CN112734698A - Cable terminal abnormity diagnosis method and device based on infrared image - Google Patents
Cable terminal abnormity diagnosis method and device based on infrared image Download PDFInfo
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Abstract
The invention discloses a cable terminal abnormity diagnosis method and device based on infrared images. Wherein, the method comprises the following steps: acquiring an infrared image of a cable terminal to be detected; analyzing the infrared image to obtain the surface temperature of the cable terminal to be detected in the infrared image; comparing the surface temperature with a preset temperature threshold value to obtain a comparison result; and determining whether the cable terminal to be tested is abnormal or not according to the comparison result. The invention solves the technical problem of low detection efficiency caused by detecting whether the cable terminal is abnormal or not in a manual mode in the prior art.
Description
Technical Field
The invention relates to the technical field of power monitoring, in particular to a cable terminal abnormity diagnosis method and device based on infrared images.
Background
With the rapid development of power grid construction, power load increases rapidly, people have higher and higher requirements on city beautification, power supply reliability, power supply quality and the like, power cables are widely applied to various voltage levels of a power system due to good electrical performance, heat resistance and mechanical performance of the power cables, and the proportion of the power cables in the city power grid is higher and higher. Therefore, whether the power cable can be operated safely becomes an important factor whether the power grid can supply power reliably. Theoretical analysis and actual operation observation show that the operation temperature generally rises after a potential problem occurs in the cable. Therefore, the operating temperature of the cable can reflect the operating state of the cable. The infrared thermal imaging temperature measurement technology has the advantages of non-contact, no interference of a high-voltage electromagnetic field, safety, high efficiency, intuition and the like, and changes of the temperature of all parts of the cable are reflected by using infrared thermal imaging, so that the infrared thermal imaging temperature measurement technology is favorable for timely finding out abnormal heating defects of the cable and guiding maintenance. However, at present, in cable routing inspection, infrared diagnosis mainly depends on manual mode to find abnormality, and autonomous analysis and judgment can not be performed on the obtained state information, so that the efficiency of abnormality detection of a cable terminal is reduced.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a cable terminal abnormity diagnosis method and device based on an infrared image, which at least solve the technical problem of low detection efficiency caused by detecting whether a cable terminal is abnormal or not in a manual mode in the prior art.
According to an aspect of an embodiment of the present invention, there is provided a cable terminal abnormality diagnosis method based on an infrared image, including: acquiring an infrared image of a cable terminal to be detected; analyzing the infrared image to obtain the surface temperature of the cable terminal to be detected in the infrared image; comparing the surface temperature with a preset temperature threshold value to obtain a comparison result; and determining whether the cable terminal to be tested is abnormal or not according to the comparison result.
Further, the cable terminal abnormality diagnosis method based on the infrared image further includes: gather the default infrared image at the different positions of cable terminal that awaits measuring, wherein, predetermine the infrared image and include: a background part and a part of cable of a cable terminal to be tested; preprocessing a preset infrared image to obtain a binary image; and training the binary image based on a deep learning network to obtain an infrared image of the cable terminal to be tested, wherein the infrared image does not contain a background part.
Further, the cable terminal abnormality diagnosis method based on the infrared image further includes: acquiring standard pixel points of a background part in a preset infrared image on a color space coordinate; acquiring an image pixel point of each pixel in a preset infrared image; calculating the Euclidean distance between the image pixel point and the standard pixel point; and obtaining the image pixel with the minimum Euclidean distance to the standard pixel point to obtain a binary image.
Further, the cable terminal abnormality diagnosis method based on the infrared image further includes: acquiring a training sample library, wherein the training sample library comprises characteristic quantities and part labels of different parts of a cable terminal; extracting characteristic quantity and part labels from a training sample library; training based on a neural network algorithm to obtain a vector machine classifier; extracting characteristic quantities in the binary images of different parts of the cable terminal to be detected; and matching the characteristic quantity in the binary image with the part label by using a vector machine classifier to obtain the infrared image.
Further, the cable terminal abnormality diagnosis method based on the infrared image further includes: analyzing the infrared image to obtain the infrared radiation power of the cable terminal to be detected; and determining the surface temperature corresponding to the infrared radiation power according to a preset relation, wherein the preset relation represents the relation between the surface absolute temperature of the object to be measured and the infrared radiation power of the object to be measured.
Further, the cable terminal abnormality diagnosis method based on the infrared image further includes: acquiring the environmental temperature of the environment where the cable terminal to be detected is located; determining a preset temperature threshold corresponding to the ambient temperature; and comparing the surface temperature with a preset temperature threshold value to obtain a comparison result.
Further, the cable terminal abnormality diagnosis method based on the infrared image further includes: under the condition that the abnormity of the cable terminal to be tested is determined according to the comparison result, acquiring a grade temperature threshold value corresponding to each fault grade at the current environment temperature; and determining the fault grade of the cable terminal to be tested according to the surface temperature and the grade temperature threshold.
According to another aspect of the embodiments of the present invention, there is also provided an infrared image-based cable terminal abnormality diagnosis apparatus, including: the acquisition module is used for acquiring an infrared image of the cable terminal to be detected; the analysis module is used for analyzing the infrared image to obtain the surface temperature of the cable terminal to be detected in the infrared image; the comparison module is used for comparing the surface temperature with a preset temperature threshold value to obtain a comparison result; and the determining module is used for determining whether the cable terminal to be tested is abnormal or not according to the comparison result.
According to another aspect of the embodiments of the present invention, there is also provided a non-volatile storage medium having a computer program stored therein, wherein the computer program is configured to execute the above-mentioned infrared image-based cable terminal abnormality diagnosis method when running.
According to another aspect of the embodiments of the present invention, there is also provided an electronic apparatus, including a storage device and a processor; the storage device is used for storing a computer program; after the processor executes the computer program, the cable terminal abnormity diagnosis method based on the infrared image is realized.
In the embodiment of the invention, the infrared image of the cable terminal is automatically analyzed, after the infrared image of the cable terminal to be detected is obtained, the surface temperature of the cable terminal to be detected in the infrared image is obtained by analyzing the infrared image, then the surface temperature is compared with a preset temperature threshold value to obtain a comparison result, and finally, whether the cable terminal to be detected is abnormal or not is determined according to the comparison result.
It is easy to notice that the above-mentioned process need not artifical the participation, and the system can be automatically carried out analysis to infrared image in order to confirm whether cable termination takes place unusually to realize carrying out the purpose of autonomic detection to cable termination, and then improved the detection efficiency that cable termination anomaly detected.
Therefore, the scheme provided by the application achieves the purpose of autonomously detecting the cable terminal, the technical effect of improving the detection efficiency of the abnormal detection of the cable terminal is achieved, and the technical problem that in the prior art, whether the detection efficiency is low due to the fact that whether the cable terminal is abnormal or not is detected in a manual mode is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a flowchart of a method for diagnosing an abnormality of a cable terminal based on an infrared image according to an embodiment of the present invention;
FIG. 2 is a schematic view of an alternative cable termination anomaly detection system according to an embodiment of the present invention;
FIG. 3 is a flow diagram of an alternative image binarization process according to an embodiment of the invention;
FIG. 4 is an alternative RGB color space coordinate system model diagram according to an embodiment of the invention;
FIG. 5 is a model diagram of training and prediction for an alternative deep learning network in accordance with embodiments of the present invention;
FIG. 6 is an alternative method for training a binarized image based on a deep learning network according to an embodiment of the present invention;
fig. 7 is a schematic view of an infrared image-based cable termination abnormality diagnosis apparatus according to an embodiment of the present invention;
fig. 8 is a schematic diagram of an electronic device according to an embodiment of the invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
According to an embodiment of the present invention, there is provided an embodiment of a cable termination abnormality diagnosis method based on infrared images, it should be noted that the steps shown in the flowchart of the attached drawings may be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that here.
Fig. 1 is a flowchart of a cable termination abnormality diagnosis method based on infrared images according to an embodiment of the present invention, as shown in fig. 1, the method including the steps of:
and S102, acquiring an infrared image of the cable terminal to be detected.
It should be noted that the cable terminal abnormality detection system may be used as an execution main body of the infrared image-based cable terminal abnormality diagnosis method in the present application, and optionally, fig. 2 shows a schematic diagram of an optional cable terminal abnormality detection system, as can be seen from fig. 2, an image acquisition device, a controller, a communication module, a database, a computer program, and an internet of things platform, where the image acquisition device may be, but is not limited to, an infrared camera.
In an alternative embodiment, the infrared image of the cable terminal to be tested only includes the cable terminal to be tested, and does not include other backgrounds. Optionally, the image acquisition device acquires a cable image containing a background, and sends the acquired cable image containing the background to the controller, and then the controller processes the acquired cable image containing the background to obtain an infrared image of the cable terminal to be detected, which does not contain the background.
And step S104, analyzing the infrared image to obtain the surface temperature of the cable terminal to be detected in the infrared image.
Optionally, the controller performs image processing on the infrared image to obtain the surface temperature of the cable terminal to be measured in the infrared image.
And S106, comparing the surface temperature with a preset temperature threshold value to obtain a comparison result.
Optionally, as shown in fig. 2, the internet of things platform establishes a communication connection with the controller through the communication module, and the internet of things platform can receive the surface temperature of the cable terminal sent by the controller, extract the preset temperature threshold in the current environment, and compare the surface temperature with the preset temperature threshold to obtain the comparison result.
And S108, determining whether the cable terminal to be tested is abnormal or not according to the comparison result.
It should be noted that, when the surface temperature of the cable terminal to be tested is too high, that is, the cable terminal to be tested is overheated, it may be determined that an abnormality occurs in the cable terminal to be tested. The Internet of things platform can determine whether the cable terminal to be detected is abnormal according to the comparison result, and generates alarm information after determining that the cable terminal to be detected is abnormal so as to remind a worker that the cable terminal to be detected sends the abnormality.
Based on the schemes defined in the above steps S102 to S108, it can be known that, in the embodiment of the present invention, after the infrared image of the cable terminal to be tested is obtained, the infrared image is analyzed to obtain the surface temperature of the cable terminal to be tested in the infrared image, and then the surface temperature is compared with the preset temperature threshold value to obtain a comparison result, and finally, whether the cable terminal to be tested is abnormal or not is determined according to the comparison result.
It is easy to notice that the above-mentioned process need not artifical the participation, and the system can be automatically carried out analysis to infrared image in order to confirm whether cable termination takes place unusually to realize carrying out the purpose of autonomic detection to cable termination, and then improved the detection efficiency that cable termination anomaly detected.
Therefore, the scheme provided by the application achieves the purpose of autonomously detecting the cable terminal, the technical effect of improving the detection efficiency of the abnormal detection of the cable terminal is achieved, and the technical problem that in the prior art, whether the detection efficiency is low due to the fact that whether the cable terminal is abnormal or not is detected in a manual mode is solved.
In an alternative embodiment, before detecting whether the cable terminal to be detected is abnormal based on the infrared image of the cable terminal to be detected, the infrared image of the cable terminal to be detected needs to be acquired first. Specifically, the image acquisition equipment gathers the preset infrared image at the different positions of the cable terminal that awaits measuring, wherein, predetermine the infrared image and include: background portion and portion of the cable at the end of the cable to be tested. Then, the controller preprocesses the preset external image to obtain a binary image, and trains the binary image based on a deep learning network to obtain an infrared image of the cable terminal to be tested, wherein the infrared image does not contain a background part.
It should be noted that the different parts of the cable termination include, but are not limited to, the termination joints, intermediate joints, and interconnection boxes of the cable termination.
Optionally, the image capturing device in fig. 2 may capture an infrared image of the cable terminal and a surrounding background (i.e., the preset infrared image), where the image capturing device is an infrared camera, and the infrared camera emits infrared rays to irradiate the cable terminal, so that the infrared rays are reflected diffusely and received by the monitoring camera again, thereby forming a video image of the cable terminal. The method comprises the steps that an infrared camera is controlled by an internet of things platform to collect infrared images of different positions of a cable terminal to obtain infrared images of the cable terminal and a surrounding background, and then a controller preprocesses the collected infrared images (namely preset infrared images) through value filtering processing, Gaussian filtering processing or wavelet filtering processing and then conducts binarization processing to obtain a binarization image. And finally, training the binary image by adopting a deep learning network to obtain an infrared image of the cable terminal.
Optionally, fig. 3 shows a flowchart of an optional image binarization process, and as can be seen from fig. 3, the process includes the following steps:
step S302, acquiring standard pixel points of a background part in a preset infrared image on a color space coordinate;
step S304, acquiring an image pixel point of each pixel in a preset infrared image;
step S306, calculating the Euclidean distance between the image pixel point and the standard pixel point;
and step S308, obtaining the image pixel with the minimum Euclidean distance to the standard pixel point to obtain a binary image.
It should be noted that, when performing binarization processing on a preset infrared image, first, a standard pixel point of a background of a cable terminal on an RGB color space coordinate is set, where fig. 4 shows an optional RGB color space coordinate system model diagram. Then, scanning each pixel point of the collected infrared images of different parts of the cable from left to right and from top to bottom, and calculating the Euclidean distance between each pixel point and a standard pixel point of the background of the cable terminal:
in the above formula, R is the pixel value of the pixel point on the R axis in the RGB color space coordinate system, and R' is the pixel value of the standard pixel point on the R axis in the RGB color space coordinate system; g is the pixel value of the pixel point on the G axis in the RGB color space coordinate system, and G' is the pixel value of the standard pixel point on the G axis in the RGB color space coordinate system; b is the pixel value of the pixel point on the B axis in the RGB color space coordinate system, and B' is the pixel value of the standard pixel point on the B axis in the RGB color space coordinate system.
Then, the controller calculates the Euclidean distance between the pixel point and the standard pixel point by substituting the scanned pixel point coordinates (R, G, B) into the formula, and calculates the pixel point with the minimum Euclidean distance to the standard pixel point of the cable terminal background to obtain a binary image of the cable terminal, namely a binary image.
Further, after the binary image is obtained, the controller trains the binary image based on a deep learning network to obtain an infrared image of the cable terminal to be tested. Optionally, fig. 5 shows a Model diagram of training and prediction of an optional deep learning network, and as can be seen from fig. 5, in the training stage, a Model is obtained by training input raw data and a classification label in a deep Model. Then, in the prediction stage, new data is input into the Model, and the Model can output the classification label corresponding to the new data.
Applying the model map of the deep learning network training and prediction to the present application, a method for training a binarized image based on the deep learning network shown in fig. 6 can be obtained, as can be seen from fig. 6, the process includes the following steps:
step S602, a training sample library is obtained, wherein the training sample library comprises characteristic quantities and part labels of different parts of the cable terminal;
step S604, extracting characteristic quantity and part labels from a training sample library;
step S606, training based on a neural network algorithm to obtain a vector machine classifier;
step S608, extracting characteristic quantities in the binary images of different parts of the cable terminal to be detected;
and step S610, matching the characteristic quantity in the binary image with the part label by using a vector machine classifier to obtain the infrared image.
It should be noted that, when performing simulation training on a binary image, feature quantities and part labels of different parts of a cable terminal are first set to construct a training sample library, where the part labels are names of the parts and include a cable terminal, a terminal connector, an intermediate connector and an interconnection box, and in addition, the collection of the feature quantities and the part labels in this embodiment is 100w each, so as to ensure that the training sample library is constructed to include massive training elements, and improve the training effect and the accuracy of calculation. Then, the controller extracts feature quantities and part labels of different parts of the cable terminal, and trains a touch vector machine classifier (namely the vector machine classifier) by using a neural network algorithm. In the prediction stage, the controller firstly extracts the characteristic quantities in the infrared images of different parts of the cable terminal to be detected, and then the trained touch vector classifier is used for matching the part labels corresponding to the characteristic quantities in the infrared images of different parts of the cable terminal to be detected, so that the infrared image of the cable terminal is obtained.
In an alternative embodiment, after the infrared image is obtained, the controller analyzes the infrared image to obtain the surface temperature of the cable terminal to be measured in the infrared image. Specifically, the controller analyzes the infrared image to obtain the infrared radiation power of the cable terminal to be detected, and then determines the surface temperature corresponding to the infrared radiation power according to a preset relation, wherein the preset relation represents the relation between the surface absolute temperature of the object to be detected and the infrared radiation power of the object to be detected.
Optionally, the correspondence between the surface absolute temperature of the object to be measured and the infrared radiation power of the object to be measured may be determined according to the stefan-boltzmann law, where the stefan-boltzmann law is:
P＝εσT4
in the above formula, P is the infrared radiation power of the object to be measured, T is the absolute temperature of the object to be measured, epsilon is the infrared emissivity of the surface of the object to be measured, and σ is the stefan-boltzmann constant, where σ ═ 10 (5067032 ± 0.00071)12。
Furthermore, after the surface temperature of the cable terminal to be detected is obtained, the internet of things platform compares the surface temperature with a preset temperature threshold value to obtain a comparison result. Specifically, the internet of things platform acquires the ambient temperature of the environment where the cable terminal to be detected is located, determines a preset temperature threshold corresponding to the ambient temperature, and then compares the surface temperature with the preset temperature threshold to obtain a comparison result.
Optionally, the internet of things platform establishes communication connection with the controller through the communication module to receive the surface temperature of the cable terminal to be tested sent by the controller, extract the preset temperature threshold value at the current ambient temperature, execute a data comparison judgment program to compare the preset temperature threshold value with the surface temperature of the cable terminal to be tested, and judge whether the cable terminal to be tested is overheated.
It should be noted that the computer program in fig. 2 includes the data comparison determination program and also includes an image processing program, where the image processing program is used to perform binarization processing on the infrared image. In addition, the preset temperature threshold has different values at different environmental temperatures, for example, when the environmental temperature is lower than 10 ℃, the highest value of the preset temperature threshold is not more than 60 ℃; when the ambient temperature is 10-25 ℃, the maximum value of the preset temperature threshold is not more than 70 ℃; when the ambient temperature is higher than 25 ℃, the maximum value of the preset temperature threshold is not higher than 80 ℃.
Further, the platform of the internet of things compares the surface temperature with a preset temperature threshold value, and determines whether the cable terminal to be detected is abnormal or not according to the comparison result. And under the condition that the abnormity of the cable terminal to be tested is determined according to the comparison result, the grade temperature threshold corresponding to each fault grade at the current environment temperature is obtained, and the fault grade of the cable terminal to be tested is determined according to the surface temperature and the grade temperature threshold.
Optionally, the fault level represents an overheating level of the cable terminal to be tested, wherein the overheating level includes, but is not limited to, a thermal fault, a severe thermal fault, and a dangerous fault. In addition, under the condition that the abnormality of the cable terminal to be detected is detected, the platform of the internet of things can also send out an alarm and output an overheating grade.
In addition, the database of fig. 2 may be used to store preset temperature thresholds, superheat level determination data, and computer programs for different ambient temperatures. Since the normal working temperature threshold of the cable terminal is 30-50 ℃, and the normal temperature threshold is changed under the influence of the ambient temperature, the setting rule of the temperature threshold in this embodiment is preferably:
(1) when the environmental temperature is lower than 10 ℃, the highest value of the preset temperature threshold is not more than 60 ℃, and at the moment, if the detected surface temperature of the cable terminal to be detected is 40 ℃, the cable terminal to be detected is judged to be in thermal fault; if the detected surface temperature of the cable terminal to be detected is 50 ℃, determining that the cable terminal to be detected has a serious thermal fault; and if the detected surface temperature of the cable terminal to be detected is 60 ℃, judging that the cable terminal to be detected is a dangerous fault.
(2) When the environmental temperature is 10-25 ℃, the maximum value of the preset temperature threshold is not more than 70 ℃, and at the moment, if the detected surface temperature of the cable terminal to be detected is 50 ℃, the cable terminal to be detected is judged to be in thermal fault; if the detected surface temperature of the cable terminal to be detected is 60 ℃, determining that the cable terminal to be detected has a serious thermal fault; and if the detected surface temperature of the cable terminal to be detected is 70 ℃, judging that the cable terminal to be detected is a dangerous fault.
(3) When the environmental temperature is higher than 25 ℃, the highest value of the preset temperature threshold is not higher than 80 ℃, and at the moment, if the detected surface temperature of the cable terminal to be detected is 60 ℃, the cable terminal to be detected is judged to be in thermal fault; if the detected surface temperature of the cable terminal to be detected is 70 ℃, determining that the cable terminal to be detected has a serious thermal fault; and if the detected surface temperature of the cable terminal to be detected is 80 ℃, judging that the cable terminal to be detected is a dangerous fault.
According to the method, the infrared camera is controlled by the platform of the Internet of things to acquire the infrared images of different positions of the cable terminal and the surrounding background, the infrared images of the cable terminal are trained and positioned by adopting the deep learning network after binarization processing, the surface temperature of the cable terminal is obtained, whether the cable terminal is overheated or not is judged according to the environmental condition, and an alarm is given and the alarm level is output if the cable terminal is overheated. Therefore, the cable terminal infrared image locating method and device can identify the cable infrared image in all directions from the infrared image with the complex background, and locate the infrared image of the cable terminal. Moreover, whether the cable terminal is overheated or not can be effectively judged, and the overheating grade can be judged, so that the automatic diagnosis of the abnormal heating phenomenon is realized. Therefore, the scheme provided by the application has practical engineering application value and can be generally applied to power cable equipment.
Example 2
According to an embodiment of the present invention, there is also provided an embodiment of an infrared image-based cable terminal abnormality diagnosis apparatus, wherein fig. 7 is a schematic diagram of an infrared image-based cable terminal abnormality diagnosis apparatus according to an embodiment of the present invention, as shown in fig. 7, the apparatus includes: an obtaining module 701, an analyzing module 703, a comparing module 705 and a determining module 707.
The acquisition module 701 is used for acquiring an infrared image of the cable terminal to be detected; the analysis module 703 is configured to analyze the infrared image to obtain a surface temperature of the cable terminal to be detected in the infrared image; a comparison module 705, configured to compare the surface temperature with a preset temperature threshold to obtain a comparison result; and the determining module 707 is configured to determine whether the cable terminal to be tested is abnormal according to the comparison result.
It should be noted that the obtaining module 701, the analyzing module 703, the comparing module 705, and the determining module 707 correspond to steps S102 to S108 in the foregoing embodiment, and the four modules are the same as the corresponding steps in the implementation example and the application scenario, but are not limited to the disclosure in embodiment 1.
Optionally, the obtaining module includes: the device comprises an acquisition module, a preprocessing module and a first training module. Wherein, collection module for gather the default infrared image at the different positions of the cable terminal that awaits measuring, wherein, predetermine infrared image and include: a background part and a part of cable of a cable terminal to be tested; the preprocessing module is used for preprocessing a preset infrared image to obtain a binary image; and the first training module is used for training the binary image based on the deep learning network to obtain an infrared image of the cable terminal to be tested, wherein the infrared image does not contain a background part.
Optionally, the preprocessing module includes: the device comprises a first acquisition module, a second acquisition module, a calculation module and a third acquisition module. The first acquisition module is used for acquiring standard pixel points of a background part in a preset infrared image on a color space coordinate; the second acquisition module is used for acquiring image pixel points of each pixel in the preset infrared image; the calculation module is used for calculating the Euclidean distance between the image pixel point and the standard pixel point; and the third acquisition module is used for acquiring the image pixel with the minimum Euclidean distance from the standard pixel point to obtain the binary image.
Optionally, the first training module includes: the device comprises a fourth acquisition module, a first extraction module, a second training module, a second extraction module and a matching module. The fourth acquisition module is used for acquiring a training sample library, wherein the training sample library comprises feature quantities and part labels of different parts of the cable terminal; the first extraction module is used for extracting characteristic quantity and part labels from a training sample library; the second training module is used for training based on a neural network algorithm to obtain a vector machine classifier; the second extraction module is used for extracting the characteristic quantities in the binary images of different parts of the cable terminal to be detected; and the matching module is used for matching the characteristic quantity in the binary image with the part label by using the vector machine classifier to obtain the infrared image.
Optionally, the analysis module includes: the device comprises a first analysis module and a first determination module. The first analysis module is used for analyzing the infrared image to obtain the infrared radiation power of the cable terminal to be detected; the first determining module is used for determining the surface temperature corresponding to the infrared radiation power according to a preset relation, wherein the preset relation represents the relation between the surface absolute temperature of the object to be measured and the infrared radiation power of the object to be measured.
Optionally, the alignment module includes: the device comprises a fifth obtaining module, a second determining module and a comparison sub-module. The fifth acquisition module is used for acquiring the environmental temperature of the environment where the cable terminal to be detected is located; the second determination module is used for determining a preset temperature threshold corresponding to the ambient temperature; and the comparison submodule is used for comparing the surface temperature with a preset temperature threshold value to obtain a comparison result.
Optionally, the determining module includes: a sixth obtaining module and a third determining module. The sixth obtaining module is used for obtaining a grade temperature threshold corresponding to each fault grade at the current environment temperature under the condition that the abnormity of the cable terminal to be tested is determined according to the comparison result; and the third determining module is used for determining the fault grade of the cable terminal to be tested according to the surface temperature and the grade temperature threshold value.
Example 3
According to another aspect of the embodiments of the present invention, there is also provided a non-volatile storage medium having a computer program stored therein, wherein the computer program is configured to execute the infrared image-based cable termination abnormality diagnosis method in embodiment 1 described above when running.
It should be noted that, the nonvolatile storage medium in this embodiment is a network cloud disk, and cloud disk storage of data can be implemented, so as to prevent data loss, and it is only necessary to upload data stored in the storage device to the cloud disk periodically during storage.
Example 4
According to another aspect of the embodiments of the present invention, there is also provided an electronic apparatus, wherein fig. 8 is a schematic diagram of the electronic apparatus according to the embodiments of the present invention, as shown in fig. 8, the electronic apparatus includes a storage device and a processor, and the storage device and the processor are connected through a bus; the storage device is used for storing a computer program; after the processor executes the computer program, the method for diagnosing the abnormality of the cable terminal based on the infrared image in the embodiment 1 is implemented.
It should be noted that the storage device used in this embodiment may be a plurality of 4TB hard disks, and the data can be backed up and transferred at any time while mass data storage is satisfied.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A cable terminal abnormity diagnosis method based on infrared images is characterized by comprising the following steps:
acquiring an infrared image of a cable terminal to be detected;
analyzing the infrared image to obtain the surface temperature of the cable terminal to be detected in the infrared image;
comparing the surface temperature with a preset temperature threshold value to obtain a comparison result;
and determining whether the cable terminal to be tested is abnormal or not according to the comparison result.
2. The method of claim 1, wherein acquiring the infrared image of the cable termination under test comprises:
gather the preset infrared image at the different positions of cable terminal that awaits measuring, wherein, preset infrared image includes: a background part and a part of cable of the cable terminal to be tested;
preprocessing the preset infrared image to obtain a binary image;
training the binary image based on a deep learning network to obtain an infrared image of the cable terminal to be tested, wherein the infrared image does not contain the background part.
3. The method according to claim 2, wherein preprocessing the preset infrared image to obtain a binarized image comprises:
acquiring standard pixel points of the background part in the preset infrared image on a color space coordinate;
acquiring an image pixel point of each pixel in the preset infrared image;
calculating the Euclidean distance between the image pixel point and the standard pixel point;
and obtaining the image pixel with the minimum Euclidean distance to the standard pixel point to obtain the binary image.
4. The method as claimed in claim 2, wherein training the binarized image based on a deep learning network to obtain the infrared image of the cable terminal to be tested comprises:
acquiring a training sample library, wherein the training sample library comprises characteristic quantities and part labels of different parts of a cable terminal;
extracting the characteristic quantity and the part label from the training sample library;
training based on a neural network algorithm to obtain a vector machine classifier;
extracting characteristic quantities in the binary images of different parts of the cable terminal to be detected;
and matching the characteristic quantity in the binary image with the part label by using the vector machine classifier to obtain the infrared image.
5. The method according to claim 1, wherein analyzing the infrared image to obtain the surface temperature of the cable terminal to be tested in the infrared image comprises:
analyzing the infrared image to obtain the infrared radiation power of the cable terminal to be detected;
and determining the surface temperature corresponding to the infrared radiation power according to a preset relation, wherein the preset relation represents the relation between the surface absolute temperature of the object to be detected and the infrared radiation power of the object to be detected.
6. The method of claim 1, wherein comparing the surface temperature to a predetermined temperature threshold results in a comparison comprising:
acquiring the environmental temperature of the environment where the cable terminal to be detected is located;
determining a preset temperature threshold corresponding to the ambient temperature;
and comparing the surface temperature with the preset temperature threshold value to obtain the comparison result.
7. The method of claim 6, wherein determining whether the cable termination under test is abnormal according to the comparison result comprises:
under the condition that the abnormity of the cable terminal to be tested is determined according to the comparison result, acquiring a grade temperature threshold value corresponding to each fault grade at the current environment temperature;
and determining the fault grade of the cable terminal to be tested according to the surface temperature and the grade temperature threshold value.
8. An infrared image-based cable termination abnormality diagnosis apparatus, comprising:
the acquisition module is used for acquiring an infrared image of the cable terminal to be detected;
the analysis module is used for analyzing the infrared image to obtain the surface temperature of the cable terminal to be detected in the infrared image;
the comparison module is used for comparing the surface temperature with a preset temperature threshold value to obtain a comparison result;
and the determining module is used for determining whether the cable terminal to be tested is abnormal or not according to the comparison result.
9. A non-volatile storage medium, wherein a computer program is stored in the non-volatile storage medium, wherein the computer program is configured to execute the infrared image-based cable termination abnormality diagnosis method according to any one of claims 1 to 7 when the computer program is executed.
10. An electronic apparatus, comprising a storage device and a processor; the storage device is used for storing a computer program; the processor, after executing the computer program, implements the infrared image-based cable termination abnormality diagnosis method according to any one of claims 1 to 7.
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