Power transmission line exception handling method, device, equipment, medium and program product

Abstract

The invention relates to an exception handling method and device for a power transmission line, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a plurality of first images of a power transmission line at different angles, inputting each first image into an anomaly detection model to obtain a first anomaly detection result including a first galloping detection result, a first sag detection result and an anomaly level, and when the anomaly level is a slight level, determining that the first anomaly detection result is a first galloping detection result; and sending the first anomaly detection result and the first images to a server, acquiring a plurality of second images of the power transmission line at different angles and sending the second images to the server, so that the server inputs the first images and the second images to an anomaly detection large model to obtain a second anomaly detection result under the condition that the server determines that the anomaly level is a slight level, and outputting an exception handling message under the condition that the second exception detection result is confirmed to be exceptional. By adopting the method, the detection accuracy of the galloping state and the sag state can be improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for handling transmission line anomalies. Background Art

[0002] With the development of Internet technology and the continuous increase in electricity demand, the construction of power equipment has begun to advance into remote areas. Taking transmission equipment as an example, transmission equipment needs to be maintained and inspected. For example, the transmission lines between transmission equipment need to be inspected for dancing and sag. If dancing and sag occur, it will affect the power transmission of the transmission lines.

[0003] In traditional technology, in order to detect the galloping and sag of the transmission line, a single device is usually used to monitor the galloping and sag status of the transmission line respectively, that is, the galloping of the transmission line is monitored by the galloping monitoring device, and the sag of the transmission line is monitored by the sag monitoring device.

[0004] However, a single device has poor detection accuracy for the galloping state and sag state when performing galloping state monitoring and sag state monitoring of transmission lines. Summary of the Invention

[0005] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for abnormal handling of transmission lines that can improve the accuracy of detecting galloping state and sag state in order to address the above technical problems.

[0006] In the first aspect, the present application provides a method for handling anomalies of a transmission line, which is applied to a terminal, and includes: collecting multiple first images of the transmission line at different angles, and inputting each first image into an anomaly detection model to obtain a first anomaly detection result; the first anomaly detection result includes a first dancing detection result, a first sag detection result and an anomaly level, the first dancing detection result is used to characterize the probability of the transmission line dancing, and the first sag detection result is used to characterize the probability of the transmission line sag; when the anomaly level is a minor level, the first anomaly detection result and each first image are sent to a server; multiple second images of the transmission line at different angles are collected, and each second image is sent to the server, so that when the server determines that the anomaly level is a minor level, each first image and each second image is input into the anomaly detection large model to obtain a second anomaly detection result, and when the second anomaly detection result is a confirmed anomaly, an anomaly handling message is output.

[0007] In one embodiment, capturing multiple first images of a power line at different angles includes: obtaining environmental parameters of the current environment through an environmental sensor, and determining an image capture strategy based on a size relationship between the environmental parameters and a parameter threshold; the image capture strategy includes an image capture angle and an image capture frequency; and capturing each first image according to the image capture strategy.

[0008] In one embodiment, after inputting each first image into the anomaly detection model and obtaining the first anomaly detection result, the method further includes: when the anomaly level is a significant level, sending the first anomaly detection result to the server, so that the server outputs an anomaly handling message when determining that the anomaly level is a significant level.

[0009] In one embodiment, after inputting each first image into the anomaly detection model and obtaining the first anomaly detection result, the method further includes: when the anomaly level is a non-abnormal level, sending the first anomaly detection result to the server, so that the server records the first anomaly detection result when determining that the anomaly level is a non-abnormal level.

[0010] In the second aspect, the present application provides another method for handling abnormalities in transmission lines, which is applied to a server, including: receiving a first abnormality detection result for the transmission line and multiple first images of the transmission line at different angles sent by a terminal; the first abnormality detection result is obtained by the terminal inputting each first image into an abnormality detection model, and the first abnormality detection result includes a first dancing detection result, a first sag detection result and an abnormality level, the first dancing detection result is used to characterize the probability of dancing in the transmission line, and the first sag detection result is used to characterize the probability of sag in the transmission line; when the abnormality level is a mild level, receiving multiple second images of the transmission line at different angles sent by the terminal, and inputting each first image and each second image into the abnormality detection large model to obtain a second abnormality detection result of the transmission line; if the second abnormality detection result is a confirmed abnormality, outputting an abnormality handling message according to the second abnormality detection result.

[0011] In one embodiment, each first image and each second image are input into an abnormality detection large model to obtain a second abnormality detection result of the transmission line, including: calculating the dancing frequency and dancing amplitude of the transmission line according to each first image and each second image by the abnormality detection large model, and generating a second dancing detection result of the transmission line according to the dancing frequency and the dancing amplitude; calculating the sag of the transmission line according to each first image and each second image by the abnormality detection large model, and generating a second sag detection result of the transmission line according to the sag; generating a second abnormality detection result according to the second dancing detection result and the second sag detection result by the abnormality detection large model.

[0012] In one embodiment, outputting an exception handling message according to the second exception detection result includes: generating an exception handling message for the transmission line based on the second exception detection result; determining an operation and maintenance device corresponding to the transmission line, and sending the exception handling message to the operation and maintenance device.

[0013] In one of the embodiments, after receiving the first abnormality detection result for the transmission line and multiple first images of the transmission line at different angles sent by the receiving terminal, the method also includes: when the abnormality level is a significant level, generating an abnormality handling message based on the first abnormality detection result, and sending the abnormality handling message to the operation and maintenance equipment corresponding to the transmission line.

[0014] On the third aspect, the present application also provides an abnormality handling device for a transmission line, including: an abnormality detection module, used to collect multiple first images of the transmission line at different angles, and input each first image into an abnormality detection model to obtain a first abnormality detection result; the first abnormality detection result includes a first dancing detection result, a first sag detection result and an abnormality level, the first dancing detection result is used to characterize the probability of the transmission line dancing, and the first sag detection result is used to characterize the probability of the transmission line sag; a data sending module, used to send the first abnormality detection result and each first image to a server when the abnormality level is a minor level; a second image sending module, used to collect multiple second images of the transmission line at different angles, and send each second image to the server, so that when the server determines that the abnormality level is a minor level, it inputs each first image and each second image into the abnormality detection model to obtain a second abnormality detection result, and outputs an abnormality handling message when the second abnormality detection result is a confirmed abnormality.

[0015] In a fourth aspect, the present application also provides another abnormality handling device for a transmission line, comprising: a data receiving module for receiving a first abnormality detection result for the transmission line and a plurality of first images of the transmission line at different angles sent by a terminal; the first abnormality detection result is obtained by the terminal inputting each first image into an abnormality detection model, the first abnormality detection result including a first dancing detection result, a first sag detection result and an abnormality level, the first dancing detection result is used to characterize the probability of dancing in the transmission line, and the first sag detection result is used to characterize the probability of sag in the transmission line; an abnormality detection module for, when the abnormality level is a mild level, receiving a plurality of second images of the transmission line at different angles sent by the terminal, and inputting each first image and each second image into the abnormality detection large model to obtain a second abnormality detection result of the transmission line; an abnormality handling message output module for outputting an abnormality handling message according to the second abnormality detection result if the second abnormality detection result is a confirmed abnormality.

[0016] In a fifth aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method described in the first aspect when executing the computer program.

[0017] In a sixth aspect, the present application further provides another computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method described in the second aspect when executing the computer program.

[0018] In a seventh aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method described in the first aspect when executed by a processor.

[0019] In an eighth aspect, the present application further provides another computer-readable storage medium having a computer program stored thereon, which implements the steps of the method described in the second aspect when executed by a processor.

[0020] In a ninth aspect, the present application further provides a computer program product, comprising a computer program, which implements the steps of the method described in the first aspect when executed by a processor.

[0021] In a tenth aspect, the present application also provides another computer program product, comprising a computer program, which implements the steps of the method described in the second aspect when executed by a processor.

[0022] The above-mentioned power line abnormality handling method, apparatus, computer device, computer-readable storage medium, and computer program product collect multiple first images of the power line at different angles through a terminal, and input the first images into an abnormality detection model configured in the terminal to obtain a first abnormality detection result. The first abnormality detection result includes a first galloping detection result, a first sag detection result, and an abnormality level. The first galloping detection result includes the probability of the power line galloping, and the first sag detection result includes the probability of the power line sag. If the abnormality level is a slight level, it indicates that there is a certain probability of the power line galloping and / or sag. The first abnormality detection result and the first image are sent to a server for abnormality detection using a larger-scale model configured in the server. Simultaneously, the terminal collects multiple second images of the power line at different angles and sends them to the server, so that the server inputs the first and second images into the large abnormality detection model for abnormality detection to obtain more accurate abnormality detection results. If the server confirms an abnormality, it outputs an abnormality handling message. Through the mutual cooperation of the terminal and the server, the terminal performs a preliminary detection and the server performs a secondary abnormality detection. The dual-terminal coordinated abnormality detection improves the detection accuracy of the galloping state and the sag state. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0024] Figure 1 FIG. 1 is an application environment diagram of a method for handling an abnormality of a power transmission line according to an embodiment;

[0025] Figure 2 1 is a flow chart of a method for handling abnormalities in a transmission line according to an embodiment;

[0026] Figure 3 201 is a flow chart of step 201 in one embodiment;

[0027] Figure 4 FIG1 is a flow chart of a method for handling abnormalities of a transmission line at a significant level in another embodiment;

[0028] Figure 5 is a flow chart of a method for handling an abnormality of a transmission line at a non-abnormal level in another embodiment;

[0029] Figure 6 is a flow chart of a method for handling an abnormality of a transmission line in one embodiment;

[0030] Figure 7 1 is a flow chart of another method for handling abnormalities of a power transmission line according to an embodiment;

[0031] Figure 8 701 in one embodiment;

[0032] Figure 9 703 in one embodiment;

[0033] Figure 10 1 is a flow chart of another method for handling abnormalities of a transmission line at a significant level in one embodiment;

[0034] Figure 11 is a structural block diagram of a transmission line abnormality processing device according to one embodiment;

[0035] Figure 12 is a structural block diagram of another apparatus for handling abnormalities of a power transmission line according to an embodiment;

[0036] Figure 13 is a diagram of the internal structure of a computer device in one embodiment;

[0037] Figure 14 FIG. 1 is a diagram showing the internal structure of another computer device in one embodiment. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0039] It should be noted that the terms "first", "second", etc. used in this application may be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "including" and "having" used in this application and any variations thereof are intended to cover non-exclusive inclusions. The term "plurality" used in this application refers to two or more. The term "and / or" used in this application refers to one of the solutions or any combination of multiple solutions.

[0040] The abnormality handling method of the transmission line provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, the application environment at least includes: a terminal 101 and a server 102.

[0041] Among them, the terminal 101 is used to collect multiple first images of the power line at different angles, and input each first image into the anomaly detection model to obtain a first anomaly detection result. When the anomaly level included in the first anomaly detection result is a minor level, the first anomaly detection result and each first image are sent to the server 102. The terminal 101 collects multiple second images of the power line at different angles and sends each second image to the server 102. The terminal 101 can be an image acquisition terminal or a camera. For example, the terminal 101 can be a fixed pan-tilt camera or a pan-tilt camera with a track. The terminal 101 can communicate with the server 102 via a wireless communication module, and can also communicate with the server 102 via a wired communication module connected to the network.

[0042] Server 102 can be used to obtain a first anomaly detection result for the power line sent by terminal 101 and multiple first images of the power line captured by the terminal at different angles. If the first anomaly detection result contains a minor anomaly level, server 102 obtains multiple second images of the power line at different angles sent by terminal 101 and inputs each first image and each second image into a large anomaly detection model to obtain a second anomaly detection result. If the second anomaly detection result is a confirmed anomaly, server 102 outputs an anomaly handling message based on the second anomaly detection result. Server 102 can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0043] In addition, the implementation environment may also include an operation and maintenance device 103, which can be used to receive and display exception handling messages sent by server 102. Operation and maintenance device 103 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart car devices, and projectors. Portable wearable devices can include smart watches, smart bracelets, head-mounted devices, and the like. Head-mounted devices can include virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, and the like.

[0044] In an exemplary embodiment, Figure 2 As shown, a method for handling abnormalities in a transmission line is provided, which is applied to Figure 1 The terminal in is taken as an example to illustrate, including the following steps 201 to 203.

[0045] Step 201 : collect a plurality of first images of the power line at different angles, and input each first image into an anomaly detection model to obtain a first anomaly detection result.

[0046] In actual scenarios, transmission lines are usually erected at high altitudes or in some sparsely populated areas. Daily inspections and status monitoring are also required for the transmission lines to prevent the transmission lines from dancing and / or sag, which affects the safety of electricity use. To this end, the present application performs status detection on the transmission lines by deploying terminals. The terminals can detect the probability of dancing and / or sag of the transmission lines by collecting images of the transmission lines and using image recognition, thereby realizing real-time dancing and sag status detection of the transmission lines.

[0047] In the present application, the first abnormality detection result includes the first dancing detection result, the first sag detection result and the abnormality level. Among them, the first dancing detection result is used to characterize the probability of the transmission line dancing; the dancing of the transmission line refers to the large-amplitude, low-frequency vibration phenomenon that occurs under specific meteorological conditions, especially when the transmission line covered with ice or snow is affected by wind. The dancing of the transmission line will affect the transmission of electric energy. The first sag detection result is used to characterize the probability of the transmission line sag; the sag of the transmission line refers to the vertical distance between the lowest point of the curve formed by the natural droop of the conductor between the suspension points in the overhead transmission line and the line connecting the suspension points. The deviation of the sag of the transmission line will affect the life of the transmission line.

[0048] In this application, the abnormality level refers to the integrated probability of transmission line galloping and sag obtained by the terminal through the abnormality detection model. The abnormality level may include multiple levels, including: no abnormality level, slight level and / or significant level. The no abnormality level indicates that the integrated probability of transmission line galloping and sag has not reached the alarm threshold, the slight level indicates that the integrated probability of transmission line galloping and sag has reached the alarm threshold but has not reached the emergency alarm threshold, and the significant level indicates that the integrated probability of transmission line galloping and sag has reached the emergency alarm level. Among them, the integrated probability is the overall probability obtained by combining and / or converting the galloping probability and the sag probability of the transmission line to characterize the galloping and / or sag of the transmission line.

[0049] During implementation, the terminal uses a configured image acquisition module to capture multiple first images of the power line at different angles. These images are then fed into an anomaly detection model, which then performs image recognition on the multiple first images to obtain a first anomaly detection result. The first anomaly detection result includes a first gallop detection result, a first sag detection result, and / or anomaly level of the power line. Furthermore, the first anomaly detection result may include environmental data collected by the terminal, such as temperature, humidity, wind speed, and / or sunlight intensity.

[0050] Among them, the anomaly detection model can be configured inside the terminal as a functional module of the terminal. The anomaly detection model can be a small model with fewer model parameters to reduce the power consumption of the terminal when performing anomaly detection through the anomaly detection model; further, the anomaly detection model can also be updated and / or upgraded through the network to which the terminal is connected, and the updated model parameters can be sent to the terminal through the server, and the anomaly detection model can be updated and / or upgraded through the terminal.

[0051] For the training process of the anomaly detection model, the sample image can be used as a sample, the anomaly detection result can be used as a label, the sample image can be input into the anomaly detection model, the calculation result can be output, the calculation result can be compared with the label, and the anomaly detection model can be corrected according to the comparison result. The anomaly detection result used as a label can be pre-set or a real dancing and / or sag result can be obtained. Similarly, the anomaly detection model training method mentioned above is used to train the anomaly detection model until the difference between the anomaly detection result calculated by the anomaly detection model and the anomaly detection result used as the label converges. The training of the anomaly detection model is completed, and the above-mentioned anomaly detection processing is performed based on the anomaly detection model. The training of the anomaly detection model can be performed on a cloud server or offline. Furthermore, the sample data can also include the measured values ​​of other sensors such as wind speed sensors, temperature and humidity sensors, so as to perform anomaly detection processing of dancing and / or sag by combining sensor data and images.

[0052] During the process of performing anomaly detection using the anomaly detection model, the anomaly detection model can calculate the dancing amplitude and / or sag degree of the transmission line based on the time intervals between the multiple first images and the movement posture of the transmission line, and detect whether the dancing amplitude and / or sag degree is greater than a preset first dancing amplitude and / or first sag degree and less than a preset second dancing amplitude and / or second sag degree. If so, it indicates that there is a medium probability of the transmission line dancing and / or sag, and a slight abnormality level is output; if the dancing amplitude and / or sag degree is greater than the preset second dancing amplitude and / or second sag degree, it indicates that there is a high probability of the transmission line dancing and / or sag, and a significant abnormality level is output; if the dancing amplitude and / or sag degree is less than the preset first dancing amplitude and / or first sag degree, it indicates that there is a low probability of the transmission line dancing and / or sag, and a non-abnormal abnormality level is output. Furthermore, anomaly detection processing can also be performed in combination with environmental parameters; even further, anomaly detection processing can also be performed in combination with network conditions. For example, network timeout or retransmission increases the probability of dancing and / or sag. Optionally, the motion parameters and / or posture parameters include dancing frequency, dancing amplitude and / or sag.

[0053] During the image acquisition process, the terminal may perform image acquisition at a fixed frequency according to a preset acquisition frequency, for example, the terminal may be configured to acquire an image every x minutes; alternatively, the terminal may perform image acquisition based on environmental data of the external environment, for example, the terminal may be configured to acquire an image when the environmental data reaches a threshold; furthermore, the terminal may perform image acquisition based on a combination of a preset acquisition frequency and environmental data, for example, the terminal performs image acquisition at a preset frequency, but immediately performs image acquisition when it detects that the environmental data reaches a threshold. The environmental data may be sent to the terminal via an environmental sensor connected to the terminal, or may be environmental data of the area obtained by the terminal via a network.

[0054] Step 202: When the abnormality level is a minor level, the first abnormality detection result and each first image are sent to the server.

[0055] During the implementation process, the terminal receives the abnormality level output by the abnormality detection model. When the abnormality level is a mild level, it indicates that there is a medium probability of galloping and / or sag in the transmission line, and a secondary detection is required through the server using a large abnormality detection model with more model parameters. The terminal sends the first abnormality detection result and the first collected image to the server.

[0056] During the execution process, in order to improve the accuracy of anomaly detection performed by the server, the terminal may pre-process the captured first image and send the pre-processed first image to the server; optionally, the terminal may adjust exposure parameters, perform image denoising, contrast enhancement and / or edge monitoring on the first image to improve image quality; further, in order to improve the efficiency of network transmission, the terminal may also compress the pre-processed image and send the compressed image to the server.

[0057] The terminal may also perform pre-processing and / or image compression on the first image with reference to the above operations before inputting the first image into the anomaly detection model to improve the accuracy of anomaly detection.

[0058] Step 203 collects multiple second images of the transmission line at different angles and sends each second image to the server, so that when the server determines that the abnormality level is a minor level, the server inputs each first image and each second image into the abnormality detection model to obtain a second abnormality detection result, and outputs an abnormality handling message when the second abnormality detection result is a confirmed abnormality.

[0059] In this application, when the abnormality level is a minor level, the terminal performs secondary image acquisition and sends it to the server, so that the server performs image recognition based on the first image and the second image to identify whether the transmission line is dancing and / or sag. Through the dual-end coordinated secondary detection method, the detection load of a single device is reduced, and the reliability and accuracy of abnormality detection are improved through secondary verification.

[0060] During the implementation process, the terminal collects multiple second images of the transmission line at different angles according to the image acquisition strategy, and sends the multiple second images to the server; after receiving the second images, the server inputs the multiple first images and the multiple second images into the abnormality detection model, and performs abnormality detection on the first images and the second images through the abnormality detection model to obtain a second abnormality detection result. When the second abnormality detection result is a confirmed abnormality, it indicates that there is indeed a probability that the transmission line will dance and / or sag, and an abnormality handling message is output to perform abnormal handling on the transmission line.

[0061] It should be noted that the terminal can interact with the server through the Internet, through a local area network, or through a wired network; the server can be an edge computing server in the area where the terminal is located, or it can be a central server of the power network; the image captured by the terminal may include more than one transmission line, that is: one terminal can perform dancing and / or sag detection on multiple transmission lines.

[0062] In the above-mentioned method for handling transmission line anomalies, a terminal collects multiple first images of the transmission line at different angles. The terminal inputs each first image into an anomaly detection model to obtain a first anomaly detection result including a first galloping detection result, a first sag detection result, and an anomaly level. The first galloping detection result is used to characterize the probability of galloping in the transmission line, and the first sag detection result is used to characterize the probability of sag in the transmission line. Galloping detection and sag detection are integrated into a single device, and both state detections are performed using a single anomaly detection model, thereby improving the utilization of the device's computing power. If the anomaly level is minor, the first anomaly detection result and each first image are sent to a server. The terminal's anomaly detection model performs preliminary detection, and the server performs secondary detection. This avoids detection errors and excessive load on a single device, thereby improving the effectiveness of anomaly detection. The terminal collects multiple second images at different angles and sends each second image to the server. The server-configured anomaly detection model performs anomaly detection based on the first and second images to obtain a second anomaly detection result. If the second anomaly detection result confirms an anomaly, an anomaly handling message is output. This dual-terminal coordinated secondary anomaly detection method improves the accuracy of detecting galloping and sag states.

[0063] Based on the embodiment of a method for handling abnormalities of a transmission line provided above, one or more embodiments are provided below to further illustrate the method for handling abnormalities of a transmission line.

[0064] In actual scenarios, dancing and / or sag usually occur in bad weather conditions, for example, dancing usually occurs in windy weather, and sag usually occurs in low-temperature and icy weather. To address this, the present application configures an environmental sensor for the terminal, determines an image acquisition strategy based on the environmental parameters of the environmental sensor, and acquires each first image based on the image acquisition strategy. In an optional implementation scheme provided by the present application, if Figure 3 As shown, the operation of acquiring multiple first images in step 201 includes steps 301 to 302.

[0065] Step 301: Acquire environmental parameters of the current environment through an environmental sensor, and determine an image acquisition strategy based on a magnitude relationship between the environmental parameters and parameter thresholds.

[0066] In this application, environmental parameters refer to the environmental data of the area where the terminal is located, which are used to characterize the weather conditions in the area. The environmental parameters may include: wind parameters, temperature parameters, humidity parameters and / or sunshine parameters. The environmental parameters can be obtained through environmental sensors. Accordingly, the environmental sensors may include: wind sensors, temperature and humidity sensors and / or light intensity sensors.

[0067] Among them, the environmental sensor can be directly connected to the terminal, and the environmental data of the sensor can be read between the terminals; in addition, the environmental sensor can also be a public environmental sensor in the area, and the terminal can obtain the environmental data of the environmental sensor through the network; or, the environmental sensor can also upload the data of the area to the environmental server after collecting it, and the terminal obtains the environmental data of the environmental sensor by calling the data interface of the environmental server.

[0068] During implementation, the terminal detects whether the environmental parameter is greater than a preset parameter threshold and determines an appropriate image acquisition strategy based on the relationship with the parameter threshold. For example, the terminal sets a parameter threshold. If the environmental parameter is less than the parameter threshold, the image acquisition strategy is determined to be: image acquisition once every half hour, each acquisition is divided into 5 angles, and the acquisition frequency of each angle is 5Hz. If the environmental parameter is greater than the parameter threshold, the image acquisition strategy is determined to be: once every 10 minutes, each acquisition is divided into 6 angles, and the acquisition frequency of each angle is 10Hz. The image acquisition strategy includes image acquisition angles and / or image acquisition frequencies.

[0069] During the execution process, a parameter threshold can also be set for each environmental parameter, and an image acquisition strategy can be set for the parameter threshold of each environmental parameter, and the image acquisition strategy corresponding to the parameter threshold is used. For example, a parameter threshold is set for wind force, and a parameter threshold is set for humidity. If any of the wind force parameter and the humidity parameter is greater than the parameter threshold, the image acquisition strategy is determined as: once every 10 minutes, 6 angles are collected each time, and the collection frequency of each angle is 10Hz.

[0070] In addition, there is also the case where the image acquisition strategy is triggered when the parameter is less than the parameter threshold. For example, when the temperature is less than the temperature threshold, the image acquisition strategy is determined. For this, numerical comparison can be performed based on the absolute value of the environmental parameter to improve data processing efficiency.

[0071] Step 302: Capture each first image according to an image acquisition strategy.

[0072] During the implementation process, the terminal collects multiple images of corresponding angles according to the determined image acquisition strategy.

[0073] In an optional implementation provided by the present application, the image acquisition strategy of the terminal is determined by sensor data, and image acquisition is performed on the transmission line in a timely manner when the environmental parameters exceed the parameter threshold, thereby improving the timeliness of the detection of dancing and / or sag, and thereby improving the effectiveness of the abnormality detection results.

[0074] In actual scenarios, there are also cases where the terminal's anomaly detection model outputs other anomaly levels. When there is a high possibility of dancing and / or sag, the anomaly level is a significant level. When there is a low possibility of dancing and / or sag, the anomaly level is a non-abnormal level. The following explains the other two anomaly levels respectively.

[0075] For the significant level, it indicates that there is a high possibility of dancing and / or sag, then there is no need for secondary detection by the server, and the abnormal processing message is directly output through the server; in an optional implementation mode provided by the present application, if Figure 4 As shown, it includes steps 401 to 402.

[0076] Step 401 : collect a plurality of first images of the power line at different angles, and input each first image into an anomaly detection model to obtain a first anomaly detection result.

[0077] Here, the implementation process of step 401 is the same as that of the above-mentioned step 201. The specific operations can be performed with reference to the operations of the above-mentioned step 201 and will not be repeated here.

[0078] Step 402: When the abnormality level is a significant level, the first abnormality detection result is sent to the server, so that the server outputs an abnormality handling message when determining that the abnormality level is a significant level.

[0079] During the implementation process, when the terminal detects that the abnormality level in the first abnormality detection result output by the abnormality detection model is a significant level, there is no need to collect the second image and the first abnormality detection result can be sent directly to the server; when the server detects that the abnormality level is a significant level, it can generate an abnormality handling message based on the first abnormality detection result and output it so that the abnormality handling personnel can perform abnormality handling.

[0080] In addition, when the abnormality level is a significant level, after sending the first abnormality detection result to the server, multiple first images can also be sent so that the server can perform further secondary detection; during the execution process, when the abnormality level is a significant level, the terminal sends the first abnormality detection result and each first image to the server; accordingly, the server receives each first image, inputs each first image into the abnormality detection model to obtain a second abnormality detection result, and outputs an abnormality handling message when the second abnormality detection result is a confirmed abnormality.

[0081] Furthermore, when the abnormality level is a significant level, the terminal may send the first abnormality detection result and each first image to the server, and send multiple second images to the server, so that the server obtains the second abnormality detection result based on each first image and each second image.

[0082] In actual applications, there are also situations where the network between the terminal and the server is poor. This may be due to the bad weather in the area where the terminal is located, which affects the wireless network or damages the wired network, such as sandstorms, blizzards, tornadoes, etc. In bad weather conditions, the probability of transmission lines dancing and / or sagging is greater; to address this, a target field can be configured in the first abnormality detection result, and the server determines whether to perform a secondary detection by reading the target field.

[0083] During the execution process, when the terminal sends each first image to the server and the abnormality level is a significant level, if it detects a sending failure, a network abnormality and / or an extremely slow transmission speed, the target field will be configured to not require a secondary detection, and the configured first abnormality detection result will be sent to the server, thereby improving the effectiveness and timeliness of abnormality detection of dancing and / or sag.

[0084] In an optional implementation provided by the present application, when the abnormality level included in the first abnormality detection result is a significant level, the terminal does not need to perform a second image acquisition, and the server can directly generate an abnormality processing message without performing a secondary abnormality detection. The abnormality processing message is quickly output by the server, thereby improving the processing efficiency of dancing and / or sag, and improving the effectiveness and timeliness of abnormality detection of dancing and / or sag.

[0085] For the non-abnormal level, it indicates that there is a small possibility of dancing and / or sag, and there is no need for secondary detection by the server. The test results are directly recorded by the server for archiving; in another optional embodiment provided by the present application, if Figure 5 As shown, it includes steps 501 to 502.

[0086] Step 501: Collect multiple first images of the power line at different angles, and input each first image into an anomaly detection model to obtain a first anomaly detection result.

[0087] During the implementation process, step 501 is implemented in the same manner as the above-mentioned step 201 and can be performed with reference to the operation of the above-mentioned step 201, which will not be repeated here.

[0088] Step 502: When the abnormality level is not an abnormality level, the first abnormality detection result is sent to the server, so that the server records the first abnormality detection result when determining that the abnormality level is not an abnormality level.

[0089] During the implementation process, when the terminal detects that the abnormality level in the first abnormality detection result output by the abnormality detection model is not an abnormality level, the terminal does not need to perform a second image acquisition, and the server does not need to perform a secondary detection. The terminal can send the first abnormality detection result to the server, and the server will archive and record it.

[0090] In addition, in order to save the data processing load of the server, the terminal can retain the first abnormality detection result by itself instead of sending it to the server when the abnormality level is not an abnormality level, that is: step 502 can be replaced by: when the abnormality level is not an abnormality level, the first abnormality detection result is stored in the data log.

[0091] In an optional implementation provided by the present application, when the abnormality level is a non-abnormal level, the abnormality detection results are recorded by the server and / or terminal, and the data is archived according to the chronological sequence of the image acquisition time, thereby improving the reliability of the dancing and / or sag detection. At the same time, the trend of the occurrence of dancing and / or sag can be predicted through the archived data, further improving the efficiency of the dancing and / or sag detection.

[0092] It should be noted that a method for handling transmission line exceptions provided in one or more of the above embodiments can be executed by a terminal, and another method for handling transmission line exceptions provided in one or more of the following embodiments can be executed by a server. The two can cooperate with each other during the execution process. Therefore, when reading the above one or more embodiments, you can refer to the contents of the following one or more embodiments. Correspondingly, when reading the following one or more embodiments, you can also refer to the contents of the above one or more embodiments.

[0093] In one embodiment, see Figure 6 , which shows a flow chart of a method for handling abnormalities in a power transmission line provided by an embodiment of the present application. Figure 6 As shown, the method for handling abnormalities in a transmission line may include the following steps:

[0094] Step 601: Acquire environmental parameters of the current environment through an environmental sensor, and determine an image acquisition strategy based on a magnitude relationship between the environmental parameters and parameter thresholds.

[0095] Step 602 : A plurality of first images of the power line at different angles are collected according to an image collection strategy, and each first image is input into an anomaly detection model to obtain a first anomaly detection result.

[0096] Step 603: When the abnormality level is a minor level, the first abnormality detection result and each first image are sent to the server.

[0097] Step 605: Collect multiple second images of the power line at different angles and send them to the server.

[0098] It should be noted that steps 601 to 603 and step 605 provided in the above embodiment can be executed by the terminal, and steps 604 and steps 606 to 608 provided in the following embodiment can be executed by the server. The two can cooperate with each other during the execution process. Therefore, when reading the above steps 601 to 603 and step 605 executed by the terminal, you can refer to the following steps 604 and steps 606 to 608 executed by the server. When reading the following steps 604 and steps 606 to 608 executed by the server, you can also refer to the above steps 601 to 603 and step 605 executed by the terminal.

[0099] It should also be noted that any one of steps 601 to 603 and any combination of multiple steps of step 605 can be selected from any one of steps 201 to 203 provided in the above embodiment or any combination of multiple steps to form a new implementation method according to the needs of implementation deployment; and any one or multiple technical features in the technical scheme composed of steps 601 to 603 and step 605 can also be selected from any one or multiple technical features in the technical scheme composed of steps 201 to 203 to form a new implementation method according to the needs of actual deployment, or the technical features in one or more optional implementation methods provided in one or more embodiments above can be selected to combine into a new implementation method, which will not be repeated here.

[0100] In an exemplary embodiment, Figure 7 As shown, another abnormality handling method for transmission lines is provided, which is applied to Figure 1 The server in is used as an example to illustrate, including the following steps 701 to 703.

[0101] Step 701: receiving a first abnormality detection result for a power transmission line and a plurality of first images of the power transmission line at different angles sent by a terminal.

[0102] Among them, the first abnormality detection result is obtained by the terminal inputting each first image into the abnormality detection model. The first abnormality detection result includes the first dancing detection result, the first sag detection result and the abnormality level. The first dancing detection result is used to characterize the probability of dancing in the transmission line, and the first sag detection result is used to characterize the probability of sag in the transmission line.

[0103] In actual scenarios, transmission lines are usually erected at high altitudes or in some sparsely populated areas. Daily inspections and status monitoring are also required for the transmission lines to prevent the transmission lines from dancing and / or sag, which affects the safety of electricity use. To this end, the present application performs status detection on the transmission lines by deploying terminals. The terminals can detect the probability of dancing and / or sag of the transmission lines by collecting images of the transmission lines and using image recognition, thereby realizing real-time dancing and sag status detection of the transmission lines.

[0104] In the present application, the first abnormality detection result includes the first dancing detection result, the first sag detection result and the abnormality level. Among them, the first dancing detection result is used to characterize the probability of the transmission line dancing; the dancing of the transmission line refers to the large-amplitude, low-frequency vibration phenomenon that occurs under specific meteorological conditions, especially when the transmission line covered with ice or snow is affected by wind. The dancing of the transmission line will affect the transmission of electric energy. The first sag detection result is used to characterize the probability of the transmission line sag; the sag of the transmission line refers to the vertical distance between the lowest point of the curve formed by the natural droop of the conductor between the suspension points in the overhead transmission line and the line connecting the suspension points. The deviation of the sag of the transmission line will affect the life of the transmission line.

[0105] In this application, the abnormality level refers to the integrated probability of transmission line galloping and sag obtained by the terminal through the abnormality detection model. The abnormality level may include multiple levels, including: no abnormality level, slight level and / or significant level. The no abnormality level indicates that the integrated probability of transmission line galloping and sag has not reached the alarm threshold, the slight level indicates that the integrated probability of transmission line galloping and sag has reached the alarm threshold but has not reached the emergency alarm threshold, and the significant level indicates that the integrated probability of transmission line galloping and sag has reached the emergency alarm level. Among them, the integrated probability is the overall probability obtained by combining and / or converting the galloping probability and the sag probability of the transmission line to characterize the galloping and / or sag of the transmission line.

[0106] During the implementation process, the server receives a plurality of first images captured and sent by the terminal when the abnormality level of the first abnormality detection result is a slight level.

[0107] Step 702: When the abnormality level is a minor level, multiple second images of the power line at different angles sent by the receiving terminal are input into the abnormality detection model to obtain a second abnormality detection result of the power line.

[0108] During the implementation process, the server receives multiple second images sent by the terminal, inputs each first image and each second image into the anomaly detection model, and performs anomaly detection through the anomaly detection model to obtain a second anomaly detection result of the power transmission line.

[0109] The "large anomaly detection model" refers to a model whose model parameters and / or neural network parameters are more complex than those of the terminal's anomaly detection model. Accordingly, the large anomaly detection model has more accurate detection capabilities than the anomaly detection model. The large anomaly detection model can be deployed within a server or independently of the server. For server-independent deployment, the large anomaly detection model can be a comprehensive large language model or an expert model for detecting galloping and / or sag. The second anomaly detection result can be a detection result indicating whether galloping and / or sag has occurred in the transmission line, and can include a confirmation of an anomaly and / or a confirmation of no anomaly. Furthermore, the second anomaly result can also include a gallop detection result and / or a sag detection result.

[0110] During the process of anomaly detection performed by the anomaly detection large model, the anomaly detection large model can calculate the motion parameters and / or posture parameters of the transmission line based on the shooting interval time of the first image and the second image and the position of the transmission line in the image, and judge whether the transmission line is dancing and / or sag through the motion parameters and / or posture parameters. If the motion parameters and / or posture parameters of the transmission line are greater than the parameter threshold, it is determined that the transmission line has an abnormality; if they are less than the parameter threshold, it is determined that the transmission line has not an abnormality; further, the anomaly detection large model can also perform anomaly detection based on the environmental parameters of the area where the terminal is located, that is, combine image recognition and environmental parameters, and calculate the integrated probability of dancing and / or sag by configuring weights, and determine the second anomaly detection result through the integrated probability.

[0111] Step 703: If the second abnormality detection result is a confirmed abnormality, an abnormality handling message is output according to the second abnormality detection result.

[0112] During implementation, if the output of the anomaly detection model indicates a confirmed anomaly, indicating a high probability of transmission line galloping and / or sag, the server generates an anomaly handling message based on the second anomaly detection result and transmits the message to the corresponding handler, enabling the handler to address the transmission line galloping and / or sag. During execution, the server may first determine the exception handler corresponding to the terminal and transmit the anomaly handling message to the corresponding handler.

[0113] Among them, the exception handling message can be a text message, sent to the exception handling party via SMS; the exception handling message can also be a voice message, sent to the exception handling party via telephone; in addition, the server, terminal and exception handling party can also be in the same system and / or program, the server sends the exception handling message to the exception handling party's device through the system and / or program, and the exception handling party receives and processes the exception handling message through the system and / or program running on the device.

[0114] In the above-mentioned transmission line abnormality handling method, a server receives a first abnormality detection result for the transmission line sent by a terminal after inputting multiple first images of the transmission line at different angles into an abnormality detection model, and also receives multiple first images sent by the terminal. The first abnormality detection result includes a first galloping detection result, a first sag detection result, and an abnormality level. The first galloping detection result is used to characterize the probability of galloping of the transmission line, and the first sag detection result is used to characterize the probability of sag of the transmission line. The terminal performs a first detection of galloping and / or sag of the transmission line. If the abnormality level is a minor level, the server receives multiple second images of the transmission line at different angles sent by the terminal and inputs each first image and each second image into the large abnormality detection model to obtain a second abnormality detection result. The secondary detection performed by the server improves the reliability of the abnormality detection result. At the same time, integrating galloping detection and sag detection into a single device improves computing power utilization. If the second abnormality detection result is a confirmed abnormality, an abnormality handling message is output based on the second abnormality detection result. The secondary galloping and / or sag abnormality detection is performed by the devices at both ends in coordination, thereby improving the detection accuracy of the galloping state and sag state.

[0115] Based on the embodiment of another method for handling abnormalities of a transmission line provided above, one or more embodiments are provided below to further illustrate the method for handling abnormalities of a transmission line.

[0116] In the process of anomaly detection by the anomaly detection large model, the motion parameters and / or posture parameters of the transmission line can be identified by image recognition, and the integrated detection results of the dancing and sag of the transmission line can be determined by the motion parameters and / or posture parameters; optionally, the motion parameters and / or posture parameters include dancing frequency, dancing amplitude and / or sag; in an optional embodiment provided by the present application, Figure 8 As shown, step 702 inputs each first image and each second image into the anomaly detection model to obtain the second anomaly detection result of the transmission line, including steps 801 to 803.

[0117] Step 801: Calculate the dancing frequency and / or dancing amplitude of the power line according to each first image and each second image through the anomaly detection model, and generate a second dancing detection result of the power line according to the dancing frequency and / or dancing amplitude.

[0118] Among them, the dancing frequency and dancing amplitude are indicators for judging whether the transmission line is dancing. The dancing frequency refers to the frequency of periodic vibrations that occur when the transmission line is affected by external forces such as wind; the dancing amplitude refers to the maximum distance that the transmission line deviates from its equilibrium position during the vibration process when the transmission line is affected by external forces (such as wind).

[0119] During the implementation process, the server inputs each first image and each second image into the anomaly detection large model. Taking the operation for each first image as an example, the anomaly detection large model calculates the dancing frequency and dancing amplitude of the transmission line during the acquisition time of the first image according to the acquisition time interval of each first image and the position of the transmission line in each first image and the acquisition angle, and calculates the dancing probability of the transmission line according to the dancing frequency and dancing amplitude, and then generates the dancing probability corresponding to each second image according to the execution method of each first image, and generates the second dancing detection result of the transmission line based on the dancing probabilities of the two.

[0120] Step 802: Calculate the sag of the transmission line based on each first image and each second image using the anomaly detection model, and generate a second sag detection result of the transmission line based on the sag.

[0121] Among them, the sag of the transmission line refers to the maximum vertical distance between the lowest point of the curve formed by the transmission line naturally drooping between two supporting points (such as poles and towers) after the transmission line is erected and the connecting line at both ends.

[0122] During the implementation process, the anomaly detection model calculates the sag of the transmission line according to the acquisition angle of each first image, and calculates the sag of the transmission line according to the acquisition angle of each second image, calculates the probability of sag in the transmission line based on the sag of the two, and generates a second sag detection result based on the probability of sag in the transmission line.

[0123] Step 803 : Generate a second anomaly detection result according to the second dancing detection result and the second sag detection result through the anomaly detection large model.

[0124] During implementation, the anomaly detection large model may integrate the second dancing detection result and the second sag detection result, and generate a unified second anomaly detection result based on the second dancing detection result and the second sag detection result.

[0125] During the execution process, if any of the detection results confirms an abnormality, a second abnormality detection result confirming the abnormality can be generated; if both detection results are about to reach the threshold, a second abnormality detection result confirming the abnormality can be generated, for example: if the threshold is 70% probability, the abnormality is confirmed, and if the probability of both is 65%, a second abnormality detection result confirming the abnormality is generated; alternatively, the second dancing detection result and the second sag detection result can be merged into one detection result, and a second abnormality detection result can be generated based on the merged detection result.

[0126] It should be noted that the above-mentioned abnormality detection large model performs abnormality detection operations, and the abnormality detection model can also refer to the above-mentioned operations to perform abnormality detection. The abnormality detection model can refer to the above-mentioned abnormality detection large model when performing abnormality detection; for example, the abnormality detection model inputs each first image into the abnormality detection model to obtain a first abnormality detection result, which may include: calculating the dancing frequency and dancing amplitude of the transmission line according to each first image through the abnormality detection model, and generating a first dancing detection result of the transmission line according to the dancing frequency and dancing amplitude; calculating the sag of the transmission line according to each first image through the abnormality detection model, and generating a first sag detection result of the transmission line according to the sag; generating a first abnormality detection result according to the first dancing detection result and the first sag detection result through the abnormality detection model.

[0127] In an optional implementation provided by the present application, a large anomaly detection model is used to simultaneously detect the galloping and sag of the transmission line through image recognition, eliminating the data fusion and installation distribution redundancy problems of two separate sensor systems, thereby improving the efficiency of anomaly detection and the timeliness of anomaly processing.

[0128] In practical applications, the maintenance and management of transmission lines are usually carried out by operation and maintenance users. After receiving the confirmation of abnormality output by the abnormality detection large model, the server can output an abnormality handling message to the operation and maintenance equipment of the operation and maintenance user, so that the operation and maintenance user can handle the galloping and / or sag of the transmission line; In an optional embodiment provided by the present application, if Figure 9 As shown, step 703 outputs the operation of the exception handling message according to the second exception detection result, including steps 901 to 902.

[0129] Step 901: Generate an abnormality handling message for the transmission line based on the second abnormality detection result.

[0130] During the implementation process, the server determines the identification of the power transmission line corresponding to the terminal according to the confirmed abnormality of the second abnormality detection result, and generates an abnormality handling message of the power transmission line according to the identification of the power transmission line and the second abnormality detection result.

[0131] During the execution process, the server can generate different exception handling messages according to the exception level. When the exception level is a minor level, an exception inspection message of the transmission line can be generated to enable the operation and maintenance user to inspect the transmission line; when the exception level is a significant level, an exception alarm message of the transmission line can be generated to enable the operation and maintenance user to handle the dancing and / or sag of the transmission line.

[0132] Step 902: Determine the operation and maintenance equipment corresponding to the transmission line, and send an exception handling message to the operation and maintenance equipment.

[0133] The operation and maintenance device can be a terminal device of the operation and maintenance user, including personal terminal devices such as mobile phones, personal computers, and tablet computers. The operation and maintenance device can also be an alarm device that can be configured to process user exception handling messages. The device identifier of the operation and maintenance device can be associated with the identifier of the transmission line and / or the identifier of the terminal. The terminal and / or transmission line identifiers can be used to determine the operation and maintenance device corresponding to the transmission line.

[0134] During the implementation process, the server can query the operation and maintenance equipment associated with the identification of the transmission line and / or terminal, and send an exception handling message to the operation and maintenance equipment so that the operation and maintenance user corresponding to the operation and maintenance equipment can handle the dancing and / or sag of the transmission line.

[0135] In an optional implementation provided by the present application, an exception handling message for the transmission line is generated based on the second exception detection result, and the exception handling message is sent to the operation and maintenance equipment corresponding to the transmission line. By sending the exception handling message in a timely manner, the operation and maintenance user can promptly handle the dancing and / or sag of the transmission line, thereby improving the exception handling efficiency of the transmission line and enhancing the safety of the transmission line.

[0136] In actual scenarios, the abnormality level in the first abnormality result sent by the terminal may also be a significant level, indicating that there is a high probability of galloping and / or sag in the transmission line. In view of this, the server may directly generate and output an abnormality handling message; in an optional implementation manner provided by the present application, if Figure 10 As shown, it also includes steps 1001 to 1002.

[0137] Step 1001: receiving a first abnormality detection result for a power transmission line and a plurality of first images of the power transmission line at different angles sent by a terminal.

[0138] Here, the implementation process of step 1001 is the same as that of step 701, and can be performed with reference to the execution method of the above-mentioned step 701, which will not be repeated here.

[0139] Step 1002: When the abnormality level is a significant level, an abnormality handling message is generated according to the first abnormality detection result, and the abnormality handling message is sent to the operation and maintenance equipment corresponding to the transmission line.

[0140] During the implementation process, when the abnormality level is a significant level, indicating that there is a high probability of the transmission line dancing and / or sag, the server may not perform a secondary detection, but directly generate an abnormality handling message based on the first abnormality detection result, and send the abnormality handling message to the operation and maintenance equipment.

[0141] In addition, when the abnormality level is a significant level, the server can also perform secondary detection based on each first image sent by the terminal. The server can receive each first image, input each first image into the abnormality detection model to obtain a second abnormality detection result, and output an abnormality handling message when the second abnormality detection result is a confirmed abnormality.

[0142] Furthermore, the server may also receive each second image sent by the terminal, and obtain a second abnormality detection result according to each first image and each second image.

[0143] In actual applications, there are also situations where the network between the terminal and the server is poor. This may be due to the bad weather in the area where the terminal is located, which affects the wireless network or damages the wired network, such as sandstorms, blizzards, tornadoes, etc. In bad weather conditions, the probability of transmission lines dancing and / or sagging is greater; to address this, a target field can be configured in the first abnormality detection result, and the server determines whether to perform a secondary detection by reading the target field.

[0144] During the execution process, the server receives the first abnormality detection result, reads the target field in the first abnormality detection result, and if the target field indicates that no secondary detection is required, generates an abnormality handling message based on the first abnormality detection result, and sends the abnormality handling message to the operation and maintenance equipment corresponding to the transmission line, thereby improving the effectiveness and timeliness of abnormality detection of dancing and / or sag.

[0145] It should be noted that the generation of the exception handling message can refer to the above-mentioned method of generating the exception handling message according to the second exception detection result, which will not be repeated here.

[0146] The present application provides an optional implementation method. When the abnormality level is a significant level, the server does not perform a secondary detection but generates an abnormality handling message based on the first abnormality detection result and sends it to the operation and maintenance equipment. Through hierarchical decision-making and hierarchical identification, the reliability of abnormality detection of the transmission line is improved while the efficiency of abnormality handling of the transmission line is improved.

[0147] In one embodiment, see Figure 6 , which shows a flow chart of a method for handling abnormalities in a power transmission line provided by an embodiment of the present application. Figure 6 As shown, the method for handling abnormalities in a transmission line may include the following steps:

[0148] Step 604: receiving the first anomaly detection result and the plurality of first images sent by the terminal.

[0149] Step 606: receiving the second images of the power line at different angles sent by the terminal, and inputting each first image and each second image into the anomaly detection model to obtain a second anomaly detection result of the power line.

[0150] Step 607: If the second abnormality detection result is a confirmed abnormality, an abnormality handling message of the transmission line is generated based on the second abnormality detection result.

[0151] Step 608: Determine the operation and maintenance equipment corresponding to the transmission line, and send an exception handling message to the operation and maintenance equipment.

[0152] It should also be noted that any one or a combination of any multiple steps of step 604 and steps 606 to 608 can be combined with any one or a combination of any multiple steps of steps 701 to 703 provided in the above embodiment to form a new implementation method according to the needs of implementation deployment; and any one or any multiple technical features in the technical scheme composed of steps 604 and steps 606 to 608 can also be combined with any one or more technical features in the technical scheme composed of steps 701 to 703 to form a new implementation method according to the needs of actual deployment, or the technical features in one or more optional implementation methods provided in one or more embodiments above can be combined to form a new implementation method, which will not be repeated here.

[0153] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily performed in sequence in the order indicated by the arrows. Unless clearly stated herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of the steps or stages in other steps or other steps. It is understandable that the various steps in different embodiments can be freely combined as needed, and the various non-contradictory schemes formed by the combination all fall within the scope of protection of this application.

[0154] Based on the same inventive concept, embodiments of the present application also provide a transmission line anomaly handling device for implementing the aforementioned transmission line anomaly handling method. The solution provided by this device is similar to the solution described in the aforementioned transmission line anomaly handling method. Therefore, the specific limitations of the one or more transmission line anomaly handling device embodiments provided below can be found in the aforementioned limitations of the transmission line anomaly handling method and will not be further elaborated here.

[0155] In an exemplary embodiment, Figure 11As shown, a transmission line abnormality handling device is provided, including: an abnormality detection module 1101, a data sending module 1102 and a second image sending module 1103, wherein: the abnormality detection module 1101 is used to collect multiple first images of the transmission line at different angles, and input each first image into an abnormality detection model to obtain a first abnormality detection result; the first abnormality detection result includes a first galloping detection result, a first sag detection result and an abnormality level, the first galloping detection result is used to characterize the probability of galloping of the transmission line, and the first sag detection result is used to characterize the probability of sag of the transmission line; the data sending module 1102 is used to send the first abnormality detection result and each first image to a server when the abnormality level is a minor level; the second image sending module 1103 is used to collect multiple second images of the transmission line at different angles, and send each second image to the server, so that when the server determines that the abnormality level is a minor level, it inputs each first image and each second image into the abnormality detection model to obtain a second abnormality detection result, and outputs an abnormality handling message when the second abnormality detection result is a confirmed abnormality.

[0156] In one embodiment, the anomaly detection module 1101 also includes an image acquisition strategy determination unit and a first image acquisition unit, wherein: the image acquisition strategy determination unit is used to obtain environmental parameters of the current environment through environmental sensors, and determine the image acquisition strategy based on the size relationship between the environmental parameters and the parameter threshold; the image acquisition strategy includes an image acquisition angle and an image acquisition frequency; the first image acquisition unit is used to acquire each first image according to the image acquisition strategy.

[0157] In one embodiment, the device also includes a module for sending a first abnormality detection result of a significant level, wherein: the module for sending a first abnormality detection result of a significant level is used to send the first abnormality detection result to the server when the abnormality level is a significant level, so that the server outputs an abnormality processing message when determining that the abnormality level is a significant level.

[0158] In one embodiment, the device also includes a first abnormality detection result sending module of non-abnormal level, wherein: the first abnormality detection result sending module of non-abnormal level is used to send the first abnormality detection result to the server when the abnormality level is non-abnormal level, so that the server records the first abnormality detection result when determining that the abnormality level is non-abnormal level.

[0159] Each module in the aforementioned power line anomaly handling device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0160] Based on the same inventive concept, embodiments of the present application also provide another transmission line anomaly handling device for implementing the aforementioned alternative transmission line anomaly handling method. The solution provided by this device is similar to the solution described in the aforementioned alternative transmission line anomaly handling method. Therefore, the specific limitations of one or more transmission line anomaly handling device embodiments provided below can be found in the aforementioned alternative transmission line anomaly handling method and will not be further elaborated here.

[0161] In an exemplary embodiment, Figure 12 As shown, a transmission line abnormality handling device is provided, comprising: a data receiving module 1201, an abnormality detection module 1202 and an abnormality handling message output module 1203, wherein: the data receiving module 1201 is used to receive a first abnormality detection result for the transmission line and multiple first images of the transmission line at different angles sent by a terminal; the first abnormality detection result is obtained by the terminal inputting each first image into an abnormality detection model, the first abnormality detection result includes a first galloping detection result, a first sag detection result and an abnormality level, the first galloping detection result is used to characterize the probability of galloping of the transmission line, and the first sag detection result is used to characterize the probability of sag of the transmission line; the abnormality detection module 1202 is used to receive multiple second images of the transmission line at different angles sent by the terminal when the abnormality level is a slight level, and input each first image and each second image into the abnormality detection large model to obtain a second abnormality detection result of the transmission line; the abnormality handling message output module 1203 is used to output an abnormality handling message according to the second abnormality detection result if the second abnormality detection result is a confirmed abnormality.

[0162] In one embodiment, the abnormality detection module 1202 includes a second dancing detection result generation unit, a second sag detection result generation unit and a second abnormality detection result generation unit, wherein: the second dancing detection result generation unit is used to calculate the dancing frequency and dancing amplitude of the transmission line according to each first image and each second image through the abnormality detection large model, and generate a second dancing detection result of the transmission line according to the dancing frequency and the dancing amplitude; the second sag detection result generation unit is used to calculate the sag of the transmission line according to each first image and each second image through the abnormality detection large model, and generate a second sag detection result of the transmission line according to the sag; the second abnormality detection result generation unit is used to generate a second abnormality detection result according to the second dancing detection result and the second sag detection result through the abnormality detection large model.

[0163] In one embodiment, the exception handling message output module 1203 includes an exception handling message generating unit and an exception handling message sending unit, wherein: the exception handling message generating unit is used to generate an exception handling message for the transmission line based on the second exception detection result; the exception handling message sending unit is used to determine the operation and maintenance equipment corresponding to the transmission line and send the exception handling message to the operation and maintenance equipment.

[0164] In one embodiment, the device also includes a significant level exception handling message sending unit, wherein: the significant level exception handling message sending unit is used to generate an exception handling message according to the first exception detection result when the exception level is a significant level, and send the exception handling message to the operation and maintenance equipment corresponding to the transmission line.

[0165] Each module in the aforementioned power line anomaly handling device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0166] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 13 As shown. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via wired or wireless communication, and the wireless communication can be implemented via Wi-Fi, a mobile cellular network, near field communication (NFC), or other technologies. When executed by the processor, the computer program implements the above-mentioned method for handling power line abnormalities.

[0167] Those skilled in the art will understand that Figure 13 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0168] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in each embodiment of the above-mentioned method for handling power line exceptions when executing the computer program.

[0169] In an exemplary embodiment, another computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 14 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store abnormality processing data of the power transmission line. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, it implements the above-mentioned another abnormality processing method of the power transmission line.

[0170] Those skilled in the art will understand that Figure 14 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0171] In an exemplary embodiment, another computer device is provided, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps in each embodiment of the above-mentioned another method for abnormality handling of a power transmission line are implemented.

[0172] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of each embodiment of the above-mentioned method for handling power line exceptions are implemented.

[0173] In one embodiment, another computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in each embodiment of the above-mentioned another method for handling power line exceptions are implemented.

[0174] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the computer program implements the steps of each embodiment of the above-mentioned method for handling power line exceptions.

[0175] In one embodiment, another computer program product is provided, comprising a computer program. When the computer program is executed by a processor, the steps of each embodiment of the above-mentioned another method for handling power line exceptions are implemented.

[0176] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0177] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0178] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0179] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for handling abnormalities in a transmission line, characterized in that: Applied to a terminal, the method includes: Acquire multiple first images of the transmission line at different angles, and input each of the first images into an anomaly detection model to obtain a first anomaly detection result; the first anomaly detection result includes a first galloping detection result, a first sag detection result, and an anomaly level; the first galloping detection result is used to characterize the probability of galloping of the transmission line, and the first sag detection result is used to characterize the probability of sag of the transmission line; When the abnormality level is a minor level, sending the first abnormality detection result and each of the first images to a server; Collect multiple second images of the transmission line at different angles, and send each second image to the server, so that when the server determines that the abnormality level is the mild level, the server inputs each first image and each second image into the abnormality detection model to obtain a second abnormality detection result, and outputs an abnormality handling message when the second abnormality detection result is a confirmed abnormality.

2. The method according to claim 1, characterized in that The collecting of a plurality of first images of the power transmission line at different angles includes: Acquire environmental parameters of the current environment through environmental sensors, and determine an image acquisition strategy based on the magnitude relationship between the environmental parameters and parameter thresholds; the image acquisition strategy includes an image acquisition angle and an image acquisition frequency; Each of the first images is acquired according to the image acquisition strategy.

3. The method according to claim 1, characterized in that After inputting each of the first images into the anomaly detection model to obtain a first anomaly detection result, the method further includes: When the abnormality level is a significant level, the first abnormality detection result is sent to the server, so that the server outputs an abnormality handling message when determining that the abnormality level is the significant level.

4. The method according to claim 1, wherein After inputting each of the first images into the anomaly detection model to obtain a first anomaly detection result, the method further includes: When the abnormality level is a non-abnormal level, the first abnormality detection result is sent to the server, so that the server records the first abnormality detection result when determining that the abnormality level is the non-abnormal level.

5. A method for handling abnormalities in a transmission line, characterized in that: Applied to a server, the method includes: receiving a first abnormality detection result for a transmission line and a plurality of first images of the transmission line at different angles, sent by a terminal; the first abnormality detection result being obtained by the terminal by inputting each of the first images into an abnormality detection model, the first abnormality detection result including a first galloping detection result, a first sag detection result, and an abnormality level; the first galloping detection result being used to characterize the probability of galloping of the transmission line, and the first sag detection result being used to characterize the probability of sag of the transmission line; When the abnormality level is a minor level, receiving a plurality of second images of the power line at different angles sent by the terminal, and inputting each of the first images and each of the second images into an abnormality detection model to obtain a second abnormality detection result of the power line; If the second abnormality detection result is a confirmed abnormality, an abnormality handling message is output according to the second abnormality detection result.

6. The method according to claim 5, characterized in that Inputting each of the first images and each of the second images into the anomaly detection model to obtain a second anomaly detection result of the transmission line includes: Calculating the dancing frequency and the dancing amplitude of the power line according to each of the first images and each of the second images by the abnormality detection large model, and generating a second dancing detection result of the power line according to the dancing frequency and the dancing amplitude; Calculating the sag of the transmission line according to each of the first images and each of the second images by the anomaly detection large model, and generating a second sag detection result of the transmission line according to the sag; The second abnormality detection result is generated according to the second dancing detection result and the second sag detection result by the abnormality detection large model.

7. The method according to claim 5, characterized in that Outputting an exception handling message according to the second exception detection result includes: generating an abnormality handling message for the power transmission line based on the second abnormality detection result; Determine the operation and maintenance equipment corresponding to the transmission line, and send the exception handling message to the operation and maintenance equipment.

8. The method according to claim 1, characterized in that After receiving the first abnormality detection result for the power transmission line and the plurality of first images of the power transmission line at different angles sent by the receiving terminal, the method further includes: When the abnormality level is a significant level, the abnormality handling message is generated according to the first abnormality detection result, and the abnormality handling message is sent to the operation and maintenance equipment corresponding to the transmission line.

9. A device for handling abnormalities in a transmission line, characterized in that: The device comprises: an anomaly detection module, configured to collect multiple first images of the transmission line at different angles, and input each of the first images into an anomaly detection model to obtain a first anomaly detection result; the first anomaly detection result includes a first galloping detection result, a first sag detection result, and an anomaly level; the first galloping detection result is used to indicate a probability of galloping of the transmission line, and the first sag detection result is used to indicate a probability of sag of the transmission line; A data sending module, configured to send the first abnormality detection result and each of the first images to a server when the abnormality level is a minor level; The second image sending module is used to collect multiple second images of the transmission line at different angles and send each second image to the server, so that when the server determines that the abnormality level is the mild level, the server inputs each first image and each second image into the abnormality detection model to obtain a second abnormality detection result, and outputs an abnormality handling message when the second abnormality detection result is a confirmed abnormality.

10. A device for handling abnormalities in a transmission line, characterized in that: The device comprises: a data receiving module, configured to receive a first abnormality detection result for a transmission line and a plurality of first images of the transmission line at different angles, sent by a terminal; the first abnormality detection result being obtained by the terminal inputting each of the first images into an abnormality detection model, the first abnormality detection result including a first galloping detection result, a first sag detection result, and an abnormality level; the first galloping detection result being used to characterize the probability of galloping of the transmission line, and the first sag detection result being used to characterize the probability of sag of the transmission line; an anomaly detection module, configured to, when the anomaly level is a minor level, receive a plurality of second images of the transmission line at different angles sent by the terminal, and input each of the first images and each of the second images into an anomaly detection large model to obtain a second anomaly detection result of the transmission line; The exception handling message output module is used to output an exception handling message according to the second exception detection result if the second exception detection result is a confirmed exception.

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