration combination set, the target detection locomotive, the simulated vibration platform, and the video capture unit; and acquire a detected vibration displacement set based on the vibration detection video stream, wherein the detected vibration displacement set includes multiple detected vibration displacements;

[0206] screening the detected vibration displacement set to obtain a target vibration displacement set;

[0207] Acquire a fitted vibration displacement set of the target detection locomotive, wherein the fitted vibration displacement set includes a plurality of fitted vibration displacements, and the fitted vibration displacements correspond one-to-one to the target detection points;

[0208] A size parameter set of a target detection locomotive is obtained, a fitting detection model is obtained based on the fitting vibration displacement set, the size parameter set and the target vibration displacement set, and the fitting detection model is sent to an initiator of a vibration test instruction using a vibration feedback unit to realize vibration measurement of a heavy-load electric locomotive.

[0209] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only exemplary, and actual implementations may have other division methods.

[0210] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0211] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0212] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0213] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A vibration measurement method for heavy-load electric locomotive based on machine vision, characterized in that: The method comprises: receiving a vibration test instruction, and determining a vibration test environment for the vibration test based on the vibration test instruction, wherein the vibration test environment includes: a heavy-load electric locomotive, a simulated vibration table, and a vibration measurement system, wherein the vibration measurement system includes: a video capture unit and a vibration feedback unit; Acquire a vibration function set and a velocity gradient set for simulating vibration, wherein the vibration function set includes a plurality of vibration functions, and the velocity gradient set includes a plurality of reference velocities, and acquire a vibration combination set based on the vibration function set and the velocity gradient set; A reference coordinate system is constructed, and a target detection locomotive is obtained using the reference coordinate system and the heavy-load electric locomotive, wherein the target detection locomotive includes a plurality of target detection points; Acquire a vibration detection video stream based on the vibration combination set, the target detection locomotive, the simulated vibration platform, and the video capture unit; and acquire a detected vibration displacement set based on the vibration detection video stream, wherein the detected vibration displacement set includes multiple detected vibration displacements; screening the detected vibration displacement set to obtain a target vibration displacement set; The step of screening the detected vibration displacement set to obtain a target vibration displacement set includes: Calculate screening values using the detected vibration displacement set and a pre-established screening relationship, aggregate the screening values to obtain a screening value set, and sort the screening values in the screening value set from largest to smallest to obtain a screening sequence, wherein the screening values correspond one-to-one to the detected vibration displacements, and the screening values in the screening sequence can express the degree of deviation between the detected vibration displacements and the overall situation; sequentially extracting the detected vibration displacements corresponding to the screening values from the screening sequence; Eliminating the extracted detected vibration displacement from the detected vibration displacement set to obtain an updated vibration displacement set, calculating an estimated value based on the updated vibration displacement set and a pre-constructed estimation relationship, and obtaining a first evaluation value using the estimated value and the extracted detected vibration displacement, wherein the estimated value refers to a value obtained by estimating the extracted detected vibration displacement using the detected vibration displacement in the updated vibration displacement set, and in the estimation, a distance factor is taken into account; the greater the distance between the detected vibration displacement in the updated vibration displacement set and the extracted detected vibration displacement, the smaller the influence of the detected vibration displacement in the updated vibration displacement set on the extracted detected vibration displacement; comparing the first evaluation value with a preset evaluation threshold; If the first evaluation value is less than or equal to the evaluation threshold, confirming that the detected vibration displacement set is a target vibration displacement set; Acquire a fitted vibration displacement set of the target detection locomotive, wherein the fitted vibration displacement set includes a plurality of fitted vibration displacements, and the fitted vibration displacements correspond one-to-one to the target detection points; A size parameter set of a target detection locomotive is obtained, a fitting detection model is obtained based on the fitting vibration displacement set, the size parameter set and the target vibration displacement set, and the fitting detection model is sent to an initiator of a vibration test instruction using a vibration feedback unit to realize vibration measurement of a heavy-load electric locomotive.

2. The machine vision-based heavy-load electric locomotive vibration measurement method according to claim 1, wherein: The obtaining of the vibration combination set based on the vibration function set and the velocity gradient set includes: Extract vibration functions from the vibration function set in sequence, and perform the following operations on the extracted vibration functions: The extracted vibration function is matched with the velocity gradient set to obtain a vibration combination, and the vibration combinations are summarized to obtain a vibration combination set, wherein the vibration combination is as follows: ； in, Indicates the vibration combination concentration A vibration combination, Represents the first A vibration function, represents the velocity gradient set, They represent the first reference speed, the second reference speed, and the Reference speed and A reference speed, Indicates that the velocity gradient is concentrated A reference speed.

3. The machine vision-based heavy-load electric locomotive vibration measurement method according to claim 2, wherein: The method of acquiring a target detection locomotive using a reference coordinate system and a heavy-load electric locomotive includes: identifying a detection area in the heavy-load electric locomotive, and acquiring a plurality of target detection points based on the reference coordinate system and the detection area; Extract target detection points from the multiple target detection points in sequence, and perform the following operations on the extracted target detection points: Obtaining a detection grayscale value based on the extracted target detection point, using the detection grayscale value as the starting point of a pre-constructed region growing algorithm, and using the region growing algorithm to obtain a target growth region, wherein the grayscale values of the pixels corresponding to the target growth region are all the same; In the detection area corresponding to the target growth area, a target positioning pattern is identified based on the detected grayscale value, wherein the target positioning pattern is a square checkerboard pattern alternating between a preset first grayscale value and a preset second grayscale value, and both the first grayscale value and the second grayscale value are different from the detected grayscale value, and a side length of the square checkerboard pattern is preset to 2q+1, where q is an integer greater than or equal to 1; Fix the center of the target positioning pattern to the target detection point to obtain the target detection locomotive.

4. The machine vision-based heavy-load electric locomotive vibration measurement method according to claim 3, characterized in that: The step of obtaining a vibration detection video stream based on the vibration combination set, the target detection locomotive, the simulated vibration platform, and the video shooting unit includes: Extract vibration combinations from the vibration combination set in sequence, and perform the following operations on the extracted vibration combinations: A plurality of target vibration combinations are obtained based on the extracted vibration combinations, wherein the target vibration combinations are as follows: ； in, Indicates the vibration combination concentration The vibration combination corresponds to the target vibration combination; Extract target vibration combinations from multiple target vibration combinations in sequence and perform the following operations on the extracted target vibration combinations: The extracted target vibration combination is input into a simulated vibration table to obtain a target vibration table, and a vibration operation is performed on the target detection locomotive using the target vibration table, wherein the duration of the vibration operation is preset, and a video shooting unit is used to shoot the target vibration table in the vibration operation to obtain a vibration detection video stream.

5. The machine vision-based vibration measurement method for heavy-load electric locomotive according to claim 4, characterized in that: The step of acquiring a detected vibration displacement set based on the vibration detection video stream includes: Based on the target detection locomotive, an initial detection image is obtained, an initial frame number of the vibration detection video stream is obtained, target detection images are sequentially extracted from the vibration detection video stream based on the initial frame number, and the following operations are performed on each of the extracted target detection images: Extract target positioning patterns from the target detection locomotive in sequence, and perform the following operations on the extracted target positioning patterns: Performing a grayscale operation on the initial detection image to obtain an initial grayscale image, and obtaining an initial detection window based on the initial grayscale image and the extracted target positioning pattern, wherein the initial detection window is a pixel area corresponding to the target positioning pattern in the initial grayscale image; Performing a grayscale operation on the extracted target detection image to obtain a target grayscale image, and obtaining a plurality of different first positioning patterns using the initial detection window and the target grayscale image; Extracting first positioning patterns sequentially from the multiple different first positioning patterns, and performing the following operations on the extracted first positioning patterns: Calculating reference similarities using a pre-constructed similarity evaluation relationship and the extracted first positioning pattern, summarizing the reference similarities to obtain a reference similarity set, identifying a second positioning pattern using the reference similarity set, wherein the second positioning pattern is the first positioning pattern corresponding to the maximum reference similarity in the reference similarity set, and obtaining a detected vibration displacement based on the second positioning pattern; The detected vibration displacements are summarized to obtain a detected vibration displacement set.

6. The machine vision-based vibration measurement method for heavy-load electric locomotive according to claim 5, characterized in that: The similarity evaluation relationship is as follows: ； in, represents the reference similarity, are all preset coefficients. Represents the grayscale value of the pixel corresponding to the target positioning pattern in the initial grayscale image, Represents the mean grayscale value of the pixel points corresponding to the target positioning pattern in the initial grayscale image, represents the grayscale value of the pixel corresponding to the first positioning pattern, represents the mean grayscale value of the pixel points corresponding to the first positioning pattern, Indicates the first positioning pattern corresponding to the Rank The pixel point or target positioning pattern of the column corresponds to the first Rank Column pixels, Indicates the coordinates of the target detection point corresponding to the target positioning pattern in the reference coordinate system, Represents the coordinates of the pre-built reference point in the reference coordinate system, The pixel area corresponding to the target positioning pattern in the initial grayscale image is OK The pixel area of the column.

7. The machine vision-based vibration measurement method for heavy-load electric locomotive according to claim 6, characterized in that: The acquiring and detecting vibration displacement based on the second positioning pattern includes: The central pixel point is retrieved from the second positioning pattern, and the pixel point is used as the displacement pixel point. The displacement coordinates are calculated using the pre-built displacement conversion relationship and the displacement pixel point. The initial detection coordinates are retrieved from the target detection locomotive using the displacement pixel point. The vibration displacement is calculated based on the detection displacement coordinates and the initial detection coordinates. The displacement conversion relationship is as follows: ； in, Indicates the calibration parameters of the video capture unit, Represents the coordinates of the displaced pixel points, represents the intrinsic parameter matrix of the video capture unit, represents the extrinsic parameter matrix of the video capture unit, Indicates the detection displacement coordinates.

8. The machine vision-based vibration measurement method for heavy-load electric locomotive according to claim 7, characterized in that: Before confirming that the detected vibration displacement set is a target vibration displacement set, the method further includes: comparing the first evaluation value with a preset evaluation threshold; If the first evaluation value is greater than the evaluation threshold, the extracted detected vibration displacement is eliminated from the detected vibration displacement set to obtain a eliminated detected displacement set, the eliminated detected displacement set is used as the detected vibration displacement set, and the process returns to the step of calculating the screening value using the detected vibration displacement set and the pre-constructed screening relationship until the first evaluation value is less than or equal to the evaluation threshold, thereby obtaining a target vibration displacement set; The screening relationship is as follows: ； in, Indicates the detection of vibration displacement concentration The screening value corresponding to the detected vibration displacement, Indicates the detection of vibration displacement concentration A detection vibration displacement, Indicates the detection of vibration displacement concentration A detection vibration displacement, Indicates the detection of vibration displacement concentration Detect vibration displacement; The estimated relationship is as follows: ； in, Indicates estimated value, represents the coordinates corresponding to the extracted detection vibration displacement, Indicates the update of the vibration displacement concentration Update vibration displacement, Indicates the updated vibration displacement concentration total Update vibration displacement, Indicates the update of the vibration displacement concentration Update the coordinates corresponding to the vibration displacement.

9. The machine vision-based vibration measurement method for heavy-load electric locomotive according to claim 8, characterized in that: The obtaining of a fitting detection model based on the fitting vibration displacement set, the size parameter set and the target vibration displacement set includes: constructing an initial locomotive model based on the size parameter set, and retrieving a plurality of model detection points in the initial locomotive model according to the position of the target detection point in the target detection locomotive, wherein the model detection points correspond one-to-one to the target detection points; According to the position of the target detection point, the fitting vibration displacement in the fitting vibration displacement set is matched with the target vibration displacement in the target vibration displacement set to obtain a plurality of matching displacement groups, wherein the matching displacement groups correspond to the target detection points one by one; Matching displacement groups are sequentially extracted from the multiple matching displacement groups, and the following operations are performed on the extracted matching displacement groups: If the target vibration displacement exists in the extracted matching displacement group, the matching displacement group is proposed as an empty set; Summarize the matching displacement groups that are not empty sets to obtain multiple target displacement groups; extracting target displacement groups from the plurality of target displacement groups in sequence, and obtaining target vibration values based on the extracted target displacement groups, wherein the target vibration value is a difference between the fitting vibration displacement and the target vibration displacement; The fitting vibration displacement, target vibration displacement and target vibration value corresponding to the target displacement group are used to perform a marking operation on the model detection points corresponding to the target displacement group to obtain a fitting detection model.

10. A heavy-load electric locomotive vibration measurement system based on machine vision, characterized in that: The system comprises: a vibration test environment confirmation module, configured to receive a vibration test instruction and, based on the vibration test instruction, confirm a vibration test environment for the vibration test, wherein the vibration test environment includes: a heavy-load electric locomotive, a simulated vibration table, and a vibration measurement system, wherein the vibration measurement system includes: a video capture unit and a vibration feedback unit; a vibration test preparation module, configured to obtain a vibration function set and a velocity gradient set for simulating vibration, wherein the vibration function set includes a plurality of vibration functions, and the velocity gradient set includes a plurality of reference velocities, and to obtain a vibration combination set based on the vibration function set and the velocity gradient set; A reference coordinate system is constructed, and a target detection locomotive is obtained using the reference coordinate system and the heavy-load electric locomotive, wherein the target detection locomotive includes a plurality of target detection points; a vibration testing module, configured to obtain a vibration detection video stream based on the vibration combination set, the target detection locomotive, the simulated vibration platform, and the video capture unit, and to obtain a detected vibration displacement set based on the vibration detection video stream, wherein the detected vibration displacement set includes a plurality of detected vibration displacements; screening the detected vibration displacement set to obtain a target vibration displacement set; The step of screening the detected vibration displacement set to obtain a target vibration displacement set includes: Calculate screening values using the detected vibration displacement set and a pre-established screening relationship, aggregate the screening values to obtain a screening value set, and sort the screening values in the screening value set from largest to smallest to obtain a screening sequence, wherein the screening values correspond one-to-one to the detected vibration displacements, and the screening values in the screening sequence can express the degree of deviation between the detected vibration displacements and the overall situation; sequentially extracting the detected vibration displacements corresponding to the screening values from the screening sequence; Eliminating the extracted detected vibration displacement from the detected vibration displacement set to obtain an updated vibration displacement set, calculating an estimated value based on the updated vibration displacement set and a pre-constructed estimation relationship, and obtaining a first evaluation value using the estimated value and the extracted detected vibration displacement, wherein the estimated value refers to a value obtained by estimating the extracted detected vibration displacement using the detected vibration displacement in the updated vibration displacement set, and in the estimation, a distance factor is taken into account; the greater the distance between the detected vibration displacement in the updated vibration displacement set and the extracted detected vibration displacement, the smaller the influence of the detected vibration displacement in the updated vibration displacement set on the extracted detected vibration displacement; comparing the first evaluation value with a preset evaluation threshold; If the first evaluation value is less than or equal to the evaluation threshold, confirming that the detected vibration displacement set is a target vibration displacement set; a vibration test analysis module, configured to obtain a fitted vibration displacement set of a target detection locomotive, wherein the fitted vibration displacement set includes a plurality of fitted vibration displacements, and the fitted vibration displacements correspond one-to-one to the target detection points; A size parameter set of a target detection locomotive is obtained, a fitting detection model is obtained based on the fitting vibration displacement set, the size parameter set and the target vibration displacement set, and the fitting detection model is sent to an initiator of a vibration test instruction using a vibration feedback unit to realize vibration measurement of a heavy-load electric locomotive.

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