CN115265445A - Power transmission line sag monitoring method and related equipment - Google Patents
Power transmission line sag monitoring method and related equipment Download PDFInfo
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Abstract
The embodiment of the invention discloses a sag monitoring method of a power transmission line and related equipment, wherein the method comprises the following steps: acquiring a satellite signal on a target power transmission line based on a satellite signal receiving device; calculating a target line sag of the target power transmission line according to the satellite signal; and leading the target line sag into a preset deep learning network model to obtain a line state risk index of the target power transmission line, wherein the preset deep learning network model comprises weight data of the target line sag, and the line state risk index represents the risk level of the current line. The target line sag of the target power transmission line is calculated by acquiring the satellite signal, and the influence of factors such as the environment of the target power transmission line, the material of the target power transmission line and the like is avoided, so that the process of monitoring the running state of the power transmission line based on the line sag is more accurate and more reliable.
Description
Technical Field
The invention relates to the technical field of line monitoring, in particular to a sag monitoring method of a power transmission line and related equipment.
Background
In recent years, with the popularization of information communication technology, the power demand is expanding, and China is building and developing power networks vigorously. The transmission line is used as the most widely distributed important component in the power system, and the safe and reliable operation of the transmission line is the basis of power grid construction and economic development. The sag of the transmission line is a key index for safe operation of the line. When meteorological conditions change or the operating load of the power transmission line is high, the line sag changes. However, when the line sag is too large, the transmission capacity of the transmission line is limited, and in severe cases, accidents such as line tripping and short circuit due to discharge can also occur. Therefore, it is very important to enhance the guarantee capability of intelligent monitoring and early warning of the sag of the transmission line.
The existing power transmission line sag monitoring method comprises a visual image method, a tension calculation method, a wire temperature calculation method and an inclination measurement method. The visual image method is susceptible to interference from the surrounding environment, such as topography around the line, growth activity of animals and plants, and weather factors such as heavy fog, flying dust, and the like. The tension calculation and the wire temperature calculation methods are influenced by the difference of the wire materials, the non-uniform wind speed and wind direction and other factors. The accuracy of the inclinometer is limited by the installation location, wire stiffness, and external environmental interference.
Disclosure of Invention
In view of the above, the invention provides a method for monitoring sag of a power transmission line and related equipment, which are used for solving the problems that in the prior art, factors influencing the calculation process of the sag of the power transmission line are many, and the error of the obtained result is large. In order to achieve one or a part of or all of the above or other purposes, the invention provides a sag monitoring method for a power transmission line, comprising the following steps: acquiring a satellite signal on a target power transmission line;
calculating a target line sag of the target power transmission line according to the satellite signal;
and importing the target line sag into a preset deep learning network model to obtain a line state risk index of the target power transmission line, wherein the preset deep learning network model comprises weight data of the line sag, and the line state risk index represents the risk level of the current line.
Optionally, the step of calculating the target line sag of the target power transmission line according to the satellite signal includes:
constructing a pseudo-range differential error equation of the target power transmission line and a carrier phase differential error equation of the target power transmission line in a differential mode based on the satellite signals;
and calculating the target line sag of the target power transmission line according to the pseudo-range differential error equation and the carrier phase differential error equation.
Optionally, the step of calculating a target line sag of the target power transmission line according to the pseudo-range differential error equation and the carrier phase differential error equation includes:
solving the pseudo-range differential error equation and the carrier phase differential error equation based on a particle swarm algorithm to obtain a three-dimensional coordinate of the lowest point on the target power transmission line and a relative three-dimensional coordinate of suspension points at two ends on the target power transmission line;
and calculating the target line sag of the target power transmission line according to the three-dimensional coordinates and the relative three-dimensional coordinates.
Optionally, before the step of calculating the target line sag of the target power transmission line according to the three-dimensional coordinates and the relative three-dimensional coordinates, the method further includes:
acquiring a reference value comparison table, wherein the reference value comparison table comprises reference values under different topographic and geomorphic data and different meteorological data conditions;
acquiring target topographic and geomorphic data and target meteorological data of the target power transmission line;
determining a target reference value according to the target topographic and geomorphic data, the target meteorological data and the reference value comparison table;
and correcting the relative three-dimensional coordinates based on the target reference value to obtain target relative three-dimensional coordinates.
Optionally, the step of introducing the target line sag into a preset deep learning network model to obtain a line state risk index of the target power transmission line includes:
and importing the monitoring information of the target line sag and the target power transmission line into the preset deep learning network model to obtain the line state risk index of the target power transmission line.
Optionally, the preset deep learning network model completes training through historical data of different power transmission lines, and is used for obtaining reference values and predicted values representing state information of the power transmission lines; and the system is also used for acquiring the weight data of the parameters corresponding to the line state risk indexes.
Optionally, the method further includes:
and generating an alarm instruction based on the line state risk index so as to enable a display device to display the running state of the target power transmission line.
On the other hand, this application embodiment provides a transmission line sag monitoring device, monitoring devices includes:
the data receiving module is used for acquiring satellite signals on the target power transmission line;
the calculation module is used for calculating the target line sag of the target power transmission line according to the satellite signal;
and the evaluation module is used for guiding the target line sag into a preset deep learning network model to obtain a line state risk index of the target power transmission line, wherein the preset deep learning network model comprises weight data of the line sag, and the line state risk index represents the risk level of the current line.
In a third aspect, an embodiment of the present application provides an electronic device, including: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating over the bus when the electronic device is running, the machine-readable instructions, when executed by the processor, performing the steps of the data acquisition method as described above.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to perform the steps of the data acquisition method as described above.
The embodiment of the invention has the following beneficial effects:
acquiring a satellite signal on a target power transmission line through a receiving device; calculating a target line sag of the target power transmission line according to the satellite signal; the influences of landform, growth activities of animals and plants, weather factors such as dense fog and dust flying around the line are avoided, and the calculation result of the line sag is more accurate; and leading the target line sag into a preset deep learning network model to obtain a line state risk index of the target power transmission line. The method mainly uses line sag data of satellite signal differential positioning and assists in data such as topographic data and meteorological data, and key parameters in line sag monitoring are corrected and predicted by combining a big data analysis and artificial intelligence method, so that the monitoring precision of the system is further improved, and a scientific basis is provided for management of the power transmission line.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Wherein:
fig. 1 is a flowchart of a sag monitoring method for a power transmission line according to an embodiment of the present disclosure;
fig. 2 is a flowchart of another power transmission line sag monitoring method provided in an embodiment of the present application
Fig. 3 is a schematic structural diagram of a sag monitoring device for a power transmission line according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application;
FIG. 5 is a schematic structural diagram of a storage medium according to an embodiment of the present disclosure;
fig. 6 is a schematic diagram of a power transmission line sag monitoring system according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, an embodiment of the present application provides a method for monitoring sag of a power transmission line, including:
s101, acquiring a satellite signal on a target power transmission line;
for example, a four-frequency satellite antenna is used as a signal acquisition device to track and acquire signals sent by a preset target satellite in real time, for example, a four-frequency Beidou satellite antenna is used to track and acquire satellite signals of four frequency bands, namely B1I, B3I, B1C and B2a, sent by a Beidou satellite in real time.
Illustratively, a satellite signal receiving device, i.e., a four-frequency satellite antenna, is respectively disposed at the lowest point of the target power transmission line conductor and at the suspension points at both ends of the target power transmission line conductor.
S102, calculating a target line sag of the target power transmission line according to the satellite signal;
in a possible implementation manner, the step of calculating the target line sag of the target power transmission line according to the satellite signal includes:
constructing a pseudo-range differential error equation of the target power transmission line and a carrier phase differential error equation of the target power transmission line in a differential mode based on the satellite signals;
and calculating the target line sag of the target power transmission line according to the pseudo-range differential error equation and the carrier phase differential error equation.
For example, data conversion is performed on a satellite signal at the lowest point of the target transmission line conductor and a satellite signal at suspension points at two ends of the target transmission line conductor, and a pseudo-range differential error equation and a carrier phase differential error equation are constructed by using various differential modes such as inter-satellite differential, inter-epoch differential, inter-frequency differential, inter-station differential and the like.
In a possible implementation manner, the step of calculating a target line sag of the target power transmission line according to the pseudo-range differential error equation and the carrier-phase differential error equation includes:
solving the pseudo-range differential error equation and the carrier phase differential error equation based on a particle swarm algorithm to obtain a three-dimensional coordinate of the lowest point on the target power transmission line and a relative three-dimensional coordinate of suspension points at two ends on the target power transmission line;
and calculating the target line sag of the target power transmission line according to the three-dimensional coordinates and the relative three-dimensional coordinates.
For example, the pseudo-range differential error equation and the carrier phase differential error equation may use a particle swarm algorithm to obtain an optimal estimation value of the three-dimensional coordinate of the lowest point of the target power transmission line and an optimal estimation value of the relative three-dimensional coordinate of the suspension points at the two ends of the target power transmission line.
Illustratively, the target line sag of the target power transmission line is calculated according to the three-dimensional coordinates and the relative three-dimensional coordinates, that is, the line sag process of the target power transmission line obtained through satellite signals is completed.
S103, guiding the target line sag into a preset deep learning network model to obtain a line state risk index of the target power transmission line, wherein the preset deep learning network model comprises weight data of the line sag, and the line state risk index represents a risk level of the current line.
Exemplarily, as shown in fig. 2, an embodiment of the present application provides another method for monitoring sag of a power transmission line, including:
reading Beidou navigation data such as satellite signals, wire temperature data and microclimate data;
calculating the line sag of the target power transmission line by combining the microclimate data around the wire and the wire temperature data and predicting the line sag of the target power transmission line in the next period, wherein the Beidou navigation data is used as the main data;
combining the line sag with monitoring data to complete risk assessment on the target power transmission line, and obtaining a line state risk index of the target power transmission line;
if the target transmission line has risks, sending an alarm instruction to enable maintenance personnel to carry out maintenance or modification according to the risk assessment of the target transmission line;
and if the target power transmission line has no risk, storing the risk assessment of the target power transmission line.
In a possible implementation manner, the step of calculating the target line sag of the target power transmission line according to the satellite signal includes:
constructing a pseudo-range differential error equation of the target power transmission line and a carrier phase differential error equation of the target power transmission line in a differential mode based on the satellite signals;
and calculating the target line sag of the target power transmission line according to the pseudo-range differential error equation and the carrier phase differential error equation.
Exemplarily, the satellite signal at the lowest point of the target transmission line conductor and the satellite signals at suspension points at two ends of the target transmission line conductor are processed according to a plurality of differential modes such as inter-satellite difference, inter-epoch difference, inter-frequency difference, inter-station difference, and the like to obtain a pseudo-range differential error equation and a carrier phase differential error equation, wherein the error equation of the pseudo-range differential is expressed as a matrix form as follows:
wherein V is an error term vector of pseudo-range difference, A is a pseudo-range difference coefficient matrix,
is the pseudorange differential vector, l is the vector of constant terms that can be measured or calculated.
The error equation for the carrier phase difference is represented in a matrix form as follows:
wherein, W is an error term vector of the carrier phase difference, B is a coefficient matrix of the carrier phase difference,
is a vector of the phase difference of the carrier wave,
is the double difference integer ambiguity of the carrier phase, K is the vector formed by constant terms which can be measured or calculated, and E is the unit matrix.
And then, calculating the target line sag of the target power transmission line according to the pseudo-range differential error equation and the carrier phase differential error equation.
In a possible implementation manner, the step of calculating the target line sag of the target power transmission line according to the pseudo-range differential error equation and the carrier phase differential error equation includes:
solving the pseudo-range differential error equation and the carrier phase differential error equation based on a particle swarm algorithm to obtain a three-dimensional coordinate of the lowest point on the target power transmission line and a relative three-dimensional coordinate of suspension points at two ends on the target power transmission line;
and calculating the target line sag of the target power transmission line according to the three-dimensional coordinates and the relative three-dimensional coordinates.
For example, the pseudo-range differential error equation and the carrier phase differential error equation can use a particle swarm algorithm to obtain the optimal estimated value of the relative three-dimensional coordinates of the lowest point of the target transmission line conductor and the suspension points at the two ends of the target transmission line conductor. In order to ensure the accuracy of the calculation result, the optimal estimation value of the relative three-dimensional coordinate needs to be corrected, and the specific correction process is as follows:
acquiring a reference value comparison table, wherein the reference value comparison table comprises reference values under different topographic and geomorphic data and different meteorological data conditions;
acquiring target topographic and geomorphic data and target meteorological data of the target power transmission line;
determining a target reference value according to the target topographic and geomorphic data, the target meteorological data and the reference value comparison table;
and correcting the relative three-dimensional coordinate based on the target reference value to obtain a target relative three-dimensional coordinate.
For example, the optimal estimated value of the relative three-dimensional coordinates of the lowest point of the target transmission line conductor and the suspension points at the two ends of the target transmission line conductor is assumed to be
Then the best estimated value obtained is obtained
The correction is made according to the following formula:
wherein, δ X, δ Y, δ Z are target reference values determined according to the reference value comparison table, (Δ X)u,ΔYu,ΔZu) The relative three-dimensional coordinates of the lowest point of the wire and the suspension point of the wire are obtained through correction.
Illustratively, if the three-dimensional coordinate of the lowest point of the target transmission line conductor and the relative three-dimensional coordinates of the hanging points at the two ends of the target transmission line conductor are respectively (Δ X)1,ΔY1,ΔZ10、(ΔX2,ΔY2,ΔZ2) Then, the line sag S of the power transmission line can be calculated by the following formula.
In a possible implementation manner, the step of introducing the target line sag into a preset deep learning network model to obtain a line state risk indicator of the target power transmission line includes:
and importing the monitoring information of the target line sag and the target power transmission line into the preset deep learning network model to obtain the line state risk index of the target power transmission line.
Illustratively, the target line sag and the monitoring information of the target power transmission line are imported into the preset deep learning network model, and the target line sag and the monitoring information are fused based on the importance degree coefficient of the target line sag and the monitoring information of the target power transmission line in deep learning network training to obtain a line state risk index of the target power transmission line.
In a possible implementation manner, the preset deep learning network model completes training through historical data of different power transmission lines and is used for obtaining reference values and predicted values representing state information of the power transmission lines; and the system is also used for acquiring the weight data of the parameters corresponding to the line state risk indexes.
Exemplarily, establishing a BP neural network model for overhead transmission line sag evaluation, and determining an input vector and an output vector;
collecting historical operation data of the overhead transmission line to be detected: load current, meteorological environment temperature, span, sag;
initializing the structure and weight of the established BP neural network model according to the collected historical operation data of the overhead transmission line to be tested;
and performing network training on the established BP neural network by using the collected historical data to obtain the preset deep learning network model.
In one possible embodiment, the method further comprises:
and generating an alarm instruction based on the line state risk index so as to enable a display device to display the running state of the target power transmission line.
Illustratively, the sag value of the overhead transmission line is evaluated in real time according to a real-time environment temperature value transmitted by a meteorological department, a real-time wire load current value transmitted by a scheduling department and an existing span data value;
and if the evaluation value is greater than a set early warning value, giving an early warning.
By means of the Beidou intelligent differential technology, real-time and high-precision monitoring of the sag of the power transmission line is achieved, the intelligent monitoring level of the power transmission line is greatly improved, and a foundation is laid for subsequent risk assessment and intelligent early warning. The method mainly comprises the steps of taking line sag data obtained based on satellite signals as a main part, taking data such as topographic and geomorphic data and meteorological data as an auxiliary part, and correcting and predicting key parameters in line sag monitoring by combining a big data analysis and artificial intelligence method, so that the monitoring precision of the system is further improved, and a scientific basis is provided for management of the power transmission line.
In a possible implementation manner, as shown in fig. 3, an embodiment of the present application provides a sag monitoring device for a power transmission line, where the monitoring device includes:
a data receiving module 201, configured to obtain a satellite signal on a target power transmission line;
a calculating module 202, configured to calculate a target line sag of the target power transmission line according to the satellite signal;
and the evaluation module 203 is configured to import the target line sag into a preset deep learning network model to obtain a line state risk index of the target power transmission line, where the preset deep learning network model includes weight data of the line sag, and the line state risk index represents a risk level of the current line.
In one possible implementation, as shown in fig. 4, an embodiment of the present application provides an electronic device 300, including: comprising a memory 310, a processor 320 and a computer program 311 stored on the memory 310 and executable on the processor 320, when executing the computer program 311, implements: acquiring a satellite signal on a target power transmission line; calculating a target line sag of the target power transmission line according to the satellite signal; and importing the target line sag into a preset deep learning network model to obtain a line state risk index of the target power transmission line, wherein the preset deep learning network model comprises weight data of the line sag, and the line state risk index represents the risk level of the current line.
In one possible implementation, as shown in fig. 5, the present application provides a computer-readable storage medium 400, on which a computer program 411 is stored, where the computer program 411 implements, when executed by a processor: acquiring a satellite signal on a target power transmission line; calculating a target line sag of the target power transmission line according to the satellite signal; and importing the target line sag into a preset deep learning network model to obtain a line state risk index of the target power transmission line, wherein the preset deep learning network model comprises weight data of the line sag, and the line state risk index represents the risk level of the current line.
In a possible implementation manner, as shown in fig. 6, the present application provides a power transmission line sag monitoring system, which specifically includes:
the system comprises a reference station network 1, an intelligent monitoring terminal 2, a data analysis center 3, a server 4, a control center 5 and a maintenance workstation 6;
wherein the reference station network 1: the target circuit sag of the target power transmission line is calculated according to the satellite signal; an optimal reference station is selected by adopting a Dglanay triangulation network technology to form a reference station network, so that a benchmark can be provided for Beidou differential positioning, and calibration can be provided for resolving a line coordinate by analyzing data of the reference station;
intelligent monitoring terminal 2: the receiving device is used for acquiring satellite signals on a target power transmission line; the data of the transmission line sag, the wire temperature and the line microenvironment can be collected in real time and sent to a data processing center;
the data analysis center 3: the system is used for correcting the optimal estimation value of the relative three-dimensional coordinate, guiding the target line sag into a preset deep learning network model to obtain a line state risk index of the target power transmission line, comprehensively evaluating monitoring data and carrying out intelligent early warning;
the server 4: the intelligent monitoring system is used for storing uploaded data provided by a reference station network, an intelligent monitoring terminal, a data analysis center, a control center and a maintenance workstation;
the control center 5: the system is used for daily management of all power transmission lines, and when early warning information appears, the most appropriate maintenance work station is scheduled in time for first-aid repair;
the maintenance work station 6: the intelligent routing inspection system is used for routing inspection of the power transmission line by using intelligent routing inspection equipment such as an unmanned aerial vehicle and the like, and rush-repair of dangerous lines according to control center scheduling.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It will be understood by those skilled in the art that the modules or steps of the invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of computing devices, and optionally they may be implemented by program code executable by a computing device, such that it may be stored in a memory device and executed by a computing device, or it may be separately fabricated into various integrated circuit modules, or it may be fabricated by fabricating a plurality of modules or steps thereof into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
It is to be noted that the foregoing description is only exemplary of the invention and that the principles of the technology may be employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in some detail by the above embodiments, the invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the invention, and the scope of the invention is determined by the scope of the appended claims.
While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (10)
1. A sag monitoring method for a power transmission line is characterized by comprising the following steps:
acquiring a satellite signal on a target power transmission line;
calculating a target line sag of the target power transmission line according to the satellite signal;
and importing the target line sag into a preset deep learning network model to obtain a line state risk index of the target power transmission line, wherein the preset deep learning network model comprises weight data of the line sag, and the line state risk index represents the risk level of the current line.
2. The sag monitoring method for an electric transmission line according to claim 1, wherein the step of calculating the sag of the target line of the target electric transmission line according to the satellite signal comprises:
constructing a pseudo-range differential error equation of the target power transmission line and a carrier phase differential error equation of the target power transmission line in a differential mode based on the satellite signals;
and calculating the target line sag of the target power transmission line according to the pseudo-range differential error equation and the carrier phase differential error equation.
3. The method of claim 2, wherein the step of calculating the target line sag of the target power transmission line according to the pseudorange differential error equation and the carrier phase differential error equation comprises:
solving the pseudo-range differential error equation and the carrier phase differential error equation based on a particle swarm algorithm to obtain a three-dimensional coordinate of the lowest point on the target power transmission line and a relative three-dimensional coordinate of suspension points at two ends on the target power transmission line;
and calculating the target line sag of the target power transmission line according to the three-dimensional coordinates and the relative three-dimensional coordinates.
4. The power transmission line sag monitoring method according to claim 3, wherein before the step of calculating the target line sag of the target power transmission line according to the three-dimensional coordinates and the relative three-dimensional coordinates, the method further comprises:
acquiring a reference value comparison table, wherein the reference value comparison table comprises reference values under different topographic and geomorphic data and different meteorological data conditions;
acquiring target topographic and geomorphic data and target meteorological data of the target power transmission line;
determining a target reference value according to the target topographic and geomorphic data, the target meteorological data and the reference value comparison table;
and correcting the relative three-dimensional coordinates based on the target reference value to obtain target relative three-dimensional coordinates.
5. The method for monitoring the sag of the power transmission line according to claim 1, wherein the step of introducing the sag of the target line into a preset deep learning network model to obtain the line state risk index of the target power transmission line comprises:
and importing the monitoring information of the target line sag and the target power transmission line into the preset deep learning network model to obtain the line state risk index of the target power transmission line.
6. The sag monitoring method for the power transmission line according to claim 1, wherein the preset deep learning network model completes training through historical data of different power transmission lines, and is used for obtaining a reference value and a predicted value representing state information of the power transmission lines; and the system is also used for acquiring the weight data of the parameters corresponding to the line state risk indexes.
7. The power transmission line sag monitoring method according to claim 1, further comprising:
and generating an alarm instruction based on the line state risk index so as to enable a display device to display the running state of the target power transmission line.
8. The utility model provides a transmission line sag monitoring device which characterized in that, monitoring devices includes:
the data receiving module is used for acquiring satellite signals on the target power transmission line;
the calculation module is used for calculating the target line sag of the target power transmission line according to the satellite signal;
and the evaluation module is used for guiding the target line sag into a preset deep learning network model to obtain a line state risk index of the target power transmission line, wherein the preset deep learning network model comprises weight data of the line sag, and the line state risk index represents the risk level of the current line.
9. An electronic device, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating over the bus when an electronic device is running, the machine-readable instructions when executed by the processor performing the steps of the power transmission line sag monitoring method according to any one of claims 1 to 7.
10. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method for monitoring power transmission line sag according to any one of claims 1 to 7.
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