Method and device for monitoring power transmission line in network-free area

Abstract

The invention discloses a method and a device for monitoring a power transmission line in a network-free area. The method comprises the following steps: acquiring original video data of a power transmission line in a network-free area by using an image acquisition part, and acquiring sensor data of the power transmission line by using a sensor part; dynamically adjusting the network topology structure of the ad hoc network according to the current network state to obtain an optimized transmission path; and sending the compressed video data and the sensor data to a control center through a plurality of optimized transmission paths by adopting a multi-path transmission strategy, and processing fusion data obtained by performing fusion analysis on the received plurality of compressed video data and the plurality of sensor data by utilizing the control center to obtain the fault degree of the power transmission line. According to the invention, the technical problems of inaccurate fault analysis and untimely processing of the power transmission line caused by difficulty in real-time data transmission and data quality guarantee when the power transmission line is monitored in a network-free coverage area in the existing power transmission monitoring method in the related technology are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system monitoring and data transmission, and in particular to a method and device for monitoring a power transmission line in a non-grid area. Background Art

[0002] The stable operation of the power system depends on the reliable monitoring and timely maintenance of the transmission lines. With the increase in power demand and the extension of transmission lines, the traditional manual inspection and fixed monitoring point methods can no longer meet the real-time monitoring needs of modern transmission lines. In recent years, the development of intelligent monitoring technology has provided new solutions for power systems. In particular, the deployment of intelligent monitoring equipment along the transmission lines and the use of wireless sensor network (WSN) technology to form self-organizing networks to achieve real-time monitoring and fault warning of transmission lines have become a hot topic in research and application. However, these technologies face huge challenges in areas without network coverage or unstable network coverage, and cannot effectively transmit monitoring data, affecting the overall reliability and response speed of the monitoring system.

[0003] To address this problem, researchers have proposed a variety of improvement plans, including the use of mobile communication technology, satellite communication technology, and drone inspections. These methods have improved the problem of data transmission to a certain extent, but there are still many shortcomings. Mobile communication technology is limited by the geographical environment and signal coverage, satellite communication is costly and has a large delay, and drone inspections are limited by weather and endurance, making it difficult to achieve continuous real-time monitoring. Therefore, finding an efficient and stable method for power transmission monitoring and data transmission, especially a solution for areas without network coverage, has become a key requirement for the development of current power system monitoring technology.

[0004] Existing power transmission monitoring technologies have many deficiencies in applications in areas without network coverage. First, traditional monitoring equipment cannot achieve real-time data transmission in areas without network coverage, resulting in delayed monitoring data and failure to timely reflect the actual operating status of the transmission line. Second, existing compression technologies are difficult to ensure the transmission quality of video data under low bandwidth conditions, often resulting in loss of key data or degradation of image quality, affecting the accuracy of fault detection. In addition, existing network topology adjustment and data transmission path optimization methods often lack flexibility and adaptability in the face of complex environmental changes, resulting in low data transmission efficiency and affecting the overall performance of the system.

[0005] The existing power transmission monitoring methods in the above-mentioned related technologies are difficult to transmit data in real time and ensure data quality when monitoring power transmission lines in areas without network coverage, resulting in inaccurate fault analysis and untimely processing of power transmission lines. No effective solution has been proposed yet. Summary of the invention

[0006] The embodiments of the present invention provide a method and device for monitoring a power transmission line in a network-free area, so as to at least solve the technical problem that the existing power transmission monitoring method in the related art is difficult to transmit data in real time and ensure data quality when monitoring the power transmission line in an area without network coverage, resulting in inaccurate fault analysis and untimely processing of the power transmission line.

[0007] According to one aspect of an embodiment of the present invention, a method for monitoring a power transmission line in a network-free area is provided, comprising: using an image acquisition component to collect original video data of the power transmission line in the network-free area, and using a sensor component to collect sensor data of the power transmission line, wherein the network-free area refers to an area without network coverage; dynamically compressing the original video data frame by frame to obtain compressed video data; dynamically adjusting the network topology of the self-organizing network according to the current network status to obtain an optimized transmission path, wherein the self-organizing network is a network formed by communication between wireless communication modules built into each monitoring device, the monitoring device is used to monitor the operating status of the power transmission line, and the optimized transmission path is a transmission path obtained by optimizing the original data transmission path during the dynamic adjustment of the network topology; using a multi-path transmission strategy to send the compressed video data and the sensor data to a control center through multiple optimized transmission paths, wherein the control center performs fusion analysis after receiving multiple compressed video data and multiple sensor data, and uses a fault determination model to process the fused data obtained by the fusion analysis to obtain the fault degree of the power transmission line.

[0008] Optionally, dynamically compressing the original video data frame by frame to obtain compressed video data includes: obtaining video frame data in the original video data; dynamically compressing the video frame data using an adaptive algorithm through a first formula to obtain intermediate compressed video data, wherein the first formula is: V c (t) represents the intermediate compressed video data at time t, m represents the number of the video frame data, N represents the total number of the video frame data, α m represents the compression parameter of the video frame data described in the mth item, F m (t) represents the mth video frame data at time t; the intermediate compressed video data is integrated by a second formula to obtain the compressed video data, wherein the second formula is: B(t)={V c (t)|t∈[t 1 , t 2 ]}, B(t) represents the time period [t 1 , t 2 ], t 1represents the starting time of the original video data, t 2 Indicates the end time of the original video data.

[0009] Optionally, dynamically adjusting the network topology of the ad hoc network according to the current network state to obtain an optimized transmission path includes: obtaining the current network state, wherein the network state at least includes: the connection state and communication quality between the monitoring devices other than the ad hoc network and the monitoring device itself; determining the monitoring device with a connection relationship as a target monitoring device according to the connection state; dynamically adjusting the network topology when determining that the communication quality between the monitoring device and the target monitoring device is lower than a communication quality threshold; after dynamically adjusting the network topology, determining the optimized transmission path by a third formula, wherein the third formula is: Wherein, u and v represent different monitoring devices respectively, T(u, v) represents the optimized transmission path between the monitoring device u and the monitoring device v, P represents the set of all transmission paths between the monitoring device u and the monitoring device v, k represents the number of the transmission path, w uv represents the path weight between the monitoring device u and the monitoring device v, P k Represents the kth transmission path.

[0010] Optionally, before adopting a multi-path transmission strategy to send the compressed video data and the sensor data to the control center through multiple optimized transmission paths, the method for monitoring power transmission lines in a network-free area also includes: sending a communication connection request to the control center, wherein the communication connection request is used to request to establish a communication connection relationship with the control center; receiving feedback information sent by the control center based on the communication connection request; and establishing the communication connection relationship with the control center when the feedback information indicates that the control center agrees to establish the communication connection relationship based on the communication connection request.

[0011] Optionally, a multi-path transmission strategy is adopted to send the compressed video data and the sensor data to the control center through multiple optimized transmission paths, including: dividing the compressed video data into multiple first data packets, and dividing the sensor data into multiple second data packets; encoding the multiple first data packets using a fourth formula to obtain corresponding multiple first encoded data packets, wherein the fourth formula is: E a =encode(D a , L), a represents the number of the first data packet, E a represents the first encoded data packet corresponding to the ath first data packet, encode() represents an encoding function, D arepresents the ath first data packet, L represents the encoding parameter; and the fifth formula is used to encode the plurality of second data packets to obtain the corresponding plurality of second encoded data packets, wherein the fifth formula is: H b =encode(O b , L), b represents the label of the second data packet, H b represents the second coded data packet corresponding to the bth second data packet, b Represents the bth second data packet; adopts a multi-path transmission strategy to transmit multiple first encoded data packets and multiple second encoded data packets to the control center through multiple optimized transmission paths.

[0012] According to another aspect of an embodiment of the present invention, a method for monitoring a power transmission line in a network-free area is provided, comprising: receiving a plurality of compressed video data and a plurality of sensor data sent by a plurality of monitoring devices, wherein the monitoring devices are used to monitor the operating status of the power transmission line in the network-free area, the network-free area refers to an area without network coverage, the compressed video data is video data obtained by the monitoring device performing frame-by-frame dynamic compression on original video data, the original video data is video data collected by an image acquisition component in the monitoring device, and the sensor data is sensor data collected by a sensor component in the monitoring device; fusing and analyzing the plurality of compressed video data and the plurality of sensor data to obtain fused data; inputting the fused data into a fault determination model to process the fused data using the fault determination model to obtain a fault degree of the power transmission line, wherein the fault determination model is trained by using a plurality of sets of training data in a machine learning manner, and each of the plurality of sets of training data includes: sample fusion data and a sample fault degree corresponding to the sample fusion data; obtaining a comparison result between the fault degree and a fault threshold, and determining a fault level according to the comparison result.

[0013] Optionally, receiving multiple compressed video data and multiple sensor data sent by multiple monitoring devices includes: receiving multiple first encoded data packets of the multiple compressed video data and multiple second encoded data packets of the multiple sensor data; decoding the multiple first encoded data packets of each compressed video data using the sixth formula to obtain the restored compressed video data, wherein the sixth formula is: V i =decode({E 1 , E 2 , …, E n}, L), decode() represents a decoding function, n represents the total number of the first coded data packets in each piece of the compressed video data, i represents the number of the monitoring device, V irepresents the compressed video data sent by the i-th monitoring device; using the seventh formula to decode multiple second encoded data packets of each sensor data to obtain the restored sensor data, wherein the seventh formula is: S i =decode({H 1 , H 2 , …, H c}, L), c represents the total number of the second encoded data packets in each of the sensor data, f represents the label of the sensor data, S i Represents the sensor data sent by the i-th monitoring device; after verifying each restored compressed video data and sensor data, obtain multiple compressed video data and multiple sensor data sent by multiple monitoring devices.

[0014] Optionally, fusing and analyzing the plurality of compressed video data and the plurality of sensor data to obtain fused data includes: performing calculations based on the plurality of compressed video data and the plurality of sensor data using an eighth formula to obtain the fused data, wherein the eighth formula is: D(t) represents the fused data at time t, τ represents the time variable, N represents the total number of monitoring devices, λ represents the first attenuation factor of the compressed video data, μ represents the second attenuation factor of the sensor data, t 0 Indicates the starting time of integration.

[0015] Optionally, the fused data is input into a fault determination model so as to process the fused data using the fault determination model to obtain the fault degree of the transmission line, including: extracting features from the fused data using a ninth formula to obtain feature parameters of the fused data, wherein the ninth formula is: F(t) represents the characteristic parameter at time t, e represents the label of the characteristic variable, M represents the total number of characteristic variables, and f e represents the e-th characteristic variable; based on the characteristic parameter, a fault analysis is performed using the tenth formula to obtain an analysis result, wherein the tenth formula is: R(t) represents the analysis result at time t, K represents the model weight, C g represents the gth weight; b g represents the g-th deviation; σ g represents the gth standard deviation; based on the analysis result, the fault detection is performed using the eleventh formula to obtain the detection result, wherein the eleventh formula is: G(t) represents the detection result at time t, and γ represents a sensitivity parameter; based on the detection result, the fault degree of the transmission line is calculated using the twelfth formula, wherein the twelfth formula is: Z(t) represents the fault degree at time t.

[0016] Optionally, the fault threshold includes: a first fault threshold, a second fault threshold and a third fault threshold, the first fault threshold is less than the second fault threshold, the second fault threshold is less than the third fault threshold, a comparison result between the fault degree and the fault threshold is obtained, and a fault level is determined according to the comparison result, including: when the comparison result is that the fault degree is not less than the first fault threshold and less than the second fault threshold, determining that the fault level is a minor fault; when the comparison result is that the fault degree is not less than the second fault threshold and less than the third fault threshold, determining that the fault level is a moderate fault; when the comparison result is that the fault degree is not less than the third fault degree, determining that the fault level is a severe fault.

[0017] Optionally, after determining the fault level according to the comparison result, the method for monitoring the transmission line in the off-grid area further includes: generating a fault maintenance strategy according to the fault level; and sending the fault maintenance strategy to a terminal device to prompt staff to perform fault maintenance on the transmission line.

[0018] According to another aspect of an embodiment of the present invention, there is also provided a monitoring device for a power transmission line in a network-free area, comprising: a collection unit, for collecting original video data of the power transmission line in the network-free area using an image collection component, and collecting sensor data of the power transmission line using a sensor component, wherein the network-free area refers to an area without network coverage; a first acquisition unit, for dynamically compressing the original video data frame by frame to obtain compressed video data; a second acquisition unit, for dynamically adjusting the network topology structure of the self-organizing network according to the current network status to obtain an optimized transmission path, wherein the self-organizing network is a wireless communication module built into each monitoring device. The network is formed by communicating between the monitoring equipment and the transmission line, the optimized transmission path is a transmission path obtained by optimizing the original data transmission path in the process of dynamically adjusting the network topology structure; the first sending unit is used to adopt a multi-path transmission strategy to send the compressed video data and the sensor data to the control center through multiple optimized transmission paths, wherein the control center performs fusion analysis after receiving the multiple compressed video data and the multiple sensor data, and uses the fault determination model to process the fused data obtained by the fusion analysis to obtain the fault degree of the transmission line.

[0019] Optionally, the first acquisition unit includes: a first acquisition module, used to acquire video frame data in the original video data; and a second acquisition module, used to dynamically compress the video frame data using an adaptive algorithm through a first formula to obtain intermediate compressed video data, wherein the first formula is: V c (t) represents the intermediate compressed video data at time t, m represents the number of the video frame data, N represents the total number of the video frame data, α m represents the compression parameter of the video frame data described in the mth item, F m (t) represents the mth video frame data at time t; a third acquisition module, used to integrate the intermediate compressed video data through a second formula to obtain the compressed video data, wherein the second formula is: B(t)={V c (t)|t∈[t 1 , t 2 ]}, B(t) represents the time period [t 1 , t 2 ], t 1 represents the starting time of the original video data, t 2 Indicates the end time of the original video data.

[0020] Optionally, the second acquisition unit includes: a fourth acquisition module, used to acquire the current network state, wherein the network state at least includes: the connection state and communication quality between the monitoring devices other than the self-organizing network in the self-organizing network; a first determination module, used to determine the monitoring device with a connection relationship as the target monitoring device according to the connection state; an adjustment module, used to dynamically adjust the network topology structure when it is determined that the communication quality between the monitoring device and the target monitoring device is lower than the communication quality threshold; a second determination module, used to determine the optimized transmission path by a third formula after dynamically adjusting the network topology structure, wherein the third formula is: Wherein, u and v represent different monitoring devices respectively, T(u, v) represents the optimized transmission path between the monitoring device u and the monitoring device v, P represents the set of all transmission paths between the monitoring device u and the monitoring device v, k represents the number of the transmission path, w uv represents the path weight between the monitoring device u and the monitoring device v, P k Represents the kth transmission path.

[0021] Optionally, the monitoring device for the power transmission line in the network-free area also includes: a second sending unit, used to send a communication connection request to the control center before adopting a multi-path transmission strategy to send the compressed video data and the sensor data to the control center through multiple optimized transmission paths, wherein the communication connection request is used to request to establish a communication connection relationship with the control center; a first receiving unit, used to receive feedback information sent by the control center based on the communication connection request; and an establishing unit, used to establish the communication connection relationship with the control center when the feedback information indicates that the control center agrees to establish the communication connection relationship based on the communication connection request.

[0022] Optionally, the first sending unit includes: a segmentation module, used to segment the compressed video data into a plurality of first data packets, and segment the sensor data into a plurality of second data packets; a fifth acquisition module, used to encode the plurality of first data packets using a fourth formula to obtain a plurality of corresponding first encoded data packets, wherein the fourth formula is: a =encode(D a , L), a represents the number of the first data packet, E a represents the first encoded data packet corresponding to the ath first data packet, encode() represents an encoding function, D a represents the ath first data packet, L represents the encoding parameter; a sixth acquisition module, used to encode multiple second data packets using the fifth formula to obtain corresponding multiple second encoded data packets, wherein the fifth formula is: H b =encode(O b , L), b represents the label of the second data packet, H b represents the second coded data packet corresponding to the bth second data packet, b Represents the bth second data packet; a transmission module, used to adopt a multi-path transmission strategy to transmit multiple first encoded data packets and multiple second encoded data packets to the control center through multiple optimized transmission paths.

[0023] According to another aspect of an embodiment of the present invention, there is also provided a monitoring device for a power transmission line in a network-free area, comprising: a second receiving unit, for receiving a plurality of compressed video data and a plurality of sensor data sent by a plurality of monitoring devices, wherein the monitoring devices are used to monitor the operating status of the power transmission line in the network-free area, the network-free area refers to an area without network coverage, the compressed video data is video data obtained by the monitoring device by dynamically compressing the original video data frame by frame, the original video data is video data collected by an image acquisition component in the monitoring device, and the sensor data is sensor data collected by a sensor component in the monitoring device; and a third acquisition unit , used for fusing and analyzing the multiple compressed video data and the multiple sensor data to obtain fused data; a fourth acquisition unit, used for inputting the fused data into a fault determination model, so as to process the fused data using the fault determination model to obtain the fault degree of the transmission line, wherein the fault determination model is trained by machine learning using multiple sets of training data, and each of the multiple sets of training data includes: sample fusion data, and a sample fault degree corresponding to the sample fusion data; a fifth acquisition unit, used for obtaining a comparison result between the fault degree and a fault threshold, and determining the fault level according to the comparison result.

[0024] Optionally, the second receiving unit includes: a receiving module, used to receive a plurality of first coded data packets of a plurality of compressed video data and a plurality of second coded data packets of a plurality of sensor data; a seventh acquisition module, used to decode the plurality of first coded data packets of each compressed video data using a sixth formula to obtain the restored compressed video data, wherein the sixth formula is: V i =decode({E 1 , E 2 , …, E n}, L), decode() represents a decoding function, n represents the total number of the first coded data packets in each piece of the compressed video data, i represents the number of the monitoring device, V i represents the compressed video data sent by the i-th monitoring device; an eighth acquisition module, used to decode the plurality of the second encoded data packets of each of the sensor data using the seventh formula to obtain the restored sensor data, wherein the seventh formula is: S i =decode({H 1 , H 2 , …, H c}, L), c represents the total number of the second encoded data packets in each of the sensor data, f represents the label of the sensor data, S iRepresents the sensor data sent by the i-th monitoring device; a ninth acquisition module, used to obtain multiple compressed video data and multiple sensor data sent by multiple monitoring devices after verifying each restored compressed video data and sensor data.

[0025] Optionally, the third acquisition unit includes: a tenth acquisition module, configured to obtain the fused data by performing calculation using an eighth formula based on the plurality of compressed video data and the plurality of sensor data, wherein the eighth formula is: D(t) represents the fused data at time t, τ represents the time variable, N represents the total number of monitoring devices, λ represents the first attenuation factor of the compressed video data, μ represents the second attenuation factor of the sensor data, t 0 Indicates the starting time of integration.

[0026] Optionally, the fourth acquisition unit includes: an eleventh acquisition module, configured to perform feature extraction on the fused data using a ninth formula to obtain feature parameters of the fused data, wherein the ninth formula is: F(t) represents the characteristic parameter at time t, e represents the label of the characteristic variable, M represents the total number of characteristic variables, and f e represents the e-th characteristic variable; a twelfth acquisition module, used to perform fault analysis based on the characteristic parameter using the tenth formula to obtain an analysis result, wherein the tenth formula is: R(t) represents the analysis result at time t, K represents the model weight, C g represents the gth weight; b g represents the g-th deviation; σ g represents the gth standard deviation; a thirteenth acquisition module, used to perform fault detection based on the analysis result using the eleventh formula to obtain a detection result, wherein the eleventh formula is: G(t) represents the detection result at time t, and γ represents a sensitivity parameter; a calculation module is used to calculate the fault degree of the transmission line based on the detection result using a twelfth formula, wherein the twelfth formula is: Z(t) represents the fault degree at time t.

[0027] Optionally, the fault threshold includes: a first fault threshold, a second fault threshold and a third fault threshold, the first fault threshold is less than the second fault threshold, the second fault threshold is less than the third fault threshold, and the fifth acquisition unit includes: a third determination module, used to determine that the fault level is a minor fault when the comparison result is that the fault degree is not less than the first fault threshold and less than the second fault threshold; a fourth determination module, used to determine that the fault level is a moderate fault when the comparison result is that the fault degree is not less than the second fault threshold and less than the third fault threshold; a fifth determination module, used to determine that the fault level is a severe fault when the comparison result is that the fault degree is not less than the third fault degree.

[0028] Optionally, the monitoring device for the transmission line in the off-grid area also includes: a generation unit, used to generate a fault maintenance strategy according to the fault level after determining the fault level according to the comparison result; a third sending unit, used to send the fault maintenance strategy to the terminal device to prompt the staff to perform fault maintenance on the transmission line.

[0029] According to another aspect of an embodiment of the present invention, a monitoring system for a power transmission line in a non-network area is provided. The monitoring system for a power transmission line in a non-network area uses any of the above-mentioned methods for monitoring a power transmission line in a non-network area.

[0030] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is further provided, wherein the computer-readable storage medium includes a stored program, wherein the program executes any one of the above-mentioned methods for monitoring a power transmission line in a non-grid area.

[0031] According to another aspect of an embodiment of the present invention, a processor is further provided, the processor being used to run a program, wherein when the program is run, any one of the above-mentioned methods for monitoring a power transmission line in a non-grid area is executed.

[0032] According to another aspect of an embodiment of the present invention, there is further provided a computer program product, comprising computer instructions, wherein when the computer instructions are executed by a processor, any one of the above-mentioned methods for monitoring a power transmission line in a non-grid area is executed.

[0033] In an embodiment of the present invention, an image acquisition component is used to collect original video data of a power transmission line in a network-free area, and a sensor component is used to collect sensor data of the power transmission line, wherein the network-free area refers to an area without network coverage; the original video data is dynamically compressed frame by frame to obtain compressed video data; the network topology of the self-organizing network is dynamically adjusted according to the current network status to obtain an optimized transmission path, wherein the self-organizing network is a network formed by communication between wireless communication modules built into each monitoring device, and the monitoring device is used to monitor the operating status of the power transmission line, and the optimized transmission path is a transmission path obtained by optimizing the original data transmission path during the dynamic adjustment of the network topology; a multi-path transmission strategy is used to send the compressed video data and sensor data to a control center through multiple optimized transmission paths, wherein the control center performs fusion analysis after receiving multiple compressed video data and multiple sensor data, and uses a fault determination model to process the fused data obtained by the fusion analysis to obtain the fault degree of the power transmission line. Through the above technical scheme, the purpose of collecting video data and sensor data by using the monitoring equipment deployed along the transmission line and transmitting them to the control center after processing is achieved, so that the control center can further analyze the received data to obtain the fault degree of the transmission line, thereby realizing the technical effect of real-time collection of relevant data of the transmission line in the network-free area and timely processing and analysis, and timely analysis and resolution of the transmission line fault, improving the accuracy and timeliness of fault detection and early warning, and thus solving the technical problem that the existing transmission monitoring method in the related technology is difficult to transmit data in real time and ensure data quality when monitoring the transmission line in the network-free area, resulting in inaccurate fault analysis and untimely processing of the transmission line. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0035] Figure 1 It is a hardware structure block diagram of a mobile terminal of a method for monitoring a power transmission line in a non-network area according to an embodiment of the present invention;

[0036] Figure 2 is a flow chart of a method for monitoring a power transmission line in a no-grid area according to an embodiment of the present invention;

[0037] Figure 3 is a flow chart of an optional method for monitoring a power transmission line in a non-grid area according to an embodiment of the present invention;

[0038] Figure 4 is a flow chart of another optional method for monitoring a power transmission line in a non-grid area according to an embodiment of the present invention;

[0039] Figure 5 is a schematic diagram of a monitoring device for a power transmission line in a no-grid area according to an embodiment of the present invention;

[0040] Figure 6 Schematic diagram of an optional monitoring device for power transmission lines in a non-grid area according to an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0042] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0043] As introduced in the background technology, the existing power transmission monitoring methods in the related art are difficult to transmit data in real time and ensure data quality when monitoring power transmission lines in areas without network coverage, resulting in inaccurate fault analysis and untimely processing of power transmission lines. In view of the above defects, a method and device for monitoring power transmission lines in areas without network coverage are provided in an embodiment of the present invention.

[0044] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0045] The method embodiments provided in the embodiments of the present invention can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a mobile terminal for a method for monitoring a power transmission line in a non-network area according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0046] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the monitoring method of the power transmission line in the network-free area in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific example of the network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0047] According to an embodiment of the present invention, a method embodiment of a method for monitoring a transmission line in a grid-free area is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0048] Figure 2 FIG. 1 is a flow chart of a method for monitoring a power transmission line in a no-grid area according to an embodiment of the present invention. Figure 2 As shown, the method comprises the following steps:

[0049] Step S202, using an image acquisition component to collect original video data of the power transmission line in a network-free area, and using a sensor component to collect sensor data of the power transmission line, wherein the network-free area refers to an area without network coverage.

[0050] Optionally, the above-mentioned image acquisition components may include but are not limited to: a camera, a scanner, etc.

[0051] Optionally, the above-mentioned sensor components may include but are not limited to: temperature sensors, humidity sensors, etc.

[0052] In this embodiment, multiple monitoring devices may be deployed along the power transmission line in the off-grid area, so as to collect the surrounding environment data of the power transmission line in real time by using the monitoring devices, so as to better monitor the power transmission line.

[0053] Combine the following Figure 3 The above embodiments of the present invention are described in detail. Figure 3 It is a flow chart of an optional method for monitoring a power transmission line in a non-grid area according to an embodiment of the present invention.

[0054] As above Figure 3 As shown in the figure, the monitoring devices deployed along the transmission line can automatically form a self-organizing network, establish a network topology through mutual communication, and can also optimize the initial network topology by combining geographic information system technology; specifically, each monitoring device deployed along the transmission line is equipped with a geographic positioning module, and the position of each monitoring device can be expressed by two-dimensional coordinates (x i ,y i ), where i represents the number of the monitoring device; after the monitoring device is started, it can automatically search and discover other nearby monitoring devices through the built-in wireless communication module for mutual communication, and can also combine geographic information system technology to optimize the network topology structure of the self-organizing network composed of monitoring devices deployed along the transmission line.

[0055] Specifically, the distance between each monitoring device can be calculated using the Euclidean distance formula: Among them, d ij represents the distance between monitoring device i and monitoring device j; through mutual communication, the monitoring devices exchange their respective geographical locations and communication status information and establish a preliminary network connection; the condition for device i to identify its neighboring device is the distance d ij Less than the communication range R: d ij ≤R; Using geographic information system (GIS) technology, combined with the geographical location data of each monitoring device, the initial network topology diagram can be generated. The preliminary network topology can be expressed as an adjacency matrix A, where: The adjacency matrix A describes the connection relationship between each monitoring device.

[0056] According to the initial network topology diagram, the shortest path algorithm (for example, Dijkstra algorithm) can be used to optimize the connection paths between the monitoring devices to ensure that each monitoring device can maintain a communication connection with at least one adjacent device, thereby forming a stable self-organizing network; the steps of the shortest path algorithm are as follows: set the source node to s, the target node to t, the initial distance to D(s) = 0, and the initial distance of other nodes to infinity D(v) = ∞; iteratively update the node distance: D(v) = min(D(v), D(u) + w uv ), where u is the node with the shortest path determined, v is the node adjacent to u, and w uv is the edge weight from node u to node v (i.e., distance d uv ); Repeat the above distance update process until the shortest paths of all nodes are determined, and the network topology structure can be optimized; the optimized network topology structure can be represented by the new adjacency matrix A ′ express: During network operation, the monitoring device continuously monitors the network connection status and dynamically adjusts the network topology according to actual conditions to cope with environmental changes and network conditions.

[0057] Step S204: dynamically compress the original video data frame by frame to obtain compressed video data.

[0058] As above Figure 3 As shown, the collected original video data can be dynamically compressed using Videox technology.

[0059] According to the above embodiment of the present invention, in the above step S204, the original video data is dynamically compressed frame by frame to obtain compressed video data, including: obtaining video frame data in the original video data; using an adaptive algorithm to dynamically compress the video frame data through a first formula to obtain intermediate compressed video data, wherein the first formula is: V c (t) represents the intermediate compressed video data at time t, m represents the number of the video frame data, N represents the total number of video frame data, α m Indicates the compression parameter of the mth video frame data, F m (t) represents the mth video frame data at time t; the intermediate compressed video data is integrated by the second formula to obtain the compressed video data, wherein the second formula is: B(t) = {V c (t)|t∈[t 1 , t 2 ]}, B(t) represents the time period [t 1 , t 2 ] compressed video data, t 1Indicates the starting time of the original video data, t 2 Indicates the end time of the original video data.

[0060] Specifically, the monitoring equipment collects high-definition video data (i.e., raw video data) of the power transmission line in real time through a built-in camera, and represents the video data stream as V(t), where t is time; the collected raw video data is dynamically compressed through Videox technology, and the compression process can be expressed as C(V(t), b(t)), where C is the compression function and b(t) is the real-time network bandwidth; according to the current network bandwidth b(t), the parameters of the compression algorithm are automatically adjusted to adjust the compression ratio r(t); the relationship between the compression ratio and the bandwidth can be expressed as: r(t) = f(b(t)), where f is the mapping function from bandwidth to compression ratio; during the compression process, the video frame is analyzed using an adaptive algorithm, and the compression rate of each frame is dynamically adjusted to ensure the continuity and quality of video transmission under low bandwidth conditions. The specific compression algorithm can be expressed as: Among them, V c (t) is the compressed video data (i.e., compressed video data), F m (t) is the video frame data, α m To dynamically adjust the compression parameters, the compressed video data is cached in the local memory and prepared for data transmission. The cached data is represented as: B(t) = {V c (t)|t∈[t 1 ,t 2 ]}, where B(t) is the time period [t 1 ,t 2 ] is a collection of compressed video data.

[0061] Step S206, dynamically adjust the network topology of the self-organizing network according to the current network status to obtain an optimized transmission path, wherein the self-organizing network is a network formed by communication between the wireless communication modules built into each monitoring device, and the monitoring device is used to monitor the operating status of the transmission line. The optimized transmission path is a transmission path obtained by optimizing the original data transmission path in the process of dynamically adjusting the network topology.

[0062] As above Figure 3 As shown, the monitoring device can dynamically adjust the topology of the ad hoc network according to the current network status and environmental changes to optimize the data transmission path.

[0063] According to the above embodiment of the present invention, in the above step S206, the network topology structure of the ad hoc network is dynamically adjusted according to the current network state to obtain an optimized transmission path, including: obtaining the current network state, wherein the network state at least includes: the connection state and communication quality between the monitoring devices other than the ad hoc network and the monitoring device itself; determining the monitoring device with a connection relationship as the target monitoring device according to the connection state; when it is determined that the communication quality between the monitoring device and the target monitoring device is lower than the communication quality threshold, dynamically adjusting the network topology structure; after dynamically adjusting the network topology structure, determining the optimized transmission path by a third formula, wherein the third formula is: Where u and v represent different monitoring devices, respectively. T(u, v) represents the optimized transmission path between monitoring device u and monitoring device v. P represents the set of all transmission paths between monitoring device u and monitoring device v. k represents the number of the transmission path. w uv represents the path weight between monitoring device u and monitoring device v, P k represents the kth transmission path.

[0064] Specifically, the monitoring device can regularly detect the current network status and environmental changes, collect network connection status and communication quality data, and generate a network status matrix S(t), where t is time; then, the connection stability and communication quality of each node are evaluated based on the network status matrix S(t), and the connection path P that may have problems is identified. k (t):P k (t)={(u,v)|S uv (t)<θ}, where S uv (t) is the connection state between node u and node v, and θ is the preset communication quality threshold; then the topology of the ad hoc network is dynamically adjusted to reconfigure the connection path between the monitoring devices to obtain the optimized network topology. The optimized network topology is expressed as a new adjacency matrix A″: Among them, S ij (t) represents the connection status between monitoring device i and monitoring device j; then combined with network coding technology, during the data transmission process, the best transmission path is selected according to the real-time network status to ensure the reliability and efficiency of data transmission. The formula for optimizing the data transmission path by network coding technology is: Where T(u,v) represents the optimal transmission path from node u to node v, P is the set of all possible paths, and w uv is the path weight.

[0065] Step S208, adopting a multi-path transmission strategy to send the compressed video data and sensor data to the control center through multiple optimized transmission paths, wherein the control center performs fusion analysis after receiving the multiple compressed video data and the multiple sensor data, and uses the fault determination model to process the fused data obtained by the fusion analysis to obtain the fault degree of the transmission line.

[0066] As above Figure 3 As shown, a multi-path transmission strategy can be adopted to transmit the collected video data and sensor data to the control center through multiple transmission paths.

[0067] According to the above embodiment of the present invention, before the above step S208, before adopting the multi-path transmission strategy to send the compressed video data and sensor data to the control center through multiple optimized transmission paths, the monitoring method of the transmission line in the network-free area also includes: sending a communication connection request to the control center, wherein the communication connection request is used to request to establish a communication connection relationship with the control center; receiving feedback information sent by the control center based on the communication connection request; and establishing a communication connection relationship with the control center when the feedback information indicates that the control center agrees to establish a communication connection relationship based on the communication connection request.

[0068] In this embodiment, after each monitoring device collects and processes the corresponding data, it needs to transmit it to the control center, so that the control center can analyze the data collected by the monitoring device and then perform fault analysis on the transmission line, so that possible faults in the transmission line can be discovered and handled in time; therefore, each monitoring device needs to establish a communication connection with the control center so that the collected monitoring data (including video data and sensor data) can be smoothly transmitted to the control center after processing.

[0069] According to the above embodiment of the present invention, in the above step S208, a multi-path transmission strategy is used to send the compressed video data and the sensor data to the control center through multiple optimized transmission paths, including: dividing the compressed video data into multiple first data packets, and dividing the sensor data into multiple second data packets; encoding the multiple first data packets using the fourth formula to obtain corresponding multiple first encoded data packets, wherein the fourth formula is: E a =encode(D a , L), a represents the number of the first data packet, E a represents the first encoded data packet corresponding to the ath first data packet, encode() represents the encoding function, D a represents the ath first data packet, L represents the encoding parameter; the fifth formula is used to encode the plurality of second data packets to obtain the corresponding plurality of second encoded data packets, wherein the fifth formula is: H b =encode(Ob , L), b represents the number of the second data packet, H b represents the second coded data packet corresponding to the b-th second data packet, O b represents the bth second data packet; a multi-path transmission strategy is adopted to transmit multiple first encoded data packets and multiple second encoded data packets to the control center through multiple optimized transmission paths.

[0070] It should be noted that when adopting a multi-path transmission strategy for transmission, the monitoring data needs to be divided into multiple data packets and encoded, and then these data packets are transmitted through multiple transmission paths. Each data packet can select the optimal transmission path for itself through the method provided in the above step S206 when transmitting, wherein the optimal transmission paths of different data packets may be the same or different; after the encoded data packet is transmitted to the receiving end, the receiving end needs to decode it to restore it to the original monitoring data, and verify it to ensure the integrity and consistency of the data.

[0071] The following uses compressed video data as an example to describe the process of transmitting it to the control center using a multipath transmission strategy. The transmission method of sensor data is the same as that of compressed video data, so it will not be repeated here. Specifically, the data transmission source node can be set as s, the target node can be set as t, and in multipath transmission, k paths P can be selected. 1 ,P 2 ,…,P k ,The path selection formula is: Among them, w uv is the path weight between node u and node v, and the compressed video data V c (t) split into multiple packets {D 1 ,D 2 ,…,D n}, each data packet is assigned to a different path P k For transmission, the allocation strategy is based on the transmission quality of the path Where f is the allocation function of data packets to paths, For path P j In the process of data packet transmission, network coding technology is combined to encode the data packet to improve the transmission efficiency and anti-interference ability; the encoded data packet is expressed as: E i =encode(D a ,L), where encode is the encoding function, L is the encoding parameter, E i is the encoded data packet; the data packet passes through multiple paths P j During the transmission process, the transmission status of each path is monitored in real time. The data packet allocation strategy is dynamically adjusted according to the path status to ensure the stability and reliability of data transmission. The transmission status matrix is: The decoding and verification process of the encoded data packet at the receiving end is described in detail in the following embodiments of the present invention and will not be repeated here.

[0072] Through the above steps, the monitoring equipment can transmit the collected monitoring data to the control center. After receiving the monitoring data transmitted by each monitoring device, the control center will integrate and analyze it to analyze and handle possible faults of the transmission line.

[0073] As can be seen from the above, through the technical solution provided by the above embodiments of the present invention, the original video data of the transmission line in the network-free area can be collected by using the image acquisition component, and the sensor data of the transmission line can be collected by using the sensor component, wherein the network-free area refers to an area without network coverage; the original video data is dynamically compressed frame by frame to obtain compressed video data; the network topology structure of the self-organizing network is dynamically adjusted according to the current network status to obtain an optimized transmission path, wherein the self-organizing network is a network formed by communication between the wireless communication modules built into each monitoring device, the monitoring device is used to monitor the operating status of the transmission line, and the optimized transmission path is a transmission path obtained by optimizing the original data transmission path in the process of dynamically adjusting the network topology structure; a multi-path transmission strategy is adopted to compress the compressed The video data and sensor data are sent to the control center through multiple optimized transmission paths. After receiving multiple compressed video data and multiple sensor data, the control center performs fusion analysis, and uses the fault determination model to process the fused data obtained by the fusion analysis to obtain the fault degree of the transmission line. The purpose of collecting video data and sensor data by using the monitoring equipment deployed along the transmission line and transmitting them to the control center after processing is achieved, so that the control center can further analyze the received data and obtain the fault degree of the transmission line. This realizes the real-time collection of relevant data of the transmission line in the off-grid area and timely processing and analysis, and timely analysis and resolution of the transmission line fault, thereby improving the accuracy and timeliness of fault detection and early warning.

[0074] Therefore, the technical solution provided by the above-mentioned embodiments of the present invention solves the technical problem that the existing power transmission monitoring method in the related technology is difficult to transmit data in real time and ensure data quality when monitoring the power transmission line in an area without network coverage, resulting in inaccurate fault analysis and untimely processing of the transmission line.

[0075] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0076] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0077] According to another aspect of an embodiment of the present invention, a method embodiment of a method for monitoring a transmission line in a grid-free area is also provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0078] Figure 4 is a flow chart of another optional method for monitoring a power transmission line in a non-grid area according to an embodiment of the present invention, such as Figure 4 As shown, the method comprises the following steps:

[0079] Step S402, receiving multiple compressed video data and multiple sensor data sent by multiple monitoring devices, wherein the monitoring devices are used to monitor the operating status of power transmission lines in a no-network area, and the no-network area refers to an area without network coverage. The compressed video data is video data obtained by the monitoring device by dynamically compressing the original video data frame by frame, the original video data is video data collected by the image acquisition component in the monitoring device, and the sensor data is sensor data collected by the sensor component in the monitoring device.

[0080] In this embodiment, the control center has a communication connection with the monitoring devices deployed along the transmission line, can receive compressed video data and sensor data transmitted by each monitoring device, and analyze the fault of the transmission line based on the received data.

[0081] According to the above embodiment of the present invention, in the above step S402, receiving multiple compressed video data and multiple sensor data sent by multiple monitoring devices includes: receiving multiple first encoded data packets of multiple compressed video data and multiple second encoded data packets of multiple sensor data; decoding the multiple first encoded data packets of each compressed video data using the sixth formula to obtain restored compressed video data, wherein the sixth formula is: V i =decode({E 1 , E 2 , …, E n}, L), decode() represents the decoding function, n represents the total number of the first coded data packets in each compressed video data, i represents the number of the monitoring device, V i represents the compressed video data sent by the i-th monitoring device; the seventh formula is used to decode the multiple second encoded data packets of each sensor data to obtain the restored sensor data, wherein the seventh formula is: S i =decode({H 1 , H 2 , …, H c}, L), c represents the total number of second coded data packets in each sensor data, f represents the number of sensor data, S i represents the sensor data sent by the i-th monitoring device; after verifying each restored compressed video data and sensor data, multiple compressed video data and multiple sensor data sent by multiple monitoring devices are obtained.

[0082] Specifically, the control center first receives the encoded data packet sent by the monitoring device, which needs to be decoded and restored to obtain the original data. The decoding process of compressed video data is described below as an example. The decoding process of sensor data is consistent with the decoding process of compressed video data, and will not be repeated here.

[0083] At the receiving end (i.e., the control center), all data packets E a is reassembled and decoded to restore the original compressed video data V c (t), the decoding process can be expressed as: V c (t) = decode({E 1 ,E 2 ,…,E n},K), where decode is the decoding function, {E 1 ,E 2 ,…,E n} is the set of received coded data packets; then the received data is verified to ensure the integrity and consistency of the data. The verification function is expressed as:

[0084]

[0085] Step S404: fusing and analyzing the plurality of compressed video data and the plurality of sensor data to obtain fused data.

[0086] As above Figure 3 As shown, after receiving the compressed video data and sensor data transmitted by each monitoring device, the control center can fuse the compressed video data and sensor data transmitted by multiple monitoring devices to obtain a comprehensive data set, namely, fused data.

[0087] According to the above embodiment of the present invention, in the above step S404, the plurality of compressed video data and the plurality of sensor data are fused and analyzed to obtain fused data, including: calculating based on the plurality of compressed video data and the plurality of sensor data using the eighth formula to obtain the fused data, wherein the eighth formula is: D(t) represents the fused data at time t, τ represents the time variable, N represents the total number of monitoring devices, λ represents the first attenuation factor of compressed video data, μ represents the second attenuation factor of sensor data, t 0 Indicates the starting time of integration.

[0088] Specifically, suppose the data transmitted by the i-th monitoring device received by the control center is {V i (t),S i (t)}, where V i (t) is the compressed video data, S i (t) is sensor data. The control center can fuse the data transmitted by all monitoring devices, remove redundant information, and align and integrate the data by time and space to form a comprehensive data set. Where D(t) represents the fused data at time t, τ represents the time variable, N represents the total number of monitoring devices, λ represents the first attenuation factor of compressed video data, μ represents the second attenuation factor of sensor data, and t 0 Indicates the starting time of integration. Fusion based on this can improve the integrity and consistency of the data and ensure the accuracy of subsequent analysis.

[0089] Step S406, inputting the fused data into the fault determination model, so as to process the fused data using the fault determination model to obtain the fault degree of the transmission line, wherein the fault determination model is trained by machine learning using multiple sets of training data, and each of the multiple sets of training data includes: sample fusion data, and sample fault degree corresponding to the sample fusion data.

[0090] In this embodiment, the fault degree of the power transmission line can be analyzed based on the fused data obtained by the control center through fusion processing of the data transmitted by all monitoring devices.

[0091] According to the above embodiment of the present invention, in the above step S406, the fused data is input into the fault determination model, and the fused data is processed by the fault determination model to obtain the fault degree of the transmission line, including: extracting features from the fused data by using the ninth formula to obtain feature parameters of the fused data, wherein the ninth formula is: F(t) represents the characteristic parameter at time t, e represents the label of the characteristic variable, M represents the total number of characteristic variables, and f e represents the e-th characteristic variable; based on the characteristic parameter, the fault analysis is performed using the tenth formula to obtain the analysis result, wherein the tenth formula is: R(t) represents the analysis result at time t, K represents the model weight, and C g represents the gth weight; b g represents the g-th deviation; σ g represents the gth standard deviation; based on the analysis results, the eleventh formula is used to perform fault detection to obtain the detection result, wherein the eleventh formula is: G(t) represents the detection result at time t, and γ represents the sensitivity parameter; based on the detection result, the fault degree of the transmission line is calculated using the twelfth formula, where the twelfth formula is: Z(t) represents the fault severity at time t.

[0092] Specifically, the control center performs big data analysis on the comprehensive data set D(t) and extracts key feature parameters F(t). The feature extraction function is: The feature extraction function can extract key features related to faults from a large amount of data to improve analysis efficiency and accuracy. Then, the pre-trained deep learning model is used to perform real-time analysis on the extracted feature parameters to ensure real-time and high efficiency. The model input is the feature parameter F(t), and the output is the analysis result R(t). The analysis process is as follows: Then, historical data and real-time monitoring data are combined to perform fault detection and prediction on the analysis result R(t) through machine learning algorithm. The fault detection model is: According to the test result G(t), determine the severity of the possible fault, that is, the degree of fault: Among them, F(t) represents the characteristic parameter at time t, e represents the label of the characteristic variable, M represents the total number of characteristic variables, and f e represents the e-th characteristic variable, R(t) represents the analysis result at time t, K represents the model weight, that is, the total number of weights in the model, and C g represents the gth weight; b g represents the g-th deviation; σ grepresents the gth standard deviation, G(t) represents the detection result at time t, γ represents the sensitivity parameter, Z(t) represents the fault degree at time t, and g here is an index variable used to traverse each item in the summation process, starting from 1 until K.

[0093] Step S408, obtaining a comparison result between the fault degree and the fault threshold, and determining the fault level according to the comparison result.

[0094] In this embodiment, the value corresponding to the fault degree Z(t) may be compared with a fault threshold to determine the fault level of the fault.

[0095] According to the above embodiment of the present invention, in the above step S408, the fault threshold includes: a first fault threshold, a second fault threshold and a third fault threshold, the first fault threshold is less than the second fault threshold, the second fault threshold is less than the third fault threshold, a comparison result between the fault degree and the fault threshold is obtained, and the fault level is determined according to the comparison result, including: when the comparison result is that the fault degree is not less than the first fault threshold and less than the second fault threshold, determining that the fault level is a minor fault; when the comparison result is that the fault degree is not less than the second fault threshold and less than the third fault threshold, determining that the fault level is a moderate fault; when the comparison result is that the fault degree is not less than the third fault degree, determining that the fault level is a severe fault.

[0096] Specifically, when 0≤Z(t)<0.4, it is considered to be a minor fault; when 0.4≤Z(t)<0.8, it is considered to be a moderate fault; when Z(t)≥0.8, it is considered to be a serious fault; here, the first fault threshold is 0, the second fault threshold is 0.4, and the third fault threshold is 0.8. Of course, other values ​​can also be taken as the corresponding fault thresholds for analysis based on actual conditions, and no specific restrictions are made here.

[0097] According to the above embodiment of the present invention, after the above step S408, that is, after determining the fault level according to the comparison result, the monitoring method of the transmission line in the network-free area also includes: generating a fault maintenance strategy according to the fault level; sending the fault maintenance strategy to the terminal device to prompt the staff to perform fault maintenance on the transmission line.

[0098] Optionally, the above-mentioned terminal devices may include but are not limited to: mobile phones, watches, bracelets, computers, tablets and other devices with communication functions.

[0099] As above Figure 3As shown, the control center can generate early warning information and corresponding fault maintenance strategies according to the fault level of each fault analyzed, and send it to the terminal devices such as the mobile phones of the staff to remind the staff to deal with the fault of the transmission line in time; the early warning information here needs to include information such as the location of the fault, the line section, the equipment location and the geographical coordinates, and the fault maintenance strategy will also be different according to the different fault levels: when a certain fault is a minor fault, the fault maintenance strategy can be to conduct routine inspections on related equipment and lines, record current monitoring data and early warning information, and maintain continuous monitoring of the fault area; when a certain fault is a medium fault, the fault maintenance strategy can be to inspect the fault area, implement preventive maintenance measures, adjust power load scheduling, increase monitoring points and monitoring frequency; when a certain fault is a serious fault, the fault maintenance strategy can be to immediately shut down, conduct a comprehensive diagnosis, replace faulty parts and equipment, conduct a comprehensive risk assessment of the fault area, assess potential secondary disasters and safety hazards, and take preventive measures.

[0100] As can be seen from the above, through the technical solution provided by the above embodiment of the present invention, multiple compressed video data and multiple sensor data sent by multiple monitoring devices can be received, wherein the monitoring device is used to monitor the operating status of the power transmission line in the no-network area, and the no-network area refers to the area without network coverage. The compressed video data is the video data obtained by the monitoring device by dynamically compressing the original video data frame by frame, the original video data is the video data collected by the image acquisition component in the monitoring device, and the sensor data is the sensor data collected by the sensor component in the monitoring device; the multiple compressed video data and the multiple sensor data are fused and analyzed to obtain fused data; the fused data is input into the fault determination model to process the fused data using the fault determination model to obtain the fault degree of the power transmission line, wherein, The fault determination model is trained by machine learning using multiple sets of training data. Each set of the multiple training data includes: sample fusion data, sample fault degree corresponding to the sample fusion data; obtaining the comparison result of the fault degree and the fault threshold, and determining the fault level according to the comparison result, thereby achieving the purpose of collecting video data and sensor data by using the monitoring equipment deployed along the transmission line and transmitting them to the control center after processing, so that the control center can further analyze the received data and obtain the fault degree of the transmission line, thereby realizing the real-time collection of relevant data of the transmission line in the off-grid area and timely processing and analysis, and timely analyzing and solving the fault of the transmission line, thereby improving the accuracy and timeliness of fault detection and early warning.

[0101] Therefore, the technical solution provided by the above-mentioned embodiments of the present invention solves the technical problem that the existing power transmission monitoring method in the related technology is difficult to transmit data in real time and ensure data quality when monitoring the power transmission line in an area without network coverage, resulting in inaccurate fault analysis and untimely processing of the transmission line.

[0102] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0103] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0104] According to an embodiment of the present invention, there is also provided a monitoring device for a power transmission line in a no-grid area for implementing the above-mentioned monitoring method for a power transmission line in a no-grid area. Figure 5 is a schematic diagram of a monitoring device for a power transmission line in a no-grid area according to an embodiment of the present invention, such as Figure 5 As shown, the device comprises: a collection unit 51, a first acquisition unit 53, a second acquisition unit 55 and a first sending unit 57. The monitoring device for the power transmission line in the no-network area is described in detail below.

[0105] The acquisition unit 51 is used to acquire original video data of the power transmission line in the no-network area by using an image acquisition component, and to acquire sensor data of the power transmission line by using a sensor component, wherein the no-network area refers to an area without network coverage.

[0106] The first acquisition unit 53 is used to dynamically compress the original video data frame by frame to obtain compressed video data.

[0107] The second acquisition unit 55 is used to dynamically adjust the network topology structure of the self-organizing network according to the current network status to obtain an optimized transmission path, wherein the self-organizing network is a network formed by communication between the wireless communication modules built into each monitoring device, and the monitoring device is used to monitor the operating status of the transmission line. The optimized transmission path is a transmission path obtained by optimizing the original data transmission path during the process of dynamically adjusting the network topology structure.

[0108] The first sending unit 57 is used to adopt a multi-path transmission strategy to send the compressed video data and the sensor data to the control center through multiple optimized transmission paths, wherein the control center performs fusion analysis after receiving the multiple compressed video data and the multiple sensor data, and uses the fault determination model to process the fused data obtained by the fusion analysis to obtain the fault degree of the transmission line.

[0109] It should be noted here that the above-mentioned acquisition unit 51, the first acquisition unit 53, the second acquisition unit 55 and the first sending unit 57 correspond to steps S202 to S208 in the above-mentioned embodiments, and the four units and the corresponding steps implement the same instances and application scenarios, but are not limited to the contents disclosed in the above-mentioned embodiments.

[0110] As can be seen from the above, in the scheme recorded in the above-mentioned embodiment of the present invention, the acquisition unit can use the image acquisition component to collect the original video data of the power transmission line in the network-free area, and use the sensor component to collect the sensor data of the power transmission line, wherein the network-free area refers to the area without network coverage; then use the first acquisition unit to dynamically compress the original video data frame by frame to obtain compressed video data; then use the second acquisition unit to dynamically adjust the network topology structure of the self-organizing network according to the current network status to obtain an optimized transmission path, wherein the self-organizing network is a network formed by communication between the wireless communication modules built into each monitoring device, the monitoring device is used to monitor the operating status of the power transmission line, and the optimized transmission path is the transmission path obtained by optimizing the original data transmission path in the process of dynamically adjusting the network topology structure; finally, use the first The sending unit adopts a multi-path transmission strategy to send the compressed video data and sensor data to the control center through multiple optimized transmission paths. After receiving the multiple compressed video data and multiple sensor data, the control center performs fusion analysis, and uses the fault determination model to process the fused data obtained by the fusion analysis to obtain the fault degree of the transmission line. The purpose of collecting video data and sensor data by using the monitoring equipment deployed along the transmission line and transmitting them to the control center after processing is achieved, so that the control center can further analyze the received data and obtain the fault degree of the transmission line, thereby realizing the real-time collection of relevant data of the transmission line in the off-grid area and timely processing and analysis, and timely analysis and resolution of the transmission line fault, thereby improving the accuracy and timeliness of fault detection and early warning.

[0111] Therefore, the technical solution provided by the above-mentioned embodiments of the present invention solves the technical problem that the existing power transmission monitoring method in the related technology is difficult to transmit data in real time and ensure data quality when monitoring the power transmission line in an area without network coverage, resulting in inaccurate fault analysis and untimely processing of the transmission line.

[0112] Optionally, the first acquisition unit includes: a first acquisition module, used to acquire video frame data in the original video data; and a second acquisition module, used to dynamically compress the video frame data using an adaptive algorithm through a first formula to obtain intermediate compressed video data, wherein the first formula is: V c (t) represents the intermediate compressed video data at time t, m represents the number of the video frame data, N represents the total number of video frame data, α m Indicates the compression parameter of the mth video frame data, F m (t) represents the mth video frame data at time t; a third acquisition module is used to integrate the intermediate compressed video data through the second formula to obtain compressed video data, wherein the second formula is: B(t) = {V c(t)|t∈[t 1 , t 2 ]}, B(t) represents the time period [t 1 , t 2 ] compressed video data, t 1 Indicates the starting time of the original video data, t 2 Indicates the end time of the original video data.

[0113] Optionally, the second acquisition unit includes: a fourth acquisition module, used to acquire the current network state, wherein the network state at least includes: the connection state and communication quality between other monitoring devices other than the self-organizing network; a first determination module, used to determine the monitoring device with a connection relationship as the target monitoring device according to the connection state; an adjustment module, used to dynamically adjust the network topology structure when it is determined that the communication quality between the target monitoring device and the target monitoring device is lower than the communication quality threshold; a second determination module, used to determine the optimized transmission path through a third formula after dynamically adjusting the network topology structure, wherein the third formula is: Where u and v represent different monitoring devices, respectively. T(u, v) represents the optimized transmission path between monitoring device u and monitoring device v. P represents the set of all transmission paths between monitoring device u and monitoring device v. k represents the number of the transmission path. w uv represents the path weight between monitoring device u and monitoring device v, P k represents the kth transmission path.

[0114] Optionally, the monitoring device for the power transmission line in the network-free area also includes: a second sending unit, used to send a communication connection request to the control center before adopting a multi-path transmission strategy to send the compressed video data and sensor data to the control center through multiple optimized transmission paths, wherein the communication connection request is used to request to establish a communication connection relationship with the control center; a first receiving unit, used to receive feedback information sent by the control center based on the communication connection request; and an establishing unit, used to establish a communication connection relationship with the control center when the feedback information indicates that the control center agrees to establish a communication connection relationship based on the communication connection request.

[0115] Optionally, the first sending unit includes: a segmentation module, used to segment the compressed video data into a plurality of first data packets, and segment the sensor data into a plurality of second data packets; a fifth acquisition module, used to encode the plurality of first data packets using a fourth formula to obtain a plurality of corresponding first encoded data packets, wherein the fourth formula is: E a =encode(D a , L), a represents the number of the first data packet, E a represents the first encoded data packet corresponding to the ath first data packet, encode() represents the encoding function, Da represents the ath first data packet, L represents the encoding parameter; a sixth acquisition module is used to encode multiple second data packets using the fifth formula to obtain corresponding multiple second encoded data packets, wherein the fifth formula is: H b =encode(O b , L), b represents the number of the second data packet, H b represents the second coded data packet corresponding to the b-th second data packet, O b Represents the bth second data packet; a transmission module, used to adopt a multi-path transmission strategy to transmit multiple first encoded data packets and multiple second encoded data packets to the control center through multiple optimized transmission paths.

[0116] According to an embodiment of the present invention, there is further provided a monitoring device for a power transmission line in a no-grid area for implementing the above-mentioned monitoring method for a power transmission line in a no-grid area. Figure 6 is a schematic diagram of an optional monitoring device for a power transmission line in a non-grid area according to an embodiment of the present invention, such as Figure 6 As shown, the device includes: a second receiving unit 61, a third obtaining unit 63, a fourth obtaining unit 65 and a fifth obtaining unit 67. The monitoring device for the power transmission line in the no-grid area is described in detail below.

[0117] The second receiving unit 61 is used to receive multiple compressed video data and multiple sensor data sent by multiple monitoring devices, wherein the monitoring devices are used to monitor the operating status of power transmission lines in a no-network area, and the no-network area refers to an area without network coverage. The compressed video data is video data obtained by the monitoring device by dynamically compressing the original video data frame by frame, the original video data is video data collected by the image acquisition component in the monitoring device, and the sensor data is sensor data collected by the sensor component in the monitoring device.

[0118] The third acquisition unit 63 is used to fuse and analyze the multiple compressed video data and the multiple sensor data to obtain fused data.

[0119] The fourth acquisition unit 65 is used to input the fused data into the fault determination model so as to process the fused data using the fault determination model to obtain the fault degree of the transmission line, wherein the fault determination model is trained by machine learning using multiple sets of training data, and each of the multiple sets of training data includes: sample fusion data and sample fault degree corresponding to the sample fusion data.

[0120] The fifth acquisition unit 67 is used to acquire a comparison result between the fault degree and the fault threshold, and determine the fault level according to the comparison result.

[0121] It should be noted here that the above-mentioned second receiving unit 61, third acquisition unit 63, fourth acquisition unit 65 and fifth acquisition unit 67 correspond to steps S402 to S408 in the above-mentioned embodiments, and the four units are the same as the instances and application scenarios implemented by the corresponding steps, but are not limited to the contents disclosed in the above-mentioned embodiments.

[0122] As can be seen from the above, in the scheme recorded in the above-mentioned embodiment of the present invention, a second receiving unit can be used to receive multiple compressed video data and multiple sensor data sent by multiple monitoring devices, wherein the monitoring device is used to monitor the operating status of the power transmission line in the off-grid area, and the off-grid area refers to the area without network coverage. The compressed video data is the video data obtained by the monitoring device by dynamically compressing the original video data frame by frame, the original video data is the video data collected by the image acquisition component in the monitoring device, and the sensor data is the sensor data collected by the sensor component in the monitoring device; then the third acquisition unit is used to fuse and analyze the multiple compressed video data and the multiple sensor data to obtain fused data; then the fourth acquisition unit is used to input the fused data into the fault determination model, so as to process the fused data using the fault determination model to obtain the fault status of the transmission line. The fault degree is obtained by training a fault determination model using multiple sets of training data by machine learning, and each set of the multiple training data includes: sample fusion data, and a sample fault degree corresponding to the sample fusion data; finally, the fifth acquisition unit is used to obtain a comparison result between the fault degree and the fault threshold, and the fault level is determined according to the comparison result, thereby achieving the purpose of collecting video data and sensor data by using the monitoring equipment deployed along the transmission line and transmitting them to the control center after processing, so that the control center can further analyze the received data to obtain the fault degree of the transmission line, thereby realizing the real-time collection of relevant data of the transmission line in the off-grid area and timely processing and analysis thereof, and timely analyzing and solving the fault of the transmission line, thereby improving the accuracy and timeliness of fault detection and early warning.

[0123] Therefore, the technical solution provided by the above-mentioned embodiments of the present invention solves the technical problem that the existing power transmission monitoring method in the related technology is difficult to transmit data in real time and ensure data quality when monitoring the power transmission line in an area without network coverage, resulting in inaccurate fault analysis and untimely processing of the transmission line.

[0124] Optionally, the second receiving unit includes: a receiving module, used to receive multiple first coded data packets of multiple compressed video data and multiple second coded data packets of multiple sensor data; a seventh acquisition module, used to decode the multiple first coded data packets of each compressed video data using the sixth formula to obtain the restored compressed video data, wherein the sixth formula is: V i =decode({E1 , E 2 , …, E n}, L), decode() represents the decoding function, n represents the total number of the first coded data packets in each compressed video data, i represents the number of the monitoring device, V i represents the compressed video data sent by the i-th monitoring device; an eighth acquisition module, used to decode the multiple second encoded data packets of each sensor data using the seventh formula to obtain the restored sensor data, wherein the seventh formula is: S i =decode({H 1 , H 2 , …, H c}, L), c represents the total number of second coded data packets in each sensor data, f represents the number of sensor data, S i Represents the sensor data sent by the i-th monitoring device; a ninth acquisition module, used to obtain multiple compressed video data and multiple sensor data sent by multiple monitoring devices after verifying each restored compressed video data and sensor data.

[0125] Optionally, the third acquisition unit includes: a tenth acquisition module, configured to calculate based on the plurality of compressed video data and the plurality of sensor data using the eighth formula to obtain fused data, wherein the eighth formula is: D(t) represents the fused data at time t, τ represents the time variable, N represents the total number of monitoring devices, λ represents the first attenuation factor of compressed video data, μ represents the second attenuation factor of sensor data, t 0 Indicates the starting time of integration.

[0126] Optionally, the fourth acquisition unit includes: an eleventh acquisition module, configured to extract features from the fused data using the ninth formula to obtain feature parameters of the fused data, wherein the ninth formula is: F(t) represents the characteristic parameter at time t, e represents the label of the characteristic variable, M represents the total number of characteristic variables, and f e represents the e-th characteristic variable; a twelfth acquisition module, used to perform fault analysis based on the characteristic parameter using the tenth formula to obtain an analysis result, wherein the tenth formula is: R(t) represents the analysis result at time t, K represents the model weight, and C k represents the kth weight; b k represents the kth deviation; σ k represents the kth standard deviation; a thirteenth acquisition module is used to perform fault detection based on the analysis result using the eleventh formula to obtain a detection result, wherein the eleventh formula is: G(t) represents the detection result at time t, and γ represents the sensitivity parameter; a calculation module is used to calculate the fault degree of the transmission line based on the detection result using the twelfth formula, wherein the twelfth formula is: Z(t) represents the fault severity at time t.

[0127] Optionally, the fault threshold includes: a first fault threshold, a second fault threshold and a third fault threshold, the first fault threshold is less than the second fault threshold, the second fault threshold is less than the third fault threshold, and the fifth acquisition unit includes: a third determination module, used to determine that the fault level is a minor fault when the comparison result is that the fault degree is not less than the first fault threshold and less than the second fault threshold; a fourth determination module, used to determine that the fault level is a moderate fault when the comparison result is that the fault degree is not less than the second fault threshold and less than the third fault threshold; a fifth determination module, used to determine that the fault level is a severe fault when the comparison result is that the fault degree is not less than the third fault degree.

[0128] Optionally, the monitoring device for the transmission line in the off-grid area also includes: a generation unit, which is used to generate a fault maintenance strategy according to the fault level after determining the fault level according to the comparison result; and a third sending unit, which is used to send the fault maintenance strategy to the terminal device to prompt the staff to perform fault maintenance on the transmission line.

[0129] According to another aspect of an embodiment of the present invention, a monitoring system for a power transmission line in a non-grid area is provided. The monitoring system for a power transmission line in a non-grid area uses any of the above-mentioned monitoring methods for a power transmission line in a non-grid area.

[0130] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium comprising a stored program, wherein the program executes any one of the above-mentioned methods for monitoring a power transmission line in a non-grid area.

[0131] Optionally, in this embodiment, the computer-readable storage medium may be located in any one of the computer terminals in a computer terminal group in a computer network, or in any one of the communication devices in a communication device group.

[0132] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: using an image acquisition component to collect original video data of a transmission line in a network-free area, and using a sensor component to collect sensor data of the transmission line, wherein the network-free area refers to an area without network coverage; dynamically compressing the original video data frame by frame to obtain compressed video data; dynamically adjusting the network topology of the self-organizing network according to the current network status to obtain an optimized transmission path, wherein the self-organizing network is a network formed by communication between wireless communication modules built into each monitoring device, and the monitoring device is used to monitor the operating status of the transmission line, and the optimized transmission path is a transmission path obtained by optimizing the original data transmission path during the dynamic adjustment of the network topology; using a multi-path transmission strategy to send the compressed video data and sensor data to the control center through multiple optimized transmission paths, wherein the control center performs fusion analysis after receiving multiple compressed video data and multiple sensor data, and uses a fault determination model to process the fused data obtained by the fusion analysis to obtain the fault degree of the transmission line.

[0133] Optionally, in this embodiment, the computer-readable storage medium is configured to store a program code for executing the following steps: obtaining video frame data in the original video data; dynamically compressing the video frame data using an adaptive algorithm through a first formula to obtain intermediate compressed video data, wherein the first formula is: V c (t) represents the intermediate compressed video data at time t, m represents the number of the video frame data, N represents the total number of video frame data, α m Indicates the compression parameter of the mth video frame data, F m (t) represents the mth video frame data at time t; the intermediate compressed video data is integrated by the second formula to obtain the compressed video data, wherein the second formula is: B(t) = {V c (t)|t∈[t 1 , t 2 ]}, B(t) represents the time period [t 1 , t 2 ] compressed video data, t 1 Indicates the starting time of the original video data, t 2 Indicates the end time of the original video data.

[0134] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: obtaining the current network state, wherein the network state at least includes: the connection state and communication quality between the monitoring devices other than the self-organizing network and the monitoring device; determining the monitoring device with a connection relationship as the target monitoring device according to the connection state; dynamically adjusting the network topology structure when determining that the communication quality between the monitoring device and the target monitoring device is lower than the communication quality threshold; after dynamically adjusting the network topology structure, determining the optimized transmission path by a third formula, wherein the third formula is: Where u and v represent different monitoring devices, respectively. T(u, v) represents the optimized transmission path between monitoring device u and monitoring device v. P represents the set of all transmission paths between monitoring device u and monitoring device v. k represents the number of the transmission path. w uv represents the path weight between monitoring device u and monitoring device v, P k represents the kth transmission path.

[0135] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for performing the following steps: sending a communication connection request to the control center, wherein the communication connection request is used to request to establish a communication connection relationship with the control center; receiving feedback information sent by the control center based on the communication connection request; and establishing a communication connection relationship with the control center when the feedback information indicates that the control center agrees to establish a communication connection relationship based on the communication connection request.

[0136] Optionally, in this embodiment, the computer-readable storage medium is configured to store a program code for executing the following steps: dividing the compressed video data into a plurality of first data packets, and dividing the sensor data into a plurality of second data packets; encoding the plurality of first data packets using a fourth formula to obtain a plurality of corresponding first encoded data packets, wherein the fourth formula is: a =encode(D a , L), a represents the number of the first data packet, E a represents the first encoded data packet corresponding to the ath first data packet, encode() represents the encoding function, D a represents the ath first data packet, L represents the encoding parameter; the fifth formula is used to encode the plurality of second data packets to obtain the corresponding plurality of second encoded data packets, wherein the fifth formula is: H b =encode(O b , L), b represents the number of the second data packet, H b represents the second coded data packet corresponding to the b-th second data packet, O brepresents the bth second data packet; a multi-path transmission strategy is adopted to transmit multiple first encoded data packets and multiple second encoded data packets to the control center through multiple optimized transmission paths.

[0137] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: receiving multiple compressed video data and multiple sensor data sent by multiple monitoring devices, wherein the monitoring devices are used to monitor the operating status of the transmission line in the no-network area, the no-network area refers to the area without network coverage, the compressed video data is the video data obtained by the monitoring device dynamically compressing the original video data frame by frame, the original video data is the video data collected by the image acquisition component in the monitoring device, and the sensor data is the sensor data collected by the sensor component in the monitoring device; fusing and analyzing the multiple compressed video data and the multiple sensor data to obtain fused data; inputting the fused data into the fault determination model to process the fused data using the fault determination model to obtain the fault degree of the transmission line, wherein the fault determination model is trained by using multiple sets of training data through machine learning, and each of the multiple sets of training data includes: sample fusion data, and a sample fault degree corresponding to the sample fusion data; obtaining a comparison result between the fault degree and the fault threshold, and determining the fault level according to the comparison result.

[0138] Optionally, in this embodiment, the computer-readable storage medium is configured to store a program code for executing the following steps: receiving a plurality of first coded data packets of a plurality of compressed video data and a plurality of second coded data packets of a plurality of sensor data; decoding the plurality of first coded data packets of each compressed video data using the sixth formula to obtain restored compressed video data, wherein the sixth formula is: V i =decode({E 1 , E 2 , …, E n}, L), decode() represents the decoding function, n represents the total number of the first coded data packets in each compressed video data, i represents the number of the monitoring device, V i represents the compressed video data sent by the i-th monitoring device; the seventh formula is used to decode the multiple second encoded data packets of each sensor data to obtain the restored sensor data, wherein the seventh formula is: S i =decode({H 1 , H 2 , …, H c}, L), c represents the total number of second coded data packets in each sensor data, f represents the number of sensor data, S irepresents the sensor data sent by the i-th monitoring device; after verifying each restored compressed video data and sensor data, multiple compressed video data and multiple sensor data sent by multiple monitoring devices are obtained.

[0139] Optionally, in this embodiment, the computer-readable storage medium is configured to store a program code for executing the following steps: performing calculations based on multiple compressed video data and multiple sensor data using the eighth formula to obtain fused data, wherein the eighth formula is: D(t) represents the fused data at time t, τ represents the time variable, N represents the total number of monitoring devices, λ represents the first attenuation factor of compressed video data, μ represents the second attenuation factor of sensor data, t 0 Indicates the starting time of integration.

[0140] Optionally, in this embodiment, the computer-readable storage medium is configured to store a program code for executing the following steps: extracting features from the fused data using the ninth formula to obtain feature parameters of the fused data, wherein the ninth formula is: F(t) represents the characteristic parameter at time t, e represents the label of the characteristic variable, M represents the total number of characteristic variables, and f e represents the e-th characteristic variable; based on the characteristic parameter, the fault analysis is performed using the tenth formula to obtain the analysis result, wherein the tenth formula is: R(t) represents the analysis result at time t, K represents the model weight, and C k represents the kth weight; b k represents the kth deviation; σ k represents the kth standard deviation; based on the analysis result, the eleventh formula is used to perform fault detection to obtain the detection result, wherein the eleventh formula is: G(t) represents the detection result at time t, and γ represents the sensitivity parameter; based on the detection result, the fault degree of the transmission line is calculated using the twelfth formula, where the twelfth formula is: Z(t) represents the fault severity at time t.

[0141] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: when the comparison result is that the fault degree is not less than the first fault threshold and less than the second fault threshold, determining the fault level to be a minor fault; when the comparison result is that the fault degree is not less than the second fault threshold and less than the third fault threshold, determining the fault level to be a moderate fault; when the comparison result is that the fault degree is not less than the third fault degree, determining the fault level to be a severe fault.

[0142] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: generating a fault maintenance strategy according to the fault level; and sending the fault maintenance strategy to the terminal device to prompt the staff to perform fault maintenance on the transmission line.

[0143] According to another aspect of an embodiment of the present invention, a processor is further provided, and the processor is used to run a program, wherein when the program is run, any one of the above-mentioned methods for monitoring a power transmission line in a non-grid area is executed.

[0144] According to another aspect of an embodiment of the present invention, there is further provided a computer program product, comprising computer instructions, which, when executed by a processor, execute any one of the above-mentioned methods for monitoring a power transmission line in a non-grid area.

[0145] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0146] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0147] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0148] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0149] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0150] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0151] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for monitoring power transmission lines in a non-grid area. It is characterized in that include: Using an image acquisition component to collect raw video data of a power transmission line in a no-network area, and using a sensor component to collect sensor data of the power transmission line, wherein the no-network area refers to an area without network coverage; Dynamically compressing the original video data frame by frame to obtain compressed video data; Dynamically adjusting the network topology of the ad hoc network according to the current network state to obtain an optimized transmission path, wherein the ad hoc network is a network formed by communication between wireless communication modules built into each monitoring device, the monitoring device is used to monitor the operating state of the transmission line, and the optimized transmission path is a transmission path obtained by optimizing the original data transmission path during the process of dynamically adjusting the network topology; A multi-path transmission strategy is adopted to send the compressed video data and the sensor data to the control center through the multiple optimized transmission paths, wherein the control center performs fusion analysis after receiving the multiple compressed video data and the multiple sensor data, and uses the fault determination model to process the fused data obtained by the fusion analysis to obtain the fault degree of the transmission line.

2. The method for monitoring power transmission lines in a no-grid area according to claim 1, It is characterized in that The original video data is dynamically compressed frame by frame to obtain compressed video data, including: Acquire video frame data from the original video data; The video frame data is dynamically compressed using a first formula using an adaptive algorithm to obtain intermediate compressed video data, wherein the first formula is: V c (t) represents the intermediate compressed video data at time t, m represents the number of the video frame data, N represents the total number of the video frame data, α m represents the compression parameter of the video frame data described in the mth item, F m (t) represents the mth video frame data at time t; The intermediate compressed video data is integrated by a second formula to obtain the compressed video data, wherein the second formula is: B(t)={V c (t)|t∈[t 1 , t 2 ]}, B(t) represents the time period [t 1 , t 2 ], t 1 represents the starting time of the original video data, t 2 Indicates the end time of the original video data.

3. The method for monitoring power transmission lines in a no-grid area according to claim 1, It is characterized in that The network topology of the ad hoc network is dynamically adjusted according to the current network status to obtain an optimized transmission path, including: Acquire the current network status, wherein the network status at least includes: the connection status and communication quality between the monitoring devices other than the self-organizing network and other monitoring devices; Determining, according to the connection status, the monitoring device with a connection relationship as a target monitoring device; When it is determined that the communication quality with the target monitoring device is lower than a communication quality threshold, dynamically adjusting the network topology structure; After dynamically adjusting the network topology, the optimized transmission path is determined by a third formula, wherein the third formula is: Wherein, u and v represent different monitoring devices respectively, T(u, v) represents the optimized transmission path between the monitoring device u and the monitoring device v, P represents the set of all transmission paths between the monitoring device u and the monitoring device v, k represents the number of the transmission path, w uv represents the path weight between the monitoring device u and the monitoring device v, P k Represents the kth transmission path.

4. The method for monitoring power transmission lines in a no-grid area according to claim 1, It is characterized in that Before adopting a multi-path transmission strategy to send the compressed video data and the sensor data to a control center through a plurality of the optimized transmission paths, the method further includes: Sending a communication connection request to the control center, wherein the communication connection request is used to request to establish a communication connection relationship with the control center; receiving feedback information sent by the control center based on the communication connection request; When the feedback information indicates that the control center agrees to establish the communication connection relationship based on the communication connection request, the communication connection relationship is established with the control center.

5. The method for monitoring power transmission lines in a no-grid area according to claim 1, It is characterized in that The compressed video data and the sensor data are sent to a control center through a plurality of optimized transmission paths using a multi-path transmission strategy, including: dividing the compressed video data into a plurality of first data packets, and dividing the sensor data into a plurality of second data packets; The fourth formula is used to encode the plurality of first data packets to obtain corresponding plurality of first encoded data packets, wherein the fourth formula is: a =encode(D a , L), a represents the number of the first data packet, E a represents the first encoded data packet corresponding to the ath first data packet, encode() represents the encoding function, D a represents the ath said first data packet, and L represents a coding parameter; The fifth formula is used to encode the plurality of second data packets to obtain corresponding plurality of second encoded data packets, wherein the fifth formula is: b =encode( b , L), b represents the label of the second data packet, H b represents the second coded data packet corresponding to the b-th second data packet, b represents the bth said second data packet; A multi-path transmission strategy is adopted to transmit a plurality of the first encoded data packets and a plurality of the second encoded data packets to the control center through a plurality of the optimized transmission paths.

6. A method for monitoring power transmission lines in a non-grid area. It is characterized in that include: Receiving multiple compressed video data and multiple sensor data sent by multiple monitoring devices, wherein the monitoring devices are used to monitor the operating status of power transmission lines in a no-network area, the no-network area refers to an area without network coverage, the compressed video data is video data obtained by the monitoring device through dynamic compression of original video data frame by frame, the original video data is video data collected by an image acquisition component in the monitoring device, and the sensor data is sensor data collected by a sensor component in the monitoring device; Performing fusion analysis on the plurality of compressed video data and the plurality of sensor data to obtain fused data; The fused data is input into a fault determination model, so as to process the fused data using the fault determination model to obtain the fault degree of the transmission line, wherein the fault determination model is trained by using multiple sets of training data in a machine learning manner, and each of the multiple sets of training data includes: sample fusion data and a sample fault degree corresponding to the sample fusion data; A comparison result between the fault degree and a fault threshold is obtained, and a fault grade is determined according to the comparison result.

7. The method for monitoring power transmission lines in a no-grid area according to claim 6, It is characterized in that Receive multiple compressed video data and multiple sensor data sent by multiple monitoring devices, including: receiving a plurality of first encoded data packets of the plurality of compressed video data and a plurality of second encoded data packets of the plurality of sensor data; The sixth formula is used to decode the plurality of the first coded data packets of each compressed video data to obtain the restored compressed video data, wherein the sixth formula is: i =decode({E 1 , E 2 , …, E n }, L), decode() represents a decoding function, n represents the total number of the first coded data packets in each piece of the compressed video data, i represents the number of the monitoring device, V i represents the compressed video data sent by the i-th monitoring device; The seventh formula is used to decode the plurality of the second encoded data packets of each of the sensor data to obtain the restored sensor data, wherein the seventh formula is: i =decode({H 1 , H 2 , …, H c }, L), c represents the total number of the second encoded data packets in each of the sensor data, f represents the label of the sensor data, S i represents the sensor data sent by the i-th monitoring device; After verifying each piece of the restored compressed video data and the sensor data, multiple pieces of the compressed video data and multiple pieces of the sensor data sent by multiple monitoring devices are obtained.

8. The method for monitoring power transmission lines in a no-grid area according to claim 6, It is characterized in that The plurality of compressed video data and the plurality of sensor data are fused and analyzed to obtain fused data, including: The fusion data is obtained by calculating based on the plurality of compressed video data and the plurality of sensor data using the eighth formula, wherein the eighth formula is: D(t) represents the fused data at time t, τ represents the time variable, N represents the total number of monitoring devices, λ represents the first attenuation factor of the compressed video data, μ represents the second attenuation factor of the sensor data, t 0 Indicates the starting time of integration.

9. The method for monitoring power transmission lines in a no-grid area according to claim 6, It is characterized in that Inputting the fused data into a fault determination model, and processing the fused data using the fault determination model to obtain the fault degree of the transmission line, including: The ninth formula is used to extract features from the fused data to obtain feature parameters of the fused data. The ninth formula is: F(t) represents the characteristic parameter at time t, e represents the label of the characteristic variable, M represents the total number of characteristic variables, and f e represents the e-th characteristic variable; Based on the characteristic parameters, the fault analysis is performed using the tenth formula to obtain the analysis result, wherein the tenth formula is: R(t) represents the analysis result at time t, K represents the model weight, C g represents the gth weight; b g represents the g-th deviation; σ g represents the gth standard deviation; Based on the analysis result, the fault detection is performed using the eleventh formula to obtain the detection result, wherein the eleventh formula is: G(t) represents the detection result at time t, and γ represents the sensitivity parameter; The fault degree of the power transmission line is calculated based on the detection result using a twelfth formula, wherein the twelfth formula is: Z(t) represents the fault degree at time t.

10. The method for monitoring power transmission lines in a no-grid area according to claim 6, It is characterized in that The fault threshold includes: a first fault threshold, a second fault threshold and a third fault threshold, the first fault threshold is less than the second fault threshold, the second fault threshold is less than the third fault threshold, obtaining a comparison result between the fault degree and the fault threshold, and determining the fault level according to the comparison result, including: When the comparison result shows that the fault degree is not less than the first fault threshold and less than the second fault threshold, determining that the fault level is a minor fault; When the comparison result shows that the fault degree is not less than the second fault threshold and less than the third fault threshold, determining that the fault level is a medium fault; When the comparison result is that the fault degree is not less than the third fault degree, the fault level is determined to be a serious fault.

11. The method for monitoring power transmission lines in a no-grid area according to claim 6, It is characterized in that After determining the fault level according to the comparison result, the method further includes: generating a fault maintenance strategy according to the fault level; The fault maintenance strategy is sent to the terminal device to prompt the staff to perform fault maintenance on the transmission line.

12. A monitoring device for power transmission lines in a no-grid area, It is characterized in that include: A collection unit, used to collect original video data of the power transmission line in the no-network area by using an image collection component, and collect sensor data of the power transmission line by using a sensor component, wherein the no-network area refers to an area without network coverage; A first acquisition unit, configured to dynamically compress the original video data frame by frame to obtain compressed video data; A second acquisition unit is used to dynamically adjust the network topology structure of the self-organizing network according to the current network state to obtain an optimized transmission path, wherein the self-organizing network is a network formed by communication between wireless communication modules built into each monitoring device, the monitoring device is used to monitor the operating state of the transmission line, and the optimized transmission path is a transmission path obtained by optimizing the original data transmission path in the process of dynamically adjusting the network topology structure; The first sending unit is used to adopt a multi-path transmission strategy to send the compressed video data and the sensor data to the control center through the multiple optimized transmission paths, wherein the control center performs fusion analysis after receiving the multiple compressed video data and the multiple sensor data, and uses a fault determination model to process the fused data obtained by the fusion analysis to obtain the fault degree of the transmission line.

13. A monitoring device for power transmission lines in a no-grid area, It is characterized in that include: a second receiving unit, configured to receive a plurality of compressed video data and a plurality of sensor data sent by a plurality of monitoring devices, wherein the monitoring devices are used to monitor the operating status of power transmission lines in a no-network area, the no-network area refers to an area without network coverage, the compressed video data is video data obtained by the monitoring device through dynamic compression of original video data frame by frame, the original video data is video data collected by an image acquisition component in the monitoring device, and the sensor data is sensor data collected by a sensor component in the monitoring device; A third acquisition unit is used to fuse and analyze the plurality of compressed video data and the plurality of sensor data to obtain fused data; a fourth acquisition unit, configured to input the fused data into a fault determination model, so as to process the fused data using the fault determination model to obtain the fault degree of the transmission line, wherein the fault determination model is obtained by training using multiple sets of training data in a machine learning manner, and each of the multiple sets of training data includes: sample fusion data and a sample fault degree corresponding to the sample fusion data; The fifth acquisition unit is used to obtain a comparison result between the fault degree and the fault threshold, and determine the fault level according to the comparison result.

14. A computer-readable storage medium, It is characterized in that The computer-readable storage medium includes a stored program, wherein the program executes the method for monitoring a power transmission line in a non-grid area as claimed in any one of claims 1 to 11.

15. A computer program product comprising computer instructions, It is characterized in that When the computer instructions are executed by the processor, the method for monitoring the power transmission line in the off-grid area as described in any one of claims 1 to 11 is performed.

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