CN116794447A - Cable line fixed-point abnormality detection system and method based on unmanned equipment - Google Patents
Cable line fixed-point abnormality detection system and method based on unmanned equipment Download PDFInfo
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Abstract
The embodiment of the application discloses a system, a method, a device and a storage medium for detecting fixed-point abnormality of a cable line based on unmanned equipment. According to the technical scheme provided by the embodiment of the application, the temperature data of each corresponding appointed temperature measuring point is determined based on the optical fiber signals, the temperature change curve of the appointed temperature measuring point is constructed based on the temperature data of different time nodes, the slope of the temperature change curve of the corresponding time node is detected to reach the set slope threshold value based on the temperature change curve, or the temperature change curve is in a set state in a set period, and the corresponding temperature measuring point is determined to be an abnormal detection node; the detection results of the external electromagnetic data and the returned waves are reported to a control background through the inspection anomaly detection node of the unmanned equipment; and carrying out abnormality verification of the abnormality detection node through a control background, and outputting an abnormality confirmation result. By adopting the technical means, accurate abnormality detection is realized, and the running safety of the line is ensured.
Description
Technical Field
The embodiment of the application relates to the technical field of cable detection, in particular to a cable line fixed-point abnormality detection system, method and device based on unmanned equipment and a storage medium.
Background
At present, in a cable line operation scene, corresponding abnormal monitoring is generally carried out on a line so as to ensure the safe operation of the cable line. In the operation process of the cable line, the situation that the line is broken, the insulation performance is reduced and the like is easy to occur frequently due to the influence of severe laying environment and the like at part of the position. Therefore, abnormal monitoring of the damage condition and the insulation performance of the line is required by manual inspection, line parameter detection and other modes, and the safe operation of the line is ensured.
However, the coverage is wider due to longer cabling. The cost is high by arranging the monitoring modules along the line. The manual inspection mode is troublesome, and abnormal conditions cannot be found in time.
Disclosure of Invention
The embodiment of the application provides a cable line fixed-point abnormality detection system, a method, a device and a storage medium based on unmanned equipment, which can determine an abnormality detection node according to line temperature change, verify abnormality through fixed-point current and guided wave detection by the unmanned equipment, realize accurate abnormality detection and ensure line operation safety.
In a first aspect, an embodiment of the present application provides an unmanned device-based cable line fixed-point anomaly detection system, including a control background, a temperature measurement optical fiber, and an unmanned device;
The temperature measuring optical fiber is arranged in the current cable line and is arranged along the current cable line;
the control background signal is connected with the temperature measuring optical fiber and the unmanned equipment and is used for collecting optical fiber signals of the temperature measuring optical fiber at each appointed temperature measuring point, determining corresponding temperature data of each appointed temperature measuring point based on the optical fiber signals, constructing a temperature change curve of the appointed temperature measuring point based on the temperature data of different time nodes, detecting that the slope of the temperature change curve reaches a set slope threshold value based on the temperature change curve, or determining that the temperature change curve is in a set state in a set period, and determining that the corresponding temperature measuring point is an abnormal detection node; acquiring a digital circuit diagram of a current cable circuit, marking the position of the abnormality detection node on the digital circuit diagram, and sending the digital circuit diagram to the unmanned equipment so as to instruct the unmanned equipment to perform abnormality detection on the abnormality detection node;
the unmanned equipment is used for positioning based on a digital circuit diagram and going to the corresponding position of the abnormal detection node, acquiring external electromagnetic data of the current cable circuit at the abnormal detection node through an electromagnetic detection module, simultaneously transmitting a guided wave signal at the position corresponding to the abnormal detection node through an excitation signal generator of a guided wave detection module, receiving a return wave of the guided wave signal through a guided wave detector, and reporting the external electromagnetic data and a detection result of the return wave to a control background;
The control background is also used for comparing the set threshold value based on the external electromagnetic data, carrying out abnormality verification on the abnormality detection node based on the comparison result and the detection result of the return wave, and outputting an abnormality confirmation result.
Further, the control background is specifically configured to determine that the temperature change curve is in a set state when the temperature data change slope between adjacent time nodes is in a set slope change range.
Furthermore, the unmanned equipment is also used for collecting a line image of the position corresponding to the abnormal detection node through a camera, inputting the line image into a pre-constructed foreign object detection model, outputting a foreign object detection result and reporting the foreign object detection result to a control background.
Further, the foreign object detection model performs model training in advance according to an image of the cable line when foreign object adhesion occurs as a training image, so as to be used for identifying the foreign object adhesion condition of the cable line.
In a second aspect, an embodiment of the present application provides a method for detecting a fixed-point anomaly of a cable line based on an unmanned device, which is applied to the system for detecting a fixed-point anomaly of a cable line based on an unmanned device according to the first aspect, and includes:
the method comprises the steps of connecting a temperature measuring optical fiber and unmanned equipment through a control background signal, collecting optical fiber signals of the temperature measuring optical fiber at each appointed temperature measuring point, determining corresponding temperature data of each appointed temperature measuring point based on the optical fiber signals, constructing a temperature change curve of the appointed temperature measuring point based on temperature data of different time nodes, detecting that the slope of the temperature change curve reaches a set slope threshold value based on the temperature change curve, or determining that the temperature change curve is in a set state in a set period, and determining that the corresponding temperature measuring point is an abnormal detection node; acquiring a digital circuit diagram of a current cable circuit, marking the position of the abnormality detection node on the digital circuit diagram, and sending the digital circuit diagram to the unmanned equipment so as to instruct the unmanned equipment to perform abnormality detection on the abnormality detection node;
The unmanned equipment is positioned based on a digital circuit diagram and goes to the corresponding position of the abnormal detection node, the electromagnetic detection module is used for collecting external electromagnetic data of the current cable line at the abnormal detection node, meanwhile, the excitation signal generator of the guided wave detection module is used for sending guided wave signals to the position corresponding to the abnormal detection node, the guided wave detector is used for receiving return waves of the guided wave signals, and detection results of the external electromagnetic data and the return waves are reported to a control background;
and comparing the set threshold value based on the external electromagnetic data through the control background, carrying out abnormality verification on the abnormality detection node based on the comparison result and the detection result of the return wave, and outputting an abnormality confirmation result.
Further, the control background is specifically configured to determine that the temperature change curve is in a set state when the temperature data change slope between adjacent time nodes is in a set slope change range.
Further, the method further comprises the following steps:
and acquiring a line image of the position corresponding to the abnormal detection node through a camera of the unmanned equipment, inputting the line image into a pre-constructed foreign object detection model, outputting a foreign object detection result and reporting the foreign object detection result to a control background.
Further, the foreign object detection model performs model training in advance according to an image of the cable line when foreign object adhesion occurs as a training image, so as to be used for identifying the foreign object adhesion condition of the cable line.
In a third aspect, an embodiment of the present application provides an apparatus for detecting a fixed-point anomaly of a cable line based on an unmanned device, which is applied to the system for detecting a fixed-point anomaly of a cable line based on an unmanned device according to the first aspect, and includes:
the detection module is used for connecting the temperature measuring optical fiber and unmanned equipment through control background signals, collecting optical fiber signals of the temperature measuring optical fiber at each appointed temperature measuring point, determining corresponding temperature data of each appointed temperature measuring point based on the optical fiber signals, constructing a temperature change curve of the appointed temperature measuring point based on the temperature data of different time nodes, detecting that the slope of the temperature change curve reaches a set slope threshold value based on the temperature change curve, or determining that the temperature change curve is in a set state in a set period, and determining that the corresponding temperature measuring point is an abnormal detection node; acquiring a digital circuit diagram of a current cable circuit, marking the position of the abnormality detection node on the digital circuit diagram, and sending the digital circuit diagram to the unmanned equipment so as to instruct the unmanned equipment to perform abnormality detection on the abnormality detection node;
The inspection module is used for positioning and going to the corresponding position of the abnormal detection node based on a digital circuit diagram through the unmanned equipment, collecting external electromagnetic data of the current cable line at the abnormal detection node through the electromagnetic detection module, simultaneously sending a guided wave signal at the position corresponding to the abnormal detection node through an excitation signal generator of the guided wave detection module, receiving a return wave of the guided wave signal through a guided wave detector, and reporting the external electromagnetic data and a detection result of the return wave to a control background;
and the verification module is used for comparing the set threshold value based on the external electromagnetic data through the control background, carrying out abnormality verification on the abnormality detection node based on the comparison result and the detection result of the return wave, and outputting an abnormality confirmation result.
In a fourth aspect, an embodiment of the present application provides a storage medium containing computer executable instructions which, when executed by a computer processor, are used to perform the unmanned device-based cabling setpoint anomaly detection method of the second aspect.
According to the embodiment of the application, the temperature measuring optical fiber and unmanned equipment are connected through control background signals, optical fiber signals of the temperature measuring optical fiber at each appointed temperature measuring point are collected, corresponding temperature data of each appointed temperature measuring point is determined based on the optical fiber signals, a temperature change curve of the appointed temperature measuring point is constructed based on the temperature data of different time nodes, the slope of the temperature change curve of the corresponding time node is detected to reach a set slope threshold value based on the temperature change curve, or the temperature change curve is in a set state in a set period, and the corresponding temperature measuring point is determined to be an abnormal detection node; acquiring a digital circuit diagram of a current cable circuit, marking the position of an abnormality detection node on the digital circuit diagram, and sending the digital circuit diagram to unmanned equipment so as to instruct the unmanned equipment to perform abnormality detection on the abnormality detection node; the method comprises the steps that an unmanned device is used for positioning based on a digital circuit diagram and going to a corresponding position of an abnormal detection node, an electromagnetic detection module is used for collecting external electromagnetic data of a current cable line at the abnormal detection node, meanwhile, a guided wave signal is sent through an excitation signal generator of the guided wave detection module at the position corresponding to the abnormal detection node, a return wave of the guided wave signal is received through a guided wave detector, and detection results of the external electromagnetic data and the return wave are reported to a control background; and setting a threshold value based on external electromagnetic data comparison through a control background, performing abnormality verification of an abnormality detection node based on a comparison result and a detection result of a return wave, and outputting an abnormality confirmation result. By adopting the technical means, the abnormal detection node can be determined according to the line temperature change, the abnormal condition is verified through the fixed-point current and guided wave detection of unmanned equipment, the accurate abnormal detection is realized, and the line operation safety is ensured.
Drawings
Fig. 1 is a flowchart of a method for detecting fixed-point anomalies of a cable line based on unmanned equipment according to a first embodiment of the present application;
fig. 2 is a schematic structural diagram of a cable line fixed-point anomaly detection system based on unmanned equipment according to an embodiment of the present application;
FIG. 3 is a schematic view of inspection of an anomaly detection node according to a first embodiment of the present application;
fig. 4 is a schematic structural diagram of a cable line fixed-point abnormality detection device based on unmanned equipment according to a second embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to a third embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the following detailed description of specific embodiments of the present application is given with reference to the accompanying drawings. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the matters related to the present application are shown in the accompanying drawings. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart depicts operations (or steps) as a sequential process, many of the operations can be performed in parallel, concurrently, or at the same time. Furthermore, the order of the operations may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figures. The processes may correspond to methods, functions, procedures, subroutines, and the like.
Embodiment one:
fig. 1 shows a flowchart of a method for detecting fixed-point anomalies of a cable line based on an unmanned device according to a first embodiment of the present application, where the method for detecting fixed-point anomalies of a cable line based on an unmanned device provided in this embodiment may be implemented by a system for detecting fixed-point anomalies of a cable line based on an unmanned device, and the system for detecting fixed-point anomalies of a cable line based on an unmanned device may be implemented by software and/or hardware, and the system for detecting fixed-point anomalies of a cable line based on an unmanned device may be formed by two or more physical entities, or may be formed by one physical entity.
The following describes an example of the unmanned-equipment-based cable line fixed-point abnormality detection system as a main body for executing the unmanned-equipment-based cable line fixed-point abnormality detection method. Referring to fig. 1, the method for detecting the fixed point abnormality of the cable line based on the unmanned equipment specifically includes:
s110, connecting a temperature measuring optical fiber and unmanned equipment through a control background signal, collecting optical fiber signals of the temperature measuring optical fiber at each appointed temperature measuring point, determining corresponding temperature data of each appointed temperature measuring point based on the optical fiber signals, constructing a temperature change curve of the appointed temperature measuring point based on the temperature data of different time nodes, detecting that the slope of the temperature change curve reaches a set slope threshold value based on the temperature change curve, or determining that the temperature change curve is in a set state in a set period, and determining that the corresponding temperature measuring point is an abnormal detection node; acquiring a digital circuit diagram of a current cable circuit, marking the position of the abnormality detection node on the digital circuit diagram, and sending the digital circuit diagram to the unmanned equipment so as to instruct the unmanned equipment to perform abnormality detection on the abnormality detection node;
S120, positioning and moving to the corresponding position of the abnormal detection node based on a digital circuit diagram through the unmanned equipment, collecting external electromagnetic data of the current cable circuit at the abnormal detection node through an electromagnetic detection module, simultaneously sending a guided wave signal at the position corresponding to the abnormal detection node through an excitation signal generator of a guided wave detection module, receiving a return wave of the guided wave signal through a guided wave detector, and reporting the external electromagnetic data and a detection result of the return wave to a control background;
s130, comparing and setting a threshold value based on the external electromagnetic data through the control background, carrying out abnormality verification on the abnormality detection node based on a comparison result and a detection result of the return wave, and outputting an abnormality confirmation result.
The embodiment of the application discloses a cable line fixed-point abnormality detection method based on unmanned equipment, which aims to detect line optical fiber signals through temperature measuring optical fibers, determine temperature data of each appointed temperature measuring point according to the optical fiber signals, and further judge and screen abnormality detection nodes according to the temperature measuring data. The unmanned equipment is used for carrying out abnormal inspection verification on the node so as to ensure timely monitoring of the running condition of the abnormal detection node, improve the line management effect and ensure the line running safety.
It can be understood that, for the position of the abnormal condition of the temperature data in the cable line, the position needs to be inspected in time, the real-time running condition of the position is determined, and the potential fault condition of the line is found in time. When the temperature abnormality occurs to the corresponding node of the line, the abnormality of the position of the line is detected in time through line inspection, so that hysteresis of abnormality processing is avoided, and the line management effect is improved. Based on the above, the embodiment of the application collects the inspection result to perform the abnormal inspection verification by inspecting the abnormal detection node, so that on one hand, the accuracy of the abnormal detection is ensured, and on the other hand, the abnormal detection efficiency is improved, and the abnormal condition of the abnormal detection node is detected and reported in time.
Specifically, referring to fig. 2, a system for detecting a fixed point abnormality of a cable line based on unmanned equipment according to an embodiment of the present application is provided, including a control background 12, a temperature measuring optical fiber 13, and an unmanned equipment 14; the temperature measuring optical fiber 13 is arranged inside the current cable line 11 and along the current cable line 11; the control background 12 is in signal connection with the temperature measuring optical fiber 13 and the unmanned equipment 14, and is used for collecting optical fiber signals of the temperature measuring optical fiber 12 at each appointed temperature measuring point, determining corresponding temperature data of each appointed temperature measuring point based on the optical fiber signals, constructing a temperature change curve of the appointed temperature measuring point based on the temperature data of different time nodes, detecting that the slope of the temperature change curve reaches a set slope threshold value based on the temperature change curve, or determining that the temperature change curve is in a set state within a set period, and determining that the corresponding temperature measuring point is an abnormal detection node; acquiring a digital circuit diagram of a current cable line, marking the position of the abnormality detection node on the digital circuit diagram, and sending the digital circuit diagram to the unmanned equipment 14 to instruct the unmanned equipment 14 to perform abnormality detection on the abnormality detection node; the unmanned equipment 14 is used for positioning and going to the corresponding position of the abnormal detection node based on a digital circuit diagram, collecting external electromagnetic data of the current cable circuit at the abnormal detection node through an electromagnetic detection module, simultaneously sending a guided wave signal at the position corresponding to the abnormal detection node through an excitation signal generator of a guided wave detection module, receiving a return wave of the guided wave signal through a guided wave detector, and reporting the external electromagnetic data and a detection result of the return wave to the control background 12; the control background 12 is further configured to compare the set threshold value with the external electromagnetic data, perform abnormality verification on the abnormality detection node based on the comparison result and the detection result of the return wave, and output an abnormality confirmation result.
Specifically, in order to timely detect and operate and maintain abnormal conditions such as line breakage and insulation performance reduction, the method includes the steps of firstly determining a position where the abnormal conditions such as line breakage and insulation performance reduction are likely to occur by detecting line temperature data, and defining the position as an abnormal detection node. And further carrying out inspection on the abnormal detection node in an unmanned equipment inspection mode. It can be understood that, for the conditions of line breakage, insulation performance degradation, etc., line abnormality such as leakage, short circuit, etc. may occur, resulting in abnormal temperature at local positions of the line. Based on the characteristics, the abnormal detection nodes with potential abnormal conditions can be screened out by detecting the temperature data of each line position, and then the abnormal detection nodes are subjected to inspection verification.
The principle of detecting real-time temperature data by the temperature measuring optical fiber is to acquire space temperature distribution information by utilizing spontaneous Raman scattering and optical time domain reflection generated when laser is transmitted in the temperature measuring optical fiber. By injecting laser pulses with certain energy and width into the temperature measuring optical fiber, the laser pulses are transmitted in the optical fiber, and simultaneously backward Raman scattered light is continuously generated. Due to thermal vibrations of the fiber molecules, the raman scattered light will comprise a stokes light with a longer wavelength than the light source and an anti-stokes light with a shorter wavelength than the light source, the intensity of the former being temperature independent and the intensity of the latter being temperature dependent. The temperature of the corresponding location point can be derived from the ratio of the intensities of the anti-stokes light signal and stokes light signal at any point in the thermometry fiber. It can be understood that after the raman scattered light is subjected to photoelectric conversion, amplification and high-speed a/D conversion, the temperature value of each point on the temperature measuring optical fiber can be calculated, and the temperature point is precisely positioned according to the transmission speed of the light and the backward light echo time, so that the distributed temperature measurement along the temperature measuring optical fiber is realized. Based on the above detection principle, a specified temperature measurement point is set on the temperature measurement optical fiber with reference to a specified detection position. When the real-time temperature data corresponding to the temperature measuring point is detected, the real-time temperature data corresponding to the detection position of the cable at the same position is obtained.
Based on the optical fiber signals of each appointed temperature measuring point, the temperature data of each appointed temperature measuring point can be determined. And then the corresponding temperature change curve can be constructed by the temperature measurement data of the temperature measurement points at different time nodes. And by analyzing the temperature change curve, whether the temperature abnormality occurs at the currently designated temperature measuring point or not can be determined.
Before the temperature data slope change condition is detected by a large amount of experimental data when the temperature of the line is abnormal, and a corresponding slope threshold value is set. And detecting the slope change of the temperature change curve, and when the slope of the temperature change curve reaches a set slope threshold, indicating that the temperature mutation of the time node is possibly caused by abnormal conditions such as electric leakage and short circuit, and at the moment, determining the corresponding temperature measuring point as an abnormal detection node.
On the other hand, the control background can also determine that the temperature change curve is in a set state when the temperature data change slope between adjacent time nodes is in a set slope change range, so as to determine that the corresponding temperature measuring point is an abnormal detection node. The slope change range when the line temperature is abnormal is detected through a large amount of experimental data, and the slope change range is further set. And comparing the temperature data change slope of the temperature change curve with the set slope change range one by one, so as to judge whether the temperature abnormality occurs at present, and further determining an abnormal temperature measuring point as an abnormal detection node.
In addition, based on the temperature change curve, the temperature change curve can be determined to be in a set change state by traversing a pre-built abnormal curve database, and when the temperature change curve is matched with an abnormal curve of the abnormal curve database, the abnormal temperature measuring point is further determined to be an abnormal detection node. The abnormal curve database constructs an abnormal curve in advance based on temperature data change under the abnormal condition of the line temperature. Different from the above-mentioned mode of detecting the slope change of the temperature change curve, the embodiment of the application can also detect the abnormality of the temperature change curve in the past set period, if the curve characteristic of the detected temperature change curve is similar to the temperature data change curve detected in the abnormal temperature state, the temperature change curve is considered to be in the set change state, i.e. the abnormal temperature condition occurs at the current corresponding temperature measuring point.
Before the process, an abnormal curve database can be constructed in advance, and when the temperature abnormality of the line is determined according to the temperature data change under the condition that the temperature abnormality occurs in the line, an abnormal curve is constructed by acquiring the temperature data in the past set period and is put into the abnormal curve database for subsequent comparison of the data curves. And when the data curves are compared later, if the current temperature change curve is found to be matched with one curve of the abnormal curve database, the abnormal temperature condition of the temperature measuring point corresponding to the current line can be judged.
After the corresponding temperature measuring point is determined to be the abnormal detecting node, the abnormal detecting node is subjected to abnormal inspection in an unmanned equipment inspection verification mode, so that the operation safety of the abnormal detecting node is ensured, and the abnormal frequency is reduced. Referring to fig. 3, the control background 12 instructs the unmanned device 14 to go to the position corresponding to the abnormality detection node 15 of the cable line by issuing a patrol task to the unmanned device 14, and performs an abnormality patrol verification on the abnormality detection node 15.
When the inspection of the abnormal detection node is carried out, the control background acquires a digital circuit diagram of the cable circuit according to the current cable circuit which needs to be inspected, and marks the position of the abnormal detection node which needs to be inspected in advance on the circuit diagram. Based on these preset anomaly detection node locations, the unmanned device may proceed to perform a patrol task based on the locations indicated by the digital circuit diagram.
The unmanned equipment is provided with an electromagnetic detection module and a guided wave detection module. When the abnormal detection node is patrolled and examined, the guided wave detection module detects whether the line is damaged at the position of the abnormal detection node in a guided wave detection mode. It can be understood that the excitation signal generator installed by the unmanned equipment is utilized to send the guided wave signal to the position of the abnormal detection node of the cable line, and the guided wave detector of the guided wave detection module is utilized to receive the return wave of the guided wave signal so as to monitor the abnormal condition of the power transmission line in real time. Because damage such as broken strands and broken strands is encountered in the guided wave, part of the wave is reflected, and the returned wave is received by the guided wave detector. Based on the principle, whether the line is broken at the position of the abnormal detection node can be judged by the return wave detection result of the guided wave detector.
The electromagnetic detection module comprises an electric field sensor and a magnetic field sensor, and the electric field sensor and the magnetic field sensor are used for respectively acquiring an electric field signal and a magnetic field signal at the position of the abnormal detection node, so that the electric field signal and the magnetic field signal are converted into external electromagnetic data at the abnormal detection node, and the external electromagnetic data comprise electric field monitoring data and magnetic field monitoring data. The electric field signal processor is used for processing the initial electric field signal acquired by the electric field sensor into an electric signal which can be identified by the system, namely electric field monitoring data. The electric field signal processor amplifies the electric signal through the signal amplifying circuit to obtain an amplified electric signal; the amplified electric signal is subjected to low-pass filtering treatment through a low-pass filtering circuit to obtain a filtered electric signal; and finally, performing AC-DC conversion on the filtered electric signal through an AC-DC conversion circuit to obtain final electric field monitoring data. Similarly, the initial magnetic field signal acquired by the magnetic field sensor is processed into a magnetic signal which can be identified by the system, namely magnetic field monitoring data by a magnetic field signal processor. The magnetic field signal processor amplifies an initial magnetic field signal through the signal amplifying circuit; and then the amplified magnetic field signal is subjected to band-pass filtering by a band-pass filtering circuit, and final magnetic field monitoring data is output. And after the electric field and magnetic field monitoring data are obtained through the analog-to-digital conversion, reporting the electric field and magnetic field monitoring data as external electromagnetic data to a control background. It will be appreciated that after the insulation of the cable has been reduced, the resistance of the insulating layer will be reduced, and at this time the cable will generate a leakage current, and a local field strength, and when the local field strength is greater than the discharge threshold, the cable may discharge air (i.e. corona occurs), and at this time the frequency of the discharge current is in a certain interval, different from 50Hz. Therefore, the embodiment of the application compares the electric field monitoring data and the magnetic field monitoring data with the preset monitoring threshold value by acquiring the electric field monitoring data and the magnetic field monitoring data, and determines the conditions of reduced insulating property, electric leakage and the like of the current line under the condition that the electric field monitoring data and the magnetic field monitoring data reach the monitoring threshold value.
And the unmanned equipment reports the detection results of the external electromagnetic data and the returned waves to the control background. And corresponding to one end of the control background, determining whether the abnormal conditions such as line breakage or insulation performance reduction, electric leakage and the like occur at the position of the abnormal detection node based on the detection results of the external electromagnetic data and the returned waves. If the detection result of the return wave indicates that the return wave is received, the current line is indicated to be damaged. And if the electromagnetic data exceeds the set threshold value, determining that the current line position has the condition of insulation performance reduction or electric leakage. And the current abnormality detection node is verified to be abnormal, and an abnormality confirmation result is output to inform operation and maintenance personnel to timely process the abnormal condition, so that the safe operation of the line is ensured.
Optionally, the unmanned device may collect a line image of a position corresponding to the abnormal detection node through a camera, input the line image into a pre-constructed foreign object detection model, output a foreign object detection result, and report the foreign object detection result to a control background. The circuit image at the position corresponding to the abnormal detection node is acquired through the camera, and the circuit image is detected through the foreign object detection model, so that an accurate abnormal detection effect is achieved. It can be understood that the line is worn due to the occurrence of branch pressure and bird pressure covering abnormality on the line, so that the leakage condition is caused, and therefore, the abnormal condition is detected by collecting the line image. The foreign object detection model is used for carrying out model training in advance according to images when foreign object attachment occurs to the cable line as training images so as to be used for identifying the foreign object attachment condition of the cable line. Before the method, images with abnormal covering pressure such as branch pressing and birds appearing on the line are collected in advance to serve as training images, the training images are input into a foreign object detection model based on a neural network to be trained, the foreign object detection model has the capability of detecting and identifying abnormal covering pressure conditions of different foreign objects, and the input line images are subsequently identified. The foreign object detection model is realized through a convolutional neural network, and an initial convolutional neural network is trained, so that the convolutional neural network can better extract the characteristics of a training sample and has certain image classification capability; the basic structure of the convolutional neural network can comprise a convolutional layer, a pooling layer and a full-connection layer, wherein the convolutional layer and the pooling layer are alternately distributed, the convolutional layer can extract the characteristics of training samples through convolutional calculation, the pooling layer can conduct downsampling processing on the training samples input into the convolutional neural network, namely, conduct shrinkage processing on the training samples, meanwhile, retain important information in the training samples, and the full-connection layer classifies images based on the image characteristics determined by the convolutional layer. In addition, in the test stage, images of coverage pressure anomalies of branches, birds and the like of a large number of lines are collected to be used as training samples for training the model, and the training sample set is marked with anomaly type information according to different anomaly types, and the foreign object detection model is trained by using the training samples marked with the anomaly type information; after each training, the trained foreign object detection model can be subjected to iterative optimization through verification of a sample set and a loss function, and finally, when the accuracy of the foreign object detection model reaches the preset accuracy or the training number of the foreign object detection model reaches the preset number of the training wheels, the training of the foreign object detection model is stopped, and the foreign object detection model after the training is stopped is used as a final foreign object detection model. Therefore, the foreign object covering abnormal type of the circuit image can be identified through the pre-trained foreign object detection model, and the corresponding foreign object detection result is output. By reporting the foreign object detection result, the patrol personnel can judge whether the current abnormal conditions such as line damage, electric leakage and the like are caused by the inclined pressure of the foreign object, so that the abnormal reasons can be known in time, the corresponding abnormal processing strategy is improved and made, and the abnormal processing effect is optimized.
In addition, the unmanned equipment can be further provided with a current detection module, and the current detection module is used for detecting current data of the position corresponding to the abnormal detection node, and for the cable wire wrapped by the insulating layer, the current is generally not generated under the condition that the cable wire is not damaged. If the current data collected by the corresponding abnormal detection node exceeds the set current threshold, the current data indicates that the corresponding abnormal detection node has leakage current, which may be caused by insulation abnormality at the position of the abnormal detection node. Based on this, insulation abnormality detection of the abnormality detection node is performed by detecting current data of the corresponding position. The leakage current is detected through unmanned equipment, so that the accuracy of line leakage detection is further improved, and the accuracy of line anomaly detection is optimized.
The method comprises the steps of connecting a temperature measuring optical fiber and unmanned equipment through a control background signal, collecting optical fiber signals of the temperature measuring optical fiber at each appointed temperature measuring point, determining temperature data of each corresponding appointed temperature measuring point based on the optical fiber signals, constructing a temperature change curve of the appointed temperature measuring point based on the temperature data of different time nodes, detecting that the slope of the temperature change curve of the corresponding time node reaches a set slope threshold value based on the temperature change curve, or determining that the temperature change curve is in a set state in a set period, and determining that the corresponding temperature measuring point is an abnormal detection node; acquiring a digital circuit diagram of a current cable circuit, marking the position of an abnormality detection node on the digital circuit diagram, and sending the digital circuit diagram to unmanned equipment so as to instruct the unmanned equipment to perform abnormality detection on the abnormality detection node; the method comprises the steps that an unmanned device is used for positioning based on a digital circuit diagram and going to a corresponding position of an abnormal detection node, an electromagnetic detection module is used for collecting external electromagnetic data of a current cable line at the abnormal detection node, meanwhile, a guided wave signal is sent through an excitation signal generator of the guided wave detection module at the position corresponding to the abnormal detection node, a return wave of the guided wave signal is received through a guided wave detector, and detection results of the external electromagnetic data and the return wave are reported to a control background; and setting a threshold value based on external electromagnetic data comparison through a control background, performing abnormality verification of an abnormality detection node based on a comparison result and a detection result of a return wave, and outputting an abnormality confirmation result. By adopting the technical means, the abnormal detection node can be determined according to the line temperature change, the abnormal condition is verified through the fixed-point current and guided wave detection of unmanned equipment, the accurate abnormal detection is realized, and the line operation safety is ensured.
Embodiment two:
on the basis of the above embodiment, fig. 4 is a schematic structural diagram of a cable line fixed-point abnormality detection device based on unmanned equipment according to a second embodiment of the present application. Referring to fig. 4, the device for detecting fixed-point abnormality of a cable line based on an unmanned apparatus provided in this embodiment specifically includes: a detection module 21, a patrol module 22 and a verification module 23.
The detection module 21 is configured to connect the temperature measurement optical fiber and the unmanned device by controlling a background signal, collect optical fiber signals of the temperature measurement optical fiber at each specified temperature measurement point, determine temperature data of each corresponding specified temperature measurement point based on the optical fiber signals, construct a temperature change curve of the specified temperature measurement point based on the temperature data of different time nodes, and determine that the corresponding temperature measurement point is an abnormal detection node based on the temperature change curve detecting that the slope of the temperature change curve reaches a set slope threshold value or the temperature change curve is in a set state within a set period; acquiring a digital circuit diagram of a current cable circuit, marking the position of the abnormality detection node on the digital circuit diagram, and sending the digital circuit diagram to the unmanned equipment so as to instruct the unmanned equipment to perform abnormality detection on the abnormality detection node;
The inspection module 22 is configured to locate and forward to a corresponding position of the anomaly detection node based on a digital circuit diagram through the unmanned device, collect external electromagnetic data of a current cable line at the anomaly detection node through the electromagnetic detection module, send a guided wave signal at the anomaly detection node through an excitation signal generator of the guided wave detection module, receive a return wave of the guided wave signal through a guided wave detector, and report detection results of the external electromagnetic data and the return wave to a control background;
the verification module 23 is configured to perform, by using the control background, abnormality verification of the abnormality detection node based on the comparison result and the detection result of the return wave, and output an abnormality confirmation result.
Further, the control background is specifically configured to determine that the temperature change curve is in a set state when the temperature data change slope between adjacent time nodes is in a set slope change range.
Further, the method further comprises the following steps:
and acquiring a line image of the position corresponding to the abnormal detection node through a camera of the unmanned equipment, inputting the line image into a pre-constructed foreign object detection model, outputting a foreign object detection result and reporting the foreign object detection result to a control background.
Further, the foreign object detection model performs model training in advance according to an image of the cable line when foreign object adhesion occurs as a training image, so as to be used for identifying the foreign object adhesion condition of the cable line.
The method comprises the steps of connecting a temperature measuring optical fiber and unmanned equipment through a control background signal, collecting optical fiber signals of the temperature measuring optical fiber at each appointed temperature measuring point, determining temperature data of each corresponding appointed temperature measuring point based on the optical fiber signals, constructing a temperature change curve of the appointed temperature measuring point based on the temperature data of different time nodes, detecting that the slope of the temperature change curve of the corresponding time node reaches a set slope threshold value based on the temperature change curve, or determining that the temperature change curve is in a set state in a set period, and determining that the corresponding temperature measuring point is an abnormal detection node; acquiring a digital circuit diagram of a current cable circuit, marking the position of an abnormality detection node on the digital circuit diagram, and sending the digital circuit diagram to unmanned equipment so as to instruct the unmanned equipment to perform abnormality detection on the abnormality detection node; the method comprises the steps that an unmanned device is used for positioning based on a digital circuit diagram and going to a corresponding position of an abnormal detection node, an electromagnetic detection module is used for collecting external electromagnetic data of a current cable line at the abnormal detection node, meanwhile, a guided wave signal is sent through an excitation signal generator of the guided wave detection module at the position corresponding to the abnormal detection node, a return wave of the guided wave signal is received through a guided wave detector, and detection results of the external electromagnetic data and the return wave are reported to a control background; and setting a threshold value based on external electromagnetic data comparison through a control background, performing abnormality verification of an abnormality detection node based on a comparison result and a detection result of a return wave, and outputting an abnormality confirmation result. By adopting the technical means, the abnormal detection node can be determined according to the line temperature change, the abnormal condition is verified through the fixed-point current and guided wave detection of unmanned equipment, the accurate abnormal detection is realized, and the line operation safety is ensured.
The cable line fixed-point abnormality detection device based on the unmanned equipment provided by the embodiment II of the application can be used for executing the cable line fixed-point abnormality detection method based on the unmanned equipment provided by the embodiment I, and has corresponding functions and beneficial effects.
Embodiment III:
an electronic device according to a third embodiment of the present application, referring to fig. 5, includes: processor 31, memory 32, communication module 33, input device 34 and output device 35. The number of processors in the electronic device may be one or more and the number of memories in the electronic device may be one or more. The processor, memory, communication module, input device, and output device of the electronic device may be connected by a bus or other means.
The memory is used as a computer readable storage medium, and can be used for storing a software program, a computer executable program and a module, and the program instructions/modules (for example, a detection module, a patrol module and a verification module in the unmanned equipment-based cable line fixed point abnormality detection device) correspond to the unmanned equipment-based cable line fixed point abnormality detection method according to any embodiment of the application. The memory may mainly include a memory program area and a memory data area, wherein the memory program area may store an operating system, at least one application program required for a function; the storage data area may store data created according to the use of the device, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some examples, the memory may further include memory remotely located with respect to the processor, the remote memory being connectable to the device through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The communication module is used for carrying out data transmission.
The processor executes various functional applications and data processing of the device by running software programs, instructions and modules stored in the memory, namely the method for detecting the fixed-point abnormality of the cable line based on the unmanned device is realized.
The input means may be used to receive entered numeric or character information and to generate key signal inputs related to user settings and function control of the device. The output means may comprise a display device such as a display screen.
The electronic device provided by the above-mentioned embodiment can be used for executing the cable line fixed-point abnormality detection method based on the unmanned device provided by the above-mentioned embodiment, and has corresponding functions and beneficial effects.
Embodiment four:
the embodiment of the application also provides a storage medium containing computer executable instructions, which when executed by a computer processor, are used for executing a method for detecting fixed-point anomalies of a cable line based on unmanned equipment, and the method for detecting fixed-point anomalies of the cable line based on unmanned equipment comprises the following steps: the method comprises the steps of connecting a temperature measuring optical fiber and unmanned equipment through a control background signal, collecting optical fiber signals of the temperature measuring optical fiber at each appointed temperature measuring point, determining corresponding temperature data of each appointed temperature measuring point based on the optical fiber signals, constructing a temperature change curve of the appointed temperature measuring point based on temperature data of different time nodes, detecting that the slope of the temperature change curve reaches a set slope threshold value based on the temperature change curve, or determining that the temperature change curve is in a set state in a set period, and determining that the corresponding temperature measuring point is an abnormal detection node; acquiring a digital circuit diagram of a current cable circuit, marking the position of the abnormality detection node on the digital circuit diagram, and sending the digital circuit diagram to the unmanned equipment so as to instruct the unmanned equipment to perform abnormality detection on the abnormality detection node; the unmanned equipment is positioned based on a digital circuit diagram and goes to the corresponding position of the abnormal detection node, the electromagnetic detection module is used for collecting external electromagnetic data of the current cable line at the abnormal detection node, meanwhile, the excitation signal generator of the guided wave detection module is used for sending guided wave signals to the position corresponding to the abnormal detection node, the guided wave detector is used for receiving return waves of the guided wave signals, and detection results of the external electromagnetic data and the return waves are reported to a control background; and comparing the set threshold value based on the external electromagnetic data through the control background, carrying out abnormality verification on the abnormality detection node based on the comparison result and the detection result of the return wave, and outputting an abnormality confirmation result.
Storage media-any of various types of memory devices or storage devices. The term "storage medium" is intended to include: mounting media such as CD-ROM, floppy disk or tape devices; computer system memory or random access memory such as DRAM, DDR RAM, SRAM, EDO RAM, lanbas (Rambus) RAM, etc.; nonvolatile memory such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. The storage medium may also include other types of memory or combinations thereof. In addition, the storage medium may be located in a first computer system in which the program is executed, or may be located in a second, different computer system connected to the first computer system through a network such as the internet. The second computer system may provide program instructions to the first computer for execution. The term "storage medium" may include two or more storage media residing in different locations (e.g., in different computer systems connected by a network). The storage medium may store program instructions (e.g., embodied as a computer program) executable by one or more processors.
Of course, the storage medium containing the computer executable instructions provided by the embodiment of the application is not limited to the method for detecting the fixed-point abnormality of the cable line based on the unmanned device, and the related operations in the method for detecting the fixed-point abnormality of the cable line based on the unmanned device provided by any embodiment of the application can be performed.
The apparatus for detecting fixed-point anomalies of a cable line based on an unmanned device, the storage medium and the electronic device provided in the foregoing embodiments may perform the method for detecting fixed-point anomalies of a cable line based on an unmanned device provided in any embodiment of the present application, and technical details not described in detail in the foregoing embodiments may be referred to the method for detecting fixed-point anomalies of a cable line based on an unmanned device provided in any embodiment of the present application.
The foregoing description is only of the preferred embodiments of the application and the technical principles employed. The present application is not limited to the specific embodiments described herein, but is capable of numerous modifications, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the application. Therefore, while the application has been described in connection with the above embodiments, the application is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit of the application, the scope of which is set forth in the following claims.
Claims (10)
1. The cable line fixed point abnormality detection system based on the unmanned equipment is characterized by comprising a control background, a temperature measuring optical fiber and the unmanned equipment;
the temperature measuring optical fiber is arranged in the current cable line and is arranged along the current cable line;
the control background signal is connected with the temperature measuring optical fiber and the unmanned equipment and is used for collecting optical fiber signals of the temperature measuring optical fiber at each appointed temperature measuring point, determining corresponding temperature data of each appointed temperature measuring point based on the optical fiber signals, constructing a temperature change curve of the appointed temperature measuring point based on the temperature data of different time nodes, detecting that the slope of the temperature change curve reaches a set slope threshold value based on the temperature change curve, or determining that the temperature change curve is in a set state in a set period, and determining that the corresponding temperature measuring point is an abnormal detection node; acquiring a digital circuit diagram of a current cable circuit, marking the position of the abnormality detection node on the digital circuit diagram, and sending the digital circuit diagram to the unmanned equipment so as to instruct the unmanned equipment to perform abnormality detection on the abnormality detection node;
the unmanned equipment is used for positioning based on a digital circuit diagram and going to the corresponding position of the abnormal detection node, acquiring external electromagnetic data of the current cable circuit at the abnormal detection node through an electromagnetic detection module, simultaneously transmitting a guided wave signal at the position corresponding to the abnormal detection node through an excitation signal generator of a guided wave detection module, receiving a return wave of the guided wave signal through a guided wave detector, and reporting the external electromagnetic data and a detection result of the return wave to a control background;
The control background is also used for comparing the set threshold value based on the external electromagnetic data, carrying out abnormality verification on the abnormality detection node based on the comparison result and the detection result of the return wave, and outputting an abnormality confirmation result.
2. The unmanned aerial vehicle-based cabling site-directed anomaly detection system of claim 1, wherein the control background is specifically configured to determine that the temperature change curve is in a set state when a temperature data change slope between adjacent time nodes is in a set slope change range.
3. The unmanned aerial vehicle-based cable line fixed-point abnormality detection system according to claim 1, wherein the unmanned aerial vehicle is further configured to collect line images of positions corresponding to the abnormality detection nodes through a camera, input the line images into a pre-constructed foreign object detection model, output a foreign object detection result, and report the foreign object detection result to a control background.
4. The unmanned aerial vehicle-based cable line fixed point abnormality detection system according to claim 3, wherein the foreign object detection model performs model training in advance based on an image when foreign object attachment occurs to the cable line as a training image for identifying the foreign object attachment condition of the cable line.
5. The unmanned equipment-based cable line fixed point abnormality detection method is applied to the unmanned equipment-based cable line fixed point abnormality detection system as claimed in claim 1, and is characterized by comprising the following steps:
the method comprises the steps of connecting a temperature measuring optical fiber and unmanned equipment through a control background signal, collecting optical fiber signals of the temperature measuring optical fiber at each appointed temperature measuring point, determining corresponding temperature data of each appointed temperature measuring point based on the optical fiber signals, constructing a temperature change curve of the appointed temperature measuring point based on temperature data of different time nodes, detecting that the slope of the temperature change curve reaches a set slope threshold value based on the temperature change curve, or determining that the temperature change curve is in a set state in a set period, and determining that the corresponding temperature measuring point is an abnormal detection node; acquiring a digital circuit diagram of a current cable circuit, marking the position of the abnormality detection node on the digital circuit diagram, and sending the digital circuit diagram to the unmanned equipment so as to instruct the unmanned equipment to perform abnormality detection on the abnormality detection node;
the unmanned equipment is positioned based on a digital circuit diagram and goes to the corresponding position of the abnormal detection node, the electromagnetic detection module is used for collecting external electromagnetic data of the current cable line at the abnormal detection node, meanwhile, the excitation signal generator of the guided wave detection module is used for sending guided wave signals to the position corresponding to the abnormal detection node, the guided wave detector is used for receiving return waves of the guided wave signals, and detection results of the external electromagnetic data and the return waves are reported to a control background;
And comparing the set threshold value based on the external electromagnetic data through the control background, carrying out abnormality verification on the abnormality detection node based on the comparison result and the detection result of the return wave, and outputting an abnormality confirmation result.
6. The unmanned aerial vehicle-based cabling site-directed anomaly detection method of claim 5, wherein the control background is specifically configured to determine that the temperature change curve is in a set state when a temperature data change slope between adjacent time nodes is in a set slope change range.
7. The unmanned aerial vehicle-based cabling targeted anomaly detection method of claim 5, further comprising:
and acquiring a line image of the position corresponding to the abnormal detection node through a camera of the unmanned equipment, inputting the line image into a pre-constructed foreign object detection model, outputting a foreign object detection result and reporting the foreign object detection result to a control background.
8. The unmanned aerial vehicle-based cable line fixed point abnormality detection method according to claim 7, wherein the foreign object detection model performs model training in advance based on an image when foreign object attachment occurs to the cable line as a training image for identifying the foreign object attachment condition of the cable line.
9. The unmanned equipment-based cable line fixed point abnormality detection device is applied to the unmanned equipment-based cable line fixed point abnormality detection system according to claim 1, and is characterized by comprising:
the detection module is used for connecting the temperature measuring optical fiber and unmanned equipment through control background signals, collecting optical fiber signals of the temperature measuring optical fiber at each appointed temperature measuring point, determining corresponding temperature data of each appointed temperature measuring point based on the optical fiber signals, constructing a temperature change curve of the appointed temperature measuring point based on the temperature data of different time nodes, detecting that the slope of the temperature change curve reaches a set slope threshold value based on the temperature change curve, or determining that the temperature change curve is in a set state in a set period, and determining that the corresponding temperature measuring point is an abnormal detection node; acquiring a digital circuit diagram of a current cable circuit, marking the position of the abnormality detection node on the digital circuit diagram, and sending the digital circuit diagram to the unmanned equipment so as to instruct the unmanned equipment to perform abnormality detection on the abnormality detection node;
the inspection module is used for positioning and going to the corresponding position of the abnormal detection node based on a digital circuit diagram through the unmanned equipment, collecting external electromagnetic data of the current cable line at the abnormal detection node through the electromagnetic detection module, simultaneously sending a guided wave signal at the position corresponding to the abnormal detection node through an excitation signal generator of the guided wave detection module, receiving a return wave of the guided wave signal through a guided wave detector, and reporting the external electromagnetic data and a detection result of the return wave to a control background;
And the verification module is used for comparing the set threshold value based on the external electromagnetic data through the control background, carrying out abnormality verification on the abnormality detection node based on the comparison result and the detection result of the return wave, and outputting an abnormality confirmation result.
10. A storage medium containing computer executable instructions which, when executed by a computer processor, are for performing the unmanned device-based cabling setpoint anomaly detection method of any one of claims 5 to 8.
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| CN117830030B (en) * | 2024-03-04 | 2024-05-28 | 吉林省远程电缆有限公司 | Cable operation supervision method and system based on Internet of things |
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